Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a ne...Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems.First,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA sequencing.Second,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health.Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.展开更多
Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale fe...Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.展开更多
Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）...Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）-automatic gain control （AGC） algorithm for a new long term evolution （LTE） communication system. Segmented digital gain control strategy is adopted to adjust the gain by only one step based on detected power status. Whether the gain needs to be adjusted is determined by current signal state derived from the change ranges of adjacent root mean square （RMS） of input signal, but not the difference between the power level of current signal and target signal. Software simulation and hardware implementing had been conducted with LTE frequency division dual （FDD） uplink signal and the results indicated that the proposed AGC algorithm can judge power status accurately and hence adjust the gain precisely in one step with a short delay, further, it can make full use of ADC digitalizing bit without oversaturation as well as keeping stable PAR. In addition, the mean error vector magnitude （EVM） was confined less than 1.6% to meet the 3rd generation partnership project （3GPP） standard well.展开更多
Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric ana...Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.展开更多
[Objectives]This study aimed to evaluate the detection sensitivity of Staphylococcus aureus in dairy products utilizing the chip digital PCR(cdPCR)technique.[Methods]Specific primers and probes were designed and synth...[Objectives]This study aimed to evaluate the detection sensitivity of Staphylococcus aureus in dairy products utilizing the chip digital PCR(cdPCR)technique.[Methods]Specific primers and probes were designed and synthesized based on the conserved sequence of the heat-resistant nuclease gene nuc of S.aureus.cdPCR was employed to detect S.aureus,and the sensitivity of this technique was systematically assessed in samples exhibiting low levels of contamination.[Results]cdPCR demonstrated precise quantification when the initial concentration of the sample enrichment solution was equal to or greater than 50 CFU/mL.The detection dynamic range extended across at least five orders of magnitude,with a minimum DNA detection limit of 0.2304 pg/μL.In artificially contaminated cheese samples,the method s lower limit of quantification for detecting S.aureus was 8×10^(2) CFU/g.Regression analysis demonstrated that the gene copy number concentration measured by cdPCR exhibited a strong linear correlation with bacterial contamination concentration across a broad range.[Conclusions]The cdPCR method developed in this study demonstrates high sensitivity and robust quantitative capabilities,offering a reliable technical approach for the precise detection of low-level S.aureus contamination in dairy products.展开更多
RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.A...RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.As visitors depart,the“digital relic vault”awakens online,where porcelain,calligraphy,jade and timepieces reveal their beauty in virtual form.History continues to breathe in the data stream.展开更多
A New Chapter of the Century-Old Palace Museum Oriental Outlook Issue 24,2025 The Palace Museum,the imperial palace of the Ming and Qing dynasties(1368-1911),opened to the public in 1925.Rather than a group of static ...A New Chapter of the Century-Old Palace Museum Oriental Outlook Issue 24,2025 The Palace Museum,the imperial palace of the Ming and Qing dynasties(1368-1911),opened to the public in 1925.Rather than a group of static ancient buildings,it stands today as a dynamic cultural organism filled with invaluable collections of cultural relics preserved by generations of artisans and now displayed to the world through digital technology in long-lasting exhibitions.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Wearable sensors have revolutionized health monitoring by transitioning from clinical diagnostics to continuous,real-time applications in daily life.The oral cavity,rich in saliva containing over 1,000 biomarkers that...Wearable sensors have revolutionized health monitoring by transitioning from clinical diagnostics to continuous,real-time applications in daily life.The oral cavity,rich in saliva containing over 1,000 biomarkers that reflect systemic health(e.g.,glucose,cortisol,and inflammatory markers)[1],offers the advantage of non-invasive sampling.Its superior environmental stability and strong connection to key physiological processes make it an ideal candidate in the field of digital medicine,serving as a natural gateway to personalized health monitoring.Therefore,the oral cavity represents not only a convenient sampling site but also a strategic interface for realizing the vision of continuous,personalized digital health monitoring.展开更多
Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This...Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.展开更多
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital paym...As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital payment systems reshaping daily life,smart manufacturing emerging across industrial zones,and modern infrastructure spreading nationwide.Yet,my recent eight-day media tour to Zhejiang Province o!ered a new and deeply thought-provoking perspective.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the...Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
