Search Results (2,965)

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease worldwide, distinguished by pronounced clinical heterogeneity and a frequent dissociation between metabolic risk factors and the degree of hepatic injury. These observations, together with the limited contribution of genetic heritability, have prompted a re-evaluation of the traditional conceptual framework of the disease. In this context, the question has emerged as to whether MASLD could be, at least in part, a transmissible condition. While there is no evidence to suggest that MASLD is contagious in humans, as no data support person-to-person transmission, gnotobiotic animal studies demonstrate that human gut microbiota can transfer susceptibility to steatosis, inflammation, and systemic metabolic disturbances through immunometabolic mechanisms, independent of host genetics. In parallel, human studies involving microbiota-targeted interventions support the concept that the gut ecosystem is a modifiable determinant of metabolic and hepatic phenotypes. Crucially, these findings do not imply natural transmission of disease, but rather underscore the functional plasticity of microbiota-host interactions. This narrative review integrates epidemiological, experimental, and clinical data to explore the hypothesis that MASLD may be functionally transmissible. MASLD is increasingly recognized as an eco-biological disease, where liver disease risk is not only shaped by host genetics and environment, but also by the ecological configuration and functional outputs of the gut microbiome. This perspective redefines disease susceptibility as, in part, context-dependent and microbiota-mediated, without implying infectiousness in the traditional sense. Full article

Accurate extraction of building vector contours from high-resolution remote sensing images is a fundamental task for urban mapping and geographic information systems. However, existing approaches often suffer from blurred boundaries and geometric distortions when dealing with buildings of complex shapes, limiting the accuracy and usability of the extracted building footprints. To address these challenges, this paper proposes a multi-level building contour refinement framework based on morphological perception. The proposed framework integrates a three-stage contour optimization strategy, including principal direction extraction, morphology-based contour reconstruction, and geometry-aware regularization, to progressively refine complex building contours under geometric constraints. In addition, a multi-dimensional contour complexity model and an adaptive threshold optimization network are introduced to dynamically adjust refinement parameters according to contour complexity. Experimental results on the WHU-Mix dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving 87.52%, 77.43%, and 87.35% in boundary F1, vertex F1, and mIoU, respectively. These results indicate that the proposed framework provides an effective and robust solution for high-precision building vector contour extraction in complex remote sensing scenarios. Full article

Introduction: Lung cancer is a leading cause of cancer-related deaths globally, with approximately 1.5 million new peripheral pulmonary lesions (PPLs) detected annually in the United States. Robotic-assisted bronchoscopy (RAB) has emerged as a promising technology, with two platforms initially approved, the Monarch platform (Auris Health Inc, Redwood City, CA, USA) and the Ion Endoluminal System (Intuitive Surgical, Sunnyvale, CA, USA), offering improved stability and distal airway visualization. As RAB adoption increases, there is a critical need for comparative effectiveness data of these systems to guide clinical decision-making and institutional investments. This study aims to compare the diagnostic yield and safety profiles of the Ion and Monarch RAB platforms after introduction at a single institution. Methods: We conducted a retrospective chart review of patients undergoing RAB in the first six months following the introduction of each platform. Demographic and radiographic data were collected. Diagnostic yield was defined as obtaining a malignant or specific benign diagnosis from bronchoscopy. Results: The study included 56 Ion and 36 Monarch procedures. Diagnostic yield was similar between Ion (75%) and Monarch (72%) groups (p = 0.8), with an adjusted odds ratio 0.89 (95% CI 0.30–2.72). Complications were low, with one pneumothorax occurring in each group. Conclusions: Early adoption and use of both RAB platforms suggests comparable diagnostic yields and safety profiles in our limited sample size. Larger studies including standardized anesthesia protocol and systematic use of real-time imaging are needed for further evaluation and comparative analysis. Full article

