AIM To investigate the correlations between clinical outcomes and biopsychological variables in female patients with knee osteoarthritis(OA).METHODS Seventy-seven patients with symptomatic knee OA were enrolled in thi...AIM To investigate the correlations between clinical outcomes and biopsychological variables in female patients with knee osteoarthritis(OA).METHODS Seventy-seven patients with symptomatic knee OA were enrolled in this study.We investigated the age,body mass index(BMI),pain catastrophizing scale(PCS)and radiographic severity of bilateral knees using a Kellgren-Lawrence(K-L)grading system of the subjects.Subsequently,a multiple linear regression was conducted to determine which variables best correlated with main outcomes of knee OA,which were pain severity,moving capacity by measuring timed-up-and-go test and Japanese Knee Osteoarthritis Measure(JKOM).RESULTS We found that the significant contributor to pain severity was PCS(β=0.555)and BMI(β=0.239),to moving capacity was K-L grade(β=0.520)and to PCS(β=0.313),and to a JKOM score was PCS(β=0.485)and K-L grade(β=0.421),respectively.CONCLUSION The results suggest that pain catastrophizing as well as biological factors were associated with clinical outcomes in female patients with knee OA,irrespective of radiographic severity.展开更多
Pain catastrophization is one of the negative emotional factors and an important psychological factor associated with patients with lumbar disc herniation(LDH).Currently,the concept of pain catastrophization of LDH is...Pain catastrophization is one of the negative emotional factors and an important psychological factor associated with patients with lumbar disc herniation(LDH).Currently,the concept of pain catastrophization of LDH is relatively mature abroad;however,there are only few research studies on this in China.To understand the status quo of pain catastrophization(PC)in patients with LDH and its influencing factors,the intervention measures of PC and their efficacy were further analyzed.In the present paper,the research status of PC at home and abroad is briefly expounded,and the influencing factors and clinical intervention measures for PC are analyzed.This paper reviews the concept of PC,the assessment tools,influencing factors,and the relevant intervention measures.In order to evaluate the pain degree of patients,understand the incidence of pain in patients,and improve the cure rate and quality of life of patients,the basic situation of patients with pain disaster is summarized to provide reference for medical personnel.展开更多
Purpose This study aimed to analyze how psychological flexibility,perfectionism,and reported injuries are related to pain catastrophizing in soccer referees.Methods Design:This was a descriptive cross-sectional study....Purpose This study aimed to analyze how psychological flexibility,perfectionism,and reported injuries are related to pain catastrophizing in soccer referees.Methods Design:This was a descriptive cross-sectional study.Setting:Data were collected online from 199 soccer referees.Pain catastrophizing was assessed with the Pain Catastrophizing Scale,psychological inflexibility with the Acceptance and Action Questionnaire,and perfectionism with the Frost Multidimensional Perfectionism Scale.Data were also gathered on other injury-related variables.Results Referees with medium–high scores on psychological inflexibility showed greater pain catastrophizing(t=5.322,P<0.001),rumination(t=4.004,P<0.001),helplessness(t=5.023,P<0.001)and magnification(t=5.590,P<0.001)than those with low scores.Psychological inflexibility emerged as a significant predictor of catastrophizing(β=0.313,P=0.006).A slight relationship was found between perfectionism and catastrophizing.For all subscales,the referees who reported mild–moderate injuries in the last three seasons showed greater pain catastrophizing,while those with severe injuries obtained higher scores on all dimensions of catastrophizing except magnification.Finally,those who reported severe injuries only obtained higher scores on rumination and helplessness.Conclusion These results provide a better understanding of the variables that influence pain perception.Possible interventions are suggested based on the observation that greater psychological flexibility was associated with lower pain catastrophizing,with the specific features of the latter depending on the presence and severity of the injury.展开更多
