Peer-reviewed international research papers published open-access with EOI assignment and global indexing across engineering, computer science, environmental science, social sciences, and more.
Agropastoralists in Kenya’s Arid and Semi-arid Lands (ASALs) face various shocks that threaten their livelihoods and expose them to significant health and economic risks. While existing studies often focus on climate-related shocks, they frequently overlook other challenges faced by these communities. A comprehensive understanding of how agropastoral communities manage diverse shocks is essential to developing effective vulnerability reduction strategies. This study examined the major shocks experienced by agropastoral households in Kenya’s ASALs and their primary coping strategies. Using cross-sectional data from 384 households in Isiolo county, this study applied descriptive analysis. From 2020 to 2024, households faced shocks such as droughts, livestock pests and diseases, livestock theft, and intercommunal conflicts. The results indicated that the county experienced higher theft and conflict rates. Coping strategies include income diversification, asset sales, increased farm labor, and reduced food expenditure. Severe droughts and conflicts led to migration, while floods caused extreme measures such as withdrawing children from school. Key factors influencing coping strategies include household demographics, farm characteristics, and market proximity. Households with more adults, higher literacy, or greater income from crops and livestock are less likely to adopt costly strategies. This study recommends integrated interventions to enhance resilience to both climate and non-climate shocks.
Climate change and variability remain one of the contributing factors to food insecurity globally, with consequences particularly extreme in arid and semi-arid areas in Africa where pastoralists-based livestock systems are an important source of income. Drought has resulted in massive livestock losses in recent years with dramatic impacts on pastoral lives. The primary contributing factor to food insecurity in Tana River County is climate change and variability as it affects the livelihoods and revenue streams of small-scale food producers by raising food prices and restricting access to food. Therefore, this article examines the impacts of climate change and variability on food security among the pastoral communities in Tana River County with focus on food availability, accessibility, utilization and stability. Climate variability in Tana River County has led to changes in the nutritional quality of some foods. Moreover, climate change and variabilities hinder the ability of pastoralists to feed their livestock leading to loss of their means of support and food insecurity.
The traditional livelihood practice of pastoralism is under threat in Marsabit County in Kenya. The past decades have seen a shift in livelihood practices from pastoralism to other livelihood practices in the arid and semi-arid lands; including small-scale crop farming, small-scale businesses, charcoal burning and selling of firewood. Through the case study areas in three villages in Laisamis subcounty, Marsabit County, this paper analyses the role of long periods of drought and the existing state of political and socio-economic marginalization in driving livelihood changes among vulnerable pastoral communities. Through descriptive design, the study sampled 384 households and used questionnaires to collect data which was analyzed through descriptive analyze. The study results indicate that climate change variability, frequent prolonged drought significantly affect livelihoods and poverty levels among pastoralist communities in Marsabit.
Urban food insecurity is increasingly recognized as a defining challenge in sub-Saharan Africa, where rising food prices, slow wage growth, and structural unemployment amplify vulnerability. The situation has been made worse with climate change that has contribute to prolonged and frequent drought. This study sought to establish the effect of drought on food security urban settlements in Nairobi, Kenya. The study was anchored on Malthusian Optimism Theory of Food Security and employed cross-sectional survey that targeted 384 households drawn from three slums settlements in Nairobi County. The households were randomly selected from each of the three slums to ensure fair representation. Semi-structured questionnaires were used for data collection. Descriptive statistical analysis that included frequencies and percentages was used for descriptive analysis while chi-square was used for inferential analysis and to establish the relationships between variables through the use of SPSS (Statistical Package for the Social Sciences). The study established that 82.4% of the household in the three slums covered by the study were food insecure. Korogocho slums registered the highest food insecurity among the covered households, followed by Mathare slums with the lowest food insecurity among the household in established in Kibera slums. The study recommended among others that there is need of encouraging sustainable food production and strategic community support as measures of addressing food insecurity in urban slums like those in Nairobi that included Mathare, Kibera and Korogocho.
Abstract
To address the challenges of industrial heavy metal discharge, this study investigates the mechanistic pathways of Pb2+, Cd2+, Zn2+, and Cu2+ removal using chemically modified wheat husk. The adsorption process was rigorously evaluated through multiple kinetic models (Pseudo-second order and Intraparticle diffusion) and isotherm frameworks (Langmuir, Freundlich, and Temkin). Results confirm that the process is governed by chemisorption and occurs on heterogeneous surfaces, with Freundlich and Temkin models providing superior fits (R2 > 0.97 for Pb). The process was found to be exothermic nature and the role of boundary layer diffusion of the materials indicate its viability for high-efficiency industrial wastewater treatment.
Photovoltaic solar panels are widely used for clean
energy generation, but their long-term performance is strongly
affected by cell-level faults such as hot spots, partial shading,
cracks, and interconnection defects. These faults may reduce
output power, accelerate degradation, and shorten the useful
lifetime of a panel. This paper presents a simulation-based
framework for solar panel life prediction through classification
of faulty cells using thermal imaging and convolutional neural
networks. A synthetic thermal-image dataset was generated to
represent healthy and faulty photovoltaic cells under controlled
temperature distributions. A dynamic thermal threshold was first
used to identify abnormal temperature regions, after which a
convolutional neural network classified cells as faulty or non
faulty. The detected fault ratio, hotspot severity, and estimated
power loss were then combined into a health-index model to
estimate the remaining useful life of the solar panel. Simulation
results show that the proposed refined CNN achieved an accuracy
of 90.0%, precision of 88.46%, recall of 92.0%, and F1-score
of 90.19%. A sample 24-cell panel case study showed that five
faulty cells reduced the estimated health index to 0.7709 and the
predicted effective life from 25 years to 19.27 years. The proposed
method provides a low-cost and scalable foundation for future
Raspberry Pi and thermal-camera-based real-time photovoltaic
monitoring systems.
