Search Results (300)

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article

Throughout the generations, traditional academic writing skills development has taught students in socio-human programs to express their knowledge and thoughts with an evidence-based foundation, helping them make a special connection with their professional fields. However, a lack of digital learning and writing resources in this process has been identified. This study of the social work field connects digital academic writing, social protection functionality, and research innovations, identifying and exploring open-access (OA) educational and social resources for social work higher education (SWHE). Applying content analyses to online documents and websites, we identified key terms characteristic of social work, following a standard approach on formulating research questions, identifying categories, creating a code book, sampling, and measuring information. The research questions were as follows: How is digital academic writing being developed in social work education programs in Romanian universities? Where do researchers, students, teachers, and professionals gather OA digital information and data for academic innovation? What kind of OA information and data are contained in websites for academic writing? We also used OA socio-educational resource analysis to derive digital, evidence-based, and academic writing codes. The frequencies of these elements in documents and websites were examined. Professional samples of four OA documents and five academic and non-academic Romanian websites with extensions were processed. Furthermore, information from a non-academic official website concerning social protection functionality was observed, identified, and measured. We concluded that academic writing is not included as an independent course in the curricula of Romanian social work programs at universities; this topic is rarely researched. Digital and evidence-based education is also a marginalized topic in socio-human scientific resources. OA information, laws, reports, and statistics were identified. Information on scientific research, academic–non-academic partnerships, descriptions of good practices, and human resources information was lacking. In conclusion, this study contributes to increasing productivity and developing digital academic skills in social work education and research. Full article

This article presents a comparative study of content generated by artificial intelligence (AI) and articles authored by professional journalists, focusing on the perspective of a Kazakhstani audience. The analysis was conducted based on several key criteria, including the structure of the article, writing style, factual accuracy, citation of sources, and completeness of the information. The study spans a variety of topics, such as politics, economics, law, sports, education, and social issues. The results indicate that AI-generated articles tend to exhibit greater structural clarity and neutrality. On the other hand, articles written by journalists score higher in terms of factual accuracy, analytical depth, and the use of verified sources. Furthermore, the research explores the significance of journalistic ethics in ensuring transparency and information completeness in content production. Ultimately, the findings emphasize the importance of upholding rigorous journalistic standards when integrating AI into media practices. Full article

The Field Guide “Paleopathology Collection at the University of Granada” includes one of the most important collections of human bone remains that present anomalies or specific characteristics that can be used to determine the existence of diseases, accidents or malformations in each subject’s life, as well as the probable cause of death. The collection consists of several thousand skeletons or parts of them. It is located at the Faculty of Medicine of the University of Granada and has been created and managed by Professor Miguel C. Botella López, founder and director of the Anthropology Laboratory of the University of Granada, between 1971 and 2024. Professor Botella is the author of the diagnoses made for each specimen from different geographical areas of Spain in a time period ranging from the Neolithic to the present day. The collection is of special interest to students and professionals in medicine, archaeology, criminology or law. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

Background: The rapid integration of artificial intelligence (AI) technologies into healthcare systems presents new opportunities and challenges, particularly regarding legal and ethical implications. In Saudi Arabia, the lack of legal awareness could hinder safe implementation of AI tools. Methods: A sequential explanatory mixed-methods design was employed. In Phase One, a structured electronic survey was administered to 357 clinicians across public and private healthcare institutions in Saudi Arabia, assessing legal awareness, liability concerns, data privacy, and trust in AI. In Phase Two, a qualitative expert panel involving health law specialists, digital health advisors, and clinicians was conducted to interpret survey findings and identify key regulatory needs. Results: Only 7% of clinicians reported high familiarity with AI legal implications, and 89% had no formal legal training. Confidence in AI compliance with data laws was low (mean score: 1.40/3). Statistically significant associations were found between professional role and legal familiarity (χ² = 18.6, p < 0.01), and between legal training and confidence in AI compliance (t ≈ 6.1, p < 0.001). Qualitative findings highlighted six core legal barriers including lack of training, unclear liability, and gaps in regulatory alignment with national laws like the Personal Data Protection Law (PDPL). Conclusions: The study highlights a major gap in legal readiness among Saudi clinicians, which affects patient safety, liability, and trust in AI. Although clinicians are open to using AI, unclear regulations pose barriers to safe adoption. Experts call for national legal standards, mandatory training, and informed consent protocols. A clear legal framework and clinician education are crucial for the ethical and effective use of AI in healthcare. Full article

