Metaverse for mental health disorders:Opportunities and challenges

Several articles on the mental health impact of the metaverse and the need to balance its potential benefits with the risks of metaverse use has recently published.The metaverse consists of a combination of immersive technologies and artificial intelligence algorithms.The metaverse differs from the preceding digital psychiatric interventions due to its complex structure and interactions between components.The diverse functions of the metaverse ensure that it may have a substantial impact on mental health.However,the evidence for its efficacy in treating mental health disorders is limited to a few trials.The mental health benefits of immersive technologies are well-documented and suggest that metaverse-based psychiatric treatment may be similarly efficacious.The mental health risks of the metaverse are largely unknown,and it is not clear whether they will be greater than other digital psychiatric interventions.Much more research is needed to determine whether metaverse-based psychiatric treatment will meet the standards of appropriate mental healthcare.

Decoding the nexus:branched-chain amino acids and their connection with sleep,circadian rhythms,and cardiometabolic health

The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and,either directly or indirectly,overall body health,encompassing metabolic and cardiovascular well-being.Given the heightened metabolic activity of the brain,there exists a considerable demand for nutrients in comparison to other organs.Among these,the branched-chain amino acids,comprising leucine,isoleucine,and valine,display distinctive significance,from their contribution to protein structure to their involvement in overall metabolism,especially in cerebral processes.Among the first amino acids that are released into circulation post-food intake,branched-chain amino acids assume a pivotal role in the regulation of protein synthesis,modulating insulin secretion and the amino acid sensing pathway of target of rapamycin.Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors,competing for a shared transporter.Beyond their involvement in protein synthesis,these amino acids contribute to the metabolic cycles ofγ-aminobutyric acid and glutamate,as well as energy metabolism.Notably,they impact GABAergic neurons and the excitation/inhibition balance.The rhythmicity of branchedchain amino acids in plasma concentrations,observed over a 24-hour cycle and conserved in rodent models,is under circadian clock control.The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood.Disturbed sleep,obesity,diabetes,and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics.The mechanisms driving these effects are currently the focal point of ongoing research efforts,since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies.In this context,the Drosophila model,though underutilized,holds promise in shedding new light on these mechanisms.Initial findings indicate its potential to introduce novel concepts,particularly in elucidating the intricate connections between the circadian clock,sleep/wake,and metabolism.Consequently,the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle.They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health,paving the way for potential therapeutic interventions.

Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

Reevaluating health metrics:Unraveling the limitations of disabilityadjusted life years as an indicator in disease burden assessment

In 1993,the World Bank released a global report on the efficacy of health promotion,introducing the disability-adjusted life years(DALY)as a novel indicator.The DALY,a composite metric incorporating temporal and qualitative data,is grounded in preferences regarding disability status.This review delineates the algorithm used to calculate the value of the proposed DALY synthetic indicator and elucidates key methodological challenges associated with its application.In contrast to the quality-adjusted life years approach,derived from multi-attribute utility theory,the DALY stands as an independent synthetic indicator that adopts the assumptions of the Time Trade Off utility technique to define Disability Weights.Claiming to rely on no mathematical or economic theory,DALY users appear to have exempted themselves from verifying whether this indicator meets the classical properties required of all indicators,notably content validity,reliability,specificity,and sensitivity.The DALY concept emerged primarily to facilitate comparisons of the health impacts of various diseases globally within the framework of the Global Burden of Disease initiative,leading to numerous publications in international literature.Despite widespread adoption,the DALY synthetic indicator has prompted significant methodological concerns since its inception,manifesting in inconsistent and non-reproducible results.Given the substantial diffusion of the DALY indicator and its critical role in health impact assessments,a reassessment is warranted.This reconsideration is imperative for enhancing the robustness and reliability of public health decisionmaking processes.

