This paper investigates the dynamic connectedness between major international stock markets (SP 500, CAC 40, DAX, Nikkei, and HSCE) and key alternative assets (gold, wheat, oil, and Bitcoin) across three structurally distinct global crises. Using a DCC-GARCH framework over the period 2006–2024, the study examines how different types of extreme events shape time-varying correlations and influence the hedging and diversification potential of non-equity assets. The results indicate that defensive properties are highly crisis-dependent: traditional assets such as gold exhibit periods of stability but do not offer uniform protection across shocks, while newer assets like Bitcoin, often described as “digital gold”, experience short-lived surges in correlation at the onset of market stress before rapidly decorrelating, raising questions about their reliability as sustained hedges. By integrating these correlation dynamics with shock-sensitive hedging assessments, the findings provide broader insights into asset behavior during crisis periods and highlight the importance of flexible, shock-aware risk-management strategies.
{"title":"Dynamic linkages between global commodity prices and stock markets in times of crisis: Evidence from a DCC-GARCH framework","authors":"Mahjouba Zakry , Lhoucine Ben Hssain , Jamal Agouram , Ghizlane Lakhnati","doi":"10.1016/j.sciaf.2026.e03221","DOIUrl":"10.1016/j.sciaf.2026.e03221","url":null,"abstract":"<div><div>This paper investigates the dynamic connectedness between major international stock markets (SP 500, CAC 40, DAX, Nikkei, and HSCE) and key alternative assets (gold, wheat, oil, and Bitcoin) across three structurally distinct global crises. Using a DCC-GARCH framework over the period 2006–2024, the study examines how different types of extreme events shape time-varying correlations and influence the hedging and diversification potential of non-equity assets. The results indicate that defensive properties are highly crisis-dependent: traditional assets such as gold exhibit periods of stability but do not offer uniform protection across shocks, while newer assets like Bitcoin, often described as “digital gold”, experience short-lived surges in correlation at the onset of market stress before rapidly decorrelating, raising questions about their reliability as sustained hedges. By integrating these correlation dynamics with shock-sensitive hedging assessments, the findings provide broader insights into asset behavior during crisis periods and highlight the importance of flexible, shock-aware risk-management strategies.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03221"},"PeriodicalIF":3.3,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188304","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}
With the growing demand for energy-efficient and sustainable electronic systems, CMOS technology plays a crucial role in advancing green electronics and had emerged as a key enabler for low-power analog hardware implementations in intelligent systems. This paper presents a comprehensive study of CMOS differential pair implementations for generating bell-shaped and sigmoid membership functions, which serve as fundamental components in fuzzy logic systems and directly influence decision-making accuracy. By employing CMOS differential-pair circuits, a hardware-efficient approach is proposed to realize smooth, continuous analog membership functions suitable for fuzzy inference applications. The study encompasses detailed mathematical modeling, circuit design methodologies, and performance evaluation through LTspice simulations of 45 nm technology. Particular emphasis is placed on noise characterization, where both flicker (1/f) noise and thermal noise contributions are analyzed to assess their impact on circuit performance. Simulation results demonstrate how circuit topology and membership function shape affect noise behavior and power consumption (below 12 W) and achieve a noise floor below 1 V/ beyond 10 kHz, confirming the circuits’ low-power and low-noise performance. A comparative analysis of the sigmoid function and bell-shaped implementations further supports these results. Additionally, the characteristic curves of both membership functions are extracted and discussed, validating their operational suitability for analog fuzzy logic systems. The findings provide valuable insights for designing low-noise analog fuzzy inference systems, thus enhancing the reliability, precision, and energy efficiency of intelligent electronic systems.
