2647 research outputs found
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Economic Development Through Strategic FDI and Technology Adoption: An Econometric Analysis for Sustainable Revenue Growth in Tanzania
Full text linkThis research article was published by Global Academic Journal of Economics and Business in November 2024This study proposes a robust strategy for bolstering the country’s
economic development. Leveraging an econometric approach model, the
research incorporates additional factors and extrapolates predictions based on
historical data. By combining qualitative and secondary data, the study ensures
a comprehensive analysis of the model through the error term and the ability to
use mathematical treatments to determine future predictions based on
historical data. The findings show that a lack of belonging, poor customer care,
and the cost factor of FDI significantly affect revenue collection. Adopting new
technology requires thorough preparation to avoid generating less revenue than
expected. The study recommends that the government establish appropriate
infrastructures for long-term strategy support for new technology adoption.
Failure to do so may result in a waste of resources. Government policy and
strategy significantly impact TRA performance. The URT government should
adopt policies from other developed countries or modify them to suit local
needs. The findings suggest serious corrective measures to prevent tax evasion
through bribery or tampering with revenue collection infrastructures
Image Segmentation Deep Learning Model for Early Detection of Banana Diseases
Full text linkThis research article was published by the Applied Artificial Intelligence An International Journal Volume 39, Issue 1, 2024Bananas are among the most widely produced perennial fruits and staple food crops that are highly affected by numerous diseases. When not managed early, Fusarium Wilt and Black Sigatoka are two of the most detrimental banana diseases in East Africa, resulting in production losses of 30% to 100%. Early detection of these banana diseases is necessary for designing proper management practices to avoid further yields and financial losses. The recent advances and successes of deep learning in detecting plant diseases have inspired this study. This study assessed a U-Net semantic segmentation deep learning model for the early detection and segmentation of Fusarium Wilt and Black Sigatoka banana diseases. This model was trained using 18,240 banana leaf and stalk images affected by these two banana diseases. The dataset was collected from the farms using mobile phone cameras with the guidance of agricultural experts and was annotated to label the images. The results showed that the U-Net model achieved a Dice Coefficient of 96.45% and an Intersection over Union (IoU) of 93.23%. The model accurately segmented areas where the banana leaves and stalks were damaged by Fusarium Wilt and Black Sigatoka diseases
Sustainable Energy Solutions in Sub-Saharan Africa: Integrating Indigenous Knowledge and Climate Resilience for Lower Carbon Emissions
Full text linkThis research article was published by Global Academic Journal of Humanities and Social Sciences 2024Promoting sustainable energy solutions in sub-Saharan countries is
crucial for addressing energy poverty, reducing carbon emissions, and fostering
long-term environmental and economic sustainability. This study explored using
indigenous knowledge and emerging technologies to reduce carbon footprints and
GHG emissions in Africa's climate hotspots in sub-Saharan countries. The study
revealed that operating institutions utilize various applications to ensure that
energy management resources can mitigate the effects of carbon emissions. The
study revealed that the most efficient use of natural resources for energy
production requires collaboration among governments, private sectors, NGOs, and
local communities. By adopting a holistic and inclusive approach, one can work
toward a more sustainable and low-carbon energy future. This paper focuses on
carbon footprint analysis and proposes solutions to address environmental issues
in implementing sustainable energy solutions in sub-Saharan countries. A
multifaceted approach involving effective strategies is needed to lower the carbon
footprint. The contribution of this study is to improve energy consumption in
communities in Africa by integrating climate resilience considerations into
sustainable energy projects to ensure long-term viability. This will involve
planning for changing climate conditions, such as extreme weather events, and
designing infrastructure that can withstand and adapt to these challenges. It has
been concluded that carbon footprint analysis is useful for determining the impacts
of carbon particles in the world’s atmosphere. The role of energy management
operations seeks to improve the assessment and analysis of carbon footprints by
allowing atmospheric measurements of carbon
Exergy analysis and performance testing of a gravitational water vortex turbine runner for small hydropower plants: An Experimental Approach
