Masters Theses and Dissertations

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Development of cloud-based integrated information system for car rental services: a case of Solidarity Car Rental Limited
(NM-AIST, 2025-07) Tuyambaze, Thacianne
Car rental businesses increasingly adopt digital technologies to enhance their operations, particularly through mobile applications and integrated information systems. This project focuses on addressing the specific operational challenges faced by Solidarity Car Rental, which, despite its use of digital tools, continues to encounter issues like overlapping bookings, weak system coordination, and limited customer feedback management. To resolve these challenges, a cloud-based, all-in-one information system was designed and implemented for the company. The project followed a mixed-methods approach, utilizing interviews, focus group discussions, and structured surveys for data collection. Quantitative data were analyzed using descriptive statistics with Microsoft Excel. Findings indicated that the developed system enables customers to easily browse car options, reserve vehicles, and complete secure payments via a mobile app. Internally, it enhances efficiency by automating tasks such as invoicing, generating real-time reports, and managing fleet operations. Data protection is ensured through encryption and secure payment systems. The use of Microsoft Azure supports scalability and integration, while SMS notifications improve communication with clients. As a result, Solidarity Car Rental has experienced reduced delays, higher customer satisfaction, and streamlined processes. The system proves to be a transformative tool that not only modernizes operations but also boosts competitiveness and revenue generation in Tanzania’s car rental sector
IoT-based system for maintaining constant taste of ginger-flavored alcohol in Eden Business Center (Rwanda)
(NM-AIST, 2025-07) Ingabire, Speciose
The flavoured alcohol industry in Rwanda faces challenges in maintaining consistent taste profiles, particularly in ginger-flavored alcohol. Eden Business Company (EBC) relies on manual ingredient measurement and fermentation testing, leading to time-consuming processes and human errors. While technological solutions such as Metal Oxide Sensors (MOS), Electronic Tongue, and Robotic Pourer (RoboBEER) have been introduced, they lack real-time intervention and notification mechanisms. This project developed a cost-effective and versatile system to monitor and control key parameters of ginger beer fermentation, including temperature, potential of hydrogen (pH), alcohol concentration, and ingredient levels. The system integrates SD18B20, pH sensors, and a refractometer to collect data, which is processed by an ESP32 microcontroller. The GSM module, buzzer, and LCD enable real-time notifications, alerts, and data display. Additionally, a load cell ensures precise ingredient measurement. The collected data is sent to the ThingSpeak webpage for remote monitoring. The results demonstrated 90% efficiency and reliability in maintaining a consistent ginger flavour. Observations showed a temperature range of 23–37℃, an alcohol level of 5.5%, and a pH of 4.0, ensuring flavour consistency. The system successfully enabled real-time monitoring and remote access, improving production accuracy and efficiency.
Development of a mobile-based climate information and advisory system for crop management: a case of Musanze District, Rwanda
(NM-AIST, 2025-08) Angelique, Mukaneza
Mobile applications and internet access enable climate change adaptations. Climatic information services package and deliver climatic data to customers, including temperature, rainfall, wind, and soil moisture. In Rwanda, farmers are accessing climate information through radios, television, trained agents through the Rwanda Meteorological Agency, and by weather applications. However, farmers claim that because of weak dissemination channels and not interacting, they are facing the main challenges in making decisions at the right time for achieving sustainable food production and security, which provides lower incomes and famine to society. We developed a bilingual (Kinyarwanda and English) mobile application using the Flutter framework with a Firebase backend to address these challenges. We integrated the Open Weather API for real-time and forecast data. An SMS gateway was incorporated to ensure notifications reach farmers even in low-connectivity areas. The system was evaluated in Musanze District, where it delivered current and forecast temperatures between 15.5 ℃ and 21.9 ℃, overcast or rainy skies, 55 % humidity, 801.5 hPa pressure, 1.5 m/s wind speed, sunrise at 07:04 AM, and sunset at 07:06 PM directly to users’ devices. Problem reporting and real-time conversation with agronomic officers provided individualized advising help. The Dfarmer app enabled farmers to make timely decisions and increase crop output by providing interactive climatic information and advisory services in their local area.
