This study addresses key hydraulic engineering challenges in turbulent pipe flow - computing flow rate (Q), hydraulic energy slope , and pipe diameter (D) - by introducing the Modified Rough Model Method (MRMM). We propose novel, high-precision explicit equations for D (Eqs. 56 and 60). These achieve maximum relative errors of 0.017 % and 0.0086 %, respectively. We also introduce an innovative friction factor equation (54) with 0.086 % error. Validated across the entire Moody diagram (, and ) using a brute-force approach with over 7 million data points, these non-iterative solutions outperform existing models. A comprehensive set of statistical metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficients (R² and Pearson's R), Bias, Mean Relative Error (MRE), Standard Deviation (SD), Coefficient of Variation (CV), and maximum relative error were employed to assess the accuracy and reliability of the proposed and existing formulas; the results of the Statistical metrics confirm their robustness, establishing a new benchmark for accuracy in pipeline design. This advancement enhances efficiency and reliability in water, oil, and gas transport systems.
{"title":"Novel high-precision explicit equations for pipe diameter in turbulent flow via modified rough model method (MRMM)","authors":"Imene Foual , Lotfi Zeghadnia , Giuseppe Oliveto , Fares Laouacheria , Kaan Yetilmezsoy","doi":"10.1016/j.sciaf.2025.e03107","DOIUrl":"10.1016/j.sciaf.2025.e03107","url":null,"abstract":"<div><div>This study addresses key hydraulic engineering challenges in turbulent pipe flow - computing flow rate (Q), hydraulic energy slope <span><math><msub><mi>S</mi><mi>f</mi></msub></math></span>, and pipe diameter (D) - by introducing the Modified Rough Model Method (MRMM). We propose novel, high-precision explicit equations for D (Eqs. 56 and 60). These achieve maximum relative errors of 0.017 % and 0.0086 %, respectively. We also introduce an innovative friction factor equation (54) with 0.086 % error. Validated across the entire Moody diagram (<span><math><mrow><mrow><mi>ε</mi></mrow><mo>/</mo><mi>D</mi><mo>=</mo><mn>0</mn><mrow><mspace></mspace><mtext>to</mtext></mrow><mspace></mspace><mn>0.05</mn></mrow></math></span>, and <span><math><mrow><mn>2300</mn><mo>≤</mo><mi>R</mi><mo>≤</mo><msup><mrow><mn>10</mn></mrow><mn>8</mn></msup></mrow></math></span>) using a brute-force approach with over 7 million data points, these non-iterative solutions outperform existing models. A comprehensive set of statistical metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficients (R² and Pearson's R), Bias, Mean Relative Error (MRE), Standard Deviation (SD), Coefficient of Variation (CV), and maximum relative error were employed to assess the accuracy and reliability of the proposed and existing formulas; the results of the Statistical metrics confirm their robustness, establishing a new benchmark for accuracy in pipeline design. This advancement enhances efficiency and reliability in water, oil, and gas transport systems.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"31 ","pages":"Article e03107"},"PeriodicalIF":3.3,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617378","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 : 2025-11-15DOI: 10.1016/j.sciaf.2025.e03096
Bocar SY, Cheikh Moustapha Wally Diouf, Ibrahima Dia
Gold exploration in West Africa increasingly demands innovative, cost-effective approaches that reduce ground-based interventions while maintaining spatial accuracy. This study introduces a novel integrative framework that combines geostatistical modeling and multispectral remote sensing to enhance gold targeting in the Sabodala region of southeastern Senegal—an area located within the prolific Birimian greenstone belt. Using a dataset of 3′103 drill-hole gold grade samples, three geostatistical interpolation methods—Ordinary Kriging (OK), Universal Kriging (UK), and Indicator Kriging (IK)—were applied to model subsurface gold distribution over a 30 km² area. Concurrently, Landsat 8 OLI/TIRS data were processed to derive surface indicators of mineralization, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Soil Index (NDSI), Silicate Index, and Land Surface Temperature.
A key innovation of this study lies in the spatial overlap analysis between kriging-based predictions and remote sensing-derived indices, which revealed strong to moderate spatial concordance—particularly between IK and the Silicate Index (62 %), and UK with NDSI (55 %). These results suggest that lithological and alteration signatures captured by remote sensing can reinforce subsurface gold predictions. Moreover, NDVI and temperature anomalies exhibited additional but weaker associations (28–47 %), supporting their value as indirect proxies in data-limited zones.
