Search Results (467)

Background: Brain health is a growing public health priority due to the high global burden of neurological and mental disorders. Promoting brain health across the lifespan supports individual and societal well-being, creativity, and productivity. Objective: To address the need for specialized education in this field, the University of Bern developed a Certificate of Advanced Studies (CAS) in Brain Health. This article outlines the program’s rationale, structure, and goals. Program Description: The one-year, 15 ECTS-credit program is primarily online and consists of four modules: (1) Introduction to Brain Health, (2) Brain Disorders, (3) Risk Factors, Protective Factors and Interventions, and (4) Brain Health Implementation. It offers a multidisciplinary, interprofessional, life-course approach, integrating theory with practice through case studies and interactive sessions. Designed for healthcare and allied professionals, the CAS equips participants with skills to promote brain health in clinical, research, and public health contexts. Given the shortage of trained professionals in Europe and globally, the program seeks to build a new generation of brain health advocates. It aims to inspire action and initiatives that support the prevention, early detection, and management of brain disorders. Conclusions: The CAS in Brain Health is an innovative educational response to a pressing global need. By fostering interdisciplinary expertise and practical skills, it enhances professional development and supports improved brain health outcomes at individual and population levels. Full article

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

Consumer-accessible and user-friendly smart products such as unmanned aerial vehicles (UAVs), or drones, have become widely used, adaptable, and acceptable devices to observe, assess, measure, and explore urban and natural environments. A drone’s relatively low cost and flexibility in the level of expertise required to operate it has enabled users from novice to industry professionals to adapt a malleable technology to various disciplines. This review examines the academic literature and maps how drones are currently being used in 93 rural and regional city councils in New South Wales, Australia. Through a systematic review of the academic literature and scrutiny of current drone use in these councils using publicly available information found on council websites, findings reveal potential uses of drone technology for local governments who want to engage with smart technology devices. We looked at how drones were being used in the management of the council’s environment; health and safety initiatives; infrastructure; planning; social and community programmes; and waste and recycling. These findings suggest that drone technology is increasingly being utilised in rural and regional areas. While the focus is on rural and regional New South Wales, a review of the academic literature and local council websites provides a snapshot of drone use examples that holds global relevance for local councils in urban and remote areas seeking to incorporate drone technology into their daily practice of city, town, or region governance. Full article

Biochar serves as an effective adsorbent for the heavy metal cadmium, with its performance significantly influenced by its physicochemical properties and various environmental features. Traditional machine learning models, though adept at managing complex multi-feature relationships, rely heavily on expertise in feature engineering and hyperparameter optimization. To address these issues, this study employs an automated machine learning (AutoML) approach, automating feature selection and model optimization, coupled with an intuitive online graphical user interface, enhancing accessibility and generalizability. Comparative analysis of four AutoML frameworks (TPOT, FLAML, AutoGluon, H₂O AutoML) demonstrated that H₂O AutoML achieved the highest prediction accuracy (R² = 0.918). Key features influencing adsorption performance were identified as initial cadmium concentration (23%), stirring rate (14.7%), and the biochar H/C ratio (9.7%). Additionally, the maximum adsorption capacity of the biochar was determined to be 105 mg/g. Optimal production conditions for biochar were determined to be a pyrolysis temperature of 570–800 °C, a residence time of ≥2 h, and a heating rate of 3–10 °C/min to achieve an H/C ratio of <0.2. An online graphical user interface was developed to facilitate user interaction with the model. This study not only provides practical guidelines for optimizing biochar but also introduces a novel approach to modeling using AutoML. Full article

Sales forecasting in make-to-order (MTO) production is particularly challenging for small- and medium-sized enterprises (SMEs) due to high product customization, volatile demand, and limited historical data. This study evaluates the practical feasibility and accuracy of statistical and machine learning (ML) forecasting methods in MTO settings across three manufacturing sectors: electrical equipment, steel, and office supplies. A cross-industry benchmark assesses models such as ARIMA, Holt–Winters, Random Forest, LSTM, and Facebook Prophet. The evaluation considers error metrics (MAE, RMSE, and sMAPE) as well as implementation aspects like computational demand and interpretability. Special attention is given to data sensitivity and technical limitations typical in SMEs. The findings show that ML models perform well under high volatility and when enriched with external indicators, but they require significant expertise and resources. In contrast, simpler statistical methods offer robust performance in more stable or seasonal demand contexts and are better suited in certain cases. The study emphasizes the importance of transparency, usability, and trust in forecasting tools and offers actionable recommendations for selecting a suitable forecasting configuration based on context. By aligning technical capabilities with operational needs, this research supports more effective decision-making in data-constrained MTO environments. Full article

This study introduces the concept of DEEPEIA, a novel deep learning (DL) platform designed to recommend the optimal export market, and its ideal foreign champion, for any product or service offered by a small and medium-sized enterprise (SME). Drawing on expertise in SME internationalization and leveraging recent advances in generative artificial intelligence (AI), this research addresses key challenges faced by SMEs in global expansion. A systematic review of existing platforms was conducted to identify current gaps and inform the conceptualization of an advanced generative DL recommender system. The Discussion section proposes the conceptual framework for such a decision optimizer within the context of contemporary technological advancements and actionable insights. The conclusion outlines future research directions, practical implementation strategies, and expected obstacles. By mapping the current landscape and presenting an original forecasting tool, this work advances the field of AI-enabled SME internationalization while still acknowledging that more empirical validation remains a necessary next step. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

