Northrise University

Background: Substance-induced psychosis (SIP) has become a growing public health concern in Zambia. Ndola Teaching Hospital Psychiatric Unit reported 535 admissions of young males aged 15-30 years with SIP in 2022-2023, reflecting a significant upward trend. Aim: To explore factors associated with the increase in SIP among young male adults aged 15-30 years at Ndola Teaching Hospital Psychiatric Unit. Methods: An exploratory-descriptive qualitative study was conducted from June to August 2023. Twenty young males diagnosed with SIP were purposively sampled. Semi-structured interviews were conducted and data were ehaviou thematically using Caulfield’s framework. The Health Belief Model guided interpretation. Results: Three themes emerged: 1) Knowledge: 80% had poor understanding of SIP and its link to substance use. 2) Attitudes: Normalization of substance use, peer pressure, and use for stress relief were common. 3) Practices: Unemployment, poverty, early onset, easy access to cannabis and alcohol, and lack of recreational opportunities were key drivers. Conclusion: The rise in SIP is driven by knowledge gaps and socio-economic vulnerabilities. Recommendations include school-based mental health education, youth livelihood and recreation programs, and strengthening Community Health Assistants to provide early screening and intervention.

This chapter examines how faith integration, ethical orientation, and artificial intelligence (AI) practices shape responsible AI adoption, pedagogical integration, academic integrity, and student outcomes at Northrise University, Zambia. Positioned within a values-driven higher education context, this chapter explores how faith-informed ethical frameworks guide responsible AI use in teaching and learning. Using a concurrent cross-sectional mixed-methods design, faculty (n = 38) and student (n = 56) perspectives are analyzed to explain how values-based governance influences AI adoption. Findings indicate that faith integration supports responsible AI guidance, ethical orientation strengthens integrity vigilance, and AI capacity-building drives pedagogical integration, while student outcomes show strong links between AI value, employability, and learning.

This study presents a systematic and engineering-oriented review of artificial intelligence (AI)-driven epidemiological forecasting models published between 2012 and 2022. A structured search was conducted across Scopus, Web of Science, PubMed, and IEEE Xplore using predefined Boolean search strings combining SEIR modelling, machine learning, deep learning, and multidisease forecasting terms. Following PRISMA 2020 guidelines, 236 records were identified, screened to 104 articles, and 18 studies met the final inclusion criteria based on methodological rigor, validation transparency, and relevance to predictive epidemic modelling. Quantitative synthesis indicates that 83% of reviewed studies focus on single-disease forecasting, only 11% incorporate co-infection dynamics, and fewer than 20% systematically integrate environmental or policy control variables. Comparative reporting across selected studies suggests that AI-based models reduce RMSE by approximately 12–18% relative to classical SEIR implementations, though heterogeneity precluded formal meta-analysis. Additionally, interpretability mechanisms such as SHAP remain underutilised in epidemiological contexts. The review identifies reproducible methodological gaps and proposes a mathematically structured hybrid SEIR-AI framework incorporating cross-pathogen interaction matrices, environmental drivers, and policy control inputs. The study contributes an engineering blueprint for scalable, interpretable multi-disease forecasting architectures suited to resource-constrained environments. Limitations include restricted sample size (n=18) and absence of statistical meta-aggregation.

This study examines recent advancements in hybrid epidemiological modelling aimed at enhancing disease outbreak forecasting. Traditional compartmental models, such as the SEIR framework, have served as foundational tools in understanding infectious disease dynamics; however, their ability to capture the complexity of real-world outbreaks is limited. Recent studies have explored the integration of real-time environmental factors—such as temperature, rainfall, and humidity—and dynamic policy interventions, including lockdowns, social distancing, and vaccination campaigns, to address these limitations. Furthermore, the emergence of machine learning techniques, particularly transformer-based artificial neural networks, has opened new avenues for modelling complex, stochastic patterns inherent in disease transmission. This study highlights the benefits of ensemble approaches that combine mechanistic insights with adaptive learning capabilities. Emphasis is placed on the integration of environmental factors and policy interventions in the development of epidemiological models to improve prediction accuracy. Experimental design results, utilising 2020–2023 Covid-19 data for Zambia, reveal that ensemble-based models incorporating environmental and policy interventions data exhibit superior accuracy of RMSE = 361.1213, MAE = 229 when compared to models oblivious of this data that had an accuracy of RMSE = 843.8066, MAE = 785.2666 and R2 = − 1.8930 at 200 epochs. Based on these results we recommend similar studies focusing on multi-disease models. The study also recommends the development of tools built on such models that can provide useful insights into the impact of policy interventions taking into consideration variable environmental factors to reduce the impact of disease epidemics.

This study investigated seasonal variations in the physico-chemical properties of leachate from the Kupferberg landfill site, examining the influence of landfill age and climatic factors. Data provided by the Windhoek Municipality during the years 2017 to 2022 facilitated the exploration of critical concerns related to groundwater protection and human health. Guided by two research questions and anchored in hypotheses tested at a 95% confidence level, the study employed Statistical Packages for Social Scientists (SPSS) for analysis. Significant temperature variations were observed across years [F (5, 21) = 4.493, p < 0.05], indicating a substantial relationship between landfill age and seasonal variations in leachate physico-chemical properties. Further ANOVA tests revealed temperature trends impacting leachate parameters, such as organic content and pH. While COD displayed a declining trend with landfill age, inorganic components like Cl- and alkalinity exhibited no distinct age-related pattern. Wet seasons demonstrated higher EC and Cl--mean values than dry seasons, correlating with elevated COD levels. The study underscored the incremental rise in parameter values over time and during the rainy season, attributed to solid waste degradation and rainwater percolation. Notably, climatic conditions significantly influenced seasonal variations in leachate physico-chemical properties (p < 0.05). Based on this outcome, null hypothesis 1 was rejected. The second null hypothesis was also rejected because climatic conditions do influence the seasonal variations in physico-chemical properties. These findings are crucial for emphasizing the need for effective leachate management strategies and providing valuable insights for arid regions. Future research can expand on a national scale, employing one-way ANOVA tests on other landfills in Namibia, and engaging local communities for comprehensive data collection on cross-contamination.