Search Results (76)

Thermodynamic free energy is used to elucidate the significance of energy dissipation-induced temperature rise on the performance, reliability, and durability of all systems, biological, chemical and physical. Transformation (a measure of reliability) and degradation (a measure of durability) are distinguished. The temperature rise mechanism is characterized by the microstructurothermal (MST) energy/entropy. A framework to quantify the contributions of the MST entropy to system transformation and degradation is introduced and demonstrated using diverse multi-physics systems: cardiovascular strain in humans, charge capacity of batteries, tribological wear of journal bearings, and shear strength of lubricating greases. Various levels of temperature-induced degradation are observed in the systems. Thermal degradation rate increases with process and energy dissipation rates. The benefits of active cooling on systems and materials are shown. This article is recommended to engineers, scientists, designers, medical doctors, and other system analysts for use in dissipation/degradation characterization and minimization. Full article

Esports is a global industry, with coaches widely regarded as having a pivotal role in facilitating player development and enhancing performance. Despite this, limited research has investigated the developmental experiences of esports coaches and how they are valued by diverse stakeholder groups. Consequently, the aim of this research is to explore global stakeholders’ perspectives towards the developmental experiences of esports coaches. Data were collected via a qualitative online survey completed by 98 participants, representing 28 nationalities, across six esports stakeholder groups (head coach, assistant coach, player, team manager, performance staff, analyst). Following a reflexive thematic analysis process, three themes were generated: (1) Speaking the same language: the importance of playing and knowing the game; (2) Walking the walk: the need for coaching experience to demonstrate competency; and (3) Formal professional learning and development: a bone of contention. By understanding how diverse stakeholders value different developmental experiences, the findings offer unique insights into the contested nature of coach development in esports. This research contributes to the esports coaching literature and provides a foundation for future empirical research into this emerging area, with recommendations and implications for esports coach education and practice discussed. Full article

There is a need for more attention to the importance of substantial parent involvement in programming for autistic children in community-based care. More encouragement is needed to ensure that practitioners prioritize parental training and involvement throughout interventions, including practitioner-led in-home applied behavioral analysis (ABA) interventions. There has been little to no research on the feasibility and efficacy of adding parental training to in-home practitioner-led ABA interventions. This study is intended to begin the consideration of efficacy by reporting on a series of focus groups involving parents of autistic children and the Board Certified Behavioral Analysts (BCBAs) and Registered Behavior Technicians (RBTs) who work with them. Method: Focus group meetings were conducted with a total of 18 participants: 7 family members, 5 RBTs, and 6 BCBAs drawn from two provider sites. Transcripts were generated, and data was analyzed using Braun & Clarke’s reflexive thematic analysis, a method for analyzing and interpreting qualitative data that involves systematically generating codes in order to develop themes. Findings: The findings are described using three main themes: (1) barriers to family involvement in applied behavioral analysis programming, (2) facilitators of family involvement in applied behavioral analysis programming, and (3) recommendations for improving family involvement in applied behavioral analysis programming. Conclusions: There are logistical challenges in involving parents in in-home interventions when they occur in evening hours when the family has multiple other responsibilities. However, being in-home also presents opportunities not available in school or clinic settings. The recommendations provided offer an initial road map to advancing parent training components. Full article

Sustained green innovation in firms is a crucial driver of sustainable economic development. Chief executive officer (CEO) openness, as a key personality trait related to leadership effectiveness, has an important but largely overlooked impact on sustained green innovation. This study aims to explore the impact of CEO openness on sustained green innovation and its boundary conditions. Using data from Chinese A-share-listed firms between 2011 and 2023, we find that CEO openness has a significant positive impact on sustained green innovation in firms. The moderating effects reveal that both digitalization level and CEO shareholding strengthen the positive effect of CEO openness on sustained green innovation. Heterogeneity analysis indicates that this positive effect is more pronounced in state-owned enterprises, firms in non-heavily polluting industries, and those with high analyst coverage. These findings provide theoretical support for understanding the determinants of sustained green innovation through the lens of CEO personality. They also enrich the growing literature on the impact of CEO openness on corporate decision-making. Furthermore, this study recommends that firms prioritize CEO openness in selection, enhance digital infrastructure, and improve equity incentive measures to ultimately foster sustained green innovation. Full article

