Background:Weight-related self-stigma(WRSS)is prevalent among individuals with different types of weight status and is associated with a range of negative health outcomes.Social support and coping models explain how i...Background:Weight-related self-stigma(WRSS)is prevalent among individuals with different types of weight status and is associated with a range of negative health outcomes.Social support and coping models explain how individuals may use different coping methods to deal with their mental health needs.Psychological distress(e.g.,depression and stress)could lead to overuse of social media and smartphones.When using social media or smartphones,individuals are likely to be exposed to negative comments regarding weight/shape/size posted on the social media.Consequently,individuals who experience problematic social media use(PSMU)or problematic smartphone use(PSPU)may develop WRSS.Therefore,the present study examined the roles of PSMU and PSPU as mediators in the relationship between psychological distress and WRSS.Methods:Using convenience sampling via an online survey,622 participants with a mean age of 23.70 years(SD=4.33)completed questions assessing sociodemographic variables,psychological distress,PSMU,PSPU,WRSS,and self-reported weight and height.Results:The hierarchical regression models showed that sex(β=0.08,p=0.01),BMI(β=0.39,p<0.001),depression(β=0.21,p=0.001),stress(β=0.18,p=0.01),PSMU(β=0.09,p=0.045),and PSPU(β=0.14,p=0.001)were significant factors for WRSS.Conclusion:The mediation models showed that both PSMU and PSPU were significant mediators in the relationships between depression and stress with WRSS.The present findings provide some evidence for understanding WRSS and has important implications for developing interventions to reduce its negative impact on individuals’health and well-being.展开更多
Non-invasive glucose monitoring remains a key challenge due to the molecule's inherently weak Raman signal.While surface-enhanced Raman spectroscopy(SERS)offers potential,it often suffers from incomplete spectral ...Non-invasive glucose monitoring remains a key challenge due to the molecule's inherently weak Raman signal.While surface-enhanced Raman spectroscopy(SERS)offers potential,it often suffers from incomplete spectral coverage.A recent work using tip-enhanced Raman scattering(TERS)successfully captures glucose's complete vibrational fingerprint.This label-free approach paves the way for highly sensitive metabolite detection and future in vivo biosensing applications.展开更多
The conversion of carbon dioxide(CO_(2))into hydrocarbons through electrochemical CO_(2)reduction reaction(eCO_(2)RR)shows a promising method to reduce CO_(2)levels and decrease reliance on fossil fuels in the years t...The conversion of carbon dioxide(CO_(2))into hydrocarbons through electrochemical CO_(2)reduction reaction(eCO_(2)RR)shows a promising method to reduce CO_(2)levels and decrease reliance on fossil fuels in the years to come.Copper-based electrocatalysts exhibit a pronounced inclination for C-C coupling,drawing considerable interest as a favored metal catalyst for generating C_(2+)products through CO_(2)RR.However,CO_(2)RR still has some obstacles including product selectivity,higher overpotential,low Faradic efficiency(FE),stability,and current density(CD).Therefore,advancement in this field enables us to comprehend the complex multi-proton electron transfer during C-C coupling and engineering strategies to improve FE and CD.Herein,this review presents some key features of Cu-based catalysts as an electrocatalyst for C_(2) product formation while addressing the industrial challenges that hinder commercialization of CO_(2)RR.In addition,recent strategies on Cu-based catalysts,synthesis strategies,advanced characterizations,and mechanistic investigations via theoretical simulations have been presented.Furthermore,recent approaches towards the composition,oxidation states,and active facets have been presented.Thus,the most favorable mechanism and possible pathways to synthesize C_(2+)products have been explained using theoretical calculations.展开更多
Background:The recently developed Depression,Anxiety,and Stress Scales–Youth Version(DASS-Y)shows promise as a tool for assessing youth mental health,but its consistency across timepoints and diverse ages remains und...Background:The recently developed Depression,Anxiety,and Stress Scales–Youth Version(DASS-Y)shows promise as a tool for assessing youth mental health,but its consistency across timepoints and diverse ages remains underexplored.The present study evaluated whether the DASS-Y reliably measured depression,anxiety,and stress among school-aged youth(aged 9–18 years)across distinct time periods and educational stages.Methods:Two studies were conducted.Study 1 examined consistency over three months using data from 736 Central Chinese high school students who completed surveys at both timepoints.Study 2 tested consistency across educational levels among 2321 primary and 1676 middle school students.Traditional confirmatory factor analysis(CFA),exploratory structural equation modeling(ESEM),and Rasch analysis were employed to assess the scale’s construct validity and measurement invariance.Results:Rasch analysis indicated acceptable DASS-Y item fit(infit/outfit statistics=0.50–1.50)and moderate test-retest reliability(ICCs=0.64–0.69).The ESEM approach demonstrated superior model fit compared to CFA,achieving a good RMSEA(0.056–0.062)and lower latent factor correlations(r=0.40–0.60),supporting longitudinal scalar invariance.Across educational levels,measurement invariance was supported,with only a small number of items exhibiting differential item functioning(DIF).Conclusion:The present study demonstrates that the DASS-Y is a reliable tool for assessing emotional health among non-clinical school-aged youth,offering educators a validated measure to monitor psychological well-being across developmental stages and time,thereby informing strategies to support youth mental health in community and educational settings.Future research among clinical populations is needed to extend its utility for diagnostic purposes.展开更多
