HydroResearch最新文献

{"title":"Electro-sequence valorization of specific enablers of aquifer vulnerability and contamination: A case of index-based model approach for ascertaining the threats to quality groundwater in sedimentary beds","authors":"N.J. George ,&nbsp;A.M. Ekanem ,&nbsp;J.E. Thomas ,&nbsp;N.I. Udosen ,&nbsp;N.M. Ossai ,&nbsp;J.G. Atat","doi":"10.1016/j.hydres.2023.11.006","DOIUrl":"10.1016/j.hydres.2023.11.006","url":null,"abstract":"<div><p>This work was set up to appraise the quality of groundwater within a newly established university using index-based techniques and the intention is to generate vulnerability potential maps that can guide in avoiding vulnerable aquifer systems. The study used geo-electrical technology to evaluate the lithological sequence of the hydrogeological units and their first-and second-order geo-electric properties, as well as the geochemical constituents of groundwater. The results showed that the hydrogeological units are composed of sequences of sands, including fine to gravelly sands minimally laced with sandy clay, according to the results of geo-electrical technology. Layers 2 and 3 were accessible by the twenty VES (V1-V20) in which current gained access to designated economic and prospective aquifer systems. The aquifer systems considered have resistivity values ranging from 26.3 to 3576.5Ωm and mean value of 832.97Ωm. The five vulnerability models deployed showed very high spatial variability and moderate negative and positive binary correlations, construed to be due to subjectivity in the weight-rate assignment. The coefficients of variation (CV) from sensitivity analysis (SA) indicated variation in the vulnerability index due to topography (9.7%) and the impact of the vadose zone (4.3%), which are marginally higher than the 1.6% displayed by other parameters. The groundwater displayed WHO's disallowed concentrations of heavy ions, and the SA indicated that CV for the groundwater geochemical contamination is variably high in cadmium ions (140.9%) followed by manganese ions (132%) and copper ions (88. 2%) across the wells. The moderately variable contribution distributions across the wells are nickel ions (48.8%), lead ions (41.6%), chromium ions (39.8%) and iron ions (35.0%). All the geophysical and geochemical indices affirm that the groundwater is vulnerable and contaminated and therefore requires monitoring and management.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 71-85"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757823000379/pdfft?md5=bd211fb61f7329185f8864e5f8cdf2a3&pid=1-s2.0-S2589757823000379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613666","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":"Characterization of the recent (2019–2022) La Plata Basin hydrological drought from a centennial-scale perspective","authors":"Juan A. Rivera","doi":"10.1016/j.hydres.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.hydres.2024.02.002","url":null,"abstract":"<div><p>Several major rivers within the La Plata Basin (LPB), the third largest basin in the world, have experienced record-low water levels between 2019 and 2022, with significant impacts for the economy of the region. This hydrological drought originated from a precipitation deficit over the headwaters of the Paraná, Paraguay, and Uruguay rivers, in response to an unusual multi-year La Niña episode. The objective of this study is to characterize the hydrological drought and quantify its unusualness by analyzing a set of indices based on daily, monthly, and annual streamflow and water levels of the main rivers of LPB, using centennial records. The results indicate that the recent hydrological drought was unprecedented in the context of the past 50 years in terms of severity and duration, featuring extreme drought conditions and duration over 25 months. The atmospheric drivers of the drought are analyzed, and future perspectives for water management are discussed.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 140-153"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000076/pdfft?md5=63992aed984a6cfa9dfe43efdbd74ff8&pid=1-s2.0-S2589757824000076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140013998","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":"Addressing water scarcity challenges through rainwater harvesting: A comprehensive analysis of potential zones and model performance in arid and semi-arid regions–A case study on Purulia, India","authors":"Subhra Halder,&nbsp;Suddhasil Bose","doi":"10.1016/j.hydres.