The influence of digital transformation on ESG performance has garnered considerable interest; however, previous research in this area has not adequately considered the influence of environmental uncertainty factors. This study utilized a dataset comprising Chinese A-share listed companies from 2009 to 2023 to explore how environmental uncertainty affects the correlation between digital transformation and ESG performance. Furthermore, we also examined potential pathways and heterogeneity. Our findings demonstrate that digital transformation significantly enhances ESG performance, with the positive effects persisting for up to three years post-implementation, although gradually diminishing in intensity. However, environmental uncertainty substantially reduces this positive impact across all pivotal technologies. Improvements in ESG performance are more pronounced in firms that are high-tech, technology-intensive, and capital-intensive and that do not produce heavy pollution. Quantile regression reveals that firms in the upper–middle ESG performance range benefit most. Our mediation analysis confirms that digital transformation enhances ESG performance by increasing firm value, media attention, and analyst coverage. Overall, this study contributes to the existing literature by providing empirical evidence of the impacts of environmental uncertainty. These findings provide strategic guidance for companies navigating digital transformation initiatives in turbulent business environments, while also offering concrete recommendations for regulatory authorities developing ESG disclosure frameworks and digital infrastructure investment priorities tailored to different uncertainty conditions. Full article

The evolving landscape of cybersecurity threats demands the modernization of Security Operations Centers (SOCs) to enhance threat detection, response, and mitigation. Security Orchestration, Automation, and Response (SOAR) platforms play a crucial role in addressing operational inefficiencies; however, traditional no-code SOAR solutions face significant limitations, including restricted flexibility, scalability challenges, inadequate support for advanced logic, and difficulties in managing large playbooks. These constraints hinder effective automation, reduce adaptability, and underutilize analysts’ technical expertise, underscoring the need for more sophisticated solutions. To address these challenges, we propose a hyper-automation SOAR platform powered by agentic-LLM, leveraging Large Language Models (LLMs) to optimize automation workflows. This approach shifts from rigid no-code playbooks to AI-generated code, providing a more flexible and scalable alternative while reducing operational complexity. Additionally, we introduce the IVAM framework, comprising three critical stages: (1) Investigation, structuring incident response into actionable steps based on tailored recommendations, (2) Validation, ensuring the accuracy and effectiveness of executed actions, (3) Active Monitoring, providing continuous oversight. By integrating AI-driven automation with the IVAM framework, our solution enhances investigation quality, improves response accuracy, and increases SOC efficiency in addressing modern cybersecurity threats. Full article

As global sustainability goals gain importance, fostering green innovation has become critical for businesses to reduce environmental impact and enhance sustainability. Green credit policies, which incentivize firms to adopt eco-friendly practices, are a powerful tool in encouraging green innovation. This study examines the impact of green credit policies on corporate green innovation in China, with a focus on high-pollution enterprises, using data from A-share listed companies from 2007 to 2023. The research highlights the significant role of analyst attention in moderating the effectiveness of these policies. The findings show that while green credit policies significantly promote the quantity and quality of green innovation, the impact is further amplified when firms receive higher levels of attention from analysts. The study also finds that larger firms and those with excess cash are more likely to respond positively to green credit policies, as they have the resources to invest in green technologies. This research provides valuable insights into the relationship between green credit policies, corporate green innovation, and the moderating effect of analyst attention, offering practical recommendations for policymakers and businesses aiming to achieve sustainable development goals. Full article

Sports analytics is a fast-evolving domain using advanced data science methods to find useful insights. This study explores the way NBA player performance metrics evolve from quarter to quarter and affect game outcomes. Using Association Rule Mining, we identify key offensive, defensive, and overall impact metrics that influence success in both regular-season and playoff contexts. Defensive metrics become more critical in late-game situations, while offensive efficiency is paramount in the playoffs. Ball handling peaks in the second quarter, affecting early momentum, while overall impact metrics, such as Net Rating and Player Impact Estimate, consistently correlate with winning. In the collected dataset we performed preprocessing, applying advanced anomaly detection and discretization techniques. By segmenting performance into five categories—Offense, Defense, Ball Handling, Overall Impact, and Tempo—we uncovered strategic insights for teams, coaches, and analysts. Results emphasize the importance of managing player fatigue, optimizing lineups, and adjusting strategies based on quarter-specific trends. The analysis provides actionable recommendations for coaching decisions, roster management, and player evaluation. Future work can extend this approach to other leagues and incorporate additional contextual factors to refine evaluation and predictive models. Full article

