Search Results (1,185)

Emergency collaboration under natural hazards depends on how hazard processes, exposed systems, and institutional arrangements interact across places, organizations, time horizons, and operational tasks. Existing research examines territorial cooperation, organizational networks, response phases, and task coordination, but these strands often rely on different units of analysis, limiting comparison across hazard contexts. This review develops an action-centered multiscale framework across four analytical dimensions: spatial, organizational, temporal, and task scales. Cross-hazard synthesis examines four recurrent collaborative problems: task reorganization, warning-to-action translation, sustained collaboration, and dynamic cross-boundary collaboration. The comparison shows that task relationships may change while overall organizational structures remain relatively stable; warning information becomes actionable when professional interpretation, authority, and operational windows align with protective tasks; sustained collaboration requires viable support and commitment arrangements alongside organizations’ continuing responsibilities; and cross-boundary response may require adjustments in timing, resource support, or decision authority without necessarily expanding formal participation. Boundary spanning, situational interpretation, coupling tasks with operational support, and feedback and adjustment explain how these configurations are supported, maintained, or revised. The framework provides a comparative research agenda and clarifies institutional conditions relevant to disaster resilience and essential-service continuity without assuming a universal organizational model or causal sequence. Full article

Universal Design for Learning (UDL), an inclusive educational framework rooted in cognitive neuroscience, presents a promising approach to confronting the challenges faced by English as a Foreign language (EFL) learners by promoting flexibility in teaching and assessment and presents evidence-based interventions to address the needs of struggling learners. This paper aims to impart EFL teachers with practical strategies that can aid them in integrating UDL principles into reading comprehension instruction. This paper underlines how the three core UDL principles—multiple means of representation, multiple means of action and expression, and multiple means of engagement—can be utilized to make reading instruction more attainable, engaging, and inclusive. Practical examples encompass the use of multimodal texts, differentiated comprehension activities, collaborative reading tasks, and technology-enhanced scaffolds to support distinct learners. The paper also discusses common difficulties that teachers may face, such as scarce resources and training needs, and presents recommendations for gradual implementation in resource-restrained contexts. Drawing on these findings, the paper presents evidence-informed strategies for teachers seeking to design inclusive reading lessons. By linking research findings to practical classroom applications, this study contributes to the growing body of evidence supporting UDL as a research-grounded approach to inclusive EFL instruction. Full article

Large language model (LLM)-based AI agents combine reasoning and planning, memory, external knowledge use, tool calling, and multi-agent collaboration to perform complex tasks. However, they face planning failures, context-maintenance limitations, collaboration instability, vulnerabilities arising from external interactions, bias, unclear attribution of responsibility, and difficulties in oversight and incident tracing. This narrative literature review examined the technological evolution and technical limitations of AI agents and developed a conceptual mapping relating these limitations to selected issues of bias and governance. Selected early theoretical studies were included; the main focus was post-2022 scholarly literature and official institutional and corporate materials through July 2026, with publication-status and evidence updates during revision in September 2026. Sources were categorized into concepts and trends, single- and multi-agent architectures, technical limitations, and socio-ethical issues; limitations were compared using a problem addressed–mitigation approach–residual limitation framework. The analysis suggests that autonomous actions performed on behalf of users, ambiguity in the scope of authority, multi-agent interactions, external knowledge use, observation of external environments, and tool calling may be linked to unclear attribution of responsibility, bias propagation and amplification, and difficulties in oversight and incident tracing. Potential mediators include multiple actors, miscoordination and conformity, inherited external-source biases, and new attack surfaces. This conceptual mapping provides a basis for future research on trustworthy AI-agent design, operation, and governance. Full article

Organizations operating in dynamic and complex environments increasingly adopt operational excellence (OPEX) to improve performance, adaptability, and sustainability. Despite its widespread implementation, OPEX initiatives often fail. Existing research mainly identifies isolated failure factors but lacks integrative, practice-oriented frameworks that translate these findings into actionable strategies. This study addresses this gap through a systematic literature review focused on leadership and management aspects of OPEX adoption. Using structured searches across major scientific databases and defined eligibility criteria, empirical and case-based studies were analyzed. Failure factors were synthesized using grounded theory-based qualitative coding, resulting in three primary organizational dimensions: top management commitment and support (TMCS), training and education deficiencies (TED), and poor organizational culture (POC). Among these, insufficient top management commitment and support is indicated as the most frequent determinant of OPEX failure. Based on these findings, a leadership framework is proposed to mitigate identified failure factors. The framework combines practical strategic deployment through Hoshin Kanri with operational alignment via Gemba Walk practices, emphasizing organizational structure, cross-functional collaboration, and knowledge transfer. By bridging strategic intent and operational execution, the model aims to improve the effectiveness of OPEX initiatives, strengthen managerial awareness, support, and commitment toward OPEX adoption and enhance the successful adoption of sustainability initiatives. Full article

