Search Results (3,358)

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

This longitudinal study examines how perceived brand globalness (PBG) influenced sustainable purchase intentions (SPI) between 2021 and 2024, incorporating factors such as perceived brand quality (PBQ), perceived brand prestige (PBP), brand–cause fit (BCF), and the moderating effect of consumer ethnocentrism (CE). Using survey responses from 415 participants, the study employed partial least squares structural equation modeling (PLS-SEM) via SmartPLS4. The findings reveal that CE emerged as significant in 2024, while PBP’s impact on SPI weakened—suggesting a growing consumer association of prestige with sustainability. Heightened post-pandemic ethical awareness further underscores the importance of brand values. Contrary to earlier research indicating low CE in developing markets, the 2024 results demonstrate an unexpected rise in CE, highlighting its evolving significance. Overall, the study emphasizes the necessity for global brands to adopt sustainable, locally attuned strategies to succeed in developing countries. Full article

Pediatric palliative care (PPC) aims to enhance the quality of life of children with life-limiting conditions and their families through individualized, interdisciplinary support. Among this population, children with neurological diseases represent a substantial and growing group, often facing prolonged disease courses, cognitive impairment, and high prognostic uncertainty. Effective communication is central to PPC; however, it remains deeply influenced by cultural, religious, and spiritual frameworks that shape family perceptions of illness, suffering, and decision-making. This narrative review explores communication strategies in PPC, with a specific focus on children with neurological conditions, highlighting conceptual foundations, cross-cultural variations, and emerging best practices. Key findings highlight the importance of culturally humble approaches, family-centered communication models, and structured tools, such as co-designed advance care planning and dignity therapy, to enhance communication. Additionally, the review highlights the presence of ethical and interdisciplinary challenges, particularly in neonatal and neurology settings, where misaligned team messaging and institutional hesitancy may compromise trust and timely referral to palliative care. Future research, policy, and clinical education priorities should advocate for models that are inclusive, ethically grounded, and tailored to the unique trajectories of neurologically ill children. Integrating cultural competence, team alignment, and family voices is essential for delivering equitable and compassionate PPC across diverse care settings. Full article

This study intends to provide insights and challenges for the shape of the B movement, an emerging paradigm that fosters cross-sectoral partnerships and encourages ethical business practices through so-called purpose-driven businesses. Focusing on Italy, the first European country to adopt this managerial model, the research investigates Italian Benefit Corporations, known as Società Benefit (SB), and their most appealing sustainability claims from a consumer perspective. The analysis intends to inform theory development by assuming the cultural and creative industry (CCI) as a field of interest, utilizing a within-subjects experimental design to analyze data from a diverse consumer sample across various contexts. The results indicate that messaging centered on economic sustainability emerged as the most effective in generating positive consumer responses, highlighting a prevailing inclination toward pragmatic factors such as affordability, economic accessibility, and tangible benefits rather than social issues. While sustainable behaviors are not yet widespread, latent ethical sensitivity for authentic, value-driven businesses suggests that economic and ethical dimensions can be strategically synthesized to enhance consumer engagement. This insight highlights the role of BCs in catalyzing a shift in consumption patterns within ethical-based and creative-driven sectors. Full article

The emergence of generative artificial intelligence (GenAI) in higher education offers new opportunities for academic support while also raising complex ethical concerns. This study explores how university students ethically evaluate the use of GenAI in three academic contexts: improving essay writing, preparing for exams, and generating complete essays without personal input. Drawing on the Multidimensional Ethics Scale (MES), the research assesses five philosophical frameworks—moral equity, relativism, egoism, utilitarianism, and deontology—based on a survey conducted among undergraduate social sciences students in Spain. The findings reveal that students generally view GenAI use as ethically acceptable when used to improve or prepare content, but express stronger ethical concerns when authorship is replaced by automation. Gender and full-time employment status also influence ethical evaluations: women respond differently than men in utilitarian dimensions, while working students tend to adopt a more relativist stance and are more tolerant of full automation. These results highlight the importance of context, individual characteristics, and philosophical orientation in shaping ethical judgments about GenAI use in academia. Full article