This integrative, multi-scale methodology offers a replicable and scalable model for the beginning-stages of the mineral exploration in other terrains. By combining spatial statistics and earth observation tools, the study bridges the gap between surface and subsurface data, improving precision in gold prospecting while reducing financial and environmental costs.
{"title":"Identification of the high gold-potential zones: A case study of the Sabodala gold mine, Senegal, by the integration of the geostatistical and remote sensing tools","authors":"Bocar SY, Cheikh Moustapha Wally Diouf, Ibrahima Dia","doi":"10.1016/j.sciaf.2025.e03096","DOIUrl":"10.1016/j.sciaf.2025.e03096","url":null,"abstract":"<div><div>Gold exploration in West Africa increasingly demands innovative, cost-effective approaches that reduce ground-based interventions while maintaining spatial accuracy. This study introduces a novel integrative framework that combines geostatistical modeling and multispectral remote sensing to enhance gold targeting in the Sabodala region of southeastern Senegal—an area located within the prolific Birimian greenstone belt. Using a dataset of 3′103 drill-hole gold grade samples, three geostatistical interpolation methods—Ordinary Kriging (OK), Universal Kriging (UK), and Indicator Kriging (IK)—were applied to model subsurface gold distribution over a 30 km² area. Concurrently, Landsat 8 OLI/TIRS data were processed to derive surface indicators of mineralization, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Soil Index (NDSI), Silicate Index, and Land Surface Temperature.</div><div>A key innovation of this study lies in the spatial overlap analysis between kriging-based predictions and remote sensing-derived indices, which revealed strong to moderate spatial concordance—particularly between IK and the Silicate Index (62 %), and UK with NDSI (55 %). These results suggest that lithological and alteration signatures captured by remote sensing can reinforce subsurface gold predictions. Moreover, NDVI and temperature anomalies exhibited additional but weaker associations (28–47 %), supporting their value as indirect proxies in data-limited zones.</div><div>This integrative, multi-scale methodology offers a replicable and scalable model for the beginning-stages of the mineral exploration in other terrains. By combining spatial statistics and earth observation tools, the study bridges the gap between surface and subsurface data, improving precision in gold prospecting while reducing financial and environmental costs.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03096"},"PeriodicalIF":3.3,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568704","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 : 2025-11-12DOI: 10.1016/j.sciaf.2025.e03090
Lindani Koketso Ncube , Albert Uchenna Ude , Enoch Nifise Ogunmuyiwa , Isaac Nongwe Beas
The major contributor of plastic pollution is the food packaging sector and efforts to replace the traditional plastic packaging with eco-friendly biocomposites can be a viable solution. Polylactic acid (PLA) is a biopolymer with potential in this regard but it has inherent challenges that could be countered by several methods including composite manufacture. In this work, biocomposites from PLA and Bambara groundnut shells (BGS) were successfully prepared by solution cast method with varying weight percentages (1 – 25 wt%) of BGS filler. Samples of the control (PLA) and the biocomposites were then characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), tensile testing, and thermogravimetric analysis (TGA) to investigate the structural, morphological, mechanical and thermal properties. The specimens were further probed for water absorption (WA), water vapour permeability (WVP), wettability, food contact migration (FCM), and soil burial biodegradability (SBD) properties. The results showed that the biocomposites successfully produced, achieved highest tensile property of 34.9 MPa with 5 wt% BGS loading, which was a 16.1 % increase when compared with the control. Wettability, WA, WVP and SBD of the PLA/BGS biocomposites were generally amplified with increase in BGS filler content. FCM analysis showed that most of the biocomposites did not exceed the overall migration limit (10 mg/dm2) and thus were acceptable for use as food packaging materials. Analysis of variance (p < 0.05) showed that the biocomposite properties with different filler loading had significant enhancements. The PLA/BGS biocomposites showed promise in application as sustainable green food packaging materials.
