Species augmentation is one of the methods used to promote biodiversity and prevent endangered species loss and extinction. The current work applies discrete-time optimal control theory to two models of species augmentation for predator-prey relationships. In discrete-time models, the order in which events occur can give different qualitative results. Two models representing different orders of events of optimal augmentation timing are considered. In one model, the population grows and predator-prey action occurs before the translocation of reserve species for augmentation. In the second model, the augmentation happens first and is followed by growth and then predator-prey action. The reserve and target populations are subjected to strong Allee effects. The optimal augmentation models employed in this work aim to maximize the prey (target population) and reserve population at the final time and minimize the associated cost at each time step. Numerical simulations in the two models are conducted using the discrete version of the forward-backward sweep method and the sequential quadratic programming iterative method, respectively. The simulation results show different population levels in the two models under varying parameter scenarios. Objective functional values showing percentage increases with optimal controls are calculated for each simulation. Different optimal augmentation strategies for the two orders of events are discussed. This work represents the first optimal augmentation results for models incorporating the predator-prey relationship with discrete events.

Multimodal music information retrieval has gained significant momentum, producing a wide range of datasets for machine learning and deep learning tasks. However, these resources remain overwhelmingly skewed toward high-resource languages and Western musical traditions. To address this gap and support more culturally inclusive artificial intelligence, this article introduces \textit{Ayoo}, a multilingual and multimodal music information retrieval dataset derived from Ghana Music Awards videos. The dataset captures the linguistic and cultural diversity of Ghana across textual, visual, and audio modalities. Extracted from historical and contemporary award-winning performances spanning 1999 to 2023, Ayoo provides a structured foundation for downstream tasks such as genre classification, music sentiment analysis, emotion recognition, lyric--audio alignment, and culturally aware recommendation systems in low-resource multilingual settings. The article describes the dataset construction pipeline, annotation process, statistical properties, benchmarking applications, and principal limitations. Ayoo contributes a culturally grounded benchmark for music information retrieval and offers a reproducible framework for computational work on African music traditions. he source code and datasets have been made open access in a GitHub repository https://github.com/CAIRGH/Ayoo-Dataset

Soil erosion threatens agricultural sustainability and food security worldwide. Yet, the independent contributions of shoot canopies versus root systems to erosion control are rarely partitioned experimentally, and multifunctional crops are seldom benchmarked against specialised erosion-control species under identical conditions. This study addressed both gaps using controlled rainfall simulation (27.3 mm h−1, 15° slope) on sandy loam soil to compare unplanted controls, root-only, and intact shoot-root treatments of Napier grass (Cenchrus purpureus (Schumach.) Morrone, syn. Pennisetum purpureum Schumach.), a species native to sub-Saharan Africa and Vetiver grass (Chrysopogon zizanioides (L.) Roberty), introduced from India. Both species reduced erosion by >94% relative to unplanted controls, with no significant difference between species. Canopy retention significantly increased the erosion-reduction efficiency beyond that of root-only treatments. Soil moisture data showed that this canopy effect coincided with what is likely transpiration-associated moisture depletion in Napier (p < 0.001) but not in Vetiver (p = 0.116), consistent with Napier's substantially greater shoot biomass. Root architectural traits provided a mechanistic basis for erosion performance: total root length was a significant predictor of erosion-reduction efficiency, and root area was a significant predictor of near-surface soil shear strength, a dynamic index of detachability. Despite a 7.8-fold difference in initial planting units between species, reflecting species-appropriate field establishment rather than deliberate experimental design, both species achieved statistically comparable erosion control. Napier grass additionally exhibited higher root tensile strength and soil reinforcement. These findings indicate that canopy retention is a primary determinant of erosion-control performance regardless of species, and that Napier grass offers a viable multifunctional alternative to the conventional default reliance on Vetiver, combining erosion control with fodder production.