基于充电阶段数据与GWO-BiLSTM模型的锂电池SOH估计方法

针对锂电池健康状态(state of health,SOH)估计过程中健康特征(health features,HFs)提取单一、估计精度较低等问题,提出一种基于充电阶段数据与灰狼优化(grey wolf optimizer,GWO)算法-双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络的锂电池SOH估计方法。首先,从电池充电阶段数据中提取五类HFs。接着,利用核主成分分析法(kernel principal component analysis,KPCA)获取HFs的关键健康因子。最后,应用GWO-BiLSTM模型对关键健康因子和SOH之间的映射关系进行动态建模,实现锂电池SOH的估计。利用NASA电池老化数据集进行验证,结果表明,所提出方法能够准确估计锂电池的SOH,均方根误差保持在1%以内,具有较高的估计精度和鲁棒性。

全健康(One Health)视角下绿色健康社区景观设计体系构建

以全健康(OneHealth)视角为出发点,保障人、环境和动物的健康对于社区健康环境规划与设计具有重要意义。在现有健康社区景观设计体系中,人、环境、物种三者关系虽被提及,但未被系统探究,健康社区建设实践也需要系统性的景观设计体系支持。基于全健康理念中人、环境和物种的相互关系,结合生态系统服务功能、康复景观理论和园艺疗法,进一步探讨人、环境、物种健康之间的关联途径。通过对全健康理念视角下的3个层次进行演绎发展,归纳总结了人的健康、环境健康和物种健康的组成要素及内容,构建了以人的健康需求为导向的社区健康设计框架,提出了以人与人际、人与环境和人与物种为三大圈层的健康社区景观设计体系。全健康视角为社区健康环境设计提供了一个系统性、整合性且具有操作性的框架,未来应重视健康社区体系中各类疗愈空间的设计与使用,以增进社区居民共享的健康福祉。

基于融合健康因子和集成极限学习机的锂离子电池SOH在线估计

锂离子电池健康状态(SOH)的在线估计对电池管理系统的安全稳定运行至关重要.为克服传统基于数据驱动的锂离子电池SOH估计方法训练时间长、计算量大、调试过程复杂的问题,提出一种基于融合健康因子和集成极限学习机的锂离子电池SOH估计方法.该方法通过dQ/dV和dT/dV曲线分析,筛选出与电池SOH相关性较高的数据区间进行多维健康特征提取,并对其进行主成分分析降维处理得到间接健康因子;利用极限学习机的随机学习算法建立间接健康因子和SOH之间的非线性映射关系.在此基础上,针对单一模型输出不稳定的特点,提出一种集成极限学习机模型,通过对估计结果设置可信度评价规则剔除单一极限学习机不可靠的输出,从而提高锂离子电池SOH的估计精度.使用NASA和牛津大学的锂离子电池老化数据集对该方法进行验证,结果表明该方法的平均绝对百分比误差小于1%,具有较高的准确性和可靠性.

多元宇宙优化估算锂离子电池的SOC与SOH

估计电池的荷电状态(SOC)和健康状态(SOH)是锂离子电池管理中最复杂的任务之一。目前,针对SOC和SOH的估计存在跟踪值误差较大、噪声误差较大和计算量大等问题,引入多元宇宙优化(MVO)算法,对照电池的实际输出电压,模型的拟合度可达95.3%。通过14次迭代得到SOC的稳定估计值,与传统的循环次数法对比,SOH估计的稳定性提高了119%,并减小了78%的计算空间需求。

基于RBF-BLS面向电动汽车低碳安全出行的SOH估计方法

电动汽车充电过程的安全性与动力电池组的健康状态(SOH)紧密相关,因此SOH的高性能实时估计是充电过程中安全检测的重要基础.由于动力电池组的SOH受复杂结构、电芯类型、驾驶习惯、环境温度和充电行为等因素的深度影响,现有基于单个或少量特定电池电芯实验数据的方法研究在面对整车动力电池组实时SOH估计时遭遇模型复杂、数据缺失、实时性差、精度不足等难题.针对建模困难、实时性和精度不足等问题,应用多方法集成融合思想,在电池经验退化模型上引入径向基函数(RBF)优化的宽度学习(BLS)神经网络,提出一种高性能的动力电池组SOH估计方法.首先,该方法采用经验退化模型和离线历史充电数据得到初步的SOH值;其次,应用RBF神经网络给出一种BLS系统中初始权重矩阵的确定方法,建立经验退化与径向基函数优化的宽度学习神经网络(RBF-BLS);再次,采用RBF-BLS神经网络和实时充电数据训练得到估计误差,并对经验退化模型得到的SOH进行补偿,从而得到更高精度的SOH估计值;最后,采用基于充电运营企业实际充电数据的计算机仿真实例来验证新方法的有效性和优越性.

