Search Results (2,115)

This article aims to explore and analyze current issues and features of environmental protection in managing local tourism destinations based on the principles of sustainable development through the relevant literature and thus to provide an insight into major environmental measures and activities that should be implemented in practice, emphasizing the importance of environmental sustainability as a key factor in the development and success of local tourist destinations in today’s business environment. Qualitative methods were used, with the literature review based on content analysis by keywords. This particularly affects the business process efficiency and the participation of destination stakeholders and in many cases leads to a low level of environmentally sustainable destination practices. In addition to this theoretical approach, this study also has direct managerial implications for destination environmental business operations. An attractive and well-preserved environment is the primary factor of tourism and local tourism destination development and its success, as well as an integrated part of the tourism product. This study addresses a critical gap in the existing literature on environmental sustainability at local destinations, where prior work has often overlooked the integration of actionable, practice-oriented frameworks tailored for both researchers and practitioners. While theoretical insights into sustainable practices abound, there remains a scarcity of holistic analyses that bridge scholarly understanding with implementable strategies for on-the-ground application. To fill this void, our research provides a comprehensive overview and systematic analysis of current practices, with targeted emphasis on co-developing scalable frameworks for improving environmentally sustainable practices at local destinations. Full article

Cardiorenal syndrome (CRS) is a term used to describe the combined dysfunction of the heart and kidneys. This complex disorder is widely acknowledged to be challenging in both its diagnosis and management, and this is the case particularly in the elderly population, due to multi-morbidity, polypharmacy, and age-related physiological changes. Given advancements in medicine and more prolonged cumulative exposure to risk factors in the elderly population, it is likely that the prevalence of chronic kidney disease (CKD) and heart failure (HF) will continue to rise going forward. Hence, understanding the mechanisms involved in the development of CRS is paramount. There are five different CRS types—they are categorised depending on the primary organ involved the acuity of disease. The pathophysiological process behind CRS is complex, involving the interplay of many processes including hemodynamic changes, neurohormonal activation, inflammation, oxidative stress, and endothelial dysfunction and vascular stiffness. The numerous diagnostic and management challenges associated with CRS are significantly further exacerbated in an elderly population. Biomarkers used to aid the diagnosis of CRS, such as serum creatinine and brain natriuretic peptide (BNP), can be challenging to interpret in the elderly population due to age-related renal senescence and multiple comorbidities. Polypharmacy can contribute to the development of CRS and therefore, before initiating treatment, coordinating a patient-centred, multi-speciality, holistic review to assess potential risks versus benefits of prescribed treatments is crucial. The overall prognosis of CRS in the elderly remains poor. Treatments are primarily directed at addressing the sequelae of the underlying aetiology, which often involves the removal of fluid through diuretics or ultrafiltration. Careful considerations when managing elderly patients with CRS is essential due to the high prevalence of frailty and functional decline. As such, in these patients, early discussions around advance care planning should be prioritised. Full article

Background/Objectives: Evidence-based nursing practice (EBNP) is essential in high-acuity settings such as intensive care units (ICUs) and emergency departments (EDs), where nurses are frequently required to make time-critical, high-stakes clinical decisions that directly influence patient safety and outcomes. Despite its recognized importance, the implementation of EBNP remains inconsistent, with frontline nurses often facing barriers to accessing and applying current evidence. Methods: This descriptive, cross-sectional study systematically mapped and prioritized clinical questions generated by ICU and ED nurses at a tertiary hospital in South Korea. Using open-ended questionnaires, 204 clinical questions were collected from 112 nurses. Each question was coded and classified according to the Nursing Interventions Classification (NIC) taxonomy (8th edition) through a structured cross-mapping methodology. Inter-rater reliability was assessed using Cohen’s kappa coefficient. Results: The majority of clinical questions (56.9%) were mapped to the Physiological: Complex domain, with infection control, ventilator management, and tissue perfusion management identified as the most frequent areas of inquiry. Patient safety was the second most common domain (21.6%). Notably, no clinical questions were mapped to the Family or Community domains, highlighting a gap in holistic and transitional care considerations. The mapping process demonstrated high inter-rater reliability (κ = 0.85, 95% CI: 0.80–0.89). Conclusions: Frontline nurses in high-acuity environments predominantly seek evidence related to complex physiological interventions and patient safety, while holistic and community-oriented care remain underrepresented in clinical inquiry. Utilizing the NIC taxonomy for systematic mapping establishes a reliable framework to identify evidence gaps and support targeted interventions in nursing practice. Regular protocol evaluation, alignment of continuing education with empirically identified priorities, and the integration of concise evidence summaries into clinical workflows are recommended to enhance EBNP implementation. Future research should expand to multicenter and interdisciplinary settings, incorporate advanced technologies such as artificial intelligence for automated mapping, and assess the long-term impact of evidence-based interventions on patient outcomes. Full article

