In this paper, the effect of of flank wear polycrystalline cubic boron nitride （PCBN） tools on residual stresses, white layer and roughness of machined workpiece surfaces is studied. Experimental results indicate th...In this paper, the effect of of flank wear polycrystalline cubic boron nitride （PCBN） tools on residual stresses, white layer and roughness of machined workpiece surfaces is studied. Experimental results indicate that with the increase of the tool wear, the surface of the machined workpiece tends to generate tensile residual stresses, and white layer becomes clearly thicker and uneven on the workpiece surface. The effect of the flank wear on the surface roughness is less within some range of flank wear value. The results show that it is possible to produce ideal surface integrality levels by controlling the tool flank wear.展开更多
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
BACKGROUND Addressing the growing challenge of hospitalizing chronic multimorbid patients,this study examines the strain these conditions impose on healthcare systems at a local level,focusing on a pilot program.Chron...BACKGROUND Addressing the growing challenge of hospitalizing chronic multimorbid patients,this study examines the strain these conditions impose on healthcare systems at a local level,focusing on a pilot program.Chronic diseases and complex patients require comprehensive management strategies to reduce healthcare burdens and improve patient outcomes.If proven effective,this pilot model has the potential to be replicated in other healthcare settings to enhance the management of chronic multimorbid patients.AIM To evaluate the effectiveness of the high complexity unit(HCU)in managing chronic multimorbid patients through a multidisciplinary care model and to compare it with standard hospital care.METHODS The study employed a descriptive longitudinal approach,analyzing data from the Basic Minimum Data Set(BMDS)to compare hospitalization variables among the HCU,the Internal Medicine Service,and other services at Antequera Hospital throughout 2022.The HCU,designed for patients with complex chronic conditions,integrates a patient-centered model emphasizing multidisciplinary care and continuity post-discharge.RESULTS The study employed a descriptive longitudinal approach,analyzing data from the BMDS to compare hospitalization variables among the HCU,the Internal Medicine Service,and other services at Antequera Hospital throughout 2022.The HCU,designed for patients with complex chronic conditions,integrates a patient-centered model emphasizing multidisciplinary care and continuity post-discharge.CONCLUSION This study demonstrates the effectiveness of the HCU in managing patients with complex chronic diseases through a multidisciplinary approach.The coordinated care provided by the HCU results in improved patient outcomes,reduced unnecessary hospitalizations,and better management of patient complexity.The superiority of the HCU compared to standard care is evident in key outcomes such as fewer readmissions and higher patient satisfaction,reinforcing its value as a model of care to be replicated.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numb...Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numbers.Originating from the reasoning paradigm of Xiangshu(象数,image-number)or phenotype-numerology thinking,TCM came with a deep correlation of clinical observations with digits and laid a strong theoretical basis for digitization.The digitization of TCM should start from the clinical aspect,solve the problem of electronic medical records,achieve standardization and informatization,and on this basis,form a TCM knowledge base through knowledge-building.This process depends on the combined efforts of multiple disciplines such as medicine,mathematics,and engineering to achieve the digitization and intelligent transformation of TCM.This era calls for TCM to break down barriers,embrace opportunities,and move towards digitization.However,during the transformation,it should maintain its essence,avoid simplistic conversions,be guided by scientific value,leverage cutting-edge technologies,and enhance the depth and breadth of the interpretation of TCM connotations.The digitization of TCM will also improve its service capabilities,create an innovative digitally-intelligent TCM service platform,and contribute to the development of“Healthy China”initiatives with wisdom and solutions.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an over...The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.展开更多
