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Enhancing the optoelectronic properties of blended triphenylamine-betalain based dyes through tailoring the anchoring unit: a theoretical investigation
Get PDFThis research article was published by Molecular Physics, 2024A series triphenyl-betalain organic dyes featuring carboxylic acid and nitro anchoring groups CH = C(X)COOH for the A1-X dyes and -CH = C(X)NO2 for the A2-X dyes, respectively, where X = CN, CH3, CCl3 and CF3 was evaluated for dye sensitised solar cells application. The geometrical structures, molecular orbitals and energies, light absorption patterns, free energies of electron injection and dye regeneration and binding to the semiconductor have been explored using DFT/TD-DFT methods. The nitro-based anchoring group resulted in pronounced red-shift in absorption spectra between 111 and 317 nm compared to carboxylic acid-based dyes. Attachment of the dyes to the semiconductor was modelled via binding to (TiO2)6H3 cluster; A2-X dyes exhibited more stable Dye@TiO2 complexes with binding energies (BEs) ranging between −4.08 and −2.88 eV compared to A1-X dyes with BEs range of −1.11 to −0.05 eV. The results evince that the dyes with CH = C(X)NO2 anchoring groups could be promising materials for light harvesting application
Image segmentation deep learning model used to Identify black Saigatoka And Fusarium wilt in banana early
Get PDFBananas are among the most widely produced perennial fruit crops. Farmers largely produce
bananas because they are important staple food and cash crops. However, bananas are highly
affected by Fusarium Wilt and Black Sigatoka diseases. These diseases cause yield losses
ranging from 30% to 100% of all the banana produce. Farmers face challenges in detecting and
mitigating the effects of these two banana diseases because of a lack of knowledge of the
diseases and the use of traditional eye observation method in detection. This study is inspired
by the success of deep learning and computer vision in detecting a wide range of plant diseases.
The study proposed the use of deep learning to automate the early detection of Fusarium Wilt
and Black Sigatoka banana diseases. Mask R-CNN and U-Net image segmentation deep
learning models were assessed for instance and semantic image segmentation. A dataset
comprising 27 360 images of banana leaves and stalks that are healthy, Fusarium Wilt infected,
and Black Sigatoka infected collected from the farm was used to train the models. An addition
of 407 images of other things apart from the banana plant were downloaded from the internet
and used to train the CNN model. From the experiments, the Mask R-CNN model achieved a
mean Average Precision of 0.045 29 in segmenting the two banana diseases. The U-Net model
achieved an Intersection over Union (IoU) of 93.23% and a Dice Coefficient of 96.45%.
Similar results were obtained by Loyani et al. (2021) when they segmented a tomato plant paste
called tuta absoluta using a U-Net model. Their model achieved a Dice Coefficient of 82.86%
and an Intersection over Union of 78.60%. Additionally, the Fusarium Wilt and Black Sigatoka
infected banana leaves and stalks were segmented using the Mask R-CNN and U-Net models.
The CNN model yielded an accuracy of 91.71% in classifying the two banana diseases. Similar
results were obtained by Sanga et al. (2020) when they deployed an Inceptionv3 model, which
achieved an accuracy of 95.41%. The CNN model was deployed in a mobile application to be
used by farmers to detect the two banana diseases early. The mobile application could detect
banana diseases early and provide research-based mitigation recommendations that
smallholder farmers and other agricultural stakeholders can use to avoid yield losses and
financial losses
HIV viral suppression and risk of viral rebound in patients on antiretroviral therapy: a two- year retrospective cohort study in Northern Tanzania
Get PDFThis research article was published by BMC Infectious Diseases Volume 24,(2024)Background
The world is moving towards the third target of the Joint United Nations Programme on HIV/AIDS to ensure most people receiving antiretroviral therapy (ART) are virologically suppressed. Little is known about viral suppression at an undetectable level and the risk of viral rebound phenomenon in sub-Saharan Africa which covers 67% of the global HIV burden.This study aimed to investigate the proportion of viral suppression at an undetectable level and the risk of viral rebound among people living with HIV receiving ART in northern Tanzania.
