Search Results (145)

The rapid transformation of rural societies and the progressive decline of traditional livelihoods have placed numerous expressions of intangible cultural heritage at risk of disappearance. In the Saraguro territory of southern Ecuador, muleteering (arriería) historically functioned as a means of transportation, trade, and cultural exchange, facilitating the transmission of knowledge, values, and practices among generations and diverse social groups. This study examines muleteering as a form of living heritage and explores its revitalization through intercultural dialogue and the recovery of ancestral knowledge. A qualitative ethnographic approach was employed, integrating documentary analysis, participant observation, and semi-structured interviews with former muleteers, community elders, cultural leaders, and local residents. The findings indicate that muleteering contributed significantly to territorial connectivity, economic exchange, collective memory, and the preservation of traditional ecological knowledge related to mobility, animal management, and community cooperation. Participants recognized muleteering as a central element of Saraguro’s cultural identity and emphasized its role in fostering intercultural interaction and inter-generational learning. The study concludes that the revitalization of muleteering through dialogue of knowledge can contribute to safeguarding intangible cultural heritage, strengthening cultural continuity, and supporting culturally sustainable development in indigenous territories. Full article

Recent advancements in sequence generation models have transformed the development of conversational chatbots, enabling more dynamic and emotionally aware interactions. While English-language chatbots have achieved notable progress through large language models (LLMs), Arabic-language systems continue to face significant challenges, particularly in handling dialectal variation, morphological complexity, and generating emotionally aligned responses. This paper introduces two innovative approaches to enhance empathetic response generation in Arabic conversational AI. The first, Emotion-Driven Response Generation (EDRG), employs a two-stage pipeline: it first classifies user emotions using marBERT and then routes inputs to the most suitable Arabic LLM (AraBERT, AraELECTRA, AraGPT-2, or MT5) for contextually appropriate response generation. The second, EmoLlama, is a Retrieval-Augmented Generation (RAG)-based framework that integrates a curated knowledge base with the LLaMA model to retrieve relevant conversational contexts before generating semantically rich and empathetic responses. To support these approaches, a large-scale open-domain Arabic dataset was curated, containing over 600,000 dialogue entries spanning empathetic and neutral responses across seven Ekman-based emotion categories. Experimental evaluations using BLEU, Perplexity (PPL), and Cosine Similarity metrics validated the effectiveness of our models. EDRG achieved strong BLEU scores across multiple emotions, reflecting high lexical alignment, while also attaining a Cosine Similarity of 0.51. In contrast, EmoLlama significantly outperformed in semantic similarity, achieving a Cosine Similarity of 0.91, demonstrating its superior ability to generate contextually and semantically rich responses. These results highlight the complementarity of lexical and semantic metrics in evaluating emotionally intelligent Arabic dialogue systems. Full article

Background/objective: Given the increasing complexity of the luminal breast cancer landscape, a proper characterization is required in everyday clinical practice, and the recurrence after the standard first-line treatment with CDK4/6 inhibitors with/without endocrine therapy should be managed. Method: The Hermione Exchange Educational Program was held in Milan, Italy, between September 2024 and January 2025. Two questionnaires were proposed regarding the use of targeted treatment or chemotherapy after progression from CDK4/6 inhibitors. The lecture and use cases enhanced the discussion during the workshops. Results: From the surveys, it emerged that most participants (69%) considered liver metastases at CDK4/6-inhibitor progression as a key reason to initiate chemotherapy, while lung progression influenced this choice for 50% of participants. Liver involvement guided the use of targeted therapy for 56%, and attitudes were divided on whether the duration of first-line CDK4/6 therapy should affect decisions (44% in agreement vs. 38% in disagreement). The willingness of patients to receive chemotherapy (88%) and comorbidities (81%) were significant drivers. Almost all participants agreed that both the duration of response and the molecular status were key aspects to consider when choosing a second line of therapy, along with the general clinical condition of the patient. In the lecture, tissue and liquid biopsy are considered powerful tools to describe tumor molecular features over time; such complexity should be harnessed by a close dialogue between oncologists, molecular biologists, and pathologists to optimize the therapeutic choice according to the mutational status of patients. The use cases illustrate three patients with visceral progression, non-visceral progression within 12 months, and non-visceral progression after 12 months following CDK4/6 inhibitors. Conclusion: Genomic testing should be considered at diagnosis and repeated during treatment to monitor the disease. The clinical experience acquired over the years must be integrated with new molecular knowledge. Full article

