A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithm...A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.展开更多
Plateaued rate of decline in neonatal mortality rate is one of the major obstacles in achieving Millennium Development Goal 4 especially in developing countries. Even in India, nationwide interventions targeting safe ...Plateaued rate of decline in neonatal mortality rate is one of the major obstacles in achieving Millennium Development Goal 4 especially in developing countries. Even in India, nationwide interventions targeting safe mother and newborn care have not yielded the desired impact, indicating the necessity to combat neonatal mortality rate at population specific level. The objective of this study is to identify the newborn care practices and beliefs, analyze their harmful or beneficial characteristics, describe the deviations from the essential newborn care practices during hospital/home delivery, explain barriers to care seeking and identify areas of potential resistance for behavior change;and utilize study findings to tailor-make cost-effective essential newborn care package. The study uses qualitative data from in-depth interview of mothers who had experienced neonatal death and key-informant interviews with healthcare personnel and birth attendants. 33 cases were randomly selected from the registered neonatal deaths across Bharuch district of Gujarat, India. Key finding of this study is less prevalent practice of essential newborn care among all cases irrespective of place of delivery and the health-care personnel facilitating delivery. Habitual traditional/tribal newborn care methods challenge the practice of prescribed essential newborn care. Clustering of deaths in few households added significantly to the existing burden of neonatal deaths, attributed to superstition “Ratewa” by tribal. Study has concluded that the introduction and implementation of essential newborn care at hospital and community/ household level are the need of the hour. Quality home based neonatal care through cost effective interventions is deemed necessary where accessing institutional care is not possible in the immediate term. Community health workers can contribute to the eradication of harmful newborn care practices and the sustenance of essential practices through community education and behavior change communication.展开更多
It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practit...It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to tackle.In this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.展开更多
Antiplatelet therapy, which reduces platelet activation and aggregation, is the corner stone of treatment for patients undergoing percutaneous coronary intervention (PCI). Clopidogrel is an established oral antiplatel...Antiplatelet therapy, which reduces platelet activation and aggregation, is the corner stone of treatment for patients undergoing percutaneous coronary intervention (PCI). Clopidogrel is an established oral antiplatelet medication of thienopyridine class, which inhibits blood clots in coronary artery disease, peripheral vascular disease, and cerebrovascular disease. Many studies have revealed that high loading dose clopidogrel in patients undergoing PCI. This review article investigates the rationale and role of high loading dose clopidogrel in patients undergoing PCI.展开更多
Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues wh...Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues when relying on local area network servers (LANs) to collect high-volume streaming social media data over long periods of time. In this paper, we present a cloud-based data collection, pre-processing, and archiving infrastructure, and argue that this system mitigates or resolves the problems most typically encountered when running social media data collection projects on LANs at minimal cloud-computing costs. We show how this approach works in different cloud computing architectures, and how to adapt the method to collect streaming data from other social media platforms. The contribution of our research lies in the development of methodologies that researchers can use to monitor and analyze phenomena including how public opinion and public discourse change in response to events, monitoring the evolution and change of misinformation campaigns, and studying how organizations and entities change how they present and frame information online.展开更多
作为一流的管理和工艺流程服务商，Accenture公司利用自己的研发工具成功地为亚洲市场研制开发了SAP Best Practices for Chemicals化工企业计算机管理平台（以下简称SAP Best），并成功地进入了中国的化工企业市场。在中国的某个化工...作为一流的管理和工艺流程服务商，Accenture公司利用自己的研发工具成功地为亚洲市场研制开发了SAP Best Practices for Chemicals化工企业计算机管理平台（以下简称SAP Best），并成功地进入了中国的化工企业市场。在中国的某个化工企业中，这一计算机管理系统从一月份开始安装，在短短不到4个月的时间里全面调试成功、投入了生产使用。软件系统中的各个模板，以及以授权登录为基础的解决方案将进一步引进到亚洲其他国家、欧洲和美国。展开更多
This paper builds a self-adaptive management process in the power system dispatching area, aiming to effectively monitor the grid operation, dynamically adjust control strategy, optimize working process and ensure the...This paper builds a self-adaptive management process in the power system dispatching area, aiming to effectively monitor the grid operation, dynamically adjust control strategy, optimize working process and ensure the continuous improvement of operational performance. By building a negative feedback and dynamic balanced management mechanism, ECPRCB （East China Power Regulation Center Branch） is able to keenly sense the internal and external changes, efficiently coordinate all kinds of resources and improve the operational performance. As a result, self-adaptive management effectively boosts ECPRCB to reach the goal of being a world-class dispatching center with high operational performance, competent internal operation, adequate resources support and strong growth motion.展开更多
