Afretec Book of Abstracts 2026

Book of Abstracts

Afretec Network Principal Investigators Meeting June 15 – 18, 2026 in Nairobi, Kenya

Table of Contents AI-Powered Voice Interfaces to Improve Financial Inclusion of Visually Impaired People When Using USSD Powered Mobile Money Services in Rwanda and Beyond (Large Grant) Kizito Nkurikiyeyezu (University of Rwanda)

4

An AI-Driven Environmental Monitoring Platform for Low-Resource Settings (Small Grant) Edwin Mugume (Carnegie Mellon Africa)

8

An Investigation of a Monitoring and Predicting Algorithm Model for Climate Change Related Diseases in Africa Urban Cities (Small Seed Grant) Immaculata Nwokoro (University of Lagos)

12

Application of AI Techniques for Extracting Carbon from Landfill Waste for Renewable Energy (Small Grant) Abdulganiyu Adelopo (University of Lagos)

15

Birds’ Detector and Repellent System for Large-Scale Smart Farming (Large Seed Grant) Emmanuel Ndashimye (Carnegie Mellon Africa)

18

Designing Organic Optical Sensors with COMSOL Multiphysics and DFT Simulations: A Pathway to Technological Advancement and Employment Growth (Small Grant) Siziwe Gqoba (University of the Witwatersrand)

24

Developing Natural-Language-Processing-Powered Digital Health Solutions for Low-Resource African Languages (Small Seed Grant) Lucienne Abrahams (University of the Witwatersrand)

28

Digital Malaria Control for the Developing World (Large Seed Grant) Carine Pierrette Mukamakuza (Carnegie Mellon Africa)

31

Enhanced Cardiovascular Diseases Discovery in Medically Underserved Communities via AI-Assisted Stethoscopy (Large Seed Grant) Khalil Elkhodary (American University in Cairo)

36

Ethical, Trustworthy, Autonomous: The Vehicles of Tomorrow (Large Grant) Amr El Mougy (American University in Cairo)

41

FINIA: Financial Inclusion via Novel Intelligence and Alternative Data (Large Seed Grant) Chimwemwe Chipeta (University of the Witwatersrand)

44

Fintech Adoption, Financial Literacy, and Financial Inclusion: Experimental Evidence of Small and Micro-Enterprises in Kenya (Small Seed Grant) Odongo Kodongo (University of the Witwatersrand)

49

Improving Digital Education and Learning innovation in the South African and Rwandan Teacher Education Systems: Towards Bridging a Digital Knowledge Divide in Africa (Large Seed Grant) Juliet Perumal (University of the Witwatersrand)

52

Integrating Artificial Intelligence and Computational Modeling in Plastic Waste Sorting, Development and Optimization of Biofiber-Reinforced Composites for Sustainable Construction Materials (Small Seed Grant) Gaudence Nyiranzeyimana (University of Rwanda)

56

Leveraging IoT and Edge-AI for Detecting and Managing Schistosomiasis (Large Seed Grant) Moustafa Youssef (American University in Cairo)

58

Modeling Artificial Intelligence Skills Gaps and Upskilling Strategies in the Construction Industries of Developing Countries: The Case of Rwanda and Nigeria (Small Seed Grant) Oluwaseun Dosumu (University of Rwanda)

61

Multilayer Biofilter System for Wastewater Treatment and Digital Monitoring in Africa (Large Grant) Nadia Arrousse (Al Akhawayn University)

63

Real-Time Noise Level Web Mapping through Crowdsourcing: Toward Creating Sustainable Urban Environments (Small Seed Grant) David Siriba (University of Nairobi

65

Strengthening Water Access and Quality in Selected African Countries (Large Seed Grant) Hakeem Amuda (University of Lagos)

68

TLC: Teaching, Learning, Creativity through Digitalization and Integrated Cross-Border Collaboration (Large Seed Grant) Reuben Dlamini (University of the Witwatersrand)

73

Towards IoT-Enabled Privacy-Preserving Large-Scale Healthcare Analytics in Africa: A Use Case on Monitoring a Cardiovascular Disease (Large Seed Grant) Tamer ElBatt (American University in Cairo)

79

Wakanda AI : Engineering Smart Solutions for Mother Tongues by Advancing Language Technologies in the African Context (Large Seed Grant) Abraham Nyete (University of Nairobi)

84

AI-Powered Voice Interfaces to Improve Financial Inclusion of Visually Impaired People When Using USSD-Powered Mobile Money Services in Rwanda and Beyond • •

I.

PI: Prof. Kizito Nkurikiyeyezu (UR) Co-PI/Co-I: Dr. Eric Umuhoza (CMU-Africa), Dr. Johannes Machinya (Wits). Prof.

Mutangana Dieudonne (UR), Nancy Biwott (CMU-Africa), and Prof. Michael Oluwatosin Bodunrin (Wits)

BACKGROUND

Across much of sub-Saharan Africa, mobile money has become the principal gateway to formal financial services, particularly for low-income populations with limited access to conventional banking. Rwanda exemplifies this transformation. According to the Rwanda FinScope Survey 2024, 86% of adults now rely on mobile phone–based services for essential transactions, including daily purchases, money transfers, school fees, healthcare payments, and government services. Many public and health services accept mobile payments exclusively, and the Government of Rwanda has set the goal of becoming a fully cashless society by 2030. Yet the dominant channel for these transactions is not the smartphone application familiar in highincome countries but Unstructured Supplementary Service Data (USSD), a legacy, text-based, menu-driven technology accessed through dial codes such as *182#.

This reliance on USSD has inadvertently marginalized people with visual impairments, who constitute approximately 1.4% of Rwanda's population aged five years and above. USSD menus render transient text overlays that screen readers cannot detect, provide no audio feedback, and impose rigid, time-sensitive navigation with inflexible session timeouts and limited input validation. As a result, visually impaired users are frequently forced to depend on caregivers to complete transactions they cannot independently verify. They also face heightened exposure to fraud since they cannot confirm recipients or amounts and are readily targeted by social-engineering tactics such as fraudulent USSD prompts delivered by SMS or voice call. Compounding these technical barriers, rapid urbanization is eroding the communal and familial support structures on which visually impaired individuals have traditionally relied, deepening the risk of financial exclusion precisely as cash is being phased out.

This project addresses a gap that no existing solution fills. Available accessibility tools are designed for the smartphone-application ecosystems of high-income settings, leaving visually impaired users in low-resource, local-language African contexts without a way to navigate USSD-based mobile money services independently. The project meets that need through a research that develops and rigorously evaluates an AI-powered voice interface tailored for Kinyarwanda, Rwanda's primary language, and generates transferable evidence on accessible digital finance for low-resource settings. In doing so, it advances Afretec's inclusive digital transformation agenda, Sustainable Development Goal 8.10 on financial inclusion, and the African Union's Digital Transformation Strategy for Africa (2020–2030).

II.

PROJECT OBJECTIVES

This project develops and evaluates an AI-powered, Kinyarwanda-first voice interface that enables visually impaired users to navigate USSD-based mobile money services independently.

The main hypothesis is that a voice interface built on Large Language Models (LLMs) fine-tuned

for Kinyarwanda will substantially increase the rate at which visually impaired users complete financial transactions without assistance, targeting an improvement of at least 60% over current methods. The work is organized using the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) and pursues six objectives: 1. Identify the accessibility barriers and user-interaction patterns that visually impaired people encounter in USSD-based mobile financial services, through a mixed-methods needs assessment.

2. Develop and optimize a smartphone application for independent USSD navigation, integrating speech recognition and natural-language processing with BERT and Whisper models fine-tuned for Kinyarwanda, a context-aware dialogue manager, and offline operation via edge computing.

3. Implement and validate security mechanisms tailored to visually impaired users, including multi-factor authentication (voice biometrics and PIN verification), behavioral analytics, and real-time fraud detection.

4. Evaluate the usability, adoption, and impact of the solution on financial inclusion through a controlled trial with more than 300 participants.

5. Create a scalable implementation blueprint, based on the Rwandan deployment, for adapting the solution across other African regions and languages.

6. Generate evidence-based policy recommendations to advance inclusive digital financial services.

III. CURRENT PROGRESS The project is in the first 3 months of its 24-month timeline, during which work has concentrated on building the methodological, ethical, and technical foundations required for fieldwork and system development.

FIGURE 1: Proposed Kinyarwanda voice interface that lets visually impaired users operate USSD mobile money by voice, with built-in security; an early prototype completes three tasks Figure 1 summarizes the problem, our proposed voice interface, and preliminary results. On the methodological and ethical side, the core data-collection instruments have been developed, including the participant questionnaire, the inclusion and exclusion criteria, an impactassessment matrix, and consent forms designed for accessible administration. Ethicalclearance documentation has been prepared, and a pre-submission of the research protocol has been lodged with the University of Rwanda for institutional pre-approval, ahead of formal

submission to the Rwanda National Council for Science and Technology (NCST). The team has also initiated discussions with the Rwanda Union of the Blind toward a formal collaboration for participant recruitment and community engagement.

On the technical side, the team has developed an early functional prototype that establishes proof of concept for voice-driven navigation of USSD-based mobile money services. The prototype combines speech recognition and on-device natural-language intent parsing with a background USSD execution layer built on Android Accessibility Services, returning results to the user through synthesized speech and haptic feedback. It currently supports a focused set of high-frequency operations such as wallet balance inquiries, airtime purchases, and fund transfers that have been chosen because they represent the most common mobile money transactions and provide a realistic basis for evaluating voice-driven USSD workflows. To address the privacy and fraud risks that arise when visually impaired users must depend on third parties, the prototype also integrates device biometric authentication, hardware-backed secure storage for sensitive credentials, and a privacy-preserving screen-blanking mechanism that conceals on-screen information during sensitive steps such as PIN entry.

Initial testing confirmed that the prototype can reliably recognize and process these basic commands and automate the corresponding USSD interactions. In preliminary trials, supported transactions were completed in approximately 12–15 seconds, which is an early indication that voice-driven automation can improve transaction efficiency while offering a more private and independent experience. These results are exploratory; they establish technical feasibility ahead of Kinyarwanda adaptation and user-centered evaluation, rather than measuring the project's primary outcomes. Research capacity has been strengthened in parallel through the recruitment of a PhD student at the University of Rwanda and of research associates at Carnegie Mellon University Africa, who are actively engaged in system implementation.

IV. FUTURE PLANS The forthcoming phase moves the project from foundation-building into active research, development, and evaluation. The immediate priority is to secure full ethical approval and to constitute a Community Advisory Board that comprises visually impaired representatives, disability organizations, telecommunications operators, and policy stakeholders to guide subsequent activities. With approvals in place, the team will conduct the mixed-methods needs assessment with more than 150 visually impaired adults across urban and rural Rwanda, producing a ranked report of accessibility barriers with quantified impact metrics and baseline measures for the later trial.

In parallel, system development will advance the prototype toward a full application. Central to this effort is fine-tuning the BERT and Whisper models on the Mozilla Common Voice Kinyarwanda corpus (more than 2,000 hours of transcribed speech), with emphasis on highfrequency financial vocabulary, so that the interface operates natively in Kinyarwanda. The team will broaden service coverage beyond the current commands to include bill payments, savings products, and transaction-history inquiries; develop a context-aware dialogue manager capable of handling a wider range of transaction types and recovering gracefully from errors; and introduce adaptive session-management techniques to accommodate USSD time-outs and improve reliability under real-world conditions. Offline operation will be implemented through TensorFlow Lite model compression and encrypted local storage so that the application

performs on mid-range devices in low-connectivity settings. The application will progress through alpha, beta, and field testing toward a deployable prototype.

A dedicated security workstream will extend the prototype's initial protections into a validated framework, delivering voice-biometric authentication, fraud-detection models, and accessible transaction-verification protocols, tested through controlled voice-spoofing and phishing simulations and a planned real-world deployment with a mobile network operator. Finally, a sixmonth controlled trial with more than 300 participants, comparing intervention and control groups and stratified by location, gender, and digital literacy will measure gains in transaction success, user confidence, and financial autonomy against the project's targets.

Later activities will translate the Rwandan deployment into a scalable implementation blueprint, modular APIs and containerized components validated for interoperability with Rwandan mobile money platforms and adaptable to other African contexts, and will produce evidence-based policy recommendations developed through a policy-gap analysis.

An AI-Driven Environmental Monitoring Platform for Low-Resource Se<ings Edwin Mugume (PI)1, Andrew Katumba2 (Co-PI), Pierre Bakunzibake3 (Co-PI) 1Carnegie Mellon University Africa, 2Makerere University, 3University of Rwanda 1. Project Objectives The world is rapidly urbanizing, with over half the world’s poplation already living in urban areas. This trend will continue, with most of this urbanization happening in Sub-Saharan Africa (SSA). This rapid urbanization has resulted in unplanned and informal seKlements that have replaced green spaces and led to poor urban livability. This has led to poor thermal comfort, increasing urban heat islands, flooding and other weather effects, reduced agricultural yields, etc [1]-[2]. Climate change disproportionally affects the poor who lack the means to guard against it yet do the least to cause it.

Most countries in SSA lack sufficient environmental and weather monitoring stations, mainly due to their high cost, which has hindered the accurate characterization and tracking of the effects of climate change in this region. Africa has an average of 1 station covering an area of 26,000 km2, which is 8 times below the minimum standards recommended by World Meteorological Organization (WMO) [3]. The same is true for air quality monitoring, where the cost of air quality reference measurement nodes is prohibitive [4]-[5]. It is therefore necessary to build low-cost nodes that can be deployed at scale to maintain large open datasets and aid the tracking of air quality and weather effects.

This project sought to design, develop, and calibrate an automatic weather station and an air quality monitoring node using low-cost sensors and accessories. Specifically, the project has the following objectives: 1. Design, develop, and calibrate a low-cost automatic weather station that measures ambient temperature, relative humidity, wind speed and direction, and rainfall.

2. Design, develop and calibrate a low-cost air quality measurement node.

3. Design, develop and test low-cost acoustics-based sensor node to measure rainfall intensity, wind speed, and wind direction.

4. Build and test a data management platform for the data storage and access of weather and air quality data.

2. Recent Progress The design and development of the hardware and firmware for the three different nodes (i.e., automatic weather station, air quality monitoring node, and acoustics-based rain and wind sensor node) have been completed. Hardware development covers custom-designed printed circuit boards (PCBs), enclosures, and radiation shields. In addition, the data management platform has been designed and tested. The platform is integrated with both the air qualoity monitor and the automatic weather station.

The automatic weather station acts as an evaluation node for various low-cost temperature and humidty sensors i.e. SHT31-D, HTU21D, BME280, and AHT20, all purchased from Adafruit for prices in the range $4-$15 [6]. Each node will be integrated with three sensors of 1

each type, and the data from these sensors will be tested for precision and accuracy against reference sensor data. The best sensor after calibration will then be chosen for final production of the automatic weather stations. The weather station has also been integrated the tipping bucket-based rain gauge from Davis Instruments (~$135) to measure rainfall [7], and a weather anemometor from Davis Instruments (~$240) to measure wind speed and direction [8]. These sensors, already part of the Vantage Pro2 weather station that costs over $700 from Davis Instruments [9], will not to be evaluated but will still be calibrated. Hence, only one of each is integrated into each weather station.

The air quality measurement node uses the PMSA5003 air quality sensor, which costs $40 on Adafruit [6]. This sensor has been favoured by other researchers who have developed similar low-cost nodes. In addition, two SHT31-D sensors are included in the air quality node; one to measure ambient temperature and humidity and the other to measure internal temperature and humidity around the MCU. As a result, the node has a radiation shield on top to house the “ambient” sensor. After evaluation of the low-cost temperature and humidity sensors, the best sensor will be included in the final node. The 3D-enclosure and custom PCB designed for the air quality node are shown in Figure 1.

Figure 1: The air quality sensor, custom PCB, 3D design and final node.

The project has also developed alternative acoustics-based low-cost rainfall and wind speed sensors. The rainfall sensor uses a MEMS microphone and Raspberry Pi Zero to record and process rain audio, which is then used to estimate rain intensity. A wakeup mechanism is implemented using a raindrop sensor integrated on a separate ESP32 MCU to ensure that audio is recorded only when rainfall is detected. Figure 2 shows a block diagram of the sensor as well as its 3D printouts. The acoustic-based wind sensor uses ultrasonic transducers to measure wind speed based on the time-of-flight (ToF) principle. The sensor emits sound pulses and measures the time they take to travel a known distance. This ToF is influenced by wind speed and can be used to calculate it. Figure 3 shows the mechanism as well as a 3D printout of the sensor. The acoustics-based sensor are considered as low-cost options compared to those from Davis Instruments.

2

Figure 2: Block diagram of the custom acoustic-based rainfall sensor and the 3D printouts.

Figure 3: ToF principle of the acoustic-based wind sensor and its 3D printout.

3. Next Targets The next stage is to complete production of the sensors, deploy them in the field, and collect data for a period of time (minimum of six months). Four weather stations and four air quality monitoring nodes will be deployed alongside highly-accurate reference nodes. The collected data will then be used to calibrate the nodes using machine learning techniques. All the nodes have been designed to run sustainably on solar power. The collected data from the three different nodes will be used to calibrate them using machine learning techniques to enhance their robustness and reliability in real-world conditions.

The weather stations will be deloyed alongside the Trans-African Hydro-Meteorological Observatory (TAHMO) nodes. TAHMO provides free access to its weather data for research purposes. Other potential reference nodes belong to Uganda National Meteorological Authority (UNMA), although we have not secured a partnership yet. For the air quality node, the project hopes to partner with AirQo, who have been in the air quality space for some years and have produced low-cost sensors of their own.

3

4. Peer-Reviewed Publications Resulting from this Project No publications to report.

References [1] [2] [3]

[4] [5]

[6] [7]

[8]

[9]

World Bank, Urban Development (2023).

World Economic Forum, Cities and Urbanization (2022).

K. Mekonnen, et al., “Accuracy of satellite and reanalysis rainfall estimates over africa: a multi-scale assessment of eight products for continental applications,” Journal of Hydrology: Regional Studies, vol. 49, 2023.

Atuyambe, L. M., et al, “The Health Impacts of Air Pollution in the Context of Climate Change in East Africa,” Annals of Global Health, Vol 90, Issue 1, 2024.

P. Green, et al., “Exploring PM2.5 variations from calibrated low-cost sensor network in Greater Kampala, during COVID-19 imposed lockdown restrictions: Lessons for Policy,” Clean Air Journal, Volume 32 (1), 2022.

Adafruit. hKps://www.adafruit.com/.

Davis Instruments, “AeroCone Rain Collector with Flat Base for Vantage Pro2 and EnviroMonitor”. Available: hKps://www.davisinstruments.com/products/aeroconerain-collector-with-flat-base-for-vantage-pro2 Davis Instruments, “Anemometer for Vantage Pro2 & EnviroMonitor”. Available: hKps://www.davisinstruments.com/products/anemometer-for-vantage-pro2vantage-pro Davis Instruments, “Wireless Vantage Pro2 Sensor Suite Weather Station”. Available: hKps://www.davisinstruments.com/products/wireless-vantage-pro2-integratedsensor-suite

4

AN INVESTIGATION OF A MONITORING AND PREDICTING ALGORITHM MODEL FOR CLIMATE CHANGE RELATED DISEASES IN AFRICA URBAN CITIES Prof. Immaculata Nwokoro1, David Rene Seagra2. Mokóḷ ádé Johnson1, Abdulganiyu Adelopo1, Tinuola Odugbemi1, Oluyemi Akindeju1, Ife Albert1, Mina Ogbanga3 1

University of Lagos, Nigeria, University of Nairobi, Kenya, 3 University of Port Harcourt, Nigeria 2

1. Project Objectives Climate change presents a growing risk to public health, particularly in urban African settings where rapid urbanization, poor waste management, and infrastructural deficits intersect with worsening environmental conditions. The increasing frequency of climate-sensitive health issues such as malaria, cholera, and heat-related morbidities demands innovative public health surveillance tools. This study seeks to address these emerging challenges by developing a community-centered, artificial intelligence-supported Interactive Voice Response (IVR) system that functions as an early warning and symptom surveillance mechanism. The main objective is to identify the information needs, user perceptions, and systemic gaps necessary to co-create and deploy a scalable digital health intervention adapted for resource-constrained and vulnerable communities. It aims to develop a comprehensive data-gathering system using Interactive Voice Response (IVR) connected to the Internet of Things (IoT), ensuring data inclusivity and confidentiality.

The project explores how digital health tools, particularly voice-based technologies accessible through basic mobile phones and multimedia, can bridge the gap in health surveillance and disease prediction. With a focus on Ìlàjẹ-Bàrígà, a climate-vulnerable urban slum community in Lagos, Nigeria, the study aims to: assess the feasibility and willingness of local users to engage with an AI-IVR system; identify the enablers and barriers to adoption; and generate communityinformed content that can guide the system’s design and implementation. Ultimately, the project aspires to create a scalable model for early detection and reporting of climate-induced health symptoms, supporting both proactive public health responses and long-term resilience strategies.

2. Recent Progress Grounded the framework in local realities, the project adopted a mixed-method approach incorporating semi-structured interviews, a structured survey, and stakeholder engagement sessions. A total of 139 stakeholders were surveyed, comprising patients, caregivers, health workers, and informal community health actors. The study population was predominantly female (77.2%), below the age of 35 (58.5%), and mainly individuals with at least a secondary school education (95.1%). Most health worker respondents were employed for less than three years and were active residents of the Ìlàjẹ-Bàrígà area. The prevalence of younger and middle-aged participants with a minimum of secondary education highlights a potentially technology-literate demographic capable of adapting to IVR solutions.

