Peer-reviewed international research papers published open-access with EOI assignment and global indexing across engineering, computer science, environmental science, social sciences, and more.
The study “Appraisal teachers’ awareness and utilization of unconventional teaching strategies in teaching agricultural science in secondary schools in Borno state, Nigeria”, was carried out using descriptive and inferential statistics survey method. Two objectives and two corresponding research questions was developed to guide the study. The finding revealed that agricultural teachers were not aware of unconventional teaching strategies as none of the parameters assessed recorded above 50% level of awareness and utilization of unconventional teaching strategies, Similarly the results show that, Lack of training and re-training of teachers to match the unconventional teaching strategies, lack of teaching material and equipment in schools, teachers unwillingness to adopt the unconventional teaching strategy without training, Teacher assume the strategy will work the first time, teachers Implement the strategy with poor lesson plan, Lack of motivation, support and encouragemsents by school administrators and Utilization of innovative instructional strategy is time consuming and very difficult in teaching and learning science, were the likely reasons why agricultural teachers are not aware and utilize unconventional teaching strategies. The study recommends that Government should organize seminars, workshops and provide adequate sensitization to agricultural teachers on awareness and utilization of unconventional instructional strategies for effective teaching and learning of agricultural science in secondary schools in Borno state, Nigeria. Keywords: Awareness, utilization, unconventional teaching strategies.
Nigeria has some core sustainable land management policies that provide the framework for the availability of land for public purposes, such as green spaces, and limit the carrying out of projects in sensitive areas that can disrupt biodiversity, degrade ecosystem functioning, and harm the environment. Despite the planning structure and mechanism for land acquisition, rapid unplanned structures have been springing up in areas that should be restricted. Thus, encouraging disorderliness and chaos, as non-approved structures suddenly spring up in green spaces and along drainage, and increase nuisance due to poor waste management practices. This study examines the importance of policy implementation in restoring urban aesthetics in Nigerian cities. A qualitative approach was adopted, using peer-reviewed journals and credible online sources. The findings reveal that a strategy that accounts for the rapidly increasing population within the policy framework will enable a controlled, sustainable infrastructure upgrade that can improve socioeconomic outcomes. The study concludes that deploying uncontrolled, unethical practices can be avoided by integrating an enhanced, technology-driven monitoring and compliance system into the sustainable land management value chain to resolve urban aesthetics issues in Nigerian cities. The study recommends a standardised approach to enforce compliance, deter offenders, and ensure that existing policies are well-planned structures that progressively address disorderly development. Keywords: Climate change, Green space, Policy implementation, Sustainable development, Urban planning.
This study analyzed the tomato production and marketing system in the Gamo Zone of South Ethiopia using a value chain approach, with a focus on identifying post-harvest handling gaps and feasible technological solutions. Tomato is an important vegetable crop in the region due to favorable soil, irrigation potential, and market access; however, producers face major challenges in maintaining a consistent supply of quality tomatoes. A three-stage sampling procedure was applied to select tomato-producing households, traders, and consumers. Data were collected through questionnaires, key informant interviews, focus group discussions, and field observations. Findings revealed significant post-harvest losses resulting from poor handling practices, lack of storage facilities, limited market information, and the dominance of brokers in price determination. Producers relied heavily on chemical inputs without adequate technical guidance, used inappropriate harvesting and packaging materials, and had limited knowledge of sorting, grading, and hygiene. A range of feasible technologies were identified and prioritized, including plastic crates, maturity testing tools, improved storage systems, and kaizen-based practices. The study concludes that addressing post-harvest inefficiencies requires coordinated actions from all actors and stakeholders. Strengthening cooperatives, improving input supply systems, enhancing extension services, and promoting appropriate technologies are essential for reducing losses and improving the competitiveness of the tomato value chain in the Gamo Zone.
This study explores investor awareness and preferences regarding green bonds, leveraging primary data from a survey of 150 participants. The research investigates the factors influencing investor behavior, including awareness levels, Primary motivations, Investment Experience, and primary barriers. Key findings reveal that awareness of green bonds remains relatively low, particularly among age group (35-44) investors, despite a growing interest in environmentally sustainable investments. The study also identifies environmental impact as a significant motivator for investment; however, it does not significantly predict willingness to invest, suggesting that financial low returns remain a primary concern for investors. Perceived barriers, such as lack of knowledge and complexity, were found to deter investment, although some investors exhibited a willingness to overcome these challenges due to strong alignment with personal values. The results underline the importance of targeted educational initiatives, transparent certification processes, and financial incentives to promote green bond adoption. To enhance the appeal and accessibility of green bonds, recommendations for policymakers, issuers, and financial institutions are proposed. This research contributes to the growing body of literature on sustainable finance and provides actionable insights to support the development of the green bond market.