This study addresses the maneuver evasion problem for medium-to-long-range air-to-air missiles by proposing a KAN-λ-PPO-based evasion algorithm.The algorithm introduces Kolmogorov-Arnold Networks(KAN)to mitigate the ...This study addresses the maneuver evasion problem for medium-to-long-range air-to-air missiles by proposing a KAN-λ-PPO-based evasion algorithm.The algorithm introduces Kolmogorov-Arnold Networks(KAN)to mitigate the catastrophic forgetting issue of Multilayer Perceptrons(MLP)in continual learning,while incorporatingλ-return to resolve sparse reward challenges in evasion scenarios.First,we model the evasion problem withλ-return and present the KAN-λ-PPO algorithm.Subsequently,we establish game environments based on the segmented ballistic characteristics of medium and long range missiles.During training,a joint reward function is designed by combining the miss distance and positional advantages to train the agent.Experiments evaluate four dimensions:(1)Performance comparison between KAN and MLP in value function approximation;(2)Catastrophic forgetting mitigation of KAN-λ-PPO in dual-task scenarios;(3)Continual learning capabilities across multiple evasion scenarios;(4)Quantitative analysis of agent strategy evolution and positional advantages.Empirical results demonstrate that KAN improves value function approximation accuracy by an order of magnitude compared with traditional MLP architectures.In continual learning tasks,the KAN-λ-PPO scheme exhibits significant knowledge retention,achieving performance improvements of 32.7% and 8.6%over MLP baselines in Task1→2 and Task2→3 transitions,respectively.Furthermore,the learned maneuver strategies outperform High-G Barrel Rolls(HGB)and S-maneuver tactics in securing positional advantages while accomplishing evasion.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics...This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.展开更多
Aiming at the problem of dynamic instability of hard-brittle jointed rock surrounding in deep tunnel/roadway engineering,combining with the support concepts of"coupling rigidity with flexibility"and"ove...Aiming at the problem of dynamic instability of hard-brittle jointed rock surrounding in deep tunnel/roadway engineering,combining with the support concepts of"coupling rigidity with flexibility"and"overcoming rigidity by flexibility",the prevention and control method with"rigid-flexible coupling(R-F-C)"was put forward.Through numerical simulation calculation,the impact damage process,acoustic emission(AE)evolution characteristics,and element stress/displacement evolution characteristics of unsupported surrounding rock structure model,rigid supporting surrounding rock structure model,and"R-F-C"supporting surrounding rock structure model under horizontal bidirectional impact loading were compared and analyzed.Based on the theory of stress wave propagation,the dynamic instability catastrophe mechanism of three kinds of supporting structure models induced by horizontal bidirectional impact loading was revealed.Based on the Mohr-Coulomb strength theory,the stress discrimination methods of dynamic catastrophe of surrounding rock induced by horizontal bidirectional impact loading under three kinds of supporting structures were proposed.Combined with the above numerical simulation study,the explosion impact physical and mechanical test of"R-F-C"surrounding rock supporting plate structure was further designed and carried out.Finally,combined with the"conceptual model of ball-cliff potential energy instability",the energy driving theory and energy transformation mechanism of impact-induced rockburst under three kinds of supporting structures were discussed deeply.The research results provided a scientific basis for further promoting the effective application of"R-F-C"supporting structure in the prevention and control of dynamic instability of deep tunnel/roadway surrounding rock.展开更多
Objective:To explore the relationship between pain degree and pain catastrophe and medical coping mode in patients with chronic pain.Methods:A visual analogue score scale,medical coping style questionnaire and pain ca...Objective:To explore the relationship between pain degree and pain catastrophe and medical coping mode in patients with chronic pain.Methods:A visual analogue score scale,medical coping style questionnaire and pain catastrophe scale were used to survey 200 patients in the pain department.Results:The average scores of pain degree of patients with chronic pain were(5.97±2.29),the average score of the total score of the Pain Catastrophe Scale was(21.21±11.56),and the average scores of facing,avoidance and surrender in the Medical Response Style Questionnaire were(17.93±3.4),(16.82±2.4),and(8.87±2.83),respectively.Pain degree was positively correlated with the yield dimension in pain catastrophe and medical coping(p<0.05).The yield dimension of medical coping was positively correlated with pain catastrophe(p<0.05).Medical coping methods played a partial mediating role between pain degree and pain catastrophe,and the mediating effect accounted for 21.59%of the total effect.Conclusion:The pain level of chronic pain patients can affect the level of pain catastrophe through medical coping,and clinical medical staff should guide patients to adopt positive coping methods to promote their healthy recovery.展开更多
