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Modelling the Impact of Human Population and Its Associated Pressure on Forest Biomass and Forest-Dependent Wildlife Population
Get PDFA Project Report submitted in Journal of Applied MathematicsVolume 2023,Mathematical models have been widely used to explain the system originating from human-nature interaction, investigate the impacts of various components, and forecast system behaviour. This paper provides a profound reference to the current state of the art regarding the application of mathematical models to study the impact of human population and population pressure on forest biomass and forest-dependent wildlife. The review focused on two aspects, namely, model formulation and model analysis. In model formulation, the review revealed that socioeconomic status influences forest resource consumption patterns, thus, stratification of the human population based on economic status is a critical phenomenon in modelling human-nature interactions; however, this component has not been featured in the reviewed models. Regarding model analysis, in most of the reviewed work, single parameter approach was utilized to perform uncertainty quantification of the model parameter; this approach has been proven to be inadequate in measuring the uncertainty and sensitivity of the parameter. Thus, the use of correlation or variance based methods, which are multidimensional parameter space methods are of significant importance. Generally, despite the limitations of many assumptions in mathematical modelling, it is revealed that mathematical models demonstrate the ability to handle complex systems originating from interactions between humans and nature
Iot based monitoring and reporting system for dosimeter wearers in radiation areas: a case study of TAEC
No full textA Project Report Submitted in Partial Fulfillment of the Requirements of the Award of the Degree of Master of Science in Embedded and Mobile Systems of The Nelson Mandela African Institution of Science and TechnologyIn today's modern society, the use of radiation sources in a wide range of activities has
increased rapidly. As a result, occupational exposure to ionizing radiation doses that cause
health effects increases. Excessive doses of 20 millisieverts (mSv) per year cause acute effects
such as sterility or cancer. The health effects of ionizing radiation fall in many countries around
the world, including Tanzania. Tanzania Atomic Energy Commission (TAEC) manages
dosimeters in order to reduce radiation hazards to radiation workers. Nonetheless, after
dispatching those dosimeters, the TAEC management room is unable to determine whether or
not each supposed wearer has worn the dosimeter. This originates from an incorrect assessment
of an individual occupational radiation dose. Therefore, an Internet of Things-based
Monitoring and Reporting System for Dosimeter Wearers in Radiation Areas is developed.
Using internet of things (IoT) technology, this project presents an effective and affordable
system for real-time remote monitoring and reporting staff wearing passive dosimeters while
near or in the area; radiation exposure is too high. The scrum method, which is based on agile
methodology, was used for system development. The system was run by an ESP32
microcontroller board that was programmed in C using the Arduino Integrated Development
Environment. The ESP 32 microcontroller could send data to the weber server via its built-in
Wi-Fi. Through the mapping web application, the end-user (TAEC Managerial Officer)
monitored and visualized radiation workers. IoT enabled the dosimeter to be monitored and
reported on at any time via the internet
Effects of zero-valent iron on sludge and methane production in anaerobic digestion of domestic wastewater
Get PDFThis research article was published in Case Studies in Chemical and Environmental Engineering Journal, Volume 8, 2023.Iron metal (Fe0) materials enhance the performance of anaerobic digestion (AD) reactors to remove pollutants. Most research focused on the materials' mechanisms and effectiveness in enhancing AD. However, there is scant information on the biogas and sludge quality and quantity and the kinetics of generated methane (CH4) of biogas from the Fe0-aided AD of domestic wastewater (DW). The information is essential for AD reactors' management. This study characterizes the sludge and biogas from Fe0-aided AD of DW and predicts the CH4 yield using the Gompertz, Logistic, and Richard models to study the impact of Fe0 materials on the composition and generation of sludge and biogas. Bench-scale reactors containing DW were fed with Fe0 and operated for 53 days in a quiescent condition, at 24 ± 3 OC room temperature, at 7.3 initial pH value. Steel wool and iron scrap were used as Fe0 sources. A parallel experiment without Fe0 was performed as an operational reference. Results indicate that Fe0 significantly enriched most of the nutrients in sludges, produced well-settling sludge (sludge volume index ≤30), and enriched the CH4 of biogas by more than 12%. Furthermore, all the tested models exhibited good fitting (error <10%) in predicting CH4 production. Fe0-aided AD produced a sludge with the potential for application in agricultural land and increased the heating value of the biogas by enriching the CH4. More than 80% of particles generated from Fe0-aided AD of DW can be settled in sedimentation tanks designed at an overflow rate ≤40 m/d. Richard was the best model for predicting methane yield from Fe0-aided AD of DW (error <1.6%)