Intelligent sensor technologies now provide unprecedented multimodal access to human affective and cognitive processes, spanning physiological, ocular, facial, kinematic, ambient, and behavioral streams. Yet, despite dramatic advances in acquisition and deep representation learning, the pipeline from raw signals to psychologically meaningful, actionable understanding remains fragile. Deep-only architectures excel at pattern extraction but struggle with contextual reasoning, uncertainty communication, and human-facing explanation; symbolic-only frameworks resist noisy, high-dimensional streams. This perspective argues that the field’s next inflection point lies not in richer sensors alone but in the interpretive layer that turns signals into states. We propose an integrative view in which neuro-symbolic fusion couples continuous sensor evidence to psychologically grounded symbolic primitives, LLMs act as auditable semantic reasoners rather than opaque classifiers, and explainability is treated as a design constraint rather than a post hoc addition. Synthesizing a decade of work on affective computing, fuzzy learner modeling, neuro-adaptive multimodal interaction, and explainable human–AI collaboration, we motivate a research agenda organized around grounded representations, uncertainty-calibrated LLM reasoning, and reflexive explanation. The aim is a class of sensing systems whose intelligence is measured not by accuracy alone but by the quality of the states they help humans understand. Full article

Cities face increasing pressure to accelerate climate action while operating within governance systems often characterised by institutional rigidity and slow regulatory adaptation. In this context, urban regulatory sandboxes have emerged as experimental instruments that enable municipalities to test innovative solutions while adapting institutional practices under conditions of uncertainty. Despite their growing implementation, limited evidence exists regarding their capacity to transform municipal governance itself. This research examines the conditions under which urban regulatory sandboxes can foster the governance capacities required for municipalities to lead the climate transition. A sequential mixed-methods design was applied, combining semi-structured interviews with municipal practitioners and a Likert-scale self-assessment instrument completed by the same practitioners. Four Spanish cities with active urban sandboxes—Barcelona, Madrid, Valencia, and Valladolid—were analysed across three governance dimensions: learning vs. isolation, flexibility vs. rigidity, and climate mission alignment. The findings suggest that, in the perception of the practitioners interviewed, urban sandboxes foster adaptive governance logics, though institutionalising these capacities exposes real tensions around intra- and inter-organisational collaboration, and the depth of transformation differs by each city context. These results carry practical implications for municipal policymakers seeking to integrate sandboxes as tools for climate-responsive urban governance and as enablers of local entrepreneurship and innovation ecosystems. This study contributes to the literature on adaptive governance and urban experimentation by positioning urban regulatory sandboxes as emerging instruments for leading climate transitions under conditions of uncertainty. Ultimately, urban sandboxes can serve as catalysts for embedding climate adaptability within municipal governance but only when accompanied by the deliberate institutional work needed to sustain them. Full article

Waterborne protozoan pathogens, particularly Cryptosporidium and Giardia, remain significant public health concerns in South Africa, disproportionately affecting children, immunocompromised individuals, and rural populations. This review synthesises recent epidemiological, environmental, and diagnostic evidence, critically examining the occurrence, transmission pathways, and public health implications of these parasites. The widespread detection of (oo)cysts in drinking, recreational, and informal water sources highlights the limitations of conventional water treatment and monitoring systems, given the environmental persistence of both organisms and their resistance to chlorination. Although advanced molecular and immunological diagnostic methods are available in selected reference laboratories, their limited integration into national surveillance programmes contributes to underreporting and fragmented disease data. Key challenges include insufficient epidemiological information across many regions, inadequate diagnostic capacity, and the limited incorporation of protozoan monitoring into water quality regulation and public health policy. Addressing these gaps requires interdisciplinary approaches that integrate molecular epidemiology, environmental health, and social science perspectives to elucidate transmission dynamics and inform targeted interventions. Priority actions include strengthening protozoan surveillance, revising water quality standards, expanding the adoption of multi-barrier and advanced treatment technologies, and implementing a national One Health framework. Sustained collaboration among government, researchers, and communities is essential to improve water safety and protect vulnerable populations. Full article

(1) Background: Traditional agricultural practices stand out for their complexity and the heterogeneity of their production processes. The objective of this study is to assess the sustainability of indigenous family farming in productive units within the Tupinambá de Olivença territory in four villages located in the municipality of Ilhéus, Bahia, Brazil. (2) Methods: We adopted the Framework for the Evaluation of Natural Resource Management Systems Incorporating Sustainability Indicators (MESMIS). A semi-structured questionnaire was administered to 67 indigenous family farming holdings. Each indicator received a score from 0 to 4 based on the qualitative responses of the families where a score of 4 fully meets the criterion and 0 fails to meet it. (3) Results: The study identified a medium level of sustainability, with an overall index of 2.01. Dimension-specific analysis revealed medium scores across all pillars: social (2.30), environmental (1.92), and economic (1.80). (4) Conclusions: To enhance indigenous farming sustainability, key actions include fostering knowledge exchange (intergenerational, academic) and supporting community-specific research; strengthening local associations through viable projects and collaboration with agricultural institutions for technical support; and implementing integrated indigenous policies addressing transport, education, credit, and commercialization, paired with culturally sensitive technical assistance from trained professionals. Full article