The use of the Unified Theory of Acceptance and Use of Technology (UTAUT) for respondents’ perception and readiness to use IVR indicated participants’ critical needs for an IVR system— specifically for saving time, improving access to health information, and enhancing the ease of symptom reporting—which correlated positively with high communalities for awareness (0.750) and benefits of IVR (0.580). This highlights the importance of designing a system that emphasizes these aspects. Crucial enablers identified included social influence and peer recommendations, community ownership and trust, and the development of culturally appropriate content that removes linguistic barriers, with a rating of 0.94 for such inclusion. Respondents emphasized the

importance of using local languages and features which would make the system more intuitive and acceptable.

The development of the Interactive Voice Response (IVR) system for this project successfully integrated several core technologies tailored to the needs of a low-resource and linguistically diverse community. The initial phase focused on implementing voice recognition technology, which enabled the system to accurately capture and process spoken responses from users during interaction. Natural Language Processing (NLP) algorithms were deployed to enhance comprehension and interpretation of the collected audio data. A cloud computing platform was used to support the high volume of data generated during user interactions. This provided scalable storage and reliable processing power necessary for real-time data management, analysis, and model updates. The cloud infrastructure also enhanced the system’s resilience and allowed for remote monitoring and maintenance.

Furthermore, full telephony integration was achieved, enabling the IVR system to handle incoming messages, route them appropriately, and provide automated responses as required. This integration ensured seamless interaction between users and the system across multiple phone networks, regardless of device sophistication.

Finally, a user-friendly interface was designed, prioritizing ease of use for individuals with varying levels of technological literacy. The menu navigation was simplified and structured logically, with audio prompts in simple, user-friendly English. This ensured that users with minimal or no experience with digital systems could still interact effectively with the IVR system. The overall design approach emphasized inclusivity, cultural relevance, and trust-building through familiar and intuitive interaction features.

In addition to the deployment of the IVR system, a comprehensive dashboard analytics platform was developed to support real-time monitoring, feedback dissemination, and data-driven decision-making. The dashboard offered layered statistical analyses tailored to the needs of different stakeholder groups—ranging from health officials and policymakers to community leaders and system administrators. These analytics visualized patterns in symptom reporting, user engagement, and message volumes, helping to interpret the broader implications of collected data within the context of climate-sensitive disease surveillance. By translating complex datasets into actionable insights, the dashboard empowered stakeholders to track the progression of reported illnesses, assess geographic disease hotspots, and evaluate the timeliness and effectiveness of public health responses.

Crucially, the analytics platform was designed to facilitate adaptive responses by allowing system administrators to customize IVR messaging and interventions based on real-time trends and user feedback. For example, if the system detected a spike in heat-related symptoms in a particular locality, automated heat safety tips or health alerts could be disseminated through follow-up IVR messages in the local language. This feedback loop made the system dynamic and responsive to emerging needs, strengthening its relevance and effectiveness. The dashboard’s user interface was intuitive and accessible, allowing non-technical users to interact with data meaningfully.

Overall, the integration of dashboard analytics enhanced the responsiveness, inclusivity, and accountability of the IVR system, making it a truly user-adaptive digital health platform for vulnerable urban populations.

From this pilot project, a total of 367 data entries had been processed and data interpreted for researchers’ evaluation. Figure 1 below provided pictorial representation of the IVR data system.

IVR Input

Cloud Data Collection Graphics

Data Visualization

Figure 1. Pictorial representation of the IVR data system

3. Next Targets Building on the foundational insights gathered from the needs assessment and stakeholder engagement, the next phase of this project will focus on the prototyping and operationalization of the AI-IVR system. This involves expanding the culturally and linguistically appropriate message scripts with local community input to develop multidialectal response recognition, and integrating IoT-enabled climate data into the predictive model. To optimize system performance and scalability, the project will expand its data collection scope and enhance analytics capabilities through cloud-based infrastructure and strengthened telephony integration.

With further funding sources, efforts will be directed toward improving end-to-end functionality—from data capture to dashboard visualization—while training workshops and digital literacy programs will be rolled out for both users and frontline health actors.

This human-centered approach will address the digital skills gap and increase system uptake. Furthermore, policy dialogues with government agencies, health ministries, and regulatory bodies will be initiated to ensure alignment with national health strategies and secure the necessary institutional support for sustained deployment. Partnerships with telecom providers, local tech hubs, and community-based organizations will also be explored to reduce operational costs and bolster backend reliability.

Ultimately, the project is poised to offer a replicable, inclusive model for symptom-based disease forecasting in Africa’s rapidly growing cities. With AI and IVR technologies, the proposed solution holds transformative potential—not only in disease surveillance but also in creating a proactive, people-centered public health framework that anticipates the health impacts of climate change and empowers communities to respond early and effectively.

Future Possibilities Review and application of the IVR model can be scaled to aid urban planning, waste management and enhance people-centric feedback on greenhouse gas emissions as it relates to climate change anomalies and the possible future health preparedness.

4. Peer-Reviewed Publications

No publications to report at the moment.

APPLICATION OF AI TECHNIQUES FOR EXTRACTING CARBON FROM LANDFILL WASTE FOR RENEWABLE ENERGY Dr. AbdulGaniyu Adelopo ¹, Prof. Siphila Wanjiku Mumenya², Dr. Babatunde Sawyerr³, Prof.

Sarath Tennakoon⁴ ¹Waste and Water Quality Unit, Works and Physical Planning Department, University of Lagos, Nigeria ²Department of Civil & Construction Engineering, University of Nairobi, Kenya ³Department of Computer Sciences, University of Lagos, Nigeria ⁴Carnegie Mellon University Africa, Kigali, Rwanda

1.0 Introduction and Objective African cities are grappling with increasing waste generation due to rapid urbanization, leading to overburdened landfills and growing environmental concerns. These landfills, often unmanaged or poorly regulated, consume valuable land, emit greenhouse gases, and lack structured data systems to support material recovery and resource reuse. Despite global advancements in digital waste management, African municipalities remain constrained by limited capacity for technology adoption, especially in leveraging data for waste to wealth.

This project explores the application of Artificial Intelligence (AI) to address these gaps by using Convolutional Neural Networks (CNNs) to predict the carbon recovery potential of landfill waste for renewable energy storage, with a specific focus on carbon conversion for use in supercapacitors. AI models have demonstrated global success in improving waste sorting accuracy and reducing operational costs; however, their integration into African landfill systems remains underexplored. Recent studies show that AI-enhanced material classification can increase recycling efficiency by over 35%, especially when supported by sensor-enabled data systems (Kalantar-Zadeh et al., 2024). Furthermore, carbonized waste materials, particularly biochar from municipal waste, are increasingly being researched as viable electrode materials for renewable energy devices (Tetteh et al., 2023).

The pilot study targets municipal landfills in Lagos (Nigeria) and Nairobi (Kenya), aiming to map the composition of waste, evaluate operational practices, and identify environmental factors that influence carbon yield. It will also assess stakeholder readiness and capacity for digital transitions, particularly among informal waste workers and municipal authorities.

The objectives of the project are to: 1. Identify the data types and parameters essential for training AI models to predict carbon yield from landfill waste.

2. Assess stakeholder needs and existing knowledge gaps in AI-based landfill monitoring.

3. Perform systematic field sampling and laboratory analysis to extract and characterize carbon from landfill materials.

4. Develop, train, and validate machine learning models (e.g., CNNs) to predict carbon extraction potential based on waste profiles.

5. Establish a standardized and scalable landfill dataset that can serve as a benchmark for future AI-driven landfill management systems.

Figure 1: Diagrammatic presentation of the project concept

2.0 Recent Progress and Key Findings This research project has successfully demonstrated the feasibility of deploying Artificial Intelligence (AI) in landfill carbon recovery efforts in African urban contexts. Strategic progress was made across both technical and stakeholder engagement fronts in the two selected study sites: Olusosun Landfill in Lagos, Nigeria, and Dandora Landfill in Nairobi, Kenya.

Field access and data collection began with the formal securing of sampling sites at both landfills.

Collaborative arrangements were established with local waste management authorities, allowing for the integration of informal and formal waste actors into the study. These partnerships facilitated the smooth execution of field activities and strengthened stakeholder buy-in.

A total of 20 key informant interviews and 879 surveys were conducted across the two sites to evaluate stakeholder readiness for digital transformation. These efforts helped map the current capacities, perceived challenges, and willingness of landfill workers and managers to adopt AIbased systems. As part of capacity building, 100 landfill workers were trained through two structured workshops held in Lagos and Nairobi, focusing on basic digital literacy, data handling, and environmental monitoring techniques.

The technical component of the research introduced a dual-data sampling procedure that integrates material sampling and optical recognition. A total of 200 representative waste samples were collected and categorized, while more than 3,500 images and 2,500 numerical entries were compiled for model training. These datasets were used to train three (3) Convolutional Neural Networks (CNNs), resulting in 95% data training and validation rates which underscore the feasibility of AI-assisted carbon estimation strategies.

Initial findings were presented at the AI Action Summit in France, providing a global platform for visibility and peer feedback. One of the standout findings was the successful conversion of landfill waste images into machine-readable formats, with a 96% accuracy rate for classification of waste into the relevant categories An analysis of the workforce demographics revealed that 64.8% of landfill workers were under the age of 36, suggesting a youth-dominated workforce with a high potential for technology adoption. Additionally, 62% of respondents had over four years of operational experience, offering valuable insights for the development of user-centric digital tools. Notably, 69.7% of the workforce occupied key operational roles such as waste pickers and truck operators, underscoring the importance of targeting digital tools at the core of landfill logistics.

Material characterization also revealed that three waste categories—polythene, inert, and organic waste—constitute approximately 70% of the total municipal solid waste at the landfills. These categories were found to possess relatively higher carbon content and thus represent a very promising feedstock for conversion into materials suitable for supercapacitor development.

A short course was developed and delivered to sixty-eight landfill workers, focusing on inclusive digital and carbon data collection techniques. The training deepen the understanding of the link between waste categorization, environmental metrics, and AI integration among the stakeholders.

The first African web-based digital inclusive landfill waste characterization and data dashboard “ALANTA Tool” had been developed with over 150 active users.

Laboratory trials on the determination of landfill-derived materials carbon content, had been completed to establish proof-of-concept for the waste-to-carbon storage quantification pathway.

3.0 Next Targets Building on the momentum from the pilot phase, the project will now focus on optimizing AI models for greater prediction precision by further validating them against laboratory-analyzed carbon content data. This validation process is critical for improving the robustness of the models and expanding their applicability across different landfill scenarios.

To enhance real-time monitoring, Internet of Things (IoT) devices will be deployed across selected landfill zones. These devices will collect live data on temperature, pH and waste images enriching the AI model inputs and enabling dynamic predictions.

Through bigger funding opportunity, the project will develop the ALANTA app to enhance user access and local language inclusion for big data collect for landfill data management, carbon potential prediction, and decision support. ALANTA will serve as a practical interface for city authorities, researchers, and waste operators, supporting smarter, circular, and data-driven landfill management practices in African cities.

It is expected that the sampling of a greater number of landfills across Africa will be carried out to strengthen the ALANTA Tool’s coverage and applicability to diverse waste streams, and to develop a scalable business model for African landfill mining while structured policy engagement will be undertaken to support enabling frameworks for AI integration into landfill circular economy waste practices.

4.0 Peer-Reviewed Publications Resulting from this Project No publications to report at the moment.

References Kalantar-Zadeh, K., Tiemann, G., & Morris, D. (2024). Smart waste systems: AI and sensorenabled waste classification in urban environments. Journal of Sustainable Smart Cities, 12(1), 45–61. https://doi.org/10.1016/j.jssc.2024.01.004 Tetteh, E. K., Kader, S. H., & Muzenda, E. (2023). Carbonized municipal solid waste as an energy storage material: A path to sustainable urban energy. Waste Management & Research, 41(8), 938–950. https://doi.org/10.1177/0734242X231161052

Project title: Birds’ Detector and Repellent System for Large-scale Smart Farming ●

Research team (names and institutions of PI, co-PI, and senior personnel): Emmanuel Ndashimye, PI (CMU-Africa) Evariste Twahirwa, Co-PI (University of Rwanda) Mutugi Kiruki, Co-PI (University of Nairobi) Christine Niyizamwiyitira Co-PI (CMU-Africa) Peace Bamurigire, Co-PI (University of Rwanda) Moise Busogi Co-PI (CMU-Africa)

●

Project Objectives:

The overall objective of this project is to develop a low-cost device that can effectively detect the presence of birds and deploy appropriate deterrent measures to protect crops in agricultural environments. This initiative aims to address the significant challenge of bird damage to agriculture, which causes economic losses, disrupts social dynamics, and even affects educational pursuits as children are often kept out of school to guard crops. On large agricultural farms, especially rice farms, current bird detection and repellent methods are often ineffective, underscoring the need for innovative solutions.

To achieve this overall objective, the specific project objectives are fourfold: 1. Vision-Based Bird Detection: Computer vision algorithms that use visual cues will be implemented.

2. Building an Acoustic Sensor-Based Bird Detection: Development of a cost-effective bird detection system using acoustic sensors by leveraging robust signal processing algorithms and machine learning.

3. Repellent System Development: Design and implementation of an environmentally friendly bird repellent system, exploring various deterrent measures. The effectiveness of repellent strategies against different bird species will be assessed, optimising for cost-effectiveness and minimal impact on non-target species.

4. Integration and Testing: Integration of acoustic and vision-based systems with the chosen repellent mechanism into a functional unit. A user-friendly interface will be developed and extensive real-world testing conducted to assess and validate the performance, accuracy, and efficiency of the system. ●

Recent Progress:

In this section, the recent progress is presented based on the main areas being addressed. The research areas are: Data Acquisition, Bird Detection, Detection System Deployment, and the Repellent System.

1. Data Acquisition:

Research focuses on determining the minimum image quality and camera settings required for effective bird information retrieval. To determine the requirements for effective retrieval of bird information, a survey has been conducted to gather statistical data on birds’ visual characteristics, such as body sizes, shapes, and flying speeds. Aggregating data collected from [4], the average body length of rice-raiding bird species ranges from 10 cm to 17 cm, and their wingspans range from 16 cm to 28 cm. The small size of those birds prompted us to aim for high-resolution Raspberry Pi cameras (64 MegaPixels Owlsight Arducam), to ensure a range above 300 meters. Learning that the birds’ speeds range from 20 mph to 30 mph, we set an upper limit of 1 frame per second to ensure near-real-time detection.

Figure 1: Examples of rice-raiding bird species and their average sizes.

(a) (b) (c) Figure 2: (a) 64 MP Arducam Owlsight camera, (b) The detector setup, (c) Another perspective of the detector.

For the acoustic data, the dataset comprised 24,840 2–3-second audio clips, drawn from custom farm recordings in Kenya and Rwanda, collected over six months. They were collected using audio moths and acoustic sensors deployed on rice and surrounding farms, processed in Audacity, and classified using the open-source Birdnet platform. Figure 3 summarises the steps in data collection. The dataset included six classes: Common waxbill, red-billed quelea, village weaver, yellow-fronted canary, other birds, and noise (other sounds on the farm and in the air).

Figure 3: Acoustic data collection process.

2. Bird Detection:

This area investigates the most efficient change detection techniques for real-time bird movement detection and the optimal image object detection algorithm for identifying birds. Achieving a more precise and reliable bird detection requires selecting and fine-tuning an effective object detection model tailored to this task. We evaluated several pretrained object detection models, from the RCNN, SSD, and the YOLO model families. Our analysis showed that the YOLO family provides lightweight models that maintain performance levels similar to other leading approaches. However, the challenge of detecting small birds due to image downscaling in YOLO models had to be addressed.

Figure 4: The bird detection algorithm’s flowchart To address the downsizing issue, we developed a more lightweight approach for bird detection that combines simple image processing steps with a compact neural network classifier. First, we perform change detection between consecutive frames and threshold the resulting difference magnitude using a high percentile, retaining only pixels corresponding to significant changes. This process yields a binary mask that highlights potential bird regions in the sky. Contour detection is then applied to this mask to identify connected regions, and bounding boxes are drawn around these candidate regions. Each corresponding image patch, defined by its bounding box, is subsequently classified using a lightweight convolutional neural network (CNN) trained to distinguish between birds and background. When evaluated on patches extracted from our collected dataset, the trained classifier achieved an F1 score of 95%. This approach reduces computational complexity by focusing classification only on regions where significant motion is detected, making it well-suited for resource-constrained systems.

Figure 5: Left: small birds detected using our approach. Right: Overall image (12 Megapixels) For Acoustic Detection, the collected data was cleaned, segmented, and processed using MFCC and spectrogram feature extraction techniques to capture important acoustic patterns. The extracted features were used to train and evaluate different models for bird sound classification. Among the models, CNN achieved the best performance with 98% accuracy. For deployment, real-time audio captured through a MEMS microphone is processed locally for classification. When the system detects a target bird species with a confidence score above 0.70, it automatically triggers the repellent mechanisms.

Figure 6: left: Acoustic detection module in a field, right: acoustic microphone hardware overview 3. The Repellent System:

This area focuses on developing the repellent system that communicates with the bird detection module.

Once birds are detected, this system will receive the bird’s location coordinates, in terms of vertical and horizontal angles. An integrated dual servo motor setup will then adjust the orientation of the repellent module to target the birds' location, enabling precise aiming of the repellent actuators. The repellent system currently comprises 3 main components: the actuator part, power supply and the communication part. The three components are briefly described in this section. Currently, the repellent actuator consists of a laser and an audio repellent. Both are powered by stepper motors and gears for rotation and precise aiming. This is depicted in Figure 7, showing the prototype’s schematic diagram and how it has been implemented.

Figure 7: From Left to Right: Laser turret diagram; Communication PCB with all components soldered; and The current prototype of the actuator The prototype is an automated bird repellent system centred on an ESP32-WROOM-32, integrating audio playback, servo-based actuation, visual indicators, and manual control. Its power architecture uses a lead-acid battery, charge controller, INA219 sensor, and LM2596 regulator to manage and monitor energy while supporting environmental sensing and real-time display feedback. Communication between detection and repellent units is achieved through Zigbee-based XBee modules, enabling low-latency wireless transmission for real-time repellent activation and deactivation. ●

Future Plans:

Next to the progress on the key research areas presented above, this section briefly presents the remaining gaps to be addressed and the methods to be used.

Cost-Effective Solutions: Focused research is being conducted to reduce the financial barriers to adopting these technologies for small-scale farmers. Current detector prototypes cost approximately 600,000 Rwf, whereas traditional human labour for bird watching is paid roughly 50,000 Rwf per month. Potential cost-reduction strategies include utilising ruggedised Android smartphones paired with high-capacity power banks to replace more expensive dedicated processing units.

Weather-Resistant Enclosures: The development of durable, weatherproof enclosures is essential for protecting sensitive electronic components from the high humidity, dust, and temperature fluctuations typical of agricultural wetlands. Current efforts involve 3D-printed designs, such as a gimbal for the repellent payload and specialised camera stands, to ensure system longevity in the field.

Project Scaling: Plans are underway to expand the deployment of the integrated system across multiple agricultural plots. Scaling requires optimising the placement of sensor nodes to maximise field coverage while minimising the total number of devices required. Future iterations will also focus on refining camera planning algorithms using terrain and bird behaviour data gathered from diverse sites like those in Rwanda's Eastern Province. ●

Peer-Reviewed Publications Resulting from this Project: ● S. O. Ooko, E. Ndashimye, E. Twahirwa, and M. Busogi, "IoT and machine learning for smart bird monitoring and repellence: Techniques, challenges, and opportunities," IoT, vol. 6, no. 3, p.

46, 2025, doi: 10.3390/iot6030046. –Published - Published ● ●

Scientific African (Elsevier) - A TinyML Model for Bird Acoustic Monitoring in - Under review Resource-Constrained Agricultural Environments -Underview Journal of Agricultural Science and Technology - A Multimodal for Bird Detection and Alert in Rural Farm Environments: A Human-Centred Design -Underview - Under review

●

o Conference Submissions/Presentation resulting from the project 2026 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)- A Multimodal Computer Vision and Acoustic Sensing Framework for Intelligent Bird Detection and Deterrence in Smart Rice Farming - -Underview Under review

Designing Organic Optical Sensors with COMSOL Multiphysics and DFT Simulations: A Pathway to Technological Advancement and Employment Growth.

Siziwe Gqoba1*, Cebisa Ella Linganiso1,2, Lusanda Nonjakazi1, Francis Otieno3*, Perez Seda3, Pascalia Kioko3, Mildred Airo4, Frederick Masara4, Marie Chantal5* 1Molecular Science Institute, School of Chemistry, University of the Witwatersrand, South Africa 2Microscopy and Microscopy and Micro-Analysis Unit, University of the Witwatersrand, South Africa 3Department

of Physics & Materials Science, School of Physical & Biological sciences, Maseno University, Kenya 4Department of Chemistry, School of Physical & Biological Sciences, Maseno University, Kenya 5Department of Physics, University of Rwanda, Rwanda

*E-mails: siziwe.gqoba@wits.ac.za; frankotienoo@maseno.ac.ke; kamirwa@gmail.com Abstract: The rapid recent advances in Internet of Things (IoT) technologies have necessitated dire need to develop next generation electronic devices that are lightweight [1] portable [2] and wearable [3].

Combined with artificial intelligence technologies, the smart sensors for IoT platforms are advancing from the lock-and-key type that monitors a single stimulus to the multiplex type that detects complex stimuli with high precision [4]. The ever-growing technological advancement has led to increased demand for key sensing materials that can collect information such as temperature, humidity, atmosphere, water quality, light and movement from surrounding environmental media [5]. A sensor is a transducer that converts one form of energy into another suitable form amenable for further processing. Although numerous types of sensors have been well established in industries, agriculture, medicine and many other areas, the development of sensing materials with high sensing capabilities is still proceeding at an unprecedented rate [6].