Background:The present study evaluated the psychometric properties of Problematic Internet Use(PIU)instruments and their correlation with psychological distress and time spent on Internet activities among university s...Background:The present study evaluated the psychometric properties of Problematic Internet Use(PIU)instruments and their correlation with psychological distress and time spent on Internet activities among university students in Ghana.Methods:In the present cross-sectional survey design study,520 participants(35.96% female)were recruited with a mean age of 19.55 years(SD=1.94)from several university departments(i.e.,Behavioral Sciences,Materials Engineering,Nursing and Midwifery,and Biochemistry and Biotechnology)of Kwame Nkrumah University of Science and Technology(KNUST)between 19 July and 04 August,2023.Participants completed a survey that included the following measures:the Gaming Disorder Test(GDT),Gaming Disorder Scale for Adolescents(GADIS-A),Internet Gaming Disorder Scale-Short Form(IGDS9-SF),Bergen Social Media Addiction Scale(BSMAS),Smartphone Application Based Addiction Scale(SABAS),Nomophobia Questionnaire(NMP-Q),and the external criterion measure:Depression Anxiety Stress Scale-21(DASS-21).Confirmatory factor analysis(CFA)was carried out to evaluate the structure of the instruments.Cronbach’s α,McDonald’s ω,and composite reliability were used to evaluate internal consistency.Pearson correlation was used to examine the associations between the scores of instruments assessing PIU,time spent on Internet activities,and the level of psychological distress.Results:Model fits confirmed the(i)unidimensional structure of the GDT,BSMAS,SABAS,IGDS9-SF,(ii)two-factor structure of the GADIS-A,and(iii)four-factor structure of the NMP-Q.Additionally,the study found that different types of PIU were significantly associated with psychological distress and time spent on related Internet activities.Conclusion:The six instruments validated in the present study demonstrated very good to excellent psychometric properties when applied to university students in Ghana.The significant associations between Internet-related disorders,time spent on Internet-related activities,and psychological distress highlight the importance of addressing issues of PIU among this population.展开更多
This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA f...This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.展开更多
Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtain...Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.展开更多
Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manu...Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model(TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.展开更多
Control of the wetting properties of biomimetic functional surfaces is a desired functionality in many applications.In this paper,the photoresist SU-8 was used as fabrication material.A silicon wafer was used as a sub...Control of the wetting properties of biomimetic functional surfaces is a desired functionality in many applications.In this paper,the photoresist SU-8 was used as fabrication material.A silicon wafer was used as a substrate to prepare a biomimetic surface with different surface roughness and micro-pillars arranged in array morphology.The evaporation dynamics and interfacial heat transfer processes of deionised water droplets on the bioinspired microstructure surface were experimentally studied.The study not only proves the feasibility of preparing hydrophilic biomimetic functional surfaces directly through photoresist materials and photolithography technology but also shows that by adjusting the structural parameters and arrangement of the surface micro-pillar structure,the wettability of the biomimetic surface can be significantly linearly regulated,thereby effectively affecting the heat and mass transfer process at the droplet liquid-vapour interface.Analysis of the results shows that by controlling the biomimetic surface microstructure,the wettability can be enhanced by about 22%at most,the uniformity of the temperature distribution at the liquid-vapour interface can be improved by about 34%,and the average evaporation rate can be increased by about 28%.This study aims to provide some guidance for the research on bionic surface design based on photoresist materials.展开更多
Esophageal cancer(EC)continues to pose a significant clinical challenge due to the absence of a reliable early detection method,leading to late-stage diagnoses and poor patient outcomes.The recent study by Liu et al p...Esophageal cancer(EC)continues to pose a significant clinical challenge due to the absence of a reliable early detection method,leading to late-stage diagnoses and poor patient outcomes.The recent study by Liu et al presents a promising breakthrough,demonstrating that plasma DNA methylation markers-SHOX2,SEPTIN9,EPO,and RNF180-offer a non-invasive approach for early EC detection with 76.19%sensitivity and 86.27%specificity.Given the urgent need for effective screening strategies,the potential integration of this assay into clinical practice could significantly enhance early diagnosis,patient monitoring,and overall survival rates.While further validation is necessary,this advancement marks an important step toward improving EC detection and management.展开更多
