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The vulnerable children refer to the special group of children with deviation in the process of children's psychological development and personality formation due to growth dilemmas. This may incur a series of serious social and family problems. The vulnerable children mainly cover the children suffering from children's psychological problems, such as childhood autism, autism, social anxiety and hypersensitivity, fear, depression, and PTSD arising from other factors. At present, the research results at home and abroad mainly focus on the psychological dynamic correlation investigation and solution discussion of a certain kind of difficult factor in the children's psychological development based on statistical data by the experimental methods, such as scale and model, and there is a blind spot in the humanistic orientation theory construction of psychological treatment for vulnerable children, causing the social reflection on children's psychological predicament from the humanistic perspective cannot be performed in related researches and going against searching for universal and integral theoretical paradigm for solving related problems. Sophisticated technologies for the observations have emerged increasingly for enabling the psychological features of vulnerable children through developmental cognitive neuroscience experiments. This paper introduces humanistic art therapy theory, focuses on the construction of a theoretical paradigm, and verifies its effectiveness based on the experimental results on the psychological development of vulnerable children, with an efficient performance. This study mainly refers to children with difficulty in social inclusion and psychological development. The results showed that two kinds of art therapy can obviously improve the psychological disorders of vulnerable children. The goal was to enhance self-cognition, strengthen emotional interaction, and implement positive motivation. [ABSTRACT FROM AUTHOR]

Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback in writing tasks based on multiple texts has almost exclusively focused on essay product and rarely included SRL processes. Objectives: We explored the viability of using product and process features to develop machine learning classifiers that identify low‐ and high‐performing essays in a multi‐text writing task. Methods: We examined learning processes and essay submissions of 163 graduate students working on an authentic multi‐text writing assignment. We utilised learners' trace data to obtain process features and state‐of‐the‐art natural language processing methods to obtain product features for our classifiers. Results and Conclusions: Of four popular classifiers examined in this study, Random Forest achieved the best performance (accuracy = 0.80 and recall = 0.77). The analysis of important features identified in the Random Forest classification model revealed one product (coverage of reading topics) and three process (elaboration/organisation, re‐reading and planning) features as important predictors of writing quality. Major Takeaways: The classifier can be used as a part of a future automated writing evaluation system that will support at scale formative assessment in writing tasks based on multiple texts in different courses. Based on important predictors of essay performance, a guidance can be tailored to learners at the outset of a multi‐text writing task to help them do well in the task. Lay Description: What is already known about this topic?: Both product and process features should be used to inform formative feedback on writing.Providing product‐ and process‐oriented feedback to learners is challenging.Automatic writing evaluation systems have mainly relied upon product features.Automated analysis of learners' trace data and their essay drafts is a promising venue. What this paper adds?: An accurate machine learning classifier that identifies low‐ and high‐scoring essays.The classifier utilized both product and process features.We obtained process features from learners' trace data in digital learning environment.We computed product features using state‐of‐the‐art text analytical methods. Implications for practice and/or policy: The classifier can be used as a part of a future automated writing evaluation system.We revealed learning processes and essay characteristics that influence performance.Based on important predictors of performance, formative feedback can be given to learners. [ABSTRACT FROM AUTHOR]

Background: As a common social phenomenon, nurses' occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention. Methods: A total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks. Results: In the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) "I feel burned out from my work", C1 "I have become more callous toward work since I took this job", and R3 "In my opinion, I am good at my job" had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 "I feel emotionally drained from my work" and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65). Conclusions: The network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice. [ABSTRACT FROM AUTHOR]