This project intends to integrate simulation and experimental approaches to design organic optical sensors that are efficient, cost-effective, and environmentally friendly using Conductor-like Screening Model (COMSOL Multiphysics) and Density Functional Theory (DFT) simulations as well as experimental validation. Such sensors have a wide range of applications in health, environmental monitoring, and sustainability relying on precise material design to achieve optimal performance. The design of these sensors is closely tied to health and environmental sustainability, as they can be used to monitor hazardous substances, detect pollutants, or assess biological parameters. Additionally, the efficiency of the sensors can be enhanced by optimizing energy consumption during operation, contributing to energy sustainability.

Objectives: 1) Use DFT simulations to explore novel organic materials for optical sensors, predicting properties such as electronic structure and light absorption efficiency.

2) To design and optimize organic optical sensors using computational techniques like DFT (Density Functional Theory) simulations.

3) Leverage COMSOL Multiphysics to simulate and analyze the mechanical, electrical, and optical properties of organic sensor materials.

4) Design and optimize organic materials that are highly sensitive to specific wavelengths of light and explore hybrid sensor designs by combining organic semiconductors with nanostructured elements for enhanced optical and electrical performance.

5) Design and develop prototypes of organic optical sensors for demonstration purposes, showcasing their potential in real-world applications.

6) Create a new partnership and enhance those existing between South Africa, Rwanda and Kenya through joint research and supervision of research students.

7) Promote gender equality and inclusion of marginalized communities in artificial intelligence technologies through smart sensors for IoT platforms among partner countries.

8) Develop early career researchers among the partner institutions that will ensure inclusive digital growth leading to socio-economic impact on the African continent.

Recent progress: Microwave Assisted synthesis of doped and non-doped carbon dots for incorporation with Rhodamine blue for optical gas sensing Carbon dots and nitrogen doped carbon dots were obtained using microwave assisted synthesis as illustrated in Figure 1 below: 5 g LAscorbic acid

500 ml deionized water

Temperature: 180°C Power: 600 W Pressure: 60 bar

15 000 RPM 6°C 15 mins

Filter

Figure 1: Schematic illustration of the synthesis of undoped carbon dots.

Their formation was confirmed by PXRD and FT-IR. The carbon dots were diluted and individually subjected to Photoluminescence studies at an excitation wavelength of 320 nm with and without Rhodamine Blue at different pH values. The undoped carbon dots exhibited an optimum luminescence at pH 8 while for the nitrogen doped carbon dots it was at pH 5. Both the undoped and the nitrogendoped carbon dots quenched the luminescence of Rhodamine Blue. Silver nanoparticles were also synthesized.

Investigation of the influence of barium titanate loading and thermal annealing on the optical absorbance of spin-coated P3HT:PCBM composite thin films Thin films were fabricated on pre-cleaned glass substrates using a solution-based spin-coating technique to obtain uniform and reproducible coatings. Post-deposition thermal treatment was carried out at selected temperatures to improve film crystallinity, morphology, and interfacial properties, while also examining the influence of annealing on the material's optoelectronic characteristics. The optical properties of the resulting thin films were investigated using UV–Visible spectroscopy to evaluate their light absorption behaviour and determine the effects of material composition and thermal processing.

Polymer-based assisted synthesis of silver aminobenzathrone for optical sensing applications

nanoparticles

mediated

2-bromo-3-

Silver nanoparticles (AgNPs) were successfully synthesized using 2-bromo-3-aminobenzathrone as a reducing and surface-functionalizing agent. The silver nanoparticles were dispersed in three different solvents: hexane, benzene, and chloroform, to examine the effect of the solvent environment on their optical properties. UV–Visible spectroscopy studies of the dye, the synthesized AgNPs and the dye– AgNP interaction systems in different solvents were undertaken to analyze the absorbance behavior and plasmonic properties. The increase in optical response in chloroform was explained as a better solubility and more effective solvent–solute interaction with the conjugated dye molecules, which led to better molecular dispersion and lesser aggregation. The absorbance of benzene was also relatively strong because of the favourable π–π interactions between the aromatic solvent and aromatic benzanthrone structure. Hexane, on the other hand, gave low absorbance intensity due to low polarity and poor interaction between the dye molecules, resulting in less dispersion and more aggregation effect. The incorporation of the silver nanoparticles resulted in an increase in the absorbance intensity in both the 1 mM as seen in Figure 2, indicating successful plasmonic interaction between the dye molecules and the surface of the silver nanoparticles.

Figure 2: Absorption spectra of the 1 mM dye-AgNPs in hexane (AH), benzene (BH) and chloroform (AC).

Photoluminescence (PL) measurements were also conducted to explore the PL characteristics and electronic interactions between the dyes and the surfaces of the nanoparticles. In addition, bioimaging experiments were performed to demonstrate the ability of the nanoparticles synthesized for application in imaging and to investigate the interaction between the nanoparticles and biological systems.

Turbidimetric measurements were also carried out to investigate the dispersion of nanoparticles, the stability of the colloids and the change in the optical density when the experimental conditions change.

Preliminary results indicate promising optical activity and stability, highlighting the potential of these materials for next-generation plasmon-enhanced sensing technologies.

Future plans: A. Carbon Dots and Ammonia Sensing Studies Complete structural characterization of the synthesized carbon dots using Raman spectroscopy, TEM, and SEM. Evaluate the ammonia sensing performance of the Rhodamine B/carbon dots nanocomposite.

Synthesize and characterize silver-doped carbon dots.

Investigate the optical properties of Rhodamine B/silver-doped carbon dots composites.

Assess the ammonia sensing performance of the Rhodamine B/silver-doped carbon dots nanocomposite.

B. Organic Photodetector Development Perform advanced optical characterization (PL and FTIR) to study charge transfer, exciton dynamics, and molecular interactions in donor–acceptor polymer blends.

Conduct electrical and optoelectronic measurements, including impedance spectroscopy, I–V, EQE, responsivity, detectivity, and response-time analysis.

Fabricate flexible organic photodetectors and evaluate their thermal, environmental, and mechanical stability.

C. Plasmonic Nanomaterials for Optical Sensing Carry out advanced optical studies using LSPR, fluorescence lifetime, and time-resolved photoluminescence spectroscopy to understand plasmon–chromophore interactions.

Perform surface and structural characterization using TEM, DLS, zeta potential, and XPS to evaluate nanoparticle size, stability, and surface functionalization.

Evaluate sensing performance by determining sensitivity, selectivity, detection limits, response time, and long-term stability toward target analytes.

D. Computational Modelling Develop COMSOL Multiphysics models to simulate optical, electrical, and sensing behavior of the synthesized materials.

Perform DFT calculations to investigate electronic structure, charge-transfer mechanisms, and optical properties, and correlate the results with experimental findings.

Peer-Reviewed Publications Resulting from this Project: No publications to report.

References: [1]

M. Gurunathan and M.A. Mahmoud, “A review and development methodology of a lightweight security model for IoT-based smart devices,” International journal of advanced computer science & applications, vol. 11, no. 2, p. 125-134, 2020.

[2]

A. Antony and S. Sarika, “A review on IoT operating systems,” International Journal of Computer Applications, vol. 176, p. 33-40, 2020.

[3]

N. Surantha, P. Atmaja, and M. Wicaksono, “A review of wearable internet-of-things device for healthcare,” Procedia Computer Science, vol. 179, p. 936-9432021.

[4]

T. Beduk, D. Beduk, M. R. Hasan, E. G. Celik, J. Kosel, J. Narang, K. N. Salama and S. Timur, “Smartphone-based multiplexed biosensing tools for health monitoring,” Biosensors, vol.12, no.

8, p. 583, 2022.

[5]

H. W. Song, W. Choi, T. Jeon, and J. H. Oh, “Recent advances in smart organic sensors for environmental monitoring systems,” ACS Applied Electronic Materials, vol. 5, no. 1, p. 77-99, 2022.

[6]

N. Parvatikar, S. Jain, S. Khasima, M. Revansiddappa, S.V. Bhoraskar, and M.V.N. Ambika Prasad, “Electrical and humidity sensing properties of polyaniline/WO 3 composites,” Sensors and Actuators B: Chemical, vol. 114, no. 2, p. 599-603, 2006.

Developing NLP-Powered Digital Solutions for Health and Nutrition Information for Low-Resource African Languages Lucienne Abrahams1, Ntsibane Ntlatlapa1, Kgopotso Magoro1, George Okeyo2, Sally Musanga2, Audrey Mbogho3, Jane W. Muchiri3, Ruth Wambua3 1 LINK Centre, University of the Witwatersrand 2 Carnegie Mellon University Africa (CMU-Africa) 3 United States International University – Africa 1. Project objectives The project, relevant to the pursuit of SDG3, aims to address language barriers in digital health information access by developing natural language processing (NLP)-powered solutions for selected low-resource African languages, namely KheLobedu (South Africa), Kidaw’ida (Kenya), and Kinyarwanda (Rwanda). The initial seed fund phase is centered on language data collection and community engagement through translation, writing, and recording. The specific objectives of the project are to: •

• • • •

Collect and curate health information language data for KheLobedu and Kidaw’ida, where limited or no health information language resources currently exist, while expanding available health information language resources for Kinyarwanda.

Develop digital artefacts and prototype solutions demonstrating language-accessible health information services for these African language communities.

Produce an initial set of digitally accessible health information content focused on selected chronic non-communicable diseases (NCDs).

Establish foundational language resources and datasets to support future artificial intelligence (AI) and NLP model development for integration into digital health information platforms.

Adopt a participatory approach involving user feedback collection, and iterative improvements.

2. Recent progress Initial progress has focused on health and nutrition information content curation, knowledge structuring, and health assistant development for the target languages. Information sheets covering hypertension, type 2 diabetes, and osteoarthritis were created using authoritative sources, to support multilingual question-answering systems.

Figure 1 presents the overall system architecture, based on a Graph Retrieval-Augmented Generation (GraphRAG) framework, that combines structured clinical knowledge topics, with contextual health information resources, to support question answering.

Figure 1: Digital health information system architecture

Building on this architecture, Figure 2 illustrates the health assistant workflow and the endto-end processing pipeline used to answer user queries.

Figure 2: Multilingual digital health information assistant workflow

To support the health assistant, the knowledge graph construction reused existing ontologies to organise disease, symptoms, exercise and food concepts and their relationships for hypertension, type 2 diabetes, and osteoarthritis.

Figure 3: Knowledge graph construction methodology

Concepts were reused from Disease Ontology (DOID), Symptom Ontology (SYMP), Exposure Ontology (EXMO), and Food Ontology (FOODON) to support semantic interoperability and vocabulary standardisation.

The design phase includes an ontology-based health knowledge graph linking diseases with symptoms, foods, and exercise concepts. Figure 4 shows a generic taxonomy-ontology relationship diagram which will be applied in structuring the language data within an ecosystem view of public health, using only the most relevant features.

A digital health information assistant and website are being developed to support health information access and communicate project progress.

3. Future plans The next phase of the project will focus on (i) language data collection activities in the three geographic locations (Mamaila TA for KheLobedu, Taita Hills for Kidaw’ida, Kigali for Rwanda), and (ii) health information assistant evaluation across the three target languages. Planned activities include conducting co-creation and participatory design engagements with language communities to ensure cultural appropriateness, expanding health content coverage and refining the digital health information assistant through usability testing, and iteratively improving the system based on user feedback.

Figure 4: A generic taxonomy-ontology relationship diagram

4. Peer-reviewed publications resulting from this project An initial literature review has been conducted. We will prepare the first draft of a journal manuscript in parallel with the process of data collection.

End.

Digital Malaria Control for the Developing World Dr. Carine Mukamakuza cmukamak@andrew.cmu.edu Carnegie Mellon University Africa Prof. Jean Paul Niyoyita jniyoyita@ur.ac.rw University of Rwanda, College of Medicine and Health Sciences

Project Objectives The persistent burden of malaria in Rwanda has created a diagnostic bottleneck that undermines treatment efficacy and surveillance efforts, especially in resource-constrained areas. Traditional microscopy-based diagnosis, still considered the gold standard, suffers from limitations in field conditions. First, it requires specialized laboratory infrastructure often unavailable in rural healthcare facilities. Second, there is a shortage of skilled technicians with expertise in parasite identification, particularly in remote communities where malaria is most prevalent. Third, the process introduces substantial delays between sample collection and results generation, often extending to days rather than hours, directly impacting treatment decisions. Fourth, traditional methods mostly struggle with diagnostic accuracy, especially for low-parasitemia cases or mixed infections that require expert interpretation. Finally, manual record-keeping of sensitive healthcare data raises privacy and security concerns. These diagnostic inefficiencies translate directly into delayed treatments, ineffective resource allocation, and compromised epidemiological monitoring.

This project addresses these gaps by developing and implementing an end-to-end malaria diagnosis system for resource-limited settings in Rwanda.

The specific objectives of this project include creating a fully functional application for malaria diagnosis that integrates AI-based detection models into a clinical workflow system at lab level. This development is being conducted in collaboration with Rwanda Biomedical Center (RBC), whose stakeholders provide essential domain expertise, validate system requirements, and facilitate implementation in healthcare settings. Additionally, we aim to build and validate a comprehensive image dataset representing the four human-affecting Plasmodium species (P. falciparum, P. vivax, P. malariae, and P. ovale). This dataset not only supports our model training and development but will also serve as an open resource for the broader malaria research community. We also seek to determine an optimal object detection model for accurate parasite detection and classification to enable reliable automated diagnosis and quantification of malaria parasites through rigorous evaluation of multiple deep learning architectures. We also seek to determine an optimal object detection model for accurate parasite detection and classification to enable reliable automated diagnosis and quantification of malaria parasites through rigorous evaluation of multiple deep learning architectures. Furthermore, our work includes developing a clinically meaningful explainability layer that grounds every automated detection in interpretable, microscopist-style morphological evidence, so that clinicians can understand and trust why the model assigns a given species, an essential requirement for safe and accountable adoption of AI-assisted diagnosis in routine practice.

Recent Progress Our implementation has successfully delivered several key components of the malaria diagnosis. We have developed a user-friendly web-based Clinical Decision Support System integrated with a parasite

Figure 1: Visual representation of malaria diagnosis digitization goals: from parasite identification to model explainability (XAI).

detection model. This platform accepts digitized microscopy images from thick blood smears, identifies parasite species and white blood cells in batch processing mode, computes parasite densities using WHOaligned metrics, and generates secure clinical reports, all within a browser-accessible framework Designed specifically for low-resource environments. The platform is hardware-independent and intuitive enough for non-specialist users while maintaining secure data management with robust authentication controls to protect sensitive patient information.

For dataset creation, over 3000 images representing the four Plasmodium species have been validated and annotated, though the dataset currently shows some class imbalance. The validated dataset includes 1099 P. falciparum, 290 P. vivax, 824 P. ovale, and 727 P. malariae.

We evaluated several state-of-the-art deep learning architectures for parasite detection, progressing from earlier models such as YOLOv5 and YOLOv10 to the attention-based YOLOv12 architecture, which offers the best combination of accuracy, speed, and efficiency, critical factors for practical use in resourceconstrained healthcare settings. On a held-out test set, our YOLOv12 model achieved an overall mAP@50 of 0.878 (mAP@50–95 of 0.677), with a mean precision of 0.84 and recall of 0.82 across the four Plasmodium species and white blood cells. We place particular emphasis on recall (sensitivity), since in malaria diagnosis the cost of missing a true infection is far higher than that of a false alarm. The model recalled the large majority of parasites for most classes, with a recall of 0.94 for white blood cells, 0.85 for P. malariae, 0.84 for P. vivax, and 0.83 for P. ovale. P. falciparum remained the most challenging species, with a recall of 0.65 (mAP@50 = 0.80), owing to its tiny size and ring-like appearance in microscopy images. Improving falciparum sensitivity is therefore a clinical priority, as it is the most lethal of the human malaria species and the most consequential to miss.

Beyond raw detection, we developed a morphological explainability framework, Segment-Grounded Morphological Concept Explanation (SGMCE), that makes each parasite prediction interpretable to clinicians. SGMCE is a post-hoc, training-free pipeline that does not modify the detection model. For every detection, it uses the model’s segmentation mask to extract a clean, background-removed crop of the parasite, algorithmically measures morphological features (shape, colour, chromatin, and pigment), and then prompts a vision–language model to produce a microscopist-style natural-language explanation, including the inferred life stage and differential reasoning for why the parasite is one species rather than another. A sample SGMCE explainability report is shown in Figure 2. Because no standard metric exists for judging such morphological explanations, we designed four complementary measures to validate them automatically. Knowledge-Base Consistency (KBC) checks whether the features the explanation claims to observe match the established morphological profile of the predicted species. CV–Claim Faithfulness (CCF) verifies that the textual claims agree with the algorithmic measurements actually extracted from the image,

guarding against ungrounded statements. Discriminativeness (DS) measures whether the explanation cites the distinguishing hallmark features that separate the predicted species from look-alikes, rather than generic descriptions. Finally, an LLM-as-judge score uses a separate large language model to rate each explanation for factual accuracy, differential specificity, and clinical usefulness. Across 737 detections, the explanations achieved high faithfulness to the underlying image measurements (CCF = 0.97) and strong agreement with established species knowledge (semantic KBC = 0.64 and DS = 0.70), confirming that the system’s reasoning is grounded in genuine morphological evidence rather than spurious cues.

Figure 2: Segment-Grounded Morphological Concept Explanation (SGMCE) report for a sample detection: the YOLOv12 detection shown in context, its segment-grounded crop with the background removed, and the generated microscopist-style morphological explanation, including the inferred life stage and differential reasoning for the predicted species.

Next Target As part of our next step, we will focus on several key priorities. First, we will complete rigorous testing and preparation of the web-based system for piloting in Rwandan healthcare facilities. A vital component of this effort will be integrating the system with existing hospital information systems to create a seamless workflow that fits naturally into clinical practice. Simultaneously, we will develop a companion mobile application that allows field healthcare workers to capture and submit blood smear images even in areas with limited connectivity.

As part of the next phase, we aim to evaluate the impact of parasite stage annotation on model performance. The initial hypothesis is that incorporating stage-specific label could improve classification accuracy, as some malaria parasites appear in multiple developmental stages, and each stage may present distinct morphological forms. To validate this assumption, we plan to retrain our current best-performing model using a newly annotated dataset that includes parasite stage labels and compare its performance against the existing model. If a significant improvement is observed, the resulting model will be used to assist in annotating the remaining P. falciparum (PF) images, thereby accelerating dataset expansion and facilitating the extension

(a) Clinician dashboard showing key case metrics, triage status, and quick actions for review.

(b) Blood smear image upload with multi-file preview and validation status.

(c) Patient diagnosis report with parasite count and density.

Figure 3: MalariaAI clinical decision support platform components for AI-assisted malaria diagnosis and reporting.

(a) P. vivax (PV)

(b) P. malariae

(c) P. ovale (PO)

Figure 4: Sample validated dataset annotations with species predictions, bounding boxes, and confidence scores.

of stage annotations to additional parasite species. Furthermore, incorporating parasite stage information is important for accurate parasitemia estimation, as not all developmental stages are considered during parasite counting. Stage-aware annotations will therefore not only support improved model performance but also contribute to more reliable parasitemia calculations and downstream clinical analyses.

We will also strengthen our morphological explainability framework (SGMCE) by carrying out validation with expert microscopists to confirm that the generated explanations are clinically trustworthy, and by integrating these interpretable, evidence-grounded explanations directly into the clinical decision support system so that each prediction is accompanied by transparent reasoning. Embedding such explanations alongside every detection is essential for building clinician trust and supporting the safe, accountable adoption of AI-assisted malaria diagnosis in routine practice.

Peer-Reviewed Publications [1] A. T. Issah, I. Seidu and C. Mukamakuza, “Detection versus Instance Segmentation for Multi-Species Malaria Diagnosis: A Head-to-Head Comparison and Multi-Dataset Validation of YOLOv12 Architectures with Small Object Optimization,” Proceedings of the 9th International Conference on Medical Imaging with Deep Learning (MIDL), PMLR, vol. 315, 2026, pp. 4683–4702, proceedings.mlr.press/v315/issah26a.html.

[2] A. T. Issah and C. Mukamakuza, “Bridging the Gap in Malaria Diagnostics: An Attention-Centric YOLO Framework with Species-Specific Augmentation for Tiny Parasite Detection in Low-Resource Settings,” Proceedings of the Second AAAI Bridge Program on AI for Medicine and Healthcare, PMLR, vol. 317, 2026, pp. 141–149, proceedings.mlr.press/v317/issah26a.html.

[3] C. P. Mukamakuza, A. D. Nishimwe Karasira, E. M. Akpo, Y. A. Bogale, P. Fasouli and M. Salem, “A Comparative Analysis of Deep Learning Models for Malaria Plasmodium Classification,” 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France, 2024, pp. 1–4, doi: 10.1109/ICECS61496.2024.10848723.

[4] H. R. Mary, C. P. Mukamakuza and E. Tuyishimire, "A Data Management Model for Malaria Control: A Case of Rwanda," 2023 IEEE AFRICON, Nairobi, Kenya, 2023, pp. 1–6, doi: 10.1109/AFRICON55910.2023.10293671.

[5] E. M. Akpo, C. P. Mukamakuza and E. Tuyishimire, “Binary Segmentation of Malaria Parasites Using U-Net Segmentation Approach: A Case of Rwanda,” 2024 Ninth International Congress on Information and Communication Technology (ICICT), Singapore, 2024, pp. 1–12, doi: 10.1007/978-981-97-4581-4_12.

[6] A. D. Nishimwe Karasira, C. P. Mukamakuza and E. Tuyishimire, “The Use of YOLOv5 as a Malaria Detection Model for the Developing World,” Proceedings of Ninth International Congress on Information and Communication Technology (ICICT 2024), London, UK, 2024, pp. 615–626, doi: 10.1007/978-981-97-3299-9_50.