Tin molybdenum mixed metal oxides catalyst for oxidative desulfurization of model diesel
Get PDFA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Materials Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThis study reports on synthesis, characterization, and catalytic activity of mesoporous mixed
metal oxides of tin (Sn) and molybdenum (Mo) for sulfur removal from model diesel. Variable
synthesis conditions, namely calcination temperature and Sn/Mo mole ratios, have been on
focus. Several techniques were used to characterize the catalysts, including powder X-ray
diffraction (XRD) for the crystal structure, orientation, and particle size; scanning electron
microscopy (SEM)-EDX for examining the morphological properties of the materials, N2
adsorption-desorption isotherms for textural properties, thermal gravimetric analysis (TGA)
for thermal stability, the Fourier transform infrared spectroscopy (FT-IR) for functionality.
Characterization results show that the adsorption-desorption isotherms are of type IV,
indicative of mesoporous materials. The surface area of the synthesized materials decreased as
calcination temperature increased due to the Ostwald ripening process and increased with the
mole ratio Sn/Mo increase. The X-ray diffraction structural analyses revealed that the
synthesized catalyst had a tetragonal structure. The presence of Mo=O and Sn‒O‒Mo bonds,
which are responsible for the catalytic reaction, is confirmed by FT-IR and Raman analyses.
The activity of the catalysts prepared at various calcination temperatures and mole ratios was
analyzed for oxidative desulfurization of model diesel, dibenzothiophene (DBT). The optimal
synthesis conditions were the calcination temperature of 450 °C and the mole ratio of Sn/Mo
of (2:1). Other multiple parameters affecting the reduction of sulfur compounds were also
investigated, including reaction temperature, catalyst loading, oxidant/sulfur ratio, and reaction
time. The (DBT) removal efficiency was 99.8 % at 60 °C, 100 mg, 5, and 30 min,
correspondingly. This high catalytic activity was due to the surface defects increase,resulting
in a high surface area with high pore distribution around the mesopore region.
Experiments examining the reaction kinetics have indicated that the reaction follows a pseudo first-order behaviour. The activation energy for the reaction has been determined to be 36 ± 4
kJ mol-1
. The rate of the heterogeneous reaction is governed by the Langmuir-Hinshelwood
mechanism. Furthermore, the maximum rate constant value of SnO2-MoO3 catalyst with
Sn/Mo (2:1) molar ratio is 0.057 min−1, which is higher than for pure SnO2 (0.017 min‒1
) and
MoO3 (0.007 min−1
), confirming a synergy between SnO2 and MoO3, promoting oxidative
desulfurization efficiency. The catalyst's high activity and reusability indicate enormous
promise for industrial catalytic desulfurization
Preliminary symbiotic performance of indigenous soybean (Glycine max)-nodulating rhizobia from agricultural soils of Tanzania
Get PDFThis research article was published in Frontiers in Sustainable Food Systems journal, 2023Globally, the increase in human population continues to threaten the sustainability of agricultural systems. Despite the fast-growing population in Sub-Saharan Africa (SSA) and the efforts in improving the productivity of crops, the increase in the yield of crops per unit area is still not promising. The productivity of crops is primarily constrained by inadequate levels of soil nutrients to support optimum crop growth and development. However, smallholder farmers occasionally use fertilizers, and the amount applied is usually small and does not meet plant requirements. This is due to the unaffordability of the cost of fertilizers, which is enough to suffice the crop requirement. Therefore, there is a need for alternative affordable and effective fertilization methods for sustainable intensification and improvement of the smallholder farming system's productivity. This study was designed to evaluate the symbiotic performance of indigenous soybean nodulating rhizobia in selected agricultural soils of Tanzania. In total, 217 rhizobia isolates were obtained from three agroecological