Measurement Sensors最新文献

{"title":"Wavelet-based convolutional neural network for non-intrusive load monitoring of next generation shipboard Power Systems","authors":"Soroush Senemmar ,&nbsp;Jie Zhang","doi":"10.1016/j.measen.2024.101298","DOIUrl":"10.1016/j.measen.2024.101298","url":null,"abstract":"<div><p>In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform signal processing and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the NILM model. The current signals are first processed through a discrete wavelet transform to create a coefficient vector that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN architecture model to monitor the load statuses in real time. The results of case studies show that the developed NILM model in comparison with benchmark methods can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks, load uncertainty, and noisy inputs.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101298"},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002745/pdfft?md5=bbb066cdab74c18014e0e798a0ba4595&pid=1-s2.0-S2665917424002745-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164724","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":"A modified CNN-IDS model for enhancing the efficacy of intrusion detection system","authors":"Ruqaya Abdulhasan Abed,&nbsp;Ekhlas Kadhum Hamza,&nbsp;Amjad J. Humaidi","doi":"10.1016/j.measen.2024.101299","DOIUrl":"10.1016/j.measen.2024.101299","url":null,"abstract":"<div><p>As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101299"},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002757/pdfft?md5=e75508d91d0b1c1b4d96b962aa0b4cfa&pid=1-s2.0-S2665917424002757-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142150680","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":"Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities","authors":"Sunkara Teena Mrudula ,&nbsp;Meenakshi ,&nbsp;Mahyudin Ritonga ,&nbsp;S. Sivakumar ,&nbsp;Malik Jawarneh ,&nbsp;Sammy F ,&nbsp;T. Keerthika ,&nbsp;Kantilal Pitambar Rane ,&nbsp;Bhaskar Roy","doi":"10.1016/j.measen.2024.101297","DOIUrl":"10.1016/j.measen.2024.101297","url":null,"abstract":"<div><p>The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101297"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002733/pdfft?md5=7ef87b6815aa628f78fb630e2df64177&pid=1-s2.0-S2665917424002733-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077264","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":"Impact of Internet of Things (IoT) applications on HR analytics and sustainable business practices in smart city","authors":"Suplab Kanti Podder ,&nbsp;Debabrata Samanta ,&nbsp;Blerta Prevalla Etemi","doi":"10.1016/j.measen.2024.101296","DOIUrl":"10.1016/j.measen.2024.101296","url":null,"abstract":"<div><h3>Aims</h3><p>The research discovers how IoT contributes to workspace optimization, utilizing occupancy sensors to streamline office layouts, improve energy efficiency, and enhance the overall work environment in smart cities in India.</p></div><div><h3>Subject and methods</h3><p>In the present research study, both descriptive and exploratory research design were implemented and respondents include the Experts, HR Analysts and Regular Employees of Services organizations. The independent and dependent variables were identified and multiple regression analysis was executed for data analysis using SPSS software.</p></div><div><h3>Results</h3><p>The results or outcomes of the research summarizes the positive response of technological upgradation in HR practices in modern organizations. HR Analytics interconnect with applications of IoT that facilitates for better resource utilization and monitoring system.</p></div><div><h3>Conclusion</h3><p>The study concludes by presenting a comprehensive framework for HR professionals to effectively integrate IoT into their analytics practices, emphasizing the need for collaboration, communication, and the establishment of clear privacy policies.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101296"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002721/pdfft?md5=32ce6ea58fbae3fba5fb960256e5a81c&pid=1-s2.0-S2665917424002721-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077263","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":"On the utility of partially corrupted flow measurement data arising from adjacent acoustic Doppler current profilers for energy yield assessment","authors":"Luke Evans ,&nbsp;Ian Ashton ,&nbsp;Brian Sellar","doi":"10.1016/j.measen.2024.101293","DOIUrl":"10.1016/j.measen.2024.101293","url":null,"abstract":"<div><p>Recommended practice for quantifying the energy resource at a tidal energy site requires the use of multiple instruments deployed across the site. However, the instruments used work by emitting an acoustic pulse and instruments working at the same time have the potential to interfere with each other through a process known as ’cross-talk’. It is important to understand the impact of cross-talk on measurements and how this can be managed and through data processing or suitable positioning of devices. The ReDAPT project conducted a measurement campaign using two Acoustic Doppler Current Profilers (ADCPs) placed upstream of an operational tidal turbine. This aimed to assess the ’in-line’ instrument placement guidelines from IEC 62600-200 for Power Performance Assessment (PPA) in real-world conditions. Consequently, the results within hold potential to support arguments for expanding these zones or adjusting their general dimensions. Despite adhering to industry standards and best practices to eliminate unreliable data in the Quality Control (QC) checks, in both concurrently measuring ADCPs at different time stamps in approximately 15 % of the returned data. This work identified for the first time interference throughout the campaign and quantified subsequent impact on estimates. A method to remove data anomalies caused by interference between closely positioned ADCPs has been developed and demonstrated, resulting in a 7 % variation in estimated Annual Energy Production (AEP). The algorithm effectively removed approximately 90 % of the corrupted measurements. Moving forward, multi-sensor deployments could use the algorithm described to double-check for interference within the data sets, although care should be taken to avoid this by choosing a suitable layout for deployment.