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Seasonal variability of vertical patterns in chlorophyll-a fluorescence in the coastal waters off Kimbiji, Tanzania
Get PDFThis research article was published in Western Indian Ocean Journal of Marine Science, Volume 20, Issue 1, 2021.A study on the vertical pattern of chlorophyll-a (Chl-a) fluorescence was undertaken in the Mafia Channel off
Kimbiji, Tanzania. Data was collected during the Southeast Monsoon (SEM) and Northeast Monsoon (NEM) seasons.
There was higher Chl-a concentration of 0.1 to 1.1 mgm-3 in the surface layer off Kimbiji to about 50 m depth due
to the presence of mixed layer depth (MLD) which allowed water mixing in the layer. A deep Chl-a maximum was
recorded at around 40 m depth during the NEM and between 40 and 70 m in the SEM. Surface water between lon gitude 39.9°E and 40.2°E had low Chl-a from the surface to about 50 m depth due to poor nutrient input. The NEM
had an insignificantly higher Chl-a value than the SEM (p > 0.05) which differed from other studies in which Chl-a
was higher during the SEM than the NEM, than, the Chl-a concentration was higher at the surface during the SEM
than during the NEM. Satellite data showed higher Chl-a in the SEM than NEM, localized along the Mafia Channel.
During the SEM season the wind pushes higher Chl-a water from the Mafia Channel towards the north and leads to
a higher concentration at Kimbij
Development of RFID based Automatic Warehouse Management System: A Case Study of ROK industries Limited Kenya
Get PDFA research article was submitted to International Journal of Advances in Scientific Research and Engineering (ijasre) Volume 7, Issue 8, August - 2021In the supply chain and logistics industry, the precision of the data assets inventory plays a crucial role in warehouse activities.
The operations like storage locations arrangement, inventory management, and maintaining the flow of incoming and out coming goods lead to the success of the warehouse. Nowadays, most people are preferring online shopping all over the world because it is faster than local trade. Thus, there's massive information in the supply chain and logistics sector to explore in order to improve operations of the warehouse such as receiving, ordering, shipping, storage assignment to facilitate the automation in the warehouse. This study is conducted to improve warehouse activities by automating storage and inventory management. A software program was developed with sets of rules, and an algorithm to optimize the inventory operations with the help of RFID technology. The predefined rules help in giving priorities to some of the selected products to store and retrieve in indicated location. The UHF RFID reader is attached to the entrance of the gate of the warehouse to facilitate the reading of incoming goods. Once the goods arrive in the warehouse, the storage location function will assign to each product the storage location and update status in the database. A handheld reader facilitates inventory management and communicates with the application via a wireless network and finally store data in the database for future use. After developing this system, a test was conducted for testing the feasibility and applicability of the system. The output showed that the inventory management operation was made strides, and the correctness of inventory location increased from 72.8% to 99%. The cycle time moreover decreases from 50 minutes to 18 minutes which is down to 28.79%
Sources of Nitrate in Ground Water Aquifers of the Semiarid Region of Tanzania
Get PDFThis research articles was published in Journals Geofluids Volume, 2021Nitrate isotopic values are often used as a tool to identify sources of nitrate in order to effectively manage ground water quality. In this study, the concentrations of NO3−, NO2−, and NH4+ from 50 boreholes and shallow wells in the Singida and Manyoni Districts were analyzed during the dry and wet seasons, followed by identification of nitrate sources using the hydrochemical method (NO3−/Cl−) and stable isotope (δ15N and δ18O) techniques. Results showed that NO2− and NH4+ concentrations were very low in both seasons due to the nitrification process. The concentrations of NO3− ranged from 2.4 ppm to 929.6 ppm with mean values of , during the dry season and from 2.4 ppm to 1620.0 ppm with mean values of , during the wet season. The higher NO3− contamination observed in the wet season could be due to rainfall which accelerated the surface runoff that collects different materials from various settings into the ground water sources. Nitrate source identification through hydrochemical technique revealed that most nitrates originated from sewage effluents and/or organic wastes such as manure. Likewise, the mean values of δ15N-NO3− ( and ) and the mean values of δ18O-NO3−( and ) suggest that 80% of boreholes and 52% of shallow wells were dominated with nitrate from sewage effluents and/or manure as most ground water sources were situated in densely populated areas with congested and poorly constructed onsite sanitation facilities such as pit latrines and manure. Therefore, to reduce nitrate pollution in the study area, a central sewer must be constructed to treat the discharged wastes. Also, groundwater harvesting should consider the proper principles for groundwater harvesting recommended by the respective authority to minimize chances of contamination and hence prevention of health ris