{"title":"Development and characterisation of Polylactic acid / Bambara groundnut (Vigna subterranea (L.) Verdc) shells biocomposite for food packaging application","authors":"Lindani Koketso Ncube , Albert Uchenna Ude , Enoch Nifise Ogunmuyiwa , Isaac Nongwe Beas","doi":"10.1016/j.sciaf.2025.e03090","DOIUrl":"10.1016/j.sciaf.2025.e03090","url":null,"abstract":"<div><div>The major contributor of plastic pollution is the food packaging sector and efforts to replace the traditional plastic packaging with eco-friendly biocomposites can be a viable solution. Polylactic acid (PLA) is a biopolymer with potential in this regard but it has inherent challenges that could be countered by several methods including composite manufacture. In this work, biocomposites from PLA and Bambara groundnut shells (BGS) were successfully prepared by solution cast method with varying weight percentages (1 – 25 wt%) of BGS filler. Samples of the control (PLA) and the biocomposites were then characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), tensile testing, and thermogravimetric analysis (TGA) to investigate the structural, morphological, mechanical and thermal properties. The specimens were further probed for water absorption (WA), water vapour permeability (WVP), wettability, food contact migration (FCM), and soil burial biodegradability (SBD) properties. The results showed that the biocomposites successfully produced, achieved highest tensile property of 34.9 MPa with 5 wt% BGS loading, which was a 16.1 % increase when compared with the control. Wettability, WA, WVP and SBD of the PLA/BGS biocomposites were generally amplified with increase in BGS filler content. FCM analysis showed that most of the biocomposites did not exceed the overall migration limit (10 mg/dm<sup>2</sup>) and thus were acceptable for use as food packaging materials. Analysis of variance (p < 0.05) showed that the biocomposite properties with different filler loading had significant enhancements. The PLA/BGS biocomposites showed promise in application as sustainable green food packaging materials.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03090"},"PeriodicalIF":3.3,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568706","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 : 2025-11-11DOI: 10.1016/j.sciaf.2025.e03088
Emmanuel Amankwah , Daniel Afedor , Napoleon Jackson Mensah , Edward Nunoo
Greywater reuse is essential in light of the growing water scarcity, which has left one-third of the world's population without access to clean water. Greywater treatment and reuse is an alternative method to alleviate freshwater withdrawal pressures and reduction of wastewater (pollutants) discharge into the environment. In this pilot-scale experimental study, a slow sand filter system (SSF) was constructed to treat greywater for non-potable use at the GETFund hostel of the University of Energy and Natural Resources (UENR), Ghana. The treatment efficiency of the constructed SSF was assessed and the results compared with the GEPA standards. A grab sampling technique was used, and six samples of greywater were collected in February 2025. The slow sand filter was set-up using fine sand, coarse sand, gravel and stones of diameters 0.2–0.5 mm, 1–2 mm, 2– 4 cm and 6 cm, respectively. The fine sand filter had a uniformity coefficient (UC) of 1.83, porosity of 0.35 and a specific density of 1.48 mg/m3. The slow sand filter has a hydraulic retention/residence time (HRT) of 12 h and a hydraulic loading rate (HLR) of 0.48 m3 per day. The treated greywater effluent was compared with the Ghana Environmental Protection Agency (GEPA) standards for discharge of wastewater. The average effluent values and corresponding percentage reduction efficiencies for physicochemical parameters, including COD, turbidity, BOD, alkalinity, EC, TDS, colour, bicarbonate, pH, and temperature, are 85.99 %, 77.95 %, 75.00 %, 70.04 %, 53.80 %, 53.67 %, 53.62 %, 8.48 %, 8.00 % and 7.80 %, respectively. SSF technology removed most contaminants, but showed low efficiency with respect to bicarbonate, pH, and temperature. The treated greywater complied with the GEPA guidelines; however, for practical usage, the test should be extended to incorporate microbiological characteristics and nutrients, which are essential for greywater reuse. This pilot study is a prelude to an extensive investigation on SSF.
