Pub Date : 2026-03-01Epub Date: 2026-01-02DOI: 10.1016/j.sciaf.2025.e03161
Jeremiah January , Gasper Mwanga , Isack E. Kibona , Nyimvua Shaban Mbare
<div><div>An optimal control model for rotavirus transmission was formulated to minimize both the cost of implementing interventions and the burden of infection among children and caregivers. The model integrates five time-dependent control functions: vaccination of children (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), public health education (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>), treatment of infected children (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span>), water treatment and sanitation (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span>), and hygiene promotion (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>). Pontryagin’s Maximum Principle was applied to derive the necessary conditions for optimality, and numerical simulations were conducted using the Runge–Kutta method to determine the optimal time-dependent control profiles and corresponding epidemiological outcomes. Simulation results at <span><math><mrow><mi>t</mi><mo>=</mo><mn>220</mn></mrow></math></span> days indicate a substantial reduction in rotavirus infections among children and caregivers when integrated controls are applied. The number of infected and hospitalized children (<span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span>) approach zero, while the vaccinated population (<span><math><msub><mrow><mi>V</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span>) reaches approximately <span><math><mrow><mn>2</mn><mo>.</mo><mn>58</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>7</mn></mrow></msup></mrow></math></span>, confirming the central role of vaccination in suppressing new infections. The concentration of environmental rotavirus particles (<span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span>) also tends to zero, highlighting the combined efficacy of hygiene and sanitation interventions in reducing environmental transmission. Among the evaluated control strategies, the combination of vaccination, treatment, and hygiene (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>13</mn></mrow></msub></math></span>) emerges as both the most cost-effective and epidemiologically impactful strategy. This approach achieves near-complete elimination of child infections at a moderate total cost of approximately $6.17<span><math><mrow><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>11</mn></mrow></msup></mrow></math></span>, yielding the best balance between health outcomes and economic feasibility. In contrast, the single-control strategies (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>–<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>)
{"title":"Modeling and optimal control of rotavirus transmission dynamics with cost effectiveness","authors":"Jeremiah January , Gasper Mwanga , Isack E. Kibona , Nyimvua Shaban Mbare","doi":"10.1016/j.sciaf.2025.e03161","DOIUrl":"10.1016/j.sciaf.2025.e03161","url":null,"abstract":"<div><div>An optimal control model for rotavirus transmission was formulated to minimize both the cost of implementing interventions and the burden of infection among children and caregivers. The model integrates five time-dependent control functions: vaccination of children (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), public health education (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>), treatment of infected children (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span>), water treatment and sanitation (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span>), and hygiene promotion (<span><math><msub><mrow><mi>u</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>). Pontryagin’s Maximum Principle was applied to derive the necessary conditions for optimality, and numerical simulations were conducted using the Runge–Kutta method to determine the optimal time-dependent control profiles and corresponding epidemiological outcomes. Simulation results at <span><math><mrow><mi>t</mi><mo>=</mo><mn>220</mn></mrow></math></span> days indicate a substantial reduction in rotavirus infections among children and caregivers when integrated controls are applied. The number of infected and hospitalized children (<span><math><msub><mrow><mi>I</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span>) approach zero, while the vaccinated population (<span><math><msub><mrow><mi>V</mi></mrow><mrow><mi>b</mi></mrow></msub></math></span>) reaches approximately <span><math><mrow><mn>2</mn><mo>.</mo><mn>58</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>7</mn></mrow></msup></mrow></math></span>, confirming the central role of vaccination in suppressing new infections. The concentration of environmental rotavirus particles (<span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span>) also tends to zero, highlighting the combined efficacy of hygiene and sanitation interventions in reducing environmental transmission. Among the evaluated control strategies, the combination of vaccination, treatment, and hygiene (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>13</mn></mrow></msub></math></span>) emerges as both the most cost-effective and epidemiologically impactful strategy. This approach achieves near-complete elimination of child infections at a moderate total cost of approximately $6.17<span><math><mrow><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>11</mn></mrow></msup></mrow></math></span>, yielding the best balance between health outcomes and economic feasibility. In contrast, the single-control strategies (<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>–<span><math><msub><mrow><mi>S</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>)","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03161"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flooding remains a major hazard in northern Morocco, where rapid urban growth and limited monitoring systems heighten the need for reliable flood susceptibility assessment. This study addresses this challenge by developing a Long Short-Term Memory (LSTM) Deep Learning (DL) model capable of predicting flood‐prone areas using satellite imagery and Geographic Information System (GIS) data. Eleven flood conditioning factors were incorporated, including elevation, slope, aspect, Stream Power Index (SPI), Topographic Position Index (TPI), Topographic Wetness Index (TWI), curvature, drainage density (DD), distance to rivers (DR), Normalized Difference Vegetation Index (NDVI), and land use (LU). Unlike previous studies relying on static GIS factors or traditional Machine Learning (ML) methods, this work evaluates how the influence of 11 conditioning factors varies across regions and tests the cross-regional transferability of the LSTM model. A balanced dataset of 1946 samples was generated through data augmentation, and optimization techniques were implemented to enhance model performance. The proposed model achieved an accuracy of 96.06 %, a precision of 94.56 %, a recall of 97.54 %, and an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 96.10 %, confirming its strong predictive capability. These findings show that LSTM can accurately capture spatial environmental patterns linked to flood occurrence, offering valuable support for urban planning, early warning systems, and improved flood risk management in data-scarce regions.