{"title":"Design and characterization of CMOS differential pair circuits for membership function generation with noise analysis in fuzzy logic systems","authors":"Hanane Sefraoui , Shuhei Amakawa , Abdechafik Derkaoui , Abdelhak Ziyyat","doi":"10.1016/j.sciaf.2026.e03220","DOIUrl":"10.1016/j.sciaf.2026.e03220","url":null,"abstract":"<div><div>With the growing demand for energy-efficient and sustainable electronic systems, CMOS technology plays a crucial role in advancing green electronics and had emerged as a key enabler for low-power analog hardware implementations in intelligent systems. This paper presents a comprehensive study of CMOS differential pair implementations for generating bell-shaped and sigmoid membership functions, which serve as fundamental components in fuzzy logic systems and directly influence decision-making accuracy. By employing CMOS differential-pair circuits, a hardware-efficient approach is proposed to realize smooth, continuous analog membership functions suitable for fuzzy inference applications. The study encompasses detailed mathematical modeling, circuit design methodologies, and performance evaluation through LTspice simulations of 45 nm technology. Particular emphasis is placed on noise characterization, where both flicker (1/<em>f</em>) noise and thermal noise contributions are analyzed to assess their impact on circuit performance. Simulation results demonstrate how circuit topology and membership function shape affect noise behavior and power consumption (below 12 <span><math><mi>μ</mi></math></span>W) and achieve a noise floor below 1 <span><math><mi>μ</mi></math></span>V/<span><math><msqrt><mrow><mtext>Hz</mtext></mrow></msqrt></math></span> beyond 10 kHz, confirming the circuits’ low-power and low-noise performance. A comparative analysis of the sigmoid function and bell-shaped implementations further supports these results. Additionally, the characteristic curves of both membership functions are extracted and discussed, validating their operational suitability for analog fuzzy logic systems. The findings provide valuable insights for designing low-noise analog fuzzy inference systems, thus enhancing the reliability, precision, and energy efficiency of intelligent electronic systems.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03220"},"PeriodicalIF":3.3,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188166","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 : 2026-01-28DOI: 10.1016/j.sciaf.2026.e03203
Kevin Otieno , Linda Chaba , Evans Omondi , Collins Odhiambo , Bernard Omolo
In dependence modeling, choosing the right copula is crucial, as different copula models can yield distinct interpretations of the relationship between variables. However, real-world applications are often constrained by the limitations of existing copula selection methods, which lack consistency and robustness across datasets. The selection methods in the literature that includes goodness-of-fit (GoF) tests and selection criteria, often yield conflicting results, thereby misrepresenting the dependence structure and leading to misleading conclusions. This study developed an integrated copula selection framework that combines GOF tests with cross-validation techniques. We integrated block-based cross-validation with GoF tests, where data was partitioned into blocks of different sizes . A copula was fitted on the training set, and its performance was validated on the test set using GoF measures. The selection process was repeated across multiple folds, and an aggregation method was applied to determine the most suitable copula. The approach was tested through Monte Carlo simulations and an empirical study was tested on weather variables in Kenya. The findings show that Kendall-based Kolmogorov–Smirnov (KendallKS) and Cramér–von Mises (KendallCvM) test statistics integrated with stratified cross-validation, when , perform better when the Benjamini–Hochberg (BH) procedure was used for aggregation. The proposed tests successfully identified the true copula and consistently rejected incorrect alternatives, with performance improving as sample size and dependence level increased. The empirical application further demonstrates the method’s robustness in real-world settings. These findings demonstrate that the proposed approach enhances the reliability and stability of copula selection.
