Scientific African最新文献

{"title":"Precise LULC classification of rural area combining elevational and reflectance characteristics using UAV","authors":"Ke Zhang ,&nbsp;Lameck Fiwa ,&nbsp;Madoka Kurata ,&nbsp;Hiromu Okazawa ,&nbsp;Kenford A.B. Luweya ,&nbsp;Mohammad Shamim Hasan Mandal ,&nbsp;Toru Sakai","doi":"10.1016/j.sciaf.2024.e02431","DOIUrl":"10.1016/j.sciaf.2024.e02431","url":null,"abstract":"<div><div>With the development of unmanned aerial vehicle (UAV) in the recent decade, very high-resolution aerial imagery has been used for precise land use/land cover classification (LULC). However, special structures in rural areas of developing countries such as traditional thatched houses have posed challenges for precise LULC classification due to their undistinctive appearance and confusable characteristics in both reflectance and structure. LULC mapping is essential particularly in rural areas which have high data scarcity and vulnerability to natural disasters. With high-resolution observation has been achieved by UAVs, it is important to propose high-precision LULC classification methods which can fully use the advantages of UAVs. To emphasize the differences among the common LULC types in rural areas, this study proposed an original index, the rural residence classification index (RCI). RCI was calculated as the product of the above ground height and the square of the difference between the NDVI value and one. Then, a comprehensive classification method was established by combining the RCI, the traditional threshold method and a machine learning method. As a result of the comparison with the traditional threshold method, object-based image analysis, and random forest methods, the method by this study achieved the highest overall accuracy (overall accuracy = 0.903, kappa = 0.875) and classification accuracy for detecting thatched houses (user's accuracy = 0.802, producer's accuracy = 0.920). These findings showed the possibility on identifying the confusable structures in rural areas using remote sensing data, which was found difficult by the previous studies so far. The method by this study can promote the further utility of UAVs in LULC classification in rural areas in developing countries, thereby providing precise and reliable material for hydrological, hydraulic or ecosystem modelling, which eventually contributes to more accurate natural hazard risk assessment, rural development, and natural resource management.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02431"},"PeriodicalIF":2.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface water quality assessment and probable health threats of metal exposure in the Tano South Municipality, Ahafo, Ghana","authors":"Jackson Adiyiah Nyantakyi ,&nbsp;Lilian Sarpong ,&nbsp;Roland Boadi Mensah ,&nbsp;Samuel Wiafe","doi":"10.1016/j.sciaf.2024.e02437","DOIUrl":"10.1016/j.sciaf.2024.e02437","url":null,"abstract":"<div><div>The prime goal of this study was to assess the surface water quality and the associated health risks for residents in the Tano South Municipality, Ghana. The WHO drinking water criteria were compared with the findings of an analysis of eight surface waters for parameters such as physiochemical parameters, nutrient levels, and concentrations of selected metals. An evaluation was performed regarding the potential non-carcinogenic and carcinogenic risks associated with metal exposure by oral and dermal absorption. The estimated water quality index indicated that seven water samples were deemed unsuitable for human consumption, while one sample indicated very low water quality. The fluoride levels in all water samples were below the limit of detection, although it guards against dental caries. All of the water samples had mean concentrations of Cd and Fe above WHO guideline values, while one water sample had a Pb concentration that was higher than recommended. Principal component analysis showed that aside from the natural source, human-induced sources such as runoff of excess chemicals and soil erosion from adjacent farm soils were responsible for the substantial levels of contaminants in surface water samples. There was a possibility of non-carcinogenic consequences for children in seven out of eight water samples. However, cancer risk for Cd and Pb was not likely for adults and children in the study area. Findings serve as a representative case study for other districts and call on water managers to treat surface waters to guard against harmful health consequences and safeguard the designated buffer zones.