Scientific African最新文献

{"title":"SNEL-DFF: Android malware detection using Siamese networks with ensemble learning","authors":"Atif Raza Zaidi ,&nbsp;Tahir Abbas ,&nbsp;Sadaqat Ali Ramay ,&nbsp;Tariq Shahzad ,&nbsp;Zahid Hussain Qaisar ,&nbsp;Muhammad Adnan Khan ,&nbsp;Adnan Abu-Mahfouz ,&nbsp;Amin Beheshti","doi":"10.1016/j.sciaf.2025.e02816","DOIUrl":"10.1016/j.sciaf.2025.e02816","url":null,"abstract":"<div><div>This paper proposes a new model simply known as Siamese Networks of Optimal Ensemble Learning with Deep Forest Feature (SNEL-DFF). The proposed model has the Deep Forest Feature extraction feature because of the complexity that is present in the data and to enhance the proficiency of the detection system. The feature vectors used in this study includes 215 attributes in android applications which are derived from samples sourced from Drebin dataset. Some of the performance evaluation results have been highlighted revealing that the proposed model yielded an accuracy of 0.99. The precision of 0.98 shows its ability to avoid miss-identification of negatives and the recall of 0.99 proves the effectiveness of using it for detection of the real malware samples. The F1 score is 0.99 and ROC-AUC value of 0.99 indicating the model has achieved 99% accuracy which points to the fact that it is balanced and provides a superior performance. These findings vindicate the hypothesis that SNEL-DFF has strong predictive accuracy as compared to the conventional machine learning algorithms. The proposed technique utilizes Siamese networks, deep forest feature enhancement, and ensemble learning, which makes it perform better than its competitors in terms of various evaluation criteria.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02816"},"PeriodicalIF":2.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702972","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":"Adsorptive removal of methylene blue as case study of adsorption phenomenon by ceramic filters: experimental study and kinetic modeling","authors":"Akuemaho Virgile Onésime Akowanou ,&nbsp;Hontonho Espérance Justine Deguenon ,&nbsp;Sena Peace Hounkpè ,&nbsp;Mohamed Moukorab Arêmou Daouda ,&nbsp;Martin Pépin Aina","doi":"10.1016/j.sciaf.2025.e02858","DOIUrl":"10.1016/j.sciaf.2025.e02858","url":null,"abstract":"<div><div>Ceramic filters are widely recognized for their effectiveness in removing microorganisms and pathogens from drinking water. However, their ability to eliminate chemical pollutants through adsorption remains underexplored. This study investigates the adsorption of methylene blue (MB) onto ceramic filters made from sintered clay materials. It is a new and detailed experimental investigation of adsorption phenomena in ceramic pot filtration, focusing on the removal of methylene blue as a model pollutant. Adsorption kinetics were examined under varying conditions, including contact time, pH, adsorbent dosage, and agitation speed. The adsorption process followed a pseudo-second-order kinetic model, indicating a strong interaction between the adsorbents and the adsorbate. Isothermal adsorption experiments revealed that the Langmuir model best described the adsorption behavior, with maximum adsorption capacities of 267.5 mg/g, 258.5 mg/g, and 252.5 mg/g for the three clay-based materials studied. This suggests that the adsorption of methylene blue occurs as a monolayer on a homogeneous surface with finite and energetically equivalent sites, highlighting the potential for optimizing surface properties to maximize adsorption efficiency. These findings confirm the significant role of adsorption on ceramic pot filter in pollutant removal by clay based ceramic filters and highlight the potential of optimizing material properties to enhance water treatment efficiency.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02858"},"PeriodicalIF":2.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680319","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":"Knowledge, perceptions, and utilization of Pre-exposure prophylaxis among youth at ntšekhe government hospital, lesotho","authors":"Teboho Lekhutla,&nbsp;Bernard Hope Taderera","doi":"10.1016/j.sciaf.2025.e02857","DOIUrl":"10.1016/j.sciaf.2025.e02857","url":null,"abstract":"<div><h3>Background</h3><div>Pre-Exposure Prophylaxis (PrEP) is an effective biomedical intervention for preventing HIV transmission among high-risk populations. However, despite its proven efficacy, the uptake of PrEP remains low among youth in Lesotho, particularly those accessing services at Ntšekhe Government Hospital. This study aims to assess the knowledge, perceptions, and utilisation of PrEP amongst the youth aged 18–24 years at the hospital.</div></div><div><h3>Methods</h3><div>A cross-sectional survey was conducted from June to July 2024, involving 152 youth who completed structured questionnaires. Descriptive statistics were used to examine socio-demographic data, knowledge levels, and perceptions, while inferential analyses explored the relationships between education, gender, and PrEP utilisation.