Scientific African最新文献

{"title":"A Frenet-frame analysis of momentum, force, and yank for variable-mass systems","authors":"Ayman Elsharkawy ,&nbsp;Doaa M.H. Ahmed ,&nbsp;Hoda K. Elsayied ,&nbsp;Maytham E. Desouky","doi":"10.1016/j.sciaf.2026.e03422","DOIUrl":"10.1016/j.sciaf.2026.e03422","url":null,"abstract":"<div><div>Variable-mass systems arise in many physical applications, yet their geometric description within differential geometry remains limited. In particular, the representation of dynamical quantities such as momentum, force, and the time derivative of force (commonly referred to as <em>yank</em>) within the Frenet–Serret framework has been largely restricted to particles of constant mass. To address this gap, this study investigates the force and <em>yank</em> associated with variable-mass motion in three-dimensional Euclidean space, where the Frenet–Serret apparatus is employed as a geometric framework for expressing and resolving these quantities. Analytical expressions for momentum, force, and <em>yank</em> are derived, and these quantities are resolved into their intrinsic geometric components along the tangential, first radial, and second radial directions in both the osculating and rectifying planes. The formulation is illustrated through several trajectories, including circular, logarithmic spiral, and helical paths, highlighting the roles of curvature and torsion in characterizing the motion and their potential relevance in risk modeling and risk analysis of dynamic systems. These results establish a geometric link between classical Newtonian mechanics and provide a framework that may support future interdisciplinary applications in actuarial science and related mathematical fields.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"32 ","pages":"Article e03422"},"PeriodicalIF":3.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184289","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}

{"title":"Leveraging advanced machine learning algorithms and GIS powered by Google Earth Engine for smart biophysical flood vulnerability assessment under climate change: Case study of Tangier-Assilah prefecture (Morocco)","authors":"Yousra El Yacoubi,&nbsp;Miriam Wahbi,&nbsp;Mustapha Maatouk,&nbsp;Omar El Kharki,&nbsp;Hakim Boulaassal,&nbsp;Otmane Yazidi Alaoui","doi":"10.1016/j.sciaf.2026.e03377","DOIUrl":"10.1016/j.sciaf.2026.e03377","url":null,"abstract":"<div><div>Climate change puts the prefecture of Tangier-Assilah under increased risk of flooding, manifested by an upsurge and intensification of rainfall episodes (40 to 130 mm), thus overloading drainage systems and heightening the area’s vulnerability to extreme weather. This study seeks to answer the key research question: how can flood vulnerability be accurately assessed using machine learning and GIS to support spatial risk management in this fast-growing flood-prone area? Six critical variables were selected for their direct impact on runoff generation, water retention capacity, flood exposure and its dynamics: Elevation, Slope, Distance to permanent waters and wetlands, Land Use/Land Cover (LULC), Soil Moisture and Precipitation. The Flood Vulnerability Index (FVI) was developed using the Principal Component Analysis (PCA) to assign weights to each normalized factor involved and reduce the dimensionality while preserving essential variability. Sample points were then extracted and classified by vulnerability level, providing labeled data to train a Support Vector Machine (SVM) model that produced a detailed flood vulnerability map for precise large-scale spatial risk assessment. The model validation showed strong performance across multiple metrics: Coefficient of Determination (R² = 0.921) and Root Mean Square Error (RMSE = 0.230)—chosen for ordinal output suitability—alongside classification metrics including accuracy (99%), Kappa (0.98), F1-score (0.99), and confusion matrix analysis, demonstrating comprehensive fit between predictions and observed data. The results highlighted a significant disparity in vulnerability, with the highest levels found in dense urban areas, low-lying terrains, the surroundings of the Mharhar, El Hachef and Tahaddart Oueds, and coastal areas, emphasizing urgent targeted mitigation and effective risk handling.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"32 ","pages":"Article e03377"},"PeriodicalIF":3.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241710","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}

