Search Results (3,133)

Global vector comparisons, which are computationally costly, symmetric by design, and frequently challenging to interpret, have historically been used to address document similarity, a fundamental task in information retrieval and text classification. A neighborhood-based document similarity framework based on ideas from mathematical topology is proposed in this paper. Instead of using exhaustive pairwise comparisons to determine similarity, local neighborhood structures are used to model documents as elements of a finite topological space induced by a similarity relation. The suggested method allows for the natural ordering of documents according to their relative proximity, supports asymmetric similarity relations, and captures local continuity using β-neighborhoods and near-open sets. A hybrid extension is presented that uses contextual embeddings produced by BERT to induce the underlying neighborhood structure in order to improve semantic representation while maintaining interpretability. Neural embeddings function as a semantic basis on which topological relations and near-set approximations are built, rather than taking the place of the topological model. Neighborhood overlap and topological refinement are then used to calculate document similarity, which enables the identification and explanation of both direct and indirect semantic relationships using explicit neighborhood paths. In comparison to TF-IDF and standalone BERT models experimental evaluation on benchmark datasets shows that the suggested topological and hybrid approaches achieve competitive or superior accuracy while enhancing scalability, asymmetry handling, and explainability. The findings show that neighborhood-based topological modeling offers a transparent and ethical framework for document similarity analysis in large-scale and interpretability-critical applications, especially when paired with neural embeddings. Full article

Generative AI tools are increasingly being adopted in education, where they function as collaborators that can provide feedback, suggest alternatives, and scaffold learning. In this paper, I conducted an autoethnographic study by examining my experience as a teacher-researcher integrating generative AI tools as a More Knowledgeable Other (MKO) within the context of game design education. Drawing on Vygotsky’s sociocultural theory, this study documents how generative AI can facilitate creative learning by extending learners’ capacity to ideate, iterate, and reflect on their design processes. This study further reflects on instructional practices and observations of learners engaging with AI-supported creative activities across workshops and training programs. My reflections reveal that generative AI tools enhance feedback loops, accelerate prototyping, and democratize access to mentorship by providing context-aware guidance. However, they also introduce challenges related to illusions of competence, a lack of internalization, and reduced iteration design depth. Future work will explore structured pedagogical models that balance human mentorship with AI-assisted guidance, aiming to establish ethical, adaptive, and creativity-centered frameworks for using generative AI in game design education. Through this lens, this study contributes to an emerging understanding of AI-enabled learning partnerships and their implications for cultivating innovation and talent in the creative industries. Full article

Euthanasia in companion animal practice represents one of the most emotionally and ethically challenging decisions in veterinary medicine, requiring clinical judgment, effective communication, and sensitivity toward both animal welfare and pet owner well-being. This mixed-methods exploratory study investigated decision-making in small animal euthanasia from both pet owner and veterinarian perspectives. An online questionnaire completed by 228 pet owners from 17 countries was supplemented by two semi-structured interviews involving small animal veterinarians with different professional backgrounds. The qualitative interview component was exploratory and was used to contextualize the survey findings. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative responses were examined thematically. Illness (63.3%) and age-related decline (31.6%) were the most frequently reported reasons for euthanasia. Most pet owners assessed their animal’s condition through personal observation (53.5%) or veterinary advice (39.2%), whereas structured quality-of-life tools were rarely used (7.3%). Emotional attachment represented the most influential factor in decision-making (69.3%). Pet owners who explicitly reported receiving emotional support from their veterinarian experienced significantly lower emotional burden (Holm-adjusted p = 0.002) and greater satisfaction with communication (Holm-adjusted p = 0.006) than those who explicitly reported no emotional support. Interview findings emphasized medical justification, individualized communication, and ethical responsibility. These findings highlight the central role of communication, emotional support, and structured end-of-life guidance in improving companion animal euthanasia decision-making. Because the study relied on voluntary online recruitment and included a limited qualitative sample, the findings should be interpreted as exploratory. Full article