Development of a medical expert system to improve the quality of antenatal care in Tanzania
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 Institution of Science and TechnologyMaternal mortality remains a global problem, with approximately 830 women dying every day
as a result of childbirth and pregnancy complications. The maternal mortality ratio is as high as
524 deaths per 100 000 live births in Tanzania. The main causes of maternal mortality in
developing countries are all linked to poor prenatal care, which is partially caused by treatment
delays. Studies show that providing women with maternal health information can help achieve
the goal of reducing global maternal mortality to less than 70 maternal deaths per 100 000 live
births by 2030. Through Natural Language Processing (NLP), we leveraged the use of BERT
question and answer model which is a pre-trained model that wasfine-tuned to develop a model
that can diagnose pregnancy complications, explain possible causes in simple language, and
provide recommendations for care and treatment, for Malaria, Pregnancy hypertension (Pre eclampsia), and miscarriage (Threatened abortion) which are the main preventable causes of
maternal mortality in Tanzania. The expert system is embedded in a maternal smartphone app,
MamaApp, that provides weekly information on fetal development, regular and concerning
pregnancy symptoms, and self-care tips. The expert system model was able to diagnose the
three conditions with confidence ranging from 79% to 100%. Validation of MamaApp in
Arusha showed high acceptance from both the expectant mothers and doctors
Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach
Get PDFThis research article was published by Taylor & Francis Group, 2021Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses
A mobile-based system for enhancing interactive communication among people in the protected area: a case study on human-wildlife conflicts management in Ngorongoro conservation area and Serengeti national park in Tanzania
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 TechnologyOne of the core human rights is the right to the best possible health for humans and a balanced
ecology for wildlife. Electric fences are the only way to prevent human-wildlife conflict, but they
are ineffective in many countries due to the high cost of power management required to operate
them. Camera trap management can help this problem, however, in underdeveloped nations like
Tanzania, it fails to owe to poor GPS usage, which prevents the information from being reported to
the protected area authority. The goal of this study is to create a mobile application A mobile-based
human-wildlife conflict Management App) that would help to solve the human and wildlife
conflicts within Tanzania’s Ngorongoro Conservation Area and Serengeti National Park. Mobile
application captures video from camera trap and allows to report the information to the park rangers
through live chatting. Interviews, observations, and questionnaires were used to gather information.
The findings suggest that 93% from interviews and observation of people thought it to be really
useful for receiving video from camera trap to the mobile app and able to report information to the
protected area authority. The remaining 7% were unable to fix the problem due to a lack of
smartphones and poor internet access within the protected area. Within the villages, the application
may be used with a smartphone and a decent internet connection. People in the protected area gave
the designed system positive feedback, with 95.2% of those who completed the system evaluation
agreeing that the App should be used. Further development of the application would necessitate
more functionality and improved internet accessibility
Rainfall variability and socio‑economic constraints on livestock production in the Ngorongoro Conservation Area, Tanzania
No full textThis research article was published by Springer Nature Switzerland AG., 2021Rainfall variability is of great importance in East Africa, where small-scale farmers and pastoralists dominate. Their livestock production activities are heavily dependent on rainfall. We assessed pastoralist perceptions on climate change, particularly rainfall variability, its impact on livestock production, and the adaptive capacity of pastoralists in the Ngorongoro Conservation Area (NCA), Tanzania. We combined 241 household interviews and information from 52 participants of Participatory Rural Appraisal (PRA) with archived data from the Ngorongoro Conservation Area Authority (NCAA). We found that most (71%) pastoralists were aware of general climate change impacts, rainfall variability, and impacts of extreme events on their livestock. Most (> 75%) respondents perceived erratic and reduced amounts of rainfall, prolonged and frequent periods of drought as the main climate change challenges. Mean annual rainfall accounted for only 46% (R2), (p = 0.076) and 32% (R2), (p = 0.22) of cattle, and sheep and goat population variability, respectively. Unexpectedly, cattle losses intensified by 10% when herd size increased (p < 0.001) and by 98% (p = 0.049) when mobility increased, implying that increasing herd sizes and mobility do not cushion households against climate change shocks. Our study highlights the need to enhance adaptive capacity of the pastoralist communities through interventions that proactively reduce vulnerability. We recommend that future research should address the profitability of pastoral cattle production under changing environmental conditions