Index Terms—Solar panel fault detection, photovoltaic cells,
thermal imaging, convolutional neural network, hotspot detec
tion, Raspberry Pi, life prediction, simulation-based study.
ABSTRACT
While frontline healthcare workers accumulated substantial experience during the COVID-19 pandemic, translating this into preparedness for chemical, biological, radiological, nuclear, and explosive incidents (CBRNEi) requires an understanding of the systemic barriers that constrain hospital emergency care. This study explored the barriers and challenges to managing CBRNEi in relation to the COVID-19 management experience, from the perspectives of key hospital stakeholders. A qualitative study was conducted in the Accident and Emergency (A&E) units of four apex hospitals in the Kandy district, Sri Lanka. Semi-structured key informant interviews (KIIs) were undertaken with purposively selected decision-makers and health managers—hospital directors, consultant emergency physicians/A&E consultants-in-charge, disaster-management focal points, sisters/masters-in-charge of A&E, and hospital accountants. Of 20 identified key informants, 15 were interviewed until data saturation, and interviews were analyzed thematically using combined inductive and deductive coding. Thirty-six codes were organized into twelve categories and seven overarching themes: limitations of facilities; limitations of planning; financial constraints and funding issues; lack of guidelines for CBRNEi management; lack of staff training; lack of emergency-response facilities; and dead body management and morgue capacity. Informants consistently described staffing shortages, absent decontamination and detection capacity, generic and outdated disaster plans, absent CBRNE-specific guidelines, insufficient specialized training, and unresolved ethical and logistical challenges in mass-fatality management. Key stakeholders identified deep, interlocking systemic barriers to CBRNEi preparedness that the COVID-19 experience revealed but did not resolve; addressing them requires comprehensive, adequately financed, and guideline-driven strategic planning with intersectoral coordination, including specialized armed-forces CBRNEi capabilities.
Introduction: Poor water quality and water contamination continue to be major public health problem in various communities especially in developing countries across the world. This in turn contributes to increases in water borne diseases affecting various populations. This study therefore, was conducted in Lilanda compound Lusaka, Zambia to determine the microbial content and compositional quality of water and the associated risk factors to contamination.
Methods: A cross sectional study design was used in this study to establish the magnitude of microbial contaminants in water and the associated risk factors. The study population was individual drinking water sources (boreholes, taps and wells) and the nearest households accessing such sources in Lilanda compound. Probability sampling method (simple random) was employed where each participant was given an equal chance of being selected. The study recruited a total of 384 participants. The study collected data by collecting water samples that were subjected to laboratory analysis. Microbial risk assessment was carried out by assessing water for microbial load (Total Coliform Count and Total Bacterial Count) and the presence of fecal coliforms such as E. coli from the same participants (n=384). Participants from whom water was collected were also relied upon to collect information using a questionnaire. The two approaches were employed in order to link the laboratory outcome to contamination risk factors. Cleaning of data for any errors was carried and later exported to STATA V.17 (STATA Corporation, TX, and USA) for analysis. Research clearance to conduct this study was sought from Rusangu University Ethics.
This study investigates the effectiveness of generative AI-prompting instruction for teaching skimming and scanning strategies to 410 EFL learners across four provinces of Pakistan. Using a convergent mixed-methods design, the research examines learners' perceptions of AI-mediated strategy instruction, prompting behaviors, metacognitive strategy use, and the perceived transfer of AI-supported skills to unassisted reading tasks. Quantitative data were collected through a 25-item Likert-scale questionnaire, while qualitative data were obtained through semi-structured interviews with 41 participants.
Pearson correlation analysis revealed significant positive relationships among all key variables, with the strongest association between prompting behaviors and metacognitive strategy use (r = .68, p < .01). Structural equation modeling demonstrated that metacognitive strategy use fully mediated the relationship between prompting behaviors and perceived transfer (indirect β = .36, p < .001) and partially mediated the relationship between prompting behaviors and learner autonomy (indirect β = .26, p < .01). Learners perceived generative AI-prompting instruction as effective for developing reading strategies (M = 4.18, SD = 0.64), with high engagement (M = 4.28, SD = 0.55) and perceived skill improvement (M = 4.14, SD = 0.68). Four prompting behavior patterns emerged: iterative refining, hierarchical questioning, verification seeking, and metacognitive monitoring. Although learners reported moderate-to-high transfer of AI-supported skills to independent tasks (M = 3.88, SD = 0.72), autonomy without AI remained moderate (M = 3.46, SD = 0.79).
The study contributes an evidence-based framework for integrating generative AI into EFL reading instruction and highlights the importance of structured scaffolding to promote learner autonomy across diverse educational contexts.
L’objectif de cet article consiste à analyser les effets de l’innovation financière et la demande de la monnaie par le modèle économétrique qui est le modèle ARDL. Cette étude comporte trois parties. Il ressort de cet article que le modèle ARDL à court terme, il est démontré que l’innovation financière a une influence sur la demande de monnaie en RDC et à long terme, cette influence est non significatif, dont la raison peut être dû au processus de son intégration, mais toutefois, son influence positive indique donc une fois bien assis, il pourrait potentiellement stimuler la demande de la monnaie, suite à l’inclusion financière de la population non bancarisée. Il est montré que les seuls déterminants sont l’inflation ainsi que le taux de change qui a des effets sur la demande de monnaie, que cela soit à court tout comme à long terme, d’où, l’innovation financière ne les supplante pas, mais au contraire interagit avec ces derniers.
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