{"title":"A novel spatially enabled neural model for flood susceptibility in Northern Morocco","authors":"Wassima Moutaouakil , Soufiane Hamida , Asmae Ouhmida , Oussama El Gannour , Bouchaib Cherradi , Abdelhadi Raihani","doi":"10.1016/j.sciaf.2025.e03148","DOIUrl":"10.1016/j.sciaf.2025.e03148","url":null,"abstract":"<div><div>Flooding remains a major hazard in northern Morocco, where rapid urban growth and limited monitoring systems heighten the need for reliable flood susceptibility assessment. This study addresses this challenge by developing a Long Short-Term Memory (LSTM) Deep Learning (DL) model capable of predicting flood‐prone areas using satellite imagery and Geographic Information System (GIS) data. Eleven flood conditioning factors were incorporated, including elevation, slope, aspect, Stream Power Index (SPI), Topographic Position Index (TPI), Topographic Wetness Index (TWI), curvature, drainage density (DD), distance to rivers (DR), Normalized Difference Vegetation Index (NDVI), and land use (LU). Unlike previous studies relying on static GIS factors or traditional Machine Learning (ML) methods, this work evaluates how the influence of 11 conditioning factors varies across regions and tests the cross-regional transferability of the LSTM model. A balanced dataset of 1946 samples was generated through data augmentation, and optimization techniques were implemented to enhance model performance. The proposed model achieved an accuracy of 96.06 %, a precision of 94.56 %, a recall of 97.54 %, and an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 96.10 %, confirming its strong predictive capability. These findings show that LSTM can accurately capture spatial environmental patterns linked to flood occurrence, offering valuable support for urban planning, early warning systems, and improved flood risk management in data-scarce regions.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03148"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes and applies a machine-learning-driven optimization framework to predict and enhance the thermomechanical performance of carbon-free adobe bricks reinforced with straw and sawdust. To move beyond trial-and-error mix design under a strength–insulation trade-off, the study establishes reproducible mix-selection rules that reduce experimental iterations. Experimental tests show that adding small amounts of straw (1% and 2%) significantly improves compressive strength, increasing it from 5.41 MPa to 9.62 MPa (+78%) and 7.93 MPa (+46.5%), respectively; however, higher dosages lead to a decrease in strength due to excessive porosity. Sawdust reduces mechanical strength but improves insulation by lowering thermal conductivity from 0.632 W/m.K for the reference brick to 0.145 W/m.K at 10% sawdust. Mixed formulations provided the best compromise: with approximately 0.5–4% sawdust and 0.5–4% straw, they maintained compressive strengths above the minimum requirement of 2.07 MPa established by the Mexican adobe construction standard. A measured dataset (density/porosity, Rc/Rf, λ and Cp) was used to train surrogate models with a 70/15/15 train–validation–test split, 5-fold cross-validation, and grid-search tuning. The machine learning models exhibited distinct predictive capabilities, achieving R² = 0.323–0.566 for compressive strength and R² = 0.794–0.991 for thermal conductivity, and multi-objective optimization (Pareto-based selection) further revealed that hybrid mixtures offer the most balanced solutions. These findings confirm the potential of agricultural waste valorization for the production of eco-friendly building materials and establish a systematic methodology that combines experimental work with artificial intelligence to optimize sustainable adobe bricks.