{"title":"Application of slow sand filter for greywater treatment at GETFund hostel, UENR, Sunyani","authors":"Emmanuel Amankwah , Daniel Afedor , Napoleon Jackson Mensah , Edward Nunoo","doi":"10.1016/j.sciaf.2025.e03088","DOIUrl":"10.1016/j.sciaf.2025.e03088","url":null,"abstract":"<div><div>Greywater reuse is essential in light of the growing water scarcity, which has left one-third of the world's population without access to clean water. Greywater treatment and reuse is an alternative method to alleviate freshwater withdrawal pressures and reduction of wastewater (pollutants) discharge into the environment. In this pilot-scale experimental study, a slow sand filter system (SSF) was constructed to treat greywater for non-potable use at the GETFund hostel of the University of Energy and Natural Resources (UENR), Ghana. The treatment efficiency of the constructed SSF was assessed and the results compared with the GEPA standards. A grab sampling technique was used, and six samples of greywater were collected in February 2025. The slow sand filter was set-up using fine sand, coarse sand, gravel and stones of diameters 0.2–0.5 mm, 1–2 mm, 2– 4 cm and 6 cm, respectively. The fine sand filter had a uniformity coefficient (UC) of 1.83, porosity of 0.35 and a specific density of 1.48 mg/m<sup>3</sup>. The slow sand filter has a hydraulic retention/residence time (HRT) of 12 h and a hydraulic loading rate (HLR) of 0.48 m<sup>3</sup> per day. The treated greywater effluent was compared with the Ghana Environmental Protection Agency (GEPA) standards for discharge of wastewater. The average effluent values and corresponding percentage reduction efficiencies for physicochemical parameters, including COD, turbidity, BOD, alkalinity, EC, TDS, colour, bicarbonate, pH, and temperature, are 85.99 %, 77.95 %, 75.00 %, 70.04 %, 53.80 %, 53.67 %, 53.62 %, 8.48 %, 8.00 % and 7.80 %, respectively. SSF technology removed most contaminants, but showed low efficiency with respect to bicarbonate, pH, and temperature. The treated greywater complied with the GEPA guidelines; however, for practical usage, the test should be extended to incorporate microbiological characteristics and nutrients, which are essential for greywater reuse. This pilot study is a prelude to an extensive investigation on SSF.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03088"},"PeriodicalIF":3.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568705","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 examines spatiotemporal variations in terrestrial water storage (TWS) and drought conditions over Ethiopia using Gravity Recovery and Climate Experiment and its Follow-On mission (GRACE (-FO)) satellite data and the water balance method. The present analyzed monthly GRACE/GRACE-FO Mascon (CSR RL06, release 0602) terrestrial water storage anomalies (TWSA) together with GLDAS over Ethiopia. The GRACE-derived TWS anomalies were compared with the Global Land Data Assimilation System (GLDAS) data to estimate groundwater storage changes. The results reveal substantial spatial variability in water storage across the country. Western Ethiopia showed significant increases in TWS, while eastern regions exhibited notable decreases, indicating potential drought conditions. Comparison of GRACE-based TWSA with a water-balance TWSA (P–ET–R from GLDAS) shows a moderate correlation (r = 0.64, two-sided p < 0.001; n ≈ 240 monthly pairs), with GRACE capturing larger amplitude variability. GRACE-derived groundwater storage and in-situ well observations (2013–2017) exhibit a moderate association (r ≈ 0.5, two-sided p < 0.001; n ≈ 55 months). Agreement between GRACE- and GLDAS-based groundwater storage is also moderate (r = 0.53, p < 0.001; n ≈ 240). Groundwater storage changes derived from GRACE (-FO) were validated using in-situ well measurements, showing a similar overall trend, although GRACE data captured more pronounced short-term fluctuations. The correlation coefficient between GRACE-derived groundwater storage and well data was moderate, suggesting that GRACE accurately reflects regional groundwater fluctuations. Drought conditions were monitored using the Total Storage Deficit Index (TSDI), showing that the western regions of Ethiopia experienced lower drought deficiency while the eastern areas exhibited higher drought severity. These findings have important implications for sustainable water resource management, particularly in drought-prone regions where accurate monitoring of water storage is crucial for mitigating the effects of climate change on agriculture and water security. The study highlights the role of satellite-based data in supporting resilient and sustainable water management strategies.