{"title":"Forest islands in West African savannas: multi-scale evidence of soil and vegetation differentiation across soil profiles and ecosystems","authors":"Samuel Ayodele Mesele ,&nbsp;Jamiu O. Azeez ,&nbsp;Godwin A. Ajiboye ,&nbsp;Amelie Bougma ,&nbsp;Vincent Logah ,&nbsp;Halidou Compaore ,&nbsp;Elmar M. Veenendaal ,&nbsp;Jonathan Lloyd","doi":"10.1016/j.sciaf.2026.e03425","DOIUrl":"10.1016/j.sciaf.2026.e03425","url":null,"abstract":"<div><div>Forest islands embedded within West African savanna landscapes represent striking examples of fine-scale ecological heterogeneity, yet the extent to which they differ from surrounding savannas remains unclear. This study evaluated soil physicochemical properties and vegetation characteristics across paired forest island and savanna sites in Nigeria, Ghana, and Burkina Faso, using a combination of profile-based sampling, mixed-effects modelling, and multivariate analysis. Topsoil (0–20 cm) comparisons showed higher mean organic carbon and total nitrogen in forest islands relative to savannas, although these differences were not statistically significant. In contrast, analysis across soil profiles revealed a significant effect of ecosystem type on soil organic carbon, with forest island soils exhibiting higher values across depths. Organic carbon declined significantly with depth, particularly below 50 cm, but this decline occurred at similar rates in both ecosystems. Multivariate analysis indicated partial differentiation between forest and savanna soils, with ecosystem type explaining 12.6% of the variation in soil chemical composition. Vegetation analyses showed clearer contrasts between ecosystems. Forest islands exhibited significantly lower Shannon diversity but significantly larger stem diameters compared to savannas, while differences in species richness, stem density, and basal area were not statistically significant. Relationships between soil properties and vegetation metrics were generally weak, with soil pH showing the only significant association, indicating a negative relationship with Shannon diversity. Overall, the results demonstrate that differences between forest islands and savannas are scale-dependent. While topsoil properties show high variability and limited statistical separation, profile-level and multivariate analyses reveal more consistent ecosystem differentiation. These findings highlight the importance of multi-scale approaches for understanding soil–vegetation dynamics in heterogeneous tropical landscapes.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"32 ","pages":"Article e03425"},"PeriodicalIF":3.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184174","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}

{"title":"Socioeconomic and environmental evaluation of rural biogas technology in Ethiopia using a multicriteria decision-making approach","authors":"Tewodros Mulu ,&nbsp;Bilhate Chala ,&nbsp;Yezbie Kassa ,&nbsp;Marelign Adugna ,&nbsp;Bernhard Freyer ,&nbsp;Dessie Tibebe","doi":"10.1016/j.sciaf.2026.e03414","DOIUrl":"10.1016/j.sciaf.2026.e03414","url":null,"abstract":"<div><div>Access to reliable and sustainable household energy remains a critical challenge in rural Ethiopia despite the country’s strong commitment to renewable energy expansion. This study applies a Multi-Criteria Decision Analysis (MCDA) framework, based on a weighted sum model, to evaluate the socioeconomic and environmental performance of rural household biogas systems in Aba Warka and Sheha Gomengie kebeles, Amhara Region, Ethiopia. A mixed-methods approach combining household surveys and qualitative interviews was conducted among 17 biogas-adopting households utilizing cow dung and water hyacinth as feedstocks. Seven evaluation criteria were assessed: energy access, health, agriculture, environmental impact, ease of use, time-saving, and community acceptance. Results indicate strong performance in energy access (5/5), health improvement (4/5), and community acceptance (4/5), contributing directly to progress toward Sustainable Development Goals (SDGs) 3 and 7. The overall performance score of 3.75/5 demonstrates significant livelihood enhancement through reduced firewood dependency and improved bio-slurry utilization for agriculture. However, operational and technical challenges—particularly feedstock preprocessing constraints and limited maintenance services—resulted in lower scores for ease of use (2/5), constraining contributions to SDGs 5 and 9. While the relatively small sample size limits broad generalization, the findings provide empirical insights into the integrated socioeconomic and environmental impacts of rural biogas technology. Strengthening local technical capacity, improving feedstock management (especially water hyacinth control), and expanding institutional and financial support mechanisms are critical to enhancing affordability, gender inclusion, and long-term sustainability, thereby accelerating progress toward SDGs 2, 5, 7, 12, 13, and 15.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"32 ","pages":"Article e03414"},"PeriodicalIF":3.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184170","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}