Precision oncology relies heavily on genomic profiling and artificial intelligence to predict therapeutic response in breast cancer. However, in low-to-middle-income countries (LMICs), these expensive modalities are inaccessible; forcing clinicians to rely on qualitative TNM staging that often fails to capture individual tumour heterogeneity. There is a critical unmet need fory “frugal innovation”—tools that convert standard histopathological data into quantitative prognostic scores. This study proposes a novel deterministic scalar mathematical model to quantify tumour resistance and predict therapeutic efficacy without high-cost infrastructure. We developed a theoretical framework that transforms qualitative pathological inputs (TNM stage, Grade, and Ki67 status) into quantitative scalar variables. The model is anchored on three derived parameters: (1) Relative Severity of Disease (RSD), a multiplier based on stage-specific survival decay; (2) a graded Tumour Proliferation Index (Ki67); and (3) the Tumour-Dependent Therapeutic Response Coefficient (TmdRxResCoef). Resistance (Res) was modelled as the product of mass (RSD) and velocity (Ki67score), while therapeutic responsiveness was defined as the inverse of resistance (1/Res). The model demonstrates a non-linear, inverse relationship between tumour burden and the potential for therapeutic gain. Crucially, the model exposed a “biological equivalence” between Stage I/High-Grade tumours (Resistance Score = 4.0) and Stage IV/Indolent tumours (Resistance Score = 3.8), challenging the dogma that anatomical stage is the sole driver of prognosis. We successfully quantified the “Resistance Gap,” demonstrating that a high-velocity early-stage tumour yields a TmdRxResCoef of <25%, mathematically defining a requirement for aggressive systemic therapy despite small anatomical size. Conversely, the model identified indolent metastatic phenotypes with stable resistance profiles suitable for de-escalated management. This scalar model bridges the gap between basic histopathology and precision medicine. By providing a mathematically transparent, calculator-ready method for quantifying tumour resistance, it empowers clinicians in resource-limited settings to make evidence-based decisions on treatment escalation or de-escalation. This framework offers a rigorous, low-cost alternative to genomic profiling and provides a scalable, mathematically transparent scaffold for future AI integration in oncology. .

In this article, we construct and analyse a stochastic mathematical model to study the co-infection dynamics of malaria and COVID-19 in a population. We derive the basic reproduction number associated with the disease-free equilibrium of the stochastic system using a Lyapunov function and establish conditions for its stability. Specifically, we calculate the threshold parameters for malaria-only $ (\mathcal {R}_{0m}<^>s), $ (R0ms), COVID-19-only $ (\mathcal {R}_{0c}<^>s) $ (R0cs), and co-infection $ (\mathcal {R}_{0mc}<^>s), $ (R0mcs), models at the disease-free equilibrium using the next-generation matrix method. We further determine the conditions for stochastic stability in malaria-only, COVID-19-only, and co-infection scenarios. Moreover, we investigate the sufficient conditions for disease extinction and persistence based on the reproductive numbers. Finally, we utilize the Euler-Maruyama numerical scheme to simulate the co-infection dynamics and support the theoretical findings.

Stochastic differential equations (SDEs) driven by Gaussian noise have proven effective for studying the dynamics of river basin discharges, while accounting for uncertainties inherent in rainfall–runoff systems. However, these uncertainties clearly exhibit many non-Gaussian characteristics, necessitating the use of more complex noises to model various levels of variability and anomalies in river basin discharges, ultimately enhancing the assessment of extreme hydrological risks. This paper considers uncertainties in rainfall–runoff systems by developing a Langevin-type SDE driven by non-Gaussian α -stable Lévy noises. The different methods’ applicability is demonstrated on the Ouémé at Bonou river basin, Benin. The SDE model parameters were estimated through a developed heuristic method based on a Monte Carlo simulation approach. To access extreme hydrological event risks, the equivalent fractional Fokker–Planck equation (FFPE) was derived and solved numerically using an adaptive finite difference method. The results showed that Lévy stable noises better capture uncertainties in rainfall–runoff systems, highlighting anomalous diffusion in daily river basin discharges. The SDE and FFPE model solutions were consistent, aligning with actual observations, and the risks of extreme events were evaluated in terms of daily and cumulative probabilities.

