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
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
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
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
Deep tight reservoirs exhibit complex stress and seepage fields due to varying pore structures,thus the seepage characteristics are significant for enhancing oil production.This study conducted triaxial compression an...Deep tight reservoirs exhibit complex stress and seepage fields due to varying pore structures,thus the seepage characteristics are significant for enhancing oil production.This study conducted triaxial compression and permeability tests to investigate the mechanical and seepage properties of tight sandstone.A digital core of tight sandstone was built using Computed Tomography(CT)scanning,which was divided into matrix and pore phases by a pore equivalent diameter threshold.A fluid-solid coupling model was established to investigate the seepage characteristics at micro-scale.The results showed that increasing the confining pressure decreased porosity,permeability,and flow velocity,with the pore phase becoming the dominant seepage channel.Cracks and large pores closed first under increasing pressure,resulted in a steep drop in permeability.However,permeability slightly decreased under high confining pressure,which followed a first-order exponential function.Flow velocity increased with seepage pressure.And the damage mainly occurred in stress-concentration regions under low seepage pressure.Seepage behavior followed linear Darcy flow,the damage emerged at seepage entrances under high pressure,which decreased rock elastic modulus and significantly increased permeability.展开更多
Digital technology and the digital economy are at the forefront of the global technological revolution and industrial transformation.They are profoundly reshaping the international landscape of competition and restruc...Digital technology and the digital economy are at the forefront of the global technological revolution and industrial transformation.They are profoundly reshaping the international landscape of competition and restructuring the world economy.Major countries and economies around the globe are increasingly competing on digital technology and the digital economy,striving to secure a strategic advantage in the new round of international competition and establish a strong position in the evolving international order.展开更多
In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in...In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper...In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).展开更多
Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-s...Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-silience study face many challenges.The introduction of digital twin technology provides an innovative solution for the resilience study of smart water distribution networks,which can more effectively support the network’s real-time monitoring and intelligent control.This paper proposes a digital twin architecture of smart water dis-tribution networks,laying the foundation for the resilience assessment of water distribution networks.Based on this,a performance evaluation model based on user satisfaction is proposed,which can more intuitively and effectively reflect the performance of urban water supply services.Meanwhile,we propose a method to quantify the importance of water distribution pipes’residual resilience,considering the time value to optimize the re-covery sequence of failed pipes and develop targeted preventive maintenance strategies.Finally,to validate the effectiveness of the proposed method,this paper applies it to a water distribution network.The results show that the proposed method can significantly improve the resilience and enhance the overall resilience of smart urban water distribution networks.展开更多
Objectives:Childhood and adolescent obesity are an increasing global health concern.This study aimed to evaluate the effectiveness of digital components and interaction types in counseling interventions for prevention...Objectives:Childhood and adolescent obesity are an increasing global health concern.This study aimed to evaluate the effectiveness of digital components and interaction types in counseling interventions for prevention and treatment.Methods:All studies were searched in online databases and grey literature,including PubMed(Medline),Web of Science,CINAHL,Scopus,IEEE Xplore Digital Library,Journal of Medical Internet Research(JMIR),MedNar,EBSco Open Dissertations.The search period is from inception to June 2023,and the languages are Finnish,English and Swedish.The research quality was evaluated using the web-based data management system Covidence for prevalence studies.The study protocol was registered with PROSPERO(registration number:CRD42021247595).Results:In this review,4,407 studies were screened,and 22 were included.These involved 3,433 participants and 264 child-parent pairs.The digital approaches included multicomponent elements like internet platforms,text messaging,video conferencing,online communities,wearable technology,and mobile apps,allowing one-way,two-way,and face-to-face interactions.Two studies showed statistically significant effects of treatment on BMI and waist-to-hip ratio.Most interventions reported positive outcomes,with no significant differences between groups,and none showed null effects during followup.Conclusions:Digital multicomponents like mobile apps and wearables can help obese children and adolescents adopt healthier lifestyles.While these interventions show promise for obesity management,further research is needed to assess their effectiveness,particularly regarding nurses'perspectives.展开更多