Objectives:The purpose of this narrative review is to offer an updated perspective on the current research on the glycoprotein Osteoprotegerin(OPG),including its potential therapeutic impact and mechanisms of action,a...Objectives:The purpose of this narrative review is to offer an updated perspective on the current research on the glycoprotein Osteoprotegerin(OPG),including its potential therapeutic impact and mechanisms of action,and interaction with bone and muscle tissues.Content:As health and social care advances people are living longer,with projections suggesting that in 2050 there will be 2 billion people who are aged over 60 years.Yet musculoskeletal health still declines into older age and as a result there is an increase in the proportion of older populations that spend more time with persistent disabilities.Although physical exercise is repeatedly demonstrated to minimise detrimental effects of ageing,it is not always a feasible intervention,and other directions must be considered.Summary and outlook:OPG,a glycoprotein decoy receptor for the receptor activator of nuclear factor kappa-βligand(RANKL)is a key regulator of bone formation yet emerging evidence has presented its potential to offer positive outcomes in regard to the preservation of skeletal muscle mass and function.Animal models have shown that OPG levels increase during exercise,and independently acts to restore losses of muscle strength and reduce bone resorption.Interventions to increase circulating OPG alongside exercise may act as a therapeutic target to combat the decline in quality of life in older age in humans.Further research is needed on the mechanisms of its action and interaction in humans in combination with exercise.展开更多
This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechani...This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechanism,the syndrome shows a large heterogeneity across presentation,cause,and disease trajectory,compli-cating diagnosis and management.Based on established prognostic scoring systems,the New Clichy Score,the BCS-transjugular intrahepatic portosystemic shunt Index,the Zeitoun Score,and the Pediatric End-stage Liver Disease score were examined.These scoring systems are used for risk stratification and thera-peutic decision-making.Although these models deliver suitability information,their static parameters,narrow validation,and limited generalizability reduce their usefulness in diverse populations.Specific challenges are highlighted in pediatric patients,pregnant females,and individuals with myeloproliferative neoplasms for whom current tools often fall short.Moreover,there remains uncertainty regarding the durability of Pediatric End-stage Liver Disease score response and longer-term risks,such as hepatocellular carcinoma.There is a need to have a dynamic prognostic model that uses imaging and genetic factors in future studies.The article discussed enhancing recruitment to improve research.Overall,this article provided a contemporary,evidence-based approach for cli-nicians to aid in the evaluation and treatment of BCS.展开更多
Rectal neuroendocrine neoplasms pose significant challenges due to their varied presentations and prognoses.Traditional prognostic models,while useful,often fall short of accurately predicting clinical outcomes for th...Rectal neuroendocrine neoplasms pose significant challenges due to their varied presentations and prognoses.Traditional prognostic models,while useful,often fall short of accurately predicting clinical outcomes for these patients.This article discusses the development and implications of a novel prognostic tool,the GATIS score,which aims to enhance predictive accuracy and guide treatment strategies more effectively than current methods.Utilizing data from a large cohort and employing sophisticated statistical models,the GATIS score integrates clinical and pathological markers to provide a nuanced assessment of prognosis.We evaluate the potential of this score to transform clinical decision-making processes,its integration into current medical practices,and future directions for its develo-pment.The integration of genetic markers and other biomarkers could further refine its predictive power,highlighting the ongoing need for innovation in the management of rectal neuroendocrine neoplasms.展开更多
Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is f...Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is fabricated,the nonlinear wavefront it generates is fixed,offering little flexibility.This limitation often necessitates the fabrication of different metasurfaces for different wavefronts,which is both time-consuming and inefficient.To address this,we combine evolutionary algorithms with spatial light modulators(SLMs)to dynamically control wavefronts using a single metasurface,reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment.We demonstrate this approach by introducing a genetic algorithm(GA)to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation(THG)in a silicon metasurface.The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions,thereby significantly boosting THG emission at resonant positions.Additionally,the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM.This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints.Our approach paves the way for self-optimized nonlinear wavefront shaping,advancing optical computation and information processing techniques.展开更多
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to event...Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.展开更多