在依赖建模中，选择正确的联结模型是至关重要的，因为不同的联结模型可以对变量之间的关系产生不同的解释。然而，现实世界的应用经常受到现有的copula选择方法的限制，这些方法缺乏跨数据集的一致性和鲁棒性。文献中的选择方法包括拟合优度（GoF）检验和选择标准，往往产生相互矛盾的结果，从而歪曲了依赖结构并导致误导性结论。本研究开发了一个综合的copula选择框架，将GOF测试与交叉验证技术相结合。我们将基于块的交叉验证与GoF测试相结合，其中数据被划分为不同大小(K)的块。在训练集上拟合了一个copula，并使用GoF度量在测试集上验证了其性能。选择过程在多个折叠中重复进行，并采用聚集法确定最合适的copula。通过蒙特卡洛模拟对该方法进行了测试，并对肯尼亚的天气变量进行了实证研究。结果表明，当K=5时，使用Benjamini-Hochberg （BH）程序进行聚合时，Kendall-based Kolmogorov-Smirnov （KendallKS）和cram - von Mises （KendallCvM）检验统计量与分层交叉验证相结合，表现更好。所提出的测试成功地识别了真正的联结，并始终拒绝不正确的替代方案，随着样本量和依赖程度的增加，性能也在提高。实证应用进一步证明了该方法在现实世界中的鲁棒性。这些结果表明，该方法提高了交配体选择的可靠性和稳定性。
{"title":"Integrating GOF tests and cross validation for copula model selection","authors":"Kevin Otieno , Linda Chaba , Evans Omondi , Collins Odhiambo , Bernard Omolo","doi":"10.1016/j.sciaf.2026.e03203","DOIUrl":"10.1016/j.sciaf.2026.e03203","url":null,"abstract":"<div><div>In dependence modeling, choosing the right copula is crucial, as different copula models can yield distinct interpretations of the relationship between variables. However, real-world applications are often constrained by the limitations of existing copula selection methods, which lack consistency and robustness across datasets. The selection methods in the literature that includes goodness-of-fit (GoF) tests and selection criteria, often yield conflicting results, thereby misrepresenting the dependence structure and leading to misleading conclusions. This study developed an integrated copula selection framework that combines GOF tests with cross-validation techniques. We integrated block-based cross-validation with GoF tests, where data was partitioned into blocks of different sizes <span><math><mrow><mo>(</mo><mi>K</mi><mo>)</mo></mrow></math></span>. A copula was fitted on the training set, and its performance was validated on the test set using GoF measures. The selection process was repeated across multiple folds, and an aggregation method was applied to determine the most suitable copula. The approach was tested through Monte Carlo simulations and an empirical study was tested on weather variables in Kenya. The findings show that Kendall-based Kolmogorov–Smirnov (KendallKS) and Cramér–von Mises (KendallCvM) test statistics integrated with stratified cross-validation, when <span><math><mrow><mi>K</mi><mo>=</mo><mn>5</mn></mrow></math></span>, perform better when the Benjamini–Hochberg (BH) procedure was used for aggregation. The proposed tests successfully identified the true copula and consistently rejected incorrect alternatives, with performance improving as sample size and dependence level increased. The empirical application further demonstrates the method’s robustness in real-world settings. These findings demonstrate that the proposed approach enhances the reliability and stability of copula selection.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03203"},"PeriodicalIF":3.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077787","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 : 2026-01-28DOI: 10.1016/j.sciaf.2026.e03193
Tabaro Kabanda
This study investigates temporal and spatial shifts on a single warmest day per year across Tanzania from 1981 to 2023, utilising ERA5 reanalysis data and advanced spatial analytics. The findings reveal significant changes in the timing and intensity of temperature peaks. Notably, northern regions influenced by Lake Victoria experience their warmest days later in the year (August–September), while coastal and central regions encounter earlier peaks (January–March). Quantitative analysis shows an average shift of 17 days later on the warmest day for 1.5% of the study area, primarily in western highlands. In comparison, coastal and central areas report a shift up to 65 days earlier. Spatial analyses, including Emerging Hot Spot and Local Outlier Analysis, identified warming trends in 0.6% of southern Tanzania, indicating persistent delays in peak temperatures. Conversely, 24.9% of the study area exhibits oscillating temperature patterns, reflecting increased climate variability. Regions with significant trends were statistically validated using the Mann-Kendall test (p < 0.05), revealing critical temporal patterns. These findings underscore the need for tailored adaptation strategies. Delayed warming in western highlands requires adjusted agricultural calendars, while earlier warming in coastal and central areas necessitates water management reforms. The study bridges critical research gaps on temperature extremes in Tanzania, providing actionable insights for agriculture, water management, and public health planning. This framework offers a model for addressing similar challenges in other regions undergoing climate variability.