{"title":"Machine learning-driven framework for groundwater quality monitoring in arid coastal aquifers","authors":"Mohamed Haythem Msaddek ,&nbsp;Yahya Moumni ,&nbsp;Lahcen Zouhri ,&nbsp;Bilel Abdelkarim ,&nbsp;Adel Zghibi","doi":"10.1016/j.sciaf.2026.e03421","DOIUrl":"10.1016/j.sciaf.2026.e03421","url":null,"abstract":"<div><div>The strategic design of groundwater quality surveillance systems (GQSS) is essential for accurate monitoring and timely detection of groundwater contamination. AI-assisted optimization approaches, particularly the Ant System meta-heuristic algorithm (AS-MHA), are effective for complex spatial allocation problems, but conventional methods rely on fixed parameters, limiting global search efficiency and slowing convergence. To overcome these limitations, a Dynamic Parameter Optimization Framework based on the Ant System (DPOF-AS) was developed for adaptive parameter tuning. The algorithm was applied to an agriculturally intensive aquifer in Mareth, Southern Tunisia, calibrated with continuous groundwater-level observations and 290 water quality samples collected from wells and boreholes between February and May 2025, and validated against an independent dataset spanning 7 May to 5 August 2025. A Support Vector Regression enhanced with Genetic Algorithm (SVR-GA) was employed as a surrogate model, reducing computational cost and accounting for uncertainties related to aquifer heterogeneity and pollution sources. The surrogate model achieved R² of 0.97 and MRE of 4.6% for the flow model, R² of 0.95 and MRE of 6.8% for nitrate transport, and R² &gt; 0.94 with MRE &lt; 7% for SVR-GA predictions, confirming high predictive accuracy. The DPOF-AS framework generated three optimized monitoring layouts (63, 28, and 15 wells for 1, 2, and 5 km surveillance radii), maintaining spatial representativeness, reducing redundancy, and achieving an average pollutant detection rate of 93.5%, outperforming conventional and random placement strategies. This study demonstrates the novelty of integrating adaptive parameter optimization with AI-enhanced surrogate modeling, providing a practical, cost-effective tool for designing reliable and timely groundwater pollution monitoring networks in complex aquifer systems.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"32 ","pages":"Article e03421"},"PeriodicalIF":3.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148184168","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}

{"title":"Integrated solar system for green hydrogen production and sustainable AC power generation using digital control","authors":"S. YAHYAOUI ,&nbsp;A. AZIZ ,&nbsp;S. DAHBI ,&nbsp;J. BLAACHA ,&nbsp;A. MESSAOUDI","doi":"10.1016/j.sciaf.2025.e03160","DOIUrl":"10.1016/j.sciaf.2025.e03160","url":null,"abstract":"<div><div>Green hydrogen production from solar energy faces critical challenges in power management and system efficiency. This work presents an optimised integrated photovoltaic energy conversion system capable of simultaneously supplying alternating current energy to local loads and producing green hydrogen by water electrolysis. Unlike conventional systems using batteries, the proposed architecture combines a Buck converter controlled by a Perturb-and-Observe algorithm, a Boost converter that stabilises the DC bus at 24 V, and a unipolar pulse width modulation bridge inverter followed by an LC filter to generate a clean sinusoidal voltage with minimal distortion. A cylindrical-electrode electrolyser is directly connected downstream of the Buck converter, where it absorbs excess power, tolerates current and voltage fluctuations, and contributes to stabilising the system which is considered as a key innovation. Experimental tests in Oujda, Morocco, under both stable and highly variable solar irradiance show that the cylindrical electrolyser absorbs up to 85 % of available PV power while maintaining system stability. The system efficiency is 11.7 % which is significantly higher than the 8-9.1 % reported for flat-plate electrolyser systems with battery storage. The proposed system ensures a hydrogen production of 40–60 mL/min with delivering a clean AC power with a total harmonic distortion below 5 %. Overall, the proposed configuration provides an effective alternative for autonomous systems in the African context by adopting a strategy in which the cylindrical electrolyser simultaneously functions as a photovoltaic power adapter and a hydrogen production unit offering a battery-free, cost-effective solution.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03160"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924875","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}