[7] Y. Bogale, C. P. Mukamakuza and E. Tuyishimire, “Intelligent Malaria Detection and Species Classification: A Case of Rwanda,” Proceedings of Ninth International Congress on Information and Communication Technology (ICICT 2024), London, UK, 2024, doi: 10.1007/978-981-97-3299-9_41.

[8] I. Umuhoza and C. Mukamakuza, “SVM Model-Based Digital System for Malaria Screening and Parasite Monitoring,” 2023 IEEE Smart Cities Conference (SCC), 2023, pp. 1–6, doi: 10.1109/SCC59637.2023.10527507.

Enhanced Cardiovascular Diseases Discovery in Medically Underserved Communities via AI-Assisted Stethoscopy

Prof. Khalil Elkhodary khalile@aucegypt.edu The American University in Cairo, Egypt Prof. Vijayakumar Bhagavatula vk16@andrew.cmu.edu Carnegie Mellon University, Africa Dr. Carine Pierrette Mukamakuza cmukamak@andrew.cmu.edu Carnegie Mellon University, Africa Prof. Francesco Renna francesco.renna@fc.up.pt University of Porto, Portugal Prof. Miguel Coimbra mcoimbra@fc.up.pt University of Porto, Portugal

Introduction Cardiovascular diseases (CVDs) remain the leading cause of death globally, accounting for an estimated 17.9 million deaths annually. In sub-Saharan Africa alone, over 1 million deaths were attributed to CVDs in 2019, representing 13% of all deaths in the region and 5.4% of global CVD-related mortality. Early diagnosis plays a crucial role in reducing CVD-related morbidity and mortality through timely clinical interventions and rehabilitation [1]. However, limited access to diagnostic infrastructure and routine screening in underserved communities continues to hinder early detection and effective management. To address this gap, there is a growing need for portable, cost-effective, and intelligent point-of-care (PoC) diagnostic systems tailored for low-resource settings [2].

To this end, this project proposes an integrated, interdisciplinary diagnostic framework that unifies three mutually reinforcing pillars: physics-driven high-fidelity simulation, experimental fluid dynamics, and machine learning-based signal analysis. High-fidelity multiphysics simulations — coupling fluid-structure interaction (FSI) with vibroacoustic modeling — generate synthetic hemodynamic and acoustic datasets across a range of pathological severities, establishing quantitative links between disease-specific flow physics and their acoustic signatures. These computational findings are experimentally grounded through a patient-specific in-vitro Mock Circulation Loop (MCL), which provides high-fidelity validation data and supports the discovery of scaling laws governing aortic hemodynamics. Together, this synthetic and experimental corpus forms the training foundation for a physics-informed machine learning pipeline capable of identifying and characterizing CVD severity from phonocardiogram (PCG) signals acquired via digital stethoscopes.

A defining commitment of this framework is physical interpretability. Physically informed neural networks (PINNs) and graph attention mechanisms embed multiphysics constraints directly into the diagnostic model, ensuring that predictions are not only accurate but clinically transparent — producing cycle-level, spectrally grounded explanations that align with established pathological descriptions and are traceable to underlying hemodynamic physics.

Objectives Deliver a low-cost point-of-care diagnostic tool for cardiovascular screening in resource-limited settings, powered by physics-informed machine learning – built on three pillars: 1. Discover flow physics via in-silico and in-vitro simulation 2. Embed physical constraints through PINNs 3. Ensure clinical transparency through explainable AI

Recent Progress -

Graph Attention Network for PCG Classification and Explainability Traditional deep learning-based approaches for heart valve disease diagnosis using heart sounds struggle at the level of clinical inference due to a lack of knowledge of the key cardiac events in the recordings. Aortic valve pathologies are known to introduce mechanical artefacts in the flow, which in turn manifests into systolic murmurs, and this prior knowledge could provide valuable insight about the type of pathology. A graph attention (GAT) based neural network is developed for accommodating the structural information of the heart sounds and identifying persistent spectral patterns from cycle to cycle for the systolic interval. The segmentation mask is generated using the previous deep learning-based method. The signals are transformed into time-frequency representations using a wavelet-based approach. Four 2D-attention-enabled autoencoders are developed for four distinct states in the phonocardiogram (PCG) signal, and the encoded features are further utilised to prepare a directed hypergraph for the input for the GAT. The model has a hierarchical decision-making framework, where the first level detects the presence of a systolic murmur, while the next one characterises it according to the disease class. The model has been trained and tested for Yaseen's dataset, specifically for the pathological cases that include systolic murmur. The attention maps are further used for explainability and description of the acoustic anomaly in disease-specific cases.

Figure 1 Hierarchical Graph Attention Network (GAT) for Sequential Heterograph-Based PCG Classification and Feature Extraction

The model achieved 100% accuracy in detecting systolic murmurs and 98% accuracy in subsequent multi-class classification. The attention-derived explanations include the description of the murmur type based on spectral content and shape for each cycle based on established pathological descriptions and a majority voting for the final decision, which can be cross-validated against the classifier prediction.

-

Self-supervised Learning While self-supervised learning (SSL) aims to reduce reliance on labeled medical data, it remains unclear whether performance gains in phonocardiogram (PCG) analysis stem from the SSL encoder or from strong handcrafted preprocessing. To resolve this, we deployed a 2×2 factorial audit, crossing preprocessing priors (filterbank vs. raw waveform) with encoder training (SSL-trained vs. randomly initialized). All configurations utilized a Mamba backbone, selected for its ability to model long-range temporal dependencies in acoustic signals with linear scaling. This architecture ensures that

performance comparisons remain consistent across three diverse benchmarks: the binary PhysioNet/CinC 2016 screening task, the 5-class Yaseen diagnostic set, and the fine-grained CirCor DigiScope murmur grading task.

Figure 3: 2x2 factorial audit design to disentangle preprocessing priors from learned representations.

Downstream performance was assessed via frozen linear probes to isolate "linear accessibility" within the embeddings, ensuring that gains were inherent to the representation quality rather than downstream classifier complexity. We compared six SSL variants (including CycleSSL and SimCLR) against a handcrafted band-energy baseline (AUROC 0.828) to further decompose frequency importance from temporal dynamics. The audit revealed that strong preprocessing can obscure SSL gains on easier tasks—where random encoders already perform near the ceiling (AUROC 0.893–0.931)—while SSL shows clearer value in raw-data settings or fine-grained clinical grading tasks (AUROC up to 0.948). Consequently, evaluation frameworks for cardiac sound classification must include random encoder baselines and task-difficulty stratification to properly attribute performance contributions and avoid overestimating the impact of learned encoders.

-

In-Vitro MCL At AUC, the experimental setup features a 1:1 scale vascular model derived from patient CT data, fabricated from compliant silicone. This Transparent Mock Circulation Loop (MCL) features a pulsatile pump, aortic valve models, and a tubing network, as illustrated in Fig. 4a

(a)

(b)

Figure 4: Schematic Representation of MCL and Particle Imaging Velocimetry (PIV) system

Fluid Flow Visualization setup: High-fidelity experimental validation was conducted at the American University in Cairo (AUC) using a patient-specific Mock Circulation Loop (MCL). High-speed Particle Image Velocimetry (PIV) was utilized to characterize transient flow instabilities and resolve complex velocity fields within the vascular model (Fig. 4a). To ensure physiological relevance, dynamic similarity was strictly maintained by scaling the Reynolds ( ) and Womersley( ) numbers via a programmable pulsatile pump, replicating the hemodynamics of various stenotic severities.

Flow instability and Turbulence Modal Decomposition : To isolate the primary physical drivers of heart valve disease, we applied Hilbert Proper Orthogonal Decomposition (HPOD) to the turbulent velocity fields captured by PIV (Fig. 4b).

This method extracts "coherent structures"—the most dominant and repeatable patterns within the chaotic flow.

Our analysis revealed that the first 10 HPOD modes capture over 90% of the total Turbulent Kinetic Energy (TKE), demonstrating that the complex turbulence is governed by a few key physical behaviors rather than random noise. By focusing on these primary modes, we effectively filter out stochastic "cycle-to-cycle" variations, revealing the "clean" physical signatures inherent in the turbulent flow. These findings establish the ground-truth benchmark required to derive the scaling laws that connect flow physics to the diagnostic heart sounds analyzed by our AI. -

In Silico Simulation: State-of-the-art multiphysics simulations of the aortic valve were developed, coupling computational structural mechanics with computational fluid dynamics to produce Fluid-Structure Interaction (FSI) simulations. The FSI outputs were subsequently used as inputs for vibroacoustic simulations employing the Boundary Element Method (BEM), enabling the artificial reproduction of acoustic signatures across varying levels of aortic stenosis.

Figure 5: Project roadmap for the computational modeling (in silico) and acoustic characterization of heart valve stenosis.

This simulation pipeline has enabled: 1. The generation of synthetic acoustic and hemodynamic datasets across a range of stenotic severities; 2. The identification of correlations between hemodynamic properties and acoustic signatures; 1. The observation of characteristic cardiac cycle features in the time domain after post-processing of raw vibroacoustic outputs. This synthetic dataset is a cornerstone resource for training the project’s AI diagnostic models.

Future Plans Machine Learning - U Porto -

Assessment of the GAT-based framework’s robustness and generalizability across multiple clinical and simulated datasets, integration of synthetic data from the in-silico pipeline, and continued development toward deployment as an accessible, digital stethoscope-based diagnostic tool for use in resource-limited settings.

Deep Learning - CMU -

-

Conduct a second round of multimodal data collection in Rwanda, focusing on the acquisition of ECG, PCG, and echocardiogram data from participants. This expanded dataset will support more robust model development, improve validation across multiple cardiovascular signal modalities, and strengthen the clinical relevance of the Aortic Stenosis diagnostic framework.

Investigate influential PCG feature domains for Aortic Stenosis classification using low-resource deep learning models and explainable AI methods. This will focus on identifying the most clinically meaningful acoustic patterns and feature representations that contribute to accurate diagnosis, while supporting model interpretability and practical deployment in resource-limited healthcare settings.

Fundamental Physics - AUC Computational In-silico Modelling: -

Refinement of the FSI and vibroacoustic simulation pipeline, including modifications to leaflet gap geometry, fluid domain extension, quadratic meshing, and turbulence modeling.

Efforts will focus on reducing artificial numerical noise from BEM simulations and minimizing spurious leaflet vibrations in the FSI model to improve the fidelity of the synthetic acoustic dataset.

In-Vitro Experimental setup: -

Conduct a systematic PIV Experimental Campaign on different degrees of Aortic Stenosis.

Benchmarking of spatiotemporal variations in aortic haemodynamics and jet morphology across various stenotic severities through comparison of dominant HPOD mode.

These findings will support the development of PIML -based cardiovascular diagnostics.

Processing and organizing audio data for machine learning model training.

Peer-Reviewed Publications Resulting from this Project [1] A. M. Ali, A. H. Hafez, K. I. Elkhodary, and M. El-Morsi, “A CFD-FFT approach to hemoacoustics that enables degree of stenosis prediction from stethoscopic signals,” Heliyon, vol. 9, no. 7, Jul. 2023. doi:10.1016/j.heliyon.2023.e17643 [2] A. M. Ali, A. A. Ghobashy, A. A. Sultan, K. I. Elkhodary, and M. El-Morsi, “A 3D scaling law for supravalvular aortic stenosis suited for stethoscopic auscultations,” Heliyon, vol. 10, no. 4, Feb. 2024. doi:10.1016/j.heliyon.2024.e26190

References: [1] N. W. Minja, D. Nakagaayi, T. Aliku, W. Zhang, I. Ssinabulya, J. Nabaale, W. Amutuhaire, S. R. de Loizaga, E. Ndagire, J. Rwebembera, and E. Okello, "Cardiovascular diseases in Africa in the twenty-first century: gaps and priorities going forward," Frontiers in Cardiovascular Medicine, vol. 9, p. 1008335, 2022.

[2] J. Rajendran and G. Slaughter, "Transforming Cardiovascular Care—Biosensors and Their Potential: A Review," IEEE Sensors Journal, 2025.

Ethical, Trustworthy, Autonomous: The Vehicles of Tomorrow PI: Assoc. Prof. Dr. Amr El Mougy | Co-PIs: Prof. Dr. Moinak Maiti, Prof. Dr. Sherif Aly, Prof. Dr. Khalil El Khodary, Dr. Daoud Siniora, Dr. Alia El Bolock The American University in Cairo (AUC) & University of the Witwatersrand (UWits) amr.elmougy@aucegypt.edu

Project overview “Ethical, Trustworthy, Autonomous: The Vehicles of Tomorrow” is a 24-month collaboration (October 2025 – October 2027) between the American University in Cairo and the University of the Witwatersrand that asks how a self-driving car can not only drive safely, but also act ethically, justify its choices, and earn the public’s trust. The work is organised around four tightly integrated research tracks. Track 1 builds an end-to-end autonomous driving system that learns to drive directly from perception while embedding ethical decision-making in both routine and nearcollision situations. Track 2 supplies the mathematical and formal-logic machinery that represents environmental risk and determines which actions are permissible — including the hard cases where ordinary traffic rules may be deliberately and verifiably relaxed to minimise harm. Track 3 constructs a digital twin of the vehicle, coupled to the CARLA simulator and visualised in virtual reality, so that human participants can experience the car’s decisions and we can measure how much they trust them. Track 4 adds an explainability layer that turns the vehicle’s internal reasoning into transparent, human-readable accounts of why it acted as it did. Together the tracks form a single pipeline — perceive, decide ethically, act, and explain — evaluated for trust at every stage (Figure 1).

Figure 1. The four tracks form a single closed loop: the vehicle perceives the scene, decides under formally specified ethical constraints, acts, and explains itself — with public trust measured throughout via a VRenabled digital twin.

Recent progress In its first six months, the project moved decisively from design to working software across all four tracks. Research-assistant teams were recruited at both AUC and UWits, and each researcher was given a focused problem statement tied to one of the four tracks, so that individual contributions accumulate toward the shared system rather than diverging.

The end-to-end driving system (Track 1) is the furthest along, and is summarised in Figure 2. We implemented a complete perception-to-control pipeline in which synthetic multi-camera and LiDAR data — generated in CARLA and SimBEV under controllable weather and traffic density — are encoded by a BEVFormer model into a unified bird’s-eye-view representation of the scene.

This 200×200 occupancy-and-semantics grid becomes the state fed to a Twin-Delayed Deep Deterministic Policy Gradient (TD3) reinforcement-learning controller that outputs steering, throttle, and braking commands. Training is already underway and shows encouraging behaviour, with the vehicle making good driving progress in simulation under reward functions shaped for lane-keeping, safe following distance, smooth and comfortable control, traffic-rule compliance, and goal progress.

In parallel, the mathematical risk-formulation track (Track 2) has produced a working model that computes an optimal action for the vehicle under dynamically changing constraints. Crucially, the formulation is built to reason about near-collision dilemmas in which strictly obeying the traffic rules — staying off the sidewalk, for instance — would cause greater harm than a controlled violation; in those moments the model is permitted to relax the rules in a principled way that minimises overall harm. A dedicated CARLA environment is now being developed to evaluate this model against live driving scenarios.

The trust-assessment track (Track 3) has advanced into experimental design: a within-subjects user study has been specified and documented, and the corresponding test scenarios are being implemented in CARLA to serve as the basis for measuring public trust in the vehicle’s decisions.

Finally, the explainability track (Track 4) has produced a complete architecture in which a domain ontology of road and driving elements — deliberately including the locally relevant agents that Western datasets omit, such as tuk-tuks and delivery motorcycles — feeds a streaming knowledge graph. Continuously mining this graph yields traceable, human-readable explanations of each scene and decision, and the design now incorporates an uncertainty-aware reasoning step that escalates from normal action, to caution, to a fail-safe warning as perception confidence drops, and that resolves conflicting signals in favour of safety.

Figure 2. The end-to-end driving pipeline (Track 1). Synthetic sensor data is encoded into a bird’s-eye-view state that drives a TD3 reinforcement-learning controller, trained with reward functions for safe, lawful, and comfortable progress.

Future plans Over the coming year the immediate priority is integration. Track 1 will complete training of the driving model and then fuse it with the codified ethical risk factors from Track 2, so that routine and near-collision driving are handled jointly under ethically guided decisions; this combined system will be tested across diverse environments and sensor modalities, from monocular and stereo cameras to LiDAR and radar with sensor fusion. Track 2 will finish and validate its CARLAbased evaluation environment. Track 3 will complete the digital twin in MATLAB, connect it to CARLA and VR, run pilot and then full-scale user studies, and analyse the resulting trust data.

Track 4 will extend the explainability model to the full decision pipeline, couple it with perceptionlevel explanations, and generate natural-language and visual accounts of the vehicle’s behaviour suitable for liability and insurance applications. These efforts converge on a single, unified ethical and explainable end-to-end AV system, with results targeted at leading journals and conferences and at national and international discussions of AV ethics policy.

Peer-reviewed publications resulting from this project No peer-reviewed publications have resulted from the project yet; several are in preparation as the individual tracks mature and integration begins.

Project Title: FINIA: Financial Inclusion via Novel Intelligence and Alternative data Principal Investigator: Prof. Chimwemwe Chipeta, University of the Witwatersrand (PI); CoPrincipal Investigators: Prof. Yudhvir Seetharam, University of the Witwatersrand; Prof. Ganesh Mani Carnegie Mellon University, Africa & Pittsburgh; Prof Patrick McSharry, Carnegie Mellon University, Africa; Prof. Edith Luhanga, Carnegie Mellon University, Africa (Senior Investigator).

Graduate Student: Pierre Ntakirutimana.

Financial Inclusion via Novel Intelligence and Alternative data (FINIA) is a pan-African research initiative that leverages AI, behavioural insights, and alternative data to address persistent gaps in financial inclusion. With a focus on digital financial literacy, crisis resilience, and start-up sustainability, the project has already delivered new evidence from cross-country surveys, machine learning models, and chatbot-based interventions. FINIA is shaping inclusive fintech ecosystems by translating data-driven insights into policy and practice.

1. Project Objectives The FINIA (Financial Inclusion via Novel Intelligence and Alternative data) project is a bold and timely initiative aimed at addressing the persistent challenges of financial exclusion in Sub-Saharan Africa. As digital technologies continue to reshape the financial landscape, FINIA seeks to harness the power of data, artificial intelligence, and behavioural insights to build inclusive, resilient, and forward-looking financial ecosystems. Financial inclusion entails not just access but the ability to use financial services effectively and sustainably. To this end, FINIA is structured around three core objectives: 1) developing robust digital financial literacy strategies (informed by pilot surveys), 2) identifying some of the policy and infrastructural enablers of digital financial services during crises, and 3) leveraging advanced data analytics to predict and mitigate financial distress among African start-ups.

Objective 1: Digital Financial Literacy FINIA focuses on crafting effective, comprehensive digital financial literacy strategies tailored to the African context. Building on the original proposal, this objective has been expanded to include the development of the first continent-wide fintech index and the use of generative AI to support financial education. The objective is motivated by the current employment trends observed on the continent. According to a Mastercard Foundation survey, the gig economy - an economic system where people engage in short-term, often digitally mediated work (gigs) - is growing 20\% every year in Africa and will employ 80 million workers by 2030.

Other surveys also reveal that the majority of Africans (an estimated 86% in 2020) are engaged in informal employment without social protections. In this context, it is essential that the population have the knowledge and skills to research and use the increasingly digital financial products and services available to ensure their long-term financial security.

Objective 2: Crisis-Resilient Digital Inclusion FINIA investigates how the COVID-19 pandemic, while accelerating digital onboarding and FinTech adoption, also introduced significant financial hardships that negatively impacted digital financial inclusion. This component of the project hypothesizes that pandemic-induced financial stress is associated with reduced access to digital financial services, but that this effect can be mitigated by both individual-level characteristics—such as education, income, age, and employment status—and country-level factors like economic growth, institutional quality, and inflation stability. Drawing on data from 31 African countries, the study employs econometric modeling to examine how these variables interact with pandemic-related

financial concerns. The findings reveal that youth, urban residents, and individuals in countries with stable inflation are more resilient to the negative effects of financial distress on digital financial inclusion. These insights underscore the need for targeted policy interventions that enhance financial resilience and ensure inclusive access to digital financial services during times of crisis.

Objective 3: Predicting Financial Distress in Start-ups FINIA focuses on identifying the drivers of success for African start-ups and developing predictive models to assess financial distress, with a strong emphasis on explainability and practical application. This work involves a structured process beginning with a literature review to ground the research in existing theory, followed by data collection and preparation, including identifying relevant features and cleaning datasets. The project will then proceed to model development, where various machine learning approaches will be explored and refined. These models will be evaluated not only for their predictive accuracy but also for their interpretability, ensuring that stakeholders can understand and trust the insights generated. The final stages will involve deploying the models, analysing results to identify robust predictors of start-up success or failure, and formulating policy recommendations and risk mitigation strategies based on these findings. This approach aims to support more resilient entrepreneurship ecosystems and inform evidence-based decision-making across the continent.

Figure1. The summary of objectives.

Figure2. The Summary of the project Key Findings.

2. Recent Progress We have completed the first task and published it in the Journal Sustainability. In this paper, we present findings from a two-phase mixed-methods study. In Phase 1, we surveyed 300 Rwandans aged 18–32 on financial knowledge, digital skills, and financial behaviors to explore key gaps in DFL. Results show modest financial knowledge and moderate digital literacy, with common budgeting and saving practices but key cybersecurity awareness-practice gaps. Gender and education disparities are also evident. To address the low loan literacy observed in Phase 1 findings, we conceived an AI-enabled mobile money loan literacy chatbot and explored user interactions with the chatbot, along with perceived usability and usefulness in Phase 2. Our findings highlight design considerations for promoting intention to adopt DFL interventions. The study aligns with the United Nations Sustainable Development Goals (SDGs) 1 (No Poverty), 5 (Gender Equality), 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure), and 10 (Reduced Inequalities).

