Sepsis one-hour bundle management combined with psychological intervention on negative emotion and sleep quality in patients with sepsis

BACKGROUND Sepsis is a serious infectious disease caused by various systemic inflammatory responses and is ultimately life-threatening.Patients usually experience depression and anxiety,which affect their sleep quality and post-traumatic growth levels.AIM To investigate the effects of sepsis,a one-hour bundle(H1B)management was combined with psychological intervention in patients with sepsis.METHODS This retrospective analysis included 300 patients with sepsis who were admitted to Henan Provincial People’s Hospital between June 2022 and June 2023.According to different intervention methods,the participants were divided into a simple group(SG,n=150)and combined group(CG,n=150).H1B management was used in the SG and H1B management combined with psychological intervention was used in the CG.The changes of negative emotion,sleep quality and post-traumatic growth and prognosis were compared between the two groups before(T0)and after(T1)intervention.RESULTS After intervention(T1),the scores of the Hamilton Anxiety scale and Hamilton Depression scale in the CG were significantly lower than those in the SG(P<0.001).Sleep time,sleep quality,sleep efficiency,daytime dysfunction,sleep disturbance dimension score,and the total score in the CG were significantly lower than those in the SG(P<0.001).The appreciation of life,mental changes,relationship with others,personal strength dimension score,and total score of the CG were significantly higher than those of the SG(P<0.001).The scores for mental health,general health status,physiological function,emotional function,physical pain,social function,energy,and physiological function in the CG were significantly higher than those in the SG(P<0.001).The mechanical ventilation time,intensive care unit stay time,and 28-d mortality of the CG were significantly lower than those of the SG(P<0.05).CONCLUSION H1B management combined with psychological intervention can effectively alleviate the negative emotions of patients with sepsis and increase their quality of sleep and life.

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

Integrity-Management Characteristics and Efficiency Evaluation of Oil and Gas Pipelines

Pipeline integrity is a cornerstone of the operation of many industrial systems, and maintaining pipeline integrity is essential for preventing economic losses and ecological damage caused by oil and gas leaks. Based on integritymanagement data published by the US Pipeline and Hazardous Materials Safety Administration, this study applied the k-means clustering and data envelopment analysis(DEA) methods to both explore the characteristics of pipeline-integrity management and evaluate its efficiency. The k-means clustering algorithm was found to be scientifically valid for classifying pipeline companies as either low-, medium-, or high-difficulty companies according to their integrity-management requirements. Regardless of a pipeline company's classification, equipment failure was found to be the main cause of pipeline failure. In-line inspection corrosion and dent tools were the two most-used tools for pipeline inspection. Among the types of repair, 180-day condition repairs were a key concern for pipeline companies. The results of the DEA analysis indicate that only three out of 34 companies were deemed to be DEA-effective. To improve the effectiveness of pipeline integrity management, we propose targeted directions and scales of improvement for non-DEA-effective companies.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Clustering-based interference management in densely deployed femtocell networks

Deploying femtoceUs underlaying macrocells is a promising way to improve the capacity and enhance the coverage of a cellular system. However, densely deployed femtocells in urban area also give rise to intra-tier interference and cross-tier issue that should be addressed properly in order to acquire the expected performance gain. In this paper, we propose an interference management scheme based on joint clustering and resource allocation for two-tier Orthogonal Frequency Division Multiplexing （OFDM）-based femtoeeU networks. We formulate an optimization task with the objective of maximizing the sum throughput of the femtocell users （FUs） under the consideration of intra-tier interference mitigation, while controlling the interference to the maeroeell user （MU） under its bearable threshold. The formulation problem is addressed by a two-stage procedure： femtoeells clustering and resource allocation. First, disjoint femtocell clusters with dynamic sizes and numbers are generated to minimize intra-tier interference. Then each cluster is taken as a resource allocation unit to share all subehannels, followed by a fast algorithm to distribute power among these subchannels. Simulation results show that our proposed schemes can improve the throughput of the FUs with acceptable complexity.

Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.