Information system for enhancing communication between members of parliament and citizens: a study of Dodoma, Tanzania
(NM-AIST, 2025-08) Mtoi, Mary
The evolution of information systems, including the internet and smart devices, has transformed the way we communicate, access information, and connect with others. Communication between members of parliament (MPs) and citizens in Tanzania remains ineffective due to the reliance on mainly formal traditional channels such as public meetings, physical visits, local government leaders, opinion boxes, visitor books, letters, broadcast media, and social media. These methods are often hindered by poor attendance, delays, unavailability of members of parliament, high costs, and limited access to smart devices and the internet. Findings revealed that 42.9% of citizens took more than three days to submit their concerns, 30.3% had never submitted, and 69% cited MP unavailability as the main challenge, highlighting agreement from both citizens and MPs that existing methods create serious obstacles. A literature review was conducted to identify technological gaps, revealing that many proposed solutions are inaccessible to users without internet-connected devices. This study developed the Public Participation Information System (PPIS). This hybrid platform combines a web application for online users with a two-way messaging system to include those without internet access or smart devices. The two-way messaging feature ensures inclusivity and timely, interactive communication for all citizens and includes Swahili and English support, with simple SMS interactions designed to accommodate users with limited internet access and low literacy levels. Furthermore, a mixed research method was used to gather system requirements from 177 participants, including both members of parliament and citizens. Findings showed that 89.1% of citizens and 98.3% of MPs were willing to adopt the system. The PPIS improved message delivery accuracy, reduced response time, and enabled citizens to track their concerns. PPIS uniquely bridges digital divides by offering inclusive two-way communication, enhancing accessibility, accountability, and citizen participation in governance, which demonstrates readiness for pilot deployment.
Deep learning-based robot for visual inspection and picking of rejected bottles: case study, Tanzania Breweries, Arusha
(NM-AIST, 2025-07) Nganyi, Jared
The current picking of rejected items in Tanzania Breweries, a food and beverage manufacturing industry in the East African (EA) region, is performed by picking rejected bottles by hand, as evidenced during this study. This being repetitive work, hence necessitated the development of an articulated industrial robotic arm prototype mimicking human behavior in picking rejected bottles that have been removed by the Empty Bottle Inspection (EBI) system due to quality non-conformity. The project requirements were obtained through quantitative and qualitative methods of data collection. Computer vision mathematical approaches were formulated that utilized Hue Situation and Value (HSV) as the key element and employed object detection through blob analysis using Gaussian blur function, erosion and dilation algorithms, moment analysis, inverse kinematics for pose estimation, and sinewave filtering functions. These were deployed in the edge computing device. The test findings revealed that the system, though implemented on an edge device, had a high throughput with the central processing unit (CPU) of about 70% to 83%, and about 1000 to 1200 megabits of random accessary memory (RAM) utilization. The pick and place speed were achieved faster than the human speed with a latency of about 100 milliseconds. In conclusion, the use of finite resource devices in yielding high throughput is notably an indication of a highly efficient developed robot control program (RCP) algorithm without the need for highly complex resource utilization robot operating system (ROS). The industry can realize efficient processes, high productivity, and increased revenues as well as the safety of the workers’ health and environmental conservation by adopting to the use of this robotic system.
Development of a web-based system to enhance monitoring laboratory order and result dissemination a case study of Softmed Company Limited
(NM-AIST, 2025-08) Abashe, Japheth
Laboratory testing plays a critical role in clinical decision-making and patient care. However, in many healthcare settings, especially in developing countries, laboratory order management remains manual and paper-based, leading to inefficiencies, errors, and delays in result dissemination. These challenges compromise the quality of care and timely treatment decisions. Despite existing studies highlighting errors in laboratory workflows, especially in the pre-analytical phase, few interventions target private, multi-hospital laboratory services in Tanzania. This study aimed to improve operational efficiency and result dissemination at SoftMed, a private pathology laboratory in Arusha, Tanzania, through the development of a web-based laboratory order and result dissemination system. The system was designed to automate test acquisition, streamline inter-facility coordination, and reduce operational bottlenecks. A qualitative approach was used, involving interviews and system validation by six clinical and laboratory staff directly involved in lab order processes. Development followed the Agile Extreme Programming (XP) methodology, using Angular for the web interface, Laravel for the backend, and MySQL for data storage. Validation assessed improvements in turnaround time, communication, and error reduction. Results indicated notable improvements in workflow efficiency, reduced turnaround time, and enhanced communication between healthcare providers and lab staff. Although the validation sample was limited, feedback confirmed the system's operational effectiveness in the specific private lab context. This study contributes practical evidence supporting lab workflow automation in Tanzania’s private healthcare sector and recommends future integration with electronic medical records (EMRs) for comprehensive health information management.
