Infected necrotizing pancreatitis(INP)remains a life-threatening complication of acute pancreatitis.Despite advancements such as endoscopic ultrasound(EUS)-guided drainage,lumen-apposing metal stents,and protocolized ...Infected necrotizing pancreatitis(INP)remains a life-threatening complication of acute pancreatitis.Despite advancements such as endoscopic ultrasound(EUS)-guided drainage,lumen-apposing metal stents,and protocolized step-up strate-gies,the clinical practice remains heterogeneous,with variability in endoscopic strategies,procedural timing,device selection,and adjunctive techniques contri-buting to inconsistent outcomes.This review synthesizes current evidence to contribute to a structured framework integrating multidisciplinary team decision-making,advanced imaging(three-dimensional reconstruction,contrast-enhanced computed tomography/magnetic resonance imaging),EUS assessment,and biomarker-driven risk stratification(C-reactive protein,procalcitonin)to optimize patient selection,intervention timing,and complication management.Key stan-dardization components include endoscopic assessment and procedural strate-gies,optimal timing of intervention,personalized approaches for complex pan-creatic collections,and techniques to reduce the number of endoscopic debride-ments and mitigate complications.This work aims to enhance clinical outcomes,minimize practice heterogeneity,and establish a foundation for future research and guideline development in endoscopic management of INP.展开更多
To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the ...To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.展开更多
Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-graine...Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.T...Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.To contribute to a better understanding of the state of the art of smart techniques for engineering projects,this paper provides a comprehensive review of multi-criteria decision-making(MCDM)techniques,intelligent techniques,and their applications in CEM.First,a comprehensive framework detailing smart technologies for construction projects is developed.Next,the characteristics of CEM are summarized.A bibliometric review is then conducted to investigate the keywords,journals,and clusters related to the application of smart techniques in CEM during 2000-2022.Recent advancements in intelligent techniques are also discussed under the following six topics:①big data technology;②computer vision;③speech recognition;④natural language processing;⑤machine learning;and⑥knowledge representation,understanding,and reasoning.The applications of smart techniques are then illustrated via underground space exploitation.Finally,future research directions for the sustainable development of smart construction are highlighted.展开更多
Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,a...Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.展开更多
This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement ...This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement of intelligent emergency,further improving the effectiveness of intelligent emergency management.First,approximately 3,900 documents from the intelligent emergency field are analyzed to determine the future research trend in intelligent emergency management.The socio-technical theory concerning technical and social systems is introduced.The emergency management system concepts of“technology enabling”and“enabling value creation”are defined according to bibliometric analysis and socio-technical theory.Second,a research framework that includes technology enabling and enabling value creation for the decision-making paradigm in emergency management according to the big data environment is constructed.A detailed analysis approach from intelligent emergency technology enabling to enabling value creation in emergency management is proposed.Finally,earthquake disasters are taken as examples,and specific analyses of the intelligent emergency enabling and enabling value creation are explored;enabling value creation is discussed based on measurable indicators.The clear concept of emergency management system technology enabling and enabling value creation,as well as the detailed analysis approach from intelligent emergency technology enabling to enabling value creation,provide a theoretical bases for scholars and practitioners to evaluate the value(performance)of intelligent emergency for the first time.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
This article comprehensively explores the relationship between anxiety and hypertensive disorders of pregnancy(HDP),covering epidemiology,potential mechanisms,and management strategies.HDP is the second leading cause ...This article comprehensively explores the relationship between anxiety and hypertensive disorders of pregnancy(HDP),covering epidemiology,potential mechanisms,and management strategies.HDP is the second leading cause of maternal and perinatal morbidity and mortality,encompassing subtypes such as gestational hypertension,preeclampsia,and eclampsia.Research indicates that anxiety is closely associated with the occurrence of HDP,potentially influencing blood pressure regulation and vascular function through neuroendocrine,inflammatory,genetic,and gut microbiota effects.Epidemiological data show that anxiety is prevalent during pregnancy and is linked to an increased risk of HDP.Biological mechanism studies reveal that anxiety can increase the risk of HDP by activating the hypothalamic-pituitary-adrenal axis,promoting inflammation,and affecting gut microbiota.In terms of treatment and management,psychological interventions(such as relaxation training,yoga,and mindfulness meditation)and pharmacological treatments(such as labetalol and nifedipine)play important roles in alleviating anxiety and improving the prognosis of HDP.Additionally,multidisciplinary collaboration and long-term postpartum follow-up are crucial for reducing the long-term risk of cardiovascular diseases.Despite significant progress in research on anxiety and HDP,many issues still require further exploration,including in-depth mechanism studies,optimization of clinical interventions,improvement of multidisciplinary collaboration models,long-term follow-up studies,and the impact of cultural and social factors.展开更多