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.hydres.2024.04.001","url":null,"abstract":"<div><p>Water scarcity in arid and semi-arid regions is a critical global concern, necessitating innovative solutions to address increasing water demands in these vulnerable areas. This study focuses on tackling this challenge by identifying and classifying rainwater harvesting zones based on their potentiality and comparing the performance of two machine learning models, Artificial Neural Network (ANN) and Random Forest (RF), for optimizing rainwater harvesting strategies. The study area is Purulia, a district in India. Extensive literature review was conducted to identify key factors influencing rainwater harvesting. Open-source remotely sensed data were employed to pinpoint rainwater harvesting potential zones. A multi-criteria decision-making technique was applied to assess the importance of various factors. Results indicated that rainfall, slope, runoff potential, soil, land cover, and drainage density are the six crucial factors for selecting suitable rainwater harvesting locations. Approximately 2% of the area is unsuitable, 8% is poorly suitable, 33% is moderately suitable, 45% is highly suitable, and the remaining 12% is extremely suitable in Purulia. Two predictive models were developed, with the RF algorithm demonstrating nearly 99% accuracy. Finally, remedial techniques for mitigating water scarcity through rainwater harvesting are discussed separately for urban and rural areas. This research article embraces a comprehensive approach to address water-related concerns, offering a replicable framework applicable globally, with a specific focus on arid and semi-arid regions.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 201-212"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000118/pdfft?md5=dd221b4db70a94d11d675df3491ceb49&pid=1-s2.0-S2589757824000118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539167","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":"Indicators of riverbank Erosion vulnerability assessment: A systematic literature review for future research","authors":"Neegar Sultana,&nbsp;Sagorika Rani Paul","doi":"10.1016/j.hydres.2024.06.002","DOIUrl":"10.1016/j.hydres.2024.06.002","url":null,"abstract":"<div><p>Despite the devastating nature of riverbank erosion, existing studies hardly address the complex nature of vulnerability affecting the livelihoods of erosion-prone residents. Hence, the indicators of riverbank erosion vulnerability need to be reviewed and aggregated within a specified framework to assess the vulnerability. This research performed a comprehensive literature review of the indicators of riverbank erosion vulnerability to address this knowledge gap. From 2000 to 2022, 22 journal articles were found using the PRISMA protocol. The review revealed that most studies were conducted in South Asia (82%), such as Bangladesh, India, and Nepal, and on local scales (68%), such as sub-districts, villages, unions etc. The analysis also highlighted the IPCC vulnerability framework is the most extensively used for riverbank erosion. The study identified a total of 28 vulnerability indicators of riverbank erosion. The findings reveal that there are substantial geographical and conceptual gaps in the assessment of riverbank erosion vulnerability.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 337-359"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000210/pdfft?md5=52e8e575f091abd7f6fc83a40cdfb57f&pid=1-s2.0-S2589757824000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141392905","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":"Evaluation of trophic state conditions in the three urban perennial lakes of the Coimbatore district, Tamil Nadu: Based on water quality parameters and rotifer composition","authors":"Bala Mohan,&nbsp;Duraisamy Prabha","doi":"10.1016/j.hydres.2024.06.003","DOIUrl":"https://doi.org/10.1016/j.hydres.2024.06.003","url":null,"abstract":"<div><p>This study aims to examine the trophic state conditions in the three major urban perennial lakes of the Coimbatore district based on the physicochemical properties and rotifer community structure. The study was conducted for a period of three months, from January 2023 to March 2023. The water and plankton samples were collected twice a month. The measured physicochemical properties showed that pH, salinity, total hardness, nitrate, and phosphate were found to be higher in Kumarasamy lake, followed by Ukkadam and Valankulam lake ecosystems. The higher rotifer population density was observed in Valankulam lake (1820 Ind./L), and lower in Kumarasamy lake (1080 Ind./L). In this study, Brachionidae species were abundantly found in the three perennial lake water environments, followed by Asplanchnidae, Lecanidae, Trichocercidae, and Filiniidae. In addition, a total of 28 freshwater rotifer species were reported for the first time from the three perennial lakes of the Coimbatore district, respectively. The determined trophic state index (TSI) (47.57) and rotifer trophic state index (TSI_ROT) (51.25) values affirm that the three perennial lakes are under meso-eutrophic conditions. The examined diversity indices values revealed that species dominance (D) and species richness (SR) are higher in Valankulam lake, followed by Ukkadam and Kumaraswamy lake ecosystems. This difference may be due to the influence of human activities and the mixing of various points and non-point sources of pollutants. The present study concluded that the three perennial lakes were moderately polluted by various sources of pollutants; therefore, continuous monitoring is needed for the conservation of flora and fauna diversity in the three perennial lakes of the Coimbatore district.