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Modeling of diamondback moth infestations in cabbages with control strategies
Full text linkThe Diamondback moth (DBM), scientifically called Plutella xylostella, a highly destructive
and rapidly spreading agricultural pest originally from Europe. This particular pest presents
a notable risk to the overall global food security, with estimates suggesting that periodic out
breaks of DBM lead to annual crop losses of up to $US 4−5 billion worldwide. Given the
potential for such substantial losses, it is crucial to employ various methods and techniques to
understand the factors affecting the interaction between DBM and cabbages, which, in turn,
impact cabbage biomass. In this research, two mathematical models were developed to assess
the effects of pesticides, inter-cropping methods, and an Integrated Pest Management strategy
on DBMinfestations in cabbage biomass. The first model focused on the impact of pesticides
through nonlinear Ordinary Differential Equations (ODEs) to analyze the interaction between
DBMandcabbage biomass. The model’s results indicated that the use of pesticides effectively
eliminate DBM in a cabbage farm. The second nonlinear ODEs model assessed the impact
of providing pest control education to farmers and deploying predators in cabbage farms. We
found that provision of pest control education alone could reduce the negative impact of the
DBM on final cabbage biomass by 10%. However, when both tactics were implemented to
gether, the pest population was reduced by 40%–80%. In both proposed models, the MATLAB
was employed for simulations to affirm the validity of the analytical results. In brief, the find
ings of this research highlighted the importance of mathematical models as useful instruments
for comprehending the management of DBM in cabbage crops
A predictive analytics-driven battery management system for sustainable e-mobility in East Africa
Full text linkIn pursuit of the United Nations Sustainable Development Goals (UN SDGs) seven and
thirteen, East African countries are swiftly transitioning to electric mobility solutions for clean
transportation and climate action. However, this transition presents a challenge in repurposing
and maintenance of used electric vehicle (EV) batteries due to limited specialized knowledge
and equipment in the region. Despite the growing popularity of electric vehicles, a significant
gap exists in understanding viable battery components for second life applications in East
Africa. This study addresses this gap by designing a predictive analytics-driven battery
management system tailored to the region's needs. The developed system integrates hardware
and software, employing a data-driven approach to analyze sensor data for decision support
and enable remote monitoring of repurposed batteries. Compared to existing works, this
research emphasizes the use of predictive algorithms to monitor battery health in second life
applications and provision for remote monitoring. This innovative approach significantly
advances the understanding and implementation of battery repurposing in East Africa. By
offering a sustainable solution for e-mobility, this study promotes a cleaner and greener future
while reducing energy costs for organizations and domestic users
Modelling the transmission dynamics and control of aflatoxins crops and its associated health risks in livestock and humans
Full text linkAflatoxin contamination poses a significant challenge to food safety and security, as it affects
both the health of consumers and the entire supply chain. Doses of aflatoxins beyond accept
able levels are dangerous and may lead to poisoning, also called aflatoxicosis, a life-threatening
illness. Liver damage or liver cancer, especially for people who may have conditions such as
hepatitis B infection, is also caused by aflatoxin consumption. This study aimed to investigate
the transmission dynamics and control of aflatoxin contamination in crops and its associated
health risks in livestock, and humans. A deterministic mathematical model to study transmis
sion dynamics was formulated and analyzed. Partial Rank Correlation Coefficients (PRCCs)
for global sensitivity analysis were calculated using Latin Hypercube Sampling (LHS) to de
termine how sensitive and significant the parameters are for each variable. Three controls,
namely good farming practices, biological control, and public education and awareness cam
paigns, were analyzed. The optimal control theory and cost-effective analysis were performed
to identify the most effective strategy for aflatoxin contamination mitigation in crops, live
stock, and humans. Four machine learning algorithms: Gaussian Process Classification (GPC),
Support Vector Machine (SVM), Random Forest Classifier (RFC), and K Nearest Neighbors
(KNN) have been used to predict aflatoxin contamination in maize and groundnuts. The anal
ysis of the mathematical model formulated shows that aflatoxin contamination-free equilib