{"title":"Prediction and optimization of the thermomechanical performance of carbon-free Adobe bricks reinforced with straw and sawdust using machine learning","authors":"Abdelmounaim Alioui , Mohamed-Amine Babay , Mohammed Benfars , Youness Azalam , Samir Idrissi Kaitouni , El Maati Bendada , Mustapha Mabrouki","doi":"10.1016/j.sciaf.2025.e03167","DOIUrl":"10.1016/j.sciaf.2025.e03167","url":null,"abstract":"<div><div>This study proposes and applies a machine-learning-driven optimization framework to predict and enhance the thermomechanical performance of carbon-free adobe bricks reinforced with straw and sawdust. To move beyond trial-and-error mix design under a strength–insulation trade-off, the study establishes reproducible mix-selection rules that reduce experimental iterations. Experimental tests show that adding small amounts of straw (1% and 2%) significantly improves compressive strength, increasing it from 5.41 MPa to 9.62 MPa (+78%) and 7.93 MPa (+46.5%), respectively; however, higher dosages lead to a decrease in strength due to excessive porosity. Sawdust reduces mechanical strength but improves insulation by lowering thermal conductivity from 0.632 W/m.K for the reference brick to 0.145 W/m.K at 10% sawdust. Mixed formulations provided the best compromise: with approximately 0.5–4% sawdust and 0.5–4% straw, they maintained compressive strengths above the minimum requirement of 2.07 MPa established by the Mexican adobe construction standard. A measured dataset (density/porosity, Rc/Rf, λ and Cp) was used to train surrogate models with a 70/15/15 train–validation–test split, 5-fold cross-validation, and grid-search tuning. The machine learning models exhibited distinct predictive capabilities, achieving R² = 0.323–0.566 for compressive strength and R² = 0.794–0.991 for thermal conductivity, and multi-objective optimization (Pareto-based selection) further revealed that hybrid mixtures offer the most balanced solutions. These findings confirm the potential of agricultural waste valorization for the production of eco-friendly building materials and establish a systematic methodology that combines experimental work with artificial intelligence to optimize sustainable adobe bricks.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03167"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We develop a novel family of distributions named heavy-tailed Topp-Leone-type II exponentiated half logistic distribution. Several mathematical properties including linear representation, Rényi entropy, quantile function, probability weighted moments, and distribution of order statistics are obtained. Estimation methods including maximum likelihood estimation, Cramér-von Mises, Anderson–Darling, right-tail Anderson–Darling, ordinary least squares and weighted least squares are compared using simulation studies to identify which method is most appropriate for the new distribution. Weibull and log–logistic distributions are used as baseline distributions to demonstrate the flexibility of the density and hazard rate functions of the new family of distributions. In addition, several actuarial measures including tail value at risk, tail variance, value at risk and tail variance premium are explored and their numerical studies are conducted to verify that the new distribution is heavy-tailed. These risk measures are used in fields such as finance to compare the risk of several investment portfolios. A special model of the new distribution called heavy tailed Topp-Leone exponentiated half logistic-log logistic is used to show its usefulness in a real life health data, where it fitted covid-19 and hydrology data sets better than competing distributions based on several goodness-of-fit statistics.
{"title":"A new and generalized family of heavy-tailed-Topp-Leone-type II exponentiated half logistic-G distribution with applications","authors":"Oarabile Lekhane , Gomolemo Jacqueline Lekono , Broderick Oluyede , Lesego Gabaitiri","doi":"10.1016/j.sciaf.2025.e03115","DOIUrl":"10.1016/j.sciaf.2025.e03115","url":null,"abstract":"<div><div>We develop a novel family of distributions named heavy-tailed Topp-Leone-type II exponentiated half logistic distribution. Several mathematical properties including linear representation, Rényi entropy, quantile function, probability weighted moments, and distribution of order statistics are obtained. Estimation methods including maximum likelihood estimation, Cramér-von Mises, Anderson–Darling, right-tail Anderson–Darling, ordinary least squares and weighted least squares are compared using simulation studies to identify which method is most appropriate for the new distribution. Weibull and log–logistic distributions are used as baseline distributions to demonstrate the flexibility of the density and hazard rate functions of the new family of distributions. In addition, several actuarial measures including tail value at risk, tail variance, value at risk and tail variance premium are explored and their numerical studies are conducted to verify that the new distribution is heavy-tailed. These risk measures are used in fields such as finance to compare the risk of several investment portfolios. A special model of the new distribution called heavy tailed Topp-Leone exponentiated half logistic-log logistic is used to show its usefulness in a real life health data, where it fitted covid-19 and hydrology data sets better than competing distributions based on several goodness-of-fit statistics.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03115"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ferrosilicon (FeSi), a metallic alloy of iron (Fe) and silicon (Si) has been a critical component in Dense Medium Separation (DMS) processes since the 1950s in the mineral processing industry. Since then, FeSi has been utilized to separate various minerals such as diamonds, gold, tin, tungsten, and iron from less valuable material, i.e. gangue. The use of FeSi as a separation medium remains prominent today. This review provides a comprehensive analysis of FeSi’s lifecycle in DMS, from production using the electric submerged arc furnace to its role in ensuring separation efficiency, medium stability, and loss mitigation strategies. Key factors affecting FeSi performance, such as viscosity, stability, contamination, and corrosion, are explored, along with emerging techniques for improving its sustainability and cost-effectiveness. Additionally, this paper proposes a case study on the potential for FeSi production in Botswana, leveraging local raw materials such as iron ore and silica to support economic growth. Future advancements in FeSi recovery and process optimization are also discussed. By addressing quality standards, environmental considerations, and economic feasibility, this review aims to provide a valuable reference for researchers and industry professionals seeking to enhance the efficiency and longevity of FeSi in DMS applications.