The prevention and treatment of cardiovascular disease (CVD) are undergoing a paradigm shift from a lipid-centric approach to a holistic metabolic perspective. Central to this evolution is the gut–fat–heart axis, a sophisticated three-dimensional communication network that integrates neural, endocrine, and immunometabolic signaling to regulate systemic lipid homeostasis. This manuscript systematically explores how the gut microbiota acts as a “metabolic organ” to remotely control host health through the production of bioactive metabolites and the modulation of molecular communication networks. At the physiological level, microbial products such as short-chain fatty acids (SCFAs) and modified bile acids regulate energy balance and lipid synthesis via the FXR-FGF15/19 axis and G protein-coupled receptors. Furthermore, gut hormones like GLP-1 and neuro-reflex pathways involving the vagus nerve provide rapid control over postprandial lipid clearance and feeding behavior. Conversely, pathological dysbiosis triggers the accumulation of harmful metabolites, such as trimethylamine N-oxide (TMAO) and lipopolysaccharides (LPS), which drive lipotoxicity, vascular inflammation, and “dysfunctional HDL” formation. These processes accelerate the progression of atherosclerosis, heart failure, and metabolic syndrome. Finally, the article outlines promising clinical translation strategies, including the development of TMA lyase inhibitors, next-generation probiotics, and the use of phytochemicals to reshape the microbial landscape. By decoding the molecular dialogues within the gut–fat–heart axis, this research provides a novel strategic vantage point for the integrated management of cardiovascular–kidney–metabolic (CKM) syndrome. Full article

As artificial intelligence (AI) becomes increasingly important in rural revitalization, building consensus among multiple stakeholders and developing participatory digital co-creation platforms has grown increasingly urgent. However, existing large language model (LLM) systems predominantly adopt a one-shot generation paradigm, making it challenging to accurately capture villagers’ cultural aspirations and frequently resulting in a significant disconnect between design outputs and community expectations. This situation reveals deficiencies in progressive deliberation mechanisms and cultural controllability. To address these issues, this study proposes a multimodal Participatory Landscape Demand Generation (PLDG) system to enhance AI-generated dialogue controllability, facilitate effective cultural translation in sensitive rural contexts, and promote sustainable development where landscape design both drives and reflects rural revitalization. The system leverages LLMs to simulate stakeholder participatory interactions in village landscape design scenarios. Using culturally distinctive Chinese villages as case studies, the research conducts multi-role simulated dialogues, multimodal semantic extraction, and iterative consensus-building, and evaluates the resultant data to generate landscape design proposals. The results indicate that the PLDG system significantly improves participation efficiency among diverse design stakeholders and enhances the sustainability of design decisions. Compared to conventional methods, metrics such as cultural compatibility, villager participation, and design innovation show substantial improvements. These findings demonstrate the considerable potential of human-AI collaboration in future rural planning. This study introduces the Culture Constraint-Driven Rural Landscape AI Collaborative Design Framework (PLDG), validating its practical efficacy in identifying culturally sensitive elements, ensuring cultural congruence, facilitating community participation, and fostering design innovation. Consequently, it provides a reusable, iterative operational tool for the digital renewal of sustainable rural landscapes. Full article

Practice-oriented or practice-based research is growing in popularity in the social, built environment and health fields for its important role in driving transformative changes at policy, programme/service and practice levels. As planning is a practice with performative characteristics occurring in a socio-political-legal context, practice-oriented research has been utilised to inform and help shape change. However, to be truly effective, practice-oriented research must be connected to day-to-day practices. In this article, we present our experience of using a Community of Practice (CoP)—that brings together people with shared interests and professions—to exchange learning and experiences and to help create knowledge to advance professional practice. In our case, we established a Community of Practice of Planners (CoPP) to help translate stage one findings into tailored knowledge resources to open up a dialogue and raise awareness on Planning for Disability Equity and Inclusion. In this article, we describe the method of CoP, how it works, including our reflections and learnings. We suggest that CoP are an underutilised method in planning practice and research. We argue that the CoP approach should be in a researcher and planner’s toolbox for more transformative progress in equity and inclusion in planning. Full article