Climate change is transforming industrial systems globally, both by exposing them to increasing environmental risks and by positioning them as key players in worldwide mitigation and adaptation efforts. This study offers a comprehensive review of how research at the climate–industry interface has developed over the past thirty years. Using a dual-method approach that combines a Systematic Literature Review (SLR) with bibliometric analysis, we examine 2458 publications from Scopus and Web of Science and visualize the field’s conceptual structure using the Thematic–Conceptual–Map (TCM) framework. Our results identify five main research themes: (1) integration of adaptation and mitigation; (2) spatial technologies and remote sensing; (3) urban heat and industrial resilience; (4) fundamental adaptation and climate resilience; and (5) connecting vulnerability with adaptive capacity. While mitigation and energy transition are predominant in industry-focused climate research, significantly fewer studies explore how industrial transformation relates to socio-ecological resilience and biodiversity conservation. This gap highlights the need for frameworks that connect decarbonization efforts with ecological preservation. By synthesizing these thematic trends, our study places industrial research at the forefront of shaping low-carbon, climate-resilient futures and offers a valuable knowledge base for scholars, practitioners, and policymakers working to integrate technology, governance, and sustainability within industrial systems. Full article

Lanthanide ions and their complexes have emerged as versatile tools in biology and medicine owing to their unique photophysical, magnetic, and coordination properties. Their applications span bioimaging, sensing, therapy and diagnostics, underpinned by their strong preference for oxygen-donor ligands, kinetic stability, and tunable luminescence. This review integrates current developments in lanthanide coordination chemistry, focusing on the mechanistic basis of their interactions with biomolecules such as nucleic acids, proteins, and peptides. Moreover, this work highlights the design principles governing complex stability and biological compatibility, summarizing key biomedical uses of lanthanides ranging from imaging and drug delivery to anticancer and antioxidant effects, and discusses their toxicity and biodistribution, and their potential for clinical translation. In particular, this review offers a mechanistically oriented synthesis of recent advances, emphasizing the interplay between coordination behavior and biological function, and identifying emerging trends that define the current landscape of lanthanide-based bioinorganic research. By correlating molecular coordination features with biological performance, the review identifies the main trends shaping lanthanide-based bioinorganic research, also including a brief discussion of complexes formed between lanthanides and naturally occurring molecules, such as amino acids. Full article

A composite fiber optic sensor based on a misaligned peanut-shaped structure and the single-mode fiber–multimode fiber–single-mode fiber (SMS) structure is proposed for simultaneous strain and temperature measurements. The misaligned peanut-shaped structure is formed by introducing a certain core-offset during fusion splicing. Through a simulation analysis of the sensor, the optical field distribution of the sensor structure under different offset amounts is obtained. The experimental results demonstrate that the sensor achieves a maximum strain sensitivity of −48.21 pm/µε with an offset of 35.61 µm under a strain range of 0–600 µε and a maximum temperature sensitivity of 124.29 pm/°C at a 24.35 µm offset with a temperature range of 35–95 °C. Meanwhile, the sensor with a 35.61 µm offset has two resonance peaks that are selected for simultaneous measurements, with strain sensitivities of −48.21 pm/µε and −47.04 pm/µε and temperature sensitivities of 75.71 pm/°C and 84.29 pm/°C, respectively. Therefore, the simultaneous measurement of the strain and temperature can be achieved through a matrix method, demonstrating that the sensor possesses a dual-parameter sensing capability for the strain and temperature. Full article