The second task has also been published in the journal Research in Globalization. The task yields important empirical and methodological insights into the relationship between crisis-induced financial anxiety and digital financial behaviours in Africa. Drawing on data from over 27,000 individuals across 31 countries, the analysis reveals a statistically significant and robust negative association between COVID-19-related financial worry and the adoption of digital financial services. This effect operates through both economic constraints, such as reduced disposable income, and behavioural barriers, including heightened risk aversion and distrust in technology. The study thus contributes to financial inclusion scholarship by elucidating the intertwined economic and psychological pathways through which crises can inhibit digital engagement.

Employing a probit regression framework and rigorous robustness checks, the research further identifies individual-level factors, such as youth status, urban residency, and remittance participation, that moderate the adverse effects of financial stress. Additionally, macroeconomic variables yield unexpected results: while low institutional quality and high inflation are associated with increased digital financial inclusion, economic growth appears to have a negative correlation. These findings suggest that inclusion outcomes are shaped less by macroeconomic advancement per se and more by context-specific behavioural adaptations and policy environments. Collectively, the results underscore the need for tailored policy interventions that address both structural inequalities and behavioural frictions in digital finance uptake.

The third completed task advances our understanding of financial distress by exploring the determinants of financial distress among small and medium-sized enterprises (SMEs) in Africa, employing both traditional econometric methods and advanced machine learning techniques. Using tools such as logistic regression, generalised least squares, mixed-effects logit, and instrumental variable methods alongside models like XGBoost, SVM, and LightGBM, the research uncovers uniquely African dynamics in firm vulnerability. Notably, the study identifies a gender-related paradox: female CEOs

are associated with increased financial distress, whereas female ownership appears to reduce it, suggesting differing implications of leadership versus investment roles. It also lends empirical support to the controversial "grease-the-wheels" hypothesis, where informal payments (‘gifting’) correlate with reduced distress, implying that, in weak institutional environments, corruption may function as an unofficial mechanism for navigating bureaucratic obstacles. Furthermore, the presence of informal competition and the use of audited financial statements are shown to significantly reduce the likelihood of financial distress.

On the technical front, we successfully launched and operationalized FindistressAI, an AI-driven financial distress prediction platform, transitioning the solution from the pilot phase to active deployment and market adoption among small and medium-sized enterprises (SMEs).We also successfully developed and launched the Africa Fintech Index through the FINIA infrastructure platform , establishing a continental benchmark and data framework for tracking and analysing the development of digital financial services and fintech ecosystems across Africa.

3. Future Plans Scaling and Refinement (Q4 2026 and Beyond) We plan to transition our current proof-of-concept into a larger-scale initiative dedicated to the expansion and technical refinement of FindistressAI.v Platform Architecture: FindistressAI is an AIpowered early-warning system built natively on the FINIA infrastructure. It synthesizes complex, multimodal data streams to predict the probability of SME financial distress across African economies: •

Multi-Dimensional Data Integration: The system integrates firm-level micro survey data, macro-financial indicators, and cutting-edge climate risk data derived from satellite imagery and remote sensing.

•

Advanced Analytics: Predictive capabilities are driven by robust machine learning models coupled with Explainable AI (XAI) components, ensuring that risk scores are transparent, interpretable, and auditable.

•

Policy-Intelligence as a Service: Beyond its technical capabilities, the platform is architected as a macro-critical policy tool. It equips governments, central banks, SME ministries, and development finance institutions (DFIs) with real-time, localized insights and actionable policy dashboards to pre-emptively mitigate systemic economic risks.

High-Impact Dissemination (October 2026) •

Wits Global Fintech Conference: Present the comprehensive methodology, empirical results, and foundational work completed during this phase of the project at the upcoming Global Fintech Conference in October 2026. This presentation will serve to validate our models among global peers and attract strategic ecosystem partners.

4. Peer Reviewed Publications/Presentations Peer Reviewed Publications •

Chipeta, C., Mani, G., McSharry, P., Seetharam, Y. and Luhanga, E. (2026). Do individual and country-level factors cushion the impact of COVID-19 financial worry on digital financial inclusion in Africa? Research in Globalization, 12, p.100332. Available at: https://doi.org/10.1016/j.resglo.2025.100332.

•

Nkuruntimana, P., Chipeta, C., Mani, G., McSharry, P., Seetharam, Y., Sowon, K. and Luhanga, E. (2026). Preliminary insights on digital financial literacy gaps among Rwandan youth and considerations for AI-powered interventions. Sustainability, 18(9), p.4155. Available at: https://doi.org/10.3390/su18094155 .

●

Chipeta, C., Mani., G, McSharry, P., Seetharam, Y., and Luhanga, E. (2024). Predictors of Financial Distress for SMEs in Africa. Under Review

Conference Presentations: ●

Nkuruntimana, P., Chipeta, C., Mani, G., McSharry, P, Seetharam, Y, and Luhanga, E. (2024). Measuring Digital Financial Inclusion in Africa: A New Fintech Index. Wits Global Fintech Conference, 3 September 2025, Cape Town.

Events Organised and Attended: ● ● ● ● ● ●

Organised and participated: 2nd Wits Global Fintech Conference. Wits Fintech Hub, Cape Town. 3 to 5 September 2025.

Participated: The Singapore Fintech Week, 25 to 28 February 2025.

Participated: The Singapore Fintech Festival, 12 to 14 November 2025.

Organised and participated in ICAIF25 on “AI for Financial Inclusion, Risk Modeling and Resilience in Emerging Markets”, 16 November 2026, Singapore.

Attended and participated in the FINEXT Conference: 10 to 11 February 2026.

Organised launch event of the Wits Fintech Society: Shaping Africa’s Fintech Future: 24 April 2026

2026 Afretec Principal Investigators Meeting Abstract Title: Fintech, financial literacy, and financial inclusion: Experimental evidence of SMEs in Kenya; Author(s): Odongo Kodongo 1, Dina Natto 2, Peterson Magutu3; Sedjro Alovokpinhou1; Affiliation(s): 1 University of the Witwatersrand, 2 Daystar University, 3 University of Nairobi; Email(s): odongo.kodongo2@wits.ac.za; dinahnatto@gmail.com; Sedjro.Alovokpinhou@wits.ac.za; magutumop@uonbi.ac.ke 1.

Project Objectives -

2.

Recent Progress -

3.

This is a new (pilot) project.

Future Plans -

4.

Examine the role of financial literacy in fostering financial inclusion amongst SMEs with different levels of adoption of fintech in Kenya.

Establish whether, holding fintech adoption constant, financial literacy has different effects on the financial inclusion of men-owned SMEs than women-owned SMEs.

Depending on the results of this pilot study, to progress to a full study.

Peer-Reviewed Publications Resulting from this Project -

No publications to report.

Project description The recent study of Kodongo (2024) documents a strong link between individual engagement with the fintech ecosystem and usage of financial services (savings, credit and insurance), and products (capital market investments) in Kenya. However, the study also finds that fintech has benefitted population segments hitherto enjoying superior access to, and consumption of, financial services more than traditionally disfavored groups (women, young adults, and the less wealthy). Moreover, Kodongo and Ngui (2024) find that despite notable improvements in fintech adoption, and in the consumption of financial services and products over time, the gender gap in financial inclusion is “alive and well” in Kenya.

Interrogating the gender gap, Kodongo and Ngui (2024) find that financial literacy might play an important moderating role on the fintech-financial inclusion nexus, especially for women. That is, low levels of financial literacy might weaken the role of fintech in closing the gender gap in financial inclusion. This is sensible given burgeoning evidence that financial literacy has a causal effect on the consumption of financial services (e.g., Grohmann et al., 2018; Ansar et al., 2023) and adoption of financial technology (Li et al., 2020; AlSuwaidi and Mertzanis, 2024).

Indeed, financial literacy is low in Kenya relative to that of countries with higher human capital

endowment: the 2024 Kenya FinAccess Financial Inclusion Survey reports that only 40.6% of the Kenyan population is financially literate.

Therefore, one might conjecture that, in the fintech age, addressing financial literacy is the antidote for disparities, including the gender gap, in effective financial inclusion (improved usage of financial services by previously disadvantaged groups) in Kenya. But the situation in Kenya is less straightforward: the 2024 FinAccess Survey documents, unlike evidence in a large body of the literature (e.g., Lusardi and Mitchell, 2014; Bucher-Koenen et al., 2017; Chen et al., 2018), that, on average, Kenyan women have a higher level of financial literacy (42.2%) than Kenyan men (39.0%). This raises an important question of empirical interest: given the potential effect of financial literacy on financial services usage (Grohmann et al., 2018; Ansar et al., 2023) and given that Kenyan women are more financially literate than Kenya men, why does the evidence point to a pervasive gender disparity in financial inclusion in Kenya?

The fintech space is a microcosm for the broader financial system. Thus, financial literacy fosters appreciation of the implications of individuals’ financial decisions and informs their prudent choices in the fintech space as much as it does financial decisions in general. That is, financial literacy is crucial for individuals to make the right financial choices and, thus, to derive higher utility from their engagement with the fintech ecosystem. This argument finds support in the empirical evidence, which provides a clear role for financial literacy in fintech adoption (Li et al., 2020; AlSuwaidi and Mertzanis, 2024). Yet, gender disparity in the engagement with the fintech ecosystem has been documented in Kenya (Kodongo, 2024). Which raises the second pertinent question: given the empirical evidence of a causal link from financial literacy to fintech adoption, and given that Kenyan women are evidently more financially literate than Kenya men, is there a role for financial literacy in addressing the gender gap in fintech adoption in Kenya?

Finally, given the findings in Kodongo and Ngui (2024) that financial literacy could be the “missing link” between fintech engagement and financial inclusion of women, the two foregoing questions in turn raise a third and more fundamental question: does financial literacy play a mediating role in the nexus between fintech adoption and usage of financial services/products?

The nature of these questions is that it is difficult to provide compressive answers to them through survey data, which do not achieve an in-depth appreciation of the cross-linkages.

Thus, we propose to attempt answers to these questions using a randomized control trial.

References AlSuwaidi, R.A. and Mertzanis, C., 2024. Financial literacy and FinTech market growth around the world. International Review of Financial Analysis, 95, 103481.

Ansar. S., Klapper, L., and Singer D., 2023. The importance of financial education for the effective use of formal financial services. Journal of Financial Literacy and Wellbeing, 1(1), 28– 46.

Bucher-Koenen, T., Lusardi, A., Alessie, R., and van Rooij, M., 2017. How financially literate are women? An overview and new insights. Journal of Consumer Affairs, 51, 255–283.

Grohmann, A. Klühs, T., and Menkhoff, L., 2018. Does financial literacy improve financial inclusion? Cross country evidence, World Development, 111, 84–96.

Kling, J. R., Liebman, J. B. and Katz, L. F., 2007. Experimental analysis of neighborhood effects. Econometrica, 75(1), 83–119.

Kodongo, O. 2024. Financial inclusion effects of engaging with the fintech ecosystem.

International Review of Economics and Finance, 96(B), 103671.

Kodongo, O. and Ngui, D., 2024. Can fintech really address gender disparity in financial inclusion? Some answers from Kenya. African Finance Journal, 26(2), 1–26.

Li, Y., Li, Z., Su, F., and Wang, Q., and Wang, Q., 2020. Fintech penetration, financial literacy, and financial decision-making: empirical analysis based on Tar, Complexity, 2020, 6696312.

Lusardi, A., and Mitchell, O. S., 2014. The Economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44.

Miguel, E., and Kremer, M., 2004. Worms: identifying impacts on education and health in the presence of treatment externalities. Econometrica, 72(1), 159–217.

Improving Digital Education and Learning innovation in the Rwandan and South African teacher education systems: Towards bridging a digital knowledge divide in Africa University of Rwanda-College of Rwanda: Dr. Innocent Twagilimana (PI); Associate Prof Epimaque Niyibizi, Associate Prof. Irenee Ndayambaje, Dr. Olivier Habimana University of the Witwatersrand: Prof. Juliet Perumal (PI), Associate Prof. Emmanuel Ojo, Associate Prof. Reuben Dlamini, Prof. Leketi Makalela 1. Project Objectives

The objectives of this project are as follows: • • • • •

develop a digital ecosystem for knowledge creation between South Africa and Rwanda; establish a laboratory for digital education and learning; promote adoption of digital teaching approaches and bridge the digital knowledge gap; enhance female participation in science, technology, engineering, arts, mathematics (STEAM); produce locally relevant digital knowledge and create a network of educational researchers

2. Recent Progress

Recent accomplishments of the project team include: • establishment a digital ecosystem for knowledge creation and sharing through the Digital Education and Learning Lab (DELab); • data collection from in-service and preservice teachers; • production of locally relevant digital knowledge from the empirical and desktop research; • adoption of digital innovative teaching practices. Using the findings of research to inform policy and practice in education; • building of research capacity among emerging researchers through (1) training teachers on how to use the DELab and digital technologies, (2) training educators about interactive and innovative digital learning methods (e.g. AI-supported education), (3) providing targeted training for preservice and in-service teachers and lecturers (emerging researchers) to address identified gaps in teaching and learning, (4) enabling emerging researchers to conduct their own research in the field of digital education, and (5) digital teaching training; • support for recent creation of two academic positions on digital and academic literacies, with the two academic lecturers working in collaboration with the Science and Technology division to facilitate the following workshops: (1) multi-literacies workshops, (2) AI literacy training, (3) computational thinking, coding and robotics; • contribution to the development of a coding & robotics curriculum and content development.

3. Future Plans

Future plans are outlined below: • • • • •

launch of DELab; adoption of digital innovative teaching practices. Using the findings of research to inform policy and practice in education (later); knowledge dissemination through publications and conference presentations; continue sharing the findings of the research with teachers and other stakeholders through publications and public dissemination platforms; project close-up: Learning sets on inclusive digital transformation; Report to funder

4. Peer-Reviewed Publications Resulting from this Project

Research on the use of digital technologies in education in South Africa and Rwanda (two papers are under review): Academic Staff’s Digital Competencies and Technology Integration in Education: Insights from a School of Education, submitted to Perspectives in Education (2026).

Pre-Service Teachers’ Technological Knowledge Competencies: A Cross-Sectional Descriptive Study, to be submitted to Journal of Technology and Teacher Education (JTATE) Journal Articles Published: 1. Obi, J. N., & Ojo, E. (2026). Mainstreaming climate change in school curricula: critical review of pedagogy, policy and Global South perspectives. Cogent Education, 13(1), 2613489.

2. Duh, I., Makda, F., & Dlamini, R. (2025). WhatsApp as a Pedagogical Tool During COVID19: A Bibliometric Review. International Journal of Educational Development in Africa, 10, 22-pages.

3. Orji, C. T. & Perumal, J. (2025). Enablers of student's satisfaction with work placement learning towards school-to-work transition: A case of Nigeria. African Journal of Applied Research, 11(2), 513–529.

4. Obi, J. N., Ojo, E., & Ujah, C. O. (2025). Decentralised renewable energy in sub-Saharan Africa: A critical review of pathways to equitable and sustainable energy transitions. Unconventional Resources, 100267.

5. Perumal, J. & Ojo, E. (2025). Bibliometric analysis of women's leadership in African universities during disruptive times. African Journal of Applied Research, 11(2), 496–512.

6. Makda, F. (2025). Digital education: Mapping the landscape of virtual teaching in higher education – a bibliometric review. Education and Information Technologies, 30(2), 2547– 2575. [Scopus indexed] 7. Twagilimana, I., Perumal, J., Habimana, O., Dlamini, R., Ndayambaje, I., Emmanuel, O., ... & Ezechiel, N. (2026). Integrating ICT in Rwandan schools to enhance access proficiency and utilization. Discover Education, 5(1), 80.

8. Twagilimana, I., Perumal, J., Habimana, O., Dlamini, R., Ndayambaje, I., Ojo, E., ... & Nsabayezu, E. (2025). Exploring ICT integration among Rwandan pre-service teachers: attitudes, barriers, and strategies. Discover Education, 4(1), 345.

9. Hidayat, H., Anwar, M., Harmanto, D., Dewi, F. K., Orji, C. T. & Mohd Isa, M. R. (2024). Two decades of project-based learning in engineering education: A 21-year meta-analysis. TEM Journal, 13(4). [Scopus indexed] 10. Hidayat, H., Harmanto, D., Orji, C. T. & Anwar, M. (2024). The implementation and empirical analysis of Android learning application toward performance among students in electronics engineering education. JOIV: International Journal on Informatics Visualization, 8(3), 1154– 1161. [Scopus indexed] 11. Hidayat, H., Ardi, Z., Ahlunnazak, A. I., Harmanto, D., Orji, C. T. & Isa, M. R. M. (2024).

Determining the influence of digital literacy on learning personal competence: The moderating role of fear of missing out. European Journal of Educational Research, 13(4), 1775–1790. [Scopus indexed] 12. Obi, J. N. & Ojo, E. (2025). Enhancing employability through vocational and technical skill development among youths and adults in Nigeria. African Journal of Applied Research, 11(2), 478–495. https://doi.org/10.26437/ajar.v11i2.1048 13. Ojo, E. (2024). Education in the eye of the storm: A bibliometric review of research on global crises and their impact on Southern African education (2000–2024). Southern African Review of Education, 29(1), 15–40. [DHET accredited] 14. Orji, C. T. & Herachwati, N. (2024). Career transition and mentorship nexus: Unmasking the mediating role of career adaptability. Higher Education, Skills and Work-Based Learning, 15(7), 82–95. [Scopus indexed]

15. Orji, C. T., Perumal, J. & Ojo, E. (2024). From being a motivated to motivated: Evidence of the efficacy of problem-based learning in practical skills training. International Journal of Home Economics, Hospitality and Allied Research, 3(1), 162–172.

Book Chapters Published: 1. Orji, C. T. (2025). Employability skills as a foundation for success: An exploration of youth experiences in skills acquisition programs. In Career Coaching and Employability Skills Acquisition (pp. 195–230). IGI Global.

2. Obi, J. N. & Ojo, E. (2025). Climate justice, renewable energy, and food security in SubSaharan Africa: A bibliometric analysis of emerging research trends. In Onyeneke, R. U., Emenekwe, C. C. & Nwajiuba, C. U. (Eds.), Energy Transition, Climate Action and Sustainable Agriculture (pp. 303–328). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-83165-2_16 3. Obi, J. N. & Ojo, E. (2025). A scoping review on exploring job search strategies for unemployed youth: Implications for the agro sector in Sub-Saharan Africa. In Otu, M. S. & Sefotho, M. M. (Eds.), Career Coaching and Employability Skills Acquisition (pp. 1–30). IGI Global. https://doi.org/10.4018/979-8-3693-4014-1.ch001 Conference Proceedings Published: 1. Orji, C. T., Dlamini, R., & Perumal, J. (2026). Effects of Flipped Project-Based Learning Approach on Critical and Reflective Thinking Skills among Graduate Students. In INTED2026 Proceedings (p. 2478). IATED. 2. Orji, C. T., & Dlamini, R. (2026). Technology-Enhanced Vocational Training: Insights into Mobile Technology Implementation for Graduate Trainees. In Reimagining Global Vocational Teacher Education Through Technology-Enhanced Learning (TEL) (pp. 1-38). IGI Global Scientific Publishing.

3. Dlamini, R., Ojo, E., Makalela, L. & Perumal, J., 2024. Comparative analysis of the status of ICT implementation and usage in education: The case of Rwanda and South Africa. In: EDULEARN24 Proceedings, 16th International Conference on Education and New Learning Technologies. Palma, Spain, 1–3 July 2024, pp. 9752–9760. IATED. https://doi.org/10.21125/edulearn.2024.2349 4. Flowers, B. & Tanner, M., 2024. Exploring the digital readiness of underprivileged secondary schools in South Africa. In: IFIP Advances in Information and Communication Technology, Vol. 708, pp. 328–341. Presented at the 18th IFIP WG 9.4 ICT4D Conference, Cape Town, South Africa, 20–22 May 2024. Springer. https://doi.org/10.1007/978-3-031-66982-8_23 5. Hassan, M.N., Marpuah, S. & Nomawati, S., 2023. The difference in ICT proficiency among secondary school Islamic education teachers based on demographic characteristics. In: AIP Conference Proceedings, Vol. 2827, Article 030034. AIP Publishing. https://doi.org/10.1063/5.0164660 6. Khunou, B. & Ojo, E., 2024. A bibliometric analysis of culturally responsive teaching in preuniversity education: Trends and impact in the South African context. In: ICERI2024 Proceedings, pp. 8104–8113. IATED. 7. Kimuli, J. & Ojo, E., 2024. Navigating success in distance education: A mixed-methods study of high-achieving students at Makerere University, Uganda. In: ICERI2024 Proceedings, pp.

9633–9639. IATED. 8. Kimuli, J. & Ojo, E., 2025. The role of technology in supporting high achievement in distance education: A uses and gratifications expectancy perspective. In: INTED2025 Proceedings, pp.

6756–6762. IATED.

9. Le Roux, J. & Perumal, J., 2024. Aspiring to lead for change: The career aspiration experiences of South African women education leaders. In: EDULEARN24 Proceedings, pp.