{"title":"Analysing shifts in the warmest day of the year in Tanzania (1981–2023): Implications for climate adaptation","authors":"Tabaro Kabanda","doi":"10.1016/j.sciaf.2026.e03193","DOIUrl":"10.1016/j.sciaf.2026.e03193","url":null,"abstract":"<div><div>This study investigates temporal and spatial shifts on a single warmest day per year across Tanzania from 1981 to 2023, utilising ERA5 reanalysis data and advanced spatial analytics. The findings reveal significant changes in the timing and intensity of temperature peaks. Notably, northern regions influenced by Lake Victoria experience their warmest days later in the year (August–September), while coastal and central regions encounter earlier peaks (January–March). Quantitative analysis shows an average shift of 17 days later on the warmest day for 1.5% of the study area, primarily in western highlands. In comparison, coastal and central areas report a shift up to 65 days earlier. Spatial analyses, including Emerging Hot Spot and Local Outlier Analysis, identified warming trends in 0.6% of southern Tanzania, indicating persistent delays in peak temperatures. Conversely, 24.9% of the study area exhibits oscillating temperature patterns, reflecting increased climate variability. Regions with significant trends were statistically validated using the Mann-Kendall test (<em>p</em> < 0.05), revealing critical temporal patterns. These findings underscore the need for tailored adaptation strategies. Delayed warming in western highlands requires adjusted agricultural calendars, while earlier warming in coastal and central areas necessitates water management reforms. The study bridges critical research gaps on temperature extremes in Tanzania, providing actionable insights for agriculture, water management, and public health planning. This framework offers a model for addressing similar challenges in other regions undergoing climate variability.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03193"},"PeriodicalIF":3.3,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188265","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 investigates the spatiotemporal variability and trends of rainfall and temperature in the Ejerie district, Ethiopia, focusing on the Dega and Weyina Dega agroecological zones. Gridded monthly rainfall data (1990–2020) and temperature records (1990–2018) from the Ethiopian Meteorology Institute were analyzed at a 4 × 4 km resolution. Variability was assessed using mean, standard deviation, coefficient of variation (CV), precipitation concentration index (PCI), and standardized rainfall anomaly (SRA). Trend analysis was conducted using the Mann–Kendall test (MK test) and innovative trend analysis (ITA). Results showed that Dega receives 1245 mm of annual rainfall, while Weyina Dega receives 907.8 mm, with both regions exhibiting low rainfall variability. Rainfall is concentrated in the summer season, followed by Belg and Bega, with notable monthly variability in November and December. The PCI analysis indicated irregular rainfall distributions, with Dega showing 87.09% irregular and 9.68% strongly irregular distributions, while Weyina Dega showed 83.87% irregular and 3.23% strongly irregular distributions. SRA analysis revealed that 83.27% of Dega and 84.48% of Weyina Dega did not experience drought, with minor drought occurrences observed in both regions. Temperature analysis showed significant seasonal differences, with Dega experiencing cooler temperatures than Weyina Dega, which has a warmer climate. Both the MK test and ITA methods yielded consistent temperature trends, with the exception of minor discrepancies in Bega season temperatures. These findings emphasize the importance of localized climate studies to inform area-specific adaptation strategies for agriculture and water resource management in the region and climate resilient livelihood.
{"title":"Spatiotemporal variability and trends in rainfall and temperature in the central highlands of Ethiopia","authors":"Marshet Tefera , Engdawork Assefa , Shiferaw Muleta","doi":"10.1016/j.sciaf.2026.e03198","DOIUrl":"10.1016/j.sciaf.2026.e03198","url":null,"abstract":"<div><div>This study investigates the spatiotemporal variability and trends of rainfall and temperature in the Ejerie district, Ethiopia, focusing on the Dega and Weyina Dega agroecological zones. Gridded monthly rainfall data (1990–2020) and temperature records (1990–2018) from the Ethiopian Meteorology Institute were analyzed at a 4 × 4 km resolution. Variability was assessed using mean, standard deviation, coefficient of variation (CV), precipitation concentration index (PCI), and standardized rainfall anomaly (SRA). Trend analysis was conducted using the Mann–Kendall test (MK test) and innovative trend analysis (ITA). Results showed that Dega receives 1245 mm of annual rainfall, while Weyina Dega receives 907.8 mm, with both regions exhibiting low rainfall variability. Rainfall is concentrated in the summer season, followed by Belg and Bega, with notable monthly variability in November and December. The PCI analysis indicated irregular rainfall distributions, with Dega showing 87.09% irregular and 9.68% strongly irregular distributions, while Weyina Dega showed 83.87% irregular and 3.23% strongly irregular distributions. SRA analysis revealed that 83.27% of Dega and 84.48% of Weyina Dega did not experience drought, with minor drought occurrences observed in both regions. Temperature analysis showed significant seasonal differences, with Dega experiencing cooler temperatures than Weyina Dega, which has a warmer climate. Both the MK test and ITA methods yielded consistent temperature trends, with the exception of minor discrepancies in Bega season temperatures. These findings emphasize the importance of localized climate studies to inform area-specific adaptation strategies for agriculture and water resource management in the region and climate resilient livelihood.