Since the enforcement of Law 98/2016 on public procurement in Romania, the inclusion of price adjustment clauses in construction contracts has become a standard practice. This paper, which presents a comprehensive analysis of the financial implications of eight adjustment formulas applied to public construction projects executed over three durations (12, 24, and 36 months) between 2018 and 2024, is a significant contribution to the field. A comparative analysis using objective indices published by Romania’s National Institute of Statistics reveals the impact of inflation and cost variations on adjusted contract values. Three scenarios, each starting in different years (2018, 2020, and 2022), are explored to determine the sensitivity of the formulas to market fluctuations. Results show that by applying the eight adjustment formulas, only two formulas tend toward annual inflation. The indices used by the construction branch are not correlated with yearly inflation, and when no advance payments are granted, they offer a reliable basis for economic equilibrium in public contracting. The study guides the selection of appropriate adjustment models to manage financial risk in a volatile construction market, providing valuable insights for academics, researchers, and professionals in civil engineering and public procurement. Full article

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement. Full article

The purpose of this research is to examine how relationships between corporate control environments and stakeholders mediate the different dimensions of pressure on auditor independence. In France, two (joint) auditors are required by law for listed companies. In this context, we analyze the experiences of higher-echelon professionals of audit firms, controllers, and managers who could elucidate the essence of pressure on auditor independence in their lived environment. An interpretative approach and empirical analysis were adopted for this study to expand on the literature and proffer an answer to the following research question: How does the relationship between a control environment and a stakeholder mediate the pressures on auditor independence? Interviews involved seven participants, mainly higher-echelon professionals of Big Four firms, as well as two members of auditee organizations, and a member of an audit committee. In addition, the narratives from the documents gathered from the EU audit legislation implementation database constitute our data corpus. Thematic coding was used to organize the results. The findings reveal that control environment best practices and down-to-earth corporate governance policies, participated in by both auditors and audited organizations, cushion the pressures on auditors. This, in turn, presents a positive and significant impact on auditor independence. Overall, the dimensions that mediate the pressures on auditors are as follows: the consciousness of pressure in itself; the reputation and experience of the audit firm; and the interactions between the auditors and corporate governance. Other factors include the cordiality of the relationship between the auditor and corporate management and the resulting healthy end of the negotiation between auditors and auditees. This study contributes to the theory and practical discussion of the relationships between the corporate control environment, corporate governance, auditing, and pressure on auditor independence. Full article

Background and Objectives: Maxillofacial trauma represents a significant global health challenge with substantial physical, psychological, and socioeconomic consequences. Materials and Methods: This narrative review analyzed 112 articles published between 2000 and 2024 examining epidemiology, prevention, economics, and outcomes of maxillofacial trauma in critical care settings. Results: Road traffic accidents remain the primary cause globally, followed by interpersonal violence and occupational injuries. Effective prevention strategies include seat belt laws, helmet legislation, and violence prevention programs. Economic burden encompasses direct healthcare costs (averaging USD 55,385 per hospitalization), productivity losses (11.8 workdays lost per incident), and rehabilitation expenses (USD 3800–18,000 per patient). Surgical management has evolved toward early intervention, minimally invasive approaches, and advanced techniques using computer-aided design and 3D printing. Complications affect 3–33% of patients, with significant functional disabilities and psychological sequelae (post-traumatic stress disorder in 27%, depression/anxiety in 20–40%). Conclusion: Maxillofacial trauma management requires multidisciplinary approaches addressing both immediate treatment and long-term rehabilitation. Despite technological advances, disparities in specialized care access persist globally. Future efforts should implement evidence-based prevention strategies, reduce care disparities, and develop comprehensive approaches addressing physical, psychological, and socioeconomic dimensions through collaboration among healthcare professionals, policymakers, and community stakeholders. Full article

Large language models (LLMs) have shown impressive general-purpose language capabilities, but their application in specialized domains such as healthcare and law remains limited due to two major challenges, namely, a lack of deep domain-specific knowledge and the inability to incorporate real-time information updates. This paper focuses on addressing these challenges by introducing parameter-sensitive low-rank adaptation (LoRA) and retrieval-augmented generation (RAG), named SensiLoRA-RAG, a two-stage framework designed to enhance LLM performance in domain-specific question-answering tasks. In the first stage, we propose a parameter-sensitive LoRA fine-tuning method that efficiently adapts LLMs to specialized domains using limited high-quality professional data, enabling rapid and resource-efficient specialization. In the second stage, we develop a chain-of-thought RAG mechanism that dynamically retrieves and integrates up-to-date external knowledge, improving the model’s ability to reason with current information and complex domain context. We evaluate our framework on tasks in the medical and legal fields, demonstrating that SensiLoRA-RAG significantly improves answer accuracy, domain relevance, and adaptability compared to baseline methods. Full article