</div></div><div><h3>Findings</h3><div>The study reveals that 71 % of participants were aware of PrEP, yet only 12.5 % had ever used it. Interestingly, a counterintuitive finding emerged: individuals with higher education, despite demonstrating significantly better knowledge of PrEP (<em>p</em> &lt; 0.001), were less likely to use it, suggesting internal or structural barriers to uptake. This unexpected trend signals a need for further qualitative investigation. Gender disparities were also evident, with males significantly less likely to report PrEP use compared to females (<em>p</em> &lt; 0.001). A moderate positive correlation (<em>r</em> = 0.431, <em>p</em> &lt; 0.001) was found between perceptions of PrEP’s effectiveness and its utilisation.</div></div><div><h3>Interpretations</h3><div>The findings highlight the need for targeted public health interventions to address knowledge gaps, dispel misconceptions, and reduce barriers to PrEP access, particularly among young people in Lesotho. Expanding PrEP education and addressing gender-specific factors are crucial steps in improving PrEP uptake and preventing HIV transmission in this high-risk population.</div></div><div><h3>Funding</h3><div>This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02857"},"PeriodicalIF":2.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702973","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":"Data-driven prediction of hemispherical solar distiller performance: Optimizing water production with machine learning","authors":"Swellam W. Sharshir ,&nbsp;Ahmed Sedik ,&nbsp;Mohamed A. Hamada ,&nbsp;T. Medhat ,&nbsp;Moustafa M. Nasralla ,&nbsp;Haleem Farman ,&nbsp;Manal E. Ali","doi":"10.1016/j.sciaf.2025.e02851","DOIUrl":"10.1016/j.sciaf.2025.e02851","url":null,"abstract":"<div><div>Artificial intelligence has found applications in numerous research domains, notably in the field of mechanical engineering. This research work introduces a novel data-driven approach to predict the thermal performance of a Hemispherical Solar Still (HSS) using five machine learning models: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The proposed prediction models are built using real experimental data that has been recorded. Leveraging real experimental data, these models were rigorously evaluated for predicting hourly productivity and instantaneous efficiency, using five statistical error metrics to assess their performance. The DT model emerged as the most accurate and efficient, achieving near-ideal R² and EVS values near to one, alongside minimal statistical error values (MSE, NAE, and Median Absolute Error). Notably, DT predicted average hourly productivity at 0.477 L/m²/day—closely matching experimental averages (0.478 L/m²)—with 45.2 % average instantaneous efficiency, outperforming other models (SVM: 46.4 %, RF: 46.4 %, KNN: 44.8 %, GB: 45.2 %). Therefore, this work demonstrates that DT-based prediction can reliably estimate HSS performance, eliminating costly and time-consuming experimental iterations. The proposed framework provides a robust, scalable solution for solar still optimization, advancing AI applications in sustainable water production.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02851"},"PeriodicalIF":2.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662150","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":"Potential of omics workflows for harnessing molecular-grade DNA polymerase and DNA-binding protein isoforms from African thermophilic microbiomes","authors":"Reuben S. Maghembe ,&nbsp;Abdalah Makaranga ,&nbsp;Samweli Bahati ,&nbsp;Deogratius Mark ,&nbsp;Christina Kidulile ,&nbsp;Emmanuel Haule ,&nbsp;Tusajigwe Mwakalinga ,&nbsp;Damian Dogbert Kalekayo ,&nbsp;Joseph Ndunguru","doi":"10.1016/j.sciaf.2025.e02854","DOIUrl":"10.1016/j.sciaf.2025.e02854","url":null,"abstract":"<div><div>Since its inception in 1983 by Kary Banks Mullis, polymerase chain reaction (PCR), has been relevant in molecular biology laboratories for detection, identifying and modifying genetic material, diagnostic research to cloning, sequencing, and mutagenesis. DNA polymerase from <em>Thermus aquaticus</em> (<em>Taq</em>), also known as <em>Taq</em> polymerase, has been the most common enzyme used globally to amplify DNA in molecular laboratories. This has leaped economies of laboratories that have successfully purified and chemically optimized the <em>Taq</em> polymerase. On the other hand, single-strand DNA-binding protein (SSB) is useful in holding one of the melted (denatured) DNA strands apart from the complementary counterpart. This allows effective DNA amplification by promoting primer annealing and preventing DNA strand rehybridization and rewinding. While these experiments work better at higher temperatures, the most reliable DNA polymerase and SSB isoforms should be sourced from similarly high temperature organisms, the thermophiles. Although Africa harbors many hot springs, African laboratories remain dependent on advanced multinational companies for <em>Taq polymerase</em> and other molecular reagents. Here we discuss the potential of omics workflows for effective bioprospecting of thermophilic microbiomes for PCR-grade DNA polymerase and SSB isoforms. We pinpoint the most recent advances in omics workflows and their relevance to the African setting. Additionally, we highlight essential conditions for molecular laboratories with capacity to utilize omics for sustainable experiments and mass production of proteins. Finally, we discuss the most forefront opportunities and challenges related to efforts to combine interdisciplinary bioinformatics to transition from basic science to innovative initiatives under the low-resource setting of Africa.