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This study contributes to existing research on how to integrate Artificial intelligence (AI) into school systems globally. This research explores in-service teachers' preparedness for integrating artificial intelligence into schools. We conducted this research within the context of the South African school system with teachers of various specializations, including sciences, social Sciences, mathematics, and languages. Drawing on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), we gathered teachers' perspectives through eight variables of technology integration, social influence, AI ethics, attitudes, TPACK, perceived self-efficacy, AI professional development, and AI preparedness. To analyze the 430 teachers' data involved in this study, we used a structural equation modeling analytical approach with SmartPLS software version 4.1.0.0. Our results indicate that technology integration, social influence, attitudes, and perceived self-efficacy influence teachers’ preparedness for AI. However, TPACK and ethics do not influence preparing teachers to integrate AI into schools. This study further presents interesting insight based on the mediation and moderation analysis of the variables. We discuss our findings and highlight their implications for practice and policy.

This study examined the impact of a pedagogy that is culturally influenced and laced with technological and contextual elements known as the culturo-techno-contextual approach (CTCA) on promoting knowledge retention of biology concepts. The research design was mixed methods, and the sample consisted of 88 senior secondary school II students selected from two Lagos State educational district V schools in Nigeria. Quantitative data was collected using the Variation and Evolution Achievement Test with a reliability value of 0.79, while qualitative data was collected using the Students' Perception of CTCA Interview Guide. All students in the two sample schools used for the experimental and control groups took a pretest before treatment, a posttest at the end of the treatment phase, and a retention test four weeks later. The ANCOVA output demonstrated a statistically significant difference in knowledge retention [F (1,85) = 134.50; p < 0.05]. This suggests that experimental students retained more.

The study investigated the potency of ethnoscience instruction on the critical thinking ability of biology students. The study employed a quasi-experimental research design, with a total of 113 senior secondary school two (the equivalent of grade 11) students from two schools in educational district V of Lagos State, Nigeria. All students in the two sample schools used for the experimental and control groups took a pretest before treatment, a posttest at the end of the treatment phase. Test of Critical Thinking in Photosynthesis (TCTP) with a reliability of 0.76 was used for data collection. The ANCOVA output demonstrated a statistically significant difference in favor of the experimental group (F (1, 110) = 14.79; p < 0.05). Ethnoscience Instruction had no differential impact on students in the experimental group based on gender. We concluded that ethnoscience instruction is a viable culturally relevant tool for improving critical thinking skill in biology.

Nearly all deformed materials possess both elastic and viscous properties through concurrent storage and dissipation of mechanical vitality. This type of novel functional fluid undergoing deformation is crucial in describing hydrodynamically the general behavior of the fluid. Thus, the working fluid carries memory attribute stored in the particles. The fractional order model for circumferentially pressure-driven flow of viscous fluid in an annulus with momentum slip is investigated theoretically. In particular, an exponentially decaying/growing pressure is considered. For the purpose of this study, the Atangana–Baleanu fractional order model is employed. A solution approach to the fractionalized model is by utilizing Laplace transformation and Tzou’s algorithm for numerical Laplace inversion. It was found that a decisive factor in boosting the speed of flow is suppressing the memory effect of the fluid particle. Also, the instability of the flow can be controlled by enhancing the memory effect.

In this study, a mathematical model for studying the dynamics of monkeypox virus transmission with non-pharmaceutical intervention is created, examined, and simulated using real-time data. Positiveness, invariance, and boundedness of the solutions are thus examined as fundamental features of mathematical models. The equilibrium points and the prerequisites for their stability are achieved. The basic reproduction number and thus the virus transmission coefficient ℜ0 were determined and quantitatively used to study the global stability of the model's steady state. Furthermore, this study considered the sensitivity analysis of the parameters according to ℜ0. The most sensitive variables that are important for infection control are determined using the normalized forward sensitivity index. Data from the United Kingdom collected between May and August 2022, which also aid in demonstrating the usefulness and practical application of the model to the spread of the disease in the United Kingdom, were used. In addition, using the Caputo–Fabrizio operator, Krasnoselskii's fixed point theorem has been used to analyze the existence and uniqueness of the solutions to the suggested model. The numerical simulations are presented to assess the system dynamic behavior. More vulnerability was observed when monkeypox virus cases first appeared recently as a result of numerical calculations. We advise the policymakers to consider these elements to control monkeypox transmission. Based on these findings, we hypothesized that another control parameter could be the memory index or fractional order.