While the use of experiments is important for developing students' scientific knowledge and skills, challenges may arise when teachers and students are conducting experiments in class, such as non‐reusable experimental resources, safety issues and difficulties simulating some specific effects. Augmented reality (AR) technology affords an alternative approach to conducting experiments by bringing students a virtual–real mixed learning environment. In this study, taking junior high school physics knowledge on the magnetic field as an example, we designed and developed an AR‐based mobile simulated experiment tool. This study investigates the effect of the AR‐based experiment on students' knowledge improvement and cognitive load compared with 3D and traditional experiments. A sample of 122 ninth‐grade students was randomly chosen and assigned to three groups (AR, 3D and Traditional). The results demonstrate that students in the AR group performed better than those in the 3D and Traditional groups in terms of their knowledge improvement. The AR group students also experienced the lowest cognitive load among the three groups. Moreover, students had positive perceptions about AR and 3D tools. The implications of this study are discussed. Lay Description: What is already known about this topic: Experiment is an important method for students' magnetic field learning.Traditional magnetic experiment consists of various procedures that may cause some inconvenience during learning.Augmented reality (AR) provides a virtual–real fusion environment to assist teaching and learning.While some studies indicate positive impact of AR on learner's cognitive load, some do not. What this paper adds: An AR‐based mobile simulated experiment tool was proposed based on the junior high school physics knowledge, the magnetic field.A Comparison was made among AR, 3D and traditional experiment for magnetic field learning.Higher knowledge improvement and lower cognitive load were observed in the AR group.3D and Traditional groups showed similar knowledge improvement and cognitive load. Implications for practice and/or policy: The AR design with hand‐held devices has unlimited scalability of educational applications.Serving the real object as marker and using the professional algorithm to simulate some specific phenomenon were recommended in future AR studies.The findings indicated that AR experimental tool had a statistically significant effect on students' knowledge improvement and cognitive load.The similar result between 3D and Traditional groups implies that not everything virtualized is good; we must consider the actual teaching and learning situations. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life. [ABSTRACT FROM AUTHOR]

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics - Volume, Variety, Velocity and Value (the 4 Vs) - that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations. [ABSTRACT FROM AUTHOR]

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

In brain MR images, the noise and low-contrast significantly deteriorate the segmentation results. In this paper, we propose an automatic unsupervised segmentation method integrating dual-tree complex wavelet transform (DT-CWT) with K-mean algorithm for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of DT-CWT and spatial position information. Then, a spatial constrained K-mean algorithm is presented as the segmentation system. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to evaluate the health‐related quality of life of nursing workers and influential factors. Background: Nursing workers are under great occupational stress. However, few population‐level studies of nursing workers' health conditions have been reported from China. Design This was a cross‐sectional study conducted in 12 medical institutions in east China between January and March 2015. Methods: Social‐demographic information, nursing workers' work‐related information and health‐related quality of life using the Chinese (mainland) version of the Short Form 36 health survey were collected by a self‐administrated questionnaire. Results: Nursing workers reported significantly lower scores of the vitality, mental health dimensions, and mental component summary of the Short Form 36 survey compared with general population reference values. They scored significantly higher for five other dimensions and comparably for the physical role dimension. Pre‐job training, families' positive attitudes to their job, and good relationships with clients were positively related with nursing workers' mental well‐being. Conclusion: To promote nursing workers' mental health, compulsory work‐related training, voluntary psychological counselling, and targeted legislation are needed. SUMMARY STATEMENT: What is already known about this topic? Shortages of professional medical staff in medical institution at all levels will not be quickly met, and nursing workers are thought to be an important supplement in China.Population‐level study on nursing workers' point of view of their work and health condition has been little reported in China. What this paper adds? Nursing workers were mentally vulnerable regarding their vitality and mental health scores.Job training, families' positive attitudes to their job, and good relationships with clients were all protective factors for nursing workers' mental well‐being. The implications of this paper: Compulsory work‐related training for nursing workers provided by intermediary companies can help promote nursing workers' well‐being.Voluntary psychological counselling provided by hospitals can also be helpful to build a supportive work environment for nursing workers.Governments need to regulate this market and protect nursing workers' rights and benefits by legislation. [ABSTRACT FROM AUTHOR]

Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications. [ABSTRACT FROM AUTHOR]