This study aims to understand the meaning nursing professionals attribute to their lived experiences of providing humanized care within a public hospital setting. Grounded in Jean Watson’s theory of human caring, the research adopts a qualitative, descriptive phenomenological design to capture the perceptions and emotions of nurses regarding humanized care. Data were collected through semi-structured interviews with nine experienced nurses, selected through purposive sampling. The interviews, conducted virtually between July and December 2024, were analyzed using Colaizzi’s method and supported by Atlas.ti software. Four main thematic categories emerged: institutional health policies, professional image and identity, strengths and challenges in care, and essential competencies for humanized care. The findings highlight the critical role of empathy, cultural sensitivity, ethical commitment, and emotional presence in delivering compassionate care. Participants emphasized that, beyond clinical procedures, humanized care requires relational and contextual sensitivity, often hindered by institutional limitations and excessive administrative burdens. The study concludes that nursing professionals are key agents in promoting ethical, empathetic, and culturally respectful practices that humanize health services. These insights offer valuable contributions for designing policies and training strategies aimed at strengthening humanized care as a cornerstone of quality healthcare systems. Full article

Nanotechnologies bring a rapid paradigm shift in hard and soft bone tissue regeneration (BTR) through unprecedented control over the nanoscale structures and chemistry of biocompatible materials to regenerate the intricate architecture and functional adaptability of bone. This review focuses on the transformative analyses and prospects of current and next-generation nanomaterials in designing bioactive bone scaffolds, emphasizing hierarchical architecture, mechanical resilience, and regenerative precision. Mainly, this review elucidated the innovative findings, new capabilities, unmet challenges, and possible future opportunities associated with biocompatible inorganic ceramics (e.g., phosphates, metallic oxides) and the United States Food and Drug Administration (USFDA) approved synthetic polymers, including their nanoscale structures. Furthermore, this review demonstrates the newly available approaches for achieving customized standard porosity, mechanical strengths, and accelerated bioactivity to construct an optimized nanomaterial-oriented scaffold. Numerous strategies including three-dimensional bioprinting, electro-spinning techniques and meticulous nanomaterials (NMs) fabrication are well established to achieve radical scientific precision in BTR engineering. The contemporary research is unceasingly decoding the pathways for spatial and temporal release of osteoinductive agents to enhance targeted therapy and prompt healing processes. Additionally, successful material design and integration of an osteoinductive and osteoconductive agents with the blend of contemporary technologies will bring radical success in this field. Furthermore, machine learning (ML) and artificial intelligence (AI) can further decode the current complexities of material design for BTR, notwithstanding the fact that these methods call for an in-depth understanding of bone composition, relationships and impacts on biochemical processes, distribution of stem cells on the matrix, and functionalization strategies of NMs for better scaffold development. Overall, this review integrated important technological progress with ethical considerations, aiming for a future where nanotechnology-facilitated bone regeneration is boosted by enhanced functionality, safety, inclusivity, and long-term environmental responsibility. Therefore, the assimilation of a specialized research design, while upholding ethical standards, will elucidate the challenge and questions we are presently encountering. Full article

Plant-derived vesicles offer several advantages, including high yield, low cost, ethical compatibility, safety, and potential health benefits. These advantages enable them to overcome technological limitations associated with vesicles of mammalian origin. Ginseng, a prominent example of a natural botanical plant, is known for its abundant bioactive components. Recent studies confirmed that ginseng-derived vesicles offer significant advantages in the treatment of human diseases. Therefore, this study reviews the extraction and purification processes of ginseng-derived vesicle-like nanoparticles (GDVLNs), their therapeutic potential, and the active ingredients in GDVLNs that may exert pharmacological activities. Furthermore, this study evaluates the research and applications of nanosized ginseng extracts, with a primary focus on ginsenosides. Full article

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article

This conceptual position is based on the diagnosis that artificial intelligence (AI) accentuates existing economic and geopolitical divides in communities in the Global South, which provide data without receiving rewards. Based on bioethical precedents of fair distribution of genetic resources, it is proposed to transfer the principle of benefit-sharing to the emerging algorithmic governance in the context of AI. From this discussion, the study reveals an algorithmic concentration in the Global North. This dynamic generates political, cultural, and labor asymmetries. Regarding the methodological design, the research was qualitative, with an interpretive paradigm and an inductive method, applying documentary review and content analysis techniques. In addition, two theoretical and two analytical categories were used. As a result, six emerging categories were identified that serve as pillars of the studied principle and are capable of reversing the gaps: equity, accessibility, transparency, sustainability, participation, and cooperation. At the end of the research, it was confirmed that AI, without a solid ethical framework, concentrates benefits in dominant economies. Therefore, if this trend does not change, the Global South will become dependent, and its data will lack equitable returns. Therefore, benefit-sharing is proposed as a normative basis for fair, transparent, and participatory international governance. Full article