{"title":"Spatiotemporal variations in terrestrial water storage: A comparative analysis using gravimetry satellite data and water balance methods over Ethiopia","authors":"Natnael Agegnehu Ayele , Andenet Ashagrie Gedamu , Wondwossen Mindahun , Muralitharan Jothimani","doi":"10.1016/j.sciaf.2025.e03086","DOIUrl":"10.1016/j.sciaf.2025.e03086","url":null,"abstract":"<div><div>This study examines spatiotemporal variations in terrestrial water storage (TWS) and drought conditions over Ethiopia using Gravity Recovery and Climate Experiment and its Follow-On mission (GRACE (-FO)) satellite data and the water balance method. The present analyzed monthly GRACE/GRACE-FO Mascon (CSR RL06, release 0602) terrestrial water storage anomalies (TWSA) together with GLDAS over Ethiopia. The GRACE-derived TWS anomalies were compared with the Global Land Data Assimilation System (GLDAS) data to estimate groundwater storage changes. The results reveal substantial spatial variability in water storage across the country. Western Ethiopia showed significant increases in TWS, while eastern regions exhibited notable decreases, indicating potential drought conditions. Comparison of GRACE-based TWSA with a water-balance TWSA (P–ET–R from GLDAS) shows a moderate correlation (<em>r</em> = 0.64, two-sided <em>p</em> < 0.001; <em>n</em> ≈ 240 monthly pairs), with GRACE capturing larger amplitude variability. GRACE-derived groundwater storage and in-situ well observations (2013–2017) exhibit a moderate association (<em>r</em> ≈ 0.5, two-sided <em>p</em> < 0.001; <em>n</em> ≈ 55 months). Agreement between GRACE- and GLDAS-based groundwater storage is also moderate (<em>r</em> = 0.53, <em>p</em> < 0.001; <em>n</em> ≈ 240). Groundwater storage changes derived from GRACE (-FO) were validated using in-situ well measurements, showing a similar overall trend, although GRACE data captured more pronounced short-term fluctuations. The correlation coefficient between GRACE-derived groundwater storage and well data was moderate, suggesting that GRACE accurately reflects regional groundwater fluctuations. Drought conditions were monitored using the Total Storage Deficit Index (TSDI), showing that the western regions of Ethiopia experienced lower drought deficiency while the eastern areas exhibited higher drought severity. These findings have important implications for sustainable water resource management, particularly in drought-prone regions where accurate monitoring of water storage is crucial for mitigating the effects of climate change on agriculture and water security. The study highlights the role of satellite-based data in supporting resilient and sustainable water management strategies.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03086"},"PeriodicalIF":3.3,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516484","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 : 2025-11-08DOI: 10.1016/j.sciaf.2025.e03083
Nuran M. Hassan , Mohamed Nejib Ouertani , Mustafa Ibrahim Ahmed Araibi , Ehab M. Almetwally , Mohammed Elgarhy , Sid Ahmed Benchiha , Ahmed M. Gemeay
Ranked set sampling (RSS) is especially effective when direct measurement of a variable is costly or time-consuming, but ranking the items on the basis of that variable is relatively simple and inexpensive. The unit compound Rayleigh (UCR) model is an effective method for capturing the properties of data sets that are affected by negatively skewed data. For the purpose of this study, a comprehensive comparison of several estimation methods is planned. These methods include maximum likelihood, Anderson–Darling, Cramer–von-Mises, maximum product of spacings, least squares, minimum spacing absolute distance, minimum spacing absolute-log distance, minimum spacing square distance, minimum spacing square-log distance, and minimum spacing Linex distance under RSS and SRS techniques. The results of the simulation demonstrate that the maximum product spacing and maximum likelihood estimation methods are preferable to alternative approaches when evaluating the quality of RSS and SRS estimates, respectively. In addition, the findings highlight the efficiency advantages of RSS over SRS, as evidenced by improved accuracy metrics. The practical importance of our findings is demonstrated by a practical application involving the survival time of 72 guinea pigs infected with the virulent tuberculosis bacillus.