Air quality is a significant public health issue, and accurate predictions of the Air Quality Index (AQI) are crucial for timely interventions. This study explores the use of supervised machine learning algorithms to forecast AQI across different neighborhoods in Accra, Ghana. Six models including Random Forest , CatBoost, Support Vector Regression (SVR), Linear Regression, Ridge and Lasso Regressions, were evaluated. Data from the Breathe Accra platform, encompassing pollutants and weather conditions in five neighborhoods, were preprocessed through data cleaning, feature selection, and normalization. Model performance was assessed using the coefficient of determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). While the initial performance of the models was less satisfactory, the incorporation of wavelet transform preprocessing significantly enhanced the results. This improvement was particularly notable for the area of Korle Bu, where the CatBoost model's R2 increased from 0.32 to 0.58, RMSE decreased from 29.03 to 22.79, and MAE dropped from 22.11 to 17.01. On average, all models except SVR performed well across all areas, as evidenced by the evaluation metrics. These findings have direct implications for enhancing air quality management and policymaking in the city of Accra, where accurate AQI predictions are vital for effective public health interventions and environmental planning.

Food security is a vital aspect of the United Nations’ Sustainable Development Goals (SDGs) which aims to promote sustainable farming in the world. Farming-driven economies such as Ghana are faced with challenges due to plant diseases. Cacao, a vital crop in Ghana is severely impacted with diseases which affect its yield and decrease exports revenue through reduced exports. Leveraging deep learning techniques offers an effective solution for early detection of diseases in cacao plants. This study adopts a comprehensive approach, starting with an Exploratory Data Analysis (EDA) of the dataset containing images of both healthy and diseased cacao plants from Ghanaian farms. Using exploratory data analysis (EDA), we can identify patterns and understand the characteristics of the dataset, laying a solid foundation for developing robust machine learning models tailored to the specific challenges faced by Ghanaian cacao farmers. Our approach involves developing and evaluating deep learning models to detect and classify cacao plant diseases. These models are designed with the Predictability, Compatibility, and Stability (PCS) framework in mind, ensuring reliability and effectiveness in disease detection. The custom convolution neural network (CNN) model outperformed other models considered in experimental analysis. This study aims to revolutionize cacao farming through precise, stable, and ethical deep learning solutions, ultimately enhancing crop resilience, productivity, and the livelihood of Ghanaian farmers.

Disease attacks on crops like maize pose a significant threat to the global food supply chain in Africa, particularly in West Africa. Maize is a staple food source and the economic backbone of the population and farmers in West Africa. In recent years, maize yields have declined due to diseases. Systematic solutions, such as visual inspection through laboratory experiments for disease diagnosis, have not led to a significant improvement in production or ensured sustainable food security in Africa. In response to this challenge, we introduce a lightweight deep-learning ensemble model for early disease detection in maize plants. The study focuses on developing and validating a model specifically designed to identify and classify diseases in maize plants. We use computer vision technology to capture intricate patterns in maize leaf images. The model is trained to recognise six classes, with five representing different diseases and one representing a healthy state. In this paper, we explore the amalgamation of Residual Network (ResNet9) and Efficient-Net-b4 (ENetb4) as a pre-training model built on convolutional neural networks (CNN) to improve accuracy and robustness in maize disease detection and prediction. The results of the study indicate significant opportunities for improvement in agricultural technology in West Africa. For instance, the ResNet9 model accurately identified diseased images of maize crops with a performance accuracy of 98.2%, while the (ENetb4) model achieved a performance accuracy of 94.3% in the same task.