9871–9880. IATED. 10. Le Roux, J. & Perumal, J., 2024. Being of service in an educational leadership terrain, laden with challenges–A case of servant leadership. In: EDULEARN24 Proceedings, pp. 9804– 9814. IATED. 11. Nkomo, S. & Matli, W., 2022. Adoption of mobile applications to improve the reading habits of rural readers in Southern Africa's secondary schools. In: 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) and 31st IAMOT Joint Conference. IEEE. https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033311 12. Nyembe, B. & Ojo, E., 2024. Mapping the education-work-graduate employability nexus in South Africa: A bibliometric analysis of research trends (2000–2023). In: ICERI2024 Proceedings, pp. 8095–8103. IATED. 13. Ojo, E., Dlamini, R., Makalela, L. & Perumal, J., 2024. Integrating technology in teacher education: A bibliometric insight into innovations in sub-Saharan Africa. In: EDULEARN24 Proceedings, p. 9681. IATED. 14. Ojo, E., Perumal, J., Makalela, L. & Dlamini, R., 2024. Decade-long evolution of digital learning labs in Africa: A comprehensive bibliometric analysis. In: EDULEARN24 Proceedings, pp. 9668–9674. IATED. 15. Orji, C.T. & Perumal, J., 2024. Impact of technology-enhanced problem-based experience on students' intrinsic motivations, ability beliefs, engagement and practical skills outcomes. In: EDULEARN24 Proceedings, pp. 8496–8504. IATED. 16. Orji, C.T. & Perumal, J., 2024. Technology-enhanced learning usage and engagement in practical skills: Does students' e-learning self-efficacy matter? In: EDULEARN24 Proceedings, pp. 8487–8495. IATED. 17. Orji, C.T. & Perumal, J., 2024. Utilization and impact of technology-enhanced learning approaches in TVET during disruptive times: A systematic review. In: ICERI2024 Proceedings, pp. 10172–10181. IATED. 18. Orji, C.T. & Perumal, J., 2025. Development needs analysis of digital technology in STEM education: Insights from rural and under-resourced schools. In: INTED2025 Proceedings, pp.

7494–7502. IATED. 19. Orji, C.T. & Perumal, J., 2025. Exploring the potential of synchronous e-tutoring for practical STEM education. In: INTED2025 Proceedings, pp. 7486–7493. IATED. 20. Perumal, J., Ojo, E., Makalela, L. & Dlamini, R., 2024. A scoping review of research on digital literacy skills and competencies among pre-service teachers, in-service teachers, and teacher educators in Africa. In: EDULEARN24 Proceedings, pp. 9769–9774. IATED. 21. Perumal, J. & Orji, C.T., 2024. Digital transformation of TVET: A bibliometric examination of innovations in sub-Saharan Africa. In: ICERI2024 Proceedings, pp. 10265–10272. IATED. 22. Tenywa, S. & Ojo, E., 2025. Enhancing educational leadership through technology: A scoping review of the impact and strategies for effective integration in South African schools. In: INTED2025 Proceedings, pp. 6731–6736. IATED. Postgraduate Students Funded by Afretec Project University of the Witwatersrand Post Doc student: -

Tobias Orji (A0083620): Improving digital education and learning, technological innovations in education, technology-enhanced learning, innovative pedagogy, problem-based learning, career transition, and employability

University of the Witwatersrand Master’s student:

Integrating Artificial Intelligence and computational modeling in plastic waste sorting, development and optimization of biofibre-reinforced composites for sustainable construction materials.

Dr. Gaudence Nyirazeyimana (ECR), University of Rwanda (Principal Investigator - 1) Prof. George Rading, University of Nairobi (Co- Principal Investigator) Prof. Thomas O. Mbuya, University of Nairobi (Principal Investigator-2) Dr. Celestin Nkundineza, University of Rwanda (Co-Principal Investigator) Dr. Maxime Binama (PhD) (ECR), University of Rwanda (Co-Principal Investiator) Mr. Boniface Ntambara (ECR), University of Rwanda (Researcher)

1. Project Objectives This research aims to support ongoing efforts in recycling plastic and food waste streams by initiating initiatives focused on the efficient upcycling of waste materials into high-quality biofibre composites for construction applications. One of the primary objectives is to integrate artificial intelligence and machine learning techniques to accurately detect and separate Polyethylene Terephthalate (PET) plastic waste from mixed waste streams. Another objective is to address the persistent problem of plastic waste misclassification, which reduces recycling purity and limits the successful recovery of valuable PET materials.

To achieve efficient sorting, the project focuses on developing an AI-driven plastic waste sorting system capable of detecting, classifying, and separating PET from non-PET materials. The study further investigates how to effectively distinguish PET from visually similar plastics under conditions of class imbalance, where false positives reduce recycling purity and missed detections limit recovery efficiency.

2. Recent Progress An AI-based plastic waste sorting system using deep learning techniques has been developed. The system utilizes EfficientNet-B0 combined with a custom CNN architecture through transfer learning. EfficientNet-B0 was selected because of its ability to capture fine texture differences between plastics while maintaining computational efficiency suitable for deployment on edge AI devices. The architecture incorporates compound scaling, MBConv layers, squeeze-and-excitation attention mechanisms, Swish activation functions, and a lightweight classification layer.

For PET/non-PET classification, a two-class dataset containing 7,492 images of size 224 × 224 pixels was created, including 1,886 PET and 5,606 non-PET samples. The images were annotated, standardized, enhanced using image augmentation techniques, and divided into training, validation, and testing sets using a 70%-15%-15% split.

The model was trained using cross-entropy loss and evaluated using accuracy, precision, recall, F1-score, confusion matrices, and AUC metrics. The proposed PET/non-PET classification model achieved promising performance with an overall accuracy of 98.64%, PET precision of 0.9913, recall of 0.9913, F1-score of 0.9913, macro F1-score of 0.9855, and weighted F1-score of 0.9864.

During validation, recall converged to 0.9982 while validation AUC approached 0.99995, demonstrating excellent capability in distinguishing PET from other waste materials.

3. Future Plans •

• •

•

•

Future work will focus on improving the robustness and real-world applicability of the proposed system. Planned improvements include better dataset balancing strategies, simulation of real-world lighting and environmental conditions, optimization of edge inference for embedded deployment, and integration of a camera-assisted mechanical sorter controller for automated waste separation. The project also aims to further enhance the performance and scalability of the AI-driven PET recovery system to support sustainable recycling and smart waste management initiatives.

Computational model to forecast the hybrid composite’s tensile modulus will also be created using representative volume element (RVE) and finite element analysis (FEA).

Then, experimental approach, combined with response surface methodology ( RSM) and the Taguchi method, will be utilized to examine how the hybridization and processing parameters influence the water absorption, as well as the flexural and tensile properties of the hybrid composite. This study will result in the development of an AI-driven PET model and a computational model and the determination of the optimum mixture formulation and processing conditions for high-performance structural applications.

Workshops will be conducted in Nairobi (from 20–24 July 2026) and Rwanda (from 7–11 September 2026) to disseminate project findings, strengthen collaboration among researchers and industry stakeholders, facilitate knowledge and technology transfer, build capacity in artificial intelligence and sustainable materials research, and promote the adoption of innovative recycling and composite manufacturing technologies within the region.

We are planning to apply for additional funding through future AFRETEC calls, as the current Seed Grant will not cover all planned research activities, including advanced testing, and prototype development.

4. Peer-Reviewed Publications Resulting from this Project No publications to report at the moment.

Leveraging IoT and Edge-AI For Detecting and Managing Schistosomiasis Moustafa Youssef, Cheikh Ahmadou Bamba Gueye, Benyl Ondeto Objective This project aims to develop an integrated IoT and Edge-AI framework to mitigate schistosomiasis risk across Africa. By continuously monitoring key physicochemical water parameters (pH, temperature, dissolved oxygen, and electrical conductivity), the system seeks to correlate these indicators with snail population dynamics, which are the primary vectors of schistosomiasis. An AI model optimized for Edge-IoT platforms enables real-time inference and delivers actionable insights to support proactive and scalable disease management in resource-constrained environments.

Current Progress The project has made substantial progress in developing an advanced water quality prediction system tailored to support schistosomiasis management. Initially, a hardware sensing kit was developed to collect physiochemical parameters of the water.

Figure 1 illustrates the IoT-based data collection and transmission system, which is built around three acquired measurement kits. Each kit is based on a Waspmote unit and is equipped with sensors for key parameters including pH, EC, DO, turbidity, TDS, temperature, and flow rate.

The collected raw data can be transmitted through two primary methods: (i) a LoRa protocol to a gateway using region-specific ISM bands, and (ii) GSM/3G/4G or Ethernet networks. Figure 2 exhibits a deployed kit in one of the field locations, showcasing the operational implementation of the IoT-based data collection system, which transmits data via cellular networks to a central database.

Next, a BERT-based transformer architecture, adapted from natural language processing, was repurposed for the temporal forecasting of environmental parameters, as illustrated in Figure 3.

Leveraging historical time-series measurements (e.g., pH and dissolved oxygen). This system demonstrated robust performance in predicting future trends of physicochemical water variables. Its accuracy significantly outperformed conventional approaches such as Long Short-Term Memory (LSTM) networks [1].

Building upon this foundation, the project subsequently addressed two critical challenges: (1) enhancing computational efficiency for deployment in resource-constrained environments and (2) extending predictive capabilities to estimate the population growth dynamics of intermediate host snails. To meet these objectives, the team implemented Fourier Network (FNet), a transformer variant that substitutes self-attention mechanisms with Fourier transforms (see Figure 4). This architectural change significantly reduced computational demands while preserving high forecasting accuracy. Controlled experiments confirmed that FNet, even with equivalent parameter counts, achieved performance comparable to the BERT model across all target variables. Notably, FNet delivered an 80% reduction in training time and a 90% decrease in inference latency per sample, as verified in [2], thereby making it highly suitable for real-time, on-device deployment.

To further improve prediction accuracy and generalization, the system was augmented with comprehensive data augmentation techniques. These enhancements yielded substantial benefits, with normalized Mean Absolute Error (MAE) reductions of up to 72%, ensuring robustness and reliability even in challenging real-world conditions.

Through these efforts, the project has delivered a highly optimized and field-ready solution capable of providing real-time, accurate, and computationally efficient water quality forecasting to inform schistosomiasis risk management strategies in low-resource settings. Ultimately, by enabling timely interventions to control the snail population, this system contributes directly to reducing human exposure to schistosomiasis-infested water sources, thereby supporting healthier communities and mitigating the burden of disease among vulnerable populations.

Building on our current framework, we are advancing the system through the implementation of a federated learning (FL) architecture, specifically designed for Edge-IoT deployments in resource-constrained, schistosomiasis-endemic regions (Figure 5). Preliminary results are highly encouraging, demonstrating that the FL approach significantly reduces communication bandwidth compared to conventional centralized learning methods, while maintaining predictive accuracy within 1.21% MAE of centralized benchmarks.

To overcome the challenges posed by non-independent and identically distributed (non-IID) data across edge devices, the system integrates FedProx optimization, which has proven effective in achieving stable convergence where traditional algorithms like FedAvg are limited. In addition to reducing bandwidth requirements, the federated approach provides exceptional resilience in real-world deployments. It maintains robust performance even under noisy sensor conditions, ensuring reliable and uninterrupted operation in dynamic and unpredictable environments. Collectively, these advancements mark a major Figure 5: Federated learning-based system architecture step forward toward a practical, scalable, and field-ready solution for distributed water quality monitoring. This system ultimately enables more effective and timely schistosomiasis risk assessment and intervention at scale.

Future Plans Looking ahead, we aim to further enhance the system’s decision-making capabilities by incorporating advanced AI techniques that integrate heterogeneous sensor data, quantify predictive uncertainty, and improve overall robustness. These will be also integrated with the information from the domain expert’s knowledge. The developments will ensure even more reliable and scalable schistosomiasis risk assessment, advancing our goal of safeguarding vulnerable populations through smarter, data-driven public health interventions.

On the hardware side, we plan to update our prototype for the IoT device to include better sensing and communication capabilities for the continuous and remote monitoring of water. These include designing and implementing efficient solutions that cover regions with inefficient or nonexistent network connectivity. Along the same lines, we will investigate the cost, availability, and power tradeoffs of using different communication technologies in the kits. This will allow a more practical and scalable deployment of the kits. For the sensing part, the knowledge of the domain expert will allow us to select and introduce new sensors to our kits, such as rainfall and salinity, which may lead to better accuracies.

Publications [1] Mohsen, Mohamed, Hamada Rizk, and Moustafa Youssef. "Enhancing Water Quality Predictions with Transformers for Schistosomiasis Management." Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies. 2024.

[2] Mohsen, Mohamed, Teegwende Zougmore, Bamba Gueye, Hamada Rizk, and Moustafa Youssef. " Efficient Real-Time Water Monitoring for Schistosomiasis Control with a Fourier Network." Proceedings of the 3rd IEEE MDM International Workshop on Mobile Crowdsensing for Smart Cities 2025.

[3] Mohsen, Mohamed, Hamada Rizk, and Moustafa Youssef. "AquaFed: Leveraging Federated Learning for RealTime Schistosomiasis Prevention Through Water Quality Monitoring." 2025 IEEE Smart World Congress (SWC). IEEE, 2025.

Modelling Artificial Intelligence (AI) Skills Gaps and Upskilling Strategies in the Construction Industry of Developing Economies: The Case of Rwanda and Nigeria Oluwaseun Sunday Dosumu1, Beauty Iniobong John2, Jesse Thornburg3, and Christine Musanase4 1

University of Rwanda, Kigali, Rwanda. o.dosumu@ur.ac.rw University of Lagos, Akoka, Nigeria. ijohn@unilag.edu.ng.

4 Carnegie Mellon University, Africa. jthornbu@andrew.cmu.edu.

4 University of Rwanda, Kigali, Rwanda. musanasechristine@gmail.com 2

Project Objectives: 1. To determine the AI technologies adopted by construction professionals at different stages of construction projects.

2. To assess the AI skills required by construction professionals at different stages of construction projects 3. To assess the current level of AI skills supply by construction professionals at different stages of construction projects 4. To analyse AI skills demand, supply, gaps and mismatch levels within the construction industry.

5. To evaluate AI upskilling strategies for construction professionals and 6. To develop a Structural Equation Model (SEM) of AI skills gaps and upskilling pathways in the construction industry.

The research project investigates the growing need for AI-related competencies within the construction industry (CI) of developing economies. Existing global studies have consistently identified significant AI skills gaps and workforce mismatches within the construction sector.

However, limited research has systematically examined the specific AI skills required for construction project delivery, the extent of AI skills gaps and mismatches among construction professionals, and the strategies necessary for effective AI upskilling in developing economies.

The research adopts the 2020 Royal Institute of British Architects RIBA Plan of Work as the framework for identifying construction project lifecycle stages and associated core professional tasks.

Recent Progress: Significant progress has been achieved under the Carnegie Mellon University and African Engineering and Technology Network (Afretec) funded research grant. A Delphi study and expert interview sessions involving AI specialists, data scientists, software engineers, programmers, and related technology experts have been completed. These engagements enabled the systematic identification and mapping of AI technologies, capability domains, construction applications, system integrations, and AI-related competency requirements across core construction tasks and lifecycle stages.

In addition, a comprehensive structured questionnaire comprising both closed-ended and openended items has been developed and deployed among construction professionals in Rwanda and Nigeria. The instrument captures AI skills demand and possession levels to enable gap and mismatch analysis, while also eliciting information on additional AI competencies, upskilling approaches, implementation challenges, and factors influencing AI adoption within construction practice. The resulting data will support the development and calibration of a Structural Equation Model examining relationships among AI skills demand, supply, mismatches, upskilling strategies, and adoption barriers. Currently, approximately 180 valid questionnaire responses have been obtained from construction professionals in Rwanda, with additional data collection ongoing in Nigeria to facilitate comparative cross-country analysis.

As part of the project outputs, a systematic review paper on AI skills capabilities in construction has been completed and prepared for conference presentation. Another paper titled “Mapping Artificial Intelligence (AI) Technologies and Capability Domains Across Construction Project Lifecycle Stages: An Expert-Informed Framework” has also been drafted and is expected to be submitted for publication following the validation workshop. In addition, a draft outline for a future scholarly book based on the broader research programme has already been developed.

Overall, the project is generating one of the first empirically grounded and practitioner-informed frameworks linking AI technologies, capability domains, skills requirements, and upskilling strategies within the construction industries of developing economies. The findings are expected to contribute significantly to policy development, curriculum enhancement, professional training, and digital transformation strategies aimed at improving AI readiness and construction sector performance in Rwanda, Nigeria, and similar contexts.

Future Plans: (1) A Focus Group Discussion and Framework Validation Workshop involving construction professionals and AI experts has been scheduled for 24 June 2026 to validate the proposed mapping and framework of Artificial Intelligence (AI) Technologies and Capability Domains to strengthen the practical relevance and reliability of the findings.

(2) A paper on“Mapping Artificial Intelligence (AI) Technologies and Capability Domains Across Construction Project Lifecycle Stages: An Expert-Informed Framework” has been drafted and will be submitted for publication following the validation workshop of June 24, 2026.

(3) A draft outline for a future scholarly book based on the broader research programme has already been developed.

(4) Analysis of skill demand, supply, gaps and mismatches in the construction industry (5) To determine the upskilling pathways for construction professionals in the construction industry.

(6) To develop a structural equation model of AI skill gaps and upskilling strategies in the construction industry.

Peer-Reviewed Publications Resulting from this Project: No publications to report at the moment.

Multilayer Biofilter System (MSL) for Wastewater Treatment and Digital Monitoring in Africa Principal Investigator: Nadia ARROUSSE, Al Akhawayn University, Morocco — n.arrousse@aui.ma Co-Principal Investigator: Hamed HASHEMI, University of the Witwatersrand, South Africa Researcher: Asmae KHALDOUN, Al Akhawayn University, Morocco Researcher: Meriem BAMAAROUF, EST, Salé, Morocco Researcher: Abdelghani El Asli, Al Akhawayn University, Morocco Project Objectives This project addresses Africa's critical water scarcity challenges by developing two complementary, sustainable water treatment technologies adapted to local aquaculture (pisciculture) needs in Morocco and South Africa: (1) A Multilayer Biofilter System (MSL) composed of alternating permeable layers (PL: gravel, pumice, perlite, zeolite) and soil mixture blocks (SMB: local soil, charcoal, wood shavings, iron shavings), designed for efficient removal of organic matter, nutrients, and heavy metals from aquaculture wastewater.

(2) Green synthesis of nanomagnetite particles using algae as a bio-reductor, leveraging the algae's bioactive metabolites (polysaccharides, phenolic compounds) to reduce Fe³⁺ ions to magnetic Fe₃O₄ nanoparticles under mild, eco-friendly conditions. These nanoparticles will be characterized and subsequently evaluated for water purification applications.

Both technologies are integrated with Machine Learning (ML) and Internet of Things (IoT) for real-time monitoring, adaptive filtration, and predictive maintenance. Additional objectives include the design of a lowcost Remotely Operated Vehicle (ROV) for submerged biofilter inspection, computational modeling (DFT, MD, CFD) of nanomagnetite-pollutant interactions, and capacity-building through training of unemployed graduates in Morocco.

Recent Progress MSL Biofilter — Optimization and Pilot Testing (Article 1, in progress) Experimental work has been conducted at the Deroua Fish Farming Station (Province of Fkih Ben Saleh, Morocco). The filtering material selected is pozzolan (volcanic rock, sourced from Aguelmouz, Middle Atlas), chosen for its high porosity, low density, and long-term mineral stability (inorganic fraction ≈ 99.9%).

Material characterization results: • Local soil (SMB component): dominated by alumino-silicates (SiO₂ 49.4%, Al₂O₃ 17.3%), with high organic matter content (90.5%), total Kjeldahl nitrogen 5.43%, and a methylene blue value (VBS) of 36 g/kg — confirming high ionic exchange capacity and strong sorption potential for pollutants, heavy metals, and phosphorus.

• Pozzolan (PL component): high inorganic fraction (99.9%), stable and non-biodegradable, with an open interconnected porosity suitable for effective hydraulic drainage. XRF shows SiO₂ (43.8%), Al₂O₃ (14.9%), and Fe₂O₃ (14.3%) as dominant oxides.

Filter configuration optimization: systematic testing of layer numbers (3 to 7 SMB layers), layer thickness (3, 5, 7 cm), and hydraulic loading rates (200 to 4000 L/m²/day) identified the optimal configuration (7 SMB layers, 5 cm thickness, 2000–3000 L/m²/day). Pilot-scale evaluation in a recirculating aquaculture system (RAS) over 11 weeks is underway, assessing COD, BOD₅, ammonium, phosphate, nitrate, and nitrite removal. Biological performance of Nile Tilapia (Oreochromis niloticus) is tracked through biometric parameters (weight gain, SGR, FCR, survival rate).

Figure 1. Experimental recirculating aquaculture system (RAS) with integrated soil-block biofilter Biosynthesis Review — Nanoparticles from Natural Resources (Article 2, in progress) A comprehensive review article on the biosynthesis of nanoparticles from natural resources has been drafted.

It covers: (i) physical, chemical, and magnetic properties of nanoparticles; (ii) mechanisms of bioreduction, nucleation, growth, and biological capping; (iii) roles of plants, algae, bacteria, and fungi as biological sources; (iv) key synthesis parameters (pH, temperature, precursor concentration, reaction time); (v) applications in water treatment, drug delivery, catalysis, and energy systems; and (vi) integration of machine learning for synthesis optimization and property prediction. The review critically examines current challenges in reproducibility, data availability, and standardization of green synthesis protocols.

Figure 2: Graphical abstract illustrating the key themes of the review, including biogenic nanoparticle synthesis, multifunctional applications, sustainability aspects, and machine learning-assisted process optimization.

Nanomagnetite Synthesis — Current Status The green synthesis of nanomagnetite (Fe₃O₄) using algae extract as a bio-reductor has been successfully initiated. Algal biomolecules (polysaccharides, phenolic compounds) facilitate the reduction of iron precursors under mild aqueous conditions, yielding magnetically responsive nanoparticles. The synthesis phase is complete. Full physicochemical characterization — including XRD (phase identification), SEM/TEM (morphology and size), FTIR (surface functional groups), VSM (magnetic properties), and zeta potential (colloidal stability) — is the immediate next step before application testing in water treatment.