Methodology
A hospital based-retrospective study recruited people living with HIV who were on ART for at least two years at Kibong’oto Infectious Disease Hospital and Mawenzi Regional Referral Hospital in Kilimanjaro Region, Tanzania. Participants’ two-year plasma HIV were captured at months 6, 12, and 24 of ART. Undetectable viral load was defined by plasma HIV of viral load (VL) less than 20copies/ml and viral rebound (VR) was considered to anyone having VL of more than 50 copies/ml after having history of undetectable level of the VL less than 20copies/ml. A multivariable log-binomial generalized linear model was used to determine factors for undetectable VL and viral VR.
Results
Among 416 PLHIV recruited, 226 (54.3%) were female. The mean (standard deviation) age was 43.7 (13.3) years. The overall proportion of undetectable VL was 68% (95% CI: 63.3–72.3) and 40.0% had viral rebound (95% CI: 34.7–45.6). Participants who had at least 3 clinic visits were 1.3 times more likely to have undetectable VL compared to those who had 1 to 2 clinic visits in a year (p = 0.029). Similarly, participants with many clinical visits ( > = 3 visits) per year were less likely to have VR compared to those with fewer visits ( = 2 visits) [adjusted relative risk (aRR) = 0.64; 95% CI: 0.44–0.93].
Conclusion
Participants who had fewer clinic visits per year(ART refills) were less likely to achieve viral suppression and more likely to experience viral rebound. Enhanced health education and close follow-up of PLHIV on antiretroviral therapy are crucial to reinforce adherence and maintain an undetectable viral load
The effect of Helichrysum shrub encroachment on orchids in a tropical, montane grassland ecosystem, Tanzania
Get PDFThis research article was published by Australian Journal of Botany Volume 72, 2024Context
Although shrub encroachment is a common phenomenon in grasslands, which often suppress co-existing herbaceous plants, little is known about how encroaching native shrubs affect endemic and threatened orchid species.
Aims
We assessed the effect of the native dwarf shrub Helichrysum species on orchid species in a protected mountainous grassland system in Tanzania.
Methods
We selected five Helichrysum shrub-dominated blocks and applied four treatments in each, i.e. no or low encroachment (50% Helichrysum cover; ‘high cover’), cutting all stems of Helichrysum shrubs to ground level (‘stem cut’) and removing both stems and roots of all Helichrysum shrubs (‘uprooted’). We then compared orchid species diversity, abundance and functional traits by using a mixed linear model across treatments.
Key results
Orchid species diversity and abundance were significantly lower in ‘high cover’ plots than in other treatments. In ‘high cover’ plots, orchid species such as Disa robusta, Satyrium acutirostrum, and S. sphaeranthum had a significantly lower chlorophyll content than they did in ‘low cover’ plots. The ‘uprooting’ treatment showed significantly higher orchid species diversity in the second field season.
Conclusion
The expansion of Helichrysum shrubs adversely affected orchid abundance, diversity, and individual vigour, which in turn affected the regenerative ability of orchids.
Implications
We suggest that management should focus on shrub removal, because only ‘cutting’ had a beneficial effect on orchids. Shrub removal should be focused on areas of high shrub cover to promote further orchid species growth in this mountainous grassland of Tanzania
Optimized Tilted Solar Radiation in Equator Region: Case Study of Seven Climatic Zones in Tanzania
Get PDFThis research article was published by Renewable Energy Research and Applications, 2024This study delves into the ongoing discourse surrounding the optimal tilt angles for solar panels to maximize solar PV power generation. Focused on seven equatorial regions in Tanzania; Dodoma, Dar es Salaam, Kilimanjaro, Kigoma, Iringa, Mtwara, and Mwanza. Multiple mathematical models are employed to ascertain the most efficient panel tilts. Leveraging solar radiation data spanning from 2000 to 2017, we developed an algorithm specifically tailored for computing suitable tilt angles in the southern hemisphere. Our investigation reveals compelling insights into the variation of optimal panel tilts throughout the year. Notably, the monthly optimal tilt angles fluctuate significantly across the regions. June emerges as the month with the highest recorded monthly optimal tilt angle, ranging from 45 degrees in Mtwara to 31 degrees in Kilimanjaro. Conversely, December showcases the lowest tilt angles, spanning from -30 degrees in Mwanza to -26 degrees in both Kigoma and Iringa. Quarterly angles exhibit peaks during the second quarter of the year, reaching 39 degrees in Mtwara and 27 degrees in Kilimanjaro, while experiencing declines in the fourth quarter, plunging to levels between -19 and -24 degrees. Additionally, our study calculates annual optimal tilt angles, revealing a range from 2 degrees in Kilimanjaro to 11 degrees in Mtwara. Crucially, the deployment of monthly optimally tilted solar PV panels demonstrates a noteworthy enhancement, yielding a 6-11% gain in solar radiation compared to horizontally mounted panels. Our study advocates for the adoption of dynamic tilt adjustment strategies of periodic angle alterations to maximize solar PV power generation