rium (ACFE) and aflatoxin contamination-persistence equilibrium (ACPE) exist. The ACFE
is globally asymptotically stable if the basic aflatoxin contamination number R0 < 1 whereas
the ACPE is globally asymptotically stable if R0 > 1. Numerical simulations showed that a
decrease in crop contamination and shedding rates and an increase in the death rate of aflatoxin
fungi in the environment by 50% reduced the basic contamination number by above 92%. Re
sults from the optimal control analysis suggest that implementation of all controls performs
better than other strategies in controlling aflatoxin contamination in crops, livestock, and hu
mans. Therefore, to control aflatoxin contamination, initiatives should focus on good farming
practices, biological control, and public education and awareness campaigns. In predicting
aflatoxin contamination, GPC outperformed other models with an accuracy of 96% and 95%
in groundnut and maize samples, respectively. Moreover, the study revealed that humidity and
rainfall have a greater influence on predicting aflatoxin contamination compared to tempera
ture
Biometric banking system for effective paperless cash withdrawals at CRDB Bank Burundi
Full text linkIn the ever-changing banking landscape, attempts have been made to improve customer
experiences and expedite operations. However, there remains a continuing difficulty at bank
teller counters, where transactions are still done on paper, resulting in operational inefficiency
and customer dissatisfaction. Despite its effort to provide excellent financial services, CRDB
Bank Burundi confronts a similar difficulty. To remedy this, our project introduces a cutting
edge biometric banking system for secure and paperless withdrawals at the bank teller desk.
The development integrates modern biometric technology, such as fingerprint scanning and
RFID card identification for customers with hand disabilities or contactless alternative. It is
guided by a detailed investigation of existing banking inefficiencies. The system promotes
client satisfaction by allowing personal interaction with bank tellers and integrating clear
notifications via SMS, WhatsApp, and email after each transaction. Furthermore, the
technology links fingerprints or cards to current bank accounts, allowing for faster withdrawals
and a more secure banking environment. Extensive testing proves the system's dependability,
efficiency, streamlined operations, and other features contributing to its effectiveness, paving
the way for deployment. This initiative represents a paradigm leap in banking, offering clients
a smooth and secure transaction experience
An automated core rolling machine for making the metering unit’s current transformer
Full text linkThe advent of automation has revolutionized various industries by simplifying complex
processes, enhancing efficiency, and reducing production time. However, many developing
countries still rely on manual labour, resulting in lower production rates and reduced
competitiveness in the global market. This study focuses on the challenges faced by Tanzania's
electrical equipment company (TANELEC) in the production of metering units, specifically the
laborious and hazardous task of rolling the core steel for the units' current transformer (CT).
Manual processes often lead to dimensional inaccuracies and gaps in the rolled core, causing a
significant number of tested metering units to be deemed unqualified. Consequently, the
industry struggles to meet customer demands effectively. The research findings emphasize the
urgent need for automating the core rolling process for metering units' CT, which became the
primary objective of this study. Employing a qualitative research approach and utilizing the
agile methodology, specifically the Extreme Programming agile method, the study aimed to
gain a comprehensive understanding of the current manual process. The outcome of the study
is a machine capable of intelligently accepting and rolling the required ferromagnetic iron
material, with users able to input desired dimensions and quantities of cores. This development
has significantly increased TANELEC's production capacity, enabling the manufacturing of up
to sixty cores per hour and alleviating the production burden. As a result, the company can now
meet customers' demands promptly
Industrial-based GSM water leakage detection, monitoring, and controlling system: a case of North Rift Valley Water Agency in Kenya
Full text linkThe current system for detecting and monitoring water leaks in Kenyan industries is manual
and costly. Despite emerging technological trends, many industries lack automated systems to
detect, monitor, and control water leakage due to high maintenance and installation costs. This
study aimed to develop an automatic, remote, real-time detection, monitoring, and control of
water leaks in North Rift Valley Water Works Agency. The system is made up of two nodes,
one at the source and another at the destination or tap. The two nodes are made up of an ESP32