zones, i.e., eastern, northern, and southern highlands. The isolates collected were screened for N2 fixing abilities under in vitro (nitrogen-free medium) and screen house conditions. The results showed varying capabilities of isolates in nitrogen-fixing both under in vitro and screen house conditions. Under in vitro experiment, 22% of soybean rhizobia isolates were identified to have a nitrogen-fixing capability on an N-free medium, with the highest N2-fixing diameter of 1.87 cm. In the screen house pot experiment, results showed that soybean rhizobia isolate significantly (P < 0.001) influenced different plant growth and yield components, where the average shoot dry weight ranged from 2.49 to 10.98 g, shoot length from 41 to 125.27 cm whilst the number of leaves per plant ranged from 20 to 66. Furthermore, rhizobia isolates significantly (P = 0.038) increased root dry weight from 0.574 to 2.17 g. In the case of symbiotic parameters per plant, the number of nodules was in the range of 0.33–22, nodules dry weight (0.001–0.137 g), shoot nitrogen (2.37–4.97%), total nitrogen (53.59–6.72 g), and fixed nitrogen (46.878–0.15 g) per plant. In addition, the results indicated that 51.39% of the tested bacterial isolates in this study were ranked as highly effective in symbiosis, suggesting that they are promising as potential alternative biofertilizers for soybean production in agricultural soils of Tanzania to increase productivity per unit area while reducing production cost
Mobile-based peer-to-peer learning prototype for smallholder dairy producers
Get PDFA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree o fMaster’s in Information Systems and Network Security of the Nelson Mandela African Institution of Science and TechnologyAbout half of Africa's animal production comes from smallholder dairy farmers, who employ
various strategies to maximize milk output. Some time-consuming and expensive heuristics are
used by smallholder dairy farmers to increase milk yield, trapping them in a cycle of failure
and lowering their incentive to continue making agricultural investments. Grouping
smallholder dairy producers with comparable characteristics makes information sharing and
interventions easier, increasing milk output. This study aimed at developing a mobile-based
peer-to-peer learning prototype which considers farmers’ homogeneity with respect to
husbandry practices and auto-allocates them to their respective production clusters. The
developed prototype's rule-based engine handles the auto-allocation procedure by grouping
farmers with similar farming characteristics into the proper production clusters. Smallholder
dairy producers exchange knowledge and expertise through these groups to increase milk
output. In Tanzania's Arusha Region, 69 smallholder dairy farmers and nine extension workers
responded to a questionnaire to provide information, which was then analyzed using R
programming. The important findings are; smallholder dairy producers were automatically
allocated to their clusters based on their milk output. Cluster position regarding milk yields was
determined using cluster performance for overall production attributes. Consequently, high yielding smallholder dairy producers are assigned to the high-yielding cluster, and vice versa,
and extension officers provide timely support. This study is unique since smallholder dairy
producers may use it to share dairy farming expertise and boost milk output. Mobile-based
peer-to-peer should be integrated with the market by engaging enterprises that process milk for
other milk product
Exploring the nexus between health status, technical efficiency, and welfare of small-scale cereal farmers in Tanzania: A stochastic frontier analysis
No full textThis articles was published by ELSEVIER,2023Cereal production is important component of Tanzania’s agricultural sector, as it provides food security and
income for a significant portion of the population. However, low levels of technical efficiency and the negative
impact of ill-health on cereal productivity have posed significant obstacles to the welfare of small-scale farmers
in the country. This study estimates the technical efficiency of cereal producers in Tanzania, investigates the
relationship between farmer health and cereal productivity, and establishes a link between technical efficiency
and the welfare of smallholder farmers. Using data from the Tanzania Agriculture Sample Census survey 2019/
20, the stochastic frontier production function was used to estimate technical efficiencies, while Tobit and
instrumental variables models analyzed the impact of health on cereal production efficiency and the effects of
efficiency on the welfare of cereal’s small-scale farmers respectively. The findings indicate that the overall
technical efficiency of cereal producers in Tanzania is 44.44%, with pure technical efficiency standing at 56.50%.
