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Thermal Performance and Technoeconomic Analysis of Solar-Assisted Heat Pump Dryer Integrated with Energy Storage Materials for Drying Cavendish Banana (Musa acuminata)
Get PDFThis research article was published by HindawiThis study examines a novel solar-assisted heat pump dryer integrated with a thermal energy storage system using soapstone as
storage material. The dryer is investigated through experimental analysis across three operating modes: mode 1 with thermal
energy storage during daytime, mode 2 without thermal energy storage during nighttime, and mode 3 without thermal energy
storage during daytime. Experiments were carried out to investigate the drying of 500 g of Cavendish banana. Thermal
performance, as well as economic, and nutritional content were examined. Three replicates of the experiment yielded
consistent results, showing a significant reduction in the moisture content of the initial sample from 74.4% to 9.6% after
undergoing distinct drying durations. Mode 1 achieved this reduction in 270 minutes, mode 2 in 390 minutes, and mode 3 in
360 minutes. The average specific moisture extraction rates for modes 1, 2, and 3 were 0.13, 0.11, and 0.12 kg/kWh,
respectively. Simultaneously, the drying rate ranged from 0.16 to 0.24% per minute. The drying efficiency varied among the
tested modes, with mode 1 achieving the highest efficiency at 23.23%. In terms of coefficient of performance, mode 1, mode 2,
and mode 3 exhibited values of 3.69, 2.57, and 2.54, respectively. The economic analysis conducted specifically for mode 1
revealed a payback period of 1.5 years, indicating the time required to recover the initial investment. Additionally, the results
indicated that the dried Cavendish banana had significantly higher concentrations of proximate parameters and minerals
compared to the fresh Cavendish banana, as evidenced by a p value less than 0.05
Enhancement for the access and utilization of library resources using machine learning techniques
Get PDFThe growing demands for online information have motivated researchers to explore the most
effectively use of digital library (DL) resource tools. The main challenges of online DL are
information search and retrieval attributes related to label relevance and feature correlation
segments. Previous research mainly relied on unbalanced multi-label data and therefore could
not develop a reliable tool to access online information. To improve availability and
usefulness of online DL, this work uses machine learning techniques to enhance the access
and utilization of library resources. The research data were collected at The Nelson Mandela
African Institution of Science and Technology (NM-AIST), Mzumbe University (MU), and
the University of Dar es Salaam (UDSM) through questionnaire and purposeful sampling
technique were then analysed with python and MAXQDA tools respectively. The survey
found that 1,217 (73%) of respondents were aware of electronic information resources (EIRs)
but faced accessibility limitations due to social and technical issues. Then, the proposed
ensemble model (PEM) for machine learning (ML) methods was used to develop a resource
discovery tool (RDT). The effectiveness of the PEM was then evaluated by comparing the
accuracy of the PEM, logistic regression (LR), support vector machine (SVM), and knearest
neighbor (kNN) algorithms. The experimental results reveal that PEM offers the highest
precision of 95%, as compared to LR's 84%, SVM's 65%, and kNN's 57%. The Web Content
Accessibility Guidelines (WCAG) 2.1 standards had been successfully used to test the four
digital library tools, the developed RDT, NM-AIST, MU, and UDSM to see how well the
developed system performs. The developed RDT had the highest established compliance
score for online content accessibility, which is 90% with only one violation, compared to
NM-AIST's 80% with 16 violations, MU's 55% with 12 violations, and UDSM's inability to
be evaluated because of the excessive number of infractions. Therefore, the results of this
study show the need to regularly check the accessibility of an online resources as well as
optimization of the digital libraries
Modelling the impacts of anthropogenic activities on forest biomass and dependent wildlife population
Get PDFThe depletion of forest biomass and declining of forest-dependent wildlife populations are ur
gent ecological and societal issues resulting from human activities such as deforestation and
land-use changes. This study aims to comprehend the influence of anthropogenic activities on
forest biomass and the population of wildlife dependent on forests, and to formulate appropri
ate management measures. Specific objectives include forecasting forest land loss in Tanzania,
analysing mathematical model describing the impact of human activities on forests and wildlife,
examining the influence of fuzzy parameters on model dynamics, and evaluating the effects of
economic measures and technological efforts on conservation. The study considered Tanzania
where local communities heavily rely on forest resources for their livelihoods. This region also
supports a rich biodiversity of wildlife species, where the forest provides essential habitats and
resources for their survival. Furthermore, the study also acknowledged the existence of vari
ous human activities carried out in the area which may have a significant impact on the forest
ecosystem and the wildlife populations that depend on it. Four models are presented: a time
series model and three dynamical system models. The key findings include: (i) The univariate
time series model accurately predicts an increase in forest land loss in Tanzania with a 96.2%