Development of an integrated loan lending mobile application for self-microfinance fund in Tanzania
(NM-AIST, 2025) Felix, Faith
The global growth of microfinance, particularly in Tanzania, is closely tied to mobile banking and lending technology. Technical hurdles, such as disjointed platforms like Oracle e-Business Suite, IMFAS, and Kopa Ada, impeded the operations of the SELF Microfinance Fund, a prominent Tanzanian lender. This fragmentation forced loan officers to rely on manual field registrations, which slowed down data processing and reduced efficiency. To address this, an integrated loan lending mobile application was developed for the SELF Microfinance Fund, incorporating their internal core microfinance system. The chosen tools for development were the Flutter framework, Dart language, MySQL database, and APIs, ensuring seamless integration and functionality. The project employed both quantitative and qualitative research methods to gain a comprehensive understanding of current loan lending processes and to gather user requirements. Quantitative data were collected via questionnaires and analysed using descriptive statistics and Python version 3.8.18 for visual representations. Qualitative data were obtained through focus group discussions and analysed using thematic analysis, revealing recurring themes and patterns through detailed transcription and coding of responses. The findings highlighted the critical need for an integrated loan lending mobile application that delivers strong security, rapid processing, user-friendliness, and efficient report generation. The developed system successfully processed various types of loan applications, provided immediate feedback to applicants, and allowed administrators full control over data functions. Evaluation of the application demonstrated user satisfaction with its intuitive interfaces, enhanced security, processing speed, and multiple language options. The application was tested through various scenarios, including stress testing, user acceptance testing, and performance evaluation. The results confirmed that the application effectively addressed the challenges posed by disjointed platforms and unlocked new opportunities for growth and improved service delivery for the SELF Microfinance Fund.
Internet of Things-based system for real-time monitoring of margarine production process: a case of Savonor in Burundi
(NM-AIST, 2025-06) Niyonzima, Ezechiel
The margarine production process at SAVONOR in Burundi faces challenges such as inadequate control mechanisms, leading to suboptimal temperature regulation, production delays, and diminished product quality. These issues necessitate modernization to enhance efficiency and product standards. This project introduces an IoT-based system for real-time monitoring of the margarine production process. The proposed system integrates various sensors, including color, pH, and DHT22 temperature and humidity sensors, along with a Peltier cooling mechanism for precise temperature control. Real-time notifications are facilitated through GSM technology, enabling proactive communication with maintenance and laboratory personnel. Additionally, an LCD provides a user-friendly interface for visualizing monitored parameters. The system was designed and tested to monitor critical indicators like margarine color and pH levels, ensuring prompt corrective actions through real-time alerts via email and SMS. By addressing the gaps in control and monitoring, this IoT-based solution improves efficiency, reduces delays, and enhances product quality. This project demonstrates the transformative potential of IoT technologies in addressing industrial challenges in resource constrained environments. By enabling real-time communication, precise control, and actionable insights, this system sets a benchmark for innovation and modernization in margarine production processes.
Development of a system to enhance community-based surveillance for disease-transmitting mosquitoes in resource-constrained settings
(NM-AIST, 2025-08) Msaky, Dickson
Mosquito-borne diseases pose a significant global health threat, particularly in resource constrained settings. However, conventional methods have significant drawbacks: they are costly and labour-intensive, making widespread or long-term data collection difficult, and they often lack active community engagement. These limitations create critical gaps in our understanding of mosquito populations, making large-scale, sustained surveillance unfeasible and leaving communities vulnerable to diseases. This study developed a system to enhance community-based surveillance for disease-transmitting mosquitoes in resource-constrained settings by enabling active community participation and providing a digital solution with integrated compensation mechanisms and expert validation. The project employed qualitative and quantitative methods using an incremental development model. Data were collected from 140 participants comprising key stakeholders and community members in the study area. Survey results revealed that 98% of participants identified malaria as the highest burden mosquito-borne disease, 78% strongly agreed that digital technology could address surveillance gaps. The system was developed using Vue.js and Python for the web-based system, Android Studio for the mobile-based system, and PostgreSQL for database management. System testing included unit testing, performance testing achieving 100% success rates with average response times of 4.44 ms, and usability testing yielding a system usability scale score of 82.4. User acceptance testing confirmed system acceptance, with 94% of participants agreed that the system is easy to use and 93% expressing comfort with regular usage. This system offers a scalable solution for enhanced vector surveillance, contributing to improved public health interventions in resource-constrained settings.