This study synthesizes the rapidly expanding evidence on how digital technologies reshape international trade, with a particular focus on small and medium-sized enterprises (SMEs). Guided by two research questions—(RQ1) How do digital tools influence the volume and composition of cross-border trade? and (RQ2) How do these effects vary by countries’ development level and firm size?—we conducted a PRISMA-compliant systematic literature review covering 2010–2024. Searches across eight major databases yielded 1857 records; after duplicate removal, title/abstract screening, full-text assessment, and Mixed Methods Appraisal Tool (MMAT 2018) quality checks, 86 peer-reviewed English-language studies were retained. Findings reveal three dominant technology clusters: (1) e-commerce platforms and cloud services, (2) IoT-enabled supply chain solutions, and (3) emerging AI analytics. E-commerce and cloud adoption consistently raise export intensity—doubling it for digitally mature SMEs—while AI applications are the fastest-growing research strand, particularly in East Asia and Northern Europe. However, benefits are uneven: firms in low-infrastructure settings face higher fixed digital costs, and cybersecurity and regulatory fragmentation remain pervasive obstacles. By integrating trade economics with development and SME internationalization studies, this review offers the first holistic framework that links national digital infrastructure and policy support to firm-level export performance. It shows that the trade-enhancing effects of digitalization are contingent on robust broadband penetration, affordable cloud access, and harmonized data-governance regimes. Policymakers should, therefore, prioritize inclusive digital-readiness programs, while business leaders should invest in complementary capabilities—data analytics, cyber-risk management, and cross-border e-logistics—to fully capture digital trade gains. This balanced perspective advances theory and practice on building resilient, equitable digital trade ecosystems. Full article

Aims/Background: Polypharmacy, or the concurrent intake of five or more medications, is a significant issue in clinical practice, particularly in multimorbid elderly individuals. Despite its importance for patient safety, medical education often lacks systematic training in recognising and managing polypharmacy within the framework of patient-centred care. We investigated the impact of a structured learning intervention introducing polypharmacy as a chronic condition, assessing whether it enhances medical students’ diagnostic competence, confidence, and interprofessional collaboration. Methods: A prospective cohort study was conducted with 50 final-year medical students who received a three-phase educational intervention. Phase 1 was interactive workshops on the principles of polypharmacy, its dangers, and diagnostic tools. Phase 2 involved simulated patient consultations and medication review exercises with pharmacists. Phase 3 involved reflection through debriefing sessions, reflective diaries, and standardised patient feedback. Student knowledge, confidence, and attitudes towards polypharmacy management were assessed using pre- and post-intervention questionnaires. Quantitative data were analysed through paired t-tests, and qualitative data were analysed thematically from reflective diaries. Results: Students demonstrated considerable improvement after the intervention in identifying symptoms of polypharmacy, suggesting deprescribing strategies, and working in multidisciplinary teams. Confidence in prioritising polypharmacy as a primary diagnostic problem increased from 32% to 86% (p < 0.01), and knowledge of diagnostic tools increased from 3.1 ± 0.6 to 4.7 ± 0.3 (p < 0.01). Standardised patients felt communication and patient-centredness had improved, with satisfaction scores increasing from 3.5 ± 0.8 to 4.8 ± 0.4 (p < 0.01). Reflective diaries indicated a shift towards more holistic thinking regarding medication burden. The small sample size limits the generalisability of the results. Conclusions: Teaching polypharmacy as a chronic condition in medical school enhances diagnostic competence, interprofessional teamwork, and patient safety. Education is a structured way of integrating the management of polypharmacy into routine clinical practice. This model provides valuable insights for designing medical curricula. Future research must assess the impact of such training on patient outcomes and clinical decision-making in the long term. Full article