Designing materials with both structural load-bearing capacity and broadband electromagnetic(EM)wave absorption properties remains a significant challenge.In this work,SiOC/SiC/SiO_(2)composite with gyroid structures ...Designing materials with both structural load-bearing capacity and broadband electromagnetic(EM)wave absorption properties remains a significant challenge.In this work,SiOC/SiC/SiO_(2)composite with gyroid structures were prepared through digital light processing(DLP)3D printing,polymer-derived ceramics(PDCs),chemical vapor infiltration(CVI),and oxidation technologies.The incorporation of the CVISiC phase effectively increases the dissipation capability,while the synergistic interaction between the gyroid structure and SiO_(2)phase significantly improves impedance matching performance.The SiOC/SiC/SiO_(2)composite achieved a minimum reflection loss(RL min)of-62.2 d B at 4.3 mm,and the effective absorption bandwidth(EAB)covered the X-band,with a thickness range of 4.1 mm-4.65 mm.The CST simulation results explain the broadband and low-frequency absorption characteristics,with an EAB of 8.4 GHz(9.6-18 GHz)and an RL min of-21.5 dB at 5 GHz.The excellent EM wave attenuation performance is associated primarily with polarization loss,conduction loss,the gyroid structure's enhancement of multiple reflections and scattering of EM waves,and the resonance effect between the structural units.The SiOC/SiC/SiO_(2)composite also demonstrated strong mechanical properties,with a maximum compressive failure strength of 31.6 MPa in the height direction.This work opens novel prospects for the development of multifunctional structural wave-absorbing materials suitable for broadband microwave absorption and load-bearing properties.展开更多
Emerging digital technology are bringing convenience to the global economy and transforming business organizations.The structural changes brought about by digitalization are particularly significant for enterprises in...Emerging digital technology are bringing convenience to the global economy and transforming business organizations.The structural changes brought about by digitalization are particularly significant for enterprises in traditional industries.Starting from the perspective of the debate between structure and agency in sociology,this paper first discusses the necessity of digital transformation of traditional manufacturing enterprises under the background of digital economy,and then explains the drivers and potential obstacles of digital transformation from the perspective of the coexistence of structure regulation and agency initiative.Based on this,this paper further studies the practice of enterprise digital transformation,and proposes a three-stage model of organizational adaptation(EEI model),namely,the experimentation period(partial digitalization),the expansion period(platform digitalization)and the integration period(ecosystem digitalization).Each of these three stages has its own independent characteristics,and is also an indispensable link in the process of enterprise digitalization,which determines the continuity and integrity of its transformation.The three-stage evolution model based on the structure and agency perspective has important theoretical and practical significance for enterprise digital development.展开更多
基金supported by the NationalNatural Science Foundation of China (No.52293442)the Special Fund from the State Key Joint Laboratory of Environment Simulation and Pollution Control (No.22Z01ESPCR)。
文摘Traditional river health assessment relies on limited water quality indices and representative organism activity,but does not comprehensively obtain biotic and abiotic information of the ecosystem.Here,we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems.First,detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination(especially emerging pollutants)and DNA/RNA sequencing.Second,supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health.Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.
基金the funding support provided to develop the present work in the project Cluster of Excellence“Internet of Production”(project:390621612).
文摘Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.
基金supported by the Smart In-Building Wireless System Using Flexible Digital Transmission Technology (SWIFT)
文摘Aiming at making full use of analog to digital converter （ADC） digitalizing bit without oversaturation while keeping peak to average ratio （PAR） stable, this paper puts forward a new segmented full-digital （SFD）-automatic gain control （AGC） algorithm for a new long term evolution （LTE） communication system. Segmented digital gain control strategy is adopted to adjust the gain by only one step based on detected power status. Whether the gain needs to be adjusted is determined by current signal state derived from the change ranges of adjacent root mean square （RMS） of input signal, but not the difference between the power level of current signal and target signal. Software simulation and hardware implementing had been conducted with LTE frequency division dual （FDD） uplink signal and the results indicated that the proposed AGC algorithm can judge power status accurately and hence adjust the gain precisely in one step with a short delay, further, it can make full use of ADC digitalizing bit without oversaturation as well as keeping stable PAR. In addition, the mean error vector magnitude （EVM） was confined less than 1.6% to meet the 3rd generation partnership project （3GPP） standard well.
基金supported by grants for development of new faculty staff,Ratchadaphiseksomphot Fund,Chulalongkorn University,Thailand.
文摘Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.
基金Supported by Science and Technology Program of Inner Mongolia Autonomous Region"Research and Demonstration of Novel Molecular Biological Identification Technology for Multiple Source Components in Milk and Dairy Products"(2025YFSH0029).
文摘[Objectives]This study aimed to evaluate the detection sensitivity of Staphylococcus aureus in dairy products utilizing the chip digital PCR(cdPCR)technique.[Methods]Specific primers and probes were designed and synthesized based on the conserved sequence of the heat-resistant nuclease gene nuc of S.aureus.cdPCR was employed to detect S.aureus,and the sensitivity of this technique was systematically assessed in samples exhibiting low levels of contamination.[Results]cdPCR demonstrated precise quantification when the initial concentration of the sample enrichment solution was equal to or greater than 50 CFU/mL.The detection dynamic range extended across at least five orders of magnitude,with a minimum DNA detection limit of 0.2304 pg/μL.In artificially contaminated cheese samples,the method s lower limit of quantification for detecting S.aureus was 8×10^(2) CFU/g.Regression analysis demonstrated that the gene copy number concentration measured by cdPCR exhibited a strong linear correlation with bacterial contamination concentration across a broad range.[Conclusions]The cdPCR method developed in this study demonstrates high sensitivity and robust quantitative capabilities,offering a reliable technical approach for the precise detection of low-level S.aureus contamination in dairy products.