{"title":"Statistical inference for the unit compound Rayleigh model under ranked set sampling with application","authors":"Nuran M. Hassan , Mohamed Nejib Ouertani , Mustafa Ibrahim Ahmed Araibi , Ehab M. Almetwally , Mohammed Elgarhy , Sid Ahmed Benchiha , Ahmed M. Gemeay","doi":"10.1016/j.sciaf.2025.e03083","DOIUrl":"10.1016/j.sciaf.2025.e03083","url":null,"abstract":"<div><div>Ranked set sampling (RSS) is especially effective when direct measurement of a variable is costly or time-consuming, but ranking the items on the basis of that variable is relatively simple and inexpensive. The unit compound Rayleigh (UCR) model is an effective method for capturing the properties of data sets that are affected by negatively skewed data. For the purpose of this study, a comprehensive comparison of several estimation methods is planned. These methods include maximum likelihood, Anderson–Darling, Cramer–von-Mises, maximum product of spacings, least squares, minimum spacing absolute distance, minimum spacing absolute-log distance, minimum spacing square distance, minimum spacing square-log distance, and minimum spacing Linex distance under RSS and SRS techniques. The results of the simulation demonstrate that the maximum product spacing and maximum likelihood estimation methods are preferable to alternative approaches when evaluating the quality of RSS and SRS estimates, respectively. In addition, the findings highlight the efficiency advantages of RSS over SRS, as evidenced by improved accuracy metrics. The practical importance of our findings is demonstrated by a practical application involving the survival time of 72 guinea pigs infected with the virulent tuberculosis bacillus.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03083"},"PeriodicalIF":3.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516351","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 investigated the Urban Heat Island (UHI) effect in Addis Ababa, Ethiopia, caused by rapid urbanization and changes in land use and land cover (LULC). The problem of increasing land surface temperatures (LST) in urban areas is linked to the expansion of impervious surfaces like asphalt and buildings, which exacerbate heat retention and impact the environment and public health. Using Landsat 8 satellite imagery, the study analyzed LST variations through remote sensing techniques, focusing on the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI). Regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were applied to assess the spatial relationship between LST and LULC changes. The results revealed that vegetated areas had a strong negative correlation (r = -0.8293) with LST, indicating a cooling effect. In contrast, built-up areas showed a positive correlation (r = 0.4491) with LST, contributing to higher temperatures. LST values in the city ranged from 10°C to 44°C, with the highest temperatures recorded in urbanized areas and the lowest in vegetated zones. These findings emphasize the importance of expanding green spaces to mitigate the UHI effect. The study's results are vital for guiding urban planning policies aimed at reducing heat-related risks in rapidly urbanizing cities, helping achieve SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). The integration of green infrastructure can play a critical role in reducing the adverse impacts of urban heat islands.
{"title":"Assessing urban heat island effects in Addis Ababa, Ethiopia using remote sensing and LULC analysis: Implications for sustainable development","authors":"Tsegaye Ayele Tadesse , Natnael Agegnehu Ayele , Talema Moged Reda , Muralitharan Jothimani","doi":"10.1016/j.sciaf.2025.e03081","DOIUrl":"10.1016/j.sciaf.2025.e03081","url":null,"abstract":"<div><div>This study investigated the Urban Heat Island (UHI) effect in Addis Ababa, Ethiopia, caused by rapid urbanization and changes in land use and land cover (LULC). The problem of increasing land surface temperatures (LST) in urban areas is linked to the expansion of impervious surfaces like asphalt and buildings, which exacerbate heat retention and impact the environment and public health. Using Landsat 8 satellite imagery, the study analyzed LST variations through remote sensing techniques, focusing on the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-Up Index (NDBI). Regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were applied to assess the spatial relationship between LST and LULC changes. The results revealed that vegetated areas had a strong negative correlation (r = -0.8293) with LST, indicating a cooling effect. In contrast, built-up areas showed a positive correlation (r = 0.4491) with LST, contributing to higher temperatures. LST values in the city ranged from 10°C to 44°C, with the highest temperatures recorded in urbanized areas and the lowest in vegetated zones. These findings emphasize the importance of expanding green spaces to mitigate the UHI effect. The study's results are vital for guiding urban planning policies aimed at reducing heat-related risks in rapidly urbanizing cities, helping achieve SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). The integration of green infrastructure can play a critical role in reducing the adverse impacts of urban heat islands.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03081"},"PeriodicalIF":3.3,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516359","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 : 2025-11-05DOI: 10.1016/j.sciaf.2025.e03079
Younes Gaga , Ajdi Mouhcine , Safaa benmassoud , Kara Mohammed , Abderrahim Bouhaddioui , Jamila Bahhou
This study demonstrates that Vetiveria zizanioides (Vetiver grass) can effectively remediate wastewater enriched in polyphenols, a major pollutant from olive-oil production. High phenolic and organic loads pose serious environmental risks and challenge conventional treatment methods. To address this, we developed an innovative hybrid treatment prototype at the Fez wastewater plant, combining trickling filters, planted filters, and activated sludge with vetiver’s extensive root system and active aeration. Wastewater containing 40–100 mg/L polyphenols was applied, and daily measurements of phenols, pH, dissolved oxygen, nitrogen, phosphorus, and chemical oxygen demand (COD) were recorded over eleven days. Unlike previous studies, this research tested high pollutant loads, implemented the system at a real wastewater treatment facility, and monitored multiple parameters daily to provide detailed pollutant-removal dynamics. Results revealed substantial removal: 91 % COD, 87.5 % phenols, 94.1 % nitrogen, and 80.72 % phosphorus, while vetiver maintained healthy growth under high contamination. These findings highlight the resilience and strong phytoremediation capacity of vetiver, demonstrating a scalable, cost-effective, and environmentally sustainable alternative to conventional wastewater treatment. The study advances current knowledge by combining hybrid treatment systems, real-world application, and high-load testing, supporting municipalities and industries in improving wastewater management, reducing treatment costs, and mitigating environmental impacts.