accuracy rate (MAPE = 0.0377), highlighting the urgency for sustainable forest management
practices and conservation policies. (ii) Depletion of forest biomass due to human activities
has severe implications for wildlife survival and ecological balance. Achieving this balance re
quires ensuring the growth rate of forest biomass and wildlife populations exceeds their rates of
utilisation and depletion, respectively. (iii) Incorporating fuzzy parameters improves model re
liability by accounting for uncertainties in climate, geography, and human activities, enhancing
decision-making processes. (iv) Economic measures and technological efforts have the po
tential to conserve forest biomass and wildlife populations. However, careful implementation
and comprehensive understanding of forest ecosystem dynamics are crucial to prevent desta
bilisation. The study emphasises the need for interdisciplinary collaboration and stakeholders
engagement to ensure sustainable forest use and conservation, considering the complexities and
uncertainties of natural systems. Balancing forest conservation with socio-economic needs is
key for the well-being of local communities and future generations
Capacitive deionization: Capacitor and battery materials, applications and future prospects
Get PDFThis research article was published by Desalination Volume 587, 15 October 2024Water scarcity all over the world attracts alternative methods to purify saline water and supplement the available dwindling freshwater resources. Capacitive deionization (CDI) is hopeful to supply water to the population due to operation at low potential along with low energy expenditure when low salinity (5 mM NaCl) feed water solutions are desalinated. Electrode material is the main controlling factor in CDI system and a lot of efforts are devoted to develop excellent materials for better CDI performance. So far, carbon materials are widely used as the electrode for CDI, though limitations such as co-ion expulsion and faradaic reactions hinder their full utilization. Alternatively, battery materials are used since their performance is great due to mitigation of co-ions ejection as well as faradaic reactions. In 2019 our group reviewed factors affecting the performance of activated carbon electrode materials and revealed lack of selectivity, co-ion expulsion, low electrical conductivity and inappropriate pore size distribution to largely contribute to its low salt removal capacity [1]. Therefore, herein, we extend the discussion beyond AC to include other carbons such as aerogels, nanotubes, graphenes etc., and battery materials such as MXenes, sodium super ionic conductors, and BiOCl to mention the few. This article also discusses the extent CDI is applied in the laboratory scale as well as in the field for desalination of real water, wastewater remediation and removal of harmful contaminants to substantiate what it can offer beyond the laboratory experiments. The work is expected to save as a complete reference for the progress of CDI electrode materials and its applications in water purification
Mathematical modeling and extraction of parameter of photovoltaic module based on modified Newton-Raphson method
Get PDFPhotovoltaic (PV) generators are represented by varoius types of electrical equivalent circuits.
Each of which describes the output current-voltage relationship under particular operating con
ditions. Ideal model, single and double diode models are examples to representation of photo
voltaic cell/module. In order to assess the performance of PV generators, one needs to extract
essential parameters of PV module. This dissertation introduces a numerical approach for
estimating four crucial physical parameters within a single-diode circuit model based on the
manufacturer’s datasheet. The methodology involves establishing a system of four non-linear
equations derived from three pivotal points in PV characteristics. Through suggested iterative
approach, the photocurrent, saturation current, ideality factor, and series resistance are deter
mined utilizing the proposed method. Validation of the suggested technique is conducted using
RTCFrance solar cell, Chloride CHL285P, and Photowatt PWP210 modules. The obtained re
sults are compared with in-field outdoor measurements, demonstrating a commendable agree
ment with the experimental data. Furthermore, the selected model is subjected to simulation
in the MATLAB environment to evaluate its response to external physical weather conditions,
specifically temperature and solar irradiance. Notably, the proposed method exhibits a faster
convergence compared to the widely utilized Newton method, emphasizing its efficiency. The
significance of modeling PV cells/modules is underscored as it plays a crucial role in predicting
the performance of photovoltaic generators under varying operating conditions. This numer
ical method contributes to the field by offering a quicker convergence compared to existing
techniques, thereby enhancing its practical utility
Unlocking nature’s pharmacy: Euphorbia hirta (L.) as a potent defense against Escherichia coli and Klebsiella pneumoniae infections in Tanzania
Get PDFThis research article was published by South African Journal of Botany Volume 177, 2025Background
Urinary tract infections (UTIs) are among the most common bacterial infections, primarily caused by Escherichia coli and Klebsiella pneumoniae, both of which have mostly developed resistance to various antibiotics. The Maasai and Meru communities in Tanzania have traditionally used Euphorbia hirta to combat resistant pathogens, particularly those causing UTIs.