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03198"},"PeriodicalIF":3.3,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188170","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 : 2026-01-27DOI: 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":"2026-01-27","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}
The exploration and exploitation of metal deposits play a crucial role in economic growth for developing countries. In Cameroon's subtropical zone, specifically within the Bangoua prospect in the Central African Orogenic Belt (CAfOB), these activities are hindered by natural factors (thick lateritic cover, dense vegetation, mountainous terrain, and scarce outcrops) as well as by the high costs of exploration. Despite the uranium potential of this prospect, it remains underexplored. In this study, Landsat-9 OLI-2/TIRS-2 and ASTER images, GIS, and field data analyses were used for mineralogical and structural mapping to identify areas suitable for uranium exploration. Following pre-processing, edge detection using Sobel filters and automatic extraction enabled the identification of lineaments. Spectral enhancement techniques, including band ratios, band combinations, Minimum Noise Fraction (MNF), Principal Component Analysis (PCA), and Linear Spectral Unmixing (LSU), were applied to delineate hydrothermal alteration zones. The lineament networks trend predominantly NE–SW, NNE–SSW, NW–SE, E–W, and N–S, and represent imprints of the Tcholliré–Banyo Shear Zone and the Central Cameroon Shear Zone, developed during the Pan-African orogeny. The extracted zones, rich in iron oxides/hydroxides, clay minerals (kaolinite, pyrophyllite, and alunite), phyllitic minerals (sericite), and propylitic minerals (calcite and epidote), show a good spatial association with the lineaments. Field investigations confirmed the presence of gummite, hydrothermal alteration zones, and metasomaties hosted by shear zones developed during the D2 and D3 deformation phases. Integrating the various thematic layers using fuzzy logic modelling enabled the production of a mineral prospectivity map highlighting high-potential areas. This study provides the first integrated remote sensing-based and modelling approach for uranium exploration in Cameroon and the CAfOB. It also offers valuable guidance for future rapid and sustainable exploration projects in comparable geological contexts worldwide.
{"title":"A multisource remote sensing – Fuzzy logic framework for uranium exploration in the newly identified bangoua prospect, Cameroon","authors":"Adélaïde Flore Masonde Kouo , Bernard Tassongwa , Victor Metang , Ghislain Ngassam Mbianya , Dérryl Médard Foko Tchanamou , Lucresse Mareline Mbouagouoré , Danielle Kamnang Fotso , Armand Sylvain Ludovic Wouatong","doi":"10.1016/j.sciaf.2026.e03202","DOIUrl":"10.1016/j.sciaf.2026.e03202","url":null,"abstract":"<div><div>The exploration and exploitation of metal deposits play a crucial role in economic growth for developing countries. In Cameroon's subtropical zone, specifically within the Bangoua prospect in the Central African Orogenic Belt (CAfOB), these activities are hindered by natural factors (thick lateritic cover, dense vegetation, mountainous terrain, and scarce outcrops) as well as by the high costs of exploration. Despite the uranium potential of this prospect, it remains underexplored. In this study, Landsat-9 OLI-2/TIRS-2 and ASTER images, GIS, and field data analyses were used for mineralogical and structural mapping to identify areas suitable for uranium exploration. Following pre-processing, edge detection using Sobel filters and automatic extraction enabled the identification of lineaments. Spectral enhancement techniques, including band ratios, band combinations, Minimum Noise Fraction (MNF), Principal Component Analysis (PCA), and Linear Spectral Unmixing (LSU), were applied to delineate hydrothermal alteration zones. The lineament networks trend predominantly NE–SW, NNE–SSW, NW–SE, E–W, and N–S, and represent imprints of the Tcholliré–Banyo Shear Zone and the Central Cameroon Shear Zone, developed during the Pan-African orogeny. The extracted zones, rich in iron oxides/hydroxides, clay minerals (kaolinite, pyrophyllite, and alunite), phyllitic minerals (sericite), and propylitic minerals (calcite and epidote), show a good spatial association with the lineaments. Field investigations confirmed the presence of gummite, hydrothermal alteration zones, and metasomaties hosted by shear zones developed during the D<sub>2</sub> and D<sub>3</sub> deformation phases. Integrating the various thematic layers using fuzzy logic modelling enabled the production of a mineral prospectivity map highlighting high-potential areas. This study provides the first integrated remote sensing-based and modelling approach for uranium exploration in Cameroon and the CAfOB. It also offers valuable guidance for future rapid and sustainable exploration projects in comparable geological contexts worldwide.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03202"},"PeriodicalIF":3.3,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188169","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}