Modelling health risks related to second hand tobacco smoke exposure
(NM-AIST, 2025-07) Matogwa, Clement
Apart from being dangerous to the smokers, tobacco smoke is also harmful to non-smokers through their exposure to secondhand tobacco smoke. It is worthy note that, increased exposure to secondhand tobacco smoke accelerates health risks and deaths to non-smokers. Eventually, this situation calls for exploration of the extent to which exposure to secondhand tobacco smoke causes health risks to non-smokers who interact with smokers at the time of smoking. More over, it sparks assessment of effective strategies to be adopted to minimize those health risks on non-smokers. In the present study, deterministic and optimal control mathematical mod els are developed to study the dynamics of secondhand tobacco smoke exposure in relation to health risks on non-smokers. Findings from the present study reveal backward bifurcation of the system signifying a possibility of emerging large outbreaks of health risks caused by sec ondhand tobacco smoke in the community even if there is a small number of smokers in that community. Additionally, numerical simulation results reveal that, the increase in interaction between smokers and non-smokers at the time of smoking by 90% leads to increase in health risks related with secondhand tobacco exposure by 7%. Moreover, findings reveal that building designated smoking areas and initiating quitting smoking campaigns concurrently is the most effective strategy to be implemented as it reduces exposure to secondhand tobacco smoke by approximately 88%. Therefore, the study recommends that efforts to minimize health risks on non-smokers should be focused on reducing interaction between smokers and non-smokers and initiating smoking cessation campaigns.
Development of a mobile application for detecting cervical cancer using deep learning model
(NM-AIST, 2025-07) Kiswaga, Dorcas
Advancements in technology are significantly enhancing healthcare, particularly in regions with limited clinical expertise, such as Tanzania. Cervical cancer remains a major public health challenge in these areas, where access to regular screening is often inadequate. This project aimed to develop a mobile application powered by deep learning to assist pathologists in detecting cervical cancer without relying on a microscope or physical observation. Traditional diagnostic methods like Pap smears, HPV testing, and colposcopy can be error-prone, especially in resource-limited settings. The dataset comprised 2,276 histological images of cervical tissue, categorized into Grade One, Grade Two, Grade Three, and Not Cancer. These images were collected with permission from SoftMed (T) Limited, a private pathology laboratory in Arusha, Tanzania, and were annotated by qualified pathologists to ensure clinical accuracy. The Design Science Research Methodology (DSRM) was adopted to guide the development and evaluation of the solution. A convolutional neural network (CNN) was implemented for image classification, and several pre-trained models ResNet50, VGG19, MobileNet, and a custom CNN were evaluated. MobileNet was selected due to its high performance and lightweight architecture, ideal for mobile deployment. The final model achieved 99.59% training accuracy and 89.6% validation accuracy. The trained model was integrated into an Android mobile application, enabling healthcare providers to upload and analyze cervical histology images directly from their devices. This application offers a fast, reliable, and accessible diagnostic tool, enhancing cervical cancer screening in underserved areas. The project demonstrates how deep learning and mobile technology can bridge critical gaps in healthcare delivery.
A remote vehicle health monitoring system: a case study of the kayoola electric vehicles
(NM-AIST, 2023-07) Kamusiime, Immaculate
Due to their complex hardware and software, vehicle systems are challenging to monitor and maintain. As a result, the absence of proper vehicle health monitoring systems leads to several issues, including resource waste, costly maintenance, and frequent breakdowns. Therefore, vehicle manufacturers and owners must prioritize implementing such systems to mitigate these negative outcomes. This project report focuses on a remote vehicle health monitoring system explicitly developed for Kiira Motors Corporation's (KMC's) electric vehicles made in Uganda. This research project involved interpreting real-time data transmitted through the vehicle's Controller Area Network (CAN) into a human-readable format using the developed embedded device and a web application to notify technical operators of the vehicle's health status remotely. The system consists of a wireless link between the embedded device and the web application and has been integrated into Kiira EV, one of KMC's concept vehicles. This embedded device is attached to the vehicles’ CAN data hub through CAN-high and CAN-low communication lines to receive live data through electric signals generated by various sensors located throughout the vehicle. The developed system was integrated into the Kiira EV, and five critical vehicle parameters, including pack voltage, motor temperature, motor speed, pack current, and vehicle location, were remotely monitored using the developed web application. The developed system enables the technical vehicle operator to remotely track, monitor, and receive notifications on the general health status of the Kiira EV based on the streamed live CAN data.
