Fair East Publishers: E-Journals
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Global food security partnerships: Aligning agricultural policies with climate-resilient development
Get PDFGlobal food security remains a pressing challenge in the face of population growth, climate change, and resource constraints. Effective partnerships—spanning governments, international organizations, the private sector, and civil society—are critical to aligning agricultural policies with climate-resilient development strategies. This review explores the role of collaborative frameworks in enhancing sustainable food systems, focusing on policy harmonization, technology transfer, financing mechanisms, and capacity-building initiatives. It examines case studies from both developed and developing regions, highlighting how integrated approaches can strengthen adaptive capacity, mitigate climate risks, and promote equitable food distribution. By synthesizing evidence on the intersections of agricultural governance, environmental sustainability, and global cooperation, this paper identifies key enablers and barriers to achieving climate-resilient food systems. The findings underscore the importance of inclusive, multi-level governance models that bridge local innovation with global policy agendas, ensuring that agricultural transformation contributes to long-term food security, environmental protection, and socio-economic resilience.
Keywords: Global Food Security, Climate-Resilient Agriculture, Policy Alignment, Sustainable Food Systems, International Partnerships, Agricultural Governance
Use of syntax in written French by French learners in the Tamale Metropolis; A case study by Senior High School french students in Tamale, Ghana
Get PDFThis study examines the use of syntax in written French by learners of French at the Senior High School (SHS) level in the Tamale Metropolis. The specific objective was to find out the areas of difficulties students have with regard to the use of syntax and suggest didactic solutions to the problems. The data collected as a basis for this study was an essay-writing test by 85 learners of the third year in five Senior High School (SHS) in the Tamale Metropolis where French is taught as a foreign language namely, Ghana Senior High School, Presbyterian Senior High School, St. Charles Senior High School, Tamale Senior High School and Tamale Girls' Senior High School. It was discovered from the study that students really have difficulties in the area previously mentioned. It was noticed in their written essays that they used inadequate syntactic constructions, wrong and repetitive syntactic structures. It was therefore suggested that some elements of textual grammar should be introduced in the teaching of essay writing at the SHS level. Teaching activities that contribute to improvement in essay-writing are also suggested in this study. It is finally recommended that the teachers introduce practical training activities in the use of syntax examined in this article and regular exercises in French which lead to the development of essay-writing competence in the learner.
Keywords: Coherence, Cohesion, Syntax
Using data mining techniques to improve the detection of credit card fraud
Get PDFThe financial industry is constantly under threat in the fight against financial fraud, requiring for strong protection. Data mining emerges as a crucial technique for detecting credit card fraud in online transactions. Detecting credit card fraud proves challenging due to the dynamic nature of legitimate and fraudulent behavior patterns and the inherent skewness in credit card datasets. The accuracy of credit card fraud detection hinges on critical factors like the sampling method of the dataset, the selection of significant variables, and the chosen detection method. This project aims to tackle these challenges by exploring various machine learning models (Random Forest, Logistic Regression, Decision Tree, Naïve Bayes) designed to predict fraudulent activity in credit card transactions. Python will serve as the primary programming language due to its extensive libraries and features for machine learning and data analysis. The objective is to enhance the precision and effectiveness of credit card fraud detection systems, contributing to the ongoing fight against financial fraud. Among the machine learning models assessed, Random Forest stands out with the highest accuracy, achieving 97%, surpassing other models in performance metrics. Our research emphasizes the development and evaluation of predictive models adaptable to the evolving nature of fraud, providing valuable insights for both financial institutions and customers.
Keywords: Data Mining, Credit Card, Fraud Detection
Hypertensive disorders in women of reproductive age: Diagnostic delays and management challenges in primary care settings in Nigeria
Get PDFHypertensive disorders in women of reproductive age are an underrecognized public health issue in Nigeria, with significant implications for maternal morbidity, adverse pregnancy outcomes, and long-term cardiovascular risk. Despite their clinical importance, early detection and effective management are often delayed within primary care settings due to a combination of systemic, clinical, and sociocultural barriers. This manuscript explores the prevalence and impact of hypertension among reproductive-age women, emphasizing its relevance beyond pregnancy. It identifies key challenges in diagnosis and management, including non-specific presentation, under-screening, limited availability of diagnostic tools such as urinalysis and ECGs, and poor follow-up systems. Cultural norms, gender-related access barriers, and provider bias further complicate timely care.