Physical, mechanical, and durability properties of concrete containing different waste synthetic fibers for green environment – A critical review
Get PDFThis research article was published by Heliyon, Volume 10 (2024)The world is facing a major challenge on ways to manage the waste synthetic materials that are potentially polluting the environment. So, by 2040 it is estimated from the total synthetic textile products that will be produced, the accumulated synthetic textile waste will be more than 73.77 %, if recycling of waste may not be managed by novel technology in different sectors. Hence, this is a great challenge coming to the world if it is not effectively recycled mainly to be used in the construction sector which covers a broad area. However, detailed critical review is needed to gather different authors result on waste synthetic fiber effectively utilized in construction materials like in a concrete. So, the present study reviewed, the effects of waste synthetic fibers specifically, which are covering many numbers of synthetic materials; polyester, nylon, and polyethylene replacement on the physical, mechanical, durability, and microstructural properties of concrete. As the review of most researchers indicates, reinforcing the waste synthetic fibers in the concrete by 0.1–1% to the weight of cement reduces workability, improves compressive, flexural, splitting tensile strength, and enhances durability. Specifically, adding around 0.5 % doses to the volume of the concrete makes good resistance to water absorption, chloride ion penetration, acidic attack, elevated temperature resistance below 600°C, and lessen concrete content hence, cost effective compared to the control concrete mixture. Besides these, the employment of waste synthetic fibers makes dense microstructure, consequently minimizes the crack occurrence and propagation
Convolutional Neural Network Deep Learning Model for Early Detection of Streak Virus and Lethal Necrosis in Maize: A Case of Northern-Highlands, Tanzania
Get PDFThis research article was published by Springer Nature Link 2024In the Tanzanian context, maize is the dominant food crop that serves as a significant common and traditional food being grown in about 45% of the country’s farmland. However, its productivity is hindered by diseases that diminutions its quality and quantity. Maize streak virus (MSV) and maize lethal necrosis (MLN) are the two diseases that have been reported by farmers to dominate for ages. These diseases are likely to be cured if early detected. Nevertheless, sophisticated tools for detecting these diseases are still lagging behind the fast pace of technology in developing countries like Tanzania. That being the case, this study aims to fill the gap by investigating the need and development of a deep learning model for early detection of these two diseases. In doing so, a deep learning solution based on Convolution Neural Networks (CNN) has been developed to predict the early occurrence of these diseases in maize leaves. A CNN model was developed from scratch with a total of 1500 datasets belonging to three classes namely; healthy, MLN, and MSV. The developed model attained a validation accuracy of 98.44%. Since the validation accuracy is more than 70% then, this model is reliable and have potential of being adopted in early prediction of MLN and MSV diseases. However, the vision transformer (ViT) model will be developed, and its efficiency be compared with CNN. The model with best results will be deployed in a mobile device, ready for use by farmers in real-life environments
Optimization of desorption parameters using response surface methodology for enhanced recovery of arsenic from spent reclaimable activated carbon: Eco-friendly and sorbent sustainability approach
Get PDFThis research article was published by Elsevier Inc. 2024Desorption and adsorbent regeneration are imperative factors that are required to be taken into account when
designing the adsorption system. From the environmental, economic, and practical points of view, regeneration
is necessary for evaluating the efficiency and sustainability of synthesized adsorbents. However, no study has
investigated the optimization of arsenic species desorption from spent adsorbents and their regeneration ability
for reuse as well as safe disposal. This study aims to investigate the desorption ability of arsenic ions adsorbed on
hybrid granular activated carbon and the optimization of the independent factors influencing the efficient re-
covery of arsenic species from the spent activated carbon using central composite design of the response surface
methodology. The activated carbon before the sorption process and after the adsorption-desorption of arsenic
ions have been characterized using SEM-EDX, FTIR, and TEM. The study found that all the investigated inde-
pendent desorption variables greatly influence the retrievability of arsenic ions from the spent activated carbon.
