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This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.74 W/m² to 149.56 W/m², thereby rendering them suitable for small-scale hybrid applications. A multi-strategy serial cuckoo search algorithm was employed to evaluate three distinct configurations, and the results indicated that the integration of PV, WT, and BS yielded the most cost-effective solution for the majority of regions, achieving a levelized cost of energy of 0.148$/kWh and a loss of power supply probability below 0.05%. Notably, alternative configurations demonstrated superior reliability in locations such as Al-Baha, Taif, and Tabuk. The rseults of this study provide valuable insights into the design of scalable, sustainable, and cost-efficient hybrid MGs tailored to regions with low wind potential, thereby contributing to enhanced energy access and economic development in remote locations.

This study reviews recent developments in optimization techniques for hybrid solar photovoltaic and wind energy systems, particularly those using artificial intelligence (AI) and hybrid algorithms. Due to the global need for sustainable energy, the study compares both traditional and modern optimization techniques. It shows that hybrid algorithms, like, Gray Wolf–Cuckoo Search Optimization (GWCSO), can speed up convergence and reduce costs by up to 25% compared with other conventional methods, such as linear programming. The study groups optimization techniques into traditional, software‐based, AI‐driven, and hybrid approaches; assessing how well they improve system efficiency, reliability, and cost. It also outlines sizing methods and their economic, technical, and environmental effects, with results showing that AI‐driven methods can lower the levelized cost of energy by 10%–15% in complex microgrids (MGs). The study further provides a structured way to size MGs, addressing a gap in optimization methods for independent hybrid systems in remote locations. Greater flexibility of hybrid algorithms in handling complex optimization problems was emphasized. Ultimately, this study offers new insights into combining AI with traditional methods, suggesting future research directions for both smart grid and MG design.

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system’s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Due to the declining supply of fossil fuels, redesigning electricity networks to integrate renewable energy is essential. This project focuses on providing reliable power to the electrical and electronics laboratory at Buea University, Cameroon, by evaluating the technical and economic performance of a grid-tied solar PV (Photovoltaic) system with storage. Total net present cost (TNPC) was used for economic analysis, and mathematical modeling was created in order to apply metaheuristic optimization for system sizing. Techno-economic decision criteria were implemented using MATLAB. Three algorithms were used: Teaching–Learning-Based Optimization (TLBO), Water Cycle Algorithm (WCA), and Improved Grey Wolf Optimization (I-GWO). TLBO performed the best out of all mentioned metaheuristic optimization techniques. The grid-tied solar PV-lithium-ion battery obtained the lowest TNPC of around 3.079798 × 10 5 $. This was determined by comparing the results of the grid-tied solar PV fuel cell (FC) energy storage system to the specified parameters of a LPSP of zero. Furthermore, solar PV-lithium-ion battery system was assessed in on grid mode for cost of energy (COE) in two different circumstances: COE load (only considering connected loads) around 0.32726504 $/kWh and COE total (including connected loads and surplus energy) around 0.12415225 $/kWh.

Background People all over the globe are becoming aware of the benefits associated with tea consumption. The use of Turkey berry ( Solanum torvum ) tea as a natural remedy and functional food high in flavonoids and other phytochemicals may improve bodily function, as well as help attain blood pressure control. Objectives This pilot study sought to assess the effect of Turkey berry ( Solanum torvum ) tea consumption on cardio-metabolic indices in people living with hypertension in Ghana. We hypothesized that Turkey berry ( Solanum torvum ) tea may improve blood pressure outcomes in people living with hypertension. Methods In this pre-post interventional pilot study, thirty (30) adults, constituting 27 females and 3 males aged 18 years and above with hypertension, were recruited from the Mampong Government Hospital Chronic Care Center in the Ashanti Region of Ghana. Following a baseline nutritional and body composition assessment (body mass index, visceral fat, body fat, and muscle mass), blood pressure readings, and lipid profile assessments, the participants received 30 bags of Turkey berry ( Solanum torvum ) tea that was consumed by each participant for 1 month (4 weeks), followed by an endline nutrition and body composition assessment, blood pressure readings, lipid profile assessment, and the assessment of Turkey berry tea compliance rate. Nutritional status was assessed using the Body Mass Index (BMI) based on the WHO cut-offs. Results The mean values of total cholesterol (TC) levels of participants improved significantly after the consumption of the Turkey berry tea from 5.20 ± 1.24 mmol/L to 2.13 ± 0.86 mmol/L ( p  < 0.001). High-Density Lipoprotein (HDL) levels of participants improved after the 30 days of Solanum torvum tea intake from 0.86 ± 0.24 mmol/L to 2.63 ± 0.76 mmol/L ( p  < 0.001). Low-density lipoprotein (LDL) levels of participants also improved from 2.39 ± 0.91 mmol/L to 1.53 ± 0.77 mmol/L ( p  < 0.001). The mean Triglycerides (TRIG) levels of participants, however, increased from 1.13 ± 0.43 mmol/L to 2.47 ± 0.77 mmol/L ( p  < 0.001). Cardiovascular Heart Disease Risk (CHDR) of the participants improved from 15.83 ± 11.04% to 11.96 ± 7.80% ( p  = 0.073). There was no significant change in the systolic blood pressure (SBP) ( p  = 0.1015), the diastolic blood pressure (DBP) ( p  = 0.485), the BMI ( p  = 0.47), the percentage body fat (% BF) ( p  = 0.26), or the visceral fat (VF) ( p  = 0.152) of the participants. Muscle mass (MM) of the participants also decreased from 27.3 to 25.3 ( p  = 0.008). Conclusion There was an improvement in the mean values of the lipid profile and a slight decrease in DBP of the participants, which supports the potential therapeutic benefits of Turkey berry tea consumption in providing cardiovascular health benefits.