Drawing on digital agency theory, expectancy–value frameworks, and self-regulated learning perspectives, this study proposes and tests a moderated mediation model explaining students’ intentions to use AI. Using data from 673 university students, we examined whether sense of positive agency (SOPA) predicts intention to use AI indirectly through perceived value and perceived benefits of AI, and whether these pathways are conditionally shaped by sense of negative agency (SONA). Conditional process analysis (PROCESS Model 59) showed that SOPA had no direct effect on intention to use AI (b = 0.013, p = 0.882). Instead, its influence was fully indirect and conditional. SOPA predicted perceived value and perceived benefits of AI only at moderate to high levels of SONA, with significant SOPA × SONA interactions for both mediators (p = 0.040). Perceived value strongly predicted intention to use AI (b = 0.385, p < 0.001), and this relationship was amplified at higher levels of negative agency (b = 0.138, p = 0.002). In contrast, the effect of perceived benefits on intention weakened as SONA increased (b = −0.125, p = 0.005), becoming non-significant at higher levels of negative agency (Johnson–Neyman point ≈ 2.99). The final model explained 50.4% of the variance in intention to use AI. Overall, the findings indicate a conditional appraisal mechanism: as negative agency increases, perceived value becomes a stronger predictor of intention, whereas the motivational contribution of perceived benefits weakens and becomes non-significant beyond the Johnson–Neyman threshold. These results support an agency-aware account of AI adoption focused on how cognitive appraisals relate to intention under different perceived agency orientations, without implying ethical reasoning or moral deliberation processes not measured in this study. Full article

A sense of belonging in science, technology, engineering, and mathematics (STEM) education is often conceptualized primarily through social dimensions, overlooking its disciplinary and epistemological contexts. In chemistry, understanding belonging requires examining how disciplinary culture, values, and norms shape who feels included and who is excluded. This perspective article explores belonging in chemistry (a STEM discipline) as a multidimensional construct, emphasizing the intersections of social, cultural, and epistemic dimensions that influence students’ participation and identity development in chemistry. Through a critical synthesis of literature informed by dominant group theory and decolonization frameworks, this work interrogates how Eurocentric definitions of merit and excellence perpetuate exclusionary practices and constrain the belonging of nondominant groups. The analysis identifies key opportunities in chemistry to support the participation of all and offers asset-based considerations for transforming departmental and instructional practices. We conclude that advancing belonging in chemistry requires reexamining disciplinary norms and adopting new approaches that validate multiple ways of knowing and contributing to the chemical sciences. Full article

The convergence of Artificial Intelligence and the Internet of Things has given rise to the Artificial Intelligence of Things (AIoT), which enables connected systems to operate with greater autonomy, adaptability, and contextual awareness. In agriculture, this evolution supports precision farming, improves resource allocation, and strengthens climate resilience by enhancing the capacity of farming systems to anticipate, absorb, and recover from environmental shocks. This review provides a structured synthesis of the transition from IoT-based monitoring to AIoT-driven intelligent agriculture and examines key applications such as smart irrigation, pest and disease detection, soil and crop health assessment, yield prediction, and livestock management. To ensure methodological rigor and transparency, this study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search and multi-stage screening procedure was conducted across major scholarly repositories, resulting in a curated selection of studies published between 2018 and 2025. These sources were analyzed thematically to identify technological enablers, implementation barriers, and contextual factors affecting adoption particularly within low-income countries where infrastructural constraints, limited digital capacity, and economic disparities shape AIoT deployment. Building on these insights, the article proposes an AIoT architecture tailored to resource-constrained agricultural environments. The architecture integrates sensing technologies, connectivity layers, edge intelligence, data processing pipelines, and decision-support mechanisms, and is supported by governance, data stewardship, and capacity-building frameworks. By combining systematic evidence with conceptual analysis, this review offers a comprehensive perspective on the transformative potential of AIoT in advancing sustainable, inclusive, and intelligent food production systems. Full article

The current teacher workforce crisis has increased policy and funding interest in employment-based pathways (EBPs), particularly in hard-to-staff schools. This qualitative study of one university’s EBP examines how [the Employing Queensland: Innovative Pathways to Teaching (EQuIPT) program] shaped pre-service teachers’ (PSTs) sense of academic belonging. Drawing on focus group data from 12 participants across four regional, rural, and remote (RRR) locations in Queensland, Australia, we examine how connections between university, schools, and communities influenced PSTs’ sense of belonging to their university, course, profession, and local communities. Structured collaboration, timely communication, and strong relationships, supported by links to wider services, were central to sustaining an academic sense of belonging. In turn, belonging supported participants’ wellbeing and plans to remain in teaching, and helped them persevere in challenging contexts. The findings suggest that, when intentionally supported, EBPs can help address workforce shortages in RRR settings. Full article