Silybum marianum L. known as milk thistle has been evaluated as anti-corrosion agent on mild steel in 1 M HCl. Research mainly targets its medicinal uses, with little attention given to its anticorrosive potential. The objective of this study is to compare the inhibition efficiency of its different parts such as seeds, leaves and stems, knowing that the phytochemicals differ between plant parts. This allows us to understand how variations in phytochemicals across different parts influence their adsorption behavior on metal surfaces which enables to evaluate whether the entire plant can be utilized as a renewable and effective green anti-corrosion agent. For this purpose, the plant parts extracts were evaluated as corrosion inhibitors by employing potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM) analysis. The study demonstrated that all the extracts exhibited a good inhibition efficiency with the effectiveness increasing with increasing the concentration. Specifically, the inhibition efficiency reached 95.5 % for the seeds extract, 94.1 % for the leaves extract and 95.1 % for the stems extract obtained at 2g/l. It follows the order: Seeds extract ≈ Stems extract > Leaves extract. Polarization studies showed that the extracts are mixed-type inhibitors, predominantly affecting the cathodic reaction. The extracts were found to follow the Langmuir adsorption isotherm, involving mainly physical adsorption. Additionally, SEM/EDX analysis provided clear evidence of surface-protective layer formed on the mild steel surface, which serves as a physical barrier against corrosion. These findings strongly indicate that milk thistle part extracts could serve as a green alternative to chemical anti-corrosive agents.
水飞蓟（又称水飞蓟）在1 M HCl溶液中作为低碳钢的防腐蚀剂进行了研究。研究主要针对其药用用途，很少关注其防腐潜力。本研究的目的是比较其不同部位如种子、叶和茎的抑制效率，了解植物各部位的植物化学物质的差异。这使我们能够了解植物化学物质在不同部位的变化如何影响它们在金属表面的吸附行为，从而能够评估整个植物是否可以用作可再生和有效的绿色防腐蚀剂。为此，通过动电位极化（PDP）、电化学阻抗谱（EIS）和扫描电镜（SEM）分析，对植物部位提取物的缓蚀剂进行了评价。研究表明，各提取物均表现出良好的抑菌效果，且抑菌效果随浓度的增加而增强。其中，当浓度为2g/l时，种子提取物的抑制率为95.5%，叶提取物为94.1%，茎提取物为95.1%。其顺序为：种子提取物≈茎提取物>；叶提取物。极化研究表明，提取物为混合型抑制剂，主要影响阴极反应。萃取物遵循Langmuir吸附等温线，主要是物理吸附。此外，SEM/EDX分析提供了明确的证据，表明在低碳钢表面形成了表面保护层，作为抗腐蚀的物理屏障。这些研究结果有力地表明，水飞蓟部分提取物可以作为化学防腐蚀剂的绿色替代品。
{"title":"Comparative analysis of corrosion inhibition efficiency of Silybum marianum L. parts on mild steel in 1 M HCl solution","authors":"Oumayma Iraqi , Issam Saber , Marouane El-Alouani , Ghizlane Doumane , Youness Taboz , Amar Habsaoui","doi":"10.1016/j.sciaf.2026.e03200","DOIUrl":"10.1016/j.sciaf.2026.e03200","url":null,"abstract":"<div><div><em>Silybum marianum</em> L. known as milk thistle has been evaluated as anti-corrosion agent on mild steel in 1 M HCl. Research mainly targets its medicinal uses, with little attention given to its anticorrosive potential. The objective of this study is to compare the inhibition efficiency of its different parts such as seeds, leaves and stems, knowing that the phytochemicals differ between plant parts. This allows us to understand how variations in phytochemicals across different parts influence their adsorption behavior on metal surfaces which enables to evaluate whether the entire plant can be utilized as a renewable and effective green anti-corrosion agent. For this purpose, the plant parts extracts were evaluated as corrosion inhibitors by employing potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM) analysis. The study demonstrated that all the extracts exhibited a good inhibition efficiency with the effectiveness increasing with increasing the concentration. Specifically, the inhibition efficiency reached 95.5 % for the seeds extract, 94.1 % for the leaves extract and 95.1 % for the stems extract obtained at 2g/l. It follows the order: Seeds extract ≈ Stems extract > Leaves extract. Polarization studies showed that the extracts are mixed-type inhibitors, predominantly affecting the cathodic reaction. The extracts were found to follow the Langmuir adsorption isotherm, involving mainly physical adsorption. Additionally, SEM/EDX analysis provided clear evidence of surface-protective layer formed on the mild steel surface, which serves as a physical barrier against corrosion. These findings strongly indicate that milk thistle part extracts could serve as a green alternative to chemical anti-corrosive agents.