2647 research outputs found
Sort by
Hydrological System and Water Balance of Ungauged Crater Lakes of the Northern Crater Highlands
Get PDFThis research article was published in the Tanzania Journal of Engineering and Technology, Vol. 42 No. 3, 2023The study aimed to unveil the hydrological system and water balance
of the ungauged crater lakes with major focus on the Emakati Lake
which occupy 46% of the Empakaai Crater associated to the East
African Rift Valley and form part of the Northern Crater Highlands.
Water samples for analysis of NO3- , Cl- and stable isotopes (2H and
18O) were collected from the Emakat lake, springs of the inner, outer
and the foot of the Empakaai Crater rims. A combination of satellite
data such as digital elevation model (DEM), Climate Hazards Group
Infrared Precipitation with Station data (CHIRPS), net shortwave solar
radiation, surface temperature, and the computation methods such as
Curve Number (CN) Model, DeBruin–Keijman (D-K) Model enabled
the computation of water balance components such as Lake level
changes, precipitation, runoff and evaporation. Results show that,
evaporation (1694.57 mm) surpasses rainfall (878.68 mm) of the
Empakaai Crater results of higher enrichments of δ18O and δ2H in the
lake ranging between 3.28⁰/₀₀ to 3.96⁰/₀₀ and 31.99 to 33.93⁰/₀₀
compared to springs which range between -5.18 to -4.05⁰/₀₀ and -26.62
to -19.48⁰/₀₀ respectively. Springs plots to the left and above of both the
GMWL and TMWL, implying that they receive direct recharge from
rainfall. The water balance in the area shows that, groundwater flow
plays a major role on the lakes hydrological system as it contributes
about 22,004,361.12 m3/year as the groundwater inflow to Emakat
Lake which is about 56% of the lake’s total inflow and about
22,734,274.00 m3/year as groundwater outflow which is about 63% of
total lake outflow. This imply that, the lake depends less on the weather
condition and hence ensuring the sustainability of the ecosystem of the
Empakaai crater and the downstream
The role of modeling in the epidemiology and control of lumpy skin disease: a systematic review
Get PDFThis research article was published by Bulletin of the National Research Centre Volume 47, 2023Background
Lumpy skin disease (LSD) is an economically important viral disease of cattle caused by lumpy disease virus (LSDV) and transmitted by blood-feeding insects, such as certain species of flies and mosquitoes, or ticks. Direct transmission can occur but at low rate and efficiency. Vaccination has been used as the major disease control method in cooperation with other methods, yet outbreaks recur and the disease still persists and is subsequently spreading into new territories. LSD has of late been spreading at an alarming rate to many countries in the world including Africa where it originated, Middle East, Asia and some member countries of the European Union except the Western Hemisphere, New Zealand and Australia. In order to take control of the disease, various research endeavors are going on different fronts including epidemiology, virology, social economics and modeling, just to mention a few. This systematic review aims at exploring models that have been formulated and/or adopted to study the disease, estimate the advancement in knowledge accrued from these studies and highlight more areas that can be further advanced using this important tool.
Main body of the abstract
Electronic databases of PubMed, Scopus and EMBASE were searched for published records on modeling of LSD in a period of ten (10) years from 2013 to 2022 written in English language only. Extracted information was the title, objectives of the study, type of formulated or adopted models and study findings. A total of 31 publications met the inclusion criteria in the systematic review. Most studies were conducted in Europe reflecting the concern for LSD outbreaks in Eastern Europe and also availability of research funding. Majority of modeling publications were focused on LSD transmission behavior, and the kernel-based modeling was more popular. The role of modeling was organized into four categories, namely risk factors, transmission behaviors, diagnosis and forecasting, and intervention strategies. The results on modeling outbreaks data identified various factors including breed type, weather, vegetation, topography, animal density, herd size, proximity to infected farms or countries and importation of animals and animal products. Using these modeling techniques, it should be possible to come up with LSD risk maps in many regions or countries particularly in Africa to advise cattle herders to avoid high risk areas. Indirect transmission by insect vectors was the major transmission route with Stomoxys calcitrans being more effective, indicating need to include insect control mechanisms in reducing the spread of LSD. However, as the disease spread further into cold climates of Russia, data show new emerging trends; in that transmission was still occurring at temperatures that preclude insect activities, probably by direct contact, and furthermore, some outbreaks were not caused by field viruses, instead, by vaccine-like viruses due to recombination of vaccine strains with field viruses. Machine learning methods have become a useful tool for diagnosing LSD, especially in resource limited countries such as in Africa. Modeling has also forecasted LSD outbreaks and trends in the foreseeable future indicating more outbreaks in Africa and stability in Europe and Asia. This brings African countries into attention to develop long-term plans to deal with LSD. Intervention methods represented by culling and vaccination are showing promising results in limiting the spread of LSD. However, culling was more successful when close to 100% of infected animals are removed. But this is complicated, firstly because the cost of its implementation is massive and secondly it needed application of diagnostic techniques in order to be able to rapidly identify the infected and/or asymptomatic animals. Vaccination was more successful when an effective vaccine, such as the homologous LSD vaccine, was used and complemented by a high coverage of above 90%. This is hard to achieve in resource-poor countries due to the high costs involved.