Diabetes mellitus has become a global health threat as well as a financial burden. According to the International Diabetes Federation (IDF) Atlas (10th edition, 2021), approximately 537 million adults live with diabetes globally, which is anticipated to rise to 643 million in 2030 and 783 million by 2045. The report shows 6.7 million deaths due to diabetes in 2021 (1 every 5 s) and health expenditure of at least 966 billion USD (316% increase over the past 15 years). This research focuses on mathematical modeling and analysis of diabetes mellitus using deterministic and stochastic models. The study is conducted without considering genetic factors. First, we construct a deterministic diabetes mellitus model and transform it into a stochastic model by incorporating Brownian motions and stochastic environmental factor intensities. We provide qualitative results for both models, including the positivity of the solution, equilibrium points, basic reproduction numbers, local stability results, and sensitivity analysis. We show that the disease-free equilibrium is locally asymptotically stable via the Routh–Hurwitz criterion. Again, the sensitivity analysis result indicates that the transmission and birth parameters at a given period have a significant role in the increase of diabetes mellitus in the population if their values increase. We further establish the existence and uniqueness of the global positive solution by employing the random Lyapunov function theory. Using the Milstein method, the numerical scheme for the stochastic model is presented, and the approximate solution using the scheme is discussed. Additionally, we simulate the dynamics of the deterministic model using the Euler–Maruyama method. The simulation results indicate that by prioritizing policies aimed at minimizing exposure to diabetes mellitus, the strain on healthcare systems can be alleviated, leading to reduced hospitalization rates and enhanced quality of life for individuals.

Multimodal music information retrieval (MIR) has gained much significance and there exists a plethora of datasets in different formats as well as machine learning and deep learning models built on these datasets for MIR tasks. However, these datasets are mostly found in high-resourced languages making the models biased to these cultures and languages. To bridge this gap, we curate a novel multimodal music information retrieval dataset that brings to bear the Akan culture and language. This Akan MIR music video dataset comprises of the different modalities, textual, visual and audio used for MIR applications such as music sentiment analysis, genre classification, emotion recognition and recommender systems. The curation of this datasets includes selecting music videos in different genres that reflect the diverse culture and language of the Akans, downloading them to extract the different modalities for MIR tasks. Python scripts were written to extract these relevant modalities. Indigenous Akan speakers well versed in the content and context of these songs and the culture and traditions labeled and translated the videos to make it easier for machine learning tasks. The source code and datasets have been made open access in a GitHub repository https://github.com/CAIRGH/Multimodal-Twi-Music.

Mathematical models of endangered competitive interactions incorporating the Allee effect with augmentation strategies have not been studied extensively. This area is however critical to ecologists since it relates to ways species can become endangered and possibly go extinct due to competition for limited resources. More importantly, the climatic change with its adverse effects has not only affected green forests but has also caused the extinction of some species. Thus, there is a need for critical augmentation strategies to safeguard such species. This paper, therefore, presents an optimal control strategy fora continuous time competition interaction model with strong Allee effects. We seek to maximize the target species at the end of each final time. We consider two objective functionals involving the populations and the cost of the controls. Using Pontryagin's Maximum Principle, we obtain the optimal control characterizations. We perform numerical simulations using the forward-backward sweep method and the approximate solutions are presented and discussed. Since there is a cost involved in the translocation of the reserve species, we adopt a minimization cost strategy. In addition, we compute the objective functional values for each simulation.

Many species are classified as threatened or endangered and are declining due to factors such as competition for limited resources. Competition affects the fitness of both populations as one organism's use of a scarce resource decreases its availability to the other. In this study, a discrete-time competition interaction model with controls is formulated. We consider four populations represented by a discrete competition model with strong Allee effects. We define two objective functionals that account for linear and nonlinear representations of the translocation costs of the reserve populations at each time step. The objective functionals seek to maximize the populations and minimize the cost of augmentation. We employ the generalization of Pontryagin's Maximum Principle for the optimal control of discrete-time state systems to obtain the necessary conditions. The discrete version of the forward-backward sweep method is employed to solve the optimality system numerically. The short-term and long-term qualitative dynamics of the model are discussed through the numerical simulations. Objective functional values indicating a percentage increase with optimal controls are calculated for each plot. The numerical simulation examines various scenarios, including the effects of cost constants, competition coefficients, and augmentation coefficients in the model.