The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of m...The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of medical services.As healthcare becomes more data-driven,cyberattacks targeting these systems continue to rise,necessitating the development of robust,domain-adapted Intrusion Detection Systems(IDS).However,current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare.To address this gap,this study introduces HCKDDCUP,a synthetic dataset modeled on the widely used KDDCUP benchmark,augmented with healthcare-relevant attributes such as patient data,treatments,and diagnoses to better simulate the unique conditions of clinical environments.This research applies standard machine learning algorithms Random Forest(RF),Decision Tree(DT),and K-Nearest Neighbors(KNN)to both the KDDCUP and HCKDDCUP datasets.The methodology includes data preprocessing,feature selection,dimensionality reduction,and comparative performance evaluation.Experimental results show that the RF model performed best,achieving 98%accuracy on KDDCUP and 99%on HCKDDCUP,highlighting its effectiveness in detecting cyber intrusions within a healthcare-specific context.This work contributes a valuable resource for future research and underscores the need for IDS development tailored to sector-specific requirements.展开更多
Background and objectives:The ongoing mpox outbreaks have garnered significant attention due to their public health implications,particularly the potential mental health impacts.Despite the growing concern,there has b...Background and objectives:The ongoing mpox outbreaks have garnered significant attention due to their public health implications,particularly the potential mental health impacts.Despite the growing concern,there has been limited exploration of the intersection between mpox and mental health within the research literature.This study aims to conduct a comprehensive bibliometric analysis to examine global research trends,regional distribution,and thematic focus areas related to mpox's psychological and psychiatric implications.Methods:We conducted a bibliometric analysis using Scopus and the Web of Science database.The analysis was carried out using the R-bibliometrics package and involved identifying literature on mpox and mental health,focusing on global research trends,regional distribution,and thematic areas of study.The analysis included 416 documents obtained from 295 sources from January 1,2014 to August 27,2024.Results:Our analysis revealed a growing but unevenly distributed literature on mpox and mental health.Most studies concentrated on the relationship between mpox and conditions such as depression and anxiety,while other psychiatric outcomes remain underexplored.The geographic distribution of research was also uneven,with regions like Europe and the Americas receiving more focus than others.Conclusions:The study highlights the need for more targeted research on the mental health sequelae of mpox,particularly for vulnerable populations and regions that are currently underrepresented in the literature.Future research should include longitudinal studies to assess the long-term effects of mpox on mental health and the development of robust methodologies to establish causality.Integrating mental health considerations into public health responses to mpox outbreaks is crucial,with significant implications for research,policy,and clinical practice.展开更多
Wearable electronic textiles(e-textiles)with embedded electronics offer promising solutions for unobtrusive,real-time health monitoring,enhancing healthcare efficiency.However,their adoption is limited by performance ...Wearable electronic textiles(e-textiles)with embedded electronics offer promising solutions for unobtrusive,real-time health monitoring,enhancing healthcare efficiency.However,their adoption is limited by performance and sustainability challenges in materials,manufacturing,and recycling.This study introduces a sustainable paradigm for the fabrication of fully inkjet-printed Smart,Wearable,and Eco-friendly Electronic Textiles(SWEET)with the first comprehensive assessments of the biodegradability and life cycle assessment(LCA).SWEET addresses existing limitations,enabling concurrent and continuous monitoring of human physiology,including skin surface temperature(at temperature coefficient of resistance,TCR value of~-4.4%℃^(-1))and heart rate(-74 beats per minute,bpm)separately and simultaneously like the industry gold standard,using consistent,versatile,and highly efficient inkjet-printed graphene and Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)-based wearable e-textiles.Demonstrations with a wearable garment on five human participants confirm the system’s capability to monitor their electrocardiogram(ECG)signals and skin temperature.Such sustainable and biodegradable e-textiles decompose by-48%in weight and lost-98%strength over 4months.Life cycle assessment(LCA)reveals that the graphene-based electrode has the lowest climate change impact of-0.037 kg CO_(2) eq,40 times lower than reference electrodes.This approach addresses material and manufacturing challenges,while aligning with environmental responsibility,marking a significant leap forward in sustainable e-textile technology for personalized healthcare management.展开更多
基金supported in part by(received funding from)the Ministry of Science and Technology,Taiwan(MOST 110-2410-H-006-115,MOST 111-2410-H-006-100)the National Science and Technology Council,Taiwan(NSTC 112-2410-H-006-089-SS2)+1 种基金the Higher Education Sprout Project,the Ministry of Education at the Headquarters of University Advancement at the National Cheng Kung University(NCKU)the 2021 Southeast and South Asia and Taiwan Universities Joint Research Scheme(NCKU 31).