The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of d...The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.展开更多
Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Amo...Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Among different approaches,replay methods have shown exceptional promise for this challenge.But performance still baffles from two aspects:(i)data in imbalanced distribution and(ii)networks with semantic inconsistency.First,due to limited memory buffer,there exists imbalance between old and new classes.Direct optimisation would lead feature space skewed towards new classes,resulting in performance degradation on old classes.Second,existing methods normally leverage previous network to regularise the present network.However,the previous network is not trained on new classes,which means that these two networks are semantic inconsistent,leading to misleading guidance information.To address these two problems,we propose BCSD(BiaMix contrastive learning and memory similarity distillation).For imbalanced distribution,we design Biased MixUp,where mixed samples are in high weight from old classes and low weight from new classes.Thus,network learns to push decision boundaries towards new classes.We further leverage label information to construct contrastive learning in order to ensure discriminability.Meanwhile,for semantic inconsistency,we distill knowledge from the previous network by capturing the similarity of new classes in current tasks to old classes from the memory buffer and transfer that knowledge to the present network.Empirical results on various datasets demonstrate its effectiveness and efficiency.展开更多
Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These altern...Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.展开更多
As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique ...As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
In order to study the failure mechanism of backfill and the reasonable matches between backfill and rock mass, and to achieve the object of safe and efficient mining in metal mine, four types of backfills were tested ...In order to study the failure mechanism of backfill and the reasonable matches between backfill and rock mass, and to achieve the object of safe and efficient mining in metal mine, four types of backfills were tested under uniaxial compression loading, with cement?tailing ratios of 0.250:1, 0.125:1, 0.100:1 and 0.083:1, respectively. With the help of the stress?strain curves, the deformation and failure characteristics of different backfills with differing cement?tailing ratios were analyzed. Based on the experimental results, the damage constitutive equations of cemented backfills with four cement?tailing ratios were proposed on the basis of damage mechanics. Moreover, comparative analysis of constitutive model and experimental results were made to verify the reliability of the damage model. In addition, an energy model using catastrophe theory to obtain the instability criteria of system was established to study the interaction between backfill and rock mass, and then the system instability criterion was deduced. The results show that there are different damage characteristics for different backfills, backfills with lower cement?tailing ratio tend to have a lower damage value when stress reaches peak value, and damage more rapidly and more obviously in failure process after peak value of stress; the stiffness and elastic modulus of rock mass with lower strength are more likely to lead to system instability. The results of this work provide a scientific basis for the rational strength design of backfill mine.展开更多
BACKGROUND Interstitial cystitis/bladder pain syndrome(IC/BPS)is an at least 6-mo noninfectious bladder inflammation of unknown origin characterized by chronic suprapubic,abdominal,and/or pelvic pain.Although the term...BACKGROUND Interstitial cystitis/bladder pain syndrome(IC/BPS)is an at least 6-mo noninfectious bladder inflammation of unknown origin characterized by chronic suprapubic,abdominal,and/or pelvic pain.Although the term cystitis suggests an inflammatory or infectious origin,no definite cause has been identified.It occurs in both sexes,but women are twice as much affected.AIM To systematically review evidence of psychiatric/psychological changes in persons with IC/BPS.METHODS Hypothesizing that particular psychological characteristics could underpin IC/BPS,we investigated in three databases the presence of psychiatric symptoms and/or disorders and/or psychological characteristics in patients with IC/BPS using the following strategy:("interstitial cystitis"OR"bladder pain syndrome")AND("mood disorder"OR depressive OR antidepressant OR depression OR depressed OR