Since their discovery in 2011,MXenes,two-dimensional transition metal carbides and nitrides,have emerged as highly promising materials for smart textile applications.They offer exceptional properties such as high elec...Since their discovery in 2011,MXenes,two-dimensional transition metal carbides and nitrides,have emerged as highly promising materials for smart textile applications.They offer exceptional properties such as high electrical conductivity,optical tunability,and mechanical flexibility.These materials can also be produced at scale and readily solution-processed into textile formats,fueling a surge of interest in integrating MXenes into various smart textile applications,from strain sensors and wearable biosensors to adaptive thermal management and electromagnetic interference(EMI)shielding.However,despite this rapid growth,existing reviews of MXene-enabled smart textiles remain narrow in scope,often focusing on single fabrication methods or specific functionalities.Such a fragmented perspective makes it difficult for researchers to gain a comprehensive understanding of how the field has evolved and where it is headed.In response,we present a quantitative bibliographic analysis of MXene–textile research from 2017 through 2024,encompassing nearly 1000 publications.This review categorizes the literature by major functional domains(sensing,energy storage/harvesting,EMI shielding,and heating)and examines their shifts over time,providing reasons and examples for these changes in research interest.Additionally,detailed analyses of functions in each category were conducted in a similar fashion.Our holistic,data-driven assessment offers guidance for future research and commercialization of MXene-functionalized smart textiles by identifying high-impact areas,emerging opportunities,and critical gaps.展开更多
With the construction of smart grid,lots of renewable energy resources such as wind and solar are deployed in power system.It might make the power system load varied complex than before which will bring difficulties i...With the construction of smart grid,lots of renewable energy resources such as wind and solar are deployed in power system.It might make the power system load varied complex than before which will bring difficulties in short-term load forecasting area.To overcome this issue,this paper proposes a new short-term load forecasting framework based on big data technologies.First,a cluster analysis is performed to classify daily load patterns for individual loads using smart meter data.Next,an association analysis is used to determine critical influential factors.This is followed by the application of a decision tree to establish classification rules.Then,appropriate forecasting models are chosen for different load patterns.Finally,the forecasted total system load is obtained through an aggregation of an individual load’s forecasting results.Case studies using real load data show that the proposed new framework can guarantee the accuracy of short-term load forecasting within required limits.展开更多
The industry sector is a very large producer and consumer of data,and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new product...The industry sector is a very large producer and consumer of data,and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services.As many of the data sources needed are distributed and outside the company,FAIR data will have a major impact,both by reducing the existing internal data silos and by enabling the efficient integration with external(public and commercial)data.Many companies are still in the early phases of internal data”FAIRification”,providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships.For a global Internet of FAIR Data&Services to thrive,also involving industry,professional tools and services are essential.FAIR metrics and certifications on individuals,data,organizations,and software,must ensure that data producers and consumers have independent quality metrics on their data.In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding”Industry GOing FAIR”.展开更多
基金supported by the Science and technology project of State Grid Information&Telecommunication Group Co.,Ltd (SGTYHT/19-JS-218)
文摘A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.
文摘Plateaued rate of decline in neonatal mortality rate is one of the major obstacles in achieving Millennium Development Goal 4 especially in developing countries. Even in India, nationwide interventions targeting safe mother and newborn care have not yielded the desired impact, indicating the necessity to combat neonatal mortality rate at population specific level. The objective of this study is to identify the newborn care practices and beliefs, analyze their harmful or beneficial characteristics, describe the deviations from the essential newborn care practices during hospital/home delivery, explain barriers to care seeking and identify areas of potential resistance for behavior change;and utilize study findings to tailor-make cost-effective essential newborn care package. The study uses qualitative data from in-depth interview of mothers who had experienced neonatal death and key-informant interviews with healthcare personnel and birth attendants. 33 cases were randomly selected from the registered neonatal deaths across Bharuch district of Gujarat, India. Key finding of this study is less prevalent practice of essential newborn care among all cases irrespective of place of delivery and the health-care personnel facilitating delivery. Habitual traditional/tribal newborn care methods challenge the practice of prescribed essential newborn care. Clustering of deaths in few households added significantly to the existing burden of neonatal deaths, attributed to superstition “Ratewa” by tribal. Study has concluded that the introduction and implementation of essential newborn care at hospital and community/ household level are the need of the hour. Quality home based neonatal care through cost effective interventions is deemed necessary where accessing institutional care is not possible in the immediate term. Community health workers can contribute to the eradication of harmful newborn care practices and the sustenance of essential practices through community education and behavior change communication.
文摘It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to tackle.In this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.