Co-word clustering analysis for nursing safety management research focuses by PubMed

Objective： To analyze hot research areas and the present research status of nursing safety management in PubMed. Methods： PubMed was searched using ＂safety management＂ for the literature on nursing safety management. BICOMB 2.0 and SPSS 20.0 software were used to analyze high-frequency keywords and conduct co-word clustering analysis. Results： We searched for totally 2353 articles related to our topic and extracted 19 high-frequency keywords （27.50%）. Five research focuses were concluded, including： study on nursing safety culture; team work to promote nursing safety; practice of nursing safety management; workplace violence against nursing staffs; nursing safety and quality evaluation standard. Conclusion： Analysis of the hotspots of nursing safety management in the past 10 years will contribute to understanding the research emphases and trend of development, and provide reference for the study and practice of nursing safety management.

A Hybrid Key Management Scheme Based on Clustered Wireless Sensor Networks

According to the weakness of session key construction based on node’s own location, we propose a hybrid key management scheme which based on clustered wireless sensor networks. The use of hierarchical thinking, reducing the amount of key storage and computing, while supporting network topology, dynamic key management for which aims to prevent leakage. Through analyzing, it shows that the scheme have certain advantages in key connectivity, security, communication and energy consumption.

UAPM: An Urgency-Aware Packet Management for Disaster Management Using Flying Ad-Hoc Networks

The ever increasing demand of adhoc networks for adaptive topology and mobility aware communication led to new paradigm of networking among Unmanned Aerial Vehicles(UAVs)known as Flying ad-hoc Networks(FANETs).Due to their dynamic topology,FANETs can be deployed for disaster monitoring and surveillance applications.During these operations,UAVs need to transmit different disaster data,which consists of different types of data packets.Among them there are packets which need to be transmitted urgently because of the emergency situation in disaster management.To handle this situation,we propose a methodology of disaster data classification using urgency level and based on these urgency levels,priority index is assigned to data packets.An approach of Urgency Aware Scheduling(UAS)is proposed to efficiently transmit high and low priority packets with minimum delays in transmission queue.We take into account different scenarios of UAVs for disaster management and for N number of UAVs,we propose bio-inspired mechanism using behavioral study of bird flocking for cluster formation and maintenance.Furthermore,we propose a priority based route selection methodology for data communication in FANET cluster.Simulationresults show that our proposed mechanism shows better performance in the presence of evaluation benchmarks like average delay,queuing time,forward percentage and fairness.

Light-Weight Simple Network Management Protocol in Wireless Sensor Networks

A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).

Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

An Efficient Key Management Protocol for Wireless Sensor Networks

Key management is a fundamental security service in wireless sensor networks. The communication security problems for these networks are exacerbated by the limited power and energy of the sensor devices. In this paper, we describe the design and implementation of an efficient key management scheme based on low energy adaptive clustering hierarchy（LEACH） for wireless sensor networks. The design of the protocol is motivated by the observation that many sensor nodes in the network play different roles. The paper presents different keys are set to the sensors for meeting different transmitting messages and variable security requirements. Simulation results show that our key management protocol based-on LEACH can achieve better performance. The energy consumption overhead introduced is remarkably low compared with the original Kerberos schemes.

基于WS-Management的分布式机群管理方案

为了解决目前机群管理中存在的缺乏统一管理标准、可扩展性差及可用性差等问题,首次把WS-Management标准引入到机群系统管理中,提出了分布式机群管理解决方案。对机群进行CIMOM建模,实现了机群的标准化管理;采用分层管理的方法,提高了机群系统的可扩展性、可用性、容错性和性价比。对该方案进行了实现和实验分析,实验结果表明了该方案性能良好。

An Efficient Key Management Scheme for Hierarchical Wireless Sensor Networks

The recent advances in integrated circuit technologies, microprocessor hardware, wireless communications, embedded systems and technologies as well as the emergence of Ad-hoc networking, made up the concept of wireless sensor networks. Regarding the nature of sensors and the nature of the environment of deployment sensor networks are exposed to many attacks more than any other networks, therefore new strategies and protocols of security must be defined for these networks taking into consideration the characteristics of sensors as well as the architecture of the network. In this paper we propose a lightweight implementation of public key infrastructure called cluster based public infrastructure (CBPKI), CBPKI is based on the security and the authenticity of the base station for executing a set of handshakes intended to establish session keys between the base station and sensors over the network used for ensuring data confidentiality and integrity.