Background/Objectives: Depression, anxiety disorders, and substance use disorders frequently coexist in clinical settings and are main factors that worsen a patient’s prognosis. However, traditional artificial intelligence-based mental health studies have limitations in capturing the complex symptoms that occur in actual counseling situations by relying on social media data or focusing on binomial classification of single diseases. This study proposes a multi-label classification model that simultaneously detects the coexistence of depression, anxiety, and substance use disorder in actual counseling dialogue texts, and applies the Shapley Additive Explanatory (SHAP) method to explain the clinical basis of model prediction. Methods: We retrospectively analyzed 1661 de-identified Korean-language counseling session transcripts obtained from the publicly available AI Hub “Mental Health Counseling Dialogue” dataset (Republic of Korea; sessions collected between 2021 and 2023 from accredited domestic mental health counseling centers). Each session averaged 30 min (≈5000 Korean characters). Labeling was performed by two licensed clinical psychologists (inter-rater Cohen’s κ = 0.82). A Hierarchical Attention Network with Bidirectional LSTM (HAN-BiLSTM) was constructed; performance was compared with six baselines (Flat LSTM, TextCNN, KR-BERT, KoBERT, KoELECTRA, KLUE-RoBERTa) using stratified 5-fold cross-validation, paired t-tests with Bonferroni correction, and McNemar’s test. Top-ranked SHAP tokens were independently rated for clinical face validity by three psychiatrists. Results: The proposed model outperformed the baseline model not only for the labels of depression (F1 = 0.90) and anxiety (F1 = 0.85) but also for substance use disorder (F1 = 0.78) with poor data, achieving a macro-averaged F1 of 0.84 (95% CI 0.82–0.86; all p < 0.001 versus baselines). As a result of the SHAP analysis, clinically significant keywords such as “I want to die,” “anxiety,” and “drink” were identified as the model’s main basis for judgment, accurately tracking the client’s state, which dynamically changed as the dialogue progressed; three independent psychiatrists rated 88.7% of the top-15 SHAP tokens per label as clinically meaningful (Fleiss’s κ = 0.76). Conclusions: This study demonstrated that a deep learning-based multi-label approach is effective in early screening of complex mental health problems. In particular, the introduction of explainable AI (XAI) increases clinicians’ trust and suggests that it can be used as an AI-based clinical decision support system (CDSS) in the future. Full article

Background/Objectives: Artificial intelligence reshapes clinical practice, and its effect on the clinician–patient relationship requires reconsideration of the frameworks that have shaped modern medical ethics. When clinicians delegate expertise to algorithms they cannot verify, it becomes unclear who bears clinical responsibility. Methods: This article applies a theoretically grounded normative approach to explore the ethical conditions under which artificial intelligence can be integrated into clinical practice without compromising the moral foundations of medicine. The analysis is primarily based on Pellegrino and Thomasma’s concept of the internal morality of medicine and the clinician’s act of profession. It further draws on Kantian ethics of human dignity, Levinasian relational ethics, virtue ethics, and Vallor’s concept of technomoral wisdom. Results: AI systems do not satisfy the conditions under which moral responsibility can be ascribed to them. Clinical moral agency lies in the capacity to bear three distinct responsibilities—epistemic, relational, and phronetic—none of which can be fulfilled by AI. The implementation of AI in healthcare, therefore, must occur strictly under the condition of Meaningful Human Control, rather than as a technical function of human oversight over algorithmic outputs. To ensure that MHC can function as an effective and ethically grounded safeguard, we propose five normative requirements: primacy of clinical judgement, prohibition of forced automation, traceability and explainability, transparency towards patients, and retaining clinical authority. Dialogue between clinicians and patients should remain the foundation of clinical decision-making. The proposed normative requirements aim to preserve the internal morality of medicine in a form that harmoniously combines both technological progress and established medical ethics. Full article

Environmental science communication has become a core competence for addressing global challenges such as climate change, glacier recession, and hydrometeorological risks. Yet university curricula often prioritize technical knowledge over communicative skills, limiting students’ ability to engage with diverse audiences. This study proposes a structured three-level framework (i.e., micro-, meso-, and macro-communication) for teaching environmental science communication. The framework is explored across six applied case studies, including glaciological thematic trails, dual-training programs, a climate-education game, an international higher-education project, immersive 360° field experiences, and an AI-enhanced scientific exhibition. Drawing on qualitative and descriptive evidence, the cross-case analysis suggests that communication competencies may develop progressively from synthesis and clarity (micro-communication), to multimodal visualization and structured argumentation (meso-communication), to stakeholder-oriented and intercultural dialogue (macro-communication). The findings indicate that multimodal, immersive, and AI-supported approaches may support accessibility, engagement, and inclusivity, while authentic learning environments contribute to the development of transferable communication skills. This study provides an exploratory and practice-based framework that may inform curriculum design and pedagogical innovation, suggesting that communication could be more systematically embedded across environmental science programs in order to strengthen evidence-informed societal engagement and support sustainable environmental governance. Full article