In addition, poor health reduces the likelihood of cereal productivity efficiency by 0.297 (p < 0.01). In addition,
efficiency was found to significantly improve household welfare, as it increases food security (0.35327, p <
0.01), household income (0.2914, p < 0.01), and nutrition status by reducing malnutrition (− 0.36607, p < 0.01).
The study recommends that rural agriculture development programs include health components to increase
productivity, sustainability, and ultimately the standard of living of rural communities
Development of an interactive mobile application for information sharing between sccult, member cooperatives (saccos), and other stakeholders
No full textA Project Report Submitted in Partial Fulfillment 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 use of mobile devices and their affiliated software has brought about a very immense
change in communication. From a different angle, marketing of goods and services for business
entities has been made easier and better. However, it has been discovered that, to know about
a specific entity one would need to have prior knowledge about it. This makes it hard for
unknown entities to become reached by potential buyers. This is the case for SACCOS and
SCCULT in Tanzania. The two become invisible to the consumer community due to their
infamous nature, making their marketing difficult and hence, stunting their growth. Savings
and Credit Cooperative Unions (SACCOS) are financial groups that provide their members
with different financial services, including loans, savings, and investments. By lending capital
to Small and Medium Enterprises, SACCOS have been a source of close to 94.7%
employments of their members. It as well contributes up to 40% of the national Gross Domestic
Product (GDP). However, only less than 10% of the national population is beneficiary of the
services offered by SACCOS in Tanzania. The purpose of this study was to bring to attention
that most individuals do not engage with SACCOS and enjoy their easy and smooth services
because they lack information. The study proved that with enough information and engagement
of the SACCOS to society, many individuals could join SACCOS and enjoy their benefits. A
mixed research method was used to gather information on the society’s awareness of SACCOS,
their knowledge, and usage of social media, and whether they have ever encountered news
about SACCOS in their social media exploration. It was discovered that more than 70% of
individuals have poor or no knowledge at all about SACCOS. The study discovered that, due
to a large number of content contexts in social media, SACCOS are not easily found in such a
pool of information. This project resulted in the development of an interactive mobile
application for information sharing between the Savings and Credit Cooperatives Union
League of Tanzania (SCCULT), their member cooperatives (SACCOS), and other stakeholders
including the Tanzanian society mass. This is a mobile application dedicated to sharing news
with society in a broader and more relevant way. Allowing individuals to have ease of access
to SACCOS’s basic information enough to help them build interest in joining them
Machine learning model for prediction of malaria in low and high endemic areas of Tanzania
Get PDFA Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of
Doctor of Philosophy in Information and Communication Science and Engineering of
the Nelson Mandela African Institution of Science and TechnologyPresumptive treatment and self-medication with anti-malaria drugs is a common practice in
most limited resource settings that hinders proper management of malaria. However, these
approaches have been considered unreliable due to the unnecessary use of malaria medication
and untreated diseases that relate to malaria. This study aimed to develop a machine-learning
model for malaria diagnosis using patients’ symptoms and non-symptomatic features in high
and low endemic areas of Tanzania. The malaria diagnosis dataset with 2556 patient’s records
and 36 features was collected in two regions of Tanzania: Morogoro and Kilimanjaro from
2015 -2019. Machine learning classifiers with the k-fold cross-validation methods were used
to train and validate the model. To improve the performance of the diagnostic model, important
features for malaria diagnosis were selected, and it was observed that the ranking of features
