AIM:To investigate decisional conflict among patients diagnosed with primary angle-closure suspect(PACS)or primary angle-closure(PAC)who are considering laser peripheral iridotomy(LPI)treatment.METHODS:A total of 111 ...AIM:To investigate decisional conflict among patients diagnosed with primary angle-closure suspect(PACS)or primary angle-closure(PAC)who are considering laser peripheral iridotomy(LPI)treatment.METHODS:A total of 111 individuals diagnosed with PACS or PAC were selected through convenient sampling from March 2023 to December 2023.These participants then completed a general information questionnaire and the Decision Conflict Scale.Data analysis was performed using multiple linear regression to reveal factors influencing decisional conflict.RESULTS:The mean Decisional Conflict Score among patients with PACS or PAC was 48.58±10.01,with 99.1%of these individuals reporting experiencing decisional conflict.Multiple linear regression analysis revealed that females(P=0.002)and patients with a shorter duration of the disease(P=0.006)had higher levels of decisional conflict.Additionally,patients diagnosed during medical visits(P=0.049),those who refused LPI treatment(P=0.032),and individuals facing significant economic burdens related to medical expenses(P=0.005)exhibited higher levels of decisional conflict.Furthermore,patients who preferred to make medical decisions independently(P=0.023)and those who favored involving family members in decisionmaking(P=0.005)experienced increased levels of decisional conflict.CONCLUSION:Patients with PACS or PAC who undergo LPI treatment often encounter significant decisional conflict.Healthcare professionals should thoroughly assess a range of factors that influence this conflict,including gender,duration of disease,method of diagnosis acquisition,LPI treatment,economic burden of medical expenses,and patient preferences regarding medical decision-making.By considering these variables,tailored decision support can be developed to address individual patient needs,ultimately reducing decisional conflict and optimizing the quality of decisions made regarding treatment options.展开更多
Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complication...Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complications,including decompensation,bleeding and liver cancer.Formation and accumulation of fibrosis in the liver is the common pathway that leads to an evolutive liver disease.Precise definition of liver fibrosis stage is essential for management of the patient in clinical practice since the presence of bridging fibrosis represents a strong indication for antiviral therapy for chronic viral hepatitis,while cirrhosis requires a specif ic follow-up including screening for esophageal varices and hepatocellular carcinoma.Liver biopsy has always represented the standard of reference for assessment of hepatic fibrosis but it has some limitations being invasive,costly and prone to sampling errors.Recently,blood markers and instrumental methods have been proposed for the non-invasive assessment of liver fibrosis.However,there are still some doubts as to their implementation in clinical practice and a real consensus on how and when to use them is not still available.This is due to an unsatisfactory accuracy for some of them,and to an incomplete validation for others.Some studies suggest that performance of non-invasive methods for liver fibrosis assessment may increase when they are combined.Combination algorithms of non-invasive methods for assessing liver fibrosis may represent a rational and reliable approach to implement non-invasive assessment of liver fibrosis in clinical practice and to reduce rather than abolish liver biopsies.展开更多
The thalamus and central dopamine signaling have been shown to play important roles in high-level cognitive processes including impulsivity. However, little is known about the role of dopamine receptors in the thalamu...The thalamus and central dopamine signaling have been shown to play important roles in high-level cognitive processes including impulsivity. However, little is known about the role of dopamine receptors in the thalamus in decisional impulsivity. In the present study,rats were tested using a delay discounting task and divided into three groups： high impulsivity（HI）, medium impulsivity（MI）, and low impulsivity（LI）. Subsequent in vivo voxel-based magnetic resonance imaging revealed that the HI rats displayed a markedly reduced density of gray matter in the lateral thalamus compared with the LI rats. In the MI rats, the dopamine D1 receptor antagonist SCH23390 or the D2 receptor antagonist eticlopride was microinjected into the lateral thalamus. SCH23390 significantly decreased their choice of a large, delayed reward and increased their omission of lever presses. In contrast,eticlopride increased the choice of a large, delayed reward but had no effect on the omissions. Together, our results indicate that the lateral thalamus is involved in decisional impulsivity, and dopamine D1 and D2 receptors in the lateral thalamus have distinct effects on decisional impulsive behaviors in rats. These results provide a new insightinto the dopamine signaling in the lateral thalamus in decisional impulsivity.展开更多
