Textual Analysis of Tweets Associated with Domestic Violence.

Background: Domestic violence is a global public health concern as stated by World Health Organization. We aimed to conduct a textual analysis of tweets associated with domestic violence through keyword identification, word trends and word collocations. The data was obtained from Twitter, focusing on publicly available tweets written in English. The objectives are to find out if the identified keywords, word trends and word collocations can help differentiate between domestic violence-related tweets and non-domestic violence-related tweets, as well as, to analyze the textual characteristics of domestic violence-related tweets and non-domestic violence-related tweets. Methods: Overall, 11,041 tweets were collected using a few keywords over a period of 15 days from 22 March 2021 to 5 April 2021. A text analysis approach was used to discover the most frequent keywords used, the word trends of those keywords and the word collocations of the keywords in differentiating between domestic violence-related or non-domestic violence-related tweets. Results: Domestic violence-related tweets and non-domestic violence-related tweets had differentiating characteristics, despite sharing several main keywords. In particular, keywords like "domestic", "violence" and "suicide" featured prominently in domestic-violence related tweets but not in non-domestic violence-related tweets. Significant differences could also be seen in the frequency of keywords and the word trends in the collection of the tweets. Conclusion: These findings are significant in helping to automate the flagging of domestic-violence related tweets and alert the authorities so that they can take proactive steps such as assisting the victims in getting medical, police and legal help as needed.

Technostress and academic motivation: direct and indirect effects on university students' psychological health.

Introduction: Research has well demonstrated that the pandemic entailed several implications among university students worldwide in terms of increased use of Information and Communication Technologies (ICTs), technostress, disruptions in academic goals and motivation processes, and growing psychological suffering. Responding to the new research need to go in-depth into the processes linking technostress and motivation dimensions to inform current research/interventions, the present study aimed to explore the direct effects of perceived Technostress dimensions (Techno-Overload, Work-Home Conflict, Pace of Change, Techno-Ease, Techno-Reliability, and Techno-Sociality) and Academic Motivation dimensions (Amotivation, Intrinsic, and Extrinsic Motivation dimensions) on students' perceived levels of Anxiety/Depression and test the potential indirect effect (mediating role) of Academic Motivation dimensions in the associations between Technostress and psychological health conditions. Methods: Overall, 1,541 students from five European countries (Czech Republic, Greece, Italy, Serbia, United Kingdom) completed a survey comprising a Background Information Form, the Technostress Scale, the Academic Motivation Scale-College, and the Hospital Anxiety and Depression Scale. Hayes' PROCESS tool was used to test direct and indirect (mediating) effects. Results: Data revealed that Techno-Overload, Work-Home Conflict, Amotivation, and Extrinsic Motivation-Introjected had a direct negative effect, whereas Techno-Ease, Techno-Reliability, Techno-Sociality, all Intrinsic Motivation dimensions, and Extrinsic Motivation-Identified had a direct protective role for students' psychological health. The significant indirect role of motivation dimensions in the associations between Technostress dimensions and Anxiety/Depression was fully supported. Discussion: Findings allow gaining further insight into the pathways of relationships between technostress, motivation, and psychological health, to be used in the current phase, featured by the complete restoration of face-to-face contacts, to inform the development of tailored research and interventions, which address lights and shadows of the technology use, and which take into account the necessity to enhance its potentials yet without impairing students' motivation and psychological health.

Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.

BACKGROUND: With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic. OBJECTIVE: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem. METHODS: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation. RESULTS: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance. CONCLUSIONS: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.

Cultural differences in visual perceptual learning.

Cultural differences in visual perceptual learning (VPL) could be attributed to differences in the way that people from individualistic and collectivistic cultures preferentially attend to local objects (analytic) or global contexts (holistic). Indeed, individuals from different cultural backgrounds can adopt distinct processing styles and learn to differentially construct meaning from the environment. Therefore, the present work investigates if cross-cultural differences in VPL can vary as a function of holistic processing. A shape discrimination task was used to investigate whether the individualistic versus collectivistic backgrounds of individuals affected the detection of global shapes embedded in cluttered backgrounds. Seventy-seven participants-including Asian (collectivistic background) and European (individualistic background) students-were trained to discriminate between radial and concentric patterns. Singelis's self-construal scale was also used to assess whether differences in learning could be attributed to independent or interdependent self-construal. Results showed that collectivists had faster learning rates and better accuracy performance than individualists following training-thereby reflecting their tendency to attend holistically when learning to extract global forms. Further, we observed a negative association between independent self-construal-which has previously been linked to analytic processing-with performance. This study provides insight into how socio-cultural backgrounds affect VPL.

Hybrid Solid Polymer Electrolytes with Two-Dimensional Inorganic Nanofillers.

Solid polymer electrolytes are of rapidly increasing importance for the research and development of future safe batteries with high energy density. The diversified chemistry and structures of polymers allow the utilization of a wide range of soft structures for all-polymer solid-state electrolytes. With equal importance is the hybrid solid-state electrolytes consisting of both "soft" polymeric structure and "hard" inorganic nanofillers. The recent emergence of the re-discovery of many two-dimensional layered materials has stimulated the booming of advanced research in energy storage fields, such as batteries, supercapacitors, and fuel cells. Of special interest is the mass transport properties of these 2D nanostructures for water, gas, or ions. This review aims at the current progress and prospective development of hybrid polymer-inorganic solid electrolytes based on important 2D materials, including natural clay and synthetic lamellar structures. The ion conduction mechanism and the fabrication, property and device performance of these hybrid solid electrolytes will be discussed.

Job satisfaction and resilience in psychiatric nurses: A study at the Institute of Mental Health, Singapore.

Job satisfaction ranks highly as one of the main factors influencing turnover rates among nurses. Mental health nursing has been reported to be a particularly stressful specialty, yet little is known about the level of job satisfaction among psychiatric nurses in Singapore. Resilience is defined as a means of adapting to stress at the workplace, and could serve as a factor influencing job satisfaction. The present study aimed to explore the current level of job satisfaction among psychiatric nurses working in the only tertiary psychiatric institution in Singapore, the influencing factors, and the relationship between resilience and job satisfaction. A survey questionnaire consisting of the following was administered to all eligible nurses working in the Institute of Mental Health between the period of 16-24 December 2014: (i) The McCloskey and Mueller Satisfaction Scale; (ii) The Resilience Scale; and (iii) sociodemographic data form. A total of 874 nurses were eligible for participation in the study, and a total of 748 nurses responded, totalling 85.6% response. A mean satisfaction score of 95.21 and mean resilience score of 125.74 were obtained. Mean satisfaction and resilience scores were the highest for nurses with longer working experience and those of older age. A positive and significant association between satisfaction and resilience scores (P = 0.001) was obtained. Psychiatric nurses in Singapore are generally satisfied with their job, but this can be further improved with the strengthening of personal resilience.