Tin halide perovskites: computational modeling of structural, electronic and thermodynamic properties towards solar cell applications
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 TechnologyIn the photovoltaic field, significant attention has been drawn to lead organo-halide
perovskite materials because of their higher ability to convert sun energy to electricity and
relatively simple process of fabrication as compared to silicon materials. Among the issues
which hinder the lead perovskites solar cells (PSCs) application, are lead toxicity and
instability of the PSCs in presence of moisture and light. The tin perovskites are thought over
as the foremost fitting substitute due to their comparable chemical nature and high-power
conversion efficiency. In this work, the methylammonium tin iodide CH3NH3SnI3 (MASnI3)
and guanidinium tin halides C(NH2)3SnX3 (GUASnX3), X = Cl, Br, I, are considered; the
electronic, structural as well as thermodynamic properties of the perovskites’ orthorhombic
phase (O-phase) have been investigated using various theoretical DFT approaches. For the
MASnI3, a direct band gap has been proved; in gamma symmetrical point of the band
structure, the band gap value Eg is computed using three different exchange-correlation (XC)
functionals: LDA 0.46 eV, PBEsol 0.98 eV and for PBE 1.12 eV; the best result has been
obtained with the PBE which follows from the comparison of the computed Eg and lattice
parameters with available experimental data. The enthalpy of the decomposition reaction of
the MASnI3 into the solid-state materials, SnI2 and CH3NH3I, with reaction enthalpy, ΔrH°(0
K) = 37 kJ mol–1
, and enthalpy of formation ΔfH°(CH3NH3SnI3, 0 K) = –390 kJ mol–1
, have
been evaluated showing the stability of the O-phase perovskite at low temperature. For the
guanidinium-tin perovskites GUASnX3, the lattice parameters are optimized using the GGA PBE functional. Computations of the materials’ band structures was carried out, and band
gaps at the gamma symmetry points were obtained: 3.00, 2.47 and 1.78 eV for the
C(NH2)3SnCl3, C(NH2)3SnBr3 and C(NH2)3SnI3, respectively. The projected state densities
are visualized, and the s-and p-states contribution of the halogens and tin to valence and
conduction bands of the perovskites assessed. For the GUASnX3 compounds, the
thermodynamic stability to different decomposition routes is examined, the standard
enthalpies of formation are obtained: –673 (GUASnCl3), –541 (GUASnBr3), and –401 kJ
mol–1
(GUASnI3). The interface between the hole transport material Cu2O and perovskite
MASnI3 has been built and analyzed; the predicted binding energy shows strong binding
between the two layers
The diversity of aphid parasitoids in East Africa and implications for biological control
Get PDFThis research article was published by Wiley Online Library in 2021BACKGROUND
Hymenopteran parasitoids provide key natural pest regulation services and are reared commercially as biological control agents. Therefore, understanding parasitoid community composition in natural populations is important to enable better management for optimized natural pest regulation. We carried out a field study to understand the parasitoid community associated with Aphis fabae on East African smallholder farms. Either common bean (Phaseolus vulgaris) or lablab (Lablab purpureus) sentinel plants were infested with Aphis fabae and deployed in 96 fields across Kenya, Tanzania, and Malawi.
RESULTS
A total of 463 parasitoids emerged from sentinel plants of which 424 were identified by mitochondrial cytochrome oxidase I (COI) barcoding. Aphidius colemani was abundant in Kenya, Tanzania and Malawi, while Lysiphlebus testaceipes was only present in Malawi. The identity of Aphidius colemani specimens were confirmed by sequencing LWRh and 16S genes and was selected for further genetic and population analyses. A total of 12 Aphidius colemani haplotypes were identified. Of these, nine were from our East African specimens and three from the Barcode of Life Database (BOLD).
CONCLUSION
Aphidius colemani and Lysiphlebus testaceipes are potential targets for conservation biological control in tropical smallholder agro-ecosystems. We hypothesize that high genetic diversity in East African populations of Aphidius colemani suggests that this species originated in East Africa and has spread globally due to its use as a biological control agent. These East African populations could have potential for use as strains in commercial biological control or to improve existing Aphidius colemani strains by selective breeding
A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
Get PDFThis research article published by IEEE, 2021Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed