The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated....The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
The microstructure of single crystal superalloy is relatively simple,consisting primarily ofγdendrites andγ/γ′eutectics.During the directional solidification process of Ni-based single crystal superalloys,withdraw...The microstructure of single crystal superalloy is relatively simple,consisting primarily ofγdendrites andγ/γ′eutectics.During the directional solidification process of Ni-based single crystal superalloys,withdrawal rate is a critical parameter affecting the spatial distribution ofγ/γ′eutectic along gravity direction.The results show that theγ/γ′eutectic fraction of the upper platform surface is always higher than that of the lower one,regardless of withdrawal rate.As the withdrawal rate decreases,there is a significant increase inγ/γ′eutectic fraction on the upper surface,while it decreases on the lower surface.The upward accumulation ofγ/γ′eutectic becomes more severe as the withdrawal rate decreases.It is also found that the percentage of Al+Ta is positively correlated with theγ/γ′eutectic fraction.Thermo-solute convection of Al and Ta solutes in the solidification front is the prime reason for the non-uniform distribution of eutectic.The non-uniform distribution ofγ/γ′eutectic cannot be eliminated even after subsequent solution heat treatment,resulting in excess eutectic on the upper surface and thus leading to the scrapping of the blade.展开更多
BACKGROUND Psychotic disorders are characterized by both positive symptoms(hallucinations,delusions)and negative symptoms(emotional blunting,anhedonia)that impair daily functioning.While antipsychotic drugs and psycho...BACKGROUND Psychotic disorders are characterized by both positive symptoms(hallucinations,delusions)and negative symptoms(emotional blunting,anhedonia)that impair daily functioning.While antipsychotic drugs and psychological interventions are effective when addressing positive symptoms,treatment of negative symptoms remains an ongoing challenge.Mindfulness-based interventions(MBIs)have been shown to reduce negative psychotic symptoms.However,as negative psychotic symptoms are assessed as a sole entity rather than a sum of manifestations,the effect of MBIs remains unclear.AIM To examine the effects of MBI in addition to integrated rehabilitation treatment(IRT)for people experiencing psychosis on each of the negative psychotic symptoms.METHODS A randomized controlled clinical trial with preintervention and postintervention measures was designed.The main outcome variable was negative psychotic symptoms assessed through the seven subscales of the Spanish version of the positive and negative syndrome scale.Data were analyzed using a repeated measures analysis of variance and reliable change index calculation.RESULTS There were no statistical differences between groups at the preintervention assessment.Statistically significant differences were found after MBI for the time in emotional withdrawal(F=37.75,P<0.001,η2=0.437)and social withdrawal(F=37.75,P<0.001,η2=0.437).CONCLUSION MBI added to IRT reduced the lack of interest and involvement with affective commitment to daily life activities,and interest and engagement in social activities increased.These negative psychotic symptoms were not improved by IRT alone.展开更多
BACKGROUND Adenoma detection rate(ADR),a key colonoscopy quality metric,varies with patient demographics and procedural factors.AIM To identify independent predictors of≥25%ADR,develop a risk model,and propose withdr...BACKGROUND Adenoma detection rate(ADR),a key colonoscopy quality metric,varies with patient demographics and procedural factors.AIM To identify independent predictors of≥25%ADR,develop a risk model,and propose withdrawal durations based on different insertion times.METHODS We retrospectively analyzed 830 cases using logistic regression and identified four key factors,validated in a prospective cohort of 5699 patients.Their importance was confirmed using random forest(RF),extreme gradient boosting(XGBoost)and light gradient boosting machine(LightGBM).Attempts to determine targetachieving withdrawal time by grouping cases based on insertion time and Cox regression were inconclusive.Using the 5699-case dataset,we developed a predictive model combining support vector machine(SVM)with XGBoost.We built a Shiny app using this model for clinical application.RESULTS Multivariate logistic regression identified age[odds ratio(OR)=1.05;95%confidence interval(CI):1.03-1.08;P<0.001],male(OR=1.79;95%CI:1.32-2.41;P=0.005),higher endoscopist experience(OR=1.79;95%CI:1.20-2.68;P=0.005),and longer withdrawal time(P<0.001)as independent risk factors for colorectal adenoma.A nomogram demonstrated strong discrimination[area under the curve(AUC)=0.720],with robust calibration and decision-curve performance.Feature importance via RF,XGBoost,and LightGBM confirmed key predictors.A hybrid model combining SVM regression for withdrawal-time estimation and XGBoost classification achieved stable results,with XGBoost reporting AUCs of 0.640 in training and 0.610 in testing,and similar validation outcomes.Deployed via a Shiny app for clinical use.However,model discrimination was modest(AUC:0.61-0.64),suggesting that clinical utility requires further refinement.CONCLUSION A hybrid SVM-XGBoost model using four key endoscopic factors was independently validated and is available as a Shiny app,delivering real-time decision support to streamline endoscopy and enhance clinical outcomes.展开更多
