Imperative for long-term management and surveillance in Kawasaki disease

Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.

Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Motivational interviewing in postoperative rehabilitation and chronic disease management: Current findings and future research directions

This editorial highlights a recently published study examining the effectiveness of music therapy combined with motivational interviewing(MI)in addressing an-xiety and depression among young and middle-aged patients following percuta-neous coronary intervention.It further explores existing evidence and potential future research directions for MI in postoperative rehabilitation and chronic disease management.MI aims to facilitate behavioral change and promote healthier lifestyles by fostering a trusting relationship with patients and enhan-cing intrinsic motivation.Research has demonstrated its effectiveness in posto-perative recovery for oncological surgery,stroke,organ transplants,and gastroin-testinal procedures,as well as in managing chronic conditions such as diabetes,obesity,and periodontal disease.The approach is patient-centered,adaptable,cost-effective,and easily replicable,though its limitations include reliance on the therapist’s expertise,variability in individual responses,and insufficient long-term follow-up studies.Future research could focus on developing individualized and precise intervention models,exploring applications in digital health management,and confirming long-term outcomes to provide more compre-hensive support for patient rehabilitation.

Association of the glycemic background patterns and the diabetes management efficacy in poorly controlled type 2 diabetes

BACKGROUND Inadequate glycemic control in patients with type 2 diabetes(T2DM)is a major public health problem and a significant risk factor for the progression of diabetic complications.AIM To evaluate the effects of intensive and supportive glycemic management strategies over a 12-month period in individuals with T2DM with glycated hemoglobin(HbA1c)≥10%and varying backgrounds of glycemic control.METHODS This prospective observational study investigated glycemic control in patients with poorly controlled T2DM over 12 months.Participants were categorized into four groups based on prior glycemic history:Newly diagnosed,previously well controlled with recent worsening,previously off-target but now worsening,and HbA1c consistently above 10%.HbA1c levels were monitored quarterly,and patients received medical,educational,and dietary support as needed.The analysis focused on the success rates of good glycemic control and the associated factors within each group.RESULTS The study showed significant improvements in HbA1c levels in all participants.The most significant improvement was observed in individuals newly diagnosed with diabetes:65%achieved an HbA1c target of≤7%.The results varied between participants with different glycemic control histories,followed by decreasing success rates:39%in participants with previously good glycemic control,21%in participants whose glycemic control had deteriorated compared to before,and only 10%in participants with persistently poor control,with mean HbA1c levels of 6.3%,7.7%,8.2%,and 9.7%,respectively.After one year,65.2%of the“newly diagnosed patients”,39.3%in the“previously controlled group”,21.9%in the“previously off-target but now worsened'”group and 10%in the“poorly controlled from the start”group had achieved HbA1c levels of 7 and below.CONCLUSION In poorly controlled diabetes,the rate at which treatment goals are achieved is associated with the glycemic background characteristics,emphasizing the need for tailored strategies.Therefore,different and comprehensive treatment approaches are needed for patients with persistent uncontrolled diabetes.

Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

Interest of D-dimer level,severity of COVID-19 and cost of management in Gabon

BACKGROUND Coronavirus disease 2019(COVID-19)is strongly associated with an increased risk of thrombotic events,including severe outcomes such as pulmonary embolism.Elevated D-dimer levels are a critical biomarker for assessing this risk.In Gabon,early implementation of anticoagulation therapy and D-dimer testing has been crucial in managing COVID-19.This study hypothesizes that elevated Ddimer levels are linked to increased COVID-19 severity.AIM To determine the impact of D-dimer levels on COVID-19 severity and their role in guiding clinical decisions.METHODS This retrospective study analyzed COVID-19 patients admitted to two hospitals in Gabon between March 2020 and December 2023.The study included patients with confirmed COVID-19 diagnoses and available D-dimer measurements at admission.Data on demographics,clinical outcomes,D-dimer levels,and healthcare costs were collected.COVID-19 severity was classified as non-severe(outpatients)or severe(inpatients).A multivariable logistic regression model was used to assess the relationship between D-dimer levels and disease severity,with adjusted odds ratios(OR)and 95%CI.RESULTS A total of 3004 patients were included,with a mean age of 50.17 years,and the majority were female(53.43%).Elevated D-dimer levels were found in 65.81%of patients,and 57.21%of these experienced severe COVID-19.Univariate analysis showed that patients with elevated D-dimer levels had 3.33 times higher odds of severe COVID-19(OR=3.33,95%CI:2.84-3.92,P<0.001),and this association remained significant in the multivariable analysis,adjusted for age,sex,and year of collection.The financial analysis revealed a substantial burden,particularly for uninsured patients.CONCLUSION D-dimer predicts COVID-19 severity and guides treatment,but the high cost of anticoagulant therapy highlights the need for policies ensuring affordable access in resource-limited settings like Gabon.

Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.

Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive Control Strategy

This paper studies the connectivity-maintaining consensus of multi-agent systems.Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption,a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved.In this paper,communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus.The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller,so there is more freedom to develop an appropriate control strategy for achieving consensus.For the multi-agent systems with this novel communication management,a predictive control based strategy is developed for achieving consensus.Simulation results indicate the effectiveness and advantages of our scheme.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management

The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution networks.Gridaware energy management,which includes the control of smart inverters and energy management systems,is a trending way to mitigate this problem.However,existing multi-agent reinforcement learning methods for grid-aware energy management have not sufficiently considered the importance of agent cooperation and the unique characteristics of the grid,which leads to limited performance.In this study,we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework(MAHGA)to stabilize the voltage.Specifically,under the paradigm of centralized training and decentralized execution,we model the power distribution network as a novel hierarchical graph containing the agent-level topology and the bus-level topology.Then a hierarchical graph attention model is devised to capture the complex correlation between agents.Moreover,we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from graphs.Experiments on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.

Research on Product Data Management Based on Multi-agent System

PDM (product data management) is one kind of techniques based on software and database, which integrates information and process related to products. But it is not enough to perform the complication of PDM in enterprises. Then the mechanism to harmonize all kinds of information and process is needed. The paper introduces a novel approach to implement the intelligent monitor of PDM based on MAS (multi agent system). It carries out the management of information and process by MC (monitor center). The paper first puts forward the architecture of the whole system, then defines the structure of MC and its interoperation mode.

Supply Chain Management System Model of Virtual Enterprises Based on Multi-Agent

Based on the analysis of a virtual enterprise and the development of supply chain management, their integration is proposed. Then, the difference between multi-agent system modeling method and the traditional modeling method is analyzed, and a method based on Java agent framework for multi-agent systems（ JAFMAS） is proposed. By using this method the virtual enterprise＇ s supply chain management system model is established.

Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Etiology and management of urethral calculi:A systematic review of contemporary series

Objective:To conduct a systematic literature review on urethral calculi in a contemporary cohort describing etiology,investigation,and management patterns.Methods:A systematic search of MEDLINE and Cochrane Central Register of Controlled Trials(CENTRAL)databases was performed.Articles,including case reports and case series on urethral calculi published between January 2000 and December 2019,were included.Full-text manuscripts were reviewed for clinical parameters including symptomatology,etiology,medical history,investigations,treatment,and outcomes.Data were collated and analyzed with univariate methods.Results:Seventy-four publications met inclusion criteria,reporting on 95 cases.Voiding symptoms(41.1%),pain(40.0%),and acute urinary retention(32.6%)were common presenting features.Urethral calculi were most often initially investigated using plain X-ray(63.2%),with almost all radio-opaque(98.3%).Urethral calculi were frequently associated with coexistent bladder or upper urinary tract calculi(16.8%)and underlying urethral pathology(53.7%)including diverticulum(33.7%)or stricture(13.7%).Urethral calculi were most commonly managed with external urethrolithotomy(31.6%),retrograde manipulation(22.1%),and endoscopic in situ lithotripsy(17.9%).Conclusion:This unique systematic review of urethral calculi provided a summary of clinical features and treatment trends with a suggested treatment algorithm.Management in contemporary urological practice should be according to calculus size,shape,anatomical location,and presence of urethral pathology.