Short conclusion
Modeling has made a significant contribution in addressing challenges associated with the epidemiology and control of LSD, especially in the areas of risk factors, disease transmission, diagnosis and forecasting as well as intervention strategies. However, more studies are needed in all these areas to address the existing gaps in knowledge
Data Synthesis Technique for Categorical Pestes Des Petits Ruminants (PPR) Data Using CTGAN Model
Get PDFThis research article was published by pre prints org,2023Data scarcity is a significant challenge in the field of Machine Learning (ML), as data
collection can be expensive, time‐consuming, and difficult, particularly in developing countries.
This challenge is exaggerated on the need to use dataset for livestock disease predictions for early
intervention and surveillance. To address this challenge, this paper presents a data synthesis
method that has been used to accurately generate new data samples from few real‐world data. With
much data available to train the ML models, overfitting is eliminated. We present the use of
Generative Adversarial Networks mainly the Conditional Tabular Generative Adversarial Network
to synthesize categorical data for training machine learning models for prediction of the Pestes des
Petits Ruminants (PPR) disease. The results showed that training score became 0.89 and the cross‐
validation score was 0.87 after synthesized data was used with Random Forest algorithm. The
resulting dataset can be used to support the prediction and surveillance of the Pestes des Petits
Ruminants (PPR) disease. The proposed method can also be applied to any domain with categorical
data, and has the potential to improve the performance of machine learning models with increased
data availability
Brix and alcohol content monitoring using wireless sensor network
Get PDFA Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of
Master of Science in Embedded and Mobile Systems of the Nelson Mandela African
Institution of Science and TechnologyThe 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
Climate-smart aquaculture in Tanzania: Assessment of transitional and heavy metals concentrations in a commonly used local feed ingredient for Tilapia farming
Get PDFThis research article was published by International Journal of Advances in Scientific
Research and Engineering (ijasre)A study conducted from January through May 2023 to assess the concentrations of heavy and transitional metals in a
commonly used local feed ingredient in a farmed Nile tilapia diet (Oreochromis niloticus) in Tanzania. Eleven fish
feed ingredients such as, sunflower seed cake (SFSC), wheat pollard (WP), maize bran (MB), fish meal (FM),
freshwater shrimp (FWS), cattle blood meal (CBM), bone meal (BM), soya bean meal (SBM), and rice bran (RB),
brewers’ spent grain (BSG) and Taro leaves (Colocasia esculenta; TL) were randomly sampled from feed
manufacturers, animal feeds’ centers and other animal feeds suppliers in Arusha and Dar es Salaam region for
inclusion in this study. Heavy metals and transition metals in feed ingredients were analyzed using the Energy-
Dispersive X-rays Fluorescence (XRF) (Xla Pro-Spectrometer/German) at the Tanzania Atomic Energy Commission
(TAEC) laboratory. The results showed that most of the fish-feed ingredients used in this study comply with the
maximum allowable concentrations in Nile tilapia diets-according to the Tanzania bureau of standards and the
European commission. However, the results showed that, the concentrations of reported metals (As, Pb, Cd, Hg, Co,
Cu, Mo, Mn, Ni, Ag, V, Cr, Fe and Zn) varied significantly (p < 0.05 ) in most of the analyzed local feed ingredients
collected in Tanzania. The study has paved the way for other researchers to further assess more feed ingredients used
not only on heavy metals but other potential contaminants in feeds to ensure sustainable fish farming in Tanzania
Internet of things (IoT)‑based on real‑time and remote boiler fuel monitoring system: a case of Raha Beverages Company Limited, Arusha‑Tanzania
No full textThis research article was published by Discover Internet of Things,In 2023RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilizes fuel in steam produc-
tion in Arusha-Tanzania, where fuel data conditions such as temperature, pressure, discharge, fuel level, and gas leakage
with humidity were a challenge to monitor, which provoked boiler malfunction and plant breakdown. Today, RABEC
manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time-consuming and gives
inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an
IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure
sensors were used to gather fuel information where the GSM module was employed to send fuel data messages to the
operator’s phone. An AT mega 328 microcontroller was used to process and analyse the fuel data and send them to the
Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the thresh-
old value, an operator was alerted by a refilling message via GSM technology. A pressure of 0.1psi, a fuel temperature of
120 ℃, and 80% of humidity, the system notifies the operator by an alert message to check the injector pressure and if
the fuel–air mixture is perfect. In addition, these data were observed on the LCD and ThingSpeak webpage. To conclude,
the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and
efficiency rate compared to the current monitoring system
Development of web and android applications for management of railway data aggregation and analysis for Tanzania railway corporatio
Get PDFA Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institute of Science and TechnologyRailway transportation is one of the oldest forms of transportation that created links to populated
and unpopulated parts of the world with railway tracks constructed through remote areas in earlier
days. The ever-growing utilization of science and technology has been implemented in railway