hyperthymic OR mania OR manic OR rapid cyclasterisk OR dysthymiasterisk OR dysphoriasterisk).RESULTS On September 27,2023,the PubMed search produced 223 articles,CINAHL 62,and the combined PsycLIT/PsycARTICLES/PsycINFO/Psychology and Behavioral Sciences Collection search 36.Search on ClinicalTrials.gov produced 14 studies,of which none had available data.Eligible were peer-reviewed articles reporting psychiatric/psychological symptoms in patients with IC/BPS,i.e.63 articles spanning from 2000 to October 2023.These studies identified depression and anxiety problems in the IC/BPS population,along with sleep problems and the tendency to catastrophizing.CONCLUSION Psychotherapies targeting catastrophizing and life stress emotional awareness and expression reduced perceived pain in women with IC/BPS.Such concepts should be considered when implementing treatments aimed at reducing IC/BPS-related pain.展开更多
Renovation system of urban villages in Xi'an City was evaluated. Influence factors of urban village renovation were analyzed on the basis of brittleness theory, and an evaluation index system established through m...Renovation system of urban villages in Xi'an City was evaluated. Influence factors of urban village renovation were analyzed on the basis of brittleness theory, and an evaluation index system established through multi-level inconsistency decomposing. By incorporating the catastrophe theory with fuzzy mathematical theory, the mathematic model was created, and catastrophe membership function was obtained as well as evaluation results. Policies for the renovation of urban villages and new direction of the renovation were interpreted. The application case proved that catastrophe progression method was objective and effective and it could provide new concepts for the evaluation and adjustment of urban village renovation. Moreover, application of brittleness theory in the research on urban village renovation is of great instruction and reference value for the present urban construction.展开更多
Aim To assess simultaneously various risk states of a system. Methods\ Using the catastrophe and fuzzy theory, the energy and uncertainty in a system are set as two control variables and the function of the system is...Aim To assess simultaneously various risk states of a system. Methods\ Using the catastrophe and fuzzy theory, the energy and uncertainty in a system are set as two control variables and the function of the system is used as the state variable for analysis. Results and Conclusion\ A risk analysis model named the cusp model is presented. Various states regarding the safety of the system such as the accident state, no accident state and miss state can be represented at will on the cusp model.展开更多
文摘AIM To investigate the correlations between clinical outcomes and biopsychological variables in female patients with knee osteoarthritis(OA).METHODS Seventy-seven patients with symptomatic knee OA were enrolled in this study.We investigated the age,body mass index(BMI),pain catastrophizing scale(PCS)and radiographic severity of bilateral knees using a Kellgren-Lawrence(K-L)grading system of the subjects.Subsequently,a multiple linear regression was conducted to determine which variables best correlated with main outcomes of knee OA,which were pain severity,moving capacity by measuring timed-up-and-go test and Japanese Knee Osteoarthritis Measure(JKOM).RESULTS We found that the significant contributor to pain severity was PCS(β=0.555)and BMI(β=0.239),to moving capacity was K-L grade(β=0.520)and to PCS(β=0.313),and to a JKOM score was PCS(β=0.485)and K-L grade(β=0.421),respectively.CONCLUSION The results suggest that pain catastrophizing as well as biological factors were associated with clinical outcomes in female patients with knee OA,irrespective of radiographic severity.
文摘Pain catastrophization is one of the negative emotional factors and an important psychological factor associated with patients with lumbar disc herniation(LDH).Currently,the concept of pain catastrophization of LDH is relatively mature abroad;however,there are only few research studies on this in China.To understand the status quo of pain catastrophization(PC)in patients with LDH and its influencing factors,the intervention measures of PC and their efficacy were further analyzed.In the present paper,the research status of PC at home and abroad is briefly expounded,and the influencing factors and clinical intervention measures for PC are analyzed.This paper reviews the concept of PC,the assessment tools,influencing factors,and the relevant intervention measures.In order to evaluate the pain degree of patients,understand the incidence of pain in patients,and improve the cure rate and quality of life of patients,the basic situation of patients with pain disaster is summarized to provide reference for medical personnel.