{"title":"Exploring the lifecycle of ferrosilicon for dense media separation in Botswana mining sector: A brief overview","authors":"Phemo T. Sebobi , Babatunde Abiodun Obadele , Prasad Raghupatruni , Enoch Nifise Ogunmuyiwa","doi":"10.1016/j.sciaf.2025.e03116","DOIUrl":"10.1016/j.sciaf.2025.e03116","url":null,"abstract":"<div><div>Ferrosilicon (FeSi), a metallic alloy of iron (Fe) and silicon (Si) has been a critical component in Dense Medium Separation (DMS) processes since the 1950s in the mineral processing industry. Since then, FeSi has been utilized to separate various minerals such as diamonds, gold, tin, tungsten, and iron from less valuable material, i.e. gangue. The use of FeSi as a separation medium remains prominent today. This review provides a comprehensive analysis of FeSi’s lifecycle in DMS, from production using the electric submerged arc furnace to its role in ensuring separation efficiency, medium stability, and loss mitigation strategies. Key factors affecting FeSi performance, such as viscosity, stability, contamination, and corrosion, are explored, along with emerging techniques for improving its sustainability and cost-effectiveness. Additionally, this paper proposes a case study on the potential for FeSi production in Botswana, leveraging local raw materials such as iron ore and silica to support economic growth. Future advancements in FeSi recovery and process optimization are also discussed. By addressing quality standards, environmental considerations, and economic feasibility, this review aims to provide a valuable reference for researchers and industry professionals seeking to enhance the efficiency and longevity of FeSi in DMS applications.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03116"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-07DOI: 10.1016/j.sciaf.2026.e03178
Jamal Abbach , Said El Moussaoui , Hajar El Talibi , Charaf Eddine Bouiss
Lake Tamda is a temporary endorheic lake located in the central Middle Atlas (Morocco) that exhibits pronounced seasonal filling and drying phases. This study aims to quantify the respective roles of climatic forcing and subsurface drainage in controlling the lake’s hydrological dynamics within a semi-arid mountain environment. We combine multi-temporal satellite observations (2011–2021) with field measurements, piezometric data, and rainfall records to track variations in lake surface area and groundwater response. Results show that lake expansion is strongly controlled by winter–spring precipitation and snowmelt, while rapid summer drawdown cannot be explained by evaporation alone. Instead, drainage through karstified carbonate bedrock and permeable zones within the landslide dam plays a major role in water loss, with structural lineaments aligning the lake with downstream wells and springs. During recent dry years (2019–2021), reduced rainfall and rising temperatures led to recurrent complete desiccation by late summer. These findings demonstrate that Lake Tamda functions as a precipitation-filled, fracture-drained seasonal lake highly sensitive to climate variability. The lake represents both a valuable geoheritage site and a vulnerable hydrosystem, highlighting the need for integrated hydrogeological monitoring to assess future impacts of climate change.
{"title":"Spatio-temporal evolution of the surface dynamics and the underground water flow of the natural lake Tamda (Middle Atlas, Morocco)","authors":"Jamal Abbach , Said El Moussaoui , Hajar El Talibi , Charaf Eddine Bouiss","doi":"10.1016/j.sciaf.2026.e03178","DOIUrl":"10.1016/j.sciaf.2026.e03178","url":null,"abstract":"<div><div>Lake Tamda is a temporary endorheic lake located in the central Middle Atlas (Morocco) that exhibits pronounced seasonal filling and drying phases. This study aims to quantify the respective roles of climatic forcing and subsurface drainage in controlling the lake’s hydrological dynamics within a semi-arid mountain environment. We combine multi-temporal satellite observations (2011–2021) with field measurements, piezometric data, and rainfall records to track variations in lake surface area and groundwater response. Results show that lake expansion is strongly controlled by winter–spring precipitation and snowmelt, while rapid summer drawdown cannot be explained by evaporation alone. Instead, drainage through karstified carbonate bedrock and permeable zones within the landslide dam plays a major role in water loss, with structural lineaments aligning the lake with downstream wells and springs. During recent dry years (2019–2021), reduced rainfall and rising temperatures led to recurrent complete desiccation by late summer. These findings demonstrate that Lake Tamda functions as a precipitation-filled, fracture-drained seasonal lake highly sensitive to climate variability. The lake represents both a valuable geoheritage site and a vulnerable hydrosystem, highlighting the need for integrated hydrogeological monitoring to assess future impacts of climate change.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03178"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.sciaf.2026.e03219
Nidal Filali Baba , Ali El Myr , Youssef Bakadir , Hamed Rahmani
This paper examines the NEET phenomenon as a socioeconomic epidemic. It addresses NEET dynamics at both the population and individual levels in North Africa. We argue that NEET status is transmitted through ideational contact and propose that proximity to Europe and migration aspirations act as non-traditional factors in this transmission. The analysis draws on multiple data sources. From a cross-country perspective, we rely on the World Bank’s and SAHWA’s surveys covering 45 African countries. From an intra-country perspective, we draw on longitudinal data from the Moroccan National Employment Survey, the World Bank (2014–2022), and local administrative archives to analyze Morocco’s NEET dynamics as a representative case for North Africa.