Drawing on program insights and facility-level reflections, the manuscript highlights missed diagnosis scenarios, health worker knowledge gaps, and weaknesses in referral pathways. However, examples from audits and pilot interventions suggest that targeted training, use of standardized protocols, and structured case review meetings can improve outcomes. To address these gaps, the paper proposes strategic solutions such as integrating blood pressure screening into antenatal care and community outreach programs, implementing task-shifting models for first-line management, and deploying mobile-based referral decision tools. Investments in digital health systems and continuing medical education for frontline providers are also recommended to strengthen the continuum of care. Overall, this paper calls for systemic reforms to enhance early detection and management of hypertensive disorders in women of reproductive age, leveraging primary healthcare systems to reduce preventable maternal morbidity and long-term complications.
Keywords: Hypertension, Primary Care, Family Physician, Maternal Health
Assessing the Contribution of Sole Proprietorship to Job Creation in Ghana
No full textThe paper investigated the role of sole proprietorships in addressing Ghana's unemployment challenges by analyzing their job creation capacity. The research aimed to quantify employment contributions, identify sectoral patterns, and assess operational constraints. The methodology combined an analysis of Ghana Statistical Service data with a literature review. Results showed sole proprietorships constituted 78% of businesses and created 48.2% of jobs, dominating services (80% of establishments) and industry (18.2% of employment). Structural barriers like unlimited liability and income instability persisted. The paper concluded with policy recommendations for skills development and regulatory reforms to enhance the employment potential of this business model while mitigating its vulnerabilities.
Keywords: Sole Proprietorship, Job Creation, Entrepreneurship, Informal Sector, Ghana Economy
Modern office equipment: Essential tools for secretaries outstanding performance in Nigeria private and public organizations
No full textThis study investigates the influence of modern office equipment as an essential apparatus for secretaries outstanding performance in public and private organizations in Nigeria. Modern office equipment has helps in no little way to activates efficiency, productivity, and overall effectiveness among secretaries across Nigeria Organizations. This paper examined the role of secretaries in the digital era and how they can adjust to the numerous technological innovations that they encounter on a daily basis. Instead of lowering secretarial performance, many pieces of contemporary office equipment and technology have developed to help improve it. The study examined the modern workplace, technical advancements, the secretary and technological advancements, the difficulties presented by contemporary technologies, and the demands placed on contemporary secretarial professionals. It was determined that the office has a significant impact on global dynamism and that professional secretaries must refresh their expertise by reskilling and upskilling to meet contemporary difficulties. Among other things, it was suggested that organizations set up refresher courses for current secretaries so they can gain a thorough understanding of new technologies that could present difficulties for them. Additionally, given the ongoing importance of secretarial professionals in achieving organizational goals and objectives, opportunities for reskilling and upskilling should be established. Even though there are many clear obstacles to adopting new technology, such as reluctance to change, a lack of training, limitations in IT infrastructure, cybersecurity, and so forth, secretaries continue to be the key to becoming productive, efficient, and relevant in our cutthroat society.
Keywords: Modern Office Equipment, Essential, Tools, Secretaries, Outstanding Performance, and Nigeria
Factors associated with sexual abstinence behavior in Chadian and Cameroonian adolescents and young adults
No full textBackground: Sexual abstinence represents a reproductive health-promoting behavior for young people and a better strategy to prevent them from the risks of early sexual initiation and negative sexual consequences. Little is known about factors associated with abstinent behavior in Chad and Cameroon. Therefore, this study examines the influence of sexual abstinence beliefs, sexual self-control, and attitudes towards sexual abstinence on sexual abstinence behaviors among school-going Chadian and Cameroonian adolescents and young adults. Methods: A total of 1,158 students from high schools aged between 15-24 years, selected using time-location sampling technique in Chad and Cameroon were involved in the study. A self-administered questionnaire composite of Beliefs in Abstinence until Marriage Scale, Attitude Scale about Sexual Abstinence, Sexual Self-Control Scale, sexual behavior, and demographic characteristics was administered. Data analyses were done with statistical software SPSS, v.20 using frequencies, means, and Generalized Linear Models. Results: Models are statistically good in explaining primary sexual abstinence, secondary sexual abstinence and intention to abstain from sex in Chad and Cameroon (p<0.05). Controlling for some sociodemographic characteristics, sexual abstinence beliefs, sexual self-control, and attitudes towards sexual abstinence were better predictors of abstinent behavior, especially primary abstinent behavior (p<0.05). Conclusion: Educational programs aiming at delaying sexual intercourse and promoting sexual abstinence should include sexual abstinence beliefs, sexual self-control, attitudes towards sexual abstinence, and pornography literacy.