Figure 3: Natural Algal Bloom Used for Green Synthesis of Magnetic Nanoparticles Future Plans The immediate priority is the complete characterization of the biosynthesized nanomagnetite particles (XRD, SEM/TEM, FTIR, VSM, zeta potential), which will confirm the crystalline structure, morphology, surface chemistry, and magnetic behavior necessary for advancing to application studies.

Once fully characterized, the nanomagnetite will be evaluated using synthetic water formulated to mimic aquaculture wastewater conditions. Its performance will then be directly compared with that of the MSL biofilter under identical and strictly controlled experimental conditions, in order to ensure a reliable assessment of removal efficiency and system performance.

Computational modeling (DFT and MD simulations, led by Co-PI Dr. Hashemi) will complement experimental data by predicting adsorption mechanisms, binding energies, and nanoparticle stability in water matrices. CFD modeling will optimize the integration of nanomagnetite into the MSL system.

The IoT sensor network and ML framework (supervised models: Random Forest, Gradient Boosting; deep learning: LSTM for time-series anomaly detection, CNN for ROV image-based fouling detection) will be deployed on the pilot biofilter system in Year 2.

Dissemination activities planned include submission of Article 1 (MSL biofilter — Deroua pilot study) and Article 2 (biosynthesis review) to peer-reviewed journals, followed by a third manuscript focused on nanomagnetite synthesis, characterization, and water treatment performance. Capacity-building workshops for unemployed graduates in Morocco and South Africa will be organized in Year 2.

Peer-Reviewed Publications Resulting from this Project No publications to report at this stage. Two manuscripts are currently in preparation.

Real-Time Noise-Level Web Mapping through Crowdsourcing: Toward Creating Sustainable Urban Environments David N. Siriba1*, Ernest Uwayezu3+, Collins M. Mwange1, Esaie Dufitimana3 Kevin Kariuki2, Linus Anari1, Christine Musanesa3 Department of Geospatial and Space Technology, University of Nairobi Department of Surveys, Ministry of Lands Public Works, Housing and Urban Development 1

2

College of Science and Technology, University of Rwanda *dnsiriba@uonbi.ac.ke; +wayezuernest@gmail.com

3

1) Project Objectives The project had three primary objectives: -

development of a real-time urban noise mapping app and platform; deployment of crowdsourcing framework for environmental data collection; generation of high-resolution urban noise evidence.

2) Recent Progress Significant achievements and evidence of progress towards impact are described below: a) Development of a Real-Time Urban Noise Mapping App and Platform An Android app capable of recording geolocated noise (sound) levels was successfully designed and deployed. The app is available on the Google Play store. A web-based GIS platform capable of visualising real-time urban noise levels (urbanearv2.vercel.app) was also developed and deployed. Crowdsourced noise data collected via smartphones was integrated with spatial referencing, enabling fine-scale urban noise mapping. b) Crowdsourcing Framework for Environmental Data Collection A participatory data collection model, allowing urban residents to contribute noise measurements was established. The newly developed technology strengthened citizen awareness of noise pollution as an environmental and public health issue. Sensitization workshops were organized in Nairobi and Kigali for Key stakeholders who included: Kenya’s National Environmental Management Authority (NEMA), Nairobi County Government, Kenya Residents Association (KARA), Kenya Bureau of Standard (KEBS), Kenya Institute of Planners (KIP), Institution of Surveyors of Kenya (ISK), Institution of Engineers of Kenya (IEK), Board of Registration of Architects and Quantity Surveyors of Kenya (BORAQS), KIPPRA among others. c) Generation of High-Resolution Urban Noise Evidence – Preliminary Findings The study collected large-scale urban noise data in Kigali and Nairobi, generating georeferenced noise measurements respectively through citizen science and

crowdsourcing approaches. Data were gathered across diverse urban seTings, including residential areas, commercial centres, transport corridors, and industrial zones, using the NoiseCapture and UrbanEar mobile applications. Each measurement recorded noise levels (dB) together with location, creating a detailed dataset capable of capturing the spatial and temporal dynamics of urban noise.

The discrete observations were transformed into continuous noise exposure surfaces, through Kriging spatial interpolation, supported by Gaussian smoothing and spatial masking to ensure consistency with urban boundaries. This enabled the generation of high-resolution noise maps that reveal paTerns of acoustic exposure across both cities.

Interpolated Noise Levels across Kigali city

Interpolated Noise Levels across Nairobi city

The results show that the highest noise levels are concentrated along major transport corridors, commercial centres, industrial zones, and airport areas, with peaks exceeding 80 dB. Interpolated maps reveal widespread exposure above recommended thresholds.

Across land-use categories, residential areas, schools, and hospitals frequently exceed acceptable limits. Critical infrastructure analysis shows elevated exposure in Kigali, with averages of 60.8 dB (schools) and 62.8 dB (health facilities), while Nairobi records significantly higher levels of 70.7 dB and 71.0 dB respectively—conditions linked to impaired learning, delayed patient recovery, sleep disturbance, and broader public health risks.

These findings indicate that urban noise is not just a maTer of environmental pollution, but a serious issue in terms of public health and city planning. It is suggested that a number of approaches be adopted to deal with this phenomenon on both the short-, medium-, and long-term bases, such as establishing quiet zones for sensitive buildings, controlling traffic and market noise, creating buffer zones and green belts, beTer insulation, continuous noise monitoring, among others.

Noise Levels vs critical infrastructure in Kigali

Noise Levels vs critical infrastructure in Nairobi

3) Future Plans Planned future work will include 1)

2)

Strengthening Citizen Engagement and Crowdsourced Urban Noise Monitoring: expanding citizen participation, increasing spatial coverage, and enhancing the sustainability of community-based monitoring initiatives. The established framework provides a strong foundation for longitudinal noise monitoring and offers opportunities for integration with land-use, mobility, environmental, and public health datasets.

Advancing Evidence-Based Urban Noise Policy and Planning: translating findings into actionable urban noise management strategies through close collaboration with city authorities, planners, and environmental regulators, while further research will develop noise exposure indicators, policy guidelines, and decision-support tools to integrate noise considerations into land-use planning, transport management, and environmental impact assessments.

4) Peer-Reviewed Publications Resulting from this Project David N. Siriba (2026). Mapping Nairobi Environmental Noise for Evidencebased Urban Noise Regulation and Policy, International Journal of Urban Sustainable Development, accepted (2026)

Project Title: Strengthening Water Access and Quality in Selected African Countries 1. Research Team: 1. Prof. M.O.H. Amuda, University of Lagos, Nigeria (Principal Investigator) 2. Assoc. Prof. F.O. Agunbiade, University of Lagos, Nigeria (Co-PI WP1) 3. Prof. T. Lawanson, University of Lagos, Nigeria (Co-PI WP1) 4. Prof. T.A. Fashanu, University of Lagos, Nigeria (WP2) 5. Dr. T.O. Mbuya, University of Nairobi, Kenya (Co-PI, Kenya) 6. Prof. U.G. Wali, University of Rwanda, Rwanda (Co-PI, Rwanda) 7. Dr. A.A. Yinusa, University of Lagos, Nigeria (WP2) 8. Engr. Micheal Ogundero, University of Lagos (Ph.D. Student) 2. Project Objectives

The overarching aim of this project is to provide a platform for harnessing human capital through multi-institutional partnerships to develop an Afrocentric knowledge ecosystem for providing solutions to some of Africa’s existential challenges via innovation and digital inclusion.

In the present project, the focus is on addressing water insecurity as an existential challenge. Specific objectives of the project are to: i.

Deploy community participation to design and validate a digital technology framework for real-time contaminant monitoring and optimal filtration policy advising. ii.

Combine citizen science with parametric contaminant transport optimization to develop scalable, fit-for-purpose smart water purification technologies that improve access. iii.

Provide a multi-institutional collaborative platform to build Africa's knowledge ecosystem, enhancing human capital, economic transformation, and job creation. iv.

Build a sustainable pathway for multi-institutional partnership through cocreated graduate learning, staff exchanges, joint research, publications, and scientific meetings. v. Implement a technology transfer and policy framework to scale the pilot project across Africa, building resilience to improve health and economic well-being.

3. Recent Progress 3.1 Strengthening Urban Resilience through Community-Led Water Management: A Case Study of Slum Communities in Lagos, Nigeria As part of our holistic study on urban water security, we evaluate community-led water management in Lagos slums (Iwaya, Ago Egun, and Ilaje). Through targeted engagement

sessions and data-driven follow-up surveys, the research identifies a critical interplay between socio-economic factors and infrastructure. Ultimately, it proves that combining community-level capacity building with policy advocacy is vital for urban water security.

3.2 A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction This project equally used machine learning to predict water potability and estimate quality parameters. Using CUAHSI data, our team developed soft sensors to estimate pH in the absence of dissolved oxygen and dissolved oxygen in the absence of pH, alongside an inference engine validated via accuracy, F1 score, and confusion matrix.

The homepage of the portability prediction system is shown in Figure 1.

(a) (b) Figure 1. Interface of the water portability e-system: (a) landing interface and (b) result of sample water portability test 3.3 A Smart IoT cum Citizen Science Framework for Real-Time Remote Water Quality Monitoring and Assessment In this activity, we leveraged Internet of Things (IoT) network deployed at Iwaya, Lagos, integrating community engagement via citizen science to monitor water quality. To ensure long-term data collection and sensor security, residents were trained as citizen scientists and empowered to demonstrate a clear understanding of the water sensor kits, successfully collecting, testing, and submitting localized water data according to protocol.

Onsite data collection was driven by a DANOPLUS water parameters sensor, which

transmitted real-time data to a custom Flask-based server. To validate critical pH and temperature metrics, the system integrated soft sensors. Users can access the live data via a custom dashboard at https://dev-water-prob.vercel.app. Furthermore, the IoT framework features an automated alerting system that broadcasts email and Slack notifications to stakeholders the moment any water parameter falls out of its optimal range. Schematic illustration of the the smart cloud-based water quality monitoring system is shown in Figure 2.

Figure 2. Schematic diagram of the smart cloud-based water quality monitoring system 3.4 Design, Construction, and Multi-Criteria Evaluation of a Low-Cost Water Filtration System for Urban Slums Urban slums in Nigeria face severe clean water deficits due to a reliance on unsafe sources and unsustainable, donor-driven interventions. Existing purification technologies are either cost-prohibitive and overly complex, or unscalable due to a lack of data-driven design. To bridge this gap, this aspect of our project developed a decentralized, low-cost, and sustainable water filtration system explicitly engineered for low-income Nigerian communities.

Constructed entirely from locally sourced materials—including sharp sand, activated carbon, and clay—the system offers an affordable, culturally acceptable, and easily replicable solution. The prototype's performance was validated against Nigerian Industrial Standards (NIS) and World Health Organization (WHO) guidelines, evaluating key parameters such as pH, turbidity, TDS, salinity, conductivity, and chloride. By addressing both technical and socioeconomic barriers, this dynamic, real-time water

quality assurance system empowers local communities and advances Sustainable Development Goal 6 (Clean Water and Sanitation) within the Nigerian context.

4. Next Targets (On-Going) i.

Developing a community scale water filtration system from previous studies ii.

Revisiting the communities to know the status of the IoT technologies iii.

Carrying out a resilience study on the communities where it is deployed Peer-Reviewed Publications Resulting from this Project 1. Umoren, V.A., Ogundero, M.A., Echeta O.C. Odinakachukwu, E.C., Yinusa, A.A., Naomi, I.I., Olua, G.S., Agunbiade, F.O., Lawanson, T.O., Fashanu, T.A. & Amuda, M. (2026). Community-Led Water Governance and Resilience in Urban Slums: Insights from Lagos, Nigeria.

Urban Water. https://doi.org/10.1080/1573062X.2026.2681861 2. Ogundero, M., Caroline, J., Yinusa, A., Umoren, Odinakachukwu, E., Daudu, U., Agunbiade, F., Fashanu, T., Lawanson, T. & Amuda, M. (2026). Design, Construction, and Multi-Criteria Evaluation of a Low-Cost Water Filtration System for Urban Slums. Next Research. https://doi.org/10.1016/j.nexres.2026.101698 3. Ogundero, M., Lawanson, T., Yinusa, A., Umoren, V., Agunbiade, F., Fashanu, T. & Amuda, M. (2025). A Smart IoT cum Citizen Science Framework for Real-Time Remote Water Quality Monitoring and Assessment. Discov Water 5, 108. https://doi.org/10.1007/s43832-025-00299-7 4. Ogundero, M., Fashanu, T., Agunbiade, F., Orolu, K., Yinusa, A., Daudu, U., & Amuda, M. (2025). A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction.

Air, Soil and Water Research. https://doi.org/10.1177/11786221251315618.

TLC: Teaching, Learning, Creativity through Digitalisation and Integrated Cross-Border Collaboration Reuben Dlamini (PI)

University of the Witwatersrand (Wits)

Reuben.Dlamini@wits.ac.za

Habimana Olivier (PI) University of Rwanda (UR)

habolivier13@gmail.com

Clement Simuja (co-PI)

Rhodes University (RU)

c.simuja@ru.ac.za

Nqobile Ndzinisa (co-PI)

University of Eswatini (UNESWA)

ntndzinisa@uneswa.ac.sz

Project Objectives • •

•

•

•

To create cross-border communities of practice to engage with the fundamental changes in education including differentiated instruction for diverse learning needs.

To explore multimodal practices where pedagogy and digital technologies converge to provide multiple access to content knowledge and transform teacher wellbeing while breaking digital divide.

To build a network of education researchers on the pedagogical integration of ICT, to help academic staff in resource-constrained contexts connect, network, and collaboratively share ideas and resources.

To create cross-border multi-institutional research collaboration to build research capacity and education initiatives harnessing digital technologies and innovations focused on the sub-Saharan unique context.

To conduct workshops and seminars supporting teacher educators to account for emerging technologies such as Artificial Intelligence to reduce routine administrative activities and create inclusive learning environments aligned with student needs.

Recent Progress Since the commencement of the project, significant progress has been made across all participating institutions in establishing the foundations necessary for successful implementation. A major priority has been securing ethical clearance, which is a mandatory requirement for all research involving human participants. To comply with institutional and national research governance requirements, each of the four participating universities prepared and submitted separate ethics applications to their respective institutional review boards as well as participating schools. Data collection activities will commence once approval has been granted by the relevant institutions and schools.

In parallel with the ethics processes, the research team has undertaken an extensive review of existing policies, frameworks, and scholarly literature related to digital transformation, multiliteracies, inclusive education, and digital pedagogies across the participating countries.

This review has informed the conceptualisation of the research and ensured that the project is grounded in current evidence and policy priorities. Building on this foundation, the research team has developed the primary research instruments, including questionnaires and interview schedules, which will be used to gather data from participants across the project sites.

Baseline activities have also been completed, including environmental scans and the identification and selection of participants who will be involved in the study. Furthermore, the project leadership structure has been established, bringing together Principal Investigators,

Co-Principal Investigators, and researchers from the participating institutions to coordinate project implementation and ensure alignment with the project's objectives.

At the University of Eswatini (UNESWA), the project was officially launched on 26 May 2026 at the Royal Villas Hotel. The launch event brought together university management and key stakeholders who will play an important role in supporting project implementation. The event served as an important platform for introducing the project's objectives, expected outcomes, and collaborative approach. Following the launch, thirteen participants were successfully recruited to participate in the research and associated capacity-building activities. These participants will enrol in the AI for Educators course offered through the UNESWA AI Academy, with training scheduled to commence on 26 June 2026. To strengthen project administration and coordination, a research assistant has also been appointed to serve as project administrator.

Figure 1 University of Eswatini (UNESWA) Project Officially Launch At the University of the Witwatersrand (Wits), project activities have focused on both research capacity development and empirical data collection. A Writing Retreat and Capacity Development Programme was held from 26 to 29 May 2026 under the theme “Strengthening Research Writing, Scholarly Productivity and Academic Capacity Development.” The programme provided participants with opportunities to engage with a range of digital tools, databases, and scholarly resources aimed at enhancing research productivity and professional development. This initiative aligns with our ongoing commitment to advancing digitalisation and the implementation of research-informed digital pedagogies in rural and resourceconstrained communities. Through the capacity development programme, we engaged 15 highly motivated educators and postgraduate students serving as tutors in the Wits School of Education.

In addition to enhancing their digital competencies, participants developed knowledge and skills in research design, methodology, and scholarly inquiry. The programme was intentionally designed to contribute to sustainable research capacity development by addressing identified

gaps in research expertise and the effective use of digital tools for research, teaching, and professional practice.

Figure 2 Project Implementation and Progress to Date In addition, substantial progress has been made in generating research outputs under an existing ethical clearance obtained in 2024. The research team completed thirty-eight semistructured interviews with Foundation Phase teachers across ten districts in a rural and resource-constrained province of South Africa. Insights from this study have informed the development of a manuscript entitled “Foundation Phase Teachers’ Engagement with Coding and Robotics in Rural South African Schools: A Qualitative Multi-Case Study,” which has been submitted to the African Journal of Research in Mathematics, Science and Technology Education and is currently under review.

Further data collection activities have focused on both pre-service and in-service teachers.

Survey data were collected from first-year pre-service teachers, with 181 responses received from a target population of 536 students, representing a response rate of 34 per cent. Analysis of these data is informing the development of a manuscript entitled “Digital Competence

among First-Year Pre-Service Teachers: A South African Perspective.” In addition, data were collected from in-service teachers, yielding 329 responses from a sample of 1,000 teachers, corresponding to a response rate of 33 per cent. These findings are contributing to the preparation of a second manuscript entitled “Examining Digital Capital and Digital Inequalities in Schools: Insights from In-Service Teachers.”

As part of the project progress report, the figure below presents the systematic approach adopted by the consortium, highlighting progress from project planning through to data collection and publication development thus far. Collectively, these activities demonstrate substantial progress towards achieving the project's objectives. Important milestones have been reached in project governance, ethical compliance, participant recruitment, research instrument development, capacity building, data collection, and scholarly dissemination. The project is therefore well positioned to enter the next phase of implementation, which will focus on expanded data collection, participant training, analysis, and knowledge production across the participating institutions.

Future Plans Beyond the development of technological knowledge and skills, participants will be exposed to quality design principles, innovative pedagogies, learning theories, and assessment models that are inclusive and aligned with the International Society for Technology in Education (ISTE) Standards for Educators. These internationally recognised standards provide a forward-looking framework for leveraging technology to enhance student learning, creativity, collaboration, and problem-solving.

Recognising that a one-size-fits-all approach to professional development is inadequate, the project adopts a multimodal pedagogical approach grounded in Activity Theory and complemented by connective and collaborative learning approaches. This framework seeks to enhance digital fluency while simultaneously addressing systemic challenges that educators face, including heavy workloads, overcrowded classrooms, limited access to resources, and insufficient institutional support. By embedding artificial intelligence (AI) integration and digital pedagogies within teacher education and professional development programmes, the research project approach aims to equip both pre-service and in-service educators not only to navigate these challenges but also to actively shape inclusive, technology-enhanced learning environments for the future.

The project seeks to train more than 200 in-service educators (lecturers & teachers) in playful learning approaches through a combination of unplugged coding activities and educational technologies such as LEGO® Mindstorms, LEGO® Education SPIKE™, Intelligent Programming Robots, and Kids First Coding and Robotics kits. These interventions are designed to foster computational thinking, creativity, problem-solving, and learner engagement across diverse educational contexts.

A key innovation of the project is the implementation of embodied adaptive AI robots to support teaching and learning in the Foundation Phase. These technologies will be used to stimulate learner engagement, strengthen foundational literacy and numeracy competencies,

and address persistent pedagogical gaps that disproportionately affect learners in underserved communities. Through carefully designed classroom interventions, the project will contribute to unlocking learners' full potential while providing teachers with practical models for integrating emerging technologies into everyday teaching practice.

The project will further promote virtual collaboration and knowledge exchange among educators across participating countries. These collaborations will facilitate the sharing of best practices, innovative teaching strategies, and contextually relevant solutions, enabling teachers to critically reflect on and transform their pedagogical approaches. Through interdisciplinary and cross-border engagement, educators will be supported in adapting teaching practices to diverse cultural, linguistic, and socio-economic contexts.

Research activities will contribute to enhancing curriculum relevance and responsiveness by generating evidence-based insights into effective AI-enabled and digitally enhanced teaching practices. The project will expose both pre-service and in-service educators to transformative classroom approaches while strengthening the research capacity required to sustain innovation in education systems. Emphasis will be placed on developing strong literacy and numeracy foundations for learners in multicultural and multilingual settings, where equitable access to quality education remains a pressing concern.

The initiative also fosters interdisciplinary collaboration by linking educational practice with scientific and technological concepts that can be applied in authentic classroom contexts.

Through virtual exchanges and collaborative activities, participants will establish cross-border Communities of Practice that support ongoing professional learning, knowledge sharing, and sustainable impact beyond the lifespan of the project.

Ultimately, the project will develop a sustainable framework for educator professional practice that supports the integration of AI-enabled educational platforms, differentiated instructional strategies, and multimodal pedagogies. To achieve this objective, the activities focusing on South Africa, Rwanda, and Eswatini will provide opportunities for comparative analysis across diverse educational systems. Guided by Critical Realism and informed by a Design-Reality Gap Analysis, the research will examine both academic staff in universities and teachers in schools as key facilitators and implementers of curriculum innovation.

Peer-Reviewed Publications Resulting from this Project Articles Underdevelopment: Article 1: Teaching, Learning, and Creativity (TLC): Digitalisation and Integrated CrossBorder Collaboration (Conceptual Article) Article 2: University Positioning in AI Policies: Comparative Insights from Eswatini, Rwanda, and South Africa (Conceptual Article)

Article 3: Digital Competence among Academic Staff: A Cross Border Perspective (Empirical Article) Article 4: Eswatini, Rwanda, and South Africa: A Comparative Analysis of Digitalisation Using a Design–Reality Gap Framework (Empirical Article) Peer-reviewed published outputs: No publications to report.