{"title":"A new hybrid Vetiver system for the treatment of polyphenol-rich wastewater from olive oil production","authors":"Younes Gaga , Ajdi Mouhcine , Safaa benmassoud , Kara Mohammed , Abderrahim Bouhaddioui , Jamila Bahhou","doi":"10.1016/j.sciaf.2025.e03079","DOIUrl":"10.1016/j.sciaf.2025.e03079","url":null,"abstract":"<div><div>This study demonstrates that <em>Vetiveria zizanioides</em> (Vetiver grass) can effectively remediate wastewater enriched in polyphenols, a major pollutant from olive-oil production. High phenolic and organic loads pose serious environmental risks and challenge conventional treatment methods. To address this, we developed an innovative hybrid treatment prototype at the Fez wastewater plant, combining trickling filters, planted filters, and activated sludge with vetiver’s extensive root system and active aeration. Wastewater containing 40–100 mg/L polyphenols was applied, and daily measurements of phenols, pH, dissolved oxygen, nitrogen, phosphorus, and chemical oxygen demand (COD) were recorded over eleven days. Unlike previous studies, this research tested high pollutant loads, implemented the system at a real wastewater treatment facility, and monitored multiple parameters daily to provide detailed pollutant-removal dynamics. Results revealed substantial removal: 91 % COD, 87.5 % phenols, 94.1 % nitrogen, and 80.72 % phosphorus, while vetiver maintained healthy growth under high contamination. These findings highlight the resilience and strong phytoremediation capacity of vetiver, demonstrating a scalable, cost-effective, and environmentally sustainable alternative to conventional wastewater treatment. The study advances current knowledge by combining hybrid treatment systems, real-world application, and high-load testing, supporting municipalities and industries in improving wastewater management, reducing treatment costs, and mitigating environmental impacts.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03079"},"PeriodicalIF":3.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516352","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 : 2025-11-05DOI: 10.1016/j.sciaf.2025.e03077
Agatha Abokwara , Chinwendu E. Madubueze , Faraimunashe Chirove
We employ PRISMA guidelines to carry out a systematic review of mathematical models of schistosomiasis organizing them into focus and approach based categories. Our analysis reveals a range of substantive challenges inherent in modelling disease dynamics, including epidemiological heterogeneity, the intrinsic complexity of disease systems, uncertainties surrounding data accuracy, and the critical need for interdisciplinary research collaboration. Furthermore, our review identifies that numerous models are formulated using ordinary differential equations, while others incorporate partial differential equations, stochastic differential equations, delay differential equations, and machine learning techniques, reflecting the methodological diversity employed to capture the multifaceted nature of infectious disease transmission. A few of them incorporate population structure as well as environmental factors such as seasonal conditions and water contact patterns. We also find that many models have limitations, and we highlight areas where future research is needed emphasizing a compelling need for better coordination and standard practices in how models are built. Models about vaccination are useful in showing how vaccines can help once they become available and models that look at co-infection show how schistosomiasis interacts with other diseases. Overall, our study shows how helpful mathematical models are in solving real-world problems. Additionally, the challenges posed by schistosomiasis underscore the importance of investing in research that utilizes mathematical modelling. We stress the need for researchers from different fields to work together more closely to fight the spread of schistosomiasis. It is recommended that future models adopt reliable methodologies that integrate multistage modelling, hybrid approaches, and agent-based frameworks. These models should also explicitly account for regional differences to enhance accuracy, relevance, and applicability across diverse contexts.