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Human immunodeficiency virus (HIV) and tuberculosis (TB) have long been known to have a synergistic relationship. This is a result of each of the diseases impacting negatively on the immune system of the infected persons. The impact of these diseases on workforce productivity is studied in this paper from the viewpoint of dynamical systems. In this paper, we present a nonlinear ordinary differential equation model to study the dynamics of HIV-TB co-infection and its effect on workforce productivity. The main model is first decoupled into two basic submodels of HIV-only and TB-only models, whose qualitative properties are presented before the qualitative properties of the main model are studied. While the HIV-only model is shown to have a globally asymptotically stable disease-free equilibrium whenever its basic reproduction number is less than unity, the TB-only model is shown to exhibit backward bifurcation under some conditions. To investigate the impact of various intervention strategies on the control of the co-infection and improvement of workforce productivity, five time-dependent controls (involving transmission prevention for the two diseases, therapy for the two diseases, and capacity building for improved workforce productivity) are incorporated into the basic model to form an optimal control problem, which is qualitatively analyzed using Pontryagin’s maximum principle and numerically simulated. Incremental cost-effectiveness analysis is conducted with the results of the numerical simulations. It is observed that the most cost-effective strategy for fighting the spread of the co-infection with enhanced productivity is that of combining both preventative and curative measures along with skills training.

The African Union Bureau of Heads of State and Government endorsed the COVID-19 Vaccine Development and Access Strategy to vaccinate at least 60% of each country’s population with a safe and efficacious vaccine by 2022, to achieve the population-level immunity needed to bring the pandemic under control. Using publicly available, country-level population estimates and COVID-19 vaccination data, we provide unique insights into the uptake trends of COVID-19 vaccinations in the 15 countries that comprise the Economic Community of West Africa States (ECOWAS). Based on the vaccination rates in the ECOWAS region after three months of commencing COVID-19 vaccinations, we provide a projection of the trajectory and speed of vaccination needed to achieve a COVID-19 vaccination coverage rate of at least 60% of the total ECOWAS population. After three months of the deployment of COVID-19 vaccines across the ECOWAS countries, only 0.27% of the region’s total population had been fully vaccinated. If ECOWAS countries follow this trajectory, the sub-region will have less than 1.6% of the total population fully vaccinated after 18 months of vaccine deployment. Our projection shows that to achieve a COVID-19 vaccination coverage of at least 60% of the total population in the ECOWAS sub-region after 9, 12 and 18 months of vaccine deployment; the speed of vaccination must be increased to 10, 7 and 4 times the current trajectory, respectively. West African governments must deploy contextually relevant and culturally acceptable strategies for COVID-19 vaccine procurements, distributions and implementations in order to achieve reasonable coverage and save lives, sooner rather than later.

A deterministic model is proposed to describe the transmission dynamics of coronavirus infection with early interventions. Epidemiological studies have employed modeling to unravel knowledge that transformed the lives of families, communities, nations and the entire globe. The study established the stability of both disease free and endemic equilibria. Stability occurs when the reproduction number, R0, is less than unity for both disease free and endemic equilibrium points. The global stability of the disease-free equilibrium point of the model is established whenever the basic reproduction number R0 is less than or equal to unity. The reproduction number is also shown to be directly related to the transmission probability (β), rate at which latently infected individuals join the infected class (δ) and rate of recruitment (Λ). It is inversely related to natural death rate (μ), rate of early treatment (τ 1), rate of hospitalization of infected individuals (θ) and Covid-induced death rate (σ). The analytical results established are confirmed by numerical simulation of the model.