This paper examines the impact of digital intelligence transformation on new quality productivity in enterprises in Fujian Province.It highlights the challenges these enterprises face,such as limited talent and infras...This paper examines the impact of digital intelligence transformation on new quality productivity in enterprises in Fujian Province.It highlights the challenges these enterprises face,such as limited talent and infrastructure,in adopting technologies like cloud computing,big data,and artificial intelligence.The research finds that digital intelligence can enhance innovation,efficiency,and market adaptability,driving significant improvements in productivity.The study emphasizes the need for organizational changes and government support to overcome barriers and accelerate transformation,offering valuable insights for both academia and industry.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
Diabetes is highly prevalent among the elderly worldwide,with the highest number of diabetes cases in China.Yet,the management of diabetes remains unsatisfactory.Recent advances in digital health technologies have fac...Diabetes is highly prevalent among the elderly worldwide,with the highest number of diabetes cases in China.Yet,the management of diabetes remains unsatisfactory.Recent advances in digital health technologies have facilitated the establishment of smart wards for diabetes patients.There is a lack of smart wards tailored specifically for older diabetes patients who encounter unique challenges in glycemic control and diabetes management,including an increased vulnerability to hypoglycemia,the presence of multiple chronic diseases,and cognitive decline.In this review,studies on digital health technologies for diabetes in China and beyond were summarized to elucidate how the adoption of digital health technologies,such as real-time continuous glucose monitoring,sensor-augmented pump technology,and their integration with 5th generation networks,big data cloud storage,and hospital information systems,can address issues specifically related to elderly diabetes patients in hospital wards.Furthermore,the challenges and future directions for establishing and implementing smart wards for elderly diabetes patients are discussed,and these challenges may also be applicable to other countries worldwide,not just in China.Taken together,the smart wards may enhance clinical outcomes,address specific issues,and eventually improve patient-centered hospital care for elderly patients with diabetes.展开更多
In the era of the digital economy,digital trade has demonstrated strong vitality,becoming a crucial driving force for the highquality development of national and regional economies.However,understanding the resilience...In the era of the digital economy,digital trade has demonstrated strong vitality,becoming a crucial driving force for the highquality development of national and regional economies.However,understanding the resilience of digital trade in the face of external crises is an important topic.Taking the backdrop of Sino-US trade friction,this paper constructs a resilience index system for digital trade.It utilizes entropy method,kernel density estimation,and ArcGIS mapping to calculate and visually analyze the resilience of China’s digital trade from 2017 to 2021.Additionally,a Tobit model is constructed to explore the main influencing factors of digital trade resilience patterns.The research findings indicate:1)temporally,during the period of Sino-US trade friction,China’s digital trade resilience shows an overall upward trend,but there are regional differences in resilience levels across the country,with a severe polarization phenomenon.2)Spatially,high resilience is observed in the eastern and central regions of China,while the western and northeastern regions exhibit low resilience.3)From a dimensional perspective,the resistance of digital trade resilience displays a spatial distribution of high values in the east and low values in the west.The recovery force is aggregated along coastal areas,and the renewal force tends to aggregate along the eastern coastline.4)Factors such as economic scale,industrial structure,urbanization rate,government fiscal expenditure,and technological talents significantly promote the enhancement of digital trade resilience.This study reveals the dynamic characteristics and influencing factors of digital trade resilience in responding to external shocks,providing theoretical basis and policy suggestions for enhancing digital trade resilience,and promoting high-quality economic development in China.展开更多
With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed...With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.展开更多
Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional ...Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.展开更多
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.
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.
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.
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 the National Natural Science Foundation of China(Nos.42272153 and 42472195)the Research Fund of PetroChina Tarim Oilfield Company(No.671023060003)the Research Fund of China National Petroleum Corporation Limited(No.2023ZZ16YJ04).