Buruli Ulcer, a devastating skin disease caused by Mycobacterium Ulcerans, poses considerable public health challenges in endemic areas. This article focuses on the use of fractional optimal control theory to prevent the spread of Buruli ulcers via integrated public health interventions. We formulated a mathematical model using the Atangana-Baleanu-Caputo fractional order derivative operator. We investigated the model's existence and uniqueness and presented numerical simulations using the predict-evaluate-correct-evaluate (PECE) method of Adam-Bashforth Moulton. We also study the fractional optimal control problem (FOCP) to minimize the spread of the disease in the endemic regions. We employ the Fractional Pontryagin's Maximum Principle (FPMP) and implement the forward-backward method to determine the extremals of the problem. Four control strategies were implemented: promoting health education on the use of protective clothing, enhancing vaccination rates, improving treatment protocols for infected individuals, and spraying insecticides to reduce water-bug populations. After examining the optimal control dynamics of the Buruli ulcer transmission model via multiple simulations with and without control, we discover that there is a substantial decrease in the population of infected humans and the water-bug population. Hence we conclude that the best strategy to implement is by applying all the control strategies suggested.

Monkeypox is a rare zoonotic disease similar to smallpox though less severe. The 2022 Monkeypox disease presented a great threat to global health as outbreaks spread mostly to countries in which Monkeypox was non-endemic. This study presents a new mathematical model with the effect of environmental and direct transmission of the Monkeypox virus in human and rodent populations. Monkeypox-Free and Monkeypox-Endemic equilibrium points are established. The basic reproduction number ℝ_0 , is derived and used to predict the future of the disease. This led to the findings: Monkeypox-Free equilibrium is globally asymptotically stable whenever ℝ_0 ≤ 1 . Infected rodents’ only endemic equilibrium point is globally asymptotically stable whenever ℝ_0n^r >1 and ℝ_0h <1 . The infected humans only endemic equilibrium point is globally asymptotically stable whenever ℝ_0n^r <1 and ℝ_0h >1 , as confirmed by Lyapunov’s method and LaSalle’s invariant principle. Additionally, five time-dependent control variables to reduce the disease are adopted. The existence of optimal control is established and using Pontryagin’s maximum principle, the optimality conditions are derived. Runge-Kutta fourth-order schemes forward and backward in time are used to obtain the solutions of the state and co-state systems respectively. Numerical simulations showing different combinations of control variables in reducing Monkeypox infection are presented. The findings of this study give useful measures for health practitioners and policymakers to implement effective and optimal control ways to curtail the Monkeypox outbreak.

Unemployment is a major problem worldwide and is one of the key factors determining a nation’s economic status. The issue of unemployment is made more difficult globally by the ongoing rise in labor force participation and the scarcity of job positions. In this work, we study the unemployment model with two distinct fractional-order derivatives: the Caputo operator and the Atangana–Baleanu operator in the sense of Caputo (ABC). These derivatives under consideration are the operators widely utilized in modeling real-world phenomena in fractional dynamics. The existence and uniqueness of the solutions to the fractional model under consideration are ascertained using the fixed-point theory. The Hyers-Ulam analysis is employed to determine stability. For the numerical results, we present an Adams-type predictor–corrector (PC) technique for Caputo derivative and an extended Adams Bashforth (ABM) method for Atangana–Baleanu derivative. The outcomes achieved with the Atangana–Baleanu–Caputo and Caputo derivatives are identical to those of the regular case when fractional order ν=1.00. However, the results obtained change slightly as fractional order assumes values smaller than one, and this variation becomes most noticeable when the fractional order ν<0.72. This is because of the fractional derivative definitions’ underlying kernel. It is shown that the Mittag–Leffler kernel derivative provides better results for smaller fractional orders.

This study presents a deterministic mathematical model of monkeypox disease transmission dynamics with an age-structured human population divided into two subgroups of children (Group 1) and adults (Group 2). Two equilibrium points, monkeypox-free, E 0 , and a unique monkeypox-endemic equilibrium point, E 1 , are established. The age-structured monkeypox basic reproduction number, ℝ 0 , is computed using the next-generation matrix approach and established to be ℝ 0 = 1.2448. The Lyapunov functions are constructed; together with LaSalle’s invariance principle, the monkeypox-free equilibrium point, E 0 , is established to be globally asymptotically stable whenever ℝ 0 ≤ 1, as confirmed by the Lyapunov stability method, and the monkeypox-endemic equilibrium point, E 1 , is globally asymptotically stable whenever ℝ 0 > 1. Sensitivity analysis of the threshold quantity ℝ 0 is performed using the Latin hypercube sampling method and Pearson’s partial rank correlation coefficient, and the results showed that the parameters β 11 , β 22 , μ , ν 1 , Λ 1 , ν 2 were the most sensitive to the spread of monkeypox infection in an age-structured population. It is, therefore, suggested that the rate of monkeypox infection can be reduced by ensuring that the rate of interaction between susceptible children and infected children and between susceptible adults and infected adults is minimized. Moreover, the spread of monkeypox infection can be curbed by emphasizing controls such as early diagnosis and treatment and hospitalization of critically ill infectives.

