Handling Multiple Objectives with Biogeography-based Optimization

Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.

Factors influencing resilience in patients with burns during rehabilitation period

Purpose:To investigate the factors that influence levels of resilience in patients with burns during rehabilitation,and to provide theoretical guidance for psychological crisis prevention and intervention.Methods:A total of 129 patients with burns and undergoing rehabilitation were investigated using a demographic questionnaire,the Connor-Davidson Resilience Scale(CD-RISC),the Social Support Rating Scale(SSRS),and the Simplified Coping Styles Questionnaire.Results:The overall resilience score of burn patients was at a middle level.Multiple regression analysis showed that gender,marital status,occupation,educational level,burn severity,and a positive coping style were all significant factors influencing patient resilience.Conclusion:During psychological crisis intervention,medical staff should guide burn patients according to their individual coping styles.Such guidance would achieve a better effect,improve patient resilience,and promote positive psychological adaptation.

The investigation of care burden and coping style in caregivers of spinal cord injury patients

Purpose:The level of care burden experienced by caregivers of patients with spinal cord injury and their coping style were evaluated in order to identify ways to improve their capacity to respond and reduce the care burden.Methods:A convenience sampling method was used to select 150 spinal cord injury patients who were discharged from the Second and the Third Affiliated Hospital of Nanchang University and their primary caregivers.The Chinese version of the Zarit Caregiver Burden Interview and the Simplified Coping Style Questionnaire were distributed to assess caregiver burden and coping strategies.Results:The care burden score of the caregivers of patients with spinal cord injury was 52.91
11.56 points.Eighty-eight percent of caregivers reported moderate or severe care burden levels.The largest proportion of caregivers were female spouses,who were less likely to use an active coping strategy.A negative coping strategy was more frequently used by caregivers compared to healthy adults,and was positively correlated with burden of care.In contrast,a negative correlation was found between the care burden and an active coping style.Conclusion:Primary caregivers of spinal cord injury patients report a heavy burden of care.As active coping strategies are more beneficial,medical care providers should encourage caregivers to make more use of these coping styles to promote physical and mental health for themselves,their patients and their family,as well as to improve the quality of care provided.

A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge concepts.Most existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning scenarios.In this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis results.Specifically,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness factors.Furthermore,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model parameters.Our UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.