This article takes the theory of power space as an starting point for an in-depth comparison of the clan hall in White Deer Plains and the scaffold in The Scarlet Letter.In White Deer Plains,The clan hall,as the core ...This article takes the theory of power space as an starting point for an in-depth comparison of the clan hall in White Deer Plains and the scaffold in The Scarlet Letter.In White Deer Plains,The clan hall,as the core place of family activities,has a spatial layout that implies a hierarchy of power,and the taboos it represents relate to various aspects such as marriage and bloodline,and maintains the order of the clan through a variety of disciplinary mechanisms.The scaffold in The Scarlet Letter is located in the town’s central square and is a symbol of Puritan social power and moral discipline,behind which Puritan taboos influence people’s behaviors and public punishments are used to achieve the discipline of the people.The research reveals the similarities and differences between the two in terms of sources of power,modes of operation and influence,and opens up new horizons for cross-cultural literary studies.展开更多
In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based bou...In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.展开更多
BACKGROUND Schizophrenia is commonly associated with comorbid depression,which exacerbates cognitive impairments and negatively impacts quality of life.Despite the high prevalence and burden of these comorbidities,eff...BACKGROUND Schizophrenia is commonly associated with comorbid depression,which exacerbates cognitive impairments and negatively impacts quality of life.Despite the high prevalence and burden of these comorbidities,effective treatment options,particularly for cognitive dysfunction,remain limited.AIM To evaluate the efficacy of computerized cognitive behavioral therapy(CCBT)with sertraline vs sertraline monotherapy in improving depressive symptoms,cognitive function,and quality of life in schizophrenia and depressive episodes.METHODS In this single-center,randomized controlled trial,68 adults[mean age(SD)=36.5(10.0),57.4%male]with schizophrenia and depressive symptoms were randomly assigned to receive either CCBT with sertraline or sertraline monotherapy during a 4-week hospitalization.The CCBT intervention involved 45-60-minute sessions twice weekly for four weeks.Outcomes included comparisons of depressive symptoms(Calgary depression scale for schizophrenia),cognitive function[MATRICS consensus cognitive battery(MCCB)],and quality of life(36-item short form survey)between the groups.RESULTS The experimental group showed greater improvements in depressive symptoms at 4,8,and 12 weeks compared to the controls,with the most notable difference at 12 weeks[mean difference(MD)=-1.7;P<0.001;Cohen’s d=0.9].Cognitive function improved across all MCCB domains in the experimental group,with higher processing speed scores(MD=4.1;P=0.043;Cohen’s d=0.5)and social cognition scores(MD=4.9;P=0.006;Cohen’s d=0.7)than in the control group.Quality of life,particularly in mental health,was significantly better in the experimental group.CONCLUSION CCBT with sertraline was more effective than sertraline monotherapy for patients with schizophrenia and depressive episodes,supporting its use as an adjunctive therapy.展开更多
To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail stee...To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.展开更多
文摘This article takes the theory of power space as an starting point for an in-depth comparison of the clan hall in White Deer Plains and the scaffold in The Scarlet Letter.In White Deer Plains,The clan hall,as the core place of family activities,has a spatial layout that implies a hierarchy of power,and the taboos it represents relate to various aspects such as marriage and bloodline,and maintains the order of the clan through a variety of disciplinary mechanisms.The scaffold in The Scarlet Letter is located in the town’s central square and is a symbol of Puritan social power and moral discipline,behind which Puritan taboos influence people’s behaviors and public punishments are used to achieve the discipline of the people.The research reveals the similarities and differences between the two in terms of sources of power,modes of operation and influence,and opens up new horizons for cross-cultural literary studies.