This study makes several original contributions. First, it conceptualizes the NEET phenomenon as a socioeconomic epidemic that spreads among individuals through contact with other NEETs. Second, it adapts the Susceptible–Infected–Recovered (SIR) model to analyze NEET dynamics in Morocco. This innovative approach allows us to construct the basic reproduction number () and to identify thresholds that govern NEET trends: stagnation, extinction, and persistence. Theoretically, this approach demonstrates the relevance of epidemiological modeling in explaining how socioeconomic phenomena like NEETs spread and persist. Practically, it identifies a critical threshold for policymakers, beyond which the spread of NEETs becomes difficult to reverse.
The findings further show that proximity to major economic centers and strong migration aspirations contribute to the expansion of NEETs. This underscores the need for integrated policy responses to curb the growth of NEET populations in their origin countries and prevent their effects from spilling over into destination countries.
{"title":"NEETs phenomenon as a socioeconomic epidemic: Support for policymakers on the role of migration aspirations and proximity to economic powers","authors":"Nidal Filali Baba , Ali El Myr , Youssef Bakadir , Hamed Rahmani","doi":"10.1016/j.sciaf.2026.e03219","DOIUrl":"10.1016/j.sciaf.2026.e03219","url":null,"abstract":"<div><div>This paper examines the NEET phenomenon as a socioeconomic epidemic. It addresses NEET dynamics at both the population and individual levels in North Africa. We argue that NEET status is transmitted through ideational contact and propose that proximity to Europe and migration aspirations act as non-traditional factors in this transmission. The analysis draws on multiple data sources. From a cross-country perspective, we rely on the World Bank’s and SAHWA’s surveys covering 45 African countries. From an intra-country perspective, we draw on longitudinal data from the Moroccan National Employment Survey, the World Bank (2014–2022), and local administrative archives to analyze Morocco’s NEET dynamics as a representative case for North Africa.</div><div>This study makes several original contributions. First, it conceptualizes the NEET phenomenon as a socioeconomic epidemic that spreads among individuals through contact with other NEETs. Second, it adapts the Susceptible–Infected–Recovered (SIR) model to analyze NEET dynamics in Morocco. This innovative approach allows us to construct the basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) and to identify thresholds that govern NEET trends: stagnation, extinction, and persistence. Theoretically, this approach demonstrates the relevance of epidemiological modeling in explaining how socioeconomic phenomena like NEETs spread and persist. Practically, it identifies a critical threshold for policymakers, beyond which the spread of NEETs becomes difficult to reverse.</div><div>The findings further show that proximity to major economic centers and strong migration aspirations contribute to the expansion of NEETs. This underscores the need for integrated policy responses to curb the growth of NEET populations in their origin countries and prevent their effects from spilling over into destination countries.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03219"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.sciaf.2026.e03208
Sounkolé Marius Gloua , Akori Elvice Esmel , Emma Georgina Hueva Zoro , Melalie Keita , Eugene Megnassan
<div><div>Protoporphyrinogen Oxidase IX (PPO) is a key target for agricultural herbicide design. In this study, we propose novel virtual PPO inhibitors using computer-aided 3D-QSAR combinatorial molecular design. Starting from the crystal structure of the PPO complex with a of fluazolate [4-Bromo-3-(5’-carboxy-4’-chloro-2’-fluorophenyl)-1-methyl-5-trifluoromethyl-pyrazol] (pdb code: 1SEZ), <em>in situ</em> modifications were made to generate first the 3D structure of the PPO-DPE1 complex and then a training set of sixteen (16) diphenyl ethers (DPE) with known experimental activities. A significant correlation was established between the relative Gibbs Free Energy (rGFE) of complex formation <span><math><mrow><mo>(</mo><mrow><mstyle><mi>Δ</mi></mstyle><mstyle><mi>Δ</mi></mstyle><msub><mi>G</mi><mrow><mi>c</mi><mi>o</mi><mi>m</mi></mrow></msub></mrow><mo>)</mo></mrow></math></span> and the observed PPO inhibitory potency <span><math><mrow><mo>(</mo><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup></mrow><mo>)</mo></mrow></math></span>, expressed as <span><math><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mo>−</mo><mn>0.1735</mn><mspace></mspace><mstyle><mi>Δ</mi></mstyle><mstyle><mi>Δ</mi></mstyle><msub><mi>G</mi><mrow><mi>c</mi><mi>o</mi><mi>m</mi></mrow></msub><mo>+</mo><mn>7.902</mn><mo>,</mo></mrow></math></span> with <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.96</mn></mrow></math></span>. A 3D-QSAR pharmacophore (PH4) model, derived from active DPE conformations, was used to screen a virtual library of 161,051 compounds. The predictive robustness of PH4 <span><math><mrow><mo>(</mo><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mspace></mspace><mn>1.0077</mn><mspace></mspace><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>p</mi><mi>r</mi><mi>e</mi></mrow></msubsup><mo>−</mo><mn>0.048</mn><mo>,</mo><mspace></mspace><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.81</mn></mrow><mo>)</mo></mrow></math></span>, validated the selection of seventy (70) novel DPE analogues, with the most active exhibiting a predicted <span><math><mrow><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>p</mi><mi>r</mi><mi>e</mi></mrow></msubsup><mo>=</mo><mn>200</mn><mrow><mspace></mspace><mtext>pM</mtext></mrow><mo>,</mo><mspace></mspace></mrow></math></span>90 times more potent than DPE1 <span><math><mrow><mo>(</mo><mrow><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mn>18</mn><mo>,</mo><mn>000</mn><mrow><mspace></mspace><mtext>pM</mtext></mrow></mrow><mo>)</mo></mrow></math></span>. Molecular dynamics simulations on the four (4) best predicted analogues and DPE1 confir
{"title":"Molecular modeling and virtual screening for computer-aided design of plant protoporphyrinogen IX oxidase (PPO) inhibitors","authors":"Sounkolé Marius Gloua , Akori Elvice Esmel , Emma Georgina Hueva Zoro , Melalie Keita , Eugene Megnassan","doi":"10.1016/j.sciaf.2026.e03208","DOIUrl":"10.1016/j.sciaf.2026.e03208","url":null,"abstract":"<div><div>Protoporphyrinogen Oxidase IX (PPO) is a key target for agricultural herbicide design. In this study, we propose novel virtual PPO inhibitors using computer-aided 3D-QSAR combinatorial molecular design. Starting from the crystal structure of the PPO complex with a of fluazolate [4-Bromo-3-(5’-carboxy-4’-chloro-2’-fluorophenyl)-1-methyl-5-trifluoromethyl-pyrazol] (pdb code: 1SEZ), <em>in situ</em> modifications were made to generate first the 3D structure of the PPO-DPE1 complex and then a training set of sixteen (16) diphenyl ethers (DPE) with known experimental activities. A significant correlation was established between the relative Gibbs Free Energy (rGFE) of complex formation <span><math><mrow><mo>(</mo><mrow><mstyle><mi>Δ</mi></mstyle><mstyle><mi>Δ</mi></mstyle><msub><mi>G</mi><mrow><mi>c</mi><mi>o</mi><mi>m</mi></mrow></msub></mrow><mo>)</mo></mrow></math></span> and the observed PPO inhibitory potency <span><math><mrow><mo>(</mo><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup></mrow><mo>)</mo></mrow></math></span>, expressed as <span><math><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mo>−</mo><mn>0.1735</mn><mspace></mspace><mstyle><mi>Δ</mi></mstyle><mstyle><mi>Δ</mi></mstyle><msub><mi>G</mi><mrow><mi>c</mi><mi>o</mi><mi>m</mi></mrow></msub><mo>+</mo><mn>7.902</mn><mo>,</mo></mrow></math></span> with <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.96</mn></mrow></math></span>. A 3D-QSAR pharmacophore (PH4) model, derived from active DPE conformations, was used to screen a virtual library of 161,051 compounds. The predictive robustness of PH4 <span><math><mrow><mo>(</mo><mrow><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mspace></mspace><mn>1.0077</mn><mspace></mspace><mi>p</mi><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>p</mi><mi>r</mi><mi>e</mi></mrow></msubsup><mo>−</mo><mn>0.048</mn><mo>,</mo><mspace></mspace><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.81</mn></mrow><mo>)</mo></mrow></math></span>, validated the selection of seventy (70) novel DPE analogues, with the most active exhibiting a predicted <span><math><mrow><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>p</mi><mi>r</mi><mi>e</mi></mrow></msubsup><mo>=</mo><mn>200</mn><mrow><mspace></mspace><mtext>pM</mtext></mrow><mo>,</mo><mspace></mspace></mrow></math></span>90 times more potent than DPE1 <span><math><mrow><mo>(</mo><mrow><mi>I</mi><msubsup><mi>C</mi><mrow><mn>50</mn></mrow><mrow><mi>e</mi><mi>x</mi><mi>p</mi></mrow></msubsup><mo>=</mo><mn>18</mn><mo>,</mo><mn>000</mn><mrow><mspace></mspace><mtext>pM</mtext></mrow></mrow><mo>)</mo></mrow></math></span>. Molecular dynamics simulations on the four (4) best predicted analogues and DPE1 confir","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03208"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.sciaf.2026.e03195
Lateef Bankole Adamolekun