文摘Background:Weight-related self-stigma(WRSS)is prevalent among individuals with different types of weight status and is associated with a range of negative health outcomes.Social support and coping models explain how individuals may use different coping methods to deal with their mental health needs.Psychological distress(e.g.,depression and stress)could lead to overuse of social media and smartphones.When using social media or smartphones,individuals are likely to be exposed to negative comments regarding weight/shape/size posted on the social media.Consequently,individuals who experience problematic social media use(PSMU)or problematic smartphone use(PSPU)may develop WRSS.Therefore,the present study examined the roles of PSMU and PSPU as mediators in the relationship between psychological distress and WRSS.Methods:Using convenience sampling via an online survey,622 participants with a mean age of 23.70 years(SD=4.33)completed questions assessing sociodemographic variables,psychological distress,PSMU,PSPU,WRSS,and self-reported weight and height.Results:The hierarchical regression models showed that sex(β=0.08,p=0.01),BMI(β=0.39,p<0.001),depression(β=0.21,p=0.001),stress(β=0.18,p=0.01),PSMU(β=0.09,p=0.045),and PSPU(β=0.14,p=0.001)were significant factors for WRSS.Conclusion:The mediation models showed that both PSMU and PSPU were significant mediators in the relationships between depression and stress with WRSS.The present findings provide some evidence for understanding WRSS and has important implications for developing interventions to reduce its negative impact on individuals’health and well-being.
文摘Non-invasive glucose monitoring remains a key challenge due to the molecule's inherently weak Raman signal.While surface-enhanced Raman spectroscopy(SERS)offers potential,it often suffers from incomplete spectral coverage.A recent work using tip-enhanced Raman scattering(TERS)successfully captures glucose's complete vibrational fingerprint.This label-free approach paves the way for highly sensitive metabolite detection and future in vivo biosensing applications.
基金the financial support from International Society of Engineering Science and Technology(ISEST)UK。
文摘The conversion of carbon dioxide(CO_(2))into hydrocarbons through electrochemical CO_(2)reduction reaction(eCO_(2)RR)shows a promising method to reduce CO_(2)levels and decrease reliance on fossil fuels in the years to come.Copper-based electrocatalysts exhibit a pronounced inclination for C-C coupling,drawing considerable interest as a favored metal catalyst for generating C_(2+)products through CO_(2)RR.However,CO_(2)RR still has some obstacles including product selectivity,higher overpotential,low Faradic efficiency(FE),stability,and current density(CD).Therefore,advancement in this field enables us to comprehend the complex multi-proton electron transfer during C-C coupling and engineering strategies to improve FE and CD.Herein,this review presents some key features of Cu-based catalysts as an electrocatalyst for C_(2) product formation while addressing the industrial challenges that hinder commercialization of CO_(2)RR.In addition,recent strategies on Cu-based catalysts,synthesis strategies,advanced characterizations,and mechanistic investigations via theoretical simulations have been presented.Furthermore,recent approaches towards the composition,oxidation states,and active facets have been presented.Thus,the most favorable mechanism and possible pathways to synthesize C_(2+)products have been explained using theoretical calculations.
基金supported by a special grant from the Taishan Scholars Project(Project No.tsqn202211130).
文摘Background:The recently developed Depression,Anxiety,and Stress Scales–Youth Version(DASS-Y)shows promise as a tool for assessing youth mental health,but its consistency across timepoints and diverse ages remains underexplored.The present study evaluated whether the DASS-Y reliably measured depression,anxiety,and stress among school-aged youth(aged 9–18 years)across distinct time periods and educational stages.Methods:Two studies were conducted.Study 1 examined consistency over three months using data from 736 Central Chinese high school students who completed surveys at both timepoints.Study 2 tested consistency across educational levels among 2321 primary and 1676 middle school students.Traditional confirmatory factor analysis(CFA),exploratory structural equation modeling(ESEM),and Rasch analysis were employed to assess the scale’s construct validity and measurement invariance.Results:Rasch analysis indicated acceptable DASS-Y item fit(infit/outfit statistics=0.50–1.50)and moderate test-retest reliability(ICCs=0.64–0.69).The ESEM approach demonstrated superior model fit compared to CFA,achieving a good RMSEA(0.056–0.062)and lower latent factor correlations(r=0.40–0.60),supporting longitudinal scalar invariance.Across educational levels,measurement invariance was supported,with only a small number of items exhibiting differential item functioning(DIF).Conclusion:The present study demonstrates that the DASS-Y is a reliable tool for assessing emotional health among non-clinical school-aged youth,offering educators a validated measure to monitor psychological well-being across developmental stages and time,thereby informing strategies to support youth mental health in community and educational settings.Future research among clinical populations is needed to extend its utility for diagnostic purposes.
基金supported in part by Higher Education Sprout Project,Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University(NCKU)supported in part by(received funding from)the National Science and Technology Council,Taiwan(NSTC 112-2410-H-006-089-SS2).