This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends kno...This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends knowledge-sharing platforms,soil protection laws,and collaborative efforts between regulatory agencies and agricultural experts.The study emphasizes the need for a balanced approach that includes natural methods alongside synthetic chemicals,particularly herbicides.Ten years ago,farmers primarily used urea,DAP,and potassium for nutrients.However,increased awareness,market forces,and government subsidies have led to a significant rise in herbicide use as a cost-effective weed management strategy.Over the past decade,synthetic fertilizer use for cotton cultivation has increased by 80%,leading to deteriorating soil quality.Paddy cultivation has decreased by 23%,while cotton cultivation has increased by 20.4%due to higher economic incentives.Currently,89.1%of farmers use herbicides,compared to 97.2%who did not a decade ago.Insecticide use has also surged,with 97.8%of farmers applying 1.5 liters or more per acre.The excessive use of chemicals threatens soil fertility and disrupts the ecosystem’s balance.This article explores the reasons behind the adoption of chemical-intensive farming practices and offers insights into farmers’decision-making processes.The careful use of synthetic chemicals is essential to safeguard soil health and maintain ecological balance.展开更多
Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,rad...Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.展开更多
Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices....Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.展开更多
Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug rese...Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the cli...BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the clinical symptoms of achalasia associated with increased LES pressure.AIM To identify the safety and efficacy of POEM for patients with normal LES integrated relaxation pressure(LES-IRP).METHODS The clinical data of patients who underwent POEM successfully in The First Medical Center of Chinese PLA General Hospital were retrospectively analyzed.A total of 481 patients who underwent preoperative high-resolution manometry(HRM)at our hospital were ultimately included in this research.According to the HRM results,the patients were divided into two groups:71 patients were included in the normal LES-IRP group(LES-IRP<15 mmHg)and 410 patients were included in the increased LES-IRP group(LES-IRP≥15 mmHg).Clinical characteristics,procedure-related parameters,adverse events,and outcomes were compared between the two groups to evaluate the safety and efficacy of POEM for patients with normal LES-IRP.RESULTS Among the 481 patients included in our study,209 were males and 272 were females,with a mean age of 44.2 years.All patients underwent POEM without severe adverse events.The median pre-treatment Eckardt scores of the normal LES-IRP and increased LES-IRP groups were 7.0 and 7.0(P=0.132),respectively,decreasing to 1.0 and 1.0 post-treatment(P=0.572).The clinical success rate of the normal LES-IRP group was 87.3%(62/71),and that of the increased LES-IRP group was 91.2%(374/410)(P=0.298).Reflux symptoms were measured by the GerdQ questionnaire,and the percentages of patients with GerdQ scores≥9 in the normal LES-IRP and increased LES-IRP groups were 8.5%and 10.7%,respectively(P=0.711).After matching,the rates of clinical success and the rates of GerdQ score≥9 were not significantly different between the two groups.CONCLUSION Our results suggest that POEM is safe and effective for achalasia and patients with normal LES-IRP.In addition,in patients with normal LES-IRP,compared with those with increased LES-IRP,POEM was not associated with a greater incidence of reflux symptoms.展开更多
The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and d...The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.展开更多
Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orien...Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
基金Supported by the National Natural Science Foundation of China（No.50875068）,and the National High Technology Research and Development Programme of China（No.2009AA044302）.
文摘In this paper, the effect of of flank wear polycrystalline cubic boron nitride （PCBN） tools on residual stresses, white layer and roughness of machined workpiece surfaces is studied. Experimental results indicate that with the increase of the tool wear, the surface of the machined workpiece tends to generate tensile residual stresses, and white layer becomes clearly thicker and uneven on the workpiece surface. The effect of the flank wear on the surface roughness is less within some range of flank wear value. The results show that it is possible to produce ideal surface integrality levels by controlling the tool flank wear.
文摘Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.
基金Supported by Fundación Progreso y Salud,No.AP-0306-2022-C3-F2.