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101293"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002691/pdfft?md5=30e40f2957963f548db06be49a880498&pid=1-s2.0-S2665917424002691-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040502","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":"Optimizing cloud service provider selection with firefly-guided fuzzy decision support system for smart cities","authors":"Surjeet Dalal ,&nbsp;Ajay Kumar ,&nbsp;Umesh Kumar Lilhore ,&nbsp;Neeraj Dahiya ,&nbsp;Vivek Jaglan ,&nbsp;Uma Rani","doi":"10.1016/j.measen.2024.101294","DOIUrl":"10.1016/j.measen.2024.101294","url":null,"abstract":"<div><p>Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101294"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002708/pdfft?md5=8b34cc351a34cf6a7ca30aa30d9fc402&pid=1-s2.0-S2665917424002708-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007061","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":"Classification of brain tumor using deep learning at early stage","authors":"P.S. Smitha,&nbsp;G. Balaarunesh,&nbsp;C. Sruthi Nath,&nbsp;Aminta Sabatini S","doi":"10.1016/j.measen.2024.101295","DOIUrl":"10.1016/j.measen.2024.101295","url":null,"abstract":"<div><p>Early detection and classification of brain tumors are crucial for patient survival. This study proposes a comprehensive deep learning approach for early brain tumor classification using medical imaging data. A diverse dataset encompassing various tumor types, stages, and healthy brain images is utilized. Preprocessing techniques like augmentation and normalization enhance data robustness. A convolutional neural network (CNN) architecture serves as the primary model, leveraging transfer learning from pre-trained models to extract relevant features even with limited data. The training process optimizes hyperparameters to prevent overfitting, and performance is evaluated using metrics like accuracy, precision, recall, F1 score, confusion matrices, and ROC curves on a separate test set. Focusing on early detection, the model explores predicting tumor growth trajectories and identifying subtle pre-tumor patterns, aligning with expert diagnoses and boosting real-world applicability. Ethical and regulatory guidelines are adhered to in data handling. Continuous improvement involves updating the model with new data and monitoring its clinical performance. This research contributes to advancing early tumor classification methods, potentially improving patient outcomes and treatment strategies.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101295"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266591742400271X/pdfft?md5=929ac12d03164a03ac2027a69f6b0393&pid=1-s2.0-S266591742400271X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048992","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":"Variability in land surface temperature concerning escalating urban development using thermal data of andsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India","authors":"Tanushri Jaiswal ,&nbsp;D.C. Jhariya ,&nbsp;Mridu Sahu","doi":"10.1016/j.measen.2024.101290","DOIUrl":"10.1016/j.measen.2024.101290","url":null,"abstract":"<div><p>Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.</p><p>Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101290"},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002666/pdfft?md5=29ae31cc445a9b532c6467aef57413c7&pid=1-s2.0-S2665917424002666-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997440","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 efficient ranking based binary salp swarm optimization for feature selection in high dimensional datasets","authors":"S. Jayachitra ,&nbsp;M. Balasubramani ,&nbsp;Abdullah Mohammed Kaleem ,&nbsp;Jayavarapu Karthik ,&nbsp;G. Keerthiga ,&nbsp;R. Mythili","doi":"10.1016/j.measen.2024.101291","DOIUrl":"10.1016/j.measen.2024.101291","url":null,"abstract":"<div><p>Feature selection is a major challenge in data mining which involves complex searching procedure to acquire relevant feature subset. The effectiveness of classification approaches is greatly susceptible to data dimensionality. The Higher dimensionality intricate numerous problems like higher computational costs and over fitting problem. The essential key factor to mitigate the problem is feature selection. The main motive is to minimize the number of features through eliminating noisy, insignificant, and redundant features from the original data. The Metaheuristic algorithm attains excellent performance for solving this kind of problems. In this paper, the grading based binary salp swarm optimization has been proposed to solve various complex problems with lesser computational time. The grading system has been used to maintain the balance among exploitation and exploration. The proposed method is examined using ten benchmark real datasets. The comparative result exhibits the promising performance of our proposed method and surpasses with other optimization interms of investigating evaluation measures.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101291"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424002678/pdfft?md5=be2cd62c32373bba12f02c48a5a0f31c&pid=1-s2.0-S2665917424002678-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048993","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":"Design industrial 5.1 air quality monitoring system and develop smart city infrastructure","authors":"Lin Wang","doi":"10.1016/j.measen.2024.101292","DOIUrl":"10.1016/j.measen.2024.101292","url":null,"abstract":"<div><p>In order to meet the requirements of urban construction and further urbanization of the country, the author proposes an industrial 5.1 air quality monitoring system to develop smart city infrastructure. The author utilizes a wireless network of lighting nodes to solve the cost and positioning accuracy issues of perception nodes covering a large area, achieving real-time alarm of urban air quality status and location of pollution occurrence. The author also adopted the latest design concept of monitoring systems combined with cloud platform interfaces, breaking the closed design of traditional IoT systems and enabling better utilization of air quality data. The test results indicate that: The communication distance of CC2530 can be maintained at around 70m under normal power, while the spacing between urban street lights is approximately 30m, which fully meets the project requirements. After two days of testing, the system alarm function and various functions are running normally.</p></div><div><h3>Conclusion</h3><p>The key parts of the system have been tested and simulated, and ideal results have been obtained.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"35 ","pages":"Article 101292"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266591742400268X/pdfft?md5=d32ce49958c8de75ea04ae778878cc5a&pid=1-s2.0-S266591742400268X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991064","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}