Microcontroller, which is used to control all the connected components. The ESP32
Microcontroller was efficient due to its ability to provide WI-FI. Aside from the solenoid valve,
which was used to turn the water flow on or off in the event of leaks, the system also includes
the FY-201 water flow sensor, which was used to gauge the amount of water flowing through
the pipe. Water leakage is detected when the water passing through the two sensors differs
slightly, indicating that a water leakage has just occurred. Thing Board, an IoT-based platform
used to monitor and visualize data from various connected devices, was used for real-time
monitoring, visualization, and control. The system administrator could log into and manage the
system by remotely turning the water leaks on and off from their phone. The database used was
MySQL DB, and a system was created using C programming language, the Arduino Integrated
Development Environment, HTML, and Cascading Style Sheet (CSS). The developed system
was tested with different water service providers, including Eldoret Water and Sanitation
Company, and the results show that the system responds positively to water leakage
parameters. The developed system could monitor water leakages in real-time with the two
nodes interacting by sharing information via the server and communicating via WI-FI enabled
by the ESP 32 Microcontroller. With new technological trends, such as the Internet of Things,
these microcontrollers will facilitate faster adoption in industrial projects. The benefits include
that a system administrator can control the system remotely without physically switching the
solenoid valve on or off. It is also lower in cost compared to other tariffs. The system can be
installed in hazardous areas, such as valleys and mountains, making it attractive. Future
research suggests more investigation into identifying leaks at particular points along water
pipes by integrating with the Global Positioning System (GPS) to determine their precise
location
Development of a Deep Learning-Based System for Enhanced Blind Spot Detection and Lane Departure Warning for the Kayoola Buses
Full text linkDeep learning-based advanced driver assistance systems (ADAS) have attracted interest from
researchers due to their impact on improving vehicle safety and reducing road traffic accidents.
In Uganda, road accidents have continued to soar with an increase of up to 42% in 2021 due to
the growing road traffic density. To curb the high rates of road accidents, especially for heavy
duty vehicles, Kiira Motors Corporation a state-owned mobility solutions enterprise needs
advanced driver assistance systems for improved safety of their market entry products the
Kayoola buses. This project presents an approach to vehicular safety enhancement through the
implementation of a Lane Departure Warning (LDW) and Blind Spot Detection system (BSD)
using advanced deep learning algorithms that will be able to alert the driver using the graphical
user interface, and auditory feedback. The system was developed based on the MobileNet
architecture and the Kayoola Buses manufactured by Kiira Motors Corporation were used as
the project case study. A purposive sampling technique was used to select the study participants
focusing on targets automotive manufacturers, bus companies, cargo truck operators, and
passengers. Two distinct datasets which included the DET dataset with raw event data from
5424 images of 1280×800 pixels and the TuSimple dataset of 6,408 road images specifically
captured on highways were used for model training. The resultant BSD and LDW system are
realized on the Raspberry Pi platform, incorporating diverse sensors which include radar
sensors, ultrasonic sensors, gyroscope and accelerometer sensors. By combining these
advanced features, the study not only bridges an essential research void but also offers a
practical resolution to pressing road safety concerns in the East African context. The
implementation of a BSD and LDW system through deep learning techniques marks a pivotal
advancement in vehicular safety. The lane detection model was tested on Dataset for Lane
Extraction (DET) and TuSimple datasets. Our model attained a mean model accuracy (F1
Score) of 77.59% and a mean IoU of 65.26% on the DET and an overall accuracy of 97.96%
on the TuSimple dataset. User acceptance tests were carried out to validate and ascertain
whether the developed system addressed the needs of the prospective users. The tests were
carried out with a total of 150 users to validate the functionality of the system. The anticipated
real-world implementation is poised to substantiate the system's effectiveness, thereby
contributing to safer roads regionally and inspiring innovation in automotive engineering by
leveraging artificial intelligence
Recent and sustainable advances in phytoremediation of heavy metals from wastewater using aquatic plant species: Green approach