This study explores the role of artificial intelligence (AI) in human resource management (HRM), with a focus on recruitment, employee retention, and performance optimization. Through a PRISMA-based systematic literature review, the paper examines many machine learning algorithms including XGBoost, SVM, random forest, and linear regression in decision-making related to employee-attrition prediction and talent management. The findings suggest that these technologies can automate HR processes, reduce bias, and personalize employee experiences. However, the implementation of AI in HRM also presents challenges, including data privacy concerns, algorithmic bias, and organizational resistance. To address these obstacles, the study highlights the importance of adopting ethical AI frameworks, ensuring transparency in decision-making, and developing effective integration strategies. Future research should focus on improving explainability, minimizing algorithmic bias, and promoting fairness in AI-driven HR practices. Full article

Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s application in sensitive sectors is hindered by challenges in meeting rigorous standards like the GDPR, with traditional setups struggling to ensure compliance and maintain trust. Addressing these issues, our research introduces an innovative Zero Trust-based DFL architecture designed for GDPR compliant systems, integrating advanced security and privacy mechanisms to ensure safe and transparent cross-node data processing. Our base paper proposed the basic GDPR-Compliant DFL Architecture. Now we validate the previously proposed architecture by formally verifying it using High-Level Petri Nets (HLPNs). This Zero Trust-based framework facilitates secure, decentralized model training without direct data sharing. Furthermore, we have also implemented a case study using the MNIST and CIFAR-10 datasets to evaluate the existing approach with the proposed Zero Trust-based DFL methodology. Our experiments confirmed its effectiveness in enhancing trust, complying with GDPR, and promoting DFL adoption in privacy-sensitive areas, achieving secure, ethical Artificial Intelligence (AI) with transparent and efficient data processing. Full article

Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and multi-task learning paradigms. We systematically categorize and analyze existing technologies based on sensing modalities (vision-based, radar-based, thermal imaging, and ambient sensing), integration strategies, and application domains. The paper examines how artificial intelligence has revolutionized this domain, transitioning from early single-modality, single-parameter approaches to sophisticated systems that combine complementary sensing technologies and simultaneously extract multiple vital sign parameters. We discuss the theoretical foundations and practical implementations of multi-modal fusion, analyzing signal-level, feature-level, decision-level, and deep learning approaches to sensor integration. Similarly, we explore multi-task learning frameworks that leverage the inherent relationships between vital sign parameters to enhance measurement accuracy and efficiency. The review also critically addresses persisting technical challenges, clinical limitations, and ethical considerations, including environmental robustness, cross-subject variability, sensor fusion complexities, and privacy concerns. Finally, we outline promising future directions, from emerging sensing technologies and advanced fusion architectures to novel application domains and privacy-preserving methodologies. This review provides a holistic perspective on contactless vital sign monitoring, serving as a reference for researchers and practitioners in this rapidly advancing field. Full article

Background/Objectives: Senior nurses in Taiwan shoulder layered responsibilities shaped by professional roles, gendered expectations, and family duty. Although Taiwan faces a persistent shortage of experienced clinical nurses, limited research has explored how long-serving nurses sustain identity and commitment across decades of caregiving. This study examines how senior staff nurses understand their journeys of becoming—and remaining—nurses within a culturally and emotionally complex landscape. Methods: Interviews were conducted between May 2019 and September 2023 in locations chosen by participants, with most sessions face-to-face and others undertaken via video conferencing during COVID-19. This narrative inquiry involved in-depth, multi-session interviews with five female senior staff nurses born in the 1970s to early 1980s. Each participant reflected on her life and career, supported by co-constructed “nursing life lines.” Thematic narrative analysis was conducted using McCormack’s five-lens framework and Riessman’s model, with ethical rigor ensured through reflexive journaling and participant validation. Results: Three overarching themes emerged: (1) inner strength and endurance, highlighting silent resilience and the ethical weight of caregiving; (2) support and responsibility in relationships, revealing the influence of family, faith, and relational duty; and (3) role navigation and professional identity, showing how nurses revisit meaning, self-understanding, and tensions across time. Participants described emotionally powerful moments, identity re-connection, and cultural values that shaped their paths. Conclusions: These narratives offer a relational and culturally embedded understanding of what it means to sustain a career in nursing. Narrative inquiry created space for reflection, meaning-making, and voice in a system where such voices are often unheard. Identity was not static—it was lived, reshaped, and held in story. Full article

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article