This paper outlines the development and delivery of a novel, collaborative, co-production approach to incorporating lived experience in the development of policy and practice in the area of domestic abuse. “SEEKERS” (Sharing Experience, Expertise and Knowledge for Effective Responses and Support) is an initiative which brings together victims and advocates, police, practitioners and researchers as equal partners. It creates opportunities for them to share their experiences, expertise, and knowledge, so that others can learn from these and use this learning in addressing domestic abuse-related issues more effectively. Throughout this paper, we discuss some of the challenges encountered in developing and delivering activities and how these were addressed. Notable benefits of the approach will be highlighted, as indicated by feedback from those involved in a range of capacities, including police and law enforcement practitioners, policy makers, councillors, service providers, support services, victim advocates and survivors of domestic abuse. It is hoped that this paper will contribute to ongoing discussions regarding the ways in which different agencies and stakeholders can work together more effectively and how we can create methods and spaces to support meaningful interaction, collaboration, and co-production with victims. Full article

Background/Objectives: The development of hypophosphatemia has been associated with intravenous iron products, with the rate of hypophosphatemia found to be higher with ferric carboxymaltose. This consensus statement provides clinical guidance on the risk of hypophosphatemia development with ferric carboxymaltose and the approaches for management. To develop consensus recommendations regarding the clinical implications of hypophosphatemia after the administration of ferric carboxymaltose, the assessment of patient risk profile, and recommended approaches for risk reduction. Methods: Consensus statements were developed from an in-person meeting of specialists with expertise in iron pathophysiology and iron therapy and further supplemented with literature review. The multidisciplinary expert panel comprised global iron specialists spanning anesthesiology, cardiology, gastroenterology, obstetrics/gynecology, hematology, nephrology, and iron molecular biology. Structured discussions were held in an in-person meeting to gather expert opinion on the evidence base regarding intravenous iron and hypophosphatemia. Consolidated summary opinions underwent further iterations of panel review to form consensus recommendation statements. Results: The expert panel developed the following consensus statements: (1) Routine serum phosphate level measurement is not recommended for low-risk patients before or after treatment with ferric carboxymaltose, as most cases of hypophosphatemia that occur following the administration of ferric carboxymaltose are asymptomatic and transient; (2) patients receiving ferric carboxymaltose should be assessed for the degree of risk for developing symptomatic or severe hypophosphatemia prior to administration; (3) monitoring serum phosphate is recommended for patients at an increased risk for developing low serum phosphate or who require repeated courses of ferric carboxymaltose treatment at higher doses; (4) prophylactic oral phosphorus after ferric carboxymaltose is unlikely to effectively elevate phosphate and is not recommended for routine clinical practice; and (5) hypophosphatemic osteomalacia is rare and the risk of development after the administration of ferric carboxymaltose, in particular single infusion, is low. Conclusions: Hypophosphatemia following ferric carboxymaltose is predominantly asymptomatic and transient. Individuals at higher risk for developing hypophosphatemia with ferric carboxymaltose treatment include those who receive multiple infusions, higher cumulative doses, or long-term iron treatment or who have underlying clinical risk factors. These consensus statements provide structured guidance on the risk of hypophosphatemia with ferric carboxymaltose and the approaches to clinical management. Full article

“Racial Injustice, Violence, and Resistance: New Approaches from Multidimensional Perspectives” is the product of a dialogue among three experts, bridging the disciplines of economics, criminology, and sociology and bringing together expertise in racial inequality, urban sociology, international immigration, Latin America, and Latino/a/x studies [...] Full article

Tainan’s Cigu, located on Taiwan’s southwestern coast, is a prominent aquaculture hub known for its extensive ponds, tidal flats, and lagoons. This study explored the novel integration of kayaking within aquavoltaic (APV) aquaculture ponds, creating a unique hybrid tourism landscape that merges industrial land use (aquaculture and energy production) with nature-based recreation. We investigated the relationships among facility maintenance and safety professionalism (FM), the perceived value of kayaking training (PV), and green energy and sustainable development recognition (GS) within these APV systems in Cigu, Taiwan. While integrating recreation with renewable energy and aquaculture is an emerging approach to multifunctional land use, the mechanisms influencing visitors’ sustainability perceptions remain underexplored. Using data from 613 kayaking participants and structural equation modeling, we tested a theoretical framework encompassing direct, mediated, and moderated relationships. Our findings reveal that FM significantly influences both PV (β = 0.68, p < 0.001) and GS (β = 0.29, p < 0.001). Furthermore, PV strongly affects GS (β = 0.56, p < 0.001). Importantly, PV partially mediates the relationship between FM and GS, with the indirect effect (0.38) accounting for 57% of the total effect. We also identified significant moderating effects of APV coverage, guide expertise, and operational visibility. Complementary observational data obtained with underwater cameras confirm that non-motorized kayaking causes minimal ecological disturbance to cultured species, exhibiting significantly lower behavioral impacts than motorized alternatives. These findings advance the theoretical understanding of experiential learning in novel technological landscapes and provide evidence-based guidelines for optimizing recreational integration within production environments. Full article