Purpose
This study aimed to evaluate the antimicrobial compounds of aqueous extracts and the antibacterial compounds in methanolic extracts of E. hirta. We specifically focused on the antibacterial activity of aqueous and methanolic extracts against E. coli and K. pneumoniae strains, which are significant contributors to UTIs.
Study design
In March 2024, we randomly collected E. hirta plant parts from the Kikwe and Kisongo wards in the Arusha region of Tanzania. The samples were washed with distilled water and shade-dried for three weeks to prevent the degradation of bioactive compounds. After drying, the samples were powdered using a laboratory grinder and stored in sterile nylon bags.
Methods
We conducted qualitative and quantitative analyses to assess the presence of various phytochemicals, including alkaloids, saponins, coumarins, terpenoids, quinones, flavonoids, and glycosides, in the aqueous and methanolic extracts of E. hirta. To identify specific phytochemical compounds in these extracts, we used gas chromatography-mass spectrometry (GC–MS) and disc diffusion assays to test their antibacterial activity against E. coli and K. pneumoniae.
Results
The GC–MS analysis identified sixteen potential bioactive compounds with antibiotic properties, including dodecanal, trans-Farnesol, phytol, 13-tetradecynoic acid, methyl ester, cis-5,8,11,14,17-Eicosapentaenoic acid, 2(4H)-Benzofuranone, 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-, (R)-, paromomycin, decanoic acid, methyl ester, azelaic acid, undecanoic acid, 10-methyl-, methyl ester, palmitoleic acid, 9,12,15-Octadecatrienoic acid (Z,Z,Z), 7-Hydroxy-3-(1,1-dimethylprop-2-enyl) coumarins, linoleyl methyl ketone, and 8,11,14-Eicosatrienoic acid, methyl ester (Z,Z,Z). When tested at a concentration of 1 g/mL, all extracts demonstrated significant antibacterial activity. The methanolic extract exhibited the highest performance, with a minimum inhibition zone of 12.0 ± 2.35 mm, followed by the aqueous cold extract at 9.25 ± 1.75 mm, and the aqueous boiled extract at 8.0 ± 1.35 mm.
Conclusion
The traditional methods employed by the Maasai and Meru communities for preparing herbal medicine from E. hirta, such as boiling and soaking in cold water, seemed to be effective in treating UTIs. Organic solvent extraction using methanol generally showed superior antibacterial activity compared to aqueous extraction. However, soaking in cold water produced extracts with higher inhibitory activity against E. coli, while boiling was more effective against K. pneumoniae. This study validates the local practices of E. hirta preparation, suggesting that water-based extracts could be both effective and safe for treating certain bacterial strains responsible for UTIs in the Arusha regio
Solvothermal liquefaction of orange peels into biocrude: An experimental investigation of biocrude yield and energy compositional dependency on process variables
Get PDFThis research article was published by Bioresource Technology / Volume 391 / January 2024The efficient valorization of biomass for energy-derived biocrudes is essential for effective waste management. However, the production of biocrudes with high energy and reduced oxygen contents during the liquefaction process requires further insight. Therefore, the impact of reaction temperature, residence time, and ethanol: acetone on the energy compositions and bioproduct’s yield enhancement were investigated. The biocrudes obtained were characterized using elemental analysis, GC–MS, FTIR, GPC and TGA to understand the effects of process parameters on the biocrudes’ compositions. An improved HHV (38.18 MJ/kg) and lower O/C ratio (0.11) were obtained at 430 °C, 35 min and 50% ethanol with a significant improvement in the enhancement factor, deoxygenation, and percentage hydrogenation of 2.63, 36.88%, and 77.87%, respectively. The presence of ketones, hydrocarbons, phenolics and aromatics of 23.74, 4.28, 37.20 and 17.81% respectively indicate the potential of the obtained biocrude as renewable energy sources upon further upgrading
Design of the data-driven software application for identification, population monitoring, and risk assessment for lions in Serengeti Tanzania
No full textThis research article was published by Computational Ecology and Software, 2025, 15(1): 1-14This study presents a design of a Data-Driven software application for identification, population monitoring,
and risk assessment for lions in Serengeti Tanzania. Lions’ populations have been declining due to poaching,
overhunting, and other ecosystem factors resulting in unmet demands for tourism and ecological balance.