文摘RENEWING THE FORBIDDEN CITY’S CENTURY-OLD LEGACY.Oriental Outlook.27 November 2025.At sunrise,the Forbidden City glows under a veil of gold;at night,it retreats into quiet dignity.But the palace never really sleeps.As visitors depart,the“digital relic vault”awakens online,where porcelain,calligraphy,jade and timepieces reveal their beauty in virtual form.History continues to breathe in the data stream.
文摘A New Chapter of the Century-Old Palace Museum Oriental Outlook Issue 24,2025 The Palace Museum,the imperial palace of the Ming and Qing dynasties(1368-1911),opened to the public in 1925.Rather than a group of static ancient buildings,it stands today as a dynamic cultural organism filled with invaluable collections of cultural relics preserved by generations of artisans and now displayed to the world through digital technology in long-lasting exhibitions.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
基金support from the Guangdong Basic and Applied Basic Research Foundation(Nos.2021A1515110388,2024A1515011707)Science and Technology Projects in Guangzhou(No.2024A04J5195)Shenzhen Natural Science Foundation(Nos.JCYJ20230807111120043).
文摘Wearable sensors have revolutionized health monitoring by transitioning from clinical diagnostics to continuous,real-time applications in daily life.The oral cavity,rich in saliva containing over 1,000 biomarkers that reflect systemic health(e.g.,glucose,cortisol,and inflammatory markers)[1],offers the advantage of non-invasive sampling.Its superior environmental stability and strong connection to key physiological processes make it an ideal candidate in the field of digital medicine,serving as a natural gateway to personalized health monitoring.Therefore,the oral cavity represents not only a convenient sampling site but also a strategic interface for realizing the vision of continuous,personalized digital health monitoring.
文摘Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
文摘As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital payment systems reshaping daily life,smart manufacturing emerging across industrial zones,and modern infrastructure spreading nationwide.Yet,my recent eight-day media tour to Zhejiang Province o!ered a new and deeply thought-provoking perspective.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
基金financially supported by National Natural Science Foundation of China(Grant Nos.12141203,52202083,W2421013)the Natural Science Foundation Project of Shaanxi Province(Grant No.2024JC-YBMS-450)+1 种基金the Sichuan Science and Technology Program(Grant No.2024YFHZ0265)the Open Project of High-end Equipment Advanced Materials and Manufacturing Technology Laboratory(Grant No.2023KFKT0005)。
文摘Designing materials with both structural load-bearing capacity and broadband electromagnetic(EM)wave absorption properties remains a significant challenge.In this work,SiOC/SiC/SiO_(2)composite with gyroid structures were prepared through digital light processing(DLP)3D printing,polymer-derived ceramics(PDCs),chemical vapor infiltration(CVI),and oxidation technologies.The incorporation of the CVISiC phase effectively increases the dissipation capability,while the synergistic interaction between the gyroid structure and SiO_(2)phase significantly improves impedance matching performance.The SiOC/SiC/SiO_(2)composite achieved a minimum reflection loss(RL min)of-62.2 d B at 4.3 mm,and the effective absorption bandwidth(EAB)covered the X-band,with a thickness range of 4.1 mm-4.65 mm.The CST simulation results explain the broadband and low-frequency absorption characteristics,with an EAB of 8.4 GHz(9.6-18 GHz)and an RL min of-21.5 dB at 5 GHz.The excellent EM wave attenuation performance is associated primarily with polarization loss,conduction loss,the gyroid structure's enhancement of multiple reflections and scattering of EM waves,and the resonance effect between the structural units.The SiOC/SiC/SiO_(2)composite also demonstrated strong mechanical properties,with a maximum compressive failure strength of 31.6 MPa in the height direction.This work opens novel prospects for the development of multifunctional structural wave-absorbing materials suitable for broadband microwave absorption and load-bearing properties.
文摘Emerging digital technology are bringing convenience to the global economy and transforming business organizations.The structural changes brought about by digitalization are particularly significant for enterprises in traditional industries.Starting from the perspective of the debate between structure and agency in sociology,this paper first discusses the necessity of digital transformation of traditional manufacturing enterprises under the background of digital economy,and then explains the drivers and potential obstacles of digital transformation from the perspective of the coexistence of structure regulation and agency initiative.Based on this,this paper further studies the practice of enterprise digital transformation,and proposes a three-stage model of organizational adaptation(EEI model),namely,the experimentation period(partial digitalization),the expansion period(platform digitalization)and the integration period(ecosystem digitalization).Each of these three stages has its own independent characteristics,and is also an indispensable link in the process of enterprise digitalization,which determines the continuity and integrity of its transformation.The three-stage evolution model based on the structure and agency perspective has important theoretical and practical significance for enterprise digital development.