文摘Purpose This study aimed to analyze how psychological flexibility,perfectionism,and reported injuries are related to pain catastrophizing in soccer referees.Methods Design:This was a descriptive cross-sectional study.Setting:Data were collected online from 199 soccer referees.Pain catastrophizing was assessed with the Pain Catastrophizing Scale,psychological inflexibility with the Acceptance and Action Questionnaire,and perfectionism with the Frost Multidimensional Perfectionism Scale.Data were also gathered on other injury-related variables.Results Referees with medium–high scores on psychological inflexibility showed greater pain catastrophizing(t=5.322,P<0.001),rumination(t=4.004,P<0.001),helplessness(t=5.023,P<0.001)and magnification(t=5.590,P<0.001)than those with low scores.Psychological inflexibility emerged as a significant predictor of catastrophizing(β=0.313,P=0.006).A slight relationship was found between perfectionism and catastrophizing.For all subscales,the referees who reported mild–moderate injuries in the last three seasons showed greater pain catastrophizing,while those with severe injuries obtained higher scores on all dimensions of catastrophizing except magnification.Finally,those who reported severe injuries only obtained higher scores on rumination and helplessness.Conclusion These results provide a better understanding of the variables that influence pain perception.Possible interventions are suggested based on the observation that greater psychological flexibility was associated with lower pain catastrophizing,with the specific features of the latter depending on the presence and severity of the injury.
文摘This study addresses the maneuver evasion problem for medium-to-long-range air-to-air missiles by proposing a KAN-λ-PPO-based evasion algorithm.The algorithm introduces Kolmogorov-Arnold Networks(KAN)to mitigate the catastrophic forgetting issue of Multilayer Perceptrons(MLP)in continual learning,while incorporatingλ-return to resolve sparse reward challenges in evasion scenarios.First,we model the evasion problem withλ-return and present the KAN-λ-PPO algorithm.Subsequently,we establish game environments based on the segmented ballistic characteristics of medium and long range missiles.During training,a joint reward function is designed by combining the miss distance and positional advantages to train the agent.Experiments evaluate four dimensions:(1)Performance comparison between KAN and MLP in value function approximation;(2)Catastrophic forgetting mitigation of KAN-λ-PPO in dual-task scenarios;(3)Continual learning capabilities across multiple evasion scenarios;(4)Quantitative analysis of agent strategy evolution and positional advantages.Empirical results demonstrate that KAN improves value function approximation accuracy by an order of magnitude compared with traditional MLP architectures.In continual learning tasks,the KAN-λ-PPO scheme exhibits significant knowledge retention,achieving performance improvements of 32.7% and 8.6%over MLP baselines in Task1→2 and Task2→3 transitions,respectively.Furthermore,the learned maneuver strategies outperform High-G Barrel Rolls(HGB)and S-maneuver tactics in securing positional advantages while accomplishing evasion.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by the National Natural Science Foundation of China(Grant No.42172316)the Major National Science and Technology Project for Deep Earth(Grant No.2024ZD100380X)the Natural Science Foundation of Hunan Province of China(2025JJ20030).
文摘This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.