Data-driven techniques can lower the negative consequences by providing mechanisms for lions’ management,
risk assessment, and monitoring in selected wildlife reserves. Lion’s whisker spots, poaching rates, prey
availability, human-conflict incidences, and pride size are key elements for achieving management,
identification, monitoring, and risk assessment for lions. The software application design aimed at providing
conceptual and logical requirements for the development of the application that will enhance lions’ monitoring
and management efforts to protect their existence and contribution to the ecosystem. The study was conducted
in the Serengeti ecosystem, including ecologists from the Tanzania Wildlife Research Institute Serengeti
Wildlife Research Center, and information systems analysts. Through a mixed research methods approach,
qualitative methods and incremental prototyping software development life cycle model were used to develop
the specific requirements. Unified Modeling Language (UML) was used to model the requirements and led to
the realization of design diagrams: application framework, database design, and artificial intelligence model
workflows. The application should equip ecologists with tools to add and identify specific lions, monitor
sightings, estimate population trends, assess risks for individual lions, and produce reports on monitoring and
sightings. This design serves as a foundation for developing the data-driven software application for
identification, population monitoring, and risk assessment for lions in Serengeti National Park Tanzania which
will enhance monitoring and management activities of lions’ population non-invasively
Prevalence of human schistosomiasis in various regions of Tanzania Mainland and Zanzibar: A systematic review and meta-analysis of studies conducted for the past ten years (2013–2023)
No full textThis research article was published by PLOS Neglected Tropical Diseases , Volume 18, 2024Schistosomiasis is a significant public health problem in Tanzania, particularly for the people living in the marginalized settings. We have conducted a systematic review with meta-analysis on the prevalence of schistosomiasis to add knowledge towards the development of effective approaches to control the disease in Tanzania. Online databases namely, Pub Med, SCOPUS and AJOL, were systematically searched and a random effect model was used to calculate the pooled prevalence of the disease. Heterogeneity and the between studies variances were determined using Cochran (Q) and Higgins (I2) tests, respectively. A total of 55 articles met the inclusion criterion for this review and all have satisfactory quality scores. The pooled prevalence of the disease in Tanzania was 26.40%. Tanzania mainland had the highest schistosomiasis prevalence (28.89%) than Zanzibar (8.95%). Sub-group analyses based on the year of publication revealed the going up of the pooled prevalence, whereby for (2013–2018) and (2018–2023) the prevalence was 23.41% and 30.06%, respectively. The prevalence of the Schistosoma mansoni and Schistosoma hematobium were 37.91% and 8.86% respectively. Mara, Simuyu, and Mwanza were the most prevalent regions, with a pooled prevalence of 77.39%, 72.26%, and 51.19%, respectively. The pooled prevalence based on the diagnostic method was 64.11% for PCR and 56.46% for POC-CCA, which is relatively high compared to other tests. Cochrans and Higgins (I2) test has shown significant heterogeneity (p-value = 0.001 and I2 = 99.6). Factors including age, region, diagnostic method and sample size have shown significant contribution to the displayed heterogeneity. The pronounced and increasing prevalence of the disease suggests potential low coverage and possibly lack of involvement of some regions in the control of the disease. This, therefore, calls for an intensive implementation of control interventions in all endemic regions, preferably using an integrated approach that targets several stages of the disease lifecycle
Towards an Artificial Intelligence Readiness Index for Africa
Get PDFThis book chapter was published by Springer Nature in 2023The applications and benefits of Artificial Intelligence (AI) for socio-economic development are immense. AI is projected to contribute approximately USD 15.7 trillion to the global Gross Domestic Product (GDP) by 2030. However, countries need to be prepared to harness such benefits. Hence, assessing the AI readiness of a country is paramount. Africa is currently the only continent without an AI readiness index tailored to its needs. It relies on the existing global indices, which may not accurately measure the progress attained by individual African countries because of the different levels of development and unique context. This paper proposes an AI readiness index for Africa. It starts by exploring what the AI readiness index needs of Africa are, examines the extent to which existing AI readiness indices meet the needs, and then looks at indicators that should constitute the AI readiness index for Africa.
The study employed a systematic literature review that aimed to explore the AI readiness needs for Africa and the extent existing indices meet these. The review focused on papers published on the AI readiness index between January 2018 to August 2022. The search strategy retrieved 301 papers, of which seven papers were selected for a detailed analysis. The study revealed that the existing indices partially meet AI readiness needs for Africa. The study also found that AI readiness index dimensions pertinent to Africa’s requirements are: Vision, Governance and Ethics, Digital Capacity, Size of the Technology Sector, Research and Development, Education, Infrastructure, Data Availability, general level of employment, employment in Data Science and AI roles, and Gross Domestic Product-Per Capita Purchasing Power Parity. This study contributes to the knowledge of AI readiness for Africa and globally. The results of this study will benefit governments, researchers, and practitioners of AI and its applications