POSTGRADUATE STUDENTS FUNDED BY AFRETEC PROJECT University of the Witwatersrand Post Doc Fellows: Tobias Orji (A0083620): TLC: Teaching, Learning, Creativity through Digitalisation and Integrated Cross-Border Collaboration

Project Title: Towards IoT-enabled Privacy-preserving Large-scale Healthcare Analytics in Africa: A Use Case on Monitoring a Cardiovascular Disease 1. Project Team PI: Tamer ElBatt, CSE Dept., The American University in Cairo, Egypt Co-PI: Dineo Tsabedze, University of Witwatersrand, South Africa Co-PI: Swarun Kumar, ECE Dept., Carnegie Mellon University, USA Co-PI: Turgay Celik, Univ. of Witwatersrand, South Africa

2. Project Objectives The objective of this project is to research, design and develop digital technologies for healthcare, with focus on wireless, sensing, and machine learning technologies for blood flow, muscle and heart monitoring and prediction of cardiovascular diseases (CVD). The project has the following main threads: • Explore wireless technologies, e.g., mmWave radar, for monitoring blood flow and muscle fatigue. To the best of our knowledge, this work is the first to introduce the concept and build a prototype.

• Design and prototype an ultra-low-cost Ballistocradiography (BCG) acquisition circuit using a piezoelectric ceramic sensor (PCS) which captures ballistic forces induced during a cardiac cycle.

• Propose a novel BCG-to-ECG signal mapping using deep learning. This work is among the first around the world to introduce and explore this promising research direction in 2025.

• Propose Federated Learning technologies for predicting Coronary Heart Disease (CHD), due to its wide prevalence, as well as feature extraction from heterogeneous tabular data, with the aid of LLMs. • Research and develop a novel ECG feature extraction machine learning model, towards automated CVD diagnosis.

• Contribute to the broader societal impact in Africa, in line with Afretec, projected to be two-fold: o Train students and junior researchers at partner institutions, towards inclusive digital transformation capacity building with focus on the vibrant area of digital health. o Target, on the long-term, the health and well-being of African citizens, especially CVD patients.

3. Recent Progress 3.1 Wireless, Sensing and Signal Acquisition Contributions 3.1.1

GigaFlex: Contactless Monitoring of Muscle Vibrations with a Chaos-Inspired Radar [1]

Muscle fatigue is a major cause of reduced motor control and injury risk during exercise. Also, Muscle tremors are often linked to CVD and neurological conditions. Under this thrust, we have developed GigaFlex, the first proof-of-concept contactless mmWave radar system for monitoring muscle vibrations during exercise and predicting impending muscle failure. Our system transmits wireless signals towards the body and measures tiny skin-surface vibrations caused by underlying muscle activity. Unlike contact-based approaches such as surface electrodes, accelerometers, or ultrasound, GigaFlex requires no sensors attached to the body, making it more suitable for natural exercise settings. During exercise, the radar

focuses on reflections from the active muscle region and extracts vibration signals from the skin surface.

As the user performs more repetitions, these vibration patterns change, revealing information about muscle activation and fatigue. A key challenge is that muscle vibrations are highly irregular and do not exhibit clean periodic patterns like breathing or heartbeats. To address this, GigaFlex uses chaos-inspired signal features to uncover hidden structure in seemingly random muscle vibrations. In particular, we analyze recurrence patterns in the radar signal to distinguish muscle vibration from body motion noise and to characterize changes associated with fatigue. We implemented GigaFlex using a 77 GHz TI mmWave radar and evaluated it with 23 subjects performing concentration bicep curls. An IMU placed on the biceps is used as a reference sensor. Results show that it can predict whether a user is within three repetitions of failure, achieving an AUC of 0.83 compared with 0.72 from a single-IMU baseline.

3.1.2

Ultra-low Cost Wrist-based BCG Acquisition Using a Piezoelectric Ceramic Sensor [2,3]

Ballistocardiography (BCG), first recorded by Yendell Henderson (1905), offers a promising, costeffective approach to scaling heart monitoring to low-income, resource-constrained settings. It detects and measures ballistic forces that give rise to subtle muscle movements caused by heartbeats. Different approaches have been introduced for BCG acquisition, e.g., bed-based, wristband-attached, earworn, ... In this thrust, we focus on wrist-based BCG acquisition using an ultra-low-cost piezoelectric ceramic sensor (PCS). The sensor converts mechanical stress, such as pressure or vibration, to voltage.

However, the raw signal captured by the PCS is often weak and noisy, mandating signal conditioning to be of practical relevance. In a pilot study, we collected sample BCG waveforms from 17 subjects (mostly students). The figure on the right shows our PCS-based BCG acquisition circuit (incl. a tailored-design, 3D-printed wristband, to house the PCS sensor). It also shows a sample of the acquired BCG signals which exhibit the main signal components, namely the H-through-N wave complex. Moreover, we demonstrate reliable heart rate estimation using BCG. The initial cost breakdown of the BCG acquisition prototype circuit is ultra-low and totals to less than $12, including the Arduino.

3.1.3

PolyPulse: Blood Flow Monitoring Using mmWave Radar [4]

A major contributor to CVD is high blood pressure, often called the “silent killer”, because it can damage arteries before any obvious symptoms. In particular, we introduce PolyPulse, the first AI-driven mmWave radar to measure pulse transit time and blood pressure at multiple key sites along the body using a single, contactless device. Our system transmits wireless signals towards a user and measures the tiny surface reflections from the pulse points. It then extracts key features from the cardiac signal indicating the start of a heart beat and the arrival of a pulse at a body site. Our approach is privacy-preserving, enables continuous, contacetless monitoring and fits within the form factor of smart home devices. We conducted a pilot study on 35 subjects across a diverse population, including healthy vs.

CVD and diabetes patuents, or a high body mass index. We aimed to measure cardiac signals at four sites:

heart, wrist, neck and head. The blood pressure derived by our system achieves a correlation of 0.92 at estimating diastolic blood pressure compared to a classic cuff-based arm blood pressure monitor.

3.2 Machine Learning Contributions for ECG and CVD Prediction 3.2.1

FedCVD: Federated learning for the Prediction of Coronary Heart Diseases (CHD) [5]

We propose a federated learning (FL) system, coined FedCVD, for predicting the risk of coronary heart disease (CHD) due to its wide prevalence among adults. As a first step, we assumed homogeneous tabular data (i.e. same attributes across clients) and employed logistic regression and support vector machine (SVM) models.

FedCVD leverages the privacy and scalability merits of FL to facilitate collaborative model training, across hospitals, using decentralized patient data. We conducted experiments for predicting the 10-year risk of developing CHD, based on the well-known Framingham Heart Study dataset*. We observe that federated SVM using random under sampling achieves the best AUC performance among the two ML models and data balancing techniques.

3.2.2

FedLLM-Align: Feature Extraction from Heterogenous Data [6]

A major obstacle in face of FL is the heterogeneity of tabular data across clients in practical settings, where schema mismatches, feature-name variations, and partially overlapping feature spaces can hinder federated aggregation. Under this thrust, we propose FedLLM-Align, a FL framework that uses a frozen pretrained transformer-based language model as a schema-level semantic alignment layer. It first serializes local tabular records into text and derives semantically aligned embeddings using a pretrained encoder, such as DistilBERT. Lightweight local classifier heads are then trained in a federated manner using standard aggregation schemes, such as FedAvg, while all raw records remain local to the clients. To quantify the merits and trade-offs of FedLLM-Align, we evaluate the framework on two binary classification tasks from different domains: i) CHD prediction using partitioned Framingham Heart Study data, and ii) Customer churn prediction using a financial dataset. Under simulated schema heterogeneity, FedLLM-Align improves the F1-score over representative federated baselines and reduces the comm. overhead by about 65% compared to FedXGBoost in the main evaluation.

3.2.3

BCG-to-ECG Signal Mapping Using Deep Learning [7,8]

High cost, hospital-grade, bulky ECG equipment challenges large-scale CVD diagnosis in developing countries, especially in underserved regions. This thrust presents a novel approach for mapping user-friendly (no electrodes) BCG waveforms to the

* https://www.framinghamheartstudy.org/

widely adopted ECG, using machine learning. This is inspired by the physiological correlation between the two signals as synchronous responses to the same cardiac cycle. In particular, we propose a hybrid Deep Learning (DL) model, composed of convolutional neural networks (CNNs) and long short-term memory (LSTM), for BCG-to-ECG signal mapping. To train and test the proposed model, a bed-based public dataset is adopted.

We employ the Pearson Correlation Coefficient (PCC) as a widely-accepted metric from statistics for quantitatively assessing signal correlation. Our approach holds a great promise as evidenced by achieving a PCC of 0.97, on the average, when correlating our deep learning-generated ECG waveform to the signal recorded by ECG equipment (ground truth). BCG-to-ECG signal mapping opens room for capitalizing on decades of the ECG rich diagnostic knowledge and the BCG affordable monitoring for early diagnosis of CVD in a low-cost, scalable manner.

3.2.4 ECG Feature Extraction Using Deep Learning–Towards Automated CVD Diagnosis [9,10] Manual analysis of ECG signals by qualified healthcare providers in rural, underserved areas is generally time-consuming, error-prone, and often inaccessible. This research thrust introduces a novel open-source deep learning model for extracting interpretable morphological (shapedbased) and temporal ECG features with high accuracy to aid physicians in monitoring and diagnosing heart abnormalities. Our proposed approach preprocesses raw ECG signals and employs a CNN for feature extraction. Using the PTB-XL+ public dataset, our model exhibits high correlation with the ground truth features, effectively capturing global and lead-specific ECG characteristics. We demonstrate that a single lead (Lead II) performs comparably to all 12 leads, significantly reducing the computational complexity and inference time.

Furthermore, a lower sampling frequency (100 Hz) preserves performance, offering an efficient alternative to the standard 500 Hz frequency for sampling ECG. This ongoing work provides a novel, practical and interpretable ECG feature extraction approach for regions with limited access to qualified healthcare professionals and costly, commercial ECG analysis software. This is work in progress with potential extensions along multiple directions currently being explored.

3.3 Research Dissemination: Public, Online Workshop The project team co-organized a public, online workshop hosted by the AUC: • Title: Vision and Innovations for Digital Health in Africa and beyond.

• Date: Tuesday April 8th, 2025.

• Format: Online, Half-day - 2:00 – 6:15 pm (Cairo Local Time).

• Program: Seven talks including three by Afretec project partners on their research activities. Hosted four invited talks by digital health experts, from Rwanda, Egypt, Italy and the United States. The Program is shown here.

• Announcement: widely disseminated in Egypt, Africa and beyond.

• Attendance: ~35 participants, from Egypt, Africa and one from Japan.

4. Future Plans •

Explore collaboration with Cardiac Subject Matter Experts: The PI had a meeting with Magdy Yacoub Heart Foundation, Egypt in Jan. 2026 to showcase the project innovations and contributions focused on digital technologies for heart monitoring, analysis and early diagnosis and prediction.

• Explore Follow on Funding Opportunities: focused on Digital Health, e.g., EU, Gates Foundation, WHO, NIH, in addition to local and regional funding sources.

• Ongoing Publications: continue the team’s work on multiple conference and journal publications in the pipeline to finalize and submit before the project end date.

• mmWave Radar: Our current system focuses on bicep curls and monitors muscle vibration in a controlled environment. Future work will extend to more muscle groups, more complex full-body exercises, and more natural gym environments. This will allow us to study whether contactless radar can support broader exercise monitoring, including fatigue tracking, injury-risk estimation, among others.

• Low-cost PCS-based BCG Acquisition: continue developing and maturing our wrist-based BCG signal acquisition system using PCS towards a promising path for heart monitoring in resource-limited settings.

• LLM-based feature extraction in federated setting: continue developing our proposed approach of Large Language Models-based feature extraction for federated clients with heterogeneous data, i.e. hosting overlapping, yet, potentially different, sets of attributes collected at different hospitals.

• BCG for CVD diagnosis: Understand and analyze the potential of BCG, as opposed to ECG, for CVD diagnosis in resource-constrained settings.

• CVD diagnosis based on multimodal data: introduce a novel generative ML model for textual diagnosis based on multimodal data, e.g., ECG, Echocardiogram and electronic health records (EHRs).

5. Peer-reviewed Publications 1. J. Zhu et al., “GigaFlex: Contactless Monitoring of Muscle Vibrations During Exercise with a ChaosInspired Radar,” ACM SenSys, May 2026.

2. H. Abd-Eltawab, A. Gaber, Y. Abuzied, T. ElBatt, ”Towards Low-cost Wrist-based BCG Heart Monitoring Using a Piezoelectric Ceramic Sensor,” under 2nd review, IEEE J-EDS, June 2026.

3. H. Abd-Eltawab, A. Gaber, Y. Abuzied, T. ElBatt, “An Ultra Low-cost Wrist-based Ballistocardiography Signal Acquisition System using a Piezoelectric Ceramic Sensor,” IEEE Healthcom, UAE, Oct. 2025.

4. J. Chan, J. Zhu, K. Yuan, A. Prabhakara, Y. Li, G. Wang, K. Michaelsen, S. Kumar, “Measuring multisite pulse transit time with an AI-enabled mmWave radar,” to appear, Nature Communications (Q1, IF: 15.7), 2026.

5. A. Gaber, H. Abd-Eltawab, T. ElBatt, “FedCVD: Towards a Scalable, Privacy-Preserving Federated Learning Model for Cardiovascular Diseases Prediction,” 8th ICMLSC, Jan. 2024.(Best Presentation) 6. A. Gaber, M. Elmahdy, Y. Abuzied, H. Abd-Eltawab, T. ElBatt, “FedLLM-Align: Feature Extraction from Heterogeneous Clients,” to appear, IEEE SmartComp Workshop (SmartAIDa), June 2026.

7. H. Abd El-tawab, A. Gaber, Y. Abuzied, T. ElBatt, “Towards Mapping Low-cost BCG to ECG using Deep Learning,” Elsevier Biomedical Signal Processing and Control, (Q1, IF: 4.9), March 2026.

8. H. Abd-Eltawab, A. Gaber, T. ElBatt, “Towards Machine Learning-based Mapping of Low-cost BCG to ECG,” IEEE Healthcom, Nara, Japan, Nov. 2024.

9. Y. Abuzied, H. Abd-Eltawab, A. Gaber, T. ElBatt, “Towards Automated CVD Diagnosis: Deep Learning-Based ECG Feature Extraction,” 5th IEEE ICTS4eHealth, Italy, July 2025.

10. Y. Abuzied, H. Abd-Eltawab, A. Gaber, T. ElBatt, “ECGXtract: Deep Learning-based ECG Feature Extraction for Automated CVD Diagnosis,” under preparation, June 2026.

Wakanda AI: Engineering Smart Solutions for Mother Tongues by Advancing Language Technologies in the African Context Bhiksha Raj, Ph.D.* 1 , Abraham Nyete, Ph.D.†2 , and Moise Busogi, Ph.D.‡3 1 Carnegie Mellon University 2 University of Nairobi

3 Carnegie Mellon University Africa

Project Objectives Despite significant publication efforts by the African Natural Language Processing (NLP) community, many of the published works have seen little to no practical use in real-world applications, especially in resource-constrained environments. Most language translation solutions for the African context are textbased and fail to serve populations where literacy levels are low, languages are primarily spoken rather than written, and technological deployment occurs in highly constrained environments. A substantial portion of the population who speak indigenous languages cannot access or benefit from text-based technologies due to limited literacy skills. Additionally, individuals with visual impairments face immense barriers in accessing written information, this includes the estimated 43 million people who are blind and 295 million with moderate to severe vision impairments. These challenges further increase the digital divide and limit the reach of digital AI solutions for machine translation in Africa.

This project proposes a speech-first framework as a more inclusive and practical alternative. By leveraging speech-to-text, text-to-speech, and speech-to-speech translation systems, we aim to build solutions that align more closely with the communication needs of African communities. The project’s core objective is to develop and deploy a scalable speech translation framework that bridges the gap between cutting-edge research and real-world usability.

To achieve this, we begin with curation of multilingual speech datasets for selected African languages, with an initial focus on Kinyarwanda. Using these datasets, we will fine-tune and adapt stateof-the-art automatic speech recognition/translation and text to speech models, optimizing them for both accuracy and efficiency in resource-constrained settings.

The final deliverable is an end-to-end speech translation system, accompanied by an open-source software toolkit. By focusing on speech applications, this solution will foster digital inclusion of nonliterate users and individuals with visual impairments. Through this initiative, we aim to create impactful and inclusive speech technologies that move beyond academic benchmarks and deliver real-world value to the African community. * Principal Investigator. Email: bhikshar@andrew.cmu.edu † ‡

Co-Principal Investigator. Email: anyete@uonbi.ac.ke Co-Principal Investigator. Email: mbusogi@andrew.cmu.edu

1

Recent Progress Over the past year the project has moved from foundational data work into active model development, systems building, and publication. The following is the progress so far on the project.

1. Low Frame-Rate Speech Codec Conventional neural speech codecs typically operate at high frame rates, producing long token sequences that are costly for autoregressive TTS models to generate. We explored the limits of low–frame-rate neural speech coding, compressing speech into far fewer tokens per second while preserving reconstruction quality. This work underpins our TTS pipeline by reducing training compute and inference latency, which is critical for our subsequent modeling and design of African-centric text-to-speech systems (see Figure 1). A paper describing this work has been submitted to Interspeech 2026 and is currently under review.

Figure 1: Model architecture for the task of interest (ASR, AST, MT and TTS)

2. Efficient Model Quantization for ASR Models Deploying large pretrained ASR models such as Whisper and MMS on consumer-grade hardware remains a barrier in many African contexts. We are investigating post-training quantization and quantizationaware training to compress multilingual ASR models while retaining accuracy on African languages.

Initial experiments focus on languages with established evaluation benchmarks to enable rigorous comparison. The resulting models will support on-device inference on affordable hardware.

3. Base Language Model To provide a strong language foundation for tasks such as Neural Machine Translation (NMT), Automatic Speech Recognition (ASR), Automatic Speech Translation (AST), and Text-to-Speech (TTS), we are pretraining a base language model on a multilingual corpus enriched with African languages across domains and regions of the continent. This model will serve as the foundation for fine-tuning across multiple tasks within the Wakanda AI ecosystem, including as the language-model component of our speech translation systems. Pretraining is being conducted on Bridges-2 H100 GPU nodes using distributed training. The base model is illustrated in Figure 1 (Aura LLM).

4. Multilingual African TTS. Building on the low frame-rate codec, we are training multilingual TTS models that synthesize speech across multiple African languages from a single model. Current efforts focus on data curation from available speech corpora and initial training runs on GPU clusters. Target languages span Bantu (Swahili, Bemba, Zulu), West African (Yoruba, Igbo, Hausa), and other families.

5. End-to-End Automatic Speech Translation.

To facilitate speech-to-speech translation for african languages we developed a pipeline that integrates both speech-to-text and text-to-speech models to convert speech from one source language into speech in a target language. The base approach to achieve this is a cascaded pipeline involving three key steps: speed recognition in the source language, translating the recognized text into the desired target language, and synthesizing speech based on the translated text. In a more efficient approach, we combine speech recognition and text translation into a single step. Figure 2 illustrates this pipeline for an end-to-end speech translation pipeline. We have built a speech translation application using the cascaded pipeline, and we are working towards refining it to achieve seamless end-to-end performance. We describe the cascaded approach in section 5.1.

Figure 2: E2E Speech to Speech Translation Application

5.1 Cascaded Speech-to-Speech Translation Application.

We have built a working cascaded speech-to-speech translation system that chains ASR, MT, and TTS components into a real-time pipeline. The system architecture uses a service-oriented design with WebRTC-based audio transport for low-latency streaming. Individual model servers handle ASR (MMS1b-all), MT (NLLB-200-1.3B), and TTS (MMS-TTS), coordinated by a custom asynchronous orchestrator. An end-to-end demonstration has been confirmed with warm latencies under 2 seconds for the

full pipeline. The system also supports an LLM-powered multilingual voice assistant mode. Figure 2 illustrates the system architecture.

6. Multilingual African Machine translation model.

Machine translation is one of the tasks that has attracted significant interest, especially in low-resource settings. We build on the general-language understanding of our base model (Section 3) and adapt it into a multilingual MT system covering 25 African languages, using English as a pivot language (i.e., African languages → English and English → African languages). The architecture is illustrated in Figure 1a.

Future Plans Moving forward, we will further optimize our models and the speech translation pipeline to enable efficient deployment in real-world environments. These efforts involve designing models that are not only accurate but also memory and computational efficient, while exploring multi-task learning and multilingual TTS with voice cloning to ensure that the synthesized target speech preserves the voice characteristics of the source speaker.

1. Quantized ASR models: Our current speech translation system has models with multi-billion parameters, and this parameter size increases the memory and computational requirement during deployment. We aim to utilize our post-training quantization recipe to quantize the speech models in our end-to-end speech translation pipeline. This quantization approach yields approximately 4x reduction in memory and compute requirements.

2. Multi-task ASR/AST task: Instead of having to design and train models per task, we intend to optimize training resources by having a unified model capable of both recognition and translation task for the languages of interest (see 1b).

3. Scaling multilingual TTS model: Building on the initial TTS work, we will extend our efforts to develop multilingual TTS that covers all 30 languages. These models will be integrated into the application (see 2) as part of the end-to-end (E2E) speech translation task.

4. Deployment and testing: We intend to deploy models in a low-bandwidth environment, including latency optimization, robustness testing, and improvements to the user-facing interface/experience.

5. Publications: We aim to publish our findings at peer-reviewed venues including Interspeech, EACL, and SLT, and release all code, models, and data pipelines as open-source resources.

Peer-Reviewed Publications Resulting from this Project A paper on low frame-rate speech codecs has been submitted to Interspeech 2026 and is currently under review.