transportation to reduce travel time and increase passenger comfortability. African countries such
as Tanzania acquired a railway network during the colonial era with the government performing
few modifications to cater to evolving passengers’ needs. There are still setbacks in generating
adequate information on how the railway sector progresses and how railway information is
disseminated to the public. This study utilizes information technology and provides means for
railway data collection, analysis, and information dissemination in Tanzania through the use of
web and mobile applications. The study was conducted through the collection of data from
Tanzania Railway Corporation (TRC) headquarters in Dar es Salaam intending to understand the
overall performance of the railway sector in terms of data acquisition and information
dissemination. The research looked into ways in which Tanzania differs from railway systems that
are statistically improved with Great Britain being the casing point and found use of fewer railway
attributes being collected and analyzed. This prompted the development of an improved railway
information system which used mixed approach in data collection involving interviews with TRC
staff, questionnaires distributed among passengers, and document reviews. From system
requirements acquired, android and web applications were successfully developed which create a
gateway to railway information, provide visual presentations of collected data from day-to-day
railway operations, create up-to-date maps of the Tanzania railway network, and foster higher
accuracy data collection.. The evaluation of the developed system showed great acceptance as a
way forward for railway data capturing, analyzing and dissemination in the near future. Inclusion
of GIS techniques proved to be of importance in creating awareness of Tanzania railway network
to the public with participants having a higher percentage of agreement in the matter. In general
acceptance, the system received more than 75% approval rating prompting more usage of railway
data attributes as evaluation keypoint in railway transportation within Tanzania
Ecological consequences of microplastic pollution in sub-Saharan Africa aquatic ecosystems: An implication to environmental health
Get PDFThis research article was published by Hydro Research volume 7 2024Microplastic pollution (MPs) emerged as a significant environmental concern due to its persistent nature. These
MPs particles endure in waters, soils, and even the atmosphere, posing potential threats to the entire ecosystem.
Aquatic organisms are at risk of ingesting MPs, leading to accumulation in tissues, ultimately affecting entire food
chain. This study aims to provide an overview of sources of MPs, distribution, and potential environmental
impacts. MPs have been documented in various substances such as bottled water, salts, seafood, and even the
air. However, the full extent of the health consequences on human exposure remains uncertain. Therefore, it is
imperative that we draw public attention to the presence of these pollutants in the environment. To mitigate
adverse effects of MPs, reducing plastic consumption, implementing improved waste management practices,
and advocating sustainable behaviors are essential for well-being of natural ecosystems and the health human
populations
Carbon dioxide removal using a novel adsorbent derived from calcined eggshell waste for biogas upgrading
Get PDFThis reseach article was published by South African Journal of Chemical EngineeringThe existence of CO2 in biogas limits its utilization in engines by lowering its energy value and density. It
contributes to global warming which is a major concern globally. The elimination of CO2 content from biogas
will significantly improve the quality of biogas. In this study, biogas upgrading by calcined eggshell waste was
systematically investigated. The influence of adsorbent particle size, mass, calcination temperature, and flow rate
on carbon dioxide removal was studied in detail. Chemical adsorption of CO2 by calcined eggshells in a packed
column having 280 and 400 μm, calcined under 800 ◦C, 850 ◦C, and 900 ◦C, with the mass of 25, 50, and 75 g
was experimentally investigated at a different flow rate as 0.03 and 0.04 m3
/h. The results revealed that a
particle size of 280 μm, calcined at 850 ◦C, flow rate of 0.03 m3
/h, and mass of 75 g perform better in the
upgrading of carbon dioxide in biogas with RE and sorption of 82.5 %, and g 5.0 g/100 g respectively, creating
methane enriched fuel. X-ray Fluorescence shows the presence of CaO automatically facilitates the whole process
of purification. The pores were mesoporous as seen in the pore distribution curve via BET analysis while
Scanning Electron Microscopy was for morphology purposes. The sorbent was successfully regenerated five times
with a RE of 79.8 % and SC of 4.97 g/100 g in the first cycle. The results proved that calcined eggshells are a
promising sorbent in biogas upgrading
Internet of Things Security in Cloud: A Review on Fog Layer Security
No full textThis research article was published byIEEE,2023Cloud computing in IoT systems enables flexible design with distributed data, infrastructure, and resources accessible from diverse industrial settings. The tremendous rise of the Internet of Things (IoT) has posed numerous issues to the centralized cloud computing architecture which are solved by fog computing. A passive rogue fog node acting as a man-in-the-middle attack poses a significant security vulnerability in the cloud fog layer, compromising data confidentiality and making identification difficult. This survey paper proposes an Intrusion Detection System (IDS) to protect the fog layer from the Man-in-the-Middle Attack (MitM/MITM/MiTM) which is present in the rogue node. Literature review methodology is employed to study various scientific articles providing a comprehensive survey of the existing security and privacy concerns in cloud computing