Medical assistance in dying (MAID) remains a sensitive and evolving issue in Europe, frequently linked with discussions about human freedom, life dignity, and healthcare policy. While national consensus in Italy is absent, the Region of Tuscany has enacted Law No. 16/2025, which establishes a MAID procedure based on recent Constitutional Court rulings. The commentary aims to provide a preliminary analysis of the new law, addressing ethical, medico-legal, and social issues that emerge in relation to the Italian and global debate on the topic. The law establishes a three-stage process based on four eligibility criteria: irreversible disease, psycho-physical suffering, life-support dependence, and informed consent. However, Tuscany’s model poses medico-legal and ethical concerns, particularly about the boundaries of regional legislative competence, the duties of healthcare professionals, and the possibility of intra-national inequity or “health migration.” In addition, critical organisational implications derived from informed consent and lethal drug self-administration impede clinical implementation in some individuals with mental or neurological disorders. The lack of clarity in the different steps of the procedure, the uncertain supervision system, and the potential consequences for specific categories of vulnerable people underline the need for comprehensive national regulation. A future regulatory framework must balance procedural clarity with individual autonomy and equitable access, bringing Italy in line with larger European context for end-of-life care. Full article

Job satisfaction is a critical factor in healthcare settings, influencing both healthcare professionals’ well-being and patient care quality. Nurses, as frontline healthcare professionals, experience various stressors that impact their job satisfaction. Organizational culture (OC) and emotional intelligence (EI) have emerged as significant determinants of nurses’ job satisfaction. However, research on how these factors interact in different cultural contexts remains limited. Objective: This study examines the impact of organizational culture and emotional intelligence on nurses’ job satisfaction. Methods: A descriptive cross-sectional study was conducted among 338 nurses working in secondary and tertiary hospitals in Greece. Data were collected using the Organizational Culture Assessment Instrument (OCAI), Wong and Law Emotional Intelligence Scale (WLEIS), and Job Satisfaction Survey (JSS). Bivariate and multivariate analyses were conducted to explore the associations between job satisfaction and study variables. Results: Nurses reported moderate job satisfaction (JSS mean score = 115.24 ± 20.84). Clan culture was the dominant organizational culture, while Hierarchy culture was the most preferred. EI was recorded at high levels among participants (WLEIS mean = 86.52 ± 12.24). Higher emotional intelligence, permanent employment status, and having children emerged as the most significant predictors of job satisfaction (p < 0.05). Notably, Hierarchy culture did not significantly predict job satisfaction, suggesting that while structure influences satisfaction, it does not solely determine it. Conclusions: The findings emphasize the importance of job security, emotional intelligence, and personal responsibilities in shaping job satisfaction. To enhance satisfaction, healthcare organizations should promote EI training, supportive leadership, and flexible policies that align organizational culture with healthcare professionals’ needs. Further research is needed to explore these relationships in diverse healthcare settings. Full article

At the crossroads of technological innovation and established practice, property valuation is experiencing a significant shift with the introduction of artificial intelligence (AI) and machine learning (ML). While these technologies offer new efficiencies and predictive capabilities, their integration raises important legal, ethical, and professional questions. This paper addresses these challenges by proposing a structured framework for incorporating Explainable Artificial Intelligence (XAI) techniques into valuation practices. The primary aim is to improve their consistency, objectivity, and transparency to ensure the internal accountability of AI-driven methodologies. Drawing from the international valuation standards, the discussion centres on the essential balance between automated precision and the professional duty of care—a balance that is crucial for maintaining trust in and upholding the integrity of property valuations. By examining the role of AI within the property market and the consequent legal debates about and requirements of transparency, this article underscores the importance of developing AI-enabled valuation models that professionals and consumers alike can trust and understand. The proposed framework calls for a concerted cross-disciplinary effort to establish industry standards that support the responsible and effective integration of AI into property valuation, ensuring that these new tools meet the same high standards of reliability and clarity expected by the industry and its clients. Full article

Teacher entrepreneurship is a fundamental aspect of today’s education. Entrepreneurial Competence (EC), as established in Organic Law 3/2020, which amends Organic Law 2/2006, on Education, and reinforced by Law 14/2013, on Support for Entrepreneurs and Their Internationalization, is key to preparing students for the challenges of the 21st century. This study follows a quantitative observational design, with data collected through a questionnaire administered to over 600 education professionals, structured into three blocks and fourteen dimensions. The research is divided into three parts, corresponding to the three blocks of the questionnaire. This article focuses on the first block, which aims to evaluate teachers’ assessment of EC and to analyse their perception of the most accessible and effective options for adequately developing this competence in educational centres. Results show that all participant groups generally rated EC highly, but perceptions differ based on gender, age, and training. Teachers with EC training express greater confidence, while those without training report more challenges. School leaders rate EC more favourably, likely due to their involvement in institutional policies. The main obstacles identified are insufficient teacher training and inadequate resources. The study emphasises the importance of enhancing teacher training and adopting active methodologies to integrate entrepreneurship into education. It also underscores practical implications for educational policy, emphasising curriculum reforms, resource allocation, and stronger school–business collaboration to foster an entrepreneurial mindset. Full article