The artificial neural networks-based smart number plate of vehicles with real-time traffic signs recognition and notification: a case study of public transport in EAST AFRICAN COMMUNITY (EAC)
(NM-AIST, 2023-08) Niyomugaba, Alexandre
The world is advancing technologically in all sectors, including intelligent transportation, whereby various vehicles' movements are monitored and controlled remotely. These technologies simplify the tasks in traffic control and increase road safety. The previous related works implemented and designed provided different technologies that can identify, locate and detect the vehicle's speed. Even though these technologies have been implemented, there is still a lack of assistance to drivers for earlier knowing and reminding the road situation and to real- time notify the dedicated authorities such as traffic police stations once an accident happens. In this project, an Artificial Neural Network based smart number plate with real-time traffic sign recognition and notification was developed. The developed smart number plate comprises two parts, the smart plate and the display. The smart plate comprises a sensing and processing unit, while the display comprises a notification unit, and both communicate through wireless communication. The sensing unit contains a speed sensor, vibration shock sensors, a Global Position System (GPS), and an AI-Thinker camera. The processing unit comprises espressif board ESP32-CAM and ESP32-S that act as controllers. The notification unit contains the Liquid crystal Display (LCD), Global System for Mobile communication (GSM), and Buzzer. With the TensorFlow model for machine learning, the smart number plate classifies and recognizes traffic speed signs with real-time notification. This smart number plate had been tested on different vehicles, and it assisted drivers in obeying the traffic speed signs earlier, and the traffic police station had been alerted for emergency support. Moreover, the remained traffic signs such as informative traffic signs were not detected and was recommended to be added into the future machine learning models.
Smart system for monitoring and controlling oxygen gas level in high purity germanium detector room: a case study of Tanzania atomic energy commission, Arusha-Tanzania
(NM-AIST, 2023-07) Uwamahoro, Yvonne
Low-oxygen air causes death all around the world. Even though the number of fatalities varies from year to year and location to location, nitrogen gas replaces oxygen in the atmosphere, increasing its percentage to less than 21% by volume. Special environment/room such as High Purity Germanium Detector Room (HPGDR) requires tailored techniques to ensure that oxygen levels are properly monitored to avoid any hazard. This study was designed for the HPGDR at Tanzania Atomic Energy Commission (TAEC). The V-Model was used which works well for small projects with clear requirements. It facilitated each step before moving on to the next level of development, resulting in the design of an error-free and high-quality system. The ESP32 microcontroller which is built in Wi-Fi was used to send data to the Blynk cloud server. The developed system is made up of four parts: The sensing component continuously monitors environmental parameters with Oxygen, MQ-135, and DHT22 sensors. The processing section processes and analyzes sensor data. The notification component alerts workers via a buzzer and Short Message Service (SMS). While the controlling component replaces the contaminated compressed air with fresh air from outside. To provide real-time monitoring, the developed system employs the Blynk Application. All processed data was accessible via mobile phones using the Blynk application. The system eliminates both danger and fear because it alerts workers through SMS and switches on exhaust fan automatically. The HPGDR workers and the administrators are the main beneficiaries of the developed system.