Full text linkThis research article was published by Journal of Environmental Management Volume 370, 2024,A key component in a nation's economic progress is industrialization, however, hazardous heavy metals that are detrimental to living things are typically present in the wastewater produced from various industries. Therefore, before wastewater is released into the environment, it must be treated to reduce the concentrations of the various heavy metals to maximum acceptable levels. Even though several biological, physical, and chemical remediation techniques are found to be efficient for the removal of heavy metals from wastewater, these techniques are costly and create more toxic secondary pollutants. However, phytoremediation is inexpensive, environmentally friendly, and simple to be applied as a green technology for heavy metal detoxification in wastewater. The present study provides a thorough comprehensive review of the mechanisms of phytoremediation, with an emphasis on the possible utilization of plant species for the treatment of wastewater containing heavy metals. We have discussed the concept, its applications, advantages, challenges, and independent variables that determine how successful and efficient phytoremediation could be in the decontamination of heavy metals from wastewater. Additionally, we argue that the standards for choosing aquatic plant species for target heavy metal removal ought to be taken into account, as they influence various aspects of phytoremediation efficiency. Following the comprehensive and critical analysis of relevant literature, aquatic plant species are promising for sustainable remediation of heavy metals. However, several knowledge gaps identified from the review need to be taken into consideration and possibly addressed. Therefore, the review provides perspectives that indicate research needs and future directions on the application of plant species in heavy metal remediation
Optimization of solvothermal liquefaction of water hyacinth over PTFE-acid mediated kaolin catalyst for enhanced biocrude production
Full text linkThis research article was published by Journal of Analytical and Applied Pyrolysis, Volume 178, March 2024The invasive nature of water hyacinth and the need for renewable energy sources have necessitated this research. Catalyst development through enhanced pore structure and process parameters optimization are requirements for effective mass transport during the biomass valorization and improved biocrude formation during solvothermal liquefaction process. In this present study, the effects of temperature (250–340 °C), residence time (10–20 min) and catalyst loading (10–13 wt%) on biocrude, biochar, gas yield, and biomass conversion were optimized using a Box-Behnken experimental design. The developed catalyst through the application of polytetrafluoroethylene (PTFE) for pore structure enhancement was characterized using SEM, BET and XRD techniques. The process optimization found maximum biocrude yield (32.0 wt%), minimum biochar yield (19.4 wt%) and maximum conversion efficiency (80.6%) at 340 °C, 20 min residence time, and 13 wt% catalyst loading. The GC-MS result of the biocrude produced at the optimum conditions (13 wt% catalyst loading) consists of ketones (32.2%), acids (22.3%) and had 65.2% carbon, 7.3% hydrogen, HHV of 29.4 MJ/kg and H/C ratio of 1.34 while an increment in catalyst loading of 20 wt% enhanced the overall biocrude properties with HHV of 35.50 MJ/kg. This result depicts the suitability of the PTFE modified acid treated kaolin for high quality biocrude production through valorization of water hyacinth into a candidate for renewable energy material
Formal Institutions In Enhancing Entrepreneurship Development In The Tanzanian Higher Learning Institutions.
Full text linkThis research article was published by the International Journal of Business Management and Economic Review
Vol. 7, No. 04; 2024This qualitative study aimed to explore how formal institutions promote entrepreneurship
development in the Tanzanian Higher Learning Institutions (HLIs) using the Sokoine University
of Agriculture (SUA) as a case study. It employed semi-structured individual interviews, focus
group discussions, and documentary review as data generation methods. Thematic analysis with
the help of Nvivo software was used to analyze the data, revealing insights from 73 respondents,
selected based on data saturation. The study applied the institutional theory as a theoretical lens to
frame both its methodology and findings interpretation. Results suggest that organized-functional,
active and interplaying formal institutions, including the HLIs’ charters, the policies (research and
development policy, innovation policy, and entrepreneurship development investment policy), and
the dedicated entrepreneurship development courses, play a crucial role in fostering
entrepreneurship development in HLIs in Tanzania. The study recommends aligning institutional
documents with entrepreneurship development and also ensuring coherence across these
instruments