This investigation examines the psychological mechanisms governing human–virtual influencer relationships and their consequential impact on environmentally-conscious consumer behavior within digital marketing ecosystems. Employing theoretical frameworks from computer-mediated communication and social psychology, this study scrutinizes how algorithmically generated social media personalities cultivate para-social relationships that drive sustainable consumption patterns. The research operationalizes five core virtual influencer characteristics—expertise, similarity, attractiveness, familiarity, and para-social interaction—as predictive variables influencing relationship commitment and subsequent eco-conscious brand engagement. Consumer innovativeness functions as a moderating variable within this theoretical model. The data collection encompassed 677 respondents demonstrating active engagement with sustainability-focused virtual influencer content, analyzed through structural equation modeling (EQS 6.4) and the PLS-SEM methodology (SmartPLS 4.0). The empirical analysis reveals significant positive correlations between virtual influencer characteristics and relationship commitment, with similarity and attractiveness demonstrating the strongest predictive validity. Relationship commitment emerged as a significant mediator influencing sustainable brand attitudes, which subsequently affected purchase intentions for environmentally responsible products. Consumer innovativeness demonstrated positive moderating effects across all virtual influencer characteristics, with particularly robust effects observed for attractiveness and para-social interaction within sustainable brand contexts. This research advances the human–AI interaction literature by elucidating the psychological mechanisms through which virtual influencers facilitate consumer relationship formation and drive behavioral outcomes toward sustainable consumption practices. The findings provide empirically validated strategic frameworks for marketers developing virtual influencer campaigns that promote environmental stewardship, emphasizing the cultivation of perceived similarity and attractiveness while incorporating audience innovativeness as a critical segmentation variable in sustainable marketing initiatives. Full article

The ongoing digitalization of industrial companies requires a structured, strategic integration of digital concepts into business processes. Digital transformation (DT) requires clearly defined roadmaps that align digital technologies with business objectives. Although there are many digital maturity models (DMMs), most are industry-specific and do not address the unique characteristics of individual companies. Even SME-focused models often struggle to close the gap between current and target maturity levels, hindering effective DT implementation. This study examines the existing academic and professional literature on DMMs for SMEs and assesses digital readiness in an industrial context. From these findings, the Dynamic Adaptive Maturity Assessment Model (DAMA-AHP) was developed. It comprises 66 DT elements in six dimensions: People and Expertise, Operability, Organization, Products and Production Processes, Strategy, and Technology. DAMA-AHP incorporates the Analytic Hierarchy Process (AHP), which has been enhanced with customizable weighting at both the dimension and element levels. This enables precise alignment with the company’s priorities and the definition of customized target maturity levels that form the basis for a tailored transformation roadmap. Validation through a case study confirmed the practical value of DAMA-AHP in measuring digital maturity and defining strategic DT priorities. It provides a comprehensive, adaptable, and dynamic framework that promotes continuous improvement and sustainable competitiveness of SMEs in the evolving digital economy. Full article

In teacher education research, the primary source of data used to measure teachers’ reflective skills are written reflection products, which are often collected in the context of field experiences following specific structural guidelines (e.g., portfolio texts). However, it is unclear how appropriate written products are for this purpose, considering teachers’ everyday professional lives, in which reflection is a mostly verbal, highly self-directed process depending on the teachers’ level of expertise. Therefore, in our study, we analyzed how teachers’ free, unstructured reflections differ from reflections structured by model-based reflection prompts. In an exploratory qualitative research design with theoretical sampling, a total of 22 prospective teachers at four different levels of expertise were asked to reflect on two standardized fictitious vignettes using a think-aloud approach. For the first vignette, participants reflected in an unstructured way. For the second vignette, the reflection was structured using simple model-based reflection prompts. On average, the participants showed a significantly better reflective performance in the structured condition, but no significant differences in relation to the level of expertise were observed. The results contribute to a better understanding of the validity of typical reflections used in teacher education as an indicator of reflective practice in the professional field. Full article

This study examines the impact of capital market openness on corporate green total factor productivity (GTFP) using a quasi-natural experiment based on the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect policies. Employing a multi-period difference-in-differences (DID) approach, the findings reveal that capital market openness significantly enhances corporate GTFP through two primary mechanisms: strengthening firms’ green financial resources and technological innovation (green “hard strength”) and improving corporate environmental governance, green information disclosure, and managerial green expertise (green “soft strength”). Further heterogeneity analysis suggests that firms with greater institutional investor engagement, higher market competition, and non-state ownership exhibit stronger responses. These results provide policy insights into leveraging financial liberalization to drive corporate sustainability and green economic growth. This study highlights the role of financial markets in supporting global carbon neutrality and sustainable development goals. Full article