Evaluation of the Impact of Refined Quality Control Management Model on the Qualified Rate of Disinfection and Sterilization of Surgical Instruments in the Sterilization Supply Center

Objective:To evaluate the application value of a refined quality control management model for a sterilization supply center.Methods:A retrospective analysis was conducted on the work situation of the sterilization supply center from January 2021 to January 2023.The work situation before January 31,2022,was classified as the control group;a routine quality control management model was implemented,and the work situation after January 31,2022,was classified as the observation group.The quality of medical device management and department satisfaction between the two groups were compared.Results:The timely recovery and supply rate,classification and cleaning pass rate,disinfection pass rate,packaging pass rate,sterilization pass rate,and department satisfaction score in the observation group were all higher than those of the control group(P<0.05).Conclusion:Implementing a refined quality control management model in the sterilization supply center can improve the quality management level of medical devices and department satisfaction and is worthy of promotion.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

The Role of Grid-Style Nursing Management Model in Critically Ill Patients

Objective:This study aims to gain insight into the effects and potential advantages of the grid-style nursing management model in the care of critically ill patients.Methods:Eighty critically ill patients admitted to our hospital between May 2020 and May 2021 were selected and randomly divided into the control group and the grid group,each with 40 patients.The control group implemented traditional nursing management,while the grid group adopted a grid-style nursing management model.The quality of care,quality of life,nursing satisfaction,and treatment adherence of the two groups were compared.Results:Compared with the control group,the grid group had significantly higher quality of care and quality of life(P<0.001);in terms of nursing satisfaction,the score of the grid group was 8.26±0.85,which was significantly higher than that of the control group(6.65±0.77)(P<0.001);90.00%(36 patients)of the grid group showed good treatment adherence,significantly higher than 70.00%(28 patients)of the control group(P<0.001).Conclusion:The implementation of the grid-style nursing management model in critically ill patients can significantly improve the quality of care,quality of life,and satisfaction of patients,and effectively promote patients’treatment adherence.These positive results provide strong support for the promotion and application of this model in clinical care.

Green Roof Performance for Stormwater Management in Equatorial Urban Areas Using Storm Water Management Model (SWMM)

Many Low Impact Developments (LIDs) have recently been developed as a sustainable integrated strategy for managing the quantity and quality of stormwater and surrounding amenities. Previous research showed that green roof is one of the most promising LIDs for slowing down rainwater, controlling rainwater volume, and enhancing rainwater quality by filtering and leaching contaminants from the substrate. However, there is no guideline for green roof design in Malaysia. Hence, Investigating the viability of using green roofs to manage stormwater and address flash flood hazards is urgently necessary. This study used the Storm Water Management Model (SWMM) to evaluate the effectiveness of green roof in managing stormwater and improving rainwater quality. The selected study area is the multistory car park (MSCP) rooftop at Swinburne University of Technology Sarawak Campus. Nine green roof models with different configurations were created. Results revealed that the optimum design of a green roof is 100 mm of berm height, 150 mm of soil thickness, and 50 mm of drainage mat thickness. With the ability to reduce runoff generation by 26.73%, reduce TSS by 89.75%, TP by 93.07%, TN by 93.16%, and improved BOD by 81.33%. However, pH values dropped as low as 5.933 and became more acidic due to the substrates in green roof. These findings demonstrated that green roofs improve water quality, able to temporarily store excess rainfall and it is very promising and sustainable tool in managing stormwater.

Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.

Clinical value of precise rehabilitation nursing in management of cerebral infarction

BACKGROUND Cerebral infarction,previously referred to as cerebral infarction or ischemic stroke,refers to the localized brain tissue experiencing ischemic necrosis or softening due to disorders in brain blood supply,ischemia,and hypoxia.The precision rehabilitation nursing model for chronic disease management is a continuous,fixed,orderly,and efficient nursing model aimed at standardizing the clinical nursing process,reducing the wastage of medical resources,and improving the quality of medical services.AIM To analyze the value of a precise rehabilitation nursing model for chronic disease management in patients with cerebral infarction.METHODS Patients(n=124)admitted to our hospital with cerebral infarction between November 2019 and November 2021 were enrolled as the study subjects.The random number table method was used to divide them into a conventional nursing intervention group(n=61)and a model nursing intervention group(n=63).Changes in the nursing index for the two groups were compared after conventional nursing intervention and precise rehabilitation intervention nursing for chronic disease management.RESULTS Compared with the conventional intervention group,the model intervention group had a shorter time to clinical symptom relief(P<0.05),lower Hamilton Anxiety Scale and Hamilton Depression Scale scores,a lower incidence of total complications(P<0.05),a higher disease knowledge mastery rate,higher safety and quality,and a higher overall nursing satisfaction rate(P<0.05).CONCLUSION The precision rehabilitation nursing model for chronic disease management improves the clinical symptoms of patients with cerebral infarction,reducing the incidence of total complications and improving the clinical outcome of patients,and is worthy of application in clinical practice.