{"title":"Study of the electronic properties of quantum rings under the influence of the electric field in the presence of two off-center impurities","authors":"M. El Hadi ,&nbsp;M. Hbibi ,&nbsp;S. Chouef ,&nbsp;R. Boussetta ,&nbsp;A. El Moussaouy ,&nbsp;O. Mommadi ,&nbsp;C.A. Duque","doi":"10.1016/j.sciaf.2025.e03155","DOIUrl":"10.1016/j.sciaf.2025.e03155","url":null,"abstract":"<div><div>This study examines the electronic properties of a GaAs quantum ring under the influence of external electric fields and donor impurity interactions, utilizing the Finite Difference Method (FDM) and the effective mass approximation. We explore the effects of quantum ring geometry, impurity positioning, and the application of electric fields on the electron–impurity binding energy and the average distance between the electron and the impurities. Our results reveal that the electron–impurity binding energy is strongly influenced by the angular position of the impurities, with stronger interactions observed when the impurities are closer to the symmetry axis of the ring. The application of an external electric field induces the Stark effect, which reduces the binding energy and alters the electron charge distribution. Furthermore, the presence of impurity interactions (<span><math><mrow><mi>κ</mi><mo>=</mo><mn>1</mn></mrow></math></span>) increases the binding energy, while the absence of impurity interactions (<span><math><mrow><mi>κ</mi><mo>=</mo><mn>0</mn></mrow></math></span>) leads to weaker binding. The study also shows that the average distance between the electron and impurities is sensitive to the angular position of the impurities and decreases under the influence of an electric field. These findings underscore the crucial role of quantum ring geometry, external fields, and impurity interactions in shaping the electronic properties of nanoscale devices, providing valuable insights for the development of advanced quantum-based technologies.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03155"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924879","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}

{"title":"A comparative study of estimation methods for the New Sine Topp-Leone Fréchet distribution","authors":"Amal S. Hassan ,&nbsp;Emadeldin I.A. Ali ,&nbsp;Hanene Hamdani ,&nbsp;Ahmed M. Gemeay ,&nbsp;Ahmed W. Shawki ,&nbsp;Mohammed Elgarhy","doi":"10.1016/j.sciaf.2025.e03142","DOIUrl":"10.1016/j.sciaf.2025.e03142","url":null,"abstract":"<div><div>Modeling and fitting complex datasets are crucial domains in various scientific fields. This article presents the sine Topp-Leone Fréchet distribution, a new probability distribution obtained by integrating the Fréchet distribution with the sine Topp-Leone-G family. The suggested model’s most noteworthy feature is its ability to accurately represent a broad variety of hazard rate patterns (such as upside-down, reversed J-shaped, decreasing, and increasing) with just three parameters. Its density may also display unimodal, reversed-J, and right-skewed shapes. We offer a number of statistical properties for the proposed distribution. Using sixteen different estimation approaches, we estimated the parameters of the suggested model and investigated a number of its distributional characteristics. We evaluate the validity of the suggested model and compare the efficacy of various parameter estimates using a Monte Carlo simulation. Numerical analysis shows that the Kolmogorov and second-order left-tail Anderson–Darling estimators exhibit superior efficiency and consistency in a variety of scenarios. They frequently receive the highest overall ranking based on the sum of all performance criteria and consistently produce estimates with low bias and mean squared error. Additionally, we evaluated the suggested distribution’s adaptability using two real-world datasets, demonstrating its remarkable ability to precisely fit the data. According to our findings, the suggested distribution performs better in terms of fit quality than a cotangent Fréchet, odd exponential-logarithmic Fréchet, Topp-Leone Fréchet, odd-generalized exponential inverse Weibull, generalized inverse Weibull distribution, arctan inverse Weibull, new arctan Fréchet, and Fréchet distributions.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03142"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924881","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}