Using the desirability function for the optimization of the independent factors as a function of desorption effi-
ciency, the optimum experimental conditions were solution pH of 2.00, eluent concentration of 0.10 M, and
temperature of 26.63 ℃, which gave maximum arsenic ions recovery efficiency of 91 %. The validation of the
quadratic model using laboratory confirmatory experiments gave an optimum arsenic ions desorption efficiency
of 97 %. Therefore, the study reveals that the application of the central composite design of the response surface
methodology led to the development of an accurate and valid quadratic model, which was utilized in the
enhanced optimization of arsenic ions recovery from the spent reclaimable activated carbon. More so, the
desorption isotherm and kinetic data of arsenic were well correlated with the Langmuir and the pseudo-second-
order models, while the thermodynamics studies indicated that arsenic ions desorption process was feasible,
endothermic, and spontaneous
Integrating Traditional Knowledge and Modern Technologies for Renewable Energy Adoption in Sub-Saharan Africa: Advancing Climate Resilience and Carbon Reduction Strategies
Get PDFThis research article was published by Global Academic Journal of Humanities and Social Sciences in November 2024This paper explores the potential of reducing carbon footprints and
greenhouse gas emissions in climate-sensitive regions of Sub-Saharan Africa by
integrating traditional knowledge with modern renewable energy technologies.
Drawing on a mixed-methods approach that combines quantitative energy data
and qualitative insights from expert interviews and policy reviews, the study
analyzes the implementation of renewable energy sources such as solar, wind,
and hydro. Data from global organizations, including the International Energy
Agency (IEA) and the World Bank, supports the investigation. The findings
highlight renewable energy's transformative potential for emissions reduction,
energy security, and economic growth, with solar energy demonstrating
exceptional promise for rural electrification. Despite its benefits, adoption is
hindered by financial constraints, inadequate infrastructure, and regulatory
challenges. The study underscores the need for climate resilience strategies
such as energy storage integration and grid upgrades to support reliable access
to renewable energy. By linking renewable energy with sustainability and
resilience theories, the research emphasizes the role of adaptive infrastructure
in fostering economic development and environmental health. Key
recommendations include improving financing mechanisms, enacting
supportive policy frameworks, strengthening regional partnerships, and
prioritizing energy storage and grid modernization. This study provides
actionable insights for policymakers, energy stakeholders, and development
organizations, emphasizing that overcoming barriers to renewable energy
adoption is critical for achieving sustainable energy access, reducing emissions,
and aligning with global climate goals
Development of an IoT-based smart irrigation system for efficient water management in Uasin Gishu County
Get PDFAgriculture is the backbone of Kenya’s economy, with Uasin Gishu county being the country’s
breadbasket. Food scarcity has recently increased due to climate change, population growth,
and decreased land available for farming. Several measures have been implemented to mitigate
food scarcity, including encouraging irrigation farming to ensure whole-year food production.
However, irrigation practice faces a water scarcity challenge. Efforts have been put in place to
improve water use efficiency. These measures include scheduled irrigation system technology.
Most of these scheduled systems target greenhouse irrigation and leave out open-field irrigation
farmers where rainfall is a factor in reducing water wastage whenever there is rainfall. This
technology does not provide a precision of plant water needs and poses a risk of under or over
irrigation. Therefore, to overcome these challenges, this project developed an IoT-based system
with sensors to monitor critical soil parameter measurements continuously. The main objective
of this project was to develop an IoT-based smart irrigation system for efficient water
management. An ESP32 microcontroller board is used to process information collected from
the sensors. Openweather API is implemented on the Thingsboard cloud platform to fetch
rainfall prediction information. While providing remote valve control, the system uses soil
moisture level and rainfall prediction parameters to automatically control the irrigation valves.
The system also offers rich farm information visualization through the Thingsboard cloud
platform dashboard, which can be accessed remotely through the Thingsboard live mobile
application, where a user can control the irrigation valves remotely. Mixed methods which
involved questionnaires and focus group discussions was used to collect data from sixteen
respondents. Purposve sampling was also used to identify the respondents during data
collection. To develop the system,agile software development methodology, specifically
extreme programming (XP) was implemented. To validate the system’s functionalities, the
system was demonstrated to twenty seven people and thereafter were allowed to interact with
the system. A questionnaire was implemented to get feedback from the respondents. Generally,
respondents agreed that the system satisfactorily met their needs. The developed system
contributes to the value chain by providing precise water input. The system can be advanced
to include other essential features needed to monitor and evaluate irrigated farms within Uasin
Gishu county and any other region