BACKGROUND The interest in shared decision making has increased considerably over the last couple of decades.Decision aids(DAs)can help in shared decision making.Especially when there is more than one reasonable optio...BACKGROUND The interest in shared decision making has increased considerably over the last couple of decades.Decision aids(DAs)can help in shared decision making.Especially when there is more than one reasonable option and outcomes between treatments are comparable.AIM To investigate if the use of DAs decreases decisional conflict in patients when choosing treatment for knee or hip osteoarthritis(OA).METHODS In this multi-center unblinded randomized controlled trial of patients with knee or hip OA were included from four secondary and tertiary referral centers.Onehundred-thirty-one patients who consulted an orthopedic surgeon for the first time with knee or hip OA were included between December 2014 and January 2016.After the first consultation,patients were randomly assigned by a computer to the control group which was treated according to standard care,or to the intervention group which was treated with standard care and provided with a DA.After the first consultation,patients were asked to complete questionnaires about decisional conflict(DCS),satisfaction,anxiety(PASS-20),gained knowledge,stage of decision making and preferred treatment.Follow-up was carried out after 26 wk and evaluated decisional conflict,satisfaction,anxiety,health outcomes(HOOS/KOOS),quality of life(EQ5D)and chosen treatment.RESULTS After the first consultation,patients in the intervention group(mean DCS:25 out of 100,SD:13)had significantly(P value:0.00)less decisional conflict compared to patients in the control group(mean DCS:39 out of 100,SD 11).The mean satisfaction score for the given information(7.6 out of 10,SD:1.8 vs 8.6 out of 10,SD:1.1)(P value:0.00),mean satisfaction score with the physician(8.3 out of 10,SD:1.7 vs 8.9 out of 10,SD:0.9)(P value:0.01)and the mean knowledge score(3.3 out of 4,SD:0.9 vs 3.7 out of,SD:0.6)(P value:0.01)were all significantly higher in the intervention group.At 26-wk follow-up,only 75 of 131 patients(57%)were available for analysis.This sample is too small for meaningful analysis.CONCLUSION Providing patients with an additional DA may have a positive effect on decisional conflict after the first consultation.Due to loss to follow-up we are unsure if this effect remains over time.展开更多
Based on decisional Difiie-Hcllman problem, we propose a simpleproxy-protected signature scheme In the random oracle model, we also carry out the strict securityproof for the proposed scheme. The security of the propo...Based on decisional Difiie-Hcllman problem, we propose a simpleproxy-protected signature scheme In the random oracle model, we also carry out the strict securityproof for the proposed scheme. The security of the proposed scheme is not loosely related to thediscrete logarithm assumption hut tightly related to the decisional Diffie-Hellman assumption in therandom oracle model.展开更多
Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When ...Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged a...The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged as a central force shaping youth decision-making across work,consumption,relationships and lifestyle choices.Unlike traditional economic rationality that prioritises income and material security,emotional value focuses on how choices make individuals feel and how they align with personal meaning.This shift is particularly evident in rapidly transforming societies such as China and Ghana,where economic restructuring,globalisation and social change have reshaped pathways to adulthood.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudi...Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.展开更多
Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When opera...Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.展开更多
Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strateg...Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.展开更多
Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high dat...Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high data efficiency and mission reliability.We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update,yielding a Markov state that couples agent poses with a probabilistic target field.On this belief–MDP we introduce a segment-conditioned latent-intent framework,in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on the fixed intent;both components are trained end-to-end with proximal updates under a centralized critic.On the 50×50 grid,coverage and discovery convergence times are reduced by up to 48%and 40%relative to a flat actor-critic benchmark,and the aggregated convergence metric improves by about 12%compared with a stateof-the-art hierarchical method.Qualitative analyses further reveal stable spatial sectorization,low path overlap,and fuel-aware patrolling,indicating that segment-conditioned latent intents provide an effective and scalable mechanism for coordinated multi-UAV search.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment...Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.展开更多
During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once ag...During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once again,3,000 exhibitors from across the globe placed their trust in the industry’s central platform in Frankfurt,presenting current collections,materials and textile solutions for holistic interior design to approximately 47,000 buyers.Under the motto“Lead the Change”,Heimtextil brought evolving market dynamics,Artificial Intelligence(AI)and new business opportunities to life.The focus was on progressive design approaches,visionary talents,functional textiles and new hospitality concepts shaping the future of interior design.A tangible sense of confidence and a clear commitment to Heimtextil as a strong industry partner resonated throughout the exhibition halls.展开更多
Mega low Earth orbit(LEO)satellite networks serve as effective complements to terrestrial networks.However,the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users.Moreover...Mega low Earth orbit(LEO)satellite networks serve as effective complements to terrestrial networks.However,the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users.Moreover,there are both ascending and descending segments in widely deployed walker-delta constellations.Even if the locations of users do not change,when the access satellites of the communicating parties are not in the same ascending or descending segment,the end-to-end latency between them will increase.To address this challenge,the self-decision handover(SDH)strategy and the joint decision handover(JDH)strategy are proposed,and they both incorporate the routing hops as a crucial handover criterion to minimize the end-to-end latency.In addition,the shortest route hop-count algorithm is designed to assist in the handover decision-making process.Simulations demonstrate that the proposed handover strategies outperform the traditional handover strategies in terms of the number of handovers and end-to-end latency.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
基金Supported by Basic Scientific Research Projects of Wenzhou(No.Y20220155).
文摘AIM:To investigate decisional conflict among patients diagnosed with primary angle-closure suspect(PACS)or primary angle-closure(PAC)who are considering laser peripheral iridotomy(LPI)treatment.METHODS:A total of 111 individuals diagnosed with PACS or PAC were selected through convenient sampling from March 2023 to December 2023.These participants then completed a general information questionnaire and the Decision Conflict Scale.Data analysis was performed using multiple linear regression to reveal factors influencing decisional conflict.RESULTS:The mean Decisional Conflict Score among patients with PACS or PAC was 48.58±10.01,with 99.1%of these individuals reporting experiencing decisional conflict.Multiple linear regression analysis revealed that females(P=0.002)and patients with a shorter duration of the disease(P=0.006)had higher levels of decisional conflict.Additionally,patients diagnosed during medical visits(P=0.049),those who refused LPI treatment(P=0.032),and individuals facing significant economic burdens related to medical expenses(P=0.005)exhibited higher levels of decisional conflict.Furthermore,patients who preferred to make medical decisions independently(P=0.023)and those who favored involving family members in decisionmaking(P=0.005)experienced increased levels of decisional conflict.CONCLUSION:Patients with PACS or PAC who undergo LPI treatment often encounter significant decisional conflict.Healthcare professionals should thoroughly assess a range of factors that influence this conflict,including gender,duration of disease,method of diagnosis acquisition,LPI treatment,economic burden of medical expenses,and patient preferences regarding medical decision-making.By considering these variables,tailored decision support can be developed to address individual patient needs,ultimately reducing decisional conflict and optimizing the quality of decisions made regarding treatment options.