Artificial intelligence (AI) is changing how firms search for, interpret, and act on externally generated knowledge in digital innovation ecosystems. Existing research motivates closer examination of how resource-constrained SMEs organize the internal workflows through which AI-supported customer, market, competitor, and platform information becomes usable for innovation. This study examines how cross-border e-commerce SMEs organize AI-supported workflows and team roles to handle external knowledge for emerging inbound open innovation. The empirical design is an exploratory, theory-elaborating qualitative multiple-case study of two contrasting SMEs. The analysis draws on ten face-to-face semi-structured interviews across strategic, managerial, technical, and frontline roles, totaling 183 min 32 s (approximately 184 recorded minutes). An abductive thematic case-analysis approach combines data-near coding, within-case analysis, cross-case synthesis, and iteration with relevant theory. The analysis suggests an analytically ordered framework rather than a verified longitudinal sequence. Case A exhibits experiment-led diffusion, whereas Case B exhibits technical-partner-led workflow design. Across the cases, participants described modular human–AI–human sequences, broader task integration, changing feedback arrangements, and greater emphasis on human review. These reported arrangements are associated with three emerging capability-related practice dimensions: external knowledge sensing, knowledge recombination, and agile implementation. Disconfirming accounts show that the patterns depend on task–AI fit and that overreliance can weaken independent market judgment. The study contributes to research on AI-enabled inbound open innovation by showing that access to AI and platform data is insufficient: external inputs become actionable when SMEs reorganize workflows and roles around prompting, interpretation, recombination, and human accountability. It also elaborates how human–AI collaboration operates as an internal microfoundation of customer-centric, platform-mediated openness. For managers, the findings suggest moving from isolated tool use toward capability-oriented workflow design while retaining human review. The conclusions offer contextualized theoretical insight rather than statistical generalization. Full article

Background: The Adverse Childhood Experiences (ACE) Pyramid has been widely used for more than two decades to illustrate pathways linking childhood adversity to health outcomes across the lifespan. Since its development, substantial advances in the science of toxic stress have expanded understanding of the biological mechanisms through which adversity influences lifelong health. Objective: To describe the development and scientific rationale for a revised ACE Pyramid that reflects contemporary evidence on toxic stress, the biological pathways linking childhood adversity to health, and opportunities for prevention and healing. Methods: A multidisciplinary revision process was conducted in collaboration with subject matter experts from government, academia, healthcare, professional associations, and community-based organizations. Iterative review and refinement were used to align the framework with current scientific evidence and practice. Results: A revised ACE Pyramid was developed that preserves the familiar structure of the existing framework while incorporating advances in the science of toxic stress, prevention, and healing. Compared with the ACE Pyramid featured on the CDC website during development of the revised framework, the revised framework more explicitly emphasizes prolonged or excessive activation of the stress response as a central mechanistic pathway linking childhood adversity to lifelong health, expands the biological layer to encompass multiple interacting biological systems beyond neurodevelopment alone, and integrates prevention and healing opportunities throughout the framework. Conclusions: The revised ACE Pyramid synthesizes more than two decades of advances in the science of toxic stress into an accessible conceptual framework. It explains how childhood adversity becomes biologically embedded to shape lifelong health while emphasizing that prevention is possible and healing can occur throughout the life course. By providing a shared framework for understanding toxic stress biology, it may help inform trauma-informed clinical care, public health action, research, and policy aimed at advancing health equity and improve outcomes for children and families. Full article

Amid rising authoritarianism, the criminalization of dissent, and the erasure of indigenous expertise from policy discourse, this paper examines community-engaged research as critical public scholarship through the Deccan Development Society’s (DDS) four-decade-plus collaboration with approximately 5000 Dalit and indigenous women farmers in Telangana, India. Against a backdrop of shrinking civic space and technocratic development agendas that delegitimize marginalized knowledge, DDS women’s sanghams have consolidated village-level assemblies into organs of local governance that claim autonomy over land, seed, food, and knowledge—positioning marginalized women as producers of evidence and theory rather than mere data sources. Drawing on complexity theory and participatory action research (PAR), this paper theorizes a community-based inquiry model where marginalized women operate as co-researchers, knowledge producers, and media creators within an emergent system characterized by distributed agency and non-hierarchical information flows. The paper interrogates key methodological, ethical, and political dynamics of critical community-based inquiry for justice and animates the concept of “autonomy as counter-policy” as a contribution to engaged scholarship and epistemic justice. Full article