文摘BACKGROUND Addressing the growing challenge of hospitalizing chronic multimorbid patients,this study examines the strain these conditions impose on healthcare systems at a local level,focusing on a pilot program.Chronic diseases and complex patients require comprehensive management strategies to reduce healthcare burdens and improve patient outcomes.If proven effective,this pilot model has the potential to be replicated in other healthcare settings to enhance the management of chronic multimorbid patients.AIM To evaluate the effectiveness of the high complexity unit(HCU)in managing chronic multimorbid patients through a multidisciplinary care model and to compare it with standard hospital care.METHODS The study employed a descriptive longitudinal approach,analyzing data from the Basic Minimum Data Set(BMDS)to compare hospitalization variables among the HCU,the Internal Medicine Service,and other services at Antequera Hospital throughout 2022.The HCU,designed for patients with complex chronic conditions,integrates a patient-centered model emphasizing multidisciplinary care and continuity post-discharge.RESULTS The study employed a descriptive longitudinal approach,analyzing data from the BMDS to compare hospitalization variables among the HCU,the Internal Medicine Service,and other services at Antequera Hospital throughout 2022.The HCU,designed for patients with complex chronic conditions,integrates a patient-centered model emphasizing multidisciplinary care and continuity post-discharge.CONCLUSION This study demonstrates the effectiveness of the HCU in managing patients with complex chronic diseases through a multidisciplinary approach.The coordinated care provided by the HCU results in improved patient outcomes,reduced unnecessary hospitalizations,and better management of patient complexity.The superiority of the HCU compared to standard care is evident in key outcomes such as fewer readmissions and higher patient satisfaction,reinforcing its value as a model of care to be replicated.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金Special Funds of the National Natural Science Foundation of China(T2341006).
文摘Digitization is the inevitable path for the natural development of traditional Chinese medicine(TCM)in the context of the Fourth Industrial Revolution.The goal of TCM digitization is to generate intelligence from numbers.Originating from the reasoning paradigm of Xiangshu(象数,image-number)or phenotype-numerology thinking,TCM came with a deep correlation of clinical observations with digits and laid a strong theoretical basis for digitization.The digitization of TCM should start from the clinical aspect,solve the problem of electronic medical records,achieve standardization and informatization,and on this basis,form a TCM knowledge base through knowledge-building.This process depends on the combined efforts of multiple disciplines such as medicine,mathematics,and engineering to achieve the digitization and intelligent transformation of TCM.This era calls for TCM to break down barriers,embrace opportunities,and move towards digitization.However,during the transformation,it should maintain its essence,avoid simplistic conversions,be guided by scientific value,leverage cutting-edge technologies,and enhance the depth and breadth of the interpretation of TCM connotations.The digitization of TCM will also improve its service capabilities,create an innovative digitally-intelligent TCM service platform,and contribute to the development of“Healthy China”initiatives with wisdom and solutions.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金the support from the National Natural Science Foundation of China(22272004,62272041)the Fundamental Research Funds for the Central Universities(YWF-22-L-1256)+1 种基金the National Key R&D Program of China(2023YFC3402600)the Beijing Institute of Technology Research Fund Program for Young Scholars(No.1870011182126)。
文摘The proliferation of wearable biodevices has boosted the development of soft,innovative,and multifunctional materials for human health monitoring.The integration of wearable sensors with intelligent systems is an overwhelming tendency,providing powerful tools for remote health monitoring and personal health management.Among many candidates,two-dimensional(2D)materials stand out due to several exotic mechanical,electrical,optical,and chemical properties that can be efficiently integrated into atomic-thin films.While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene,the rapid development of new 2D materials with exotic properties has opened up novel applications,particularly in smart interaction and integrated functionalities.This review aims to consolidate recent progress,highlight the unique advantages of 2D materials,and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices.We begin with an in-depth analysis of the advantages,sensing mechanisms,and potential applications of 2D materials in wearable biodevice fabrication.Following this,we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body.Special attention is given to showcasing the integration of multi-functionality in 2D smart devices,mainly including self-power supply,integrated diagnosis/treatment,and human–machine interaction.Finally,the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of2D materials for advanced biodevices.