Keywords: Sexual Abstinence Beliefs, Sexual Self-Control, Attitudes, Young People, Sexual Abstinence, Chad, Cameroon
Optimizing cross-selling strategies with machine learning: A case study of car loan adoption among remittance recipients in Uzbekistan
No full textDespite the significant flow of remittances into Uzbekistan, the adoption rate of financial services and products among remittance recipients remains relatively low. Identifying which remittance recipients are more suitable for cross-selling retail banking products is crucial. The primary objective of this study is developing a robust model that assists financial institutions to identify potential customers with a higher likelihood of adopting car loans. We use a unique dataset of remittance transactions and vehicle financing data provided by a commercial bank in Uzbekistan. To balance data, we apply Synthetic Minority Over-sampling Technique for Nominal and Continuous variables (SMOTE-NC) and combination of over-sampling approach of SMOTE with an under-sampling method called Edited Nearest Neighbors (SMOTE-ENN). We compare the performance of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) models in these two datasets. Our analysis reveals than all models perform better on the SMOTEEN dataset. DT and RF outperform the other models. Based on these insights, we recommend using DT, RF and SMOTEEN techniques on imbalanced datasets. The results of this study offer practical implications for data scientists and financial institutions in remittance-receiving countries aiming to leverage remittance flows and boost cross-selling.
Keywords: Cross-Selling, Machine-Learning, Remittances, Vehicle Loans, Uzbekistan
Barriers and drivers for the adoption of Building Information Modelling (BIM) in the Nigerian construction industry
No full textInternational recognition has been accorded to the potential of Building Information Modelling (BIM) to revolutionize the construction industry. There is a lot of potential for BIM to enhance project efficiency, lower costs, and achieve excellent project outcomes. However, because of several enablers and barriers, the Nigerian construction sector has been slow to adopt it and faces a number of challenges, which this study aims to identify. This study explores the state of building information modeling (BIM) adoption in Nigeria's construction sector, highlighting the main obstacles and drives influencing its adoption. It also looks at the factors that encourage and hinder BIM adoption in Nigeria. Conducting a comprehensive study of the current state of the construction industry, we investigate the key elements that either foster or impede the adoption of BIM. The study employs a mixed-methods (qualitative and quantitative) approach, combining a case study analysis with surveys (Questionnaire) to collect data from construction and built environment professionals in the field. The results highlight the urgent need to overcome technological, human, and regulatory barriers in order to facilitate the adoption of BIM, while also acknowledging the increasing impact of government initiatives and the need for improved project quality. The outcomes of these findings are discussed, recommendations are made for stakeholders in the conclusion, the limitations of the research are acknowledged, and valuable information regarding the level of preparation of the Nigerian construction industry for BIM is provided. It also offers suggestions for quickening the adoption of BIM.
Keywords: Building Information Modelling (BIM), BIM adoption, Barriers, Drivers, Regulations, Construction professionals, Awareness
Strengthening security, privacy, and trust in artificial intelligence software for critical infrastructure in the United States
No full textAs artificial intelligence (AI) systems become increasingly embedded in critical infrastructure such as energy, transportation, and healthcare, ensuring their security, privacy, and trustworthiness is essential. This study investigates the application of machine learning models to enhance the protection of AI software operating within these sensitive environments. The research specifically focuses on classifying malicious PortScan network activity using the Friday Afternoon Port Scan dataset from the NSL-KDD repository. A quantitative approach was adopted, employing two ensemble machine learning algorithms: Random Forest and Gradient Boosting. The dataset underwent preprocessing, including handling of missing and infinite values, feature selection, and multicollinearity checks using a correlation matrix and Variance Inflation Factor (VIF). Both models were tuned using GridSearchCV with five-fold cross-validation and evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The results revealed that both models achieved perfect classification performance, with all evaluation metrics, including AUC, recording a score of 1.000. Confusion matrix analysis showed extremely low misclassification rates, with Random Forest slightly outperforming Gradient Boosting. Feature importance plots identified forward packet characteristics as dominant in decision-making. Despite these impressive outcomes, the study recognizes the possibility of overfitting and emphasizes the need for external validation using real-world traffic. Based on the findings, the study recommends embedding secure-by-design principles in AI development, enforcing robust data privacy controls, and ensuring transparency through explainable AI techniques. These practices are vital for maintaining public trust and operational integrity in AI-powered critical systems.
Keywords: Artificial Intelligence, Critical Infrastructure, Security, Privacy, Machine Learning, Gradient Boosting, Intrusion Detection