In the contemporary medical landscape,the burgeoning interest in natural therapies,particularly for managing gastrointestinal disorders,has brought traditional Chinese medicine(TCM)to the forefront.This article explai...In the contemporary medical landscape,the burgeoning interest in natural therapies,particularly for managing gastrointestinal disorders,has brought traditional Chinese medicine(TCM)to the forefront.This article explains the core principles and clinical applications of TCM in treating these conditions,furthering the discourse through an examination of integrated TCM strategies,as demonstrated in the study by Zhou et al.While TCM has shown promising clinical outcomes,it encounters significant hurdles in standardization,mechanistic research,and clinical validation.Future investigations should aim to solidify the scientific underpinnings of TCM and expand its use in gastrointestinal disease management,striving for a seamless fusion of traditional and contemporary medical practices.展开更多
Flexible multifunctional polymer-based electromagnetic interference(EMI)shielding composite films play a pivotal role in 5 G communication technology,smart wearables,automotive electronics,and aerospace.In this work,(...Flexible multifunctional polymer-based electromagnetic interference(EMI)shielding composite films play a pivotal role in 5 G communication technology,smart wearables,automotive electronics,and aerospace.In this work,(Ti_(3)C_(2)T_(x) MXene/cellulose nanofibers(CNF)-(hydroxy‑functionalized BNNS(BNNS-OH)/CNF)composite films(TBCF)with Janus structure are prepared via vacuum-assisted filtration of BNNS-OH/CNF and Ti_(3)C_(2)T_(x)/CNF suspension by one after another.Then ionic bonding-strengthened TBCF(ITBCF)is obtained by Ca^(2+)ion infiltration and cold-pressing technique.The Janus structure endows ITBCF with the unique“conductive on one side and insulating on the other”property.When the mass ratio of Ti_(3)C_(2)T_(x) and BNNS is 1:1 and the total mass fraction is 70 wt.%,the electrical conductivity(σ)of the Ti_(3)C_(2)T_(x)/CNF side of ITBCF reaches 166.7 S/cm,while the surface resistivity of the BNNS-OH/CNF side is as high as 304 MΩ.After Ca^(2+)ion infiltration,the mechanical properties of ITBCF are significantly enhanced.The tensile strength and modulus of ITBCF are 73.5 MPa and 15.6 GPa,which are increased by 75.9%and 46.2%compared with those of TBCF,respectively.Moreover,ITBCF exhibits outstanding EMI shielding effectiveness(SE)of 57 dB and thermal conductivity(λ)of 9.49 W/(m K).In addition,ITBCF also presents excellent photothermal and photoelectric energy conversion performance.Under simulated solar irradiation with a power density of 120 mW/cm^(2),the surface stabilization temperature reaches up to 65.3°C and the maximum steady state voltage reaches up to 58.2 mV.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode sche...This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.展开更多
With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have i...With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.展开更多
Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers ...Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers and lacks theoretical guidance.This paper proposes a concise graph representation method based on graph theory for fuel thermal management systems,which can represent all possible connections between subsystems.A generalized optimization algorithm is proposed for fuel thermal management system architecture to minimize the heat sink.This algorithm can autonomously arrange subsystems with heat production differences and efficiently utilize the architecture of the fuel heat sink.At the same time,two evaluation indices are proposed from the perspective of subsystems.These indices intuitively and clearly show that the reason for the high efficiency of heat sink utilization is the balanced and moderate cooling of each subsystem and verify the rationality of the architecture optimization method.A set of simulations are also conducted,which demonstrate that the fuel tank temperature has no effect on the performance of the architecture.This paper provides a reference for the architectural design of aircraft fuel thermal management systems.The metrics used in this paper can also be utilized to evaluate the existing architecture.展开更多
Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and ...Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.展开更多
Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD pat...Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.展开更多
BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and familie...BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.展开更多
This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal disease...This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
基金Supported by the Education and Teaching Reform Project of the First Clinical College of Chongqing Medical University,No.CMER202305Natural Science Foundation of Xizang Autonomous Region,No.XZ2024ZR-ZY100(Z)Program for Youth Innovation in Future Medicine,Chongqing Medical University,China,No.W0138.