文摘Deep tight reservoirs exhibit complex stress and seepage fields due to varying pore structures,thus the seepage characteristics are significant for enhancing oil production.This study conducted triaxial compression and permeability tests to investigate the mechanical and seepage properties of tight sandstone.A digital core of tight sandstone was built using Computed Tomography(CT)scanning,which was divided into matrix and pore phases by a pore equivalent diameter threshold.A fluid-solid coupling model was established to investigate the seepage characteristics at micro-scale.The results showed that increasing the confining pressure decreased porosity,permeability,and flow velocity,with the pore phase becoming the dominant seepage channel.Cracks and large pores closed first under increasing pressure,resulted in a steep drop in permeability.However,permeability slightly decreased under high confining pressure,which followed a first-order exponential function.Flow velocity increased with seepage pressure.And the damage mainly occurred in stress-concentration regions under low seepage pressure.Seepage behavior followed linear Darcy flow,the damage emerged at seepage entrances under high pressure,which decreased rock elastic modulus and significantly increased permeability.
文摘Digital technology and the digital economy are at the forefront of the global technological revolution and industrial transformation.They are profoundly reshaping the international landscape of competition and restructuring the world economy.Major countries and economies around the globe are increasingly competing on digital technology and the digital economy,striving to secure a strategic advantage in the new round of international competition and establish a strong position in the evolving international order.
文摘In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
文摘In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).
基金the financial support for this research from the Program for the Program for young backbone teachers in Universities of Henan Province(No.2021GGJS007).
文摘Resilient smart urban water distribution networks are essential to ensure smooth urban operation and maintain daily water services.However,the dynamics and complexity of smart water distribution networks make its re-silience study face many challenges.The introduction of digital twin technology provides an innovative solution for the resilience study of smart water distribution networks,which can more effectively support the network’s real-time monitoring and intelligent control.This paper proposes a digital twin architecture of smart water dis-tribution networks,laying the foundation for the resilience assessment of water distribution networks.Based on this,a performance evaluation model based on user satisfaction is proposed,which can more intuitively and effectively reflect the performance of urban water supply services.Meanwhile,we propose a method to quantify the importance of water distribution pipes’residual resilience,considering the time value to optimize the re-covery sequence of failed pipes and develop targeted preventive maintenance strategies.Finally,to validate the effectiveness of the proposed method,this paper applies it to a water distribution network.The results show that the proposed method can significantly improve the resilience and enhance the overall resilience of smart urban water distribution networks.
文摘Objectives:Childhood and adolescent obesity are an increasing global health concern.This study aimed to evaluate the effectiveness of digital components and interaction types in counseling interventions for prevention and treatment.Methods:All studies were searched in online databases and grey literature,including PubMed(Medline),Web of Science,CINAHL,Scopus,IEEE Xplore Digital Library,Journal of Medical Internet Research(JMIR),MedNar,EBSco Open Dissertations.The search period is from inception to June 2023,and the languages are Finnish,English and Swedish.The research quality was evaluated using the web-based data management system Covidence for prevalence studies.The study protocol was registered with PROSPERO(registration number:CRD42021247595).Results:In this review,4,407 studies were screened,and 22 were included.These involved 3,433 participants and 264 child-parent pairs.The digital approaches included multicomponent elements like internet platforms,text messaging,video conferencing,online communities,wearable technology,and mobile apps,allowing one-way,two-way,and face-to-face interactions.Two studies showed statistically significant effects of treatment on BMI and waist-to-hip ratio.Most interventions reported positive outcomes,with no significant differences between groups,and none showed null effects during followup.Conclusions:Digital multicomponents like mobile apps and wearables can help obese children and adolescents adopt healthier lifestyles.While these interventions show promise for obesity management,further research is needed to assess their effectiveness,particularly regarding nurses'perspectives.