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03200"},"PeriodicalIF":3.3,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187811","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 was conducted to investigate the integration of a Hydraulic Variable Valve Actuation (HVVA) system into a Lifan 177F single-cylinder gasoline engine to enhance performance, fuel efficiency, and emissions control. Unlike conventional valve mechanisms, HVVA systems are designed to dynamically adjust valve timing and lift through hydraulic actuation, allowing combustion to be optimized across a range of engine loads and speeds. MATLAB and GT-Suite simulations were used to model and evaluate the behavior of various valve spring configurations, including single-valve, dual-valve, and double-spring pendulum designs under dynamic operating conditions. It was found that engine power output was increased by 10%, fuel consumption was reduced by 5.64%, and torque delivery was significantly improved at mid to high RPM ranges. Among the configurations examined, the double-spring pendulum mechanism was shown to be the most effective in minimizing hydraulic oscillations, improving valve stability, and enabling smoother operation at higher speeds. These results highlight the practical potential of HVVA systems for use in compact and cost-sensitive engine platforms, where compliance with stringent emissions regulations and improved efficiency are increasingly demanded. Through precise and adaptive valve control, the proposed HVVA approach is shown to support cleaner combustion and align with broader sustainability objectives. In future work, efforts will be directed toward overcoming integration challenges and refining control strategies to further optimize HVVA system performance in practical automotive applications.
{"title":"Performance enhancement of a single-cylinder gasoline engine through hydraulic variable valve actuation (HVVA) system integration","authors":"Dimamu Biru, kumlachew Yeneneh, Bisrat Yoseph, Tatek Mamo","doi":"10.1016/j.sciaf.2026.e03196","DOIUrl":"10.1016/j.sciaf.2026.e03196","url":null,"abstract":"<div><div>This study was conducted to investigate the integration of a Hydraulic Variable Valve Actuation (HVVA) system into a Lifan 177F single-cylinder gasoline engine to enhance performance, fuel efficiency, and emissions control. Unlike conventional valve mechanisms, HVVA systems are designed to dynamically adjust valve timing and lift through hydraulic actuation, allowing combustion to be optimized across a range of engine loads and speeds. MATLAB and GT-Suite simulations were used to model and evaluate the behavior of various valve spring configurations, including single-valve, dual-valve, and double-spring pendulum designs under dynamic operating conditions. It was found that engine power output was increased by 10%, fuel consumption was reduced by 5.64%, and torque delivery was significantly improved at mid to high RPM ranges. Among the configurations examined, the double-spring pendulum mechanism was shown to be the most effective in minimizing hydraulic oscillations, improving valve stability, and enabling smoother operation at higher speeds. These results highlight the practical potential of HVVA systems for use in compact and cost-sensitive engine platforms, where compliance with stringent emissions regulations and improved efficiency are increasingly demanded. Through precise and adaptive valve control, the proposed HVVA approach is shown to support cleaner combustion and align with broader sustainability objectives. In future work, efforts will be directed toward overcoming integration challenges and refining control strategies to further optimize HVVA system performance in practical automotive applications.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03196"},"PeriodicalIF":3.3,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077792","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}
The present study investigated the effects of Moringa stenopetala (Baker f.) Cufod. trees on soil physico-chemical properties and maize (Zea mays L.) grain yield in the Karfura watershed, located in the Rift valley of Ethiopia. A total of 36 composite soil samples and corresponding maize yield data were collected at three radial distances (1 m, 3 m, and 10 m) from the trunks of M. stenopetala trees across three altitudinal zones (low, mid, and high elevations). Soil samples were analyzed for texture, moisture content, bulk density, pH, total nitrogen (TN), available phosphorus (AP), organic carbon (OC), and electrical conductivity (EC). Maize grain yield was measured from plots established at the same radial distances around each tree. Results showed that soil bulk density increased significantly with distance from the tree trunk, whereas soil moisture, TN, AP, OC, and EC decreased. Maize grain yield was consistently higher under M. stenopetala canopies than in adjacent open fields. Elevation had a strong influence on soil and yield parameters: soil nutrient concentrations, moisture content, and maize yield all declined significantly with increasing altitude. These variations were likely attributed to microclimatic differences, reduced organic matter input, steeper slopes, and more intensive land use at higher elevations. Overall, the findings demonstrate the ecological and agronomic significance of M. stenopetala in smallholder farming systems. Scattered M. stenopetala trees enhance soil fertility and maize productivity, particularly in degraded landscapes. The study highlights the potential of indigenous parkland agroforestry systems as sustainable land management strategies for improving soil quality and strengthening food security. Future research should explore litter decomposition dynamics, soil microbial activity, carbon sequestration potential, and the long-term impacts of M. stenopetala orchard establishment on soil health and productivity.