Wastewater surveillance (WWS) has quickly emerged as an invaluable tool for public health surveillance, particularly in the wake of the COVID-19 pandemic. Its long-term utility is constrained, however, by fragmented data systems, inconsistent metadata practices, and poor interoperability. The Public Health and Environmental Surveillance Open Data Model (PHES-ODM) was developed as an open, collaborative framework to standardize WWS data and support transparent, ethical data use aligned with FAIR principles in response to these challenges. Building on the success and global adoption of earlier versions, this paper introduces version 3 of the model, expanding to address persistent barriers to interoperability and data utility. Key enhancements include improved metadata capture, support for complex relational linkages across sites, samples, measures, and populations, and new tables for public health actions, external data linkages, and analytical workflows. Tools for mapping across existing standards and supporting long and wide data formats are also introduced. Balancing robustness with usability, PHES-ODM v3 provides a scalable, modular infrastructure adaptable to diverse WWS programmes. The model offers comprehensive solutions for improving data quality, accessibility, and integration, supporting more effective public health decision-making in an increasingly complex global surveillance landscape. Full article

Introduction: Neurosurgery has evolved from an anatomy-driven analog discipline into a digitally augmented field supported by multimodal imaging, neuronavigation, intraoperative imaging, neurophysiological monitoring, robotics, augmented reality, and artificial intelligence. Objective: To examine how this transition has altered professional responsibility, informed consent, training, and medico-legal accountability in neurosurgical practice. Methods: We performed a structured narrative review of the literature on digital neurosurgery and its ethical and professional implications, focusing on publications from 1990 onward and supplemented by landmark historical papers. Sources were selected for relevance to cranial, spinal, skull base, stereotactic, and neuro-oncological neurosurgery, and then synthesized into thematic domains including brain shift, eloquent cortex preservation, stereotactic accuracy, intraoperative neurophysiology, workflow integration, equity, and liability. Results: Digital systems improve lesion localization, function-preserving surgery, stereotactic precision, documentation, and training, but they also introduce new vulnerabilities related to registration error, brain shift, platform dependence, data overload, cost, cybersecurity, deskilling, and diffuse accountability. Conclusions: Digital augmentation expands rather than diminishes the neurosurgeon’s responsibility. The neurosurgeon remains accountable for surgical indication, interpretation of technology-generated information, intraoperative override, and communication of technology-specific risks. The central ethical challenge is to integrate digital tools without weakening patient-centered judgment. Full article

Artificial intelligence (AI) is increasingly embedded in development systems, enabling new capabilities for poverty prediction, social protection targeting and service delivery optimisation. However, the implications of these technologies for poverty governance—the institutional mechanisms for designing and delivering poverty reduction strategies—remain fragmented in the literature. This study conducted a PRISMA 2020-guided systematic review of peer-reviewed journal articles and scholarly book chapters published between 2015 and 2025 and retrieved from Scopus, Web of Science and DOAJ. Following title/abstract screening, full-text eligibility assessment and quality appraisal, 48 studies were selected, thematically identifying cross-cutting patterns related to system performance, implementation processes, governance considerations and contextual constraints. The reviewed literature indicates that AI can improve poverty governance through multimodal data integration, enhanced targeting accuracy and automated administrative processes. However, persistent challenges include biased datasets, infrastructural limitations, regulatory gaps and ethical risks such as algorithmic bias and digital exclusion, which may reinforce structural inequalities. The review contributes an integrated evidence base and introduces a conceptual framework for understanding AI in poverty governance, highlighting that developmental gains depend on robust data governance, inclusive digital infrastructure, context-sensitive design, algorithmic transparency and institutional capacity. Future research should prioritise impact evaluation, fairness-aware AI, participatory design and scalable approaches for low-resource environments. Full article