文摘Antiplatelet therapy, which reduces platelet activation and aggregation, is the corner stone of treatment for patients undergoing percutaneous coronary intervention (PCI). Clopidogrel is an established oral antiplatelet medication of thienopyridine class, which inhibits blood clots in coronary artery disease, peripheral vascular disease, and cerebrovascular disease. Many studies have revealed that high loading dose clopidogrel in patients undergoing PCI. This review article investigates the rationale and role of high loading dose clopidogrel in patients undergoing PCI.
文摘Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which most typically involve collecting data from public-use APIs, often encounter issues when relying on local area network servers (LANs) to collect high-volume streaming social media data over long periods of time. In this paper, we present a cloud-based data collection, pre-processing, and archiving infrastructure, and argue that this system mitigates or resolves the problems most typically encountered when running social media data collection projects on LANs at minimal cloud-computing costs. We show how this approach works in different cloud computing architectures, and how to adapt the method to collect streaming data from other social media platforms. The contribution of our research lies in the development of methodologies that researchers can use to monitor and analyze phenomena including how public opinion and public discourse change in response to events, monitoring the evolution and change of misinformation campaigns, and studying how organizations and entities change how they present and frame information online.
文摘作为一流的管理和工艺流程服务商，Accenture公司利用自己的研发工具成功地为亚洲市场研制开发了SAP Best Practices for Chemicals化工企业计算机管理平台（以下简称SAP Best），并成功地进入了中国的化工企业市场。在中国的某个化工企业中，这一计算机管理系统从一月份开始安装，在短短不到4个月的时间里全面调试成功、投入了生产使用。软件系统中的各个模板，以及以授权登录为基础的解决方案将进一步引进到亚洲其他国家、欧洲和美国。
文摘This paper builds a self-adaptive management process in the power system dispatching area, aiming to effectively monitor the grid operation, dynamically adjust control strategy, optimize working process and ensure the continuous improvement of operational performance. By building a negative feedback and dynamic balanced management mechanism, ECPRCB （East China Power Regulation Center Branch） is able to keenly sense the internal and external changes, efficiently coordinate all kinds of resources and improve the operational performance. As a result, self-adaptive management effectively boosts ECPRCB to reach the goal of being a world-class dispatching center with high operational performance, competent internal operation, adequate resources support and strong growth motion.
文摘Since their discovery in 2011,MXenes,two-dimensional transition metal carbides and nitrides,have emerged as highly promising materials for smart textile applications.They offer exceptional properties such as high electrical conductivity,optical tunability,and mechanical flexibility.These materials can also be produced at scale and readily solution-processed into textile formats,fueling a surge of interest in integrating MXenes into various smart textile applications,from strain sensors and wearable biosensors to adaptive thermal management and electromagnetic interference(EMI)shielding.However,despite this rapid growth,existing reviews of MXene-enabled smart textiles remain narrow in scope,often focusing on single fabrication methods or specific functionalities.Such a fragmented perspective makes it difficult for researchers to gain a comprehensive understanding of how the field has evolved and where it is headed.In response,we present a quantitative bibliographic analysis of MXene–textile research from 2017 through 2024,encompassing nearly 1000 publications.This review categorizes the literature by major functional domains(sensing,energy storage/harvesting,EMI shielding,and heating)and examines their shifts over time,providing reasons and examples for these changes in research interest.Additionally,detailed analyses of functions in each category were conducted in a similar fashion.Our holistic,data-driven assessment offers guidance for future research and commercialization of MXene-functionalized smart textiles by identifying high-impact areas,emerging opportunities,and critical gaps.
文摘With the construction of smart grid,lots of renewable energy resources such as wind and solar are deployed in power system.It might make the power system load varied complex than before which will bring difficulties in short-term load forecasting area.To overcome this issue,this paper proposes a new short-term load forecasting framework based on big data technologies.First,a cluster analysis is performed to classify daily load patterns for individual loads using smart meter data.Next,an association analysis is used to determine critical influential factors.This is followed by the application of a decision tree to establish classification rules.Then,appropriate forecasting models are chosen for different load patterns.Finally,the forecasted total system load is obtained through an aggregation of an individual load’s forecasting results.Case studies using real load data show that the proposed new framework can guarantee the accuracy of short-term load forecasting within required limits.
文摘The industry sector is a very large producer and consumer of data,and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services.As many of the data sources needed are distributed and outside the company,FAIR data will have a major impact,both by reducing the existing internal data silos and by enabling the efficient integration with external(public and commercial)data.Many companies are still in the early phases of internal data”FAIRification”,providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships.For a global Internet of FAIR Data&Services to thrive,also involving industry,professional tools and services are essential.FAIR metrics and certifications on individuals,data,organizations,and software,must ensure that data producers and consumers have independent quality metrics on their data.In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding”Industry GOing FAIR”.