文摘Background:The present study evaluated the psychometric properties of Problematic Internet Use(PIU)instruments and their correlation with psychological distress and time spent on Internet activities among university students in Ghana.Methods:In the present cross-sectional survey design study,520 participants(35.96% female)were recruited with a mean age of 19.55 years(SD=1.94)from several university departments(i.e.,Behavioral Sciences,Materials Engineering,Nursing and Midwifery,and Biochemistry and Biotechnology)of Kwame Nkrumah University of Science and Technology(KNUST)between 19 July and 04 August,2023.Participants completed a survey that included the following measures:the Gaming Disorder Test(GDT),Gaming Disorder Scale for Adolescents(GADIS-A),Internet Gaming Disorder Scale-Short Form(IGDS9-SF),Bergen Social Media Addiction Scale(BSMAS),Smartphone Application Based Addiction Scale(SABAS),Nomophobia Questionnaire(NMP-Q),and the external criterion measure:Depression Anxiety Stress Scale-21(DASS-21).Confirmatory factor analysis(CFA)was carried out to evaluate the structure of the instruments.Cronbach’s α,McDonald’s ω,and composite reliability were used to evaluate internal consistency.Pearson correlation was used to examine the associations between the scores of instruments assessing PIU,time spent on Internet activities,and the level of psychological distress.Results:Model fits confirmed the(i)unidimensional structure of the GDT,BSMAS,SABAS,IGDS9-SF,(ii)two-factor structure of the GADIS-A,and(iii)four-factor structure of the NMP-Q.Additionally,the study found that different types of PIU were significantly associated with psychological distress and time spent on related Internet activities.Conclusion:The six instruments validated in the present study demonstrated very good to excellent psychometric properties when applied to university students in Ghana.The significant associations between Internet-related disorders,time spent on Internet-related activities,and psychological distress highlight the importance of addressing issues of PIU among this population.
基金funded by the Office of Gas and Electricity Markets(Ofgem)and supported by De Montfort University(DMU)and Nottingham Trent University(NTU),UK.
文摘This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.
基金supported by National Natural Science Foundation of China(12374358,91950207)Guangdong Basic and Applied Basic Research Foundation(2024A1515010420).
文摘Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.
基金co-supported by the National Natural Science Foundation of China(Nos.61806219,61876189 and 61703426)the Young Talent Fund of University Association for Science and Technology in Shaanxi,China(Nos.20190108 and 20220106)the Innovation Talent Supporting Project of Shaanxi,China(No.2020KJXX-065).
文摘Modern air battlefield operations are characterized by flexibility and change, and the battlefield evolves rapidly and intricately. However, traditional air target intent recognition methods, which mainly rely on manually designed neural network models, find it difficult to maintain sustained and excellent performance in such a complex and changing environment. To address the problem of the adaptability of neural network models in complex environments, we propose a lightweight Transformer model(TransATIR) with a strong adaptive adjustment capability, based on the characteristics of air target intent recognition and the neural network architecture search technique. After conducting extensive experiments, it has been proved that TransATIR can efficiently extract the deep feature information from battlefield situation data by utilizing the neural architecture search algorithm, in order to quickly and accurately identify the real intention of the target. The experimental results indicate that TransATIR significantly improves recognition accuracy compared to the existing state-of-the-art methods, and also effectively reduces the computational complexity of the model.
基金supported by H2020-MSCA-RISE-778104–ThermaSMART,Royal Society(IEC\NSFC\211210)doctoral degree scholarship of China Scholarship Council(CSC).
文摘Control of the wetting properties of biomimetic functional surfaces is a desired functionality in many applications.In this paper,the photoresist SU-8 was used as fabrication material.A silicon wafer was used as a substrate to prepare a biomimetic surface with different surface roughness and micro-pillars arranged in array morphology.The evaporation dynamics and interfacial heat transfer processes of deionised water droplets on the bioinspired microstructure surface were experimentally studied.The study not only proves the feasibility of preparing hydrophilic biomimetic functional surfaces directly through photoresist materials and photolithography technology but also shows that by adjusting the structural parameters and arrangement of the surface micro-pillar structure,the wettability of the biomimetic surface can be significantly linearly regulated,thereby effectively affecting the heat and mass transfer process at the droplet liquid-vapour interface.Analysis of the results shows that by controlling the biomimetic surface microstructure,the wettability can be enhanced by about 22%at most,the uniformity of the temperature distribution at the liquid-vapour interface can be improved by about 34%,and the average evaporation rate can be increased by about 28%.This study aims to provide some guidance for the research on bionic surface design based on photoresist materials.
文摘Esophageal cancer(EC)continues to pose a significant clinical challenge due to the absence of a reliable early detection method,leading to late-stage diagnoses and poor patient outcomes.The recent study by Liu et al presents a promising breakthrough,demonstrating that plasma DNA methylation markers-SHOX2,SEPTIN9,EPO,and RNF180-offer a non-invasive approach for early EC detection with 76.19%sensitivity and 86.27%specificity.Given the urgent need for effective screening strategies,the potential integration of this assay into clinical practice could significantly enhance early diagnosis,patient monitoring,and overall survival rates.While further validation is necessary,this advancement marks an important step toward improving EC detection and management.