Research on sensitive human narratives is increasingly constrained by ethical and privacy regulations that limit access to primary data, creating a structural small-data challenge that limits deep computational analysis. To address this limitation, this study validates a Natural Language Processing protocol that scales 946 real breakup narratives from r/breakups to 6000 human-validated high-fidelity synthetic records across five BERTopic clusters. The architecture employs MPNet, UMAP, and HDBSCAN to map latent space and thematically cluster texts, extracts seed documents using the Kneedle algorithm, and orchestrates DeepSeek V3.2 with stochastic sampling and small batches (k = 5). Automated validation via Cosine Similarity with a P10 threshold attained a mean semantic similarity of 0.7204 (range 0.6413–0.7855) and a fidelity rate of 99.08%. Expert human review by two researchers of this investigation evaluated 1732 posts on topic adherence and emotional authenticity using Gwet’s AC2. Five of six clusters achieved AC2 ≥ 0.70 on both dimensions; Topic 3 showed marginal adherence (AC2 = 0.660) while maintaining acceptable authenticity (AC2 = 0.817), and the 1200 synthetic posts for Topic 5 failed human validation (AC2 < 0.50) due to documented LLM safety-filter limitations and are excluded from the final corpus. These results demonstrate that the proposed protocol enables the research community to generate validated, privacy-preserving synthetic data ecosystems while establishing empirical boundary conditions for sensitive topic analysis. Full article

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA. Full article

Vaccination stands as a preeminent public health measure in the fight against infectious diseases, with a proven track record of significantly reducing morbidity and mortality rates. However, the presence of vaccine hesitancy and misinformation, particularly evident during the course of the pandemic, has emerged as a significant challenge. The present study analyzes public perceptions of vaccination by examining YouTube comments on 215 vaccine-related videos, which total over 94,000 comments. Employing advanced topic modeling techniques, such as Hierarchical Dirichlet Process (hLDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), the study identifies key themes, including vaccine safety, side effects, pharmaceutical ethics, and public trust in healthcare authorities. The findings indicate that debates frequently center on political, social, and scientific concepts. Vaccine hesitancy has emerged as a pervasive global phenomenon that transcends cultural boundaries. The dissemination of misinformation regarding the efficacy of vaccines and the safety of treatments, such as ivermectin, is a prevalent phenomenon on social media platforms. This poses significant challenges to public health efforts. The subjects of child vaccination and parental standpoints are also recurring topics of concern. This study underscores the pivotal function of digital platforms such as YouTube in influencing public attitudes regarding vaccination. This underscores the necessity for targeted communication strategies, advanced digital literacy, and proactive policies by social media platforms to address misinformation and promote evidence-based information. Such precautions are imperative to sustaining elevated vaccination rates and safeguarding public health in the digital age. Full article

Kazakhstan has one of the highest suicide rates globally, yet little is known about how suicide-related content is structured and expressed on VKontakte, the country’s widely used social network. This study aimed to qualitatively analyze VKontakte communities associated with suicide, depression, and self-harm, with a focus on naming conventions, thematic characteristics, and potential indicators relevant for digital prevention strategies. A qualitative content analysis was conducted on 50 public VKontakte communities selected from a larger dataset of 2353 communities collected between December 2021 and March 2025. Communities were included if suicide- or self-harm-related references appeared in their names, descriptions, posts, or visual elements and if they had at least one subscriber with a probable connection to Kazakhstan. Textual and visual content was examined manually at the community level. Six naming typologies were identified: explicitly suicidal, self-harm-focused, depressive, ironic, supportive, and non-related. Community content ranged from direct expressions of suicidal ideation to aestheticized or romanticized representations of pain and death. Some communities contained material that encouraged or normalized self-harm with minimal moderation, while others combined supportive interactions with potentially harmful content. Overall, VKontakte communities linked to users from Kazakhstan represent a heterogeneous digital environment in which supportive and risk-related elements may coexist. These findings highlight challenges for automated detection and suggest that patterns of engagement with specific community types may serve as descriptive indicators for future ethically guided research. Full article

Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed instructional hours. Moving beyond mere technical stacking, the model establishes a closed-loop data ecosystem that integrates “pre-class adaptive diagnosis, in-class contextualized internalization, and post-class personalized transfer,” while deeply embedding engineering ethics and sustainability issues related to carbon neutrality. A one-semester quasi-experimental study (Experimental N = 60, Control N = 60) was conducted, utilizing a triangulated assessment of final exam scores, platform-based behavioral trajectories, and semi-structured interviews. The results showed that the experimental group achieved significantly higher final assessment scores than the control group (82.4 ± 5.7 vs. 73.2 ± 6.9), with normality tests supporting the use of parametric analysis and Analysis of Covariance (ANCOVA) indicating a significant instructional effect after controlling for Grade Point Average (GPA) and pre-test scores. Furthermore, behavioral analysis confirms that the LA mechanism significantly enhances students’ self-regulated learning and engagement by increasing the visibility of the learning process. This study provides an evidence-based reform paradigm for engineering curricula to achieve the synergistic cultivation of knowledge acquisition, competency development, and value alignment within constrained instructional timeframes. Full article