基金Supported by An unrestricted grant from Roche-Italia
文摘Chronic hepatitis B and C together with alcoholic and non-alcoholic fatty liver diseases represent the major causes of progressive liver disease that can eventually evolve into cirrhosis and its end-stage complications,including decompensation,bleeding and liver cancer.Formation and accumulation of fibrosis in the liver is the common pathway that leads to an evolutive liver disease.Precise definition of liver fibrosis stage is essential for management of the patient in clinical practice since the presence of bridging fibrosis represents a strong indication for antiviral therapy for chronic viral hepatitis,while cirrhosis requires a specif ic follow-up including screening for esophageal varices and hepatocellular carcinoma.Liver biopsy has always represented the standard of reference for assessment of hepatic fibrosis but it has some limitations being invasive,costly and prone to sampling errors.Recently,blood markers and instrumental methods have been proposed for the non-invasive assessment of liver fibrosis.However,there are still some doubts as to their implementation in clinical practice and a real consensus on how and when to use them is not still available.This is due to an unsatisfactory accuracy for some of them,and to an incomplete validation for others.Some studies suggest that performance of non-invasive methods for liver fibrosis assessment may increase when they are combined.Combination algorithms of non-invasive methods for assessing liver fibrosis may represent a rational and reliable approach to implement non-invasive assessment of liver fibrosis in clinical practice and to reduce rather than abolish liver biopsies.
基金supported by the National Natural Science Foundation(81471353)the National Basic Research Program of China(2015CB553500)the Science Fund for Creative Research Groups from of National Natural Science Foundation of China(81521063)
文摘The thalamus and central dopamine signaling have been shown to play important roles in high-level cognitive processes including impulsivity. However, little is known about the role of dopamine receptors in the thalamus in decisional impulsivity. In the present study,rats were tested using a delay discounting task and divided into three groups： high impulsivity（HI）, medium impulsivity（MI）, and low impulsivity（LI）. Subsequent in vivo voxel-based magnetic resonance imaging revealed that the HI rats displayed a markedly reduced density of gray matter in the lateral thalamus compared with the LI rats. In the MI rats, the dopamine D1 receptor antagonist SCH23390 or the D2 receptor antagonist eticlopride was microinjected into the lateral thalamus. SCH23390 significantly decreased their choice of a large, delayed reward and increased their omission of lever presses. In contrast,eticlopride increased the choice of a large, delayed reward but had no effect on the omissions. Together, our results indicate that the lateral thalamus is involved in decisional impulsivity, and dopamine D1 and D2 receptors in the lateral thalamus have distinct effects on decisional impulsive behaviors in rats. These results provide a new insightinto the dopamine signaling in the lateral thalamus in decisional impulsivity.
文摘BACKGROUND The interest in shared decision making has increased considerably over the last couple of decades.Decision aids(DAs)can help in shared decision making.Especially when there is more than one reasonable option and outcomes between treatments are comparable.AIM To investigate if the use of DAs decreases decisional conflict in patients when choosing treatment for knee or hip osteoarthritis(OA).METHODS In this multi-center unblinded randomized controlled trial of patients with knee or hip OA were included from four secondary and tertiary referral centers.Onehundred-thirty-one patients who consulted an orthopedic surgeon for the first time with knee or hip OA were included between December 2014 and January 2016.After the first consultation,patients were randomly assigned by a computer to the control group which was treated according to standard care,or to the intervention group which was treated with standard care and provided with a DA.After the first consultation,patients were asked to complete questionnaires about decisional conflict(DCS),satisfaction,anxiety(PASS-20),gained knowledge,stage of decision making and preferred treatment.Follow-up was carried out after 26 wk and evaluated decisional conflict,satisfaction,anxiety,health outcomes(HOOS/KOOS),quality of life(EQ5D)and chosen treatment.RESULTS After the first consultation,patients in the intervention group(mean DCS:25 out of 100,SD:13)had significantly(P value:0.00)less decisional conflict compared to patients in the control group(mean DCS:39 out of 100,SD 11).The mean satisfaction score for the given information(7.6 out of 10,SD:1.8 vs 8.6 out of 10,SD:1.1)(P value:0.00),mean satisfaction score with the physician(8.3 out of 10,SD:1.7 vs 8.9 out of 10,SD:0.9)(P value:0.01)and the mean knowledge score(3.3 out of 4,SD:0.9 vs 3.7 out of,SD:0.6)(P value:0.01)were all significantly higher in the intervention group.At 26-wk follow-up,only 75 of 131 patients(57%)were available for analysis.This sample is too small for meaningful analysis.CONCLUSION Providing patients with an additional DA may have a positive effect on decisional conflict after the first consultation.Due to loss to follow-up we are unsure if this effect remains over time.
