BACKGROUND Since being declared as a pandemic on March 11,2020,coronavirus disease 2019(COVID-19)has profoundly influenced heart and lung transplant programs,impacting donor availability,patient management,and healthc...BACKGROUND Since being declared as a pandemic on March 11,2020,coronavirus disease 2019(COVID-19)has profoundly influenced heart and lung transplant programs,impacting donor availability,patient management,and healthcare resources.This study offers a citation-based review of the research output on this subject,seeking to understand how the transplant community has responded to these challenges.Through a review of literature from the beginning of the pandemic to early 2023,we evaluate the shifts in academic emphasis and the emerging trends in heart and lung transplantation during the COVID-19 period.AIM To assess the impact of COVID-19 on heart and lung transplantation research,highlighting key themes,contri-butions,and trends in the literature during the pandemic.METHODS We conducted an extensive search of the Web of Science database on February 9,2023.We employed the terms"transplant"and"transplantation",as well as organ-specific terms like"heart","cardiac",and"lung",combined with COVID-19-related terms such as"COVID-19","coronavirus",and"SARS-CoV-2".The search encompassed public-ations from March 11,2020 to February 9,2023.Data on authors,journals,countries,institutions,and publication types(articles,reviews,conference papers,letters,notes,editorials,brief surveys,book chapters,and errata)were analyzed.The data was visualized and processed with VOSviewer 1.6.18 and Excel.RESULTS We included 847 research items.There were 392 articles(46.3%)and 88 reviews(10.3%).The studies included were referenced 7757 times,with an average of 9.17 citations per article.The majority of the publications(n=317)were conducted by institutes from the United States with highest citations(n=4948)on this subject,followed by Germany,Italy,and France.The majority of papers(n=101)were published in the Journal of Heart and Lung Transplantation.CONCLUSION To the fullest extent of our knowledge,this is the first bibliometric study of COVID-19's impact on heart and lung transplantation to offer a visual analysis of the literature in order to predict future frontiers and provide an over-view of current research hotspots.展开更多
The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s o...The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.展开更多
Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin ...Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.展开更多
文摘BACKGROUND Since being declared as a pandemic on March 11,2020,coronavirus disease 2019(COVID-19)has profoundly influenced heart and lung transplant programs,impacting donor availability,patient management,and healthcare resources.This study offers a citation-based review of the research output on this subject,seeking to understand how the transplant community has responded to these challenges.Through a review of literature from the beginning of the pandemic to early 2023,we evaluate the shifts in academic emphasis and the emerging trends in heart and lung transplantation during the COVID-19 period.AIM To assess the impact of COVID-19 on heart and lung transplantation research,highlighting key themes,contri-butions,and trends in the literature during the pandemic.METHODS We conducted an extensive search of the Web of Science database on February 9,2023.We employed the terms"transplant"and"transplantation",as well as organ-specific terms like"heart","cardiac",and"lung",combined with COVID-19-related terms such as"COVID-19","coronavirus",and"SARS-CoV-2".The search encompassed public-ations from March 11,2020 to February 9,2023.Data on authors,journals,countries,institutions,and publication types(articles,reviews,conference papers,letters,notes,editorials,brief surveys,book chapters,and errata)were analyzed.The data was visualized and processed with VOSviewer 1.6.18 and Excel.RESULTS We included 847 research items.There were 392 articles(46.3%)and 88 reviews(10.3%).The studies included were referenced 7757 times,with an average of 9.17 citations per article.The majority of the publications(n=317)were conducted by institutes from the United States with highest citations(n=4948)on this subject,followed by Germany,Italy,and France.The majority of papers(n=101)were published in the Journal of Heart and Lung Transplantation.CONCLUSION To the fullest extent of our knowledge,this is the first bibliometric study of COVID-19's impact on heart and lung transplantation to offer a visual analysis of the literature in order to predict future frontiers and provide an over-view of current research hotspots.
基金This work was supported by Universiti Sains Malaysia under external grant(Grant Number 304/PNAV/650958/U154).
文摘The Software-Defined Networking(SDN)technology improves network management over existing technology via centralized network control.The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues.However,despite the advantages of centralized control,concern about its security is rising.The more traditional network switched to SDN technology,the more attractive it becomes to malicious actors,especially the controller,because it is the network’s brain.A Distributed Denial of Service(DDoS)attack on the controller could cripple the entire network.For that reason,researchers are always looking for ways to detect DDoS attacks against the controller with higher accuracy and lower false-positive rate.This paper proposes an entropy-based approach to detect low-rate and high-rate DDoS attacks against the SDN controller,regardless of the number of attackers or targets.The proposed approach generalized the Rényi joint entropy for analyzing the network traffic flow to detect DDoS attack traffic flow of varying rates.Using two packet header features and generalized Rényi joint entropy,the proposed approach achieved a better detection rate than the EDDSC approach that uses Shannon entropy metrics.
文摘Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.