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 360-371"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000222/pdfft?md5=a8188df6e412e8c551a4616cb071e5bc&pid=1-s2.0-S2589757824000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540929","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":"Development of intensity – duration – frequency (IDF) curves using combined rain gauge (RG) and remote sense (TRMM) datasets for Weta Traditional Area in Ghana","authors":"Mawufemor Dzamposu Agakpe ,&nbsp;Mexoese Nyatuame ,&nbsp;Francis Ampiaw","doi":"10.1016/j.hydres.2024.01.003","DOIUrl":"https://doi.org/10.1016/j.hydres.2024.01.003","url":null,"abstract":"<div><p>This research developed IDF curves and derived empirical equations for the Weta Area to estimate design rainfall. Daily rainfall data of Rain Gauge and Tropical Rainfall Measuring Mission (TRMM) 3B42 over 22 years for <em>Weta</em> were obtained from the Ghana Meteorological Services and National Aeronautics and Space Administration, respectively. The Indian Meteorological Department formula was used to disaggregate the daily data into hourly synthetic series to develop the IDF curves. The combined dataset was subjected to frequency analysis to determine the distribution which best characterize the dataset. The results demonstrated that the IDF curves provided higher intensities for shorter durations (0.08-h, 0.17-h, 0.25 h, and 0.5 h) while lower intensities for longer durations (1 h, 2-h, 6-h, 12 h and 24 h). The rainfall intensities and return periods are parallel and high intensities correlated with brief durations for the same return period. TRMM overestimated rainfall compared to Rain Gauge data. The IDF curves and derived empirical equations are useful scientific tools for hydraulic infrastructure design and water resources planning and management.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 109-121"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000039/pdfft?md5=34c3466feb30395be3f54e1d5805f2cf&pid=1-s2.0-S2589757824000039-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549119","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":"Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin","authors":"Abinash Sahoo ,&nbsp;Swayamshu Satyapragnya Parida ,&nbsp;Sandeep Samantaray ,&nbsp;Deba Prakash Satapathy","doi":"10.1016/j.hydres.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.hydres.2024.04.006","url":null,"abstract":"<div><p>For flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrastructure across the world. Development of effective flood forecasting and prediction models is critical for minimising deaths and mitigating damages. This study employs hybrid deep learning Long Short Term Memory (LSTM) algorithms like LSTM, Convolution LSTM (Conv-LSTM) and Convolutional Neural Network LSTM (CNN-LSTM) to predict likelihood flood events using daily precipitation, daily temperature and daily relative humidity from two flood-forecasting stations i.e., Champua (Baitarani River, Odisha) and Jarikela (Brahmani River, Odisha) over a 20-year period. The results show that CNN-LSTM performed best followed by Conv-LSTM and LSTM in terms of R² = 0.98055, 0.96564, and 0.93244, RMSE = 19.137, 35.635, and 49.347, MAE = 18.372, 33.766, and 47.058, NSE = 0.971, 0.9517 and 0.9257 respectively. The findings support the claim that machine learning models and algorithms, in particular CNN-LSTM model, can be applied to flood forecasting with high accuracy, thereby enhancing water and hazard management.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 272-284"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757824000167/pdfft?md5=877f227a2a194f80c8c9d584ef048e10&pid=1-s2.0-S2589757824000167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894456","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":"Ecological consequences of microplastic pollution in sub-Saharan Africa aquatic ecosystems: An implication to environmental health","authors":"Edward Moto ,&nbsp;Miraji Hossein ,&nbsp;Ramadhani Bakari ,&nbsp;Alfred Said Mateso ,&nbsp;Juma Rajabu Selemani ,&nbsp;Salma Nkrumah ,&nbsp;Asha Ripanda ,&nbsp;Mwemezi J. Rwiza ,&nbsp;Elias Charles Nyanza ,&nbsp;Revocatus L. Machunda","doi":"10.1016/j.hydres.2023.11.003","DOIUrl":"10.1016/j.hydres.2023.11.003","url":null,"abstract":"<div><p>Microplastic pollution (MPs) emerged as a significant environmental concern due to its persistent nature. These MPs particles endure in waters, soils, and even the atmosphere, posing potential threats to the entire ecosystem. Aquatic organisms are at risk of ingesting MPs, leading to accumulation in tissues, ultimately affecting entire food chain. This study aims to provide an overview of sources of MPs, distribution, and potential environmental impacts. MPs have been documented in various substances such as bottled water, salts, seafood, and even the air. However, the full extent of the health consequences on human exposure remains uncertain. Therefore, it is imperative that we draw public attention to the presence of these pollutants in the environment. To mitigate adverse effects of MPs, reducing plastic consumption, implementing improved waste management practices, and advocating sustainable behaviors are essential for well-being of natural ecosystems and the health human populations.