{"title":"Determinants of child survival in Ghana: A multi-level survival analysis of individual and contextual factors","authors":"Dieu Donne Yormekpe ,&nbsp;Benedicta Osei","doi":"10.1016/j.sciaf.2025.e03124","DOIUrl":"10.1016/j.sciaf.2025.e03124","url":null,"abstract":"<div><div>This study investigates child survival determinants in Ghana using a multi-level survival analysis of the 2006, 2011 and 2018 Multiple Indicator Cluster Surveys (n=13,125). We employed Cox proportional hazards, parametric accelerated failure time (AFT) and gamma frailty models to assess individual and contextual factors influencing under-five mortality, accounting for unobserved heterogeneity. Key findings reveal a wealth-mortality relationship, with middle-wealth households showing lower mortality (HR=0.60, TR=1.44) compared to low-wealth, while rich households exhibited higher risks (HR=1.84, TR=0.64). Maternal education significantly reduces mortality risk (primary: HR=0.69, TR=1.28; secondary+: HR=0.86, TR=1.12). Institutional deliveries lower mortality by 29%–43% (TR=1.29–1.43). Northern regions (Upper West: HR=2.53, TR=0.50; Northern: HR=2.24, TR=0.56) face elevated risks, reflecting structural disparities. Gamma frailty models revealed a significant frailty variance (<span><math><mrow><mi>θ</mi><mo>&gt;</mo><mn>0</mn></mrow></math></span>), confirming substantial unobserved heterogeneity across regions and indicating that unmeasured, region-specific factors influence child mortality. The Weibull AFT model provided the best fit (AIC=21,999.45), highlighting early-life mortality risks. The policy implications are clear that interventions must be spatially targeted and move beyond simple wealth stratification, requiring specific environmental and infrastructural improvements tailored to high-risk and economically disparate areas of Ghana.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03124"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683114","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}

{"title":"Forecasting temperature data for two counties in California using the stress–strength reliability of the MO q-GEVL distribution: A return level based approach and time series","authors":"Eslam Abdelhakim Seyam ,&nbsp;Shimaa Wasfy Sadk ,&nbsp;Ibrahim A. Fares ,&nbsp;Rasha Abd El-Wahab Attwa","doi":"10.1016/j.sciaf.2026.e03207","DOIUrl":"10.1016/j.sciaf.2026.e03207","url":null,"abstract":"<div><div>In this article, the stress–strength reliability (<span><math><mrow><mi>S</mi><mi>S</mi><mi>R</mi><mo>=</mo><mi>P</mi><mrow><mo>(</mo><mi>X</mi><mo>&lt;</mo><mi>Y</mi><mo>)</mo></mrow></mrow></math></span>), where both stress <span><math><mi>X</mi></math></span> and strength <span><math><mi>Y</mi></math></span> follow a new model called Marshall Olkin’s q extended generalized extreme value linear distribution (<span><math><mi>M</mi></math></span>-<span><math><mrow><mi>q</mi><mi>GEVL</mi></mrow></math></span>) under type II progressive censoring, is investigated. The <span><math><mrow><mi>S</mi><mi>S</mi><mi>R</mi></mrow></math></span> estimation is considered using both maximum likelihood estimation (<span><math><mrow><mi>M</mi><mi>L</mi><mi>E</mi></mrow></math></span>) and Bayesian estimation (<span><math><mrow><mi>B</mi><mi>E</mi></mrow></math></span>). Since the <span><math><mrow><mi>M</mi><mi>L</mi><mi>E</mi></mrow></math></span> equations are extremely complex, a metaheuristic optimization technique called the Whale Optimization Algorithm (WOA) is utilized. The <span><math><mrow><mi>B</mi><mi>E</mi></mrow></math></span> is considered for both informative and non informative cases using both Linex and square error loss functions. Temperature data from Sacramento and Los Angeles counties in California are also used to demonstrate the proposed statistical methodology, showing that the <span><math><mrow><mi>M</mi><mtext>-</mtext><mi>q</mi><mi>G</mi><mi>E</mi><mi>V</mi><mi>L</mi></mrow></math></span> model provides a better fit than both <span><math><mrow><mi>q</mi><mi>G</mi><mi>E</mi><mi>V</mi><mi>L</mi></mrow></math></span> and <span><math><mrow><mi>G</mi><mi>E</mi><mi>V</mi><mi>L</mi></mrow></math></span> based on lower <span><math><mrow><mi>S</mi><mi>K</mi></mrow></math></span>, <span><math><mrow><mi>A</mi><mi>I</mi><mi>C</mi></mrow></math></span>, and <span><math><mrow><mi>B</mi><mi>I</mi><mi>C</mi></mrow></math></span> values. Time series analysis, return level, and <span><math><mrow><mi>S</mi><mi>S</mi><mi>R</mi></mrow></math></span> are applied to predict future temperatures, and the results consistently indicate higher predicted temperatures in Los Angeles than in Sacramento.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03207"},"PeriodicalIF":3.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077788","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}