文摘This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends knowledge-sharing platforms,soil protection laws,and collaborative efforts between regulatory agencies and agricultural experts.The study emphasizes the need for a balanced approach that includes natural methods alongside synthetic chemicals,particularly herbicides.Ten years ago,farmers primarily used urea,DAP,and potassium for nutrients.However,increased awareness,market forces,and government subsidies have led to a significant rise in herbicide use as a cost-effective weed management strategy.Over the past decade,synthetic fertilizer use for cotton cultivation has increased by 80%,leading to deteriorating soil quality.Paddy cultivation has decreased by 23%,while cotton cultivation has increased by 20.4%due to higher economic incentives.Currently,89.1%of farmers use herbicides,compared to 97.2%who did not a decade ago.Insecticide use has also surged,with 97.8%of farmers applying 1.5 liters or more per acre.The excessive use of chemicals threatens soil fertility and disrupts the ecosystem’s balance.This article explores the reasons behind the adoption of chemical-intensive farming practices and offers insights into farmers’decision-making processes.The careful use of synthetic chemicals is essential to safeguard soil health and maintain ecological balance.
基金Supported by the Natural Science Foundation of Jilin Province,No.YDZJ202401182ZYTSJilin Provincial Key Laboratory of Precision Infectious Diseases,No.20200601011JCJilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases,Jilin Province Development and Reform Commission,No.2022C036.
文摘Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Category B,XDB1090000).
文摘Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.
基金upported by the National Key Research and Development Program of China(Grant No.:2023YFF1204904)the National Natural Science Foundation of China(Grant Nos.:U23A20530 and 82173746)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
文摘BACKGROUND Most patients who were included in previous studies on achalasia had increased lower esophageal sphincter(LES)pressure.Peroral endoscopic myotomy(POEM)has been confirmed to be effective at relieving the clinical symptoms of achalasia associated with increased LES pressure.AIM To identify the safety and efficacy of POEM for patients with normal LES integrated relaxation pressure(LES-IRP).METHODS The clinical data of patients who underwent POEM successfully in The First Medical Center of Chinese PLA General Hospital were retrospectively analyzed.A total of 481 patients who underwent preoperative high-resolution manometry(HRM)at our hospital were ultimately included in this research.According to the HRM results,the patients were divided into two groups:71 patients were included in the normal LES-IRP group(LES-IRP<15 mmHg)and 410 patients were included in the increased LES-IRP group(LES-IRP≥15 mmHg).Clinical characteristics,procedure-related parameters,adverse events,and outcomes were compared between the two groups to evaluate the safety and efficacy of POEM for patients with normal LES-IRP.RESULTS Among the 481 patients included in our study,209 were males and 272 were females,with a mean age of 44.2 years.All patients underwent POEM without severe adverse events.The median pre-treatment Eckardt scores of the normal LES-IRP and increased LES-IRP groups were 7.0 and 7.0(P=0.132),respectively,decreasing to 1.0 and 1.0 post-treatment(P=0.572).The clinical success rate of the normal LES-IRP group was 87.3%(62/71),and that of the increased LES-IRP group was 91.2%(374/410)(P=0.298).Reflux symptoms were measured by the GerdQ questionnaire,and the percentages of patients with GerdQ scores≥9 in the normal LES-IRP and increased LES-IRP groups were 8.5%and 10.7%,respectively(P=0.711).After matching,the rates of clinical success and the rates of GerdQ score≥9 were not significantly different between the two groups.CONCLUSION Our results suggest that POEM is safe and effective for achalasia and patients with normal LES-IRP.In addition,in patients with normal LES-IRP,compared with those with increased LES-IRP,POEM was not associated with a greater incidence of reflux symptoms.
基金supported by the National Key Research and Development Program of China(2021YFA1101303)the National Natural Science Foundation of China(62374115)the Innovation Program of Shanghai Municipal Education Commission(2021-01-07-00-07-E00096).
文摘The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.
基金Under the auspices of National Natural Science Foundation of China(No.42293270)。
文摘Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