Background: The retention of Clinical Research Associates (CRAs) is critical for the integrity and sustainability of clinical trials in South Africa, an emerging hub for global clinical research. High CRA turnover threatens trial quality, data continuity, and site relationships, yet the context-specific drivers of turnover within the South African clinical research landscape remain poorly understood. This study explores the factors influencing job satisfaction and turnover intentions among CRAs to inform targeted retention strategies. Methods: A qualitative, interpretivist study was conducted using semi-structured interviews. Twelve CRAs with experience in South African Contract Research Organizations (CROs) were sampled on LinkedIn using purposive sampling. Data were analyzed iteratively using thematic analysis within Atlas.ti 26.0.1.33961 software, guided by Herzberg’s Two-Factor Theory and Mobley’s Turnover Model. Results: The analysis revealed a complex model of turnover drivers. Compensation was the most salient factor, operating not only as a hygiene factor but also as a direct motivator for job mobility in a competitive market. Unsustainable workload and a culture stigmatizing discussions of overload were key push factors. Intrinsic motivators were equally decisive: misalignment with therapeutic area preferences caused profound dissatisfaction, while alignment fostered engagement. Career growth manifested dual pathways: ambition for vertical progression and a redefined search for horizontal growth into roles offering greater work-life flexibility. Conclusions: CRA turnover is driven by an interplay of extrinsic pressures and intrinsic motivational deficits. To enhance retention, managers must adopt a multi-pronged strategy: implement market-competitive, well-being-oriented compensation; foster a culture that supports open workload dialogue; create transparent career architectures with dual progression tracks; and facilitate internal mobility across therapeutic areas. This study provides a foundational framework for developing context-sensitive retention policies, thereby contributing to the stability and quality of clinical research in South Africa. Full article

Automated clinical documentation based on clinician-patient conversations is an emerging application of deep learning, driven by advances in medical speech recognition and natural language processing. Despite technological progress, real-world adoption remains limited. This review analyzes deep learning–based medical speech-to-text systems, focusing on methodologies, evaluation strategies, and barriers to clinical implementation. A systematic review of 31 studies was conducted, covering automatic speech recognition, clinical dialogue processing, and large language model-based documentation pipelines. Speech recognition accuracy varies considerably in noisy, multi-speaker, and spontaneous clinical environments. Downstream tasks such as entity extraction and summarization are highly sensitive to transcription errors and constrained by limited real-world datasets. Most systems lack external clinical validation and are tested in controlled settings. Key challenges include speaker diarization, domain adaptation, privacy protection, and the need for standardized evaluation frameworks. Although LLMs demonstrate strong potential, concerns remain regarding hallucinations and factual reliability, necessitating improved robustness and clinician oversight. Full article

Artificial intelligence (AI) is transforming how organizations develop human potential, offering scalable and data-driven support for coaching and capability building. This study proposes and validates a conceptual framework for integrating AI into organizational coaching processes to enhance competence development and strategic alignment. AI-supported coaching in this research is treated as an emerging organizational technology whose potential organizational value depends less on model capability and more on governance design, decision rights, and auditable evaluation outputs. Following a mixed-methods, multi-phase design, the research combined a Systematic Literature Review (SLR) with the construction of a layered design architecture in which OSCAR serves as the primary coaching-process scaffold, complemented by KSA for competency specification, Situational Leadership for adaptive guidance, and KPIs for monitoring and governance. The framework structures AI-supported coaching across 10 interrelated phases, from contextual anchoring to review and measurement, while preserving iterative re-entry to earlier phases whenever review evidence, contextual change, or insufficient progress makes adjustment necessary. Prototyping demonstrated feasibility and coherence across models, while the focus group provided qualitative expert feedback on the framework’s clarity, governance needs, and perceived usefulness for competence development. At this stage, however, the KPI structures generated by the framework and the descriptive comparison across AI tools should be interpreted as prototype-level outputs rather than as empirically validated performance measures or evidence of added value over baseline approaches. Because the evaluation relied on two fictional prototyping scenarios and a small expert-oriented focus group (n = 6), the findings should be interpreted as evidence of prototype demonstration and qualitative refinement rather than of real-world effectiveness or organizational impact. The study also does not include a control group or comparison with traditional human coaching, so the added value of the AI-supported framework over alternative coaching arrangements remains a question for future empirical testing. Findings suggest that AI can usefully support organizational coaching by personalizing dialogue, structuring reflection, and generating auditable development artefacts, provided ethical safeguards and human oversight remain integral. The research contributes a preliminarily validated, ethics-informed, and governance-aware framework for AI adoption in organizational coaching and offers practical insights for embedding AI-enabled development in learning organizations. Full article