文摘In recent years,decomposition-based evolutionary algorithms have become popular algorithms for solving multi-objective problems in real-life scenarios.In these algorithms,the reference vectors of the Penalty-Based boundary intersection(PBI)are distributed parallelly while those based on the normal boundary intersection(NBI)are distributed radially in a conical shape in the objective space.To improve the problem-solving effectiveness of multi-objective optimization algorithms in engineering applications,this paper addresses the improvement of the Collaborative Decomposition(CoD)method,a multi-objective decomposition technique that integrates PBI and NBI,and combines it with the Elephant Clan Optimization Algorithm,introducing the Collaborative Decomposition Multi-objective Improved Elephant Clan Optimization Algorithm(CoDMOIECO).Specifically,a novel subpopulation construction method with adaptive changes following the number of iterations and a novel individual merit ranking based onNBI and angle are proposed.,enabling the creation of subpopulations closely linked to weight vectors and the identification of diverse individuals within them.Additionally,new update strategies for the clan leader,male elephants,and juvenile elephants are introduced to boost individual exploitation capabilities and further enhance the algorithm’s convergence.Finally,a new CoD-based environmental selection method is proposed,introducing adaptive dynamically adjusted angle coefficients and individual angles on corresponding weight vectors,significantly improving both the convergence and distribution of the algorithm.Experimental comparisons on the ZDT,DTLZ,and WFG function sets with four benchmark multi-objective algorithms—MOEA/D,CAMOEA,VaEA,and MOEA/D-UR—demonstrate that CoDMOIECO achieves superior performance in both convergence and distribution.
基金Supported by Fuzhou Science and Technology Plan Project,No.2023-S-028.
文摘BACKGROUND Schizophrenia is commonly associated with comorbid depression,which exacerbates cognitive impairments and negatively impacts quality of life.Despite the high prevalence and burden of these comorbidities,effective treatment options,particularly for cognitive dysfunction,remain limited.AIM To evaluate the efficacy of computerized cognitive behavioral therapy(CCBT)with sertraline vs sertraline monotherapy in improving depressive symptoms,cognitive function,and quality of life in schizophrenia and depressive episodes.METHODS In this single-center,randomized controlled trial,68 adults[mean age(SD)=36.5(10.0),57.4%male]with schizophrenia and depressive symptoms were randomly assigned to receive either CCBT with sertraline or sertraline monotherapy during a 4-week hospitalization.The CCBT intervention involved 45-60-minute sessions twice weekly for four weeks.Outcomes included comparisons of depressive symptoms(Calgary depression scale for schizophrenia),cognitive function[MATRICS consensus cognitive battery(MCCB)],and quality of life(36-item short form survey)between the groups.RESULTS The experimental group showed greater improvements in depressive symptoms at 4,8,and 12 weeks compared to the controls,with the most notable difference at 12 weeks[mean difference(MD)=-1.7;P<0.001;Cohen’s d=0.9].Cognitive function improved across all MCCB domains in the experimental group,with higher processing speed scores(MD=4.1;P=0.043;Cohen’s d=0.5)and social cognition scores(MD=4.9;P=0.006;Cohen’s d=0.7)than in the control group.Quality of life,particularly in mental health,was significantly better in the experimental group.CONCLUSION CCBT with sertraline was more effective than sertraline monotherapy for patients with schizophrenia and depressive episodes,supporting its use as an adjunctive therapy.
基金supported by the Key Technologies Research and Development Program under Grant 2021YFB1600300.
文摘To examine stress redistribution phenomena in bridges subjected to varying operational conditions,this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge.An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns.XGBoost(eXtreme Gradient Boosting),a gradient-boosting machine learning(ML)algorithm,was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution.Unlike traditional numerical models that rely on extensive assumptions and idealizations,XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors,such as temperature,traffic load,and structural geometry.This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant.Results indicate a clear shift of stress concentrations frombeamends toward mid-span regions following the commencement of metro operations,reflecting both structural adaptation and localized overstress near arch ribs.Furthermore,the model generates robust predictions of stress evolution,demonstrating potential applications in early warning systems and fatigue risk assessment.This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges.In addition,a Stress Redistribution Index(SRI)is proposed,derived from this monitoring study and finite-element-based transverse load distributions,to quantify temporal stress shifts between midspan and edge beams.The results provide both theoretical contributions and practical guidance for the design,inspection,and maintenance of complex bridge structures.