Development of early warning system for human wildlife conflict using deep learning, iot and sms
(NM-AIST, 2023-08) Ronoh, Emmanuel
Human-wildlife conflict is a significant challenge to communities living in areas close to wildlife game parks and reserves. It is more evident in the United Republic of Tanzania whose economy depends on agriculture and wildlife tourism as a significant source of income for her citizens and foreign exchange respectively. The proposed system is a low-power and low-cost early-warning system using deep learning, Internet of Things (IoT) and Short Message Service (SMS) to support human-wildlife conflict response teams in mitigating these problems. The proposed system comprises three basic units: sensing unit, processing unit, and alerting unit. The sensing unit consists of a Global Positioning System (GPS) module, a passive infrared (PIR) sensor, and a Raspberry Pi camera. The PIR sensor module detects animal nearby using its heat signature, the GPS collects and records the current system location while the Raspberry pi camera takes an image after the PIR sensor has detected the animal nearby using its heat signature. The processing unit with the main unit uses a Raspberry microcomputer to perform image inferencing using the “you look only once” (YOLO) algorithm and data processing. The last unit is an alerting unit that uses Global System for Mobile Communications module to send an alerting SMS message to the community response team leader and the human-wildlife conflict response team whenever wild animals are detected near the park’s border. Therefore, the system detects, identifies, and reports wild animals detected using SMS. General Packet Radio Service cellular network provides internet connectivity for the purpose of data collection to enable monitoring and storage in the cloud. An online visualization system was developed using google maps to show the location of wildlife detected by the camera trap. The park rangers track the wildlife online to acquire important information before the wildlife wanders out of the park. This system was developed using the open-source Raspberry pi which is cost- effective even for low-income communities who are targeted by the system.
Brix and alcohol content monitoring using wireless sensor network
(NM-AIST, 2023-08) Willa, Victor
The fermentation process plays a vital role in the production of wine and beer by converting Brix (Sugar) into alcohol. Consequently, monitoring this fermentation is crucial for breweries to ensure the quality of their products. This project’s main objective was to enhance alcohol quality monitoring the by-products of fermentation, namely Brix (Sugar concentration) and alcohol levels. Each stage of fermentation results in varying Brix and alcohol percentages by volume. To achieve this, a system utilizing wireless communication protocols was proposed. Sensor nodes were strategically placed to collect data, which was then transmitted to a centralization station for monitoring and visualization. The sampling technique used was non- probability purposive, as it allowed the project to gather essential information from knowledgeable personnel in the field of study, contributing to a deeper understanding of the problem. The implementation of an IoT (Internet of Things) and Wireless Sensor Network solution proved to be highly advantageous for the brewery industry. This approach facilitated the seamless transfer of real-world fermentation processes into the digital realm, enabling optimization of these processes. Through this project study a novel and automated method with commendable accuracy was developed for estimating Brix and alcohol content during fermentation process. This innovative solution promises to improve the overall quality of alcohol production and enhance the efficiency of monitoring and control in brewery.
Real-time IoT-based air quality monitoring and health hazards indicator system for mines regions: a case study of Bulyanhulu gold mine
(NM-AIST, 2023-07) Flavian, Daudi
Air quality in mining regions is a significant concern due to the potential release of pollutants from mining activities and associated processes. The proximity of mining operations to communities can have detrimental effects on the air quality and pose health risks to residents. Despite the well-known harmful effects of breathing in contaminated air, yet, this concern is commonly neglected due to a lack of information regarding air quality and levels of air quality. The study indicates that the concentration of pollutants such as PM2.5/PM10, CO, CO2, SO2, and NO2 can lead to developing chronic diseases such as respiratory issues, coughs, asthma, ischemic heart diseases, and cancer; due to inhaling hazardous air. This study proposes a real- time IoT-based air quality monitoring and health hazards indicator system for mining regions. The study implements a reliable and long-range (LoRa) wireless sensing system that collects real-time air quality data and updates it to the cloud. The developed real-time IoT-based air quality monitoring system for mines region is composed of numerous sensors (MQ7, MQ135, MQ136, MiCS4514, PMS7003, DHT22), Raspberry Pi, ATmega328 microcontroller, LoRa shields, and the ThingSpeak IoT server. The system collects air pollutants such as carbon monoxide (CO), carbon dioxide (CO2), sulfur dioxide (SO2), particulate matter (PM2.5/PM10), nitrogen dioxide (NO2), temperature, and related humidity. The system is self-contained, using a solar charger shield to link a photovoltaic solar panel to a rechargeable battery for continuous operation. The smart sensing device constantly monitors air quality and uploads the results to a cloud via the coordinator node and the LoRa gateway shield, which in turn uploads the information to the ThingSpeak IoT server. The data collected are processed to calculate the Air Quality Index (AQI), which is then analyzed to generate early warnings and an indication of diseases and dangerous health hazards when exposed to such environments for a certain time. The results are displayed on a developed web-based dashboard that users can easily access and visualize the results. The system is very reliable as developed to simplify the monitoring process and provide accurate data on pollutant levels. The system helps environmental stakeholders in the air quality data aggregation, analysis, Air Quality Index (AQI) calculation, Reporting, and easy way of air quality data communication to the public as well as the indication of health hazards, allowing for informed decision-making, policy formulation, and mitigation strategies.