文摘Infected necrotizing pancreatitis(INP)remains a life-threatening complication of acute pancreatitis.Despite advancements such as endoscopic ultrasound(EUS)-guided drainage,lumen-apposing metal stents,and protocolized step-up strate-gies,the clinical practice remains heterogeneous,with variability in endoscopic strategies,procedural timing,device selection,and adjunctive techniques contri-buting to inconsistent outcomes.This review synthesizes current evidence to contribute to a structured framework integrating multidisciplinary team decision-making,advanced imaging(three-dimensional reconstruction,contrast-enhanced computed tomography/magnetic resonance imaging),EUS assessment,and biomarker-driven risk stratification(C-reactive protein,procalcitonin)to optimize patient selection,intervention timing,and complication management.Key stan-dardization components include endoscopic assessment and procedural strate-gies,optimal timing of intervention,personalized approaches for complex pan-creatic collections,and techniques to reduce the number of endoscopic debride-ments and mitigate complications.This work aims to enhance clinical outcomes,minimize practice heterogeneity,and establish a foundation for future research and guideline development in endoscopic management of INP.
基金National Key R&D Program of China of the 13th Five-Year Plan(No.2018YFD1100704)。
文摘To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.
基金supported by National Natural Science Foundation of China(Grant Nos.42072126,42372139)the Natural Science Foundation of Sichuan Province(Grant Nos.2022NSFSC0990).
文摘Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金funded by the project of Guangdong Provincial Basic and Applied Basic Research Fund Committee(2022A1515240073)the Pearl River Talent Recruitment Program(2019CX01G338),Guangdong Province.
文摘Construction engineering and management(CEM)has become increasingly complicated with the increasing size of engineering projects under different construction environments,motivating the digital transformation of CEM.To contribute to a better understanding of the state of the art of smart techniques for engineering projects,this paper provides a comprehensive review of multi-criteria decision-making(MCDM)techniques,intelligent techniques,and their applications in CEM.First,a comprehensive framework detailing smart technologies for construction projects is developed.Next,the characteristics of CEM are summarized.A bibliometric review is then conducted to investigate the keywords,journals,and clusters related to the application of smart techniques in CEM during 2000-2022.Recent advancements in intelligent techniques are also discussed under the following six topics:①big data technology;②computer vision;③speech recognition;④natural language processing;⑤machine learning;and⑥knowledge representation,understanding,and reasoning.The applications of smart techniques are then illustrated via underground space exploitation.Finally,future research directions for the sustainable development of smart construction are highlighted.
基金Supported by the CNPC Major Science and Technology Project(2021DJ1806).
文摘Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.
基金the National Natural Science Foundation of China(Grant No.:71771061).
