Analysis of the Application Effect of Family Collaborative Care Model on Elderly Patients with Type 2 Diabetes Mellitus and the Situation of Self-Care Ability

Objective: To study the application effect of the family collaborative care model on elderly patients with type 2 diabetes mellitus and its influence on self-care ability. Methods: The elderly type 2 diabetes mellitus patients (400 cases) treated in our hospital between March 2020 and July 2023 were divided into two groups by randomized grouping method;the control group received the conventional nursing program, while the observation group received the family collaborative nursing model. Blood glucose level, self-care ability, and quality of life were compared between the groups. Results: The blood glucose level of the observation group was lower than that of the control group (P < 0.05). The self- care ability and quality of life scores of the observation group were higher than those of the control group (P < 0.05). Conclusion: The family collaborative care model for elderly patients with type 2 diabetes mellitus can promote their self- care ability, improve the effect of glycemic control, and improve their quality of life, and is suitable for further promotion and application.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

ChatModeler:基于大语言模型的人机协作迭代式需求获取和建模方法

需求获取和建模是需求工程中的关键步骤,影响后续系统设计与实现.传统的需求获取和建模方法通常由需求提供者、需求分析师等多类干系人共同协作、反复迭代完成,需要耗费大量的人力.如何减轻需求提供者与需求分析师的负担、提高获取和建模的效率有着重要意义.现有工作中有的使用知识库来提供更多知识,以辅助获取或者建模,有的利用自然语言处理等技术对获取或者建模过程进行自动化,但是它们并没有减轻需求提供者的负担.利用大语言模型(large language models,LLMs)的生成能力,提供了一种人机协作的迭代式需求获取和建模框架ChatModeler.具体来说,根据真实世界中需求团队的分工及协作关系,将部分需求提供者、需求分析师等角色的工作由大语言模型承担,而需求提供者只需要进行确认.为大语言模型扮演的各种角色进行了提示词设计,该提示词会随需求的元模型而变化.ChatModeler在7个需求案例上与3种需求模型的自动建模方法进行了14组对比实验,证明了ChatModeler在降低需求提供者的负担和生成高质量需求模型2个方面上的优越性.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

Exploration of the Training Mode of School-enterprise Collaborative AI Innovative Talents Based on PaddlePaddle

This paper takes the school-enterprise cooperation between the University of Electronic Science and Technology of China(UESTC)and Baidu(China)Co.,Ltd.as an example to build the Paddle Paddle(Sichuan)AI Education Innovation Center by enjoying the best of both UESTC and Baidu(China),and cooperating with 16 universities in Sichuan Province.With the support of this center,both the school and the enterprise successfully built a school-enterprise collaborative AI innovative talent training model,which mainly serves the universities,industries,and districts.Furthermore,this training model is able to facilitate the update of the industrial intelligence and the regional economic development in southwest China,and also provide a reference for deep integration of school-enterprise collaboration and the cultivation of innovative talents in electronic information fields.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Assessing the host genetic background effects on type 2 diabetes and obesity development in response to mixed–oral bacteria and high-fat diet using the collaborative cross mouse model

Background: Host genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes(T2 D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross(CC), for dissecting host susceptibility for the development of T2 D and obesity, showing significant variations following high-fat(42% fat) diet(HFD). Here, we aimed to assessing the host genetic background and sex effects on T2 D and obesity development in response to oral-mixed bacterial infection and HFD using the CC lines.Materials and Methods: Study cohort consists of 97 mice from 2 CC lines(both sexes), maintained on either HFD or Standard diet(CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis(Pg) and Fusobacterium nucleatum(Fn) bacteria(control groups without infection). Body weight(BW) and glucose tolerance ability were assessed at the end time point of the experiment.Results: The CC lines varied(P <.05) at their BW gain and glucose tolerance ability(with sex effect) in response to diets and/or infection, showing opposite responses despite sharing the same environmental conditions. The combination of diet and infection enhances BW accumulation for IL1912, while restraints it for IL72. As for glucose tolerance ability, only females(both lines) were deteriorated in response to infection.Conclusions: This study emphasizes the power of the CC mouse population for the characterization of host genetic makeup for defining the susceptibility of the individual to development of obesity and/or impaired glucose tolerance.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

A Platform for Collaborative Modeling

A collaborative virtual modeling environment based on VRML is presented as a sugarcane harvester modeling framework, and is illustrated with the implemented software that can using I;EAS, Smarteam, and Conceptual Innovation Design System （CIDS） as the supporting platform, which is powerful enough to meet the demands of different real life applications. It is possible to perform collaborative interactive modifications with concurrent synchronous visualization at each client computer with any required level of detail. The platform provides a product modeling and assembling environment, and the product data among I;EAS, Smarteam and CIDS can be exchanged.