Sustainability challenges are frequently characterized by a persistent attitude–behavior gap, particularly within competitive frameworks. This phenomenon is exemplified by voluntary carbon offsetting in aviation, where passengers’ stated willingness to pay consistently exceeds their actual transactional behavior. Prevailing strategies in Education for Sustainable Development (ESD) typically address this discrepancy by either reinforcing individual value systems or advocating for post-capitalist shifts to circumvent market competition. Given the inherent limitations of both approaches, this paper delineates an alternative conceptual path. By transposing a research framework from the field of institutional ethics to the domain of ESD, we aim to integrate this perspective into the academic ESD discourse and facilitate its practical implementation. We present a simple game-theoretic ESD model from which we derive specific guidelines for practical application. We contend that sustainability issues are best addressed by restructuring the ‘rules of the game.’ Consequently, this necessitates a strategic shift in ESD: prioritizing the analysis of incentive structures, governance mechanisms and their modification over a sole reliance on individual motivational drivers. Full article

This article examines dialogic presence as articulated by Martin Buber and explores its continued relevance within contemporary technoetic and AI-mediated new media art. Drawing on Buber’s early writings on art, theatre, and dance—particularly Daniel (1913)—the article first analyses the dialogic relations between artist, art form, and viewer, with attention to the aesthetic principles of distance, unity, and presence that structure the I–Thou encounter. The second part explores the correlation between Buber’s dialogic philosophy and the principles of technoetic art as theorised by Roy Ascott, focusing on the telematic installation Aspects of Gaia: Digital Pathways across the Whole Earth (1989) as a paradigmatic example of dialogic encounter within technologically mediated environments. The third part examines seven artworks from the Infinite Self Pavilion, curated for The Wrong Biennale (2025–2026), as illustrative examples. These works engage AI-mediated aesthetics to interrogate the relation between Self and Other through modes of dialogic encounter and presence induced by orbital apparatus, installation, and screen practices, positioning the viewer at the centre of the encounter while challenging the limits of human consciousness. The article concludes by foregrounding Buber’s ethical stance toward advanced technologies, emphasising relational responsibility and humility in dialogue with Ascott’s technoetic ethics. Full article

Background/Objectives: Laryngeal disorders are among the most prevalent conditions in otolaryngology, yet they remain challenging to diagnose without specialized expertise. Artificial intelligence (AI) systems leveraging machine learning (ML) and deep learning (DL) have demonstrated promising performance for the automatic detection and classification of voice disorders and laryngeal lesions. Methods: This review synthesizes findings from 88 studies published between 2015 and 2025 on AI-based laryngeal disorder detection, considering physioacoustic mechanisms, databases and acquisition protocols, AI architectures and validation strategies, and diagnostic performance. Results: The current literature supports high internal accuracies for binary healthy versus pathological detection (88–99%); meanwhile, performance decreases for higher-level tasks such as pathophysiological category classification and identification, particularly under external validation. From a clinical perspective, clinicians do not infer specific diagnoses from isolated acoustic parameters such as percent jitter or shimmer. Instead, they rely on how these perturbation patterns dynamically evolve during connected speech, where alterations guide perceptual differentiation between underlying disorders. Recurrent sources of bias include dependence on a limited number of historical vowel-based databases, class and demographic imbalance, and limited ecological validity of recording protocols. Additional concerns involve the predominant use of internal cross-validation and insufficient reproducibility or code sharing. Conclusions: Drawing on the literature, an integrative three-level clinical recognition framework is proposed, delineating realistic use cases for AI as a decision-support tool rather than an autonomous diagnostic system. Key priorities for future personalized medicine and research are also identified, including diversified multi-center datasets, standardized methodological reporting, rigorous external validation, and compliance with regulatory and ethical requirements for medical AI deployment. Full article