文摘This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment.It addresses the neglect of the application value(performance)measurement of intelligent emergency,further improving the effectiveness of intelligent emergency management.First,approximately 3,900 documents from the intelligent emergency field are analyzed to determine the future research trend in intelligent emergency management.The socio-technical theory concerning technical and social systems is introduced.The emergency management system concepts of“technology enabling”and“enabling value creation”are defined according to bibliometric analysis and socio-technical theory.Second,a research framework that includes technology enabling and enabling value creation for the decision-making paradigm in emergency management according to the big data environment is constructed.A detailed analysis approach from intelligent emergency technology enabling to enabling value creation in emergency management is proposed.Finally,earthquake disasters are taken as examples,and specific analyses of the intelligent emergency enabling and enabling value creation are explored;enabling value creation is discussed based on measurable indicators.The clear concept of emergency management system technology enabling and enabling value creation,as well as the detailed analysis approach from intelligent emergency technology enabling to enabling value creation,provide a theoretical bases for scholars and practitioners to evaluate the value(performance)of intelligent emergency for the first time.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
文摘This article comprehensively explores the relationship between anxiety and hypertensive disorders of pregnancy(HDP),covering epidemiology,potential mechanisms,and management strategies.HDP is the second leading cause of maternal and perinatal morbidity and mortality,encompassing subtypes such as gestational hypertension,preeclampsia,and eclampsia.Research indicates that anxiety is closely associated with the occurrence of HDP,potentially influencing blood pressure regulation and vascular function through neuroendocrine,inflammatory,genetic,and gut microbiota effects.Epidemiological data show that anxiety is prevalent during pregnancy and is linked to an increased risk of HDP.Biological mechanism studies reveal that anxiety can increase the risk of HDP by activating the hypothalamic-pituitary-adrenal axis,promoting inflammation,and affecting gut microbiota.In terms of treatment and management,psychological interventions(such as relaxation training,yoga,and mindfulness meditation)and pharmacological treatments(such as labetalol and nifedipine)play important roles in alleviating anxiety and improving the prognosis of HDP.Additionally,multidisciplinary collaboration and long-term postpartum follow-up are crucial for reducing the long-term risk of cardiovascular diseases.Despite significant progress in research on anxiety and HDP,many issues still require further exploration,including in-depth mechanism studies,optimization of clinical interventions,improvement of multidisciplinary collaboration models,long-term follow-up studies,and the impact of cultural and social factors.
基金Supported by the 2023 Government Funded Project of the Outstanding Talents Training Program in Clinical Medicine,No.ZF2023165Key Research and Development Projects of Hebei Province,No.18277731DNatural Science Foundation of Hebei Province,No.H202423105.
文摘In the contemporary medical landscape,the burgeoning interest in natural therapies,particularly for managing gastrointestinal disorders,has brought traditional Chinese medicine(TCM)to the forefront.This article explains the core principles and clinical applications of TCM in treating these conditions,furthering the discourse through an examination of integrated TCM strategies,as demonstrated in the study by Zhou et al.While TCM has shown promising clinical outcomes,it encounters significant hurdles in standardization,mechanistic research,and clinical validation.Future investigations should aim to solidify the scientific underpinnings of TCM and expand its use in gastrointestinal disease management,striving for a seamless fusion of traditional and contemporary medical practices.
基金financially supported by the National Natural Science Foundation of China(Nos.52303090,52403132,52403112,52473083)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-QN-0168,2024JC-TBZC-04)+6 种基金the Innovation Capability Support Plan of Shaanxi Province(No.2024ZC-KJXX-022)the Shaanxi Province Key Research and Development Plan Project(No.2023-YBGY-461)the Innovation Capability Support Program of Shaanxi(No.2024RS-CXTD-57)the Natural Science Foundation of Chongqing,China(No.2023NSCQ-MSX2547)the Youth Talent Promotion Project of Shaanxi Science and Technology Association(No.20240426)The Special Scientific Research Plan of Education Department of Shaanxi Province(No.23JK0376)the authors would also like to thank Shiyaniia lab for the sup-port of SEM and XPS tests.