Cardiovascular diseases (CVDs) are the leading cause of global mortality, accounting for nearly 18 million deaths annually. Early detection is crucial for effective management and prevention of fatal outcomes. However, traditional diagnostic methods—such as ECGs, stress tests, and blood analyses—often require specialized resources and are time-consuming; making them less accessible, especially in resource-constrained settings. This study explores the use of machine learning algorithms for early prediction of cardiovascular disease using publicly available clinical data. Four models—Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN)—were developed and evaluated on a dataset comprising 303 patient records with features such as age, cholesterol, blood pressure, and ECG results. The Random Forest Classifier emerged as the best-performing model, achieving an accuracy of 88.5% and a ROC-AUC score of 93.4%. Feature importance analysis revealed that chest pain type, maximum heart rate, ST depression, and cholesterol were key indicators of heart disease. The findings suggest that machine learning models, particularly ensemble-based methods, can provide accurate, interpretable, and efficient tools for early detection of cardiovascular conditions. This research contributes to the growing body of evidence supporting the integration of artificial intelligence into clinical decision-making, especially for early diagnosis. Future work should focus on expanding the dataset, incorporating additional features like lifestyle and genetic factors, and evaluating models in real-world healthcare environments.
This study investigated a sustainable approach to enhancing cotton fabric by applying biochar derived from cotton stalk waste, using flat silk-screen printing. The goal was to impart novel, multifunctional properties. The research rigorously tested parameters, including air and water permeability, color characteristics, color fastness, moisture absorbance, and, critically, the ability to reduce common air pollutants. SEM and FTIR analyses were conducted to characterize the biochar's integration. Results demonstrate that biochar-printed fabrics effectively reduce air pollutants in a dose-dependent manner. High biochar concentrations, specifically 50 g/kg, achieved over 90% VOC reduction. This efficacy stems from biochar's adsorptive capabilities and its physical occlusion within the fabric's pores, creating a tortuous path that decreases air permeability. A slight reduction in water permeability was observed; the biochar treatment created a unique "hidden hydrophobic effect" on the printed surface, due to a double-faced textile with both hydrophilic and hydrophobic characteristics. This resulted in faster drying times and increased water vapor permeability, alongside demonstrable odor-adsorption capabilities. SEM and EDX confirmed successful biochar deposition, altering fabric aesthetics, notably reducing lightness and increasing color difference in proportion to biochar concentration. The functionalization proved durable, exhibiting consistent wash and perspiration fastness to a robust binder system. This research establishes biochar printing as a viable method for creating multifunctional textiles that adsorb air pollutants and modify water transport, demonstrating promise for applications in filtration and smart textiles.
Ethiopia is a low-income country with a population of 120 million. It has historically experienced an extremely high maternal and neonatal mortality ratio in Africa, most fatalities stem due to preventable causes like postpartum haemorrhage (PPH), hypertensive disorders, infections, and neonatal asphyxia. The rates remain unacceptably high, particularly in rural areas. The main issue was the capacity of Ethiopia’s frontline health workforce to manage obstetric and neonatal emergencies effectively. Despite the expansion policy of health workers, there were persistent gaps in knowledge, retaining skills, and confidence which undermined the quality of care during emergencies. To fill the gap, the Maternity Foundation partnered with academic institutions to create the Safe Delivery Application (SDA), which was initially evaluated in Ethiopia in 2014 before being expanded across the country's maternal healthcare initiatives. The outcome from the national reports and research studies found significant increase in the knowledge, skills, and confidence level of the frontline healthcare workers with high rates of user acceptance and continued application. it also faced some challenges like cultural and literacy related factors, insufficient supplies of critical medications, substandard facility infrastructure, and reliance on external funding sources. Ethiopia’s experience shows that digital health tools work best when they are adapted to local culture and context, supported by strong government commitment, and embedded within broader health system improvements. The Ethiopia case study is providing a scalable model for other low-resource countries working toward SDG targets on maternal and child survival.
This systematic review examines the interplay between mental health and workplace productivity, highlighting how psychological well-being, job stress, work-life balance, and organizational support influence employee performance. The review synthesizes global, regional, and local evidence to explore the mechanisms through which mental health impacts productivity, emphasizing the role of mental health interventions in reducing absenteeism, presenteeism, and improving task performance. Findings indicate that good mental health is a key determinant of higher employee engagement and productivity, while poor mental health leads to reduced work efficiency and increased workplace stress. The review also identifies methodological gaps and suggests areas for future research, particularly in underrepresented regions such as Africa. Policy and practical implications for integrating mental health support into organizational frameworks are discussed, emphasizing the need for comprehensive mental health programs as a strategic productivity too.