Aging is a multifactorial process that affects various physiological functions, including masticatory performance, which is crucial for oral health and nutritional well-being. Impaired masticatory function, often due to factors such as tooth loss, reduced salivation, or muscle atrophy, can lead to significant nutritional challenges and compromise the overall health of elderly individuals. Recent research has illuminated the interconnectedness of masticatory function, oral microbiota, and gut health, suggesting that altered chewing ability may disrupt oral microbial communities, which in turn affect gastrointestinal health and systemic inflammation. This commentary review provides a comprehensive analysis of the role of masticatory function in aging, exploring its impact on the oral microbiota, gut health, and broader nutritional status. We discuss the potential consequences of impaired mastication, including malnutrition, dysbiosis, and gastrointestinal disorders, and explore possible strategies for improving masticatory function and maintaining a healthy gut microbiome through interventions like dietary modifications, oral care, and rehabilitation. We aim to underscore the importance of integrating masticatory function management into the broader context of aging-related healthcare, promoting holistic, multidisciplinary approaches to support nutritional needs and quality of life in older adults. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Educational institutions are facing a crisis characterized by the need to address diverse learning styles and vocational aspirations, exacerbated by ongoing financial pressures. To navigate these challenges effectively, there is an urgent need to innovate educational practices and learning environments, ensuring they are adaptable and responsive to the evolving needs of students and the workforce. The adoption of the Industry 5.0 framework offers a promising solution, providing a holistic approach that emphasizes the integration of human creativity and advanced technologies to transform educational institutions into resilient, human-centric, and sustainable learning environments. In this context, this article presents a transdisciplinary methodology that integrates Asset Management (AM) with Social Innovation (SI) through Design Thinking (DT) to co-design Educational Facilities 5.0 with stakeholders. The application of the proposed approach in an AgroLab case study—a food and agricultural laboratory—demonstrates how the methodology enables the definition of an Educational Facility 5.0 and generates AM Design Knowledge to support informed decision-making in the subsequent design, implementation, and operation phases. Following DT principles—where knowledge emerges through iterative experimentation and insights from practical applications—this article also discusses the role of SI and DT in AM, the role of Large Language Models in convergent processes, and a vision for Educational Facilities 5.0. Full article

Sustainable farmland management is vital for global food security and for mitigating environmental degradation and climate change. While individual practices such as crop rotation and no-tillage are well-documented, this review synthesizes current evidence to illuminate the critical synergistic effects of integrating four key strategies: crop rotation, conservation tillage, organic amendments, and soil microbiome management. Crop rotation enhances nutrient cycling and disrupts pest cycles, while conservation tillage preserves soil structure, reduces erosion, and promotes carbon sequestration. Organic amendments replenish soil organic matter and stimulate biological activity, and a healthy soil microbiome boosts plant resilience to stress and enhances nutrient acquisition through key functional groups like arbuscular mycorrhizal fungi (AMFs). Critically, the integration of these practices yields amplified benefits that far exceed their individual contributions. Integrated management systems not only significantly increase crop yields (by up to 15–30%) and soil organic carbon but also deliver profound global ecosystem services, with a potential to sequester 2.17 billion tons of CO₂ and reduce soil erosion by 2.41 billion tons annually. Despite challenges such as initial yield variability, leveraging these synergies through precision agriculture represents the future direction for the field. This review concludes that a holistic, systems-level approach is essential for building regenerative and climate-resilient agroecosystems. Full article