{"title":"Understanding schistosomiasis transmission: A systematic review of mathematical models","authors":"Agatha Abokwara , Chinwendu E. Madubueze , Faraimunashe Chirove","doi":"10.1016/j.sciaf.2025.e03077","DOIUrl":"10.1016/j.sciaf.2025.e03077","url":null,"abstract":"<div><div>We employ PRISMA guidelines to carry out a systematic review of mathematical models of schistosomiasis organizing them into focus and approach based categories. Our analysis reveals a range of substantive challenges inherent in modelling disease dynamics, including epidemiological heterogeneity, the intrinsic complexity of disease systems, uncertainties surrounding data accuracy, and the critical need for interdisciplinary research collaboration. Furthermore, our review identifies that numerous models are formulated using ordinary differential equations, while others incorporate partial differential equations, stochastic differential equations, delay differential equations, and machine learning techniques, reflecting the methodological diversity employed to capture the multifaceted nature of infectious disease transmission. A few of them incorporate population structure as well as environmental factors such as seasonal conditions and water contact patterns. We also find that many models have limitations, and we highlight areas where future research is needed emphasizing a compelling need for better coordination and standard practices in how models are built. Models about vaccination are useful in showing how vaccines can help once they become available and models that look at co-infection show how schistosomiasis interacts with other diseases. Overall, our study shows how helpful mathematical models are in solving real-world problems. Additionally, the challenges posed by schistosomiasis underscore the importance of investing in research that utilizes mathematical modelling. We stress the need for researchers from different fields to work together more closely to fight the spread of schistosomiasis. It is recommended that future models adopt reliable methodologies that integrate multistage modelling, hybrid approaches, and agent-based frameworks. These models should also explicitly account for regional differences to enhance accuracy, relevance, and applicability across diverse contexts.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03077"},"PeriodicalIF":3.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516353","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}
Accurate flood susceptibility assessment remains a critical challenge, yet spatial machine learning offers next-generation data-driven solutions for robust and scalable flood risk prediction. Traditional flood susceptibility models based on hydrodynamic and statistical approaches are often constrained by extensive data requirements, complex calibration, and high computational costs, which limit their application in data-scarce regions. This study advances existing approaches by integrating Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP) with spatial machine learning to optimize the weighting of flood conditioning factors prior to model training. Expert and literature derived weights for nine spatial predictors were normalized and used as input layers to three algorithms—Random Forests (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN)—for flood susceptibility mapping in Chinhoyi, Zimbabwe. A total of 564 flood and 925 non-flood locations were mapped using CNN, 432 flood and 564 non-flood locations using RF, and 569 flood and 908 non-flood locations using SVM. Model performance was assessed using accuracy metrics and receiver operating characteristics to determine predictive capability and generalization. Results revealed that CNN outperformed RF and SVM, producing superior spatial precision and reliability. The methodological integration of AHP-MCDA with deep spatial learning represents a novel advancement in flood susceptibility modelling, enhancing model generalization, interpretability, and applicability in data-limited environments. The study contributes to the advancement of geospatial artificial intelligence applications in hydrological hazard modelling, offering practical insights for resilient urban planning, early warning systems, and sustainable disaster risk management in vulnerable landscapes.
{"title":"Next generation data-driven flood susceptibility modelling with spatial machine learning","authors":"Nobert Tafadzwa Mukomberanwa, Honest Komborero Madamombe","doi":"10.1016/j.sciaf.2025.e03082","DOIUrl":"10.1016/j.sciaf.2025.e03082","url":null,"abstract":"<div><div>Accurate flood susceptibility assessment remains a critical challenge, yet spatial machine learning offers next-generation data-driven solutions for robust and scalable flood risk prediction. Traditional flood susceptibility models based on hydrodynamic and statistical approaches are often constrained by extensive data requirements, complex calibration, and high computational costs, which limit their application in data-scarce regions. This study advances existing approaches by integrating Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP) with spatial machine learning to optimize the weighting of flood conditioning factors prior to model training. Expert and literature derived weights for nine spatial predictors were normalized and used as input layers to three algorithms—Random Forests (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN)—for flood susceptibility mapping in Chinhoyi, Zimbabwe. A total of 564 flood and 925 non-flood locations were mapped using CNN, 432 flood and 564 non-flood locations using RF, and 569 flood and 908 non-flood locations using SVM. Model performance was assessed using accuracy metrics and receiver operating characteristics to determine predictive capability and generalization. Results revealed that CNN outperformed RF and SVM, producing superior spatial precision and reliability. The methodological integration of AHP-MCDA with deep spatial learning represents a novel advancement in flood susceptibility modelling, enhancing model generalization, interpretability, and applicability in data-limited environments. The study contributes to the advancement of geospatial artificial intelligence applications in hydrological hazard modelling, offering practical insights for resilient urban planning, early warning systems, and sustainable disaster risk management in vulnerable landscapes.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"30 ","pages":"Article e03082"},"PeriodicalIF":3.3,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516350","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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