基金Innovation and Entrepreneurship Training Program of Minjiang University,“Research on the Impact of Digital Intelligence Empowerment on the New Quality Productivity of Specialized and Innovative Enterprises and the Realization Path”(Project No.:202410395029)。
文摘This paper examines the impact of digital intelligence transformation on new quality productivity in enterprises in Fujian Province.It highlights the challenges these enterprises face,such as limited talent and infrastructure,in adopting technologies like cloud computing,big data,and artificial intelligence.The research finds that digital intelligence can enhance innovation,efficiency,and market adaptability,driving significant improvements in productivity.The study emphasizes the need for organizational changes and government support to overcome barriers and accelerate transformation,offering valuable insights for both academia and industry.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
基金Supported by Post-Subsidy Funds from the National Clinical Research Center,Ministry of Science and Technology of China,No.303-01-001-0272-08Beijing Municipal Administration of Hospitals Incubating Program,No.PX2022032Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes(PWD&RPP-MRI),No.JYY2023-13.
文摘Diabetes is highly prevalent among the elderly worldwide,with the highest number of diabetes cases in China.Yet,the management of diabetes remains unsatisfactory.Recent advances in digital health technologies have facilitated the establishment of smart wards for diabetes patients.There is a lack of smart wards tailored specifically for older diabetes patients who encounter unique challenges in glycemic control and diabetes management,including an increased vulnerability to hypoglycemia,the presence of multiple chronic diseases,and cognitive decline.In this review,studies on digital health technologies for diabetes in China and beyond were summarized to elucidate how the adoption of digital health technologies,such as real-time continuous glucose monitoring,sensor-augmented pump technology,and their integration with 5th generation networks,big data cloud storage,and hospital information systems,can address issues specifically related to elderly diabetes patients in hospital wards.Furthermore,the challenges and future directions for establishing and implementing smart wards for elderly diabetes patients are discussed,and these challenges may also be applicable to other countries worldwide,not just in China.Taken together,the smart wards may enhance clinical outcomes,address specific issues,and eventually improve patient-centered hospital care for elderly patients with diabetes.
基金Under the auspices of National Natural Science Foundation of China(No.42471205)the General Scientific Research Project of Zhejiang Provincial Department of Education(No.2024JYTYB12)the Philosophy and Social Science Planning Project of Zhejiang Province(No.23NDJC109YB)。
文摘In the era of the digital economy,digital trade has demonstrated strong vitality,becoming a crucial driving force for the highquality development of national and regional economies.However,understanding the resilience of digital trade in the face of external crises is an important topic.Taking the backdrop of Sino-US trade friction,this paper constructs a resilience index system for digital trade.It utilizes entropy method,kernel density estimation,and ArcGIS mapping to calculate and visually analyze the resilience of China’s digital trade from 2017 to 2021.Additionally,a Tobit model is constructed to explore the main influencing factors of digital trade resilience patterns.The research findings indicate:1)temporally,during the period of Sino-US trade friction,China’s digital trade resilience shows an overall upward trend,but there are regional differences in resilience levels across the country,with a severe polarization phenomenon.2)Spatially,high resilience is observed in the eastern and central regions of China,while the western and northeastern regions exhibit low resilience.3)From a dimensional perspective,the resistance of digital trade resilience displays a spatial distribution of high values in the east and low values in the west.The recovery force is aggregated along coastal areas,and the renewal force tends to aggregate along the eastern coastline.4)Factors such as economic scale,industrial structure,urbanization rate,government fiscal expenditure,and technological talents significantly promote the enhancement of digital trade resilience.This study reveals the dynamic characteristics and influencing factors of digital trade resilience in responding to external shocks,providing theoretical basis and policy suggestions for enhancing digital trade resilience,and promoting high-quality economic development in China.
基金supported by the National Fund Cultivation Project from China People’s Police University(Grant Number:JJPY202402)National Natural Science Foundation of China(Grant Number:62172165).
文摘With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFB3710204)Guangxi Science and Technology Major Program(Grant No.AA23023028-1)+1 种基金Natural Science Foundation of Heilongjiang Province of China for Distinguished Young Scientists(Grant No.JQ2022E007)Xinjiang Production and Construction Corps Science and Technology Plan(Grant No.2023AA003).
文摘Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.