This study aims to investigate the impacts of urbanization and propose strategies to mitigate urban vulnerability. The study first defines and examines urban vulnerability within the context of spatial management, allowing for the introduction of indicators derived from the Sustainable Territorial Economic/Environmental Management Approach (STeMA), complemented by maps, to assess urban vulnerability. These indicators offer a holistic view of urban areas, highlighting the key areas of concern. Secondly, the study identifies highly vulnerable areas and provides targeted intervention recommendations. A European city with a population of more than 500,000 is considered as a case study. By integrating quantitative analysis and qualitative insights, this study aims to provide actionable solutions for urban planners and policymakers in order to improve sustainability in rapidly urbanizing regions. Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

This paper focuses on the complex dynamics that concern assembly line balance in the context of mass customization within manufacturing. In fact, the increase in demand for customized products has heightened the complexities associated with achieving optimal efficiency, productivity, product quality, and customer satisfaction. The research proposes a multi-criteria analysis of statistical methods to determine the fluctuation of parameters affecting the state of balance of an assembly line. A 3D matrix model is suggested to analyze the parameters managing the assembly line. This representation is executed using the MATLAB R2024b tool, and a methodology for finding the variability of parameters affecting balance through statistical approaches is proposed. We observed that changes in parameters such as task times, worker efficiency, or material flow led to significant changes in the line’s overall balance. As a result, static balancing becomes inadequate to deal with the complexities introduced by these highly variable parameters. The novelty of this paper consists of the innovative integration of multi-criteria statistical analysis and 3D matrix modeling to detect parameter variability and optimize assembly line balancing. Conventional static approaches are often unable to capture the process-dynamic aspect of modern manufacturing. This work presents a systematic methodology capable of identifying, quantifying, and moderating the variability of key operating parameters. This methodology, carried out using MATLAB-based simulations, is based on principal component analysis (PCA) and correlation analysis to detect critical factors influencing balancing efficiency. By structuring assembly line parameters in a 3D matrix representation, this research gives a holistic, data-based method for improving decision-making in balancing procedures. The research goes beyond theoretical modeling by applying the approach to a real automotive assembly line, validating its effectiveness and demonstrating its practical applicability in industrial conditions. Full article

In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which serves as a pivotal cornerstone for effective maintenance and operational decision-making. While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. This study endeavors to bridge this gap through the following contributions: (1) A rigorous analysis and systematic categorization of application scenarios for equipment RUL prediction, elucidating their distinct characteristics and requirements. (2) A comprehensive summary and comparative evaluation of several AI algorithms deemed suitable for RUL prediction, delineating their respective strengths and limitations. (3) An in-depth comparative analysis of the applicability of AI algorithms across varying application contexts, informed by a nuanced understanding of different application scenarios and AI algorithm research. (4) An insightful discussion on the current challenges confronting AI-based RUL prediction technology, coupled with a forward-looking examination of its future prospects. By furnishing a meticulous and holistic understanding of the traits of various AI algorithms and their contextual applicability, this study aspires to facilitate the attainment of optimal application outcomes in the realm of equipment RUL prediction. Full article

Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. Interviews with current and former EKO Cobra members reveal key risk factors, including overconfidence, insufficient training, inadequate equipment, and the challenges of high-stakes scenarios. Using a structured yet thematically flexible interview analysis approach, the study adopts group dynamics theory as its framework and applies a semi-inductive, semi-deductive qualitative methodology. It examines risk categorisation in ad hoc operations, as well as the interplay between risk perception and training, proposing actionable strategies to enhance safety and preparedness through tailored training programmes. The findings underscore the transformative impact of intensive scenario-based and high-stress training, which enhances situational awareness and reinforces team-based responses through cohesion and effective communication. Group dynamics, including cohesion and effective communication, play a pivotal role in mitigating risks and ensuring operational success. Importantly, this research advocates for continuous, adaptive, and specialised training to address evolving challenges. By linking theoretical frameworks with practical and actionable insights, this study proposes a holistic training approach that promotes both resilience and long-term sustainability in police operations. These findings offer valuable guidance to elite units like EKO Cobra for broader policy frameworks by providing insights that make police operations safer and more effective and resilient. Full article