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02437"},"PeriodicalIF":2.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aloe ferox leaf gel extracts attenuate redox imbalance in oxidative renal injury and stimulates glucose uptake, whilst inhibiting key enzymes linked to diabetes and obesity","authors":"Huda Ismail,&nbsp;Almahi I. Mohamed,&nbsp;Md. Shahidul Islam","doi":"10.1016/j.sciaf.2024.e02425","DOIUrl":"10.1016/j.sciaf.2024.e02425","url":null,"abstract":"<div><div>The worldwide prevalence of diabetes and obesity is growing rapidly. Both metabolic disorders are linked to chronic adverse complications, which include kidney dysfunctions. Medicinal plants play a crucial role in traditional healthcare, especially in developing countries. <em>Aloe ferox</em>, native to South Africa, has a long history of medicinal use, but its pharmacological potential is less studied compared to other <em>Aloe</em> species, necessitating further investigation. Therefore, the present study was conducted to determine the antioxidative, anti-obesogenic, and antidiabetic effects of <em>A. ferox</em> leaf gel extracts using <em>in vitro, ex vivo</em>, and <em>in silico</em> experimental models, with oxidative renal damage induced by ferrous sulfate (FeSO₄). <em>A. ferox</em> leaf gel extracts exhibited strong antioxidant activity, inhibited carbohydrate and lipid digesting enzymes, and significantly improved glucose uptake in yeast. The aqueous extract demonstrated better antioxidant efficacy, leading to higher reduced glutathione (GSH) level, and superoxide dismutase (SOD) and catalase enzymes activity, along with a concurrent reduction of nitric oxide (NO) and malondialdehyde (MDA) levels. Likewise, the aqueous extract showed more potent <em>in vitro</em> DPPH, NO, and OH• radical scavenging activity with IC₅₀ values of 3.64 ± 0.3 μg/mL, 110.73±0.1 μg/mL, and 331.13 ± 0.7 μg/mL, respectively. The aqueous extract had a better inhibition of the enzyme, α-amylase (IC₅₀ = 25.73±2.5 µg/mL), whilst the ethanolic extract inhibited α-glucosidase (IC₅₀ =663.2 ± 0.3 µg/ml), and pancreatic lipase (IC₅₀ =122.01±5.9 µg/mL) more strongly. Incubation of the extracts with yeast cells stimulated glucose uptake dose dependently, when ethanolic extract (IC₅₀ = 308.01±0.7 µg/mL) showed the better effects compared to the aqueous extract (IC₅₀ = 338.79±7.05 µg/mL). Furthermore, LC-MS analysis led to the identification of many compounds, when chlorogenic acid demonstrated a stronger molecular interaction with the active site amino acids of α-amylase and catalase compared to other compounds. However, Aloin B showed the highest binding affinity with α-glucosidase, when 5-Hydroxyaloin A showed the lowest binding energy with lipase and SOD enzymes. These results suggest the reno-protective effects of A<em>. ferox</em> leaf gel extracts against FeSO₄-induced oxidative stress along with its anti-hyperglycaemic activity. Given these observed effects, <em>A. ferox</em> leaf gel could be valuable in developing natural therapies and potential drug development for the management of these disorders. Further studies in animal models and humans are needed to ascertain the results of this study.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02425"},"PeriodicalIF":2.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of single and multi-date Landsat image classifications using random forest algorithm in a semi-arid savanna of Ghana, West Africa","authors":"Eric Adjei Lawer","doi":"10.1016/j.sciaf.2024.e02434","DOIUrl":"10.1016/j.sciaf.2024.e02434","url":null,"abstract":"<div><div>Accurate detection and quantification of land use and land cover (LULC) change is critical for understanding landscape patterns in heterogeneous semi-arid environments. This study investigates the performance of single-date and multi-date Landsat images as well as the relationship between different LULC schemes (simple [2 and 4 classes] and complex [6 and 9 classes]) and the resulting classification accuracy. Specifically, the random forest algorithm was applied to Landsat data comprised of different combinations of image dates (single-date and multi-date) captured in June, October, and December for multiple levels of LULC (scheme) mapping and accuracy evaluations due to its high performance when dealing with large data and heterogeneous landscapes. Results indicated that multi-date images consistently produced higher classification accuracies than single-date images. Significant negative correlations observed between the number of classes in LULC schemes and overall accuracy and kappa coefficient indicate that