differs among regions and when combined dataset. Significant features selected are residence
area, fever, age, general body malaise, visit date, and headache. Random Forest and Decision
Tree algorithms were the best performing classifiers in modelling malaria diagnosis datasets
and attained 96%, 99% and 98% prediction accuracy for Kilimanjaro, Combined and Morogoro
dataset respectively. These best-performing classifiers were evaluated using the unseen malaria
diagnosis dataset and performed well in classifying malaria patients from sick patients. The
final developed model showed that only a specific combination of features can predict malaria
accurately. The results of this study revealed that malaria diagnosis using patients’ symptoms
and demographic features is possible. Also, the study results offer additional knowledge and
shed light on the state diagnosis of malaria in the country. The developed machine learning
model enables prediction of patient’s malaria state using symptoms observed and non-
symptomatic features before prescription of anti-malaria drugs. Apart from that the output of
this study will be a necessary step in designing a malaria diagnosis decision support system
through the developed model. Furthermore, towards reducing drug resistance, the results of
this study can be used by the policymakers and the Ministry of Health for better management
of malaria disease in health facilities and drug dispensing outlets to avoid self-medication and
presumptive treatment
Status, physiognomies and economic viability of hydroponic lettuce production in selected areas of Sothern TANZANIA and Central UGANDA
Get PDFA Thesis Submitted in Fulfillment of the Requirements for the Degree of Doctor of
Philosophy in Life Sciences of the Nelson Mandela African Institution of Science and
TechnologyThere is an increasing interest and research in soilless farming due to its ability to enhance food
production amidst challenges presented by urbanization. However, the adoption of this technology
is still very limited in East Africa. This main objective of the research was to study the
appropriateness of hydroponics as a feasible urban cropping system for improved vegetable
production and accessibility in Uganda. An assessment on status of hydroponics in Northern
Tanzania and Central Uganda was carried out using google questionnaires and face-face interviews
which revealed limited uptake of the technology majorly due to the high initial costs required. An
experiment was set up in central Uganda to evaluate the performance of red and green leafy lettuce
produced using a non-greenhouse and non-circulating hydroponic system. Parameters assessed
included; plant height, root length, number of leaves, leaf width, fresh weight and dry matter
content. Data was analyzed using 2 sample T-test under origin software. A significant difference
was noted at harvest for dry matter content (P=0.02, P=0.01), fresh weight (P=0.03, P=0.02) and
root length (P=0.01, P=0.02) between red and green lettuce grown under soil and hydroponics in
that order at P < 0.05. An economic analysis was done on the system to assess its profitability.
Budgeting techniques results showed: Net present value (16.37$), Internal rate of return (12.57%),
Profitability index (1.1) and non-discounted payback period (4,5) for annual crop production. Net
present value was sensitive to changes in discount rate and unit price while revenue varied with a
change in quantities sold and unit price. Regression analysis showed that a variation in the unit
price of lettuce was stronger and negatively affected the quantity sold (R=0.91) than the influence
the same independent variable on revenue earned (R=0.84). Based on the study results,
hydroponics has the potential to act as a suitable alternative in vegetable production system and
improve accessibility to vegetables across urban areas in a cost-effective manner. This will also
assist in contributing to sustatinable develeopment goals; 3 (good health and wellbeing) and 11
(sustainable cities and communities). There is need to study the perfomance of other vegetables
as well as various factors that can improve crop perfomance using the hydroponic system inorder
to boost; crop yield, adoption of the system and hence vegetable accessibility and food security.
Policy makers and governments should put more efforts in training farming communities about
hydroponics