The human papillomavirus (HPV) is a common sexually transmitted infection and a leading cause of cervical cancer. The yearly hospitalization rate for diseases linked to HPV is alarming. However, the mathematical study of HPV disease using fractal–fractional derivatives has received less attention from researchers globally. In this study, we develop a compartmental model of HPV transmission dynamics that includes a hospitalized compartment. We investigate the dynamics of our model equations through fractal and fractional analysis using the Mittag-Leffler law. The fractional order enables us to capture the memory effects, and the fractal dimension helps to capture self-similarities in the HPV model. The fixed point theorem is employed to establish the existence and uniqueness of solutions for the proposed fractal–fractional model. We conduct a stability analysis utilizing Hyers-Ulam criteria to demonstrate that the model’s equation exhibits robust and stable behavior. A novel numerical scheme is discussed and the numerical simulations are conducted using this proposed scheme. The simulation results reveal that the fractal dimension and fractional order significantly influence the dynamics of the HPV fractal–fractional model. Both factors substantially affect the model’s trajectories, as demonstrated by numerical simulations. Moreover, higher fractional order and fractal dimension values lead to a decrease in individuals across the different compartments as the simulation progresses. The numerical simulations illustrate the need to employ fractal–fractional derivatives in studying infectious diseases like HPV. For researchers and other interested stakeholders, we recommend modeling the coinfection of HPV and other sexually transmitted diseases in future extensions.

This article studies the action of predators and the predator-dependent functional response in the fishery resource model. We employ the Atangana-Baleanu-Caputo fractional derivative to study the proposed fractional fishery resource model in the presence of predators with Crowley- Martin functional response. We present a theoretical and numerical analysis of the governing nonlinear differential equations of the model consisting of the biomass density of the fish population inside the unrestricted fishing zone, the biomass density of the fish population inside the reserve or restricted fishing zone, and the predator population. Using the fixed point theory and nonlinear analysis, we establish the existence and uniqueness results of the proposed ABC fractional fishery resource model. We establish stability analysis of the fishery model using the Ulam-Hyers stability approach. The numerical scheme of the fractional Adams-Bashforth method is provided and the approximate solutions for the model under consideration are given and discussed. We observe that an increase in the fish capturing rates increases the size of the predator population and reduces the fish subpopulations. To maintain a high number of fish species, we recommend a control measure to reduce the fish capturing rate by the predators.

The advent of social media (SM) platforms has transformed communications, information dissemination, and interpersonal relationships on a global scale. As SM continues to evolve and proliferate, its impact on various aspects of society has become increasingly complex and multifaceted. For this reason and over the past decades, several controversies have been held about whether SM is good or bad. However, the mathematical modeling technique inculcating SM impacts (positive and negative) has not been studied in the existing works. This article considers a mathematical model approach using the ABC-fractional derivative technique to study the dynamics of SM impacts. We provide the various definitions and the properties needed to study the model. Also, we use the fixed point theorem and a nonlinear analytic approach to demonstrate the theoretical solutions of the existence of solutions for the proposed model. Furthermore, the fundamental reproduction number is computed, and the stability of the model is achieved using the Ulam–Hyers (HU) criteria. We again perform a sensitivity study for the SM impact model and the effects of the sensitive parameters are presented in 3D and contour plots. In addition, a numerical algorithm of the predictor–corrector type of the Adams–Bashforth method for determining the approximate solution of the model is developed and the results are discussed. The effects of the most sensitive parameters on affected individuals in the model with a constant fractional order are shown and discussed. The simulation results indicate that as individuals become aware of the negative impacts of SM, the number of positively impacted individuals rises.