Discussion on Management Model of Medical Equipment Maintenance in Hospital

With the development of technology, medical equipment plays a more important role in the modern hospital. Medical equipment provides images, physiological results or other schematics of the patients to help doctors deal with the routines. It is also the major tool to the lab assistant in the clinical teaching and studies. It becomes one of a symbol of the technological level in modern hospital. The director of the hospital should establish an effi cacious management pattern to manage the medical equipment, so that it will be operated in good condition to insure the safety of the patients. Since the maintenance is an important part of medical equipment management, it is significant that the hospital should establish a system to fit for itself. The study reviews the issues in hospital medical equipment maintenance management, the development of the maintenance management model and the characteristics, as well as the status at home and abroad. The research has been fi nished by using literature research method, questionnaire survey method and data analysis method. Then we analyze the current status of hospital medical equipment maintenance management model to fi nd the problems. Based on the authentic and effective data from the survey, we can deduce the overview about the maintenance model in Beijing. The paper discusses the advantages and the disadvantages between the different model, and it makes suggestions on the follow aspects, such as system management, quality management, personnel management, cost management, information management perspective.

Determination of potential management zones from soil electrical conductivity,yield and crop data

One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper,the variables of soil electrical conductivity (EC) data,cotton yield data and normalized differ-ence vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources,and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones,fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georefer-enced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and produc-tivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone,and management zone 3 presented the highest nutrient level and potential crop productivity,whereas management zone 1 the lowest. Based on these findings,we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils,and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.

Lightweight Key Management Scheme Using Fuzzy Extractor for Wireless Mobile Sensor Network

The mature design of wireless mobile sensor network makes it to be used in vast verities of applications including from home used to the security surveillance.All such types of applications based on wireless mobile sensor network are generally using real time data,most of them are interested in real time communication directly from cluster head of cluster instead of a base station in cluster network.This would be possible if an external user allows to directly access real time data from the cluster head in cluster wireless mobile sensor network instead of accessing data from base station.But this leads to a serious security breach and degrades the performance of any security protocol available in this domain.Most existing schemes for authentication and cluster key management for external users,exchange a number of messages between cluster head and base station to allow external to access real time data from the base station instead of cluster head.This increase communication cost and delay in such real time access information.To handle this critical issue in cluster wireless mobile sensor network,we propose a lightweight authentication and key management scheme using a fuzzy extractor.In this scheme,any external user can access data directly from the cluster head of any cluster without the involvement of the base station.The proposed scheme only uses the one-way hash functions and bitwise XOR operations,apart from the fuzzy extractor method for the user biometric verification at the login phase.The presented scheme supports scalability for an increasing number of nodes using polynomials.The proposed scheme increases the life-time of the network by decreasing the key pool size.

A New Approach for Database Fragmentation and Allocation to Improve the Distributed Database Management System Performance

The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.

Investigating Road Safety Management Systems in the European Countries: Patterns and Particularities

A road safety management (RSM) system can be defined as “a complex institutional structure that involves cooperating and interacting bodies which support the tasks and processes necessary for the prevention and reduction of road traffic injuries”. RSM should promote the road safety progress of the country. However, the details of this relationship are generally lacking. This study explored the RSM systems in European countries based on the information collected through interviews with experts and officials, in each country, and using a “good practice” criteria questionnaire. The dataset included 14 countries with fifty items related to five RSM areas: institutional organization;policy formulation and adoption;policy implementation and funding;monitoring and evaluation;scientific support, information and capacity building. Cluster analyses and correlations were used to identify country groups with similar RSM components, to recognize typical RSM structures if available and to examine the relationship between RSM and road safety performance of the countries. The findings showed that all the countries are different when RSM systems are considered as a whole, making it impossible to identify typical RSM structures or a single best working model at a national level. However, it is possible to compare countries when the RSM areas are considered separately, where the clusters of countries recognized by the study present the patterns common for those European countries. Across the analyses, a number of countries with a consistently higher and lower availability of the RSM components were identified, enabling a final countries’ ranking into a number of groups. The latter actually reflects the level of RSM in the country, in terms of its correspondence to the “good practice” criteria. A further analysis indicated a positive correlation between the higher level of the RSM system and better safety performance of the countries.