Practice of Perioperative Rational Use of Antibacterial Drugs Based on Drug Pathway

[Objectives]To establish a new management model for rational use of perioperative antibacterial drugs in surgical departments.[Methods]Based on evidence-based medicine,the department s drug pathway was formulated,and the new mode of rational drug use control was established by using fine pharmaceutical technology intervention,and the intervention effect was evaluated by the intensity of antibacterial drug use,per capita drug costs and the proportion of drugs.[Results]After adopting drug pathway in departments,the intensity of antibacterial drug use,per capita drug costs and the proportion of drugs decreased significantly,and the effect of rational drug use control was remarkable.[Conclusions]The drug pathway provides a new management and control mode for the rational use of perioperative antibacterial drugs in surgical departments of hospitals.Thus,it is worthy of popularization and application.

Multi-objective Optimization Design of Wing Structure with the Model Management Framework

Evolutionary algorithm is time-consuming because of the large number of evolutions and much times of finite element analysis, when it is used to optimize the wing structure of a certain high altitude long endurance unmanned aviation vehicle（UAV）. In order to improve efficiency it is proposed to construct a model management framework to perform the multi-objective optimization design of wing structure. The sufficient accurate approximation models of objective and constraint functions in the wing structure optimization model are built when using the model management framework, therefore in the evolutionary algorithm a number of finite element analyses can he avoided and the satisfactory multi-objective optimization results of the wing structure of the high altitude long endurance UAV are obtained.

Inclusive Multiple Models(IMM)for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.

The 3D simulation and optimized management model of groundwater systems based on eco-environmental water demand

Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.

A comparison of the management models of protected areas between China and the southern Africa region

A comparison of the management models of protected areas between China and the African south region allows reading and evaluating the similarities and differences in the use of management model as a management tool for protected areas and specifi- cally some positive and negative features of the management approaches in these two regions. Previous to this study it verified the designation of protected areas as increasing at a faster rate than ever before, comparatively much faster now in China than southern Africa regions. With the aim of evaluating similarities and differences in the use of management model as a management tool for pro- tected areas in China and southern Africa Region, both qualitative and quantitative data were collected. Qualitative data were mainly from policy documents, scientific articles and magazine reports, whereas quantitative （secondary data） statistical data from Interna- tional Union for Conservation of Nature Resources （IUCN） and World Database on Protected Areas （WDPA）. In view of the data sources above, the study found that in China all the protected areas are state owned whilst in southern Africa regions there are some protected areas that are privately owned; also that the models or typology of governance applied are successful for the fact that they both combine co-management or collaborative management, community-conserved areas and private protected areas that are subject to greater success and can help design planning and management than those who use exclusively government management. To this, the study concluded that the use of management model is influenced by the type of governance a country applies to its reserved areas.

Development of Crisis Management Models Combined with Cloud Computing

Three monitor models of enterprise crisis were introduced,i.e.,the monitoring model of enterprise crisis based on intelligent Meta search,the enterprise crisis management model based on artificial neural network and the combined early-warning model.Combined with the advantages of cloud computing,the prominent crisis management models are improved and more efficient,comprehensive and accurate in enterprise crisis management.Through the empirical study of the models,cloud computing makes the early warning structures of enterprise crisis tend to be more simple and efficient,cloud computing can effectively enhance the recognition ability and learning ability of the crisis management,and cloud computing can keep data information updating and realize the dynamic management of enterprise joint early-warning.At the same time,according to the comparative analysis and the experimental result,the crisis management models based on cloud computing also need some improvements.

Efficient Energy-Aware Resource Management Model(EEARMM)Based Dynamic VM Migration

In cloud environment,an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands.The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure.However,it is very difficult to persuade the objectives of the Cloud Service Providers(CSPs)and end users in an impulsive cloud domain with random changes of workloads,huge resource availability and complicated service policies to handle them,With that note,this paper attempts to present an Efficient Energy-Aware Resource Management Model(EEARMM)that works in a decentralized manner.Moreover,the model involves in reducing the number of migrations by definite workload management for efficient resource utilization.That is,it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine.The Estimation Model Algorithm(EMA)is given for determining the virtual machine migration.Further,VM-Selection Algorithm(SA)is also provided for choosing the appropriate VM to migrate for resource management.By the incorporation of these algorithms,overloading of VM instances can be avoided and energy efficiency can be improved considerably.The performance evaluation and comparative analysis,based on the dynamic workloads in different factors provides evidence to the efficiency,feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.

Numerical modeling and simulation of PEM fuel cells: Progress and perspective

This paper provides a comprehensive review on the research and development in multi-scale numerical modeling and simulation of PEM fuel cells. An overview of recent progress in PEM fuel cell modeling has been provided. Fundamental transport phenomena in PEM fuel cells and the corresponding mathematical formulation of macroscale models are analyzed. Various important issues in PEM fuel cell modeling and simulation are examined in detail, including fluid flow and species transport, electron and proton transport, heat transfer and thermal management, liquid water transport and water management, transient response behaviors, and cold-start processes. Key areas for further improvements have also been discussed.