This invited commentary discusses the recent study by Atay et al,which investigated relapse rates following the spontaneous withdrawal of maintenance 5-aminosalicylates in ulcerative colitis.The discussion focuses,in ...This invited commentary discusses the recent study by Atay et al,which investigated relapse rates following the spontaneous withdrawal of maintenance 5-aminosalicylates in ulcerative colitis.The discussion focuses,in this patient setting,on the possible reasons that might prompt clinicians to pursue such exit strategies,and on the importance of exercising caution in these decisions,given the extremely narrow subsets of patients for whom international guidelines allow any degree of leeway.展开更多
BACKGROUND 5-aminosalicylates(5-ASA)are the primary treatment for mild to moderate ulcerative colitis(UC).Maintenance therapy with 5-ASA has been shown to reduce both the risk of relapse and colorectal cancer.AIM To e...BACKGROUND 5-aminosalicylates(5-ASA)are the primary treatment for mild to moderate ulcerative colitis(UC).Maintenance therapy with 5-ASA has been shown to reduce both the risk of relapse and colorectal cancer.AIM To evaluate the outcomes of 5-ASA withdrawal due to non-adherence in UC patients while in remission on monotherapy.METHODS Adult patients with UC who were followed up between July 2019 and April 2025 were screened.Patients in remission receiving 5-ASA monotherapy who experienced treatment withdrawal due to non-adherence were included in this study.RESULTS Among 880 patients with UC,30(3.4%)had 5-ASA withdrawal due to nonadherence while in remission on monotherapy.Twelve patients(40%)had disease relapse after a median of 20 months.The rate of patients in remission was 89%in the first year,decreasing to 73%in the second year,and to 64%in the third year.There were no significant differences between patients with and without relapse in terms of demographics,disease extent,remission duration before 5-ASA withdrawal,previous medications,steroid dependence,5-ASA formulation,baseline inflammatory markers,or partial and endoscopic Mayo scores.Most patients(75%)who experienced relapse were successfully treated with 5-ASA monotherapy,while one-fourth of them required corticosteroids.No patients required biologic agents,hospitalization,or surgical intervention.CONCLUSION Intermittent therapy may be safe and feasible for UC patients,especially those in long-term remission,with treatment interruption up to one year considered acceptable.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning secur...An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.展开更多
基金supported by the National Natural Science Foundation of China(No.52090041).
文摘The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金Shenzhen Science and Technology Program(JSGG20220831092800001)。
文摘The microstructure of single crystal superalloy is relatively simple,consisting primarily ofγdendrites andγ/γ′eutectics.During the directional solidification process of Ni-based single crystal superalloys,withdrawal rate is a critical parameter affecting the spatial distribution ofγ/γ′eutectic along gravity direction.The results show that theγ/γ′eutectic fraction of the upper platform surface is always higher than that of the lower one,regardless of withdrawal rate.As the withdrawal rate decreases,there is a significant increase inγ/γ′eutectic fraction on the upper surface,while it decreases on the lower surface.The upward accumulation ofγ/γ′eutectic becomes more severe as the withdrawal rate decreases.It is also found that the percentage of Al+Ta is positively correlated with theγ/γ′eutectic fraction.Thermo-solute convection of Al and Ta solutes in the solidification front is the prime reason for the non-uniform distribution of eutectic.The non-uniform distribution ofγ/γ′eutectic cannot be eliminated even after subsequent solution heat treatment,resulting in excess eutectic on the upper surface and thus leading to the scrapping of the blade.
基金Supported by the R+D Project funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and by FEDER,EU,No.PID2021-122987OA-I00.
文摘BACKGROUND Psychotic disorders are characterized by both positive symptoms(hallucinations,delusions)and negative symptoms(emotional blunting,anhedonia)that impair daily functioning.While antipsychotic drugs and psychological interventions are effective when addressing positive symptoms,treatment of negative symptoms remains an ongoing challenge.Mindfulness-based interventions(MBIs)have been shown to reduce negative psychotic symptoms.However,as negative psychotic symptoms are assessed as a sole entity rather than a sum of manifestations,the effect of MBIs remains unclear.AIM To examine the effects of MBI in addition to integrated rehabilitation treatment(IRT)for people experiencing psychosis on each of the negative psychotic symptoms.METHODS A randomized controlled clinical trial with preintervention and postintervention measures was designed.The main outcome variable was negative psychotic symptoms assessed through the seven subscales of the Spanish version of the positive and negative syndrome scale.Data were analyzed using a repeated measures analysis of variance and reliable change index calculation.RESULTS There were no statistical differences between groups at the preintervention assessment.Statistically significant differences were found after MBI for the time in emotional withdrawal(F=37.75,P<0.001,η2=0.437)and social withdrawal(F=37.75,P<0.001,η2=0.437).CONCLUSION MBI added to IRT reduced the lack of interest and involvement with affective commitment to daily life activities,and interest and engagement in social activities increased.These negative psychotic symptoms were not improved by IRT alone.
基金Supported by the Young and Middle-Aged Talents Program of Wuxi Health Commission,No.BJ2020011Cohort Research Program of Wuxi Medical Center,Nanjing Medical University,No.WMCC202314Wuxi People’s Hospital 2024“Wild Goose Array Talent”Reserve Discipline Leader,No.2024-YZ-HBDTR-YC-2024.
文摘BACKGROUND Adenoma detection rate(ADR),a key colonoscopy quality metric,varies with patient demographics and procedural factors.AIM To identify independent predictors of≥25%ADR,develop a risk model,and propose withdrawal durations based on different insertion times.METHODS We retrospectively analyzed 830 cases using logistic regression and identified four key factors,validated in a prospective cohort of 5699 patients.Their importance was confirmed using random forest(RF),extreme gradient boosting(XGBoost)and light gradient boosting machine(LightGBM).Attempts to determine targetachieving withdrawal time by grouping cases based on insertion time and Cox regression were inconclusive.Using the 5699-case dataset,we developed a predictive model combining support vector machine(SVM)with XGBoost.We built a Shiny app using this model for clinical application.RESULTS Multivariate logistic regression identified age[odds ratio(OR)=1.05;95%confidence interval(CI):1.03-1.08;P<0.001],male(OR=1.79;95%CI:1.32-2.41;P=0.005),higher endoscopist experience(OR=1.79;95%CI:1.20-2.68;P=0.005),and longer withdrawal time(P<0.001)as independent risk factors for colorectal adenoma.A nomogram demonstrated strong discrimination[area under the curve(AUC)=0.720],with robust calibration and decision-curve performance.Feature importance via RF,XGBoost,and LightGBM confirmed key predictors.A hybrid model combining SVM regression for withdrawal-time estimation and XGBoost classification achieved stable results,with XGBoost reporting AUCs of 0.640 in training and 0.610 in testing,and similar validation outcomes.Deployed via a Shiny app for clinical use.However,model discrimination was modest(AUC:0.61-0.64),suggesting that clinical utility requires further refinement.CONCLUSION A hybrid SVM-XGBoost model using four key endoscopic factors was independently validated and is available as a Shiny app,delivering real-time decision support to streamline endoscopy and enhance clinical outcomes.
文摘This invited commentary discusses the recent study by Atay et al,which investigated relapse rates following the spontaneous withdrawal of maintenance 5-aminosalicylates in ulcerative colitis.The discussion focuses,in this patient setting,on the possible reasons that might prompt clinicians to pursue such exit strategies,and on the importance of exercising caution in these decisions,given the extremely narrow subsets of patients for whom international guidelines allow any degree of leeway.
文摘BACKGROUND 5-aminosalicylates(5-ASA)are the primary treatment for mild to moderate ulcerative colitis(UC).Maintenance therapy with 5-ASA has been shown to reduce both the risk of relapse and colorectal cancer.AIM To evaluate the outcomes of 5-ASA withdrawal due to non-adherence in UC patients while in remission on monotherapy.METHODS Adult patients with UC who were followed up between July 2019 and April 2025 were screened.Patients in remission receiving 5-ASA monotherapy who experienced treatment withdrawal due to non-adherence were included in this study.RESULTS Among 880 patients with UC,30(3.4%)had 5-ASA withdrawal due to nonadherence while in remission on monotherapy.Twelve patients(40%)had disease relapse after a median of 20 months.The rate of patients in remission was 89%in the first year,decreasing to 73%in the second year,and to 64%in the third year.There were no significant differences between patients with and without relapse in terms of demographics,disease extent,remission duration before 5-ASA withdrawal,previous medications,steroid dependence,5-ASA formulation,baseline inflammatory markers,or partial and endoscopic Mayo scores.Most patients(75%)who experienced relapse were successfully treated with 5-ASA monotherapy,while one-fourth of them required corticosteroids.No patients required biologic agents,hospitalization,or surgical intervention.CONCLUSION Intermittent therapy may be safe and feasible for UC patients,especially those in long-term remission,with treatment interruption up to one year considered acceptable.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金The National Natural Science Foundation of China(No.60403027,60773191,70771043)the National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z403)
文摘An access control model is proposed based on the famous Bell-LaPadula （BLP） model.In the proposed model,hierarchical relationships among departments are built,a new concept named post is proposed,and assigning security tags to subjects and objects is greatly simplified.The interoperation among different departments is implemented through assigning multiple security tags to one post, and the more departments are closed on the organization tree,the more secret objects can be exchanged by the staff of the departments.The access control matrices of the department,post and staff are defined.By using the three access control matrices,a multi granularity and flexible discretionary access control policy is implemented.The outstanding merit of the BLP model is inherited,and the new model can guarantee that all the information flow is under control.Finally,our study shows that compared to the BLP model,the proposed model is more flexible.