No full textThis research article was published by Tanzania Journal of Science / Vol. 50 (2024)their low initial investment, simple design, ease of maintenance, and low head utilization. However, the technology suffers from poor performance issues caused by the non-optimized parameters of its crucial components, such as the runner. In this study, the performance of a runner (16° blade-hub angle, six blades, and a curved blade profile) for a GWVPP was experimentally examined. The study also employed an exergy analysis. The experimental results revealed that the efficiency of the GWVPP system was in the range of 9.84% to 25.35%, the torque was in the range of 0.08 to 0.23 Nm, and the output power was in the range of 2.96 to 7.33 W. Furthermore, an exergy analysis of the system showed an exergy efficiency of 43.58%. Additionally, the error analysis of the GWVPP revealed ranges of 0.1 - 0.5 W for power, 0.01 - 0.03 Nm for torque, and 1.3–3.1% for efficiency, suggesting that the experimental setup and instrumentation of this study were reasonably accurate. Based on the results, the new vortex runner and GWVPP system are recommended for energy generation in low-head, low-flow small hydropower plants
Coastal Mariculture: Status, challenges , and future perspectives of Milkfish (Chanos chanos) farming in Tanzania
Full text linkThis research article was published by Iranian Journal of Fisheries Sciences, volume 24, 2025An investigative field survey was performed from October to
November 2023 at nine villages within five districts in four
selected regions, aimed to assess the status, challenges, and
future perspectives of coastal mariculture development along
the coastline in Mainland Tanzania. During this study, both
purposive and snowball sampling techniques were used. A
structured questionnaire forms were used as an assessment
tool to gather fish farmers’s information, followed by a
focussed group discussion and key informants’ interviews
with government officials. A total of 162 fish farmers,
government officials and animal feed sellers were assessed.
Demographic data indicated that most farmers were male
accounting for 67.9% and females (32.5), aged between
between 20 and 40 years old (56.8%), with primary education
level, accounted for 82.7%. On the other hand, milkfish were
mostly stocked at 2-3 fish/m2 in an earthen pond system, and
under monoculture were mostly fed local feed ingredients
(88%). The study showed that three major income-generating
activities: Milkfish (85%), crab fattening (12%), tilapia (2%), and sea cucumber (1%) were practiced along the coast to
support blue economy initiatives. Additionally, the results
indicated that government subsidies (89), farm inputs (81%),
and capital investment were the major challenges that
constrained milkfish development along the coastline of
mainland Tanzania. Further, current data indicated that
milkfish farming is solely practiced at the subsistence level
and needs a scale-up to sustain the blue economy. The present
study highlighted the status, challenges, and plan for the
future development of coastal mariculture in Tanzania
A comprehensive survey on linear programming and energy optimization methods for maximizing lifetime of wireless sensor network
Full text linkThis research article was published by Springer Link volume 27 2024The wireless sensor network (WSN) is considered as a network, encompassing small-embedded devices named sensors that are wirelessly connected to one another for data forwarding within the network. These sensor nodes (SNs) follow an ad-hoc configuration and are connected with the Base Station (BS) through the internet for data sharing. When more amounts of data are shared from several SNs, traffic arises within the network, and controlling and balancing the traffic loads (TLs) are significant. The TLs are the amount of data shared by the network in a given time. Balancing these loads will extend the network’s lifetime and reduce the energy consumption (EC) rate of SNs. Thus, the Load Balancing (LB) within the network is very efficient for the network’s energy optimization (EO). However, this EO is the major challenging part of WSN. Several existing research concentrated and worked on energy-efficient LB optimization to prolong the lifetime of the WSN. Therefore, this review collectively presents a detailed survey of the linear programming (LP)-based optimization models and alternative optimization models for energy-efficient LB in WSN. LP is a technique used to maximize or minimize the linear function, which is subjected to linear constraints. The LP methods are utilized for modeling the features, deploying, and locating the sensors in WSN. The analysis proved the efficacy of the developed model based on its fault tolerance rate, latency, topological changes, and EC rates. Thus, this survey briefly explained the pros and cons of the developed load-balancing schemes for EO in WSN
Computational and experimental performance analysis of a runner for gravitational water vortex power plant
Full text linkDissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyEnergy generation through water is one of the most economic sources of power. On the other
hand, isolated and rural communities can both benefit from using micro-hydropower to power
their homes. The gravitational water vortex power plant (GWVPP) has recently attracted
interest due to its low initial investment, straightforward design, simple maintenance
requirements, and low head requirements. However, the technology suffers a low performance
caused by unoptimised parameters of its crucial components, such as the GWVPP runner. This
study presents the results of numerical simulation and experimental approaches for the
GWVPP runner. To understand how each factor affected the efficiency of GWVPP runner,
four parameters (hub-blade angle, speed, runner profile, and number of blades) were examined.