The digitalization of nuclear power plants (NPPs) enhances operational efficiency but exposes critical control networks to sophisticated cyber threats, necessitating intelligent, adaptive, and data-driven security solutions beyond traditional rule-based methods. Network intrusions in NPPs arise from vulnerabilities in legacy systems, network architecture, human errors, increased connectivity, and sophisticated threat exploitation, necessitating adaptive, real-time detection mechanisms. This research presents a novel Machine Learning-based Intrusion Detection System (ML-IDS) tailored for NPP control networks, addressing the critical need for robust cybersecurity in safety-critical industrial environments. Leveraging a hybrid SVM-KNN model, termed SVM-Plus, the proposed framework combines the global decision-making capability of Support Vector Machines (SVM) with the local sensitivity of k-Nearest Neighbors (KNN) to detect both widespread and subtle anomalies in network traffic. The study utilizes a carefully simulated NPP network dataset, capturing both normal operations and diverse intrusion scenarios, to overcome the challenges posed by restricted access to real operational data. Extensive preprocessing, feature engineering, and normalization were applied to ensure reliable model training. The SVM-Plus model demonstrated exceptional performance, achieving an accuracy of 99.71%, precision of 0.8735, recall of 0.83, and an F1 score of 0.8476, outperforming traditional classifiers including Random Forest, SVM, XGBoost, and Bagging. The research also emphasizes interpretability, probabilistic confidence scoring, and ethical data governance to ensure operator trust and regulatory compliance. This work provides a practical, high-performance framework for real-time intrusion detection in nuclear control systems, enhancing resilience against cyber threats and establishing a foundation for future AI-driven security solutions in critical infrastructure networks.
Mortgage financing refers to the borrowing money from a lender to purchase residential property or alternative real estate. Mortgage enables one to purchase a home without paying the total price upfront, as mortgage lenders, as financial institutions, provide housing loans with tenors of up to 20 years at significantly lower interest rates. With increased access to capital through mortgage financing, affordable housing government projects can achieve their potential. The study targeted, and through purpose sampling, selected branches of the four leading commercial banks in assets and customer base (Kenya Commercial Bank, Cooperative Bank of Kenya, Equity Bank, and Family Bank) and 2 micro-financial institutions (Kenya Women Micro Finance and Rafiki Micro Finance) who are possible lenders to micro-enterprises that most of the targeted homeowners save with in the specific project areas. Data was collected through a semi-structured questionnaire. Descriptive statistical analytics presented data results in the form of mean, frequencies, percentages and standard deviation, while inferential statistical analysis comprised of Pearson Correlation and Multi-Linear Regression. The study established that access to capital factors of high interest rates, lack of collateral, high risk of default, and a poor credit profile significantly affect the performance of government affordable housing programs in Kenya. The study recommended that there is a need for necessary policies to explore alternative credit scoring techniques, creative incremental housing loans and micro-mortgages. The government should also work with financial institutions in order to develop appropriate products that address low-income earners' needs, which include products with low interest rates, processing mortgages with the houses are securities, in addition to financial literacy in need of paying debts and maintaining a good credit record.
This study assessed the status, practices, and challenges across cassava production, marketing, processing, postharvest handling, and utilization in Gofa Zone, South Ethiopia. Data were collected from 98 cassava-producing households and 21 traders using a two-stage sampling design, complemented by qualitative interviews and secondary sources. Key actors identified along the cassava value chain include producers, assemblers, retailers, processors, and wholesalers. Cassava producers face multiple constraints such as disease and pest prevalence, limited access to improved varieties, weak agronomic practices, and the absence of value‑adding technologies. Marketing challenges include unorganized markets, dominance of informal intermediaries, lack of standardized measurement, limited access to reliable market information, and the absence of cassava-focused cooperatives. Due to the highly perishable nature of cassava, total postharvest qualitative losses were estimated at 36.93%, consistent with regional loss estimates (30–50%). Despite these constraints, cassava offers opportunities as a climate‑smart crop with growing regional demand and its role as a substitute for staple cereals. Utilization practices include direct boiling and blending cassava flour with maize and teff for bread and injera. The study recommends introduction of early‑maturing and disease‑tolerant varieties, establishment of cassava‑processing cooperatives, promotion of improved value‑addition technologies, and enhanced extension support for production and post-harvest management.