基于IMOCS-BP神经网络的锂离子电池SOH估计

锂离子电池随着循环充放电次数的增长,其健康状态SOH(state-of-health)会随之发生一定程度的衰减。针对以上问题,设计了一种基于改进的多目标布谷鸟搜索IMOCS(improved multi-objective Cuckoo search)-BP神经网络的锂离子电池健康状态估计方法,在避免算法陷入局部最优的同时自适应改变布谷鸟搜索CS(Cuckoo search)算法更新概率和搜索步长,解决CS算法收敛速度慢和求解精度低的问题。以IMOCS算法和BP神经网络结合,对节点空间范围进行全局搜索,降低权值和阈值的初值对BP神经网络的影响,实现参数优化。通过Matlab仿真,验证了基于IMOCS-BP神经网络的SOH估计算法误差低、性能强,实现了锂电池SOH的精准预测。

基于SSA-BPNN的锂离子电池SOH估算

锂离子电池已被广泛应用于储能系统与电动汽车中,精确地估算锂离子电池健康状态SOH(state-of-health)是保证系统安全可靠运行的必要条件。从容量的角度分析SOH,在恒流-恒压CC-CV(constant current-constant voltage)充电电压和温度曲线中提取了7个健康特征HI(health indicator)作为输入,基于数据驱动法提出了麻雀搜索算法-反向传播神经网络SSA-BPNN(sparrow search algorithm-back propagation neural network)的锂离子电池SOH估算方法,并应用数据增强进一步提高模型的鲁棒性,最终在NASA锂离子电池随机使用数据集上进行验证。通过与未采取数据增强的传统BP神经网络相比,获得SOH估算精度有明显提升,测试集SOH估算的最大绝对误差和均方根误差分别小于3%和1.32%,实验结果表明该方法兼顾误差小,收敛快,全局搜索能力且能够适应电池老化差异特性。

基于EKF算法的纯电动汽车锂电池SOC与SOH联合估算

为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。

基于BP神经网络与H∞滤波的锂电池SoH-SoC联合估计研究

锂电池健康状态(SoH)和荷电状态(SoC)的精确估计是新能源汽车安全运行的重要保障。针对SoH-SoC联合估计精度低、鲁棒性差的问题,提出一种基于变学习率BP神经网络和自适应渐消扩展H∞滤波的SoH-SoC联合估计方法。首先,提出一种基于单位充电压差时间间隔的新型SoH特征参数;其次,通过设计新型变学习率BP神经网络,提高传统BP网络误差收敛速度及缩短权值寻优时间;最后,通过设计新型自适应衰减因子对传统扩展H∞滤波误差协方差矩阵进行加权,建立自适应渐消扩展H∞滤波算法,减小陈旧量测值对估计结果的影响,提高扩展H∞滤波的估计精度及鲁棒性。实验结果表明,本文所提算法SoH估计误差小于0.35%,SoC估计误差小于0.5%,展现出较高的估计精度和鲁棒性。

基于改进DELM在不同放电工况下的锂电池SOH预测方法研究

锂电池性能受到多种因素的影响,尤其在不同放电工况下其健康状态(SOH)预测难度极大增加.因此,研究以基于深度极限学习机为基础的锂电池SOH预测方法为基础,利用改进鲸鱼优化算法解决深度极限学习机中存在的随机权重和偏置问题,旨在提高锂电池SOH预测的精确率.对比实验结果表明,研究提出的预测方法算法的误差在[-0.02,0.04]之间,平均精确率达到96.23%,相比利用麻雀搜索算法、灰狼优化算法,改进的深度极限学习机算法分别提升了9.67%和5.05%.平均召回率为90.83%,平均误报率为3.75%.此外,高负载工况下,100次充放电循环后锂电池SOH值仅有0.221,下降幅度最大.不同工况下研究提出的预测方法平均精确率达到96.07%.研究提出的锂电池健康状态预测方法对于提高电池性能、延长寿命、提高安全性和降低环境影响具有重要的实际意义.