The COVID-19 pandemic has had a significant impact on countries worldwide, including the United Kingdom (UK). The UK has faced numerous challenges, but its response, including the rapid vaccination campaign, has been noteworthy. While progress has been made, the study of the pandemic is important to enable us to properly prepare for future epidemics. Collaboration, vigilance, and continued adherence to public health measures will be crucial in navigating the path to recovery and building resilience for the future. In this article, we propose an overview of the COVID-19 situation in the UK using both mathematical (a nonlinear differential equation model) and statistical (time series modeling on a moving window) models on the transmission dynamics of the COVID-19 virus from the beginning of the pandemic up until July 2022. This is achieved by integrating a hybrid model and daily empirical case and death data from the UK. We partition this dataset into before and after vaccination started in the UK to understand the influence of vaccination on disease dynamics. We used the mathematical model to present some mathematical analyses and the calculation of the basic reproduction number (R0). Following the sensitivity analysis index, we deduce that an increase in the rate of vaccination will decrease R0. Also, the model was fitted to the data from the UK to validate the mathematical model with real data, and we used the data to calculate time-varying R0. The homotopy perturbation method (HPM) was used for the numerical simulation to demonstrate the dynamics of the disease with varying parameters and the importance of vaccination. Furthermore, we used statistical modeling to validate our model by performing principal component analysis (PCA) to predict the evolution of the spread of the COVID-19 outbreak in the UK on some statistical predictor indicators from time series modeling on a 14-day moving window for detecting which of these indicators capture the dynamics of the disease spread across the epidemic curve. The results of the PCA, the index of dispersion, the fitted mathematical model, and the mathematical model simulation are all in agreement with the dynamics of the disease in the UK before and after vaccination started. Conclusively, our approach has been able to capture the dynamics of the pandemic at different phases of the disease outbreak, and the result presented will be useful to understand the evolution of the disease in the UK and future and emerging epidemics.

A co-infection model for onchocerciasis and Lassa fever (OLF) with periodic variational vectors and optimal control is studied and analyzed to assess the impact of controls against incidence infections. The model is qualitatively examined in order to evaluate its asymptotic behavior in relation to the equilibria. Employing a Lyapunov function, we demonstrated that the disease-free equilibrium (DFE) is globally asymptotically stable; that is, the related basic reproduction number is less than unity. When it is bigger than one, we use a suitable nonlinear Lyapunov function to demonstrate the existence of a globally asymptotically stable endemic equilibrium (EE). Furthermore, the necessary conditions for the presence of optimum control and the optimality system for the co-infection model are established using Pontryagin’s maximum principle. The model is quantitatively analyzed by studying how sensitive the basic reproduction number is to the model parameters and the model simulation using Runge–Kutta technique of order 4 is also presented to study the effects of the treatments. We deduced from the quantitative analysis that, if there is an effective treatment and diagnosis of those exposed to and infected with the disease, the spread of the viral disease can be effectively managed. The results presented in this work will be useful for the proper mitigation of the disease.

The transmission dynamics of onchocerciasis in two interacting populations are examined using a deterministic compartmental model with nonlinear incidence functions. The model undergoes qualitative analysis to examine how it behaves near disease-free equilibrium (DFE) and endemic equilibrium. Using the Lyapunov function, it is demonstrated that the DFE is globally stable when the threshold parameter R0≤1 is taken into account. When R0>1, it suffices to show globally how asymptotically stable the endemic equilibrium is and its existence. We conduct the bifurcation analysis by looking at the possibility of the model’s equilibria coexisting at R0<1 but near R0=1 using the Center Manifold Theory. We use the sensitivity analysis method to understand how some parameters influence the R0, hence the transmission and mitigation of the disease dynamics. Furthermore, we simulate the model developed numerically to understand the population dynamics. The outcome presented in this article offers valuable understanding of the transmission dynamics of onchocerciasis, specifically in the context of two populations that interact with each other, considering the presence of nonlinear incidence.

Objective: The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. Methods: Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. Results: This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. Conclusion: This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.