文摘Based on decisional Difiie-Hcllman problem, we propose a simpleproxy-protected signature scheme In the random oracle model, we also carry out the strict securityproof for the proposed scheme. The security of the proposed scheme is not loosely related to thediscrete logarithm assumption hut tightly related to the decisional Diffie-Hellman assumption in therandom oracle model.
基金funded by Henan Key Laboratory of General Aviation Technology,grant number ZHKF-240202。
文摘Evaluating Unmanned Aerial Vehicle(UAV)systems within a System-of-Systems(SoS)environment helps clarify their contribution to the overall combat capability and supports effectiveness-oriented system optimization.When assessing decision systems in such an environment,cross-level modeling and simulation are required,which often face a trade-off between low modeling cost and high simulation accuracy,while the credibility of results remains challenging to ensure.To address these issues,this study proposes a hybrid-granularity Hardware-In-the-Loop(HIL)SoS environment construction method based on Graphical Evaluation and Review Technique(GERT).The method employs GERT to analyze the relationships between simulation systems,the System Under Test(SUT),and mission outcomes,thereby determining the required model precision for different systems.A dynamic resource allocation algorithm is applied to adjust model granularity on demand,ensuring high-fidelity simulation under constrained total cost.Additionally,GERT estimates the computational frequency and communication bandwidth requirements of the SUT,guiding hardware selection to enhance simulation credibility.A UAV maritime combat case study was conducted for validation.The results demonstrate that,compared to the flat modeling approach,the hybrid-granularity scenario based on GERT analysis achieves higher simulation accuracy with lower overall model complexity.The coefficient of variation in evaluation results significantly decreases in HIL simulations compared to virtual simulations,confirming improved credibility.Under the hybrid-granularity HIL scenario,the decision system was evaluated from an effectiveness perspective,identifying the most sensitive performance parameter.Subsequent targeted optimization led to an 11.90%improvement in effectiveness,validating the method's practical utility.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
文摘The Double Take column looks at a single topic from an African and Chinese perspective.This month,we discuss how to understand the growing emphasis on emotional returns among young people.Emotional value has emerged as a central force shaping youth decision-making across work,consumption,relationships and lifestyle choices.Unlike traditional economic rationality that prioritises income and material security,emotional value focuses on how choices make individuals feel and how they align with personal meaning.This shift is particularly evident in rapidly transforming societies such as China and Ghana,where economic restructuring,globalisation and social change have reshaped pathways to adulthood.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
文摘Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.
文摘Embodied intelligent systems integrate perception,control,and decision-making within physical agents,and have become a cornerstone of modern aerospace,autonomous driving,and cooperative robotic applications.When operating in uncertain and dynamic environments,such systems must address challenges arising from incomplete sensing,unpredictable maneuvers,communication constraints,disturbances,and evolving network structures.