Tuberculosis (TB) remains a major infectious disease worldwide and continues to pose a critical public health challenge, particularly in developing countries where access to rapid diagnostic tools is limited. Chest X-ray imaging is a widely available and cost-effective screening technique, but its interpretation requires expertise and is susceptible to human error. Recent advancements in deep learning and transfer learning provide new opportunities to automate TB detection with high accuracy. This study proposes a transfer learning-based approach for Tuberculosis Diagnosis using pretrained algorithms such as DenseNet121, VGG16, and InceptionV3. A publicly available dataset was preprocessed, augmented, and used to fine-tune these models. The models’ performance was evaluated using precision, recall, F1-score, accuracy, AUC, and Cohen’s Kappa. The study results show that DenseNet121 outperformed the other pre-trained architectures, achieving an accuracy of 99.52%, AUC of 99.91%. The findings confirm the effectiveness of transfer learning for TB diagnosis and highlight the potential of deep learning in enhancing medical screening, especially in low-resource regions.
—This research scrutinizes ubiquitous learning and online testing as determinants of academic performance in senior high school students at Lapasan National High School. The researcher uses purposive descriptive correlational research, a non-experimental quantitative study design that uses purposive sampling to describe specific characteristics of a pre-identified population and the statistical relationships between two or more variables within that group. The subjects of this research are 115 senior high students. The study made use of the online survey questionnaire in determining the demographic profile and the general weighted average (GWA), and of course the attributes of the U-learning and online testing design characteristics. In gathering the data, the researcher used the modified questionnaire, based on the characteristics of u-learning, and categorized the learning paradigms into six characteristics: concept, permanency, accessibility, immediacy, interactivity, and context-awareness. The questionnaire was composed of 22 questions and follows the 5-point Likert scale format. The questionnaire was drawn out based on what the researcher found to be relevant to the study; the requirements in designing a good data collection instrument were considered. The researcher carefully examines the result using a myriad of statistical techniques. Testing the data on measures of central tendency and measures of dispersion. Inferential statistics will measure the correlational value and data’s variability in terms of p-value to determine significance level alpha, where it is at 95% CI. The findings of the research conclude that (1) there was a significant relationship between ubiquitous learning and online testing characteristics of permanency, accessibility, immediacy, interactivity, adaptability, and relevance in the academic performance of the students anchoring principles of u-learning. u-learning, ubiquitous learning and online testing, e-learning, m-learning
Forty five different brands of water sachets produced in Central Region were analyzed for activity concentrations of Gross Alpha/ Beta, for 228Ra, and 226Ra. In order to quantify the radionuclides of interest in the water samples, the analysis was conducted in the Alpha Spectrometry Laboratory under Radiation Protection Institute at the Ghana Atomic Energy Commission using Gross Alpha/Beta Counter System. The results from the research revealed that the Alpha activity concentration ranged from 1.02±0.01 to 3.36±0.03 mBq/L, with an average of 2.09±0.01 mBq/L. The average beta activity concentration was 27.98±0.02mBq/L, with a range of 15.12±0.01 to 45.07±0.03mBq/L. All the sachet water samples had Gross Alpha/Beta activity concentration values below the 0.1 Bq/L and 1.0 Bq/L, respectively, Ghana Standard Authority and World Health Organization permitted guideline thresholds set for drinking water quality. According to the data, there is no substantial radiological health risk to the residents from drinking sachet water produced in Central Region This study would provide some useful monitoring data for setting a regulatory limit (base-line radiometric radioactivity) on radiation in Ghanaian-produced sachet water and public drinking water. These data could serve as baselines for future research in the study area and give the regulatory authority useful information (base-line radiometric values) to assess future changes. Keywords: Activity Concentration, Sachet water, Gross Alpha Activity, Gross Beta Activity.