本研究考察了辣木（Moringa stenopetala, Baker f.）的抗氧化作用。Cufod。在位于埃塞俄比亚裂谷的Karfura流域，树木对土壤理化性质和玉米（Zea mays L.）粮食产量的影响。在低、中、高海拔3个垂直带，在距窄叶木树干1 m、3 m和10 m 3个径向距离处，采集了36个复合土壤样品和相应的玉米产量数据。分析土壤样品的质地、含水量、容重、pH、全氮（TN）、速效磷（AP）、有机碳（OC）和电导率（EC）。玉米籽粒产量是在每棵树周围相同径向距离处建立的地块上测量的。结果表明：随着离树干距离的增加，土壤容重显著增加，土壤水分、总氮、总磷、有机碳和总碳含量显著降低；冠层下玉米籽粒产量始终高于邻近露地。海拔对土壤和产量参数的影响较大，土壤养分浓度、水分含量和玉米产量均随海拔的升高而显著下降。这些变化可能归因于小气候差异、有机质输入减少、坡度更陡以及高海拔地区土地利用集约。综上所述，该研究结果表明了窄叶藻在小农农业系统中的生态和农艺意义。分散的狭叶松树可提高土壤肥力和玉米生产力，特别是在退化的景观中。该研究强调了本土公园农林业系统作为改善土壤质量和加强粮食安全的可持续土地管理战略的潜力。未来的研究应进一步探讨凋落物分解动态、土壤微生物活性、固碳潜力以及建园对土壤健康和生产力的长期影响。
{"title":"The impact of agroforestry on soil health and maize productivity in the Rift valley, Ethiopia","authors":"Gezahegn Gelebo , Gezahegn Kassa , Aynalem Gochera , Yashwant S. Rawat","doi":"10.1016/j.sciaf.2026.e03194","DOIUrl":"10.1016/j.sciaf.2026.e03194","url":null,"abstract":"<div><div>The present study investigated the effects of <em>Moringa stenopetala</em> (Baker f.) Cufod. trees on soil physico-chemical properties and maize (<em>Zea mays</em> L.) grain yield in the Karfura watershed, located in the Rift valley of Ethiopia. A total of 36 composite soil samples and corresponding maize yield data were collected at three radial distances (1 m, 3 m, and 10 m) from the trunks of <em>M. stenopetala</em> trees across three altitudinal zones (low, mid, and high elevations). Soil samples were analyzed for texture, moisture content, bulk density, pH, total nitrogen (TN), available phosphorus (AP), organic carbon (OC), and electrical conductivity (EC). Maize grain yield was measured from plots established at the same radial distances around each tree. Results showed that soil bulk density increased significantly with distance from the tree trunk, whereas soil moisture, TN, AP, OC, and EC decreased. Maize grain yield was consistently higher under <em>M. stenopetala</em> canopies than in adjacent open fields. Elevation had a strong influence on soil and yield parameters: soil nutrient concentrations, moisture content, and maize yield all declined significantly with increasing altitude. These variations were likely attributed to microclimatic differences, reduced organic matter input, steeper slopes, and more intensive land use at higher elevations. Overall, the findings demonstrate the ecological and agronomic significance of <em>M. stenopetala</em> in smallholder farming systems. Scattered <em>M. stenopetala</em> trees enhance soil fertility and maize productivity, particularly in degraded landscapes. The study highlights the potential of indigenous parkland agroforestry systems as sustainable land management strategies for improving soil quality and strengthening food security. Future research should explore litter decomposition dynamics, soil microbial activity, carbon sequestration potential, and the long-term impacts of <em>M. stenopetala</em> orchard establishment on soil health and productivity.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03194"},"PeriodicalIF":3.3,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188187","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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