This study examines the relationship between Environmental, Social, and Governance (ESG) disclosures and perceived financial performance within the context of Islamic finance, with a focus on Maqasid al-Shariah—the overarching goals of Islamic law. Using a quantitative approach, the study surveyed 350 stakeholders in Saudi Arabia’s Islamic finance sector, including corporate managers, investment professionals, and financial analysts, over a six-month period (May to October 2024). The findings indicate that stakeholders perceive a positive relationship between ESG disclosures and financial performance, particularly when companies align their ESG practices with Islamic finance principles. However, the study does not measure actual financial performance; rather, it assesses stakeholders’ perceptions of ESG’s influence on corporate governance, risk management, and investment attractiveness. Results suggest that companies integrating ESG principles with Maqasid al-Shariah foster greater stakeholder trust, enhance corporate responsibility, and promote long-term sustainability. However, variations in trust and investment decisions exist based on industry type, ESG disclosure levels, and demographic factors such as experience and familiarity with ESG practices. The study provides novel insights into how Islamic finance principles shape ESG disclosure practices, offering practical recommendations for improving corporate governance and sustainability. By emphasizing transparency, ethical investment, and regulatory alignment, these findings contribute to ongoing discussions on sustainable finance and the role of ESG in shaping Islamic financial institutions. Full article

Soxhlet extraction is a method recommended by the Association of Official Analytical Chemists (AOAC) to determine the lipid content in plant samples. Generally, n-hexane (toxicity grade 5) is used as the solvent (≈300 mL; ≈30 g sample) at boiling temperatures (69 °C) for long times (≤16 h) under a chilled water reflux (≈90 L/h), proportionally aggravated by the number of repetitions and samples determined. In this sense, the technique is neither safe nor sustainable for the analyst or the environment. This article presents the development of an alternative and more sustainable procedure for determining the lipid content in raw Arabica coffee beans. A 3³ full factorial design was used to perform direct hot solid–liquid extractions in 4 mL vials, varying the ground grains and solvent ratios, temperatures, and times. An optimal condition resulted in an extractive yield statistically equivalent to Soxhlet, without variation in the composition of the oil fatty acids determined by GC-MS after hole oil transesterification. This procedure was presented as a sustainable alternative to Soxhlet extraction because it does not require water for cooling and needs a smaller volume of solvent (2 mL) and sample mass (0.2 g); it also has a smaller generated residue, as well as requiring a shorter time (1.5 h) and less energy expenditure for extraction. Full article

Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives. To enhance the effectiveness of these tests, Machine Learning (ML) has been integrated, showing significant potential for identifying anomalies across various security areas through detailed detection of underlying malicious patterns. However, pentesting environments are unpredictable and intricate, requiring analysts to make extensive efforts to understand, explore, and exploit them. This study considers these challenges, proposing a recommendation system based on a context-rich, vocabulary-aware transformer capable of processing questions related to the target environment and offering responses based on necessary pentest batteries evaluated by a Reinforcement Learning (RL) estimator. This RL component assesses optimal attack strategies based on previously learned data and dynamically explores additional attack vectors. The system achieved an F1 score and an Exact Match rate over $97.0$%, demonstrating its accuracy and effectiveness in selecting relevant pentesting strategies. Full article

The Chinese stock market, marked by rapid growth and significant volatility, presents unique challenges for investors and analysts. A-share stocks, traded on the Shanghai and Shenzhen exchanges, are crucial to China’s financial system and offer opportunities for both domestic and international investors. Accurate stock recommendation tools are vital for informed decision making, especially given the ongoing regulatory changes and economic reforms in China. Current stock recommendation methods often fall short, as they typically fail to capture the complex inter-company relationships and rely heavily on financial reports, neglecting the potential of unlabeled data and historical price trends. In response, we propose a novel approach that combines graph-based structures with historical price data to develop self-learned stock embeddings for A-share recommendations. Our method leverages self-supervised learning, bypassing the need for human-generated labels and autonomously uncovering latent relationships and patterns within the data. This dual-input strategy enhances the understanding of market dynamics, leading to more accurate stock predictions. Our contributions include a novel framework for label-free stock recommendations with modeling stock connections and pricing information, and empirical evidence demonstrating the robustness and adaptability of our approach in the volatile Chinese stock market. Full article

In recent years, we have witnessed a massive increase in the number of startups, which are also producing significant amounts of digital data. This poses a new challenge for expert analysts due to their limited attention spans and knowledge, also considering the low success rate of empirical startup evaluation. However, this new era also presents a great opportunity for the application of artificial intelligence (AI) towards intelligent startup investments. There are only a few works that have considered the potential of AI for startup recommendation, and they have not paid attention to the actual requirements of investors, also neglecting to investigate the desirability, feasibility, and value proposition of this venture. In this paper, we answer these questions by conducting a survey in collaboration with three major organizations of the Greek startup ecosystem. Furthermore, this paper also presents the design specifications for an AI-based decision support system for forecasting startup sustainability that is aligned with the requirements of expert analysts. Preliminary experiments with 44 Greek startups demonstrate Random Forest’s strong ability to predict sustainability scores. Full article

The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens’ opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study’s material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project’s implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes. Full article

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset. Full article