Activity concentrations of Gross Alpha/Beta, in thirty five brands of sachet water produced in the Western Region of Ghana were measured. The analysis was carried out in the Alpha Spectrometry Laboratory at Radiation Protection Center of Ghana Atomic Energy Commission using gross alpha/beta counter system to screen for radiological contaminant of interest in the water samples. Activity concentration for Gross Alpha values ranged from 1.06±0.01- 3.29±0.02 mBq/L and Gross beta values ranged from 15.01±0.01 - 38.44±0.02 mBq/L during the radiological evaluation; the corresponding average levels of activity were 1.85±0.01 and 22.55±0.02 mBq/L. This indicates the radiological safety of the sachet drinking water. Just 2% of all the sachets water samples did not met the national and international standard. The concentration values of gross - alpha and gross -beta for all the sachet water samples were within the Ghana Standard Authority and World Health Organization recommended guideline levels set for drinking water quality which is 0.1Bq/L and 1.0Bq/L, and 0.5Bq/L and 1.0Bq/L respectively. The results obtained indicated that the inhabitants in the Western region were not exposed to any significant radiological health hazard associated with drinking sachet water. This research would provide some useful data (base –line radiometric values) to be used by the regulatory authority to evaluate possible changes in the future. Keywords: Activity Concentrations; Sachet water; Ghana Standard Authority; Western Region and WHO
This study explores the multifaceted ways of cultural background influences communication styles, including verbal and non-verbal interactions, language barriers, and contextual nuances. By examining the impact of different cultural norms and values on the dynamics of the workplace, the study emphasizes the problems and capabilities of multicultural labor. Using qualitative analysis and cases from different sectors, the study identifies key strategies to improve intercultural communication, such as cultural competence education and inclusive communication practices. The results emphasize the need for organizations to prioritize cultural awareness in communication strategies to improve teamwork, reduce misunderstandings, and create a more harmonious working environment. Ultimately, this article contributes to a constant discourse about the importance of cultural diversity in shaping effective communication. Keywords: Organizational Culture, Employee Communication Satisfaction, workplace performance; cross-cultural communication
The Influence of Mobile Learning Applications on Study Habits Of Bachelor of Science in Information Technology Students at Aemilianum College Inc. This study aimed to determine the influence of mobile learning applications on the study habits of Bachelor of Science in Information Technology (BSIT) students at Aemilianum College Inc. It employed a quantitative-descriptive and correlational research design to examine how the use of mobile learning platforms affected students’ study habits and academic performance. A total of 25 BSIT students participated as respondents. Data were gathered through a structured questionnaire and analyzed using frequency, mean, and correlation to measure the extent of application usage and its relationship with students’ learning behavior. The findings revealed that the most frequently used mobile learning applications among BSIT students were Google Classroom (40%), YouTube (26%), and Zoom/Google Meet/MS Teams (10%), while Moodle/School LMS and Duolingo were not used. Students often used these applications to access lecture materials, engage in self-study, and communicate with teachers and classmates, with an overall mean of 3.8. Mobile learning applications moderately aided students in managing their time (m = 3.29, Neutral), improved concentration and understanding (m = 3.67, Agree), and enhanced motivation to study (m = 3.88, Agree). However, students encountered several challenges such as poor internet connection, limited mobile data, and distractions from non-academic applications (overall mean = 3.85, Agree). A positive correlation was found between the use of mobile learning applications and academic performance (m = 3.8, Agree). Respondents suggested incorporating interactive and hands-on features, minimizing distractions, and promoting self-discipline. It was concluded that mobile learning applications had significantly influenced students’ study habits by providing flexibility, accessibility, and improved academic engagement. Despite technical and behavioral challenges, these tools contributed positively to learning outcomes. It was recommended that institutions, educators, and developers continue to enhance mobile learning environments, address connectivity issues, and integrate interactive and structured learning approaches to promote effective and disciplined use among students. Keywords: Academic Performance, E-Learning, Digital Education, Information Technology Students, Learning Behavior, Mobile Learning Applications, Mobile Technology, Online Learning, and Study Habits
L’article analyse les besoins stratégiques en audit pour un secteur d’entreprises publiques, en identifiant les risques économiques et financières comme axes prioritaires. L’approche combine : Risques économiques : Mesurés par l’EBE pour évaluer la performance opérationnelle et la capacité de générer des ressources internes ; Risques financiers : Evalués via des ratios financiers (solvabilité, liquidité, indépendance, rentabilité financière et rentabilité économique) et une régression logistique pour prédire les probabilités de défaillance ou de vulnérabilité.
This study examines prevention strategies for simple diarrhea among children under five in the Trois Rivières neighborhood, Bandundu. Using a descriptive methodology involving 86 households, the research assessed knowledge, attitudes, and practices related to hygiene, sanitation, water treatment, and use of oral rehydration salts. Findings indicate partial awareness of preventive measures, low water treatment (24.42%), and insufficient sanitary facilities (60.47% without hygienic toilets). The study concludes that prevention strategies are limited by inadequate community awareness and weak health education programs. It recommends the implementation of an integrated community health approach focusing on hygiene promotion, behavioral change, and environmental improvements.
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