the more complex the LULC scheme, the lower the accuracy produced. Nevertheless, improvement in overall accuracy was negligible for simple schemes (e.g., ∼1 % for two LULC classes), while it was moderate for complex schemes (∼5 %) when using the best-performing images for multi-date (June-October-December) compared to single-date (October) classifications: however, the improvement was considerable when compared to the least performing single-date image (June, 8–15 %). These varying classification accuracies were due to differences or similarities in spectral responses of target classes in the various LULC schemes applied to the investigated images. Consequently, the resulting differences in the spatial distribution and quantification of LULC classes produced by the different approaches can affect policy and land management decisions, especially if inappropriate image dates are used for LULC mapping. Overall, the findings highlight the reliability of appropriate single-date and multi-date images for mapping LULC change using simple and complex schemes in heterogeneous semi-arid savanna landscapes.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02434"},"PeriodicalIF":2.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and forecasting mobile money customer transaction volumes in rural and semi-urban Malawi: An autoregressive integrated moving average spatial decomposition","authors":"Danny Namakhwa ,&nbsp;Betchani Henry Mbuyampungatete Tchereni ,&nbsp;Winford Masanjala ,&nbsp;Collins Duke Namakhwa ,&nbsp;Steven Limbanazo Kuchande ,&nbsp;Wisdom Richard Mgomezulu","doi":"10.1016/j.sciaf.2024.e02430","DOIUrl":"10.1016/j.sciaf.2024.e02430","url":null,"abstract":"<div><div>Mobile money technologies in Malawi have revolutionised banking and monetary transactions across geographical barriers. Prospects of profit have drawn mobile money agents to invest in the business but find it is more profitable when substantial customers subscribe to the cash-in and cash-out facilities of mobile money. Despite the initial success, several challenges have emerged, including regulatory hurdles, network reliability issues, and the need for increased financial literacy among users. The volumes of transactions in the rural areas are observably lower compared to urban areas. This study uses Bvumbwe township in Malawi to model and forecast the discrepancy of mobile money transactions in rural and semi-urban Malawi. The study uses ARIMA modelling to understand the temporal manifestation of mobile money subscription in these localities. Using ARMA (1,1) models decomposed for the semi-urban and rural area, the study finds that the semi-urban area has a disproportionately higher and lasting volume of mobile money transactions compared to the rural area. The study also finds that mobile money transactions are more susceptible to long-lasting effects of external shocks in the rural area compared to the urban area. Intuitively, the day-to-day relationship in the transactions is also stronger in the rural area. These findings highlight the need for tailored policy interventions to enhance mobile money adoption and utilization in different geographical contexts.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02430"},"PeriodicalIF":2.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deployment of mobile application using a novel CNN model for the detection of COVID-19 thoracic disease","authors":"Steve Okyere-Gyamfi ,&nbsp;Vivian Akoto-Adjepong ,&nbsp;Kwabena Adu ,&nbsp;Mighty Abra Ayidzoe ,&nbsp;Obed Appiah ,&nbsp;Peter Appiahene ,&nbsp;Patrick Kwabena Mensah ,&nbsp;Michael Opoku ,&nbsp;Faiza Umar Bawah ,&nbsp;Nicodemus Songose Awarayi ,&nbsp;Samuel Boateng ,&nbsp;Peter Nimbe ,&nbsp;Adebayo Felix Adekoya","doi":"10.1016/j.sciaf.2024.e02432","DOIUrl":"10.1016/j.sciaf.2024.e02432","url":null,"abstract":"<div><div>In January 2021 and January 2022, COVID-19 caused roughly 13,000 and 6,000 deaths respectively per day. In August 2022, 26,000 deaths per day were estimated to be caused by COVID-19, followed by 13,000 deaths per day in February 2024. The timely identification and treatment of malignant diseases can potentially lower the mortality rate. Nonetheless, the use of manual methods for diagnosing these conditions requires a meticulous and comprehensive examination, making it susceptible to errors, burdensome for healthcare professionals, and time-intensive. Hence, the objective of this study is to design and deploy a novel deep-learning model for the detection of COVID-19 thoracic diseases. A Convolutional Neural Network (CNN) with less