Effect analysis of applying high-quality service model to surgical nursing

BACKGROUND Surgical care of the hand plays a crucial role in the medical field,as problems with the hand can profoundly affect a patient's quality of life and function.In order to meet the needs of patients,improve patient satisfaction and improve treatment outcomes,high-quality service models have been introduced in the field of nursing.AIM To explore the effect analysis of applying high-quality service model to surgical nursing.METHODS We conducted a retrospective study of patients who underwent hand surgery at our hospital between 2019 and 2022,using a quality service model that included improved patient education,pain management,care team collaboration,and effective communication.Another group of patients received traditional care as a control group.We compared postoperative recovery,satisfaction,complication rate,and length of hospital stay between the two groups.Inferential statistics were used to compare the difference between the two groups by independent sample t test,Chi-square test and other methods to evaluate the effect of intervention measures.RESULTS Postoperative recovery time decreased from 17.8±2.3 d to 14.5±2.1 d,pain score decreased from 4.7±1.9 to 3.2±1.4,and hand function score increased from 78.4±7.1 to 88.5±6.2.In terms of patient satisfaction,the quality service model group scored 87.3±5.6 points,which was significantly higher than that of the traditional care group(74.6±6.3 points).At the same time,patients'understanding of medical information also improved from 6.9±1.4 to 8.6±1.2.In terms of postoperative complications,the application of the quality service model reduced the incidence of postoperative complications from 26%to 10%,the incidence of infection from 12%to 5%,and the incidence of bleeding from 10%to 3%.The reduction in these data indicates that the quality service model plays a positive role in reducing the risk of complications.In addition,the average hospital stay of patients in the quality service model group was shortened from 6.8±1.5 d to 5.2±1.3 d,and the hospitalization cost was also reduced from 2800±600 yuan to 2500±500 yuan.CONCLUSION Applying a quality service model to hand surgery care can significantly improve patient clinical outcomes,including faster recovery,less pain,greater satisfaction,and reduced complication rates.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Edge-Cloud Collaborative Optimization Scheduling with Micro-Service Architecture

The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.

Study of the ship design process model for collaborative design

The ship design process model is the basis for developing the ship collaborative design system under network environment.According to the characteristics of the ship design, a method for dividing the ship design process into three layers is pat forward, that is project layer, design task layer and design activity layer, then the formalized definitions of the ship design process model, the decomposing principles of the ship design process and the architecture of the ship collaborative design (SDPM) system are presented. This method simplifies the activity network, makes the optimization and adjustment of the design plan convenient and also makes the design process easier to control and change, at last the architecture of the ship collaborative design system is discussed.

Efficient protocols and methods for high-throughput utilization of the Collaborative Cross mouse model for dissecting the genetic basis of complex traits

The Collaborative Cross(CC)mouse model is a next‐generation mouse genetic reference population(GRP)designated for a high‐resolution quantitative trait loci(QTL)mapping of complex traits during health and disease.The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild‐derived strains.Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions.In this article,we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases,including type 2 diabetes and metabolic diseases,body composition,immune response,colorectal cancer,susceptibility to Aspergillus fumigatus,Klebsiella pneumoniae,Pseudomonas aeruginosa,sepsis,and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum,which were conducted at our laboratory using the CC mouse population.These traits are observed at multiple levels of the body systems,including metabolism,body weight,immune profile,susceptibility or resistance to the development and progress of infectious or chronic diseases.Herein,we present full protocols and step‐by‐step methods,implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines,mapping the gene underlying the host response to these infections and chronic diseases.The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits,including chronic and infectious diseases.

Multi-Knapsack Model of Collaborative Portfolio Configurations in Multi-Strategy Oriented

Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organization of the multiple strategical guidance and multi-knapsack model. Furthermore, the organizing resource utility and risk management of portfolio were considered. The experiments were conducted on three main technological markets which contain communication, transportation and industry. The results demonstrated that the proposed model and algorithm were feasible and reliable.

Research on the Collaborative Innovation Model in Regional Social Governance

Reforms in social governance are promoted by changes in social environments.Social governance models cannot be constant,and the rise of every innovative governance model is inseparable from an ever-changing society.The theoretical connotation of the collaborative innovation model in regional social governance is mainly reflected in its agents,actions,and processes.The fundamental requirements of the collaborative innovation model in regional social governance are de-administration and market-based.The key elements of the collaborative regional social governance model include:delegating power from central authorities,transferring selected social governance affairs,enhancing the development of governance capabilities in social governance agents,diversifying these agents by giving full play to the agency to strengthen the concept of collaborative governance for social governance agents,strengthening the establishment of the governance system,and ensuring the normal progress of the governance processes.The collaborative innovation model in regional social governance should be built in three aspects:a system of institutional norms,an evaluation indicator system,and law-based collaborative governance.

The Effectiveness of a Multidisciplinary Collaborative Model for the Diagnosis and Treatment of Vertigo

Objective:To investigate the effectiveness of applying a multidisciplinary collaborative model for the diagnosis and treatment of patients with vertigo.Methods:The study was carried out in Xianyang Hospital of Yan’an University,in which 100 patients with vertigo were selected from April 2021 to April 2022 and were divided into two groups:the control group was under the single diagnosis and treatment model,whereas the experimental group was under the multidisciplinary collaborative diagnosis and treatment model,with 50 cases in each group.The diagnostic effects of the two groups were compared.Results:The diagnostic and therapeutic efficiency of the patients in the experimental group were 94%and 98%,respectively,while those of the patients in the control group were 78%and 82%,respectively,with a significant difference between the two groups(p<0.05).The balance scores of the patients in both groups were low before the treatment,in which the difference was not significant(p>0.05);after the treatment,the scores improved,with those of the patients in the experimental group being significantly higher than those in the control group(p<0.05).Moreover,the satisfaction rate of patients in the experimental group(98%)was significantly higher than that of the control group(80%)(p<0.05).Conclusion:The application of the multidisciplinary collaborative diagnosis and treatment model in the diagnosis of patients with vertigo is effective.The multidisciplinary model can improve clinical diagnosis,enhance the treatment effect,improve the clinical symptoms of patients,and increase the satisfaction of patient care.Hence,it is of high clinical application value.