基金financially supported by the National Key Research and Development Program of China(2024YFD1700104 and 2022YFE0209200-03)the National Natural Science Foundation of China(42161144002 and 41977156)+3 种基金the Guangxi Natural Science Foundation,China(2022GXNSFBA035625)the Guangxi Technology Base and Talent Subject,China(Guike AD22035927)the Shandong Key Research and Development Project,China(2022TZXD0045)the State Key Laboratory of Earth System Numerical Modeling and Application,Institute of Atmospheric Physics,Chinese Academy of Sciences。
文摘Intervention strategies to control non-point source nitrogen(N)and phosphorus(P)pollution in agriculture are expensive and there is a trade-off between engineering cost and treatment effectiveness.Implementing strategies often result in unsatisfactory outcomes and massive engineering costs when managing diffusive pollution in agricultural catchments.To address this issue,this paper proposes a robust,handy,catchment N&P decision support system(CNPDSS),an Android-based smartphone system integrated with a web-based geographic information system(GIS).The CNPDSS aims to provide artificial intelligence-driven decisions that minimize N&P loadings and engineering costs for mitigating pollution in agricultural catchments.It consists of four components:a general user interface(GUI),GIS,N&P pollution modeling(NPPM),and a DSS.The CNPDSS simplifies the GUI and integrates GIS modules to create a user-friendly interface,enabling non-professional users to operate the system easily through intuitive actions.The NPPM uses straightforward empirical models to predict N&P loadings,enhancing efficiency by avoiding excessive parameters.Taking into account the N&P movement pathway in the catchment,the DSS incorporates three control measures:source reduction in farmland(before migration stage),process retention by ecological ditch(midway transport stage),and down-end purification by constructed wetland(waterbody discharge stage),to formulate a comprehensive ternary controlling strategy.To optimize the cost-effectiveness of any proposed N&P control strategies for sub-catchments,a differential evolution algorithm(DEA)is employed in CNPDSS to carry out a dual-objective decision-making optimization computation.In this study,the CNPDSS is applied to a case study in an agricultural catchment in Central China to develop the most cost-effective ternary N&P control strategies that ensure the catchment water quality within Criterion Ⅲ of the Chinese Surface Water Quality Standard GB3838-2002 is met(total N concentration≤1.0 mg L^(-1)and total P concentration≤0.2 mg L^(-1)).Our results demonstrate that the CNPDSS is feasible and also possesses an adaptive design and flexible architecture to enable its generalization and extension to support strong hands-on applications in other catchments.
文摘Cooperative multi-UAV search requires jointly optimizing wide-area coverage,rapid target discovery,and endurance under sensing and motion constraints.Resolving this coupling enables scalable coordination with high data efficiency and mission reliability.We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update,yielding a Markov state that couples agent poses with a probabilistic target field.On this belief–MDP we introduce a segment-conditioned latent-intent framework,in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on the fixed intent;both components are trained end-to-end with proximal updates under a centralized critic.On the 50×50 grid,coverage and discovery convergence times are reduced by up to 48%and 40%relative to a flat actor-critic benchmark,and the aggregated convergence metric improves by about 12%compared with a stateof-the-art hierarchical method.Qualitative analyses further reveal stable spatial sectorization,low path overlap,and fuel-aware patrolling,indicating that segment-conditioned latent intents provide an effective and scalable mechanism for coordinated multi-UAV search.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.
文摘During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once again,3,000 exhibitors from across the globe placed their trust in the industry’s central platform in Frankfurt,presenting current collections,materials and textile solutions for holistic interior design to approximately 47,000 buyers.Under the motto“Lead the Change”,Heimtextil brought evolving market dynamics,Artificial Intelligence(AI)and new business opportunities to life.The focus was on progressive design approaches,visionary talents,functional textiles and new hospitality concepts shaping the future of interior design.A tangible sense of confidence and a clear commitment to Heimtextil as a strong industry partner resonated throughout the exhibition halls.
基金supported by the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster(MS01240103)the National Natural Science Foundation of China(62071146,62431009)+2 种基金the National 2011 Collaborative Innovation Center of Wireless Communication Technologies(2242022k60006)the Research Project Fund of Songjiang Laboratory(SL20230104)Heilongjiang Province Postdoctoral General Foundation(LBH-Z22133)。
文摘Mega low Earth orbit(LEO)satellite networks serve as effective complements to terrestrial networks.However,the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users.Moreover,there are both ascending and descending segments in widely deployed walker-delta constellations.Even if the locations of users do not change,when the access satellites of the communicating parties are not in the same ascending or descending segment,the end-to-end latency between them will increase.To address this challenge,the self-decision handover(SDH)strategy and the joint decision handover(JDH)strategy are proposed,and they both incorporate the routing hops as a crucial handover criterion to minimize the end-to-end latency.In addition,the shortest route hop-count algorithm is designed to assist in the handover decision-making process.Simulations demonstrate that the proposed handover strategies outperform the traditional handover strategies in terms of the number of handovers and end-to-end latency.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