基于迁移学习与GRU神经网络结合的锂电池SOH估计

为解决退役电池梯次利用过程中单体剩余使用寿命估计困难、测试流程复杂与能耗高等问题,提出迁移学习与GRU网络结合的锂离子电池健康状态估计方法;设计的基础模型结构为输入层+GRU层+全连接层+输出层;根据健康因子的得分,选择训练基础模型的数据集、划分电池相似度等级并制定对应的迁移学习策略。实验结果表明:与其他模型相比,分别使用数据集的前40%与前25%训练得到的基础模型与迁移学习模型,两者的精度分别最大提高42.48%与95.28%,而预测稳定性分别最大提高55.38%与93.55%。

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

基于数据驱动与组合模型的锂离子电池SOH估计

锂离子电池的健康监测很重要。结合数据驱动模型和经验模型,提出一种组合估计模型。首先使用基于长短期记忆网络(LSTM)模型的数据驱动方式进行电池健康状态(SOH)初步估计,然后将工况中的在线估计值用于拟合双指数经验模型,再进一步通过观测值和估计值的误差和增益,更新双指数模型对应的参数,以此进行迭代,实现更精确的SOH实时估计。该组合估计模型能够准确估计锂离子电池的SOH,且当观测器的修正周期为单个循环周期时,估计结果的平均绝对误差(MAE)均值和均方根误差(RMSE)均值分别为0.0033和0.0042,优于单纯LSTM数据驱动下的SOH估计性能。

A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.

Alexa,let's train now!——A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging

Background:There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging.Consequently,the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions.In this context,digital and home-based physical training interventions might be a promising alternative to center-based intervention programs.Thus,this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance.Methods:In this pre-registered systematic review(PROSPERO;ID:CRD42022320031),5 electronic databases(PubMed,Web of Science,Psyclnfo,SPORTDiscus,and Cochrane Library)were searched by 2 independent researchers(FH and PT)to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults.The systematic literature search yielded 8258 records(extra17 records from other sources),of which 27 controlled trials were considered relevant.Two reviewers(FH and PT)independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale).Results:Of the 27 reviewed studies,15 reported positive effects on cognitive and motor-cognitive outcomes(i.e.,performance improvements in measures of executive functions,working memory,and choice stepping reaction test),and a considerable heterogeneity concerning study-related,population-related,and intervention-related characteristics was noticed.A more detailed analysis suggests that,in particular,interventions using online classes and technology-based exercise devices(i.e.,step-based exergames)can improve cognitive performance in healthy older adults.Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence(≤85%)and monitoring of the level of regular physical activity in the control group.Conclusion:The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall,though there is limited evidence that specific types of digital and home-based physical training interventions(e.g.,online classes and step-based exergames)can be an effective strategy for improving cognitive performance in older adults.However,due to the limited number of available studies,future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Shifting the Paradigm:A Fresh Look at Physical Activity Frequency and Its Impact on Mental Health,Life Satisfaction,and Self-Rated Health in Adolescents

As adolescent mental health problems are becoming a more serious issue globally,this paper explores the relationship of physical activity in adolescents and its frequency on mental health as well as examines the mediating effects of life satisfaction and self-rated health in order to provide a reference for the promotion of mental health in adolescents.A sample of 3578 Chinese high school students completed questionnaires assessing their mental health,physical activity frequency,life satisfaction,and self-rated health.The mean SCL-90 value for adolescents was found to be 1.629%,and 24.73%of adolescents had varying degrees of mental health issue.Increased physical activity frequency is positively associated with improved mental health(p<0.001).Additionally,life satisfaction and self-rated health were found to play significant mediating roles in the relationship between physical activity frequency and mental health.Specifically,low-frequency physical activity had the most pronounced mediating effect on mental health through life satisfaction,while high-frequency physical activity exerted the most significant mediating effect on mental health through self-rated health.These findings underscore the importance of promoting physical activity among adolescents and highlight the distinct pathways through which physical activity frequency can influence mental health outcomes.Further research is needed to explore these relationships in diverse populations and settings,as well as to develop targeted intervention.