The needs and opportunities for housing improvement for malaria control in Southern Tanzania
(NM-AIST, 2023-07) Bofu, Ramadhani Mohamedi
There is evidence that mosquito-proofed houses can reduce malaria risk. However, housing improvement is rarely included in malaria control toolboxes. This study assessed the need, magnitude, and opportunities for housing improvement to control malaria in Tanzania. The exploratory mixed-methods study was conducted in 19 villages across four councils in southern Tanzania. A structured survey was administered to 1292 community members to assess need, perceptions, and opportunities for housing improvement. Direct observations of 802 houses and surrounding environments were done to identify the needs, opportunities, and to validate the survey findings. A market survey was done to assess availability, cost of resources and services necessary for mosquito-proofing homes. Focus group discussions were conducted with key stakeholders to explore insights on the potential and challenges of housing improvement. Of the 735 respondents who needed housing improvements, a majority needed window screening (91.1%), repairs of holes in walls (79.4%), door covers (41.6%), closing eave spaces (31.2%) and bettering roofs (19.0%). Community members invested significant efforts to improve their homes against malaria and other dangers, but these efforts were delayed due to high costs and limited incomes. Study participants suggested several mechanisms of support to improve their homes, including loans and subsidies. Addressing the need for housing improvement is a critical component of malaria control. A majority of the community members needed modest modifications and had plans to work on it. Thus, it is crucial to bring together key players across sectors to reduce barriers and making housing improvement accessible and affordable to residents.
Development of a remote road traffic accidents tracking and reporting system for Kayoola buses
(NM-AIST, 2023-08) Maniranzi, Mireille
Motor vehicles continue to improve in safety each year, resulting in a decline in fatality rates. However, car crashes remain a significant concern for the automotive industry as the goal is to eliminate accidents altogether. Unfortunately, effective strategies for managing road traffic accidents are not frequently implemented by transport companies. The use of digital or paper forms for manual accident reporting leads to the loss or inaccurate and untimely reporting of valuable information regarding these accidents. To address this issue, this project proposed to develop a remote road traffic accidents tracking and reporting system catered to the transport industry. By integrating various components such as a gyroscope for tilt detection, a load cell for impact force measurement, a web application serving as a centralized reporting platform, a GSM module enabling SMS alerts, an SD card for report backup, and an Arduino Nano 33 IoT for overall system control, a comprehensive solution has been achieved. This system enables real-time accident detection, and immediate transmission of alerts. Ultimately, the project endeavors to enhance the safety and efficiency of the transport industry by delivering a reliable and highly effective road traffic accidents tracking and reporting system for informed decision making.
Modeling transmission dynamics and control of anthrax
(NM-AIST, 2019-03) Efraim, Joely E
Anthrax is a zoonotic disease caused by Bacillus anthraces. In this study the deterministic mathematical models for transmission dynamics of anthrax in absence and presence of control strategies in humans and animals are presented and analyzed to determine which parameters are sensitive to the disease and how will control strategies help to eradicate the disease. Using normalized sensitivity index, sensitivity index of each parameter with respect to basic repro- duction number R0 is derived and find that, parameters such as anthrax transmission’s rate β , animal’s recruitment rate ba, animal’s natural death rate, and pathogen’s natural death rate are most sensitive to the transmission dynamics of anthrax. Stability analysis for equilibrium states by linearization, Metzler matrix, and Lyapunov function shows that the disease-free equilibrium is locally and globally asymptotically stable when R0 < 1 and endemic equilibrium is globally asymptotically stable when R0 > 1. The analysis shows that when free pathogens are destroyed with fumigants both susceptible humans and animals flourish while infected humans and an- imals decrease. It is also found that pathogens and carcasses decrease due to the fumigation effect. The analysis also shows that when carcasses are incinerated and removed from the af- fected area both humans and animals increase while infected humans and animals decrease. The analysis also shows that incineration and complete removal of carcasses makes the population of carcasses and pathogens decrease. The study also found that when all control strategies such as fumigation, incineration and complete removal of carcasses, animal’s treatment, and humans treatment are all administered both susceptible humans and animals increase, infected humans and animals decrease and carcasses and pathogens decrease.

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