Analysis on Sustainable Development Management Model of Village Banks——Based on Bayan Rongxing Village Bank in Heilongjiang Province

This paper summarized the management models of village banks in China. We compared and analyzed different management models of four village banks in different regions on four aspects： regional agriculture feature, loan for farmers, loan for enterprises and the construction of network, then came to the conclusion that, if the village banks＇ health development can realize, they must pay attention to innovating in loan patterns as well as material loan and starting out the network development.

Hydraulics Management Model of Response Function Solved by Laplace Transform Boundary Element Method

In solving a response function by the boundary element method, the use of the singular valued method and the Laplace transform in a time domain makes the solving process be simplified and the result be accurate. The restricted condition matrix formed by the response matrix method is much smaller than that by embedding method. In addition, the response function may realize directly the management decision making. So it is efficient for establishing and solving hydraulics management models.

A method to manage the model base in DSS

How to manage and use models in DSS is a most important subject. Generally, it costs a lot of money and time to develop the model base management system in the development of DSS and most are simple in function or cannot be used efficiently in practice. It is a very effective, applicable, and economical choice to make use of the interfaces of professional computer software to develop a model base management system. This paper presents the method of using MATLAB, a well-known statistics software, as the development platform of a model base management system. The main functional framework of a MATLAB-based model base management system is discussed. Finally, in this paper, its feasible application is illustrated in the field of construction projects.

Statecharts for Distributed Product Data Management System Modelling

Product data management (PDM) has been accepted as an important tool for the manufacturing industries. In recent years, more and mor e researches have been conducted in the development of PDM. Their research area s include system design, integration of object-oriented technology, data distri bution, collaborative and distributed manufacturing working environment, secur ity, and web-based integration. However, there are limitations on their rese arches. In particular, they cannot cater for PDM in distributed manufacturing e nvironment. This is especially true in South China, where many Hong Kong (HK) ma nufacturers have moved their production plants to different locations in Pearl R iver Delta for cost reduction. However, they retain their main offices in HK. Development of PDM system is inherently complex. Product related data cover prod uct name, product part number (product identification), drawings, material speci fications, dimension requirement, quality specification, test result, log size, production schedules, product data version and date of release, special tooling (e.g. jig and fixture), mould design, project engineering in charge, cost spread sheets, while process data includes engineering release, engineering change info rmation management, and other workflow related to the process information. Accor ding to Cornelissen et al., the contemporary PDM system should contains manageme nt functions in structure, retrieval, release, change, and workflow. In system design, development and implementation, a formal specification is nece ssary. However, there is no formal representation model for PDM system. Theref ore a graphical representation model is constructed to express the various scena rios of interactions between users and the PDM system. Statechart is then used to model the operations of PDM system, Fig.1. Statechart model bridges the curr ent gap between requirements, scenarios, and the initial design specifications o f PDM system. After properly analyzing the PDM system, a new distributed PDM (DPDM) system is proposed. Both graphical representation and statechart models are constructed f or the new DPDM system, Fig.2. New product data of DPDM and new system function s are then investigated to support product information flow in the new distribut ed environment. It is found that statecharts allow formal representations to capture the informa tion and control flows of both PDM and DPDM. In particular, statechart offers a dditional expressive power, when compared to conventional state transition diagr am, in terms of hierarchy, concurrency, history, and timing for DPDM behavioral modeling.

Bi-Level Energy Management Model of Grid-Connected Microgrid Community

As the proportion of renewable energy power generation continues to increase,the number of grid-connected microgrids is gradually increasing,and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community(MGC).In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community,this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid.The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid.The upper-level model optimizes the goal ofmaximizing the socialwelfare of themicrogrid.Taking amicrogrid community with four microgrids as an example,the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid,improve the overall revenue of the microgrid community,and reduce the power interaction pressure on the main grid.

Establishment of Integrated Diagnosis and Treatment and Whole-Course Management Model of Major Depressive Disorder

Integrated diagnosis,treatment and whole-course management model of major depressive disorder(MDD)is an integrated drug-psychological-physical comprehensive treatment model based on rapid biological-psychological-social evaluation for treating patients with major depressive disorder.This paper comprehensively evaluates the clinical efficacy and biochemical indexes of patients,and carries out symptom evaluation,problem classification,disease diagnosis and etiological analysis of visitors from the three dimensions about physiology,psychology and society.Then,according to the symptoms and causes of different dimensions,this paper formulates personalized drug,psychological and physical therapy programs,and constantly optimizes and adjusts the treatment plan in the treatment process,so as to cure both the symptoms and the root causes,providing a reliable strategy for the treatment of clinical MDD,and establishing a standardized characteristic model for further promotion and application of this technology.At present,the project has been used in the member units of Taihe Medical Group and Shiyan City and its surrounding areas,the market response is good,and will be gradually promoted to the whole country in the later stage.