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02854"},"PeriodicalIF":2.7,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654760","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":"Predicting Working Poor and Total Employment in Kenya in-line with SDG norms","authors":"Rohil Ahuja ,&nbsp;Dhruvil Borda ,&nbsp;Asi Vasu Deva Reddy ,&nbsp;Saleena B. ,&nbsp;Prakash B.","doi":"10.1016/j.sciaf.2025.e02812","DOIUrl":"10.1016/j.sciaf.2025.e02812","url":null,"abstract":"<div><div>This research seeks to bridge the gap between the intuitive understanding that jobs are essential for poverty reduction and economic growth. Focusing on Kenya, a Lower-Middle-Income country, this research examines how Total Factor Productivity (TFP) plays a pivotal role in long-term economic growth. This research lists all the key determinants contributing to the enhancement of TFP in-line with SDG 1, 2 &amp; 8 and investigates the complex interrelationships between Contribution to GDP and the Growth of GDP to predict Working Poor and Total Employment for each industry. The dataset for analysis was accumulated from the Statistical reports from 2011 to 2023, published by the Kenya National Bureau of Statistics (KNBS). This empirical research employs Deep Learning (DL) and Machine Learning (ML) algorithms to predict the number of Working Poor and Total Employment, based on economic growth metrics. After comparing various ML and DL algorithms, the research concludes that the Decision Tree is the most suitable for predicting the Working Poor with 94.76% accuracy, while the Random Forest is most effective for predicting Total Employment with 94.57%. These predictions are intended to assist policymakers in making informed decisions to reduce the Working Poor, thereby increasing TFP and, in turn, bolstering the Kenyan economy.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02812"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665637","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":"Decision tree-based statistical learning and quantile regression adjustment: Insights from pregnant women in Benin","authors":"Yémalin Bill-Lucas Kintomonho ,&nbsp;Mintodê Nicodème Atchadé ,&nbsp;Donatien Daddah","doi":"10.1016/j.sciaf.2025.e02832","DOIUrl":"10.1016/j.sciaf.2025.e02832","url":null,"abstract":"<div><div>This research investigated factors influencing pregnancy outcomes (stillbirth or live birth) and newborn weight distribution among pregnant women in Benin. It covered 1,048 pregnant women who attended the five referral hospitals in Couffo in 2020, with data on 952 live births. We used random forests for feature selection, CART decision trees for pregnancy outcome prediction, and quantile regression to examine newborn weight distribution. The CART model identified key risk factors for adverse pregnancy outcomes, including young maternal age (18–24 years), a history of HIV, two or more prior stillbirths, and multiparity (four or more living children). In contrast, favorable pregnancy outcomes were associated with maternal age (25–40 years), at least three prenatal consultations, and the absence of major medical conditions. The neonatal death rate was 91.6 per 1000 live births. Quantile regression analysis revealed that low birth weight was associated with young maternal age (14–24 years), strenuous maternal occupation, paternal occupation, pregnancy complications (hypertension, preeclampsia, anemia, premature rupture of membranes), twin pregnancy, late prenatal care (third trimester), fewer than four prenatal visits, and a history of cesarean section. Macrosomia was linked to maternal age (25–45 years), parental occupation, and the absence of medical or pregnancy-related complications. Notably, macrosomia was strongly associated with a high cesarean section rate (64.44%). These findings highlight critical factors influencing pregnancy risks and newborn health, emphasizing the need for targeted prenatal care strategies in Benin.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02832"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632481","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":"Control strategies of hybrid RESs for off-grid water pumping technologies: An overview","authors":"Haneen M. Bawayan ,&nbsp;Mohamed A. Enany ,&nbsp;Mahmoud M. Elymany ,&nbsp;Ahmed A. Shaier ,&nbsp;Marwa M. Ahmed","doi":"10.1016/j.sciaf.2025.e02856","DOIUrl":"10.1016/j.sciaf.2025.e02856","url":null,"abstract":"<div><div>Hybrid Renewable Energy Systems (HRESs) that combine photovoltaic (PV) and wind energy (WE) offer a sustainable solution for off-grid water pumping, particularly in remote or agricultural areas with limited or unreliable grid access. This review critically examines control strategies used in Hybrid Renewable Energy Water Pumping Systems (HREWPS), focusing on improvements in energy efficiency, reliability, cost-effectiveness, and adaptability to changing environmental conditions. The manuscript categorizes and compares a range of control methodologies. These include maximum power point tracking (MPPT) algorithms such as perturb and observe (P&amp;O), incremental conductance (IC), fuzzy logic, artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and