This article develops a comparative metaphysical inquiry into real time through a dialogue structured by formal analogy between al-Ghazālī’s theology of continuous creation (tajdīd al-khalq) and Lee Smolin’s relational, law-evolving physics. Against both timeless determinism and accounts of becoming that deny any further ontological grounding, it argues that real time may be understood as a structured horizon of actualization in which openness is progressively articulated into determinate actuality under constraint. Employing a non-reductive method of formal analogy, the analysis maps shared problem-structures—discreteness, contingency, openness, and directionality—while foregrounding controlled disanalogies, especially the contrast between volitional grounding in al-Ghazālī and system-level, naturalistic actualization in Smolin. The article proposes three interpretive claims: (i) both frameworks may be read as relocating order within time rather than above it; (ii) the comparison brings into focus the philosophical problem of actualization, rather than mere succession, in accounts of real temporality; and (iii) stability and regularity are more plausibly understood as articulated within time than as timeless givens. The result is a layered account of temporal order in which volitional maintenance, ontological stabilization, and mathematical framing intersect, suggesting a way of viewing real time as ontologically significant and epistemically consequential within the present comparison. Full article

In November 2024, a two-day meeting entitled “PBBM—Best Scientific Practices to Define Drug Product Performance: Latest Regulatory and Industry Perspectives” was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics Focus group and hosted by MHRA in London, UK. Physiologically based biopharmaceutics modelling, referred to as PBBM, is used to inform drug product quality strategies and provide a more detailed understanding of how medicines can interact with the human body. Industrial, academic, regulatory, and software company scientists came together to discuss the latest developments in PBBM and to debate key topics relevant to the establishment of best practices and improved implementation. Case study presentations and breakout sessions highlighted how companies are using PBBM in their portfolio decision-making (early development through post-approval changes). Discussions highlighted how the exploration of drug product quality risks has evolved over time, moving from the more empirical BCS classification approach to a more detailed in vivo and mechanistic understanding, where sponsors have and continue to invest in building clinical drug product knowledge. Regulatory scientists shared how they are building experience in using PBBM to set clinically relevant drug product quality specifications, including how they would like to see the area grow in the future. Although significant progress has certainly been made in this field over the last 10 years, the need to continue to bring industry and regulators closer together in the future remains a key topic. Guideline evolution, training and continued dialogue will be essential in reaching a harmonised approach to the use of PBBM to develop drug product strategies and set quality specifications. Full article

This paper investigates women’s movements in German-speaking Europe that operate at the intersection of academic theology and activism, challenging the assumption that gender parity within theological institutions has been achieved. Despite broader European progress toward gender equality, theological faculties continue to exhibit structural disparities, including women’s underrepresentation in senior positions and persistent obstacles such as the “leaky pipeline,” the “glass ceiling,” and restrictive ecclesial procedures like the Nihil Obstat. These dynamics intensify the vulnerability of women theologians, particularly those advocating for gender justice within Church structures that do not consistently recognize women as full participants. The study also highlights the vulnerability experienced by women theologians who advocate for gender equality within ecclesial institutions that do not consistently recognize women as full participants. Interdisciplinary dialogue between theology and the social sciences is often met with suspicion, as religion is frequently portrayed as a source of division rather than a catalyst for transformation. Moreover, extremist and fundamentalist movements instrumentalize gender issues, polarizing European societies and suppressing interfaith initiatives that promote justice, care, and cooperation. The paper argues for transversal, intersectional, and inclusive approaches that bridge academic and activist networks. By fostering collaboration, critical reflection, and shared praxis, these movements reimagine the role of women in both Church and society, offering transformative models grounded in justice, dignity, and equality. Full article