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 39-54"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757823000343/pdfft?md5=dfd9c21ef81135ab93a7c4b44a466e32&pid=1-s2.0-S2589757823000343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616339","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":"Hydrometeorological variation in the middle and upper reaches of the Yellow River Basin (1960–2019)","authors":"Xin Liu ,&nbsp;Xue Yang ,&nbsp;Geng Cui ,&nbsp;Yan Liu ,&nbsp;Wei Yang ,&nbsp;Xiangning Qu ,&nbsp;Lei Wang ,&nbsp;Shouzheng Tong","doi":"10.1016/j.hydres.2023.11.005","DOIUrl":"https://doi.org/10.1016/j.hydres.2023.11.005","url":null,"abstract":"<div><p>The hydrological and meteorological elements of the Yellow River Basin have undergone fluctuations in response to both climate change and human activities. Understanding its evolutionary trends is essential for the development of effective water conservation measures. Employing linear fitting, Mann–Kendall, and wavelet analysis techniques, this study scrutinizes the trends and periodicity in precipitation, air temperature, and runoff within the middle and upper reaches of the Yellow River Basin from 1960 to 2019. The findings elucidate that temperatures in the middle and upper reaches of the Yellow River have been progressively increasing on an annual basis, while precipitation exhibits periodic fluctuations, and annual runoff demonstrates a declining trajectory. Sudden changes in runoff and temperature transpired in the years 1985 and 1997, respectively, whereas sudden changes in annual precipitation occurred in 1961 and 2012. On an annual scale, the influence of temperature and precipitation on runoff displays no evident prominent cycle. Notably, a significant negative correlation exists between temperature and runoff, with the noteworthy impact of temperature on runoff primarily manifesting in the 6-year sub-cycle spanning 1986 to 1992, as well as the 9-year sub-cycle encompassing 1963 to 1972. Conversely, precipitation and runoff exhibit a significant positive correlation. The substantial effect of precipitation on runoff is mainly concentrated within the 9-year sub-cycle from 1963 to 1972 and the 8-year sub-cycle from 1972 to 1980. Precipitation emerges as the primary driving force behind watershed runoff. In essence, the study's outcomes unveil the implications of climate change on runoff within the middle and upper reaches of the Yellow River Basin, thereby offering pertinent insights for effective water resource management strategies in the face of evolving climatic conditions.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 32-38"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757823000367/pdfft?md5=2b694260e33fcbb1c9a98f776179ba43&pid=1-s2.0-S2589757823000367-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489944","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":"Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches","authors":"Rijurekha Dasgupta,&nbsp;Subhasish Das,&nbsp;Gourab Banerjee,&nbsp;Asis Mazumdar","doi":"10.1016/j.hydres.2023.11.001","DOIUrl":"https://doi.org/10.1016/j.hydres.2023.11.001","url":null,"abstract":"<div><p>Understanding hydrological processes is achieved using modeling approaches due to the extensive and complex interactions between various environmental elements. Hydrological modeling is based on empirical equations that require parameter calibration and model validation to improve performance and evaluate results. This process requires the implementation of absent or lacking data in many ungauged catchments. Therefore, Hydrological Modeling in Ungauged Catchments (HMUC) is an important research area in hydrology. Many researchers tried to develop appropriate technology for this purpose. This review article describes regionalization, satellite observation and machine learning based technologies used for this purpose and presents relevant issues. Key studies worldwide using regionalization, satellite observations and machine learning approaches to develop HMUC have been reviewed here. This study promotes research on HMUC by describing the performances of these methods in different climatic, and geographic conditions. It identifies potential application limitations to guide the framing of future requirements and opportunities for HMUC.</p></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"7 ","pages":"Pages 15-31"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589757823000331/pdfft?md5=e06eb11b139cb5d297e0cc16f00ac58c&pid=1-s2.0-S2589757823000331-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138474842","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}