trainable parameters was implemented. This proposed model was deployed on a mobile device using Android Studio and Flutter for the detection of COVID-19 thoracic diseases. Specificity, accuracy, precision, sensitivity, f1-score, ROC, and PR curves were used to evaluate the model's performance. Moreover, the carbon footprint as well as how responsible the proposed model is according to Responsible AI rules was also assessed. The model's evaluation results show an overall accuracy of 93.27 %, specificity of 97.33 %, precision of 93.75 %, sensitivity of 94.42 %, F1-Score of 94.06 %, ROC rate of 98.0 %, PR rate of 96.8 %. The evaluation of the mobile application shows higher generalizability on the COVID-19 dataset. Also, the overall FACETS Score representing responsible AI is 83 % and the carbon footprint (representing the amount of carbon emission emitted into the environment during model training and testing) of 416.73 g with equivalent tree months of 0.45 was obtained. This application with better performance and a low carbon footprint was deployed using Android Studio and Flutter and can assist physicians in the diagnosis of COVID-19 and related diseases.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02432"},"PeriodicalIF":2.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine","authors":"Filagot Mengistu ,&nbsp;Binyam Tesfaw Hailu ,&nbsp;Temesgen Alemayehu Abera ,&nbsp;Janne Heiskanen ,&nbsp;Tadesse Terefe Zeleke ,&nbsp;Tino Johansson ,&nbsp;Petri Pellikka","doi":"10.1016/j.sciaf.2024.e02415","DOIUrl":"10.1016/j.sciaf.2024.e02415","url":null,"abstract":"<div><div>In semi-arid agro-pastoral environments of Africa, beekeeping is widely recognized as an important activity to improve and diversify livelihoods. Although the scientific identification of suitable honey bees (<em>Apis mellifera ssps.</em>) forages may guide beekeepers to set up apiaries or to timely move honey bee colonies to exploit bee forage resources available in various landscapes, the characterization and mapping of bee forage classes is challenging. We evaluated how various data sources and classification algorithms in Google Earth Engine (GEE) affect the accuracy of honey bee forage class mapping in a semi-arid region of Ethiopia. Predictors derived from multi-source satellite data, such as high-resolution Planet imagery (P), Sentinel 1 RADAR (S1), Sentinel 2 multispectral (S2), and Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) were tested and best predictors were identified using Forward Feature Selection (FFS). Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. The results show that the highest accuracy is obtained by all four algorithms when combining P, S1, S2, and DEM compared to using predictors from a single data source or any other combinations. GTB had higher overall accuracy (90.9 %) than RF (88.2 %), CART (85.5 %), or SVM (79.9 %). Nonetheless, the highest overall accuracy (94.7 %) was obtained when integrating the four machine learning algorithms in an Ensemble Learning Approach (ELA). Applying ELA improved the classification accuracy by 3.8 %, 6.5 %, 9.2 %, and 14.8 % compared to single learner classification algorithms (i.e., GTB, RF, CART, and SVM, respectively). This study demonstrates an improved classification accuracy for honey bee forage class mapping in tropical rangeland by applying ELA, which can provide a better approach for monitoring and managing bee forage resources.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02415"},"PeriodicalIF":2.7,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing shell morphology in freshwater mussels (Mollusca: Bivalvia: Unionidae) from the Maâden River, Tunisia: Insights from geometric morphometrics and shape descriptors","authors":"Chiheb Fassatoui ,&nbsp;Moez Shaiek ,&nbsp;Mohamed Salah Romdhane","doi":"10.1016/j.sciaf.2024.e02427","DOIUrl":"10.1016/j.sciaf.2024.e02427","url":null,"abstract":"<div><div>Identifying freshwater mussel species within the Unionidae family can be challenging due to systematic confusion. This study investigates shell shapes to identify four sympatric species of freshwater mussels (<em>Potamida littoralis, Unio durieui, U. gibbus</em>, and <em>U. ravoisieri</em>) in the Maâden river (northern Tunisia) using two morphological approaches: global shape descriptors and geometric morphometrics of shell contours. The results revealed significant differences in global shape descriptors among the species. More in-depth analyses showed distinct shape variations, with <em>P. littoralis</em> and <em>U. gibbus</em> significantly differing from <em>U. durieui</em> and <em>U. ravoisieri</em> across most descriptors. Notably, <em>U. durieui</em> and <em>U. ravoisieri</em> exhibited similar shape traits, differing primarily in roundness, characterized by low circularity and high ellipticity. Principal Component Analyses depicted substantial morphological variance, with Procrustes data requiring the first two components to fully elucidate dispersion. Linear Discriminant Analysis revealed effective discrimination among species, with an accuracy rate of over 74 % in classifying individuals into their respective populations across both approaches. Geospatial analyses indicated varying influences of latitude, longitude, and altitude on shell morphology among species. Latitude had noteworthy impacts on <em>P. littoralis</em>, approaching significance for shape descriptors and Procrustes data. Conversely, <em>U. durieui</em> and <em>U. gibbus</em> displayed minimal geographic influence on shell morphology, while <em>U. ravoisieri</em> showed significant variations across these parameters. This study highlights the distinct morphological differences among sympatric mussel species in northern Tunisia, providing valuable insights for conservation efforts, especially for species vulnerable to climate change.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02427"},"PeriodicalIF":2.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal changes in the abundance and distribution of large terrestrial mammals in old Oyo National Park, Nigeria","authors":"Umar Lawal Mohammed ,&nbsp;Shahrul Anuar Mohd Sah","doi":"10.1016/j.sciaf.2024.e02428","DOIUrl":"10.1016/j.sciaf.2024.e02428","url":null,"abstract":"<div><div>Seasonal distribution of large terrestrial mammals in African protected areas is shaped by factors such as food and water availability, breeding cycles, and predation risks. This study utilised encounter rates derived from diurnal distance sampling to examine seasonal variations in the abundance and distribution of these mammals within Old Oyo National Park, Nigeria. Notably, the abundance and distribution in the Marguba range, characterised by mixed open savanna woodland, differed significantly between the dry and wet seasons, as well as among various sub-habitats and ranges during the dry season. The impact of key factors—(1) resource availability (food and water) and/or (2) poaching threat—remains critical and potentially influential in driving these seasonal differences. Further investigation to ascertain the effect of poaching and determine the impact of other controlling factors is crucial. Understanding the effects of these factors is essential for the effective and sustainable management and conservation of large terrestrial mammals in the study area.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02428"},"PeriodicalIF":2.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal sizing of battery energy storage system (BESS) for multiple applications using regression analysis and deep sleep optimizer algorithm","authors":"Chukwuemeka Emmanuel Okafor,&nbsp;Komla Agbenyo Folly","doi":"10.1016/j.sciaf.2024.e02424","DOIUrl":"10.1016/j.sciaf.2024.e02424","url":null,"abstract":"<div><div>The multifunctional applications of battery energy storage system in a power system network will reduce the significant slack time of use as evident in many single-based applications. In order to deploy BESS for multiple applications, it is of utmost importance that the optimal size for the desired multiple functions, firstly be determined. This work proposes a novel methodology for the optimal sizing of battery energy storage system for frequency support, power loss minimization and voltage deviation mitigations. The suggested sizing methodology takes into account the level of penetration of the renewable energy sources in the power network. Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size. The robustness of the proposed method was tested by using IEEE modified 39-bus system. Simulation results show that with the BESS optimal size integrated into the network, voltage deviations were mitigated by about 20 % and power losses were reduced from 65.3 MW to 59.68 MW. Also, the system frequency nadir during the outage of the largest single generating, was sustained at 59.60 Hz whereas, without BESS it was 59.15 Hz. This is critical because such a frequency decline may activate the underfrequency load shedding relays.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"26 ","pages":"Article e02424"},"PeriodicalIF":2.7,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}