基金Project(2023AH051167)supported by the Natural Science Research Project of Anhui Educational Committee,ChinaProject(AHBP2024B-04)supported by the Foundation of Anhui Engineering Research Center of New Explosive Materials and Blasting Technology,China+1 种基金Project(GXZDSYS2023103)supported by the Open Fund for Anhui Key Laboratory of Mining Construction Engineering,ChinaProjects(52274071,52404155)supported by the National Natural Science Foundation of China。
文摘Aiming at the problem of dynamic instability of hard-brittle jointed rock surrounding in deep tunnel/roadway engineering,combining with the support concepts of"coupling rigidity with flexibility"and"overcoming rigidity by flexibility",the prevention and control method with"rigid-flexible coupling(R-F-C)"was put forward.Through numerical simulation calculation,the impact damage process,acoustic emission(AE)evolution characteristics,and element stress/displacement evolution characteristics of unsupported surrounding rock structure model,rigid supporting surrounding rock structure model,and"R-F-C"supporting surrounding rock structure model under horizontal bidirectional impact loading were compared and analyzed.Based on the theory of stress wave propagation,the dynamic instability catastrophe mechanism of three kinds of supporting structure models induced by horizontal bidirectional impact loading was revealed.Based on the Mohr-Coulomb strength theory,the stress discrimination methods of dynamic catastrophe of surrounding rock induced by horizontal bidirectional impact loading under three kinds of supporting structures were proposed.Combined with the above numerical simulation study,the explosion impact physical and mechanical test of"R-F-C"surrounding rock supporting plate structure was further designed and carried out.Finally,combined with the"conceptual model of ball-cliff potential energy instability",the energy driving theory and energy transformation mechanism of impact-induced rockburst under three kinds of supporting structures were discussed deeply.The research results provided a scientific basis for further promoting the effective application of"R-F-C"supporting structure in the prevention and control of dynamic instability of deep tunnel/roadway surrounding rock.
文摘Objective:To explore the relationship between pain degree and pain catastrophe and medical coping mode in patients with chronic pain.Methods:A visual analogue score scale,medical coping style questionnaire and pain catastrophe scale were used to survey 200 patients in the pain department.Results:The average scores of pain degree of patients with chronic pain were(5.97±2.29),the average score of the total score of the Pain Catastrophe Scale was(21.21±11.56),and the average scores of facing,avoidance and surrender in the Medical Response Style Questionnaire were(17.93±3.4),(16.82±2.4),and(8.87±2.83),respectively.Pain degree was positively correlated with the yield dimension in pain catastrophe and medical coping(p<0.05).The yield dimension of medical coping was positively correlated with pain catastrophe(p<0.05).Medical coping methods played a partial mediating role between pain degree and pain catastrophe,and the mediating effect accounted for 21.59%of the total effect.Conclusion:The pain level of chronic pain patients can affect the level of pain catastrophe through medical coping,and clinical medical staff should guide patients to adopt positive coping methods to promote their healthy recovery.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number RI-44-0833.
文摘The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.
基金supported by the National Natural Science Foundation of China(62176188).
文摘Class-incremental learning studies the problem of continually learning new classes from data streams.But networks suffer from catastrophic forgetting problems,forgetting past knowledge when acquiring new knowledge.Among different approaches,replay methods have shown exceptional promise for this challenge.But performance still baffles from two aspects:(i)data in imbalanced distribution and(ii)networks with semantic inconsistency.First,due to limited memory buffer,there exists imbalance between old and new classes.Direct optimisation would lead feature space skewed towards new classes,resulting in performance degradation on old classes.Second,existing methods normally leverage previous network to regularise the present network.However,the previous network is not trained on new classes,which means that these two networks are semantic inconsistent,leading to misleading guidance information.To address these two problems,we propose BCSD(BiaMix contrastive learning and memory similarity distillation).For imbalanced distribution,we design Biased MixUp,where mixed samples are in high weight from old classes and low weight from new classes.Thus,network learns to push decision boundaries towards new classes.We further leverage label information to construct contrastive learning in order to ensure discriminability.Meanwhile,for semantic inconsistency,we distill knowledge from the previous network by capturing the similarity of new classes in current tasks to old classes from the memory buffer and transfer that knowledge to the present network.Empirical results on various datasets demonstrate its effectiveness and efficiency.
基金This work was funded in part by the Alliance to Feed the Earth in Disasters(ALLFED).
文摘Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20598 and 52104107)the"Qinglan Project"of Jiangsu Colleges and Universities,Young Elite Scientists Sponsorship Program of Jiangsu Province(Grant No.TJ-2023-086).