The rapid increase in population drives a growing demand for expanded infrastructure. Cement quality, crucial for infrastructure performance, is largely influenced by a well-controlled raw mix lime saturation factor (LSF). Accurate LSF estimation relies on integrating precise mathematical formulas into elemental composition analyzers. However, the formulas traditionally utilized in the cement industry, often fall short of capturing underlying complexities of the process. Thus, there is need for more robust mathematical formula to accurately estimate LSF. This study develops LSF predictive models by employing artificial neural networks (ANN) optimized with particle swarm optimization (PSO), Levenberg–Marquardt (LM), and genetic algorithms (GA), using two thousand four hundred and sixty data points obtained via cross belt-analyzer. Dependable variables selected were lime, silica, alumina, and iron oxide. To enhance the practicality and ease of use, the models (LM-ANN, PSO-ANN, and GA-ANN) were converted into mathematical equations and further integrated into software application, in form of simple calculator. The models were validated using 5-fold cross-validation with random sampling, demonstrating consistent, generalization capability, and reliable performance across key metrics including coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). The models' performance was benchmarked against the established model proposed by Bogue (1966). The LM-ANN model outperformed both Bogue’s and the other evaluated models, achieving superior results across key metrics: R² = 0.9885, RMSE = 1.7828, relative squared error (RSE) = 9.99 × 10⁻⁷. While all three models are suitable for practical deployment, the LM-ANN model is strongly recommended for industrial applications. The mathematical model can be integrated into elemental composition analyzers to enhance real-time process optimization and improve cement production efficiency. Meanwhile, the software application will serve as a smart tool for rapid LSF estimation and consistent monitoring of analyzer reliability in cement production.
{"title":"Optimized ANN-based mathematical model and software application for predicting raw mix lime saturation factor for high-quality cement production","authors":"Lateef Bankole Adamolekun","doi":"10.1016/j.sciaf.2026.e03195","DOIUrl":"10.1016/j.sciaf.2026.e03195","url":null,"abstract":"<div><div>The rapid increase in population drives a growing demand for expanded infrastructure. Cement quality, crucial for infrastructure performance, is largely influenced by a well-controlled raw mix lime saturation factor (LSF). Accurate LSF estimation relies on integrating precise mathematical formulas into elemental composition analyzers. However, the formulas traditionally utilized in the cement industry, often fall short of capturing underlying complexities of the process. Thus, there is need for more robust mathematical formula to accurately estimate LSF. This study develops LSF predictive models by employing artificial neural networks (ANN) optimized with particle swarm optimization (PSO), Levenberg–Marquardt (LM), and genetic algorithms (GA), using two thousand four hundred and sixty data points obtained via cross belt-analyzer. Dependable variables selected were lime, silica, alumina, and iron oxide. To enhance the practicality and ease of use, the models (LM-ANN, PSO-ANN, and GA-ANN) were converted into mathematical equations and further integrated into software application, in form of simple calculator. The models were validated using 5-fold cross-validation with random sampling, demonstrating consistent, generalization capability, and reliable performance across key metrics including coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE). The models' performance was benchmarked against the established model proposed by Bogue (1966). The LM-ANN model outperformed both Bogue’s and the other evaluated models, achieving superior results across key metrics: R² = 0.9885, RMSE = 1.7828, relative squared error (RSE) = 9.99 × 10⁻⁷. While all three models are suitable for practical deployment, the LM-ANN model is strongly recommended for industrial applications. The mathematical model can be integrated into elemental composition analyzers to enhance real-time process optimization and improve cement production efficiency. Meanwhile, the software application will serve as a smart tool for rapid LSF estimation and consistent monitoring of analyzer reliability in cement production.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03195"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-22DOI: 10.1016/j.sciaf.2025.e03153
A.M.N. Shatri , Y Lemmer , L Kalombo , V Mandiwana , D.R. Mumbengegwi