swarm intelligence techniques. Advanced motor control methods like field-oriented control (FOC), direct torque control (DTC), scalar control, and sliding mode control are also reviewed. These techniques are evaluated across motor types including induction motors (IM), brushless DC motors (BLDCM), permanent magnet synchronous motors (PMSM), switched reluctance motors (SRM), synchronous reluctance motors (SynRM), and open-end induction motors (OEIM). This study also emphasizes battery-less configurations, hybrid storage systems, and AI-enhanced energy management frameworks that optimize real-time performance and increase system resilience. Emerging technologies such as model predictive control (MPC), IoT-based remote monitoring, and blockchain-enabled microgrid energy trading are discussed as future enablers for advanced HREWPS. By integrating recent advancements and experimental findings, this review outlines a roadmap for sustainable, intelligent, and adaptive off-grid water pumping systems. It underscores the role of smart control solutions in addressing global water and energy challenges.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02856"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654758","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":"Energy, emissions, and economics of institutional solar PV cooking: evidence from an experimental study","authors":"Flavio Odoi-Yorke ,&nbsp;Richard Opoku ,&nbsp;Eunice A. Adjei ,&nbsp;Samuel Antwi ,&nbsp;Francis Davis ,&nbsp;George Yaw Obeng ,&nbsp;Francis Kofi Forson","doi":"10.1016/j.sciaf.2025.e02853","DOIUrl":"10.1016/j.sciaf.2025.e02853","url":null,"abstract":"<div><div>For many countries in the global south, especially in Africa, cooking is done using biomass fuels, with severe indoor air pollution, negative health hazards, and environmental pollution. Therefore, transitioning to cleaner cooking fuels using renewable energy is critical to decarbonize the cooking sector. In this study, analyses were conducted on the energy, emissions, and economics of an innovative solar PV electric steam cooker (SESC), and the results were compared with a traditional biomass cookstove system (BCS) for institutional cooking. The SESC has sand as thermal energy storage incorporated into the design. Experiments were conducted on the SESC and the BCS to cook a common staple food (kenkey) in four senior high schools in Ghana. The experimental results show that the SESC reduces energy consumption by approximately 90 %, achieves an average thermal efficiency of 37.4 % (compared to 14.8 %-14.9 % for BCS), and eliminates direct emissions. The energy savings of the SESC are due to minimal heat losses because of its brick insulation, and the fact that the heated sand is able to hold heat and sustain cooking even when the solar PV energy source is turned OFF. In addition, the lifecycle cost analysis indicates that the SESC has a lower levelized cost of cooking a meal (4.8–6.3 USD/meal) compared to the BCS (8.4–10.5 USD/meal). The payback period for the SESC ranges between 4.8 and 6.8 years, making it financially viable for large-scale institutional cooking. The findings highlight the potential of solar steam cooking as a viable institutional clean cooking solution, with implications for policy interventions and subsidy programmes to promote adoption in large-scale food preparation settings.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02853"},"PeriodicalIF":2.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632479","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":"The dynamics of hepatitis B virus via a stochastic epidemic model","authors":"Tahir Khan ,&nbsp;II Hyo Jung ,&nbsp;Gul Zaman ,&nbsp;Ebenezer Bonyah","doi":"10.1016/j.sciaf.2025.e02837","DOIUrl":"10.1016/j.sciaf.2025.e02837","url":null,"abstract":"<div><div>Hepatitis B is a highly contagious disease affecting approximately two billion people globally. Mathematical modeling of hepatitis B virus (HBV) transmission is crucial not only for understanding its current spread but also for predicting future dynamics. In this study, we develop a stochastic differential equation (SDE) model that captures the key characteristics of HBV transmission, acknowledging its inherent randomness. The model incorporates two infectious compartments: acutely infected and chronically infected individuals, reflecting the clinical significance of both groups. Notably, some individuals may progress directly to the chronic stage without a prior acute phase. Successfully vaccinated individuals are considered recovered, given that the hepatitis B vaccine provides protective immunity in approximately 95% of cases. We establish the model’s well-posedness by proving the existence and uniqueness of solutions, and we analyze conditions for disease extinction and persistence. Sensitivity analysis is also performed to evaluate the impact of key epidemiological parameters on disease dynamics. Finally, numerical simulations are conducted using the stochastic Runge–Kutta (SRK) method to validate and support the analytical findings.</div></div>","PeriodicalId":21690,"journal":{"name":"Scientific African","volume":"29 ","pages":"Article e02837"},"PeriodicalIF":2.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606103","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}