The (custom) design tool of Design-Optimal Expert was used to create twenty-four (24)
experimental runs. Commercial Computational Fluid Dynamics (CFD) software, specifically
Ansys CFX, was employed to simulate these runs and assess the system's efficiency. R2 values
of 0.9507 and 0.9603 for flat and curved profiles indicate a better model fitting to actual data.
Additionally, the numerical analysis led to a 3.65% improvement in the efficiency of the curved
blade profile runner, while the flat runner profile's efficiency increased by 1.69% compared to
non-optimized scenarios. The validation process revealed that the comparison between the
numerical investigation and experimental results demonstrated a promising agreement, further
supporting the accuracy of the numerical analysis. The experimental finding depicts that the
efficiency was 9.84 - 25.35%, torque was 0.08 – 0.23 Nm, and the output power was 2.96 –
7.33 W. Furthermore, the results portray the numerical efficiency to be slightly greater by
0.54% than the experimental efficiency, presumably because the frictional forces were not
incorporated in the numerical analysis. Additionally, the exergy analysis of the system revealed
a value of 43.58%. The power error range was between 0.1 and 0.5 W, with a low variation in
the data points. The torque error range was relatively lower than the power error range, ranging
from 0.01 to 0.03 Nm, and the torque measurements showed a low variation in the data points.
The efficiency error range was generally low, with most errors falling within the 1.3-3.1%
range. Therefore, the GWVPP runner efficiency can be improved significantly through
numerical analysis and experimental studies. Also, based on the above results, the GWVPP
runner and GWVPP system are recommended for energy generation in low-head and flowrate
water sources
Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania
Full text linkThis research article was published by Scientific African volume 25 2024The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction
Optimizing ciprofloxacin removal from water using jamun seed (Syzygium cumini) biochar: A sustainable approach for ecological protection
Full text linkThis research article was published by HydroResearch,Volume 7, 2024Scientific interest in antimicrobial pollutants, such as ciprofloxacin, has increased. Due to spread of antibiotic-resistant bacteria, resistance genes, and their dissemination to the environment. Therefore, their remediation is necessary to ensure ecological sustainability. The current study aimed to optimise the removal of ciprofloxacin from synthetic water using jamun seed (JS) (Syzygium cumini) biochar using a response surface methodology (RSM). Result indicates ciprofloxacin elimination efficiency ranged between 32.46 and 94.95%, indicating the material can be improved and used for remediation of organics. The residual standard error of 4.4% were found for the predicted model, implying that the model is credible and can be used to predict future experimental findings. The R-squarred value for the improved Langmuir model's R2 is 0.9681 which is inclose agreement with the Freundlich isotherm, R2 0.9757. Therefore, JS biochar could be utilized for the remediation of ciprofloxacin from contaminated water and wastewater for ecological safety and sustainabilit
Dataset of Virginia Flue-cured Tobacco Leaf images based on stalk leaf position for classification tasks: A case of Tanzania
Full text linkThis research article was published by Science Direct Volume 56 2024Nicotiana tabacum is a kind of plant cultivated for its leaves used for manufacturing medicine and cigarettes. With the common name, the Tobacco plant is grown in many countries including China, Indonesia, Malawi and Tanzania just to mention a few. Literatures suggest a technical gap in the proper identification of grade labels for various parts of the plant. In addition, manual grading has resulted in various gaps and biases. To mitigate this, a data-driven grading solution is necessary. However, relevant datasets to train grade classifiers from various countries become of the essence. This article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels. Due to high rainfall which swiped away the applied fertilizer on the tobacco plants in the farms, we failed to get images of one grade. Therefore, this research could capture and label 22 grade labels. Images of tobacco leaves based on the tobacco plant position were collected in Tanzania through participatory community research. Canon 5D mark III cameras with 100 mm micro lens were used to take pictures of tobacco leaves based on the tobacco plant position. Domain experts were used for image labelling and cleaning according to tobacco grade labels identified in Tanzania. The dataset carries 49,779 images, which can be used to develop machine learning models for tobacco leaf grade label identification. The collected dataset can be used to train models and enhance the performance of pre-trained models in any country of interest