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Transition Expertise and Identity:A Study of Individuals Who Succeeded Repeatedly in Life and Career Transitions
No full textThrough a systematic review of relevant literature and an analysis of in-depth interviews with key expert performers, this book examines the nature of expertise that enables individuals to make repeated successful transitions over the course of their career. Focusing on business, sports, and music, it examines the roles of motivation, cognitive flexibility, personal intelligence, generative thinking, and contextual intelligence in this process. It further shows how identity changes and adapts during a career transition and how self concept evolves over the course of a career. This book has wide appeal for academics in psychology, sports, music, and business, as well as coaches, mentors, talent management, and training organisations across these domains
Guidelines for reporting action simulation studies (GRASS): Proposals to improve reporting of research in motor imagery and action observation
No full textA fog-edge-enabled intrusion detection system for smart grids
No full textThe Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field
A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance
Get PDFThe liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol-based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning-based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement
Correction:The Omega-3 Fatty Acid Eicosapentaenoic Acid Accelerates Disease Progression in a Model of Amyotrophic Lateral Sclerosis
No full text[This corrects the article DOI: 10.1371/journal.pone.0061626.].</p
The BERA Guide to Mental Health and Wellbeing in Schools:Exploring Frontline Support in Educational Research and Practice
No full textPositioning wellbeing at the heart of a well-functioning school community, this timely guide brings the debate on the escalating child and adolescent mental health crisis to a broad audience. A research-informed yet accessible introduction to both the scholarly discourse and real-world cases of mental health in education, The BERA Guide to Mental Health and Wellbeing in Schools focuses on the UK while providing messages and practical tips for an international readership. Chapters examine policy approaches and the pivotal role schools have acquired within the government’s settings-based approach to mental health. The first section presents the theoretical framework and policy context, and the second is dedicated to a selection of case studies from schools in the UK, drawing attention to current issues encountered in education, showcasing exemplars of good practice and sharing innovative approaches to tackling poor mental wellbeing. Published in partnership between the British Educational Research Association (BERA) and Emerald Publishing, The BERA Guides are short, research-informed yet accessible introductions to key, interdisciplinary topics impacting education research and practice for a broad academic audience.<br/
The impact of earnings opacity on corporate social responsibility: insights from Vietnamese listed firms
No full textPurposeThis paper studies the association between earnings opacity and corporate social responsibility disclosures of firms listed on the Vietnamese Stock Exchange.Design/methodology/approachWe utilize a dataset comprising a sample of all listed Vietnamese firms for the period of 2014–2022. Data regarding corporate social responsibility information are gathered manually. Following Dechow et al. (1995), Kothari et al. (2005) and Bhattacharya et al. (2003), earnings opacity is measured by using three proxies, including abnormal accruals, earnings smoothing and loss avoidance. Our hypothesis was tested via ordinary least squares (OLS) regressions. To address endogeneity problems, we use the two-stage instrumental variable method (IV-2SLS) as well as the generalized method of moments (GMM) to ensure the robustness of our results.FindingsWe find that earnings opacity is positively related to corporate social responsibility disclosures. Cross-sectional analyses indicate that managers of firms disguise their opportunistic behaviour by disclosing more information about corporate social responsibility. The evidence also shows that firms experience long-run underperformance when having higher earnings opacity and greater sustainability disclosures. Our results remain robust even after correcting for endogeneity using the IV approach and the GMM method.Practical implicationsEvidence from this study can serve as a warning signal to the investment community, highlighting that some methods aimed at enhancing a firm’s corporate social responsibility disclosures might be used to obstruct other unethical activities. Moreover, the results of this study can help regulators gain a better comprehension of firms' reporting patterns concerning corporate social responsibility initiatives. It should not only reform the corporate social responsibility regulation but also impose stronger litigation for firms to enhance the quality of corporate social responsibility disclosures.Originality/valueWe are the first to present evidence regarding the relationship between earnings opacity and corporate social responsibility disclosure in Vietnam