Background/Objectives: Personalized medicine represents a transformative approach to healthcare in Saudi Arabia. However, a significant research gap exists regarding the specific regulatory, implementation, and practical challenges of integrating genomic data into personalized oncology care in the Saudi context. We aimed to conduct an exploratory assessment of the current regulatory landscape, practices, and challenges related to genomic data use in personalized oncology in Saudi Arabia and to identify regulatory and practical gaps, propose actionable solutions, and outline future directions for enhancing genomic integration into cancer treatment. Methods: A mixed-methods exploratory design incorporating qualitative expert interviews and a narrative regulatory review was adopted. Results: We integrated qualitative insights from four subject-matter experts with evidence from a non-systematic literature review and a narrative review of regulatory documents issued by Saudi stakeholders and international authorities. The findings identified six key themes comprising (1) the emerging role of personalized medicine in cancer care, (2) current practice and Saudi genomic initiatives, (3) regulatory and policy gaps, (4) ethical and data privacy concerns, (5) infrastructure gaps and genetic testing barriers, and (6) stakeholder collaboration and data sharing, with subthemes including healthcare providers’ skills, patient awareness, expert recommendations, future perspectives, and a descriptive regulatory comparison. However, these themes represent preliminary findings requiring further validation. Conclusions: We provide preliminary insights highlighting significant challenges in integrating genomic data into personalized oncology in Saudi Arabia, emphasizing the need for a national regulatory framework, clear guidelines, robust infrastructure, and strong stakeholder collaboration. These findings offer a foundational roadmap for future research and policy development. Full article

Slovene-medium schools in Italy are public schools in which Slovene is the language of instruction and the protected language of the Slovene national community, yet many pupils encounter Slovene mainly through schooling. This creates a didactic situation that cannot be adequately described as first-, second-, foreign- or heritage-language education. This article analyses how a minority-language didactic model was developed through a six-year collaborative action research process in Slovene-medium schools in Italy (2019–2025). Using retrospective documentary analysis, the study reconstructs four accredited professional development programmes (2019–2022) and subsequent phases of consultation, curriculum refinement, teaching-material development, documented practice-based revision and extension into assessment development. The bounded analytical corpus comprised 36 sources: 22 documentary sources and 14 teaching units. Qualitative analysis mapped the corpus chronologically, coded recurring didactic problems and compared outputs across cycles. Findings show a two-dimensional model: a process dimension connecting sociolinguistic diagnosis, collaborative professional learning, curriculum development, whole-school language support, material development, implementation and assessment alignment, and an instructional dimension based on differentiation, adapted input, contextualised grammar, communicative integration, scaffolding, formative feedback and school-wide language modelling. The article contributes an analytically transferable model for supporting multilingual students in minority-language-medium education where the language of schooling is socially less dominant outside school. Full article

Industry–education integration is the core direction of the practical teaching reform of urban and rural planning in the new era. Taking Fengqing College Students’ Social Practice Teaching Base for Rural Planning as the research object, this paper discusses the internal mechanism and implementation path of industry–education integration in the practical teaching of rural planning. Aiming at the pain points of the traditional teaching mode, such as the disconnection between theory and practice, the mismatch between talent training and local needs, and the separation between social services and the teaching system, this paper constructs three core systems based on the long-term local practice of the base: the integrated management mechanism of local action and professional teaching, the undergraduate–postgraduate integrated curriculum system, and the government–enterprise–university collaborative education community. This research realizes the two-way empowerment of university education and rural revitalization, promotes the transformation of the planning teaching mode, and tempers students’ professional ability and professional literacy. It provides a practical paradigm for the practical teaching reform of urban and rural planning and university–local collaboration in serving rural revitalization. Full article

The existing research on digital–intelligent innovation collaboration networks has not effectively connected the evolution of real collaborative relationships with governance methods, and governance models often struggle to accommodate both empirically identified evolutionary mechanisms and sequential decision-making capability. Using Chinese digital–intelligent innovation co-patent data from 2016 to 2025, this study develops a machine learning analytical framework centered on relationship evolution and collaboration resilience governance and employs OSR-DDQN to optimize dynamic intervention strategies under limited budgets. The results show that state transitions in digital–intelligent innovation collaboration exhibit pronounced path specificity, with the innovation capability, relational capital, and technological complementarity displaying differentiated conditional associations across transition paths. Further simulation experiments show that, across different budget conditions, OSR-DDQN improves the mean reward by 11.56% to 12.83% relative to the best-performing standard deep Q-network. Within the specified governance environment, once the budget reaches a minimally sufficient level, further performance gains arise primarily from optimizing the action timing, intervention targets, and stopping decisions rather than from simply increasing governance resources. Full article