Objective: To explore the practice and application of infection prevention and control strategies in risk departments during the COVID-19 epidemic, and to formulate the infection prevention and control measures to pro...Objective: To explore the practice and application of infection prevention and control strategies in risk departments during the COVID-19 epidemic, and to formulate the infection prevention and control measures to provide advice and guidance in risk departments. Methods: According to the latest plan of diagnosis and treatment, prevention and control issued by the National Health Commission, expert advice and consensus, combined with the actual situation in our hospital, a series of infection prevention and control measures of COVID-19 in risk department was formulated. Results: During the epidemic period, the prevention and control measures of nine risk departments including emergency operation, anesthesiology, endoscopy center, blood purification center, otolaryngology, stomatology, medical imaging department, medical cosmetology department and pulmonary function room were established from six aspects, including pre-examination and screening, medical technology control, personnel management, personal protection, environmental disinfection, medical waste disposal, etc. Conclusion: During the epidemic period, the infection prevention and control strategy of risk departments is one of the key links to control the spread of the epidemic, and risk departments must pay attention to and strictly implement various infection prevention and control measures.展开更多
Vanadium oxide cathode materials with stable crystal structure and fast Zn^(2+) storage capabilities are extremely important to achieving outstanding electrochemical performance in aqueous zinc‐ion batteries.In this ...Vanadium oxide cathode materials with stable crystal structure and fast Zn^(2+) storage capabilities are extremely important to achieving outstanding electrochemical performance in aqueous zinc‐ion batteries.In this work,a one‐step hydrothermal method was used to manipulate the bimetallic ion intercalation into the interlayer of vanadium oxide.The pre‐intercalated Cu ions act as pillars to pin the vanadium oxide(V‐O)layers,establishing stabilized two‐dimensional channels for fast Zn^(2+) diffusion.The occupation of Mn ions between V‐O interlayer further expands the layer spacing and increases the concentration of oxygen defects(Od),which boosts the Zn^(2+) diffusion kinetics.As a result,as‐prepared Cu_(0.17)Mn_(0.03)V_(2)O_(5−□)·2.16H_(2)O cathode shows outstanding Zn‐storage capabilities under room‐and lowtemperature environments(e.g.,440.3 mAh g^(−1) at room temperature and 294.3 mAh g^(−1)at−60°C).Importantly,it shows a long cycling life and high capacity retention of 93.4%over 2500 cycles at 2 A g^(−1) at−60°C.Furthermore,the reversible intercalation chemistry mechanisms during discharging/charging processes were revealed via operando X‐ray powder diffraction and ex situ Raman characterizations.The strategy of a couple of 3d transition metal doping provides a solution for the development of superior room‐/lowtemperature vanadium‐based cathode materials.展开更多
In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and...In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.展开更多
Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social ...Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.展开更多
文摘Objective: To explore the practice and application of infection prevention and control strategies in risk departments during the COVID-19 epidemic, and to formulate the infection prevention and control measures to provide advice and guidance in risk departments. Methods: According to the latest plan of diagnosis and treatment, prevention and control issued by the National Health Commission, expert advice and consensus, combined with the actual situation in our hospital, a series of infection prevention and control measures of COVID-19 in risk department was formulated. Results: During the epidemic period, the prevention and control measures of nine risk departments including emergency operation, anesthesiology, endoscopy center, blood purification center, otolaryngology, stomatology, medical imaging department, medical cosmetology department and pulmonary function room were established from six aspects, including pre-examination and screening, medical technology control, personnel management, personal protection, environmental disinfection, medical waste disposal, etc. Conclusion: During the epidemic period, the infection prevention and control strategy of risk departments is one of the key links to control the spread of the epidemic, and risk departments must pay attention to and strictly implement various infection prevention and control measures.
基金National Natural Science Foundation of China,Grant/Award Numbers:52372188,51902090,51922008,520721142023 Introduction of studying abroad talent program,the China Postdoctoral Science Foundation,Grant/Award Number:2019 M652546+3 种基金Xinxiang Major Science and Technology Projects,Grant/Award Number:21ZD001Henan Province Postdoctoral Start‐Up Foundation,Grant/Award Number:1901017Henan Center for Outstanding Overseas Scientists,Grant/Award Number:GZS2018003Overseas Expertise Introduction Project for Discipline Innovation,Grant/Award Number:D17007。
文摘Vanadium oxide cathode materials with stable crystal structure and fast Zn^(2+) storage capabilities are extremely important to achieving outstanding electrochemical performance in aqueous zinc‐ion batteries.In this work,a one‐step hydrothermal method was used to manipulate the bimetallic ion intercalation into the interlayer of vanadium oxide.The pre‐intercalated Cu ions act as pillars to pin the vanadium oxide(V‐O)layers,establishing stabilized two‐dimensional channels for fast Zn^(2+) diffusion.The occupation of Mn ions between V‐O interlayer further expands the layer spacing and increases the concentration of oxygen defects(Od),which boosts the Zn^(2+) diffusion kinetics.As a result,as‐prepared Cu_(0.17)Mn_(0.03)V_(2)O_(5−□)·2.16H_(2)O cathode shows outstanding Zn‐storage capabilities under room‐and lowtemperature environments(e.g.,440.3 mAh g^(−1) at room temperature and 294.3 mAh g^(−1)at−60°C).Importantly,it shows a long cycling life and high capacity retention of 93.4%over 2500 cycles at 2 A g^(−1) at−60°C.Furthermore,the reversible intercalation chemistry mechanisms during discharging/charging processes were revealed via operando X‐ray powder diffraction and ex situ Raman characterizations.The strategy of a couple of 3d transition metal doping provides a solution for the development of superior room‐/lowtemperature vanadium‐based cathode materials.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.U20A20197,62306187the Foundation of Ministry of Industry and Information Technology TC220H05X-04.
文摘In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.
基金supported by the NationalNatural Science Foundation of China(61972136)the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation(T201410,T2020017)+1 种基金the Natural Science Foundation of Xiaogan City(XGKJ2022010095,XGKJ2022010094)the Science and Technology Research Project of Education Department of Hubei Province(No.Q20222704).
文摘Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.