Phytotherapy has been used to treat gastroenteritis in many African countries, with medicinal plant extracts from Grewia tenax, Corchorus tridens, and Lantana camara showing strong antibacterial properties against bacteria that cause gastroenteritis. However, issues such as uncontrolled metabolism by gastric juices and instability in the gastrointestinal tract due to varying pH levels reduce the effectiveness of these phytomedicines. This has limited their use as an alternative or complementary treatment for gastroenteritis. To address this, nanotechnology has been employed to improve the pharmacokinetic and pharmacodynamic properties of phytomedicines. This study aimed to develop biodegradable, plant-based, chitosan-modified poly(lactic-co-glycolic acid) (CMPLGA) microparticles for targeted release in the lower gastrointestinal tract. Nanoparticles were created by mixing 12. 5 mg/ml of polymers with 120 mg/ml of antibacterial extracts from G. tenax, C. tridens. and L. camara using a modified double emulsion (W 1/O/W 2) and solvent evaporation method. The size and zeta potential of the nanoparticles were measured using photon correlation spectroscopy and electrophoretic laser Doppler anemometry. Scanning Electron Microscopy was used to examine morphology, and the encapsulation efficiency was determined via UV- vis spectroscopy. In vitro, the release kinetics of the plant extracts from the nanoparticles were investigated using sample separation techniques in simulated gastric and intestinal fluids, without the presence of enzymes. The plant-based CMPLGA nanoparticles were spherical, with sizes ranging from 524 ± 18 nm. 92 nm to 2582 ± 123 nm, and zeta potential from 2. 68 ± 0. 08 mV to 44. 2 ± 0. 100 mV; encapsulation efficiency was greater than 89.8 %. The release of phytomedicine from the nanoparticles depended on pH, with <2 % release at pH 1. 2 and over 50 % release at pH 7. 7.4. These CMPLGA nanoparticles improved the stability of the antibacterial phytomedicine in acidic conditions similar to those in the upper GI tract. They may serve as an effective vehicle for future drug delivery targeting gastrointestinal pathogens in the lower GI tract.
{"title":"Synthesis of chitosan-modified poly (lactic-co-glycolic acid) microparticles with pH-dependent controlled-release kinetics to enhance the delivery of potential antidiarrheal medicinal plant extract to the lower gastrointestinal tract","authors":"A.M.N. Shatri , Y Lemmer , L Kalombo , V Mandiwana , D.R. Mumbengegwi","doi":"10.1016/j.sciaf.2025.e03153","DOIUrl":"10.1016/j.sciaf.2025.e03153","url":null,"abstract":"<div><div>Phytotherapy has been used to treat gastroenteritis in many African countries, with medicinal plant extracts from <em>Grewia tenax, Corchorus tridens</em>, and <em>Lantana camara</em> showing strong antibacterial properties against bacteria that cause gastroenteritis. However, issues such as uncontrolled metabolism by gastric juices and instability in the gastrointestinal tract due to varying pH levels reduce the effectiveness of these phytomedicines. This has limited their use as an alternative or complementary treatment for gastroenteritis. To address this, nanotechnology has been employed to improve the pharmacokinetic and pharmacodynamic properties of phytomedicines. This study aimed to develop biodegradable, plant-based, chitosan-modified poly(lactic-co-glycolic acid) (CMPLGA) microparticles for targeted release in the lower gastrointestinal tract. Nanoparticles were created by mixing 12. 5 mg/ml of polymers with 120 mg/ml of antibacterial extracts from <em>G. tenax, C. tridens</em>. and <em>L. camara</em> using a modified double emulsion (W 1/O/W 2) and solvent evaporation method. The size and zeta potential of the nanoparticles were measured using photon correlation spectroscopy and electrophoretic laser Doppler anemometry. Scanning Electron Microscopy was used to examine morphology, and the encapsulation efficiency was determined via UV- vis spectroscopy. In vitro, the release kinetics of the plant extracts from the nanoparticles were investigated using sample separation techniques in simulated gastric and intestinal fluids, without the presence of enzymes. The plant-based CMPLGA nanoparticles were spherical, with sizes ranging from 524 ± 18 nm. 92 nm to 2582 ± 123 nm, and zeta potential from 2. 68 ± 0. 08 mV to 44. 2 ± 0. 100 mV; encapsulation efficiency was greater than 89.8 %. The release of phytomedicine from the nanoparticles depended on pH, with <2 % release at pH 1. 2 and over 50 % release at pH 7. 7.4. These CMPLGA nanoparticles improved the stability of the antibacterial phytomedicine in acidic conditions similar to those in the upper GI tract. They may serve as an effective vehicle for future drug delivery targeting gastrointestinal pathogens in the lower GI tract.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03153"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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