文摘Flexible multifunctional polymer-based electromagnetic interference(EMI)shielding composite films play a pivotal role in 5 G communication technology,smart wearables,automotive electronics,and aerospace.In this work,(Ti_(3)C_(2)T_(x) MXene/cellulose nanofibers(CNF)-(hydroxy‑functionalized BNNS(BNNS-OH)/CNF)composite films(TBCF)with Janus structure are prepared via vacuum-assisted filtration of BNNS-OH/CNF and Ti_(3)C_(2)T_(x)/CNF suspension by one after another.Then ionic bonding-strengthened TBCF(ITBCF)is obtained by Ca^(2+)ion infiltration and cold-pressing technique.The Janus structure endows ITBCF with the unique“conductive on one side and insulating on the other”property.When the mass ratio of Ti_(3)C_(2)T_(x) and BNNS is 1:1 and the total mass fraction is 70 wt.%,the electrical conductivity(σ)of the Ti_(3)C_(2)T_(x)/CNF side of ITBCF reaches 166.7 S/cm,while the surface resistivity of the BNNS-OH/CNF side is as high as 304 MΩ.After Ca^(2+)ion infiltration,the mechanical properties of ITBCF are significantly enhanced.The tensile strength and modulus of ITBCF are 73.5 MPa and 15.6 GPa,which are increased by 75.9%and 46.2%compared with those of TBCF,respectively.Moreover,ITBCF exhibits outstanding EMI shielding effectiveness(SE)of 57 dB and thermal conductivity(λ)of 9.49 W/(m K).In addition,ITBCF also presents excellent photothermal and photoelectric energy conversion performance.Under simulated solar irradiation with a power density of 120 mW/cm^(2),the surface stabilization temperature reaches up to 65.3°C and the maximum steady state voltage reaches up to 58.2 mV.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62204235。
文摘This paper introduces a high-precision bandgap reference(BGR)designed for battery management systems(BMS),fea-turing an ultra-low temperature coefficient(TC)and line sensitivity(LS).The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset.A low dropout regulator(LDO)and a pre-regula-tor enhance output driving and LS,respectively.Curvature compensation enhances the TC by addressing higher-order nonlinear-ity.These approaches,effective near room temperature,employs trimming at both 20 and 60°C.When combined with fixed cur-vature correction currents,it achieves an ultra-low TC for each chip.Implemented in a CMOS 180 nm process,the BGR occu-pies 0.548 mm²and operates at 2.5 V with 84μA current draw from a 5 V supply.An average TC of 2.69 ppm/℃ with two-point trimming and 0.81 ppm/℃ with multi-point trimming are achieved over the temperature range of-40 to 125℃.It accommo-dates a load current of 1 mA and an LS of 42 ppm/V,making it suitable for precise BMS applications.
基金supported in part by the National Key R&D Program of China under Grant 2020YFB1806104in part by Innovation and Entrepreneurship of Jiangsu Province High-level Talent Program+1 种基金in part by Natural Sciences and Engineering Research Council of Canada (NSERC)the support from Huawei
文摘With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet coverage.Meanwhile,the civil aviation communications have increased dramatically,especially for providing airborne Internet services.However,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service management.Firstly,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight management.Secondly,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service continuity.Finally,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated problem.Simulation results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.
文摘Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers and lacks theoretical guidance.This paper proposes a concise graph representation method based on graph theory for fuel thermal management systems,which can represent all possible connections between subsystems.A generalized optimization algorithm is proposed for fuel thermal management system architecture to minimize the heat sink.This algorithm can autonomously arrange subsystems with heat production differences and efficiently utilize the architecture of the fuel heat sink.At the same time,two evaluation indices are proposed from the perspective of subsystems.These indices intuitively and clearly show that the reason for the high efficiency of heat sink utilization is the balanced and moderate cooling of each subsystem and verify the rationality of the architecture optimization method.A set of simulations are also conducted,which demonstrate that the fuel tank temperature has no effect on the performance of the architecture.This paper provides a reference for the architectural design of aircraft fuel thermal management systems.The metrics used in this paper can also be utilized to evaluate the existing architecture.
基金supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number(R.G.P.2/93/45).
文摘Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.
文摘Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.
基金Supported by Guiding Project of Hebei Provincial Health Commission,No.20201190 and 20180220.
文摘BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.
基金Supported by National Research Foundation of Korea,No.NRF-2021S1A5A8062526.
文摘This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