文摘As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
基金Projects(2013BAB02B05,2012BAB08B01)supported by the National Science and Technology Support Program of ChinaProject(2013JSJJ029)supported by the Teacher Foundation of Central South University,ChinaProject(51074177)supported by the Joint Funding of National Natural Science Foundation and Shanghai Baosteel Group Corporation,China
文摘In order to study the failure mechanism of backfill and the reasonable matches between backfill and rock mass, and to achieve the object of safe and efficient mining in metal mine, four types of backfills were tested under uniaxial compression loading, with cement?tailing ratios of 0.250:1, 0.125:1, 0.100:1 and 0.083:1, respectively. With the help of the stress?strain curves, the deformation and failure characteristics of different backfills with differing cement?tailing ratios were analyzed. Based on the experimental results, the damage constitutive equations of cemented backfills with four cement?tailing ratios were proposed on the basis of damage mechanics. Moreover, comparative analysis of constitutive model and experimental results were made to verify the reliability of the damage model. In addition, an energy model using catastrophe theory to obtain the instability criteria of system was established to study the interaction between backfill and rock mass, and then the system instability criterion was deduced. The results show that there are different damage characteristics for different backfills, backfills with lower cement?tailing ratio tend to have a lower damage value when stress reaches peak value, and damage more rapidly and more obviously in failure process after peak value of stress; the stiffness and elastic modulus of rock mass with lower strength are more likely to lead to system instability. The results of this work provide a scientific basis for the rational strength design of backfill mine.
文摘BACKGROUND Interstitial cystitis/bladder pain syndrome(IC/BPS)is an at least 6-mo noninfectious bladder inflammation of unknown origin characterized by chronic suprapubic,abdominal,and/or pelvic pain.Although the term cystitis suggests an inflammatory or infectious origin,no definite cause has been identified.It occurs in both sexes,but women are twice as much affected.AIM To systematically review evidence of psychiatric/psychological changes in persons with IC/BPS.METHODS Hypothesizing that particular psychological characteristics could underpin IC/BPS,we investigated in three databases the presence of psychiatric symptoms and/or disorders and/or psychological characteristics in patients with IC/BPS using the following strategy:("interstitial cystitis"OR"bladder pain syndrome")AND("mood disorder"OR depressive OR antidepressant OR depression OR depressed OR hyperthymic OR mania OR manic OR rapid cyclasterisk OR dysthymiasterisk OR dysphoriasterisk).RESULTS On September 27,2023,the PubMed search produced 223 articles,CINAHL 62,and the combined PsycLIT/PsycARTICLES/PsycINFO/Psychology and Behavioral Sciences Collection search 36.Search on ClinicalTrials.gov produced 14 studies,of which none had available data.Eligible were peer-reviewed articles reporting psychiatric/psychological symptoms in patients with IC/BPS,i.e.63 articles spanning from 2000 to October 2023.These studies identified depression and anxiety problems in the IC/BPS population,along with sleep problems and the tendency to catastrophizing.CONCLUSION Psychotherapies targeting catastrophizing and life stress emotional awareness and expression reduced perceived pain in women with IC/BPS.Such concepts should be considered when implementing treatments aimed at reducing IC/BPS-related pain.
文摘Renovation system of urban villages in Xi'an City was evaluated. Influence factors of urban village renovation were analyzed on the basis of brittleness theory, and an evaluation index system established through multi-level inconsistency decomposing. By incorporating the catastrophe theory with fuzzy mathematical theory, the mathematic model was created, and catastrophe membership function was obtained as well as evaluation results. Policies for the renovation of urban villages and new direction of the renovation were interpreted. The application case proved that catastrophe progression method was objective and effective and it could provide new concepts for the evaluation and adjustment of urban village renovation. Moreover, application of brittleness theory in the research on urban village renovation is of great instruction and reference value for the present urban construction.
文摘Aim To assess simultaneously various risk states of a system. Methods\ Using the catastrophe and fuzzy theory, the energy and uncertainty in a system are set as two control variables and the function of the system is used as the state variable for analysis. Results and Conclusion\ A risk analysis model named the cusp model is presented. Various states regarding the safety of the system such as the accident state, no accident state and miss state can be represented at will on the cusp model.