文摘Objectives:The purpose of this narrative review is to offer an updated perspective on the current research on the glycoprotein Osteoprotegerin(OPG),including its potential therapeutic impact and mechanisms of action,and interaction with bone and muscle tissues.Content:As health and social care advances people are living longer,with projections suggesting that in 2050 there will be 2 billion people who are aged over 60 years.Yet musculoskeletal health still declines into older age and as a result there is an increase in the proportion of older populations that spend more time with persistent disabilities.Although physical exercise is repeatedly demonstrated to minimise detrimental effects of ageing,it is not always a feasible intervention,and other directions must be considered.Summary and outlook:OPG,a glycoprotein decoy receptor for the receptor activator of nuclear factor kappa-βligand(RANKL)is a key regulator of bone formation yet emerging evidence has presented its potential to offer positive outcomes in regard to the preservation of skeletal muscle mass and function.Animal models have shown that OPG levels increase during exercise,and independently acts to restore losses of muscle strength and reduce bone resorption.Interventions to increase circulating OPG alongside exercise may act as a therapeutic target to combat the decline in quality of life in older age in humans.Further research is needed on the mechanisms of its action and interaction in humans in combination with exercise.
文摘This editorial narrative review discussed Budd-Chiari syndrome(BCS),which re-presents a rare but critical vascular liver disease resulting in an obstruction of he-patic venous outflow.Despite having a unifying mechanism,the syndrome shows a large heterogeneity across presentation,cause,and disease trajectory,compli-cating diagnosis and management.Based on established prognostic scoring systems,the New Clichy Score,the BCS-transjugular intrahepatic portosystemic shunt Index,the Zeitoun Score,and the Pediatric End-stage Liver Disease score were examined.These scoring systems are used for risk stratification and thera-peutic decision-making.Although these models deliver suitability information,their static parameters,narrow validation,and limited generalizability reduce their usefulness in diverse populations.Specific challenges are highlighted in pediatric patients,pregnant females,and individuals with myeloproliferative neoplasms for whom current tools often fall short.Moreover,there remains uncertainty regarding the durability of Pediatric End-stage Liver Disease score response and longer-term risks,such as hepatocellular carcinoma.There is a need to have a dynamic prognostic model that uses imaging and genetic factors in future studies.The article discussed enhancing recruitment to improve research.Overall,this article provided a contemporary,evidence-based approach for cli-nicians to aid in the evaluation and treatment of BCS.
文摘Rectal neuroendocrine neoplasms pose significant challenges due to their varied presentations and prognoses.Traditional prognostic models,while useful,often fall short of accurately predicting clinical outcomes for these patients.This article discusses the development and implications of a novel prognostic tool,the GATIS score,which aims to enhance predictive accuracy and guide treatment strategies more effectively than current methods.Utilizing data from a large cohort and employing sophisticated statistical models,the GATIS score integrates clinical and pathological markers to provide a nuanced assessment of prognosis.We evaluate the potential of this score to transform clinical decision-making processes,its integration into current medical practices,and future directions for its develo-pment.The integration of genetic markers and other biomarkers could further refine its predictive power,highlighting the ongoing need for innovation in the management of rectal neuroendocrine neoplasms.
基金support from the Biotechnology and Biological Council Doctoral Training Programme(BBSRC DTP)the support from the Royal Society and Wolfson Foundation(RSWF\FT\191022).
文摘Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is fabricated,the nonlinear wavefront it generates is fixed,offering little flexibility.This limitation often necessitates the fabrication of different metasurfaces for different wavefronts,which is both time-consuming and inefficient.To address this,we combine evolutionary algorithms with spatial light modulators(SLMs)to dynamically control wavefronts using a single metasurface,reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment.We demonstrate this approach by introducing a genetic algorithm(GA)to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation(THG)in a silicon metasurface.The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions,thereby significantly boosting THG emission at resonant positions.Additionally,the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM.This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints.Our approach paves the way for self-optimized nonlinear wavefront shaping,advancing optical computation and information processing techniques.
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
基金supported by the SC&SS,Jawaharlal Nehru University,New Delhi,India.
文摘Electric Vehicles(EVs)have emerged as a cleaner,low-carbon,and environmentally friendly alternative to traditional internal combustion engine(ICE)vehicles.With the increasing adoption of EVs,they are expected to eventually replace ICE vehicles entirely.However,the rapid growth of EVs has significantly increased energy demand,posing challenges for power grids and infrastructure.This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road.To address these challenges,various deep learning(DL)models,such as Recurrent Neural Networks(RNNs)and Long Short-Term Memory(LSTM)networks,have been employed for predicting energy demand at EV charging stations(EVCS).However,these models face certain limitations.They often lack interpretability,treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction.Additionally,these models process data sequentially,which makes them computationally slower and less efficient when dealing with large datasets.In the context of these limitations,this paper introduces a novel Attention-Augmented Long Short-Term Memory(AA-LSTM)model.The proposed model integrates an attention mechanism to focus on the most relevant time steps,thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy.By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism,the AA-LSTM model delivers superior performance.The attention mechanism selectively prioritizes critical parts of the input sequence,reducing the computational burden and making the model faster and more effective.The AA-LSTM model achieves impressive results,demonstrating a Mean Absolute Percentage Error(MAPE)of 3.90%and a Mean Squared Error(MSE)of 0.40,highlighting its accuracy and reliability.These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS,offering improved performance and efficiency compared to contemporary approaches.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2501).
文摘The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of medical services.As healthcare becomes more data-driven,cyberattacks targeting these systems continue to rise,necessitating the development of robust,domain-adapted Intrusion Detection Systems(IDS).However,current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare.To address this gap,this study introduces HCKDDCUP,a synthetic dataset modeled on the widely used KDDCUP benchmark,augmented with healthcare-relevant attributes such as patient data,treatments,and diagnoses to better simulate the unique conditions of clinical environments.This research applies standard machine learning algorithms Random Forest(RF),Decision Tree(DT),and K-Nearest Neighbors(KNN)to both the KDDCUP and HCKDDCUP datasets.The methodology includes data preprocessing,feature selection,dimensionality reduction,and comparative performance evaluation.Experimental results show that the RF model performed best,achieving 98%accuracy on KDDCUP and 99%on HCKDDCUP,highlighting its effectiveness in detecting cyber intrusions within a healthcare-specific context.This work contributes a valuable resource for future research and underscores the need for IDS development tailored to sector-specific requirements.
文摘Background and objectives:The ongoing mpox outbreaks have garnered significant attention due to their public health implications,particularly the potential mental health impacts.Despite the growing concern,there has been limited exploration of the intersection between mpox and mental health within the research literature.This study aims to conduct a comprehensive bibliometric analysis to examine global research trends,regional distribution,and thematic focus areas related to mpox's psychological and psychiatric implications.Methods:We conducted a bibliometric analysis using Scopus and the Web of Science database.The analysis was carried out using the R-bibliometrics package and involved identifying literature on mpox and mental health,focusing on global research trends,regional distribution,and thematic areas of study.The analysis included 416 documents obtained from 295 sources from January 1,2014 to August 27,2024.Results:Our analysis revealed a growing but unevenly distributed literature on mpox and mental health.Most studies concentrated on the relationship between mpox and conditions such as depression and anxiety,while other psychiatric outcomes remain underexplored.The geographic distribution of research was also uneven,with regions like Europe and the Americas receiving more focus than others.Conclusions:The study highlights the need for more targeted research on the mental health sequelae of mpox,particularly for vulnerable populations and regions that are currently underrepresented in the literature.Future research should include longitudinal studies to assess the long-term effects of mpox on mental health and the development of robust methodologies to establish causality.Integrating mental health considerations into public health responses to mpox outbreaks is crucial,with significant implications for research,policy,and clinical practice.
基金funding from Commonwealth Scholarship Commission(CSC)U.K.for a Ph.D.scholarship for Marzia DulalUKRI Research England the Expanding Excellence in England(E3)grant.
文摘Wearable electronic textiles(e-textiles)with embedded electronics offer promising solutions for unobtrusive,real-time health monitoring,enhancing healthcare efficiency.However,their adoption is limited by performance and sustainability challenges in materials,manufacturing,and recycling.This study introduces a sustainable paradigm for the fabrication of fully inkjet-printed Smart,Wearable,and Eco-friendly Electronic Textiles(SWEET)with the first comprehensive assessments of the biodegradability and life cycle assessment(LCA).SWEET addresses existing limitations,enabling concurrent and continuous monitoring of human physiology,including skin surface temperature(at temperature coefficient of resistance,TCR value of~-4.4%℃^(-1))and heart rate(-74 beats per minute,bpm)separately and simultaneously like the industry gold standard,using consistent,versatile,and highly efficient inkjet-printed graphene and Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)-based wearable e-textiles.Demonstrations with a wearable garment on five human participants confirm the system’s capability to monitor their electrocardiogram(ECG)signals and skin temperature.Such sustainable and biodegradable e-textiles decompose by-48%in weight and lost-98%strength over 4months.Life cycle assessment(LCA)reveals that the graphene-based electrode has the lowest climate change impact of-0.037 kg CO_(2) eq,40 times lower than reference electrodes.This approach addresses material and manufacturing challenges,while aligning with environmental responsibility,marking a significant leap forward in sustainable e-textile technology for personalized healthcare management.