Mobile technologies are rapidly growing and shaping the tourism industry. Nonetheless, remote locations have specific characteristics that could restrain the deployment and use of technologies and jeopardize the sense of safety, affecting tourism experiences. There is a lack of empirical research that studies the importance of mobile technologies and security networks in remote destinations. A survey based on the Technology Acceptance Model (TAM) was conducted on 738 tourists during their stay in the Autonomous Region of the Azores, a nine-island Portuguese archipelago, to analyze the importance and impact of mobile technologies and security services. Since tourists have a high intensity of smartphone usage during their stay (86% use mobile internet and almost 50% use smartphones once per hour), mobile communication services and technologies need to be in place. Internet access and Wi-Fi are highly important for tourists for browsing and messaging, especially in urban areas, but also in rural and maritime areas. The availability of emergency and security networks is critical for destination selection and to engage in tourism activities. This paper contributes to the study of mobile tourism in remote destinations, with inputs regarding tourists’ behavior, and has implications for governance and industry stakeholders regarding destination management and the creation of meaningful and sustainable experiences with a high value for digital and smart tourists in the post-pandemic era. Full article

Rapid urbanization and industrialization have significantly reshaped rural landscapes in metropolitan fringe areas, resulting in “hybridized” characteristics. This study establishes an analytical framework to systematically characterize hybrid rural landscapes, diagnose specific local issues, reveal their spatial differentiation patterns and driving mechanisms, and propose targeted governance strategies. Taking 124 rural units in Baoshan District, Shanghai as a case, multi-source data from the latest available years (2020–2023) were compiled as a cross-sectional snapshot, and a comprehensive indicator system integrating landscape pattern (P), social function (F), and spatial vitality (V) was developed. Utilizing multi-source geospatial data—including land-use maps, points of interest, and mobile signaling data—Gaussian Mixture Models were applied to classify typical hybrid landscape types. Spatial evolution processes and underlying driving forces were further interpreted through remote sensing imagery analysis, field investigations, and policy document reviews. Eleven distinctive hybrid rural landscape types (HTs) were characterized, forming a spatial gradient from urban to rural, encompassing “high-density urbanized” → “ecologically embedded” → “production–living integrated” → “traditional rural landscapes”. Additionally, five representative evolutionary patterns—“urban restructuring”, “ecological orientation”, “industrial-driven transition”, “transitional hybridization”, and “traditional preservation”—were identified, shaped by spatial configuration, planning policies, industrial investments, and demographic dynamics. The framework enhances understanding of the complexity and evolutionary dynamics of rural landscapes, providing theoretical insights and practical guidance for effective typological governance and targeted policy interventions. Full article

Teacher well-being is increasingly shaped by rapid technological change in education. As digital innovation accelerates, teachers’ well-being is closely linked to technological self-efficacy, understood as confidence in using digital tools alongside a sense of professional meaning, agency, and control. This qualitative study explores the relationship between teacher well-being and technological self-efficacy through an examination of teachers’ experiences in the “Artificial Intelligence for Beginners” professional development program. Reflective narratives from 18 participating teachers were analyzed to examine how engagement in the program was experienced as supporting the development of techno-pedagogical self-efficacy, professional learning, and well-being. Thematic analysis revealed several interconnected themes, including increased technological confidence and reduced anxiety toward digital innovation, the development of practical applications for personalized learning, heightened awareness of ethical and privacy considerations, and the emergence of a collaborative professional learning community. Participants also reported developing strategies for coping with digital complexity and experiencing a renewed sense of professional identity. Overall, the findings suggest that structured professional development in artificial intelligence may contribute to teachers’ perceived competence, autonomy, and sense of purpose. Strengthening technological self-efficacy through such programs may support individual teacher well-being and the collective professional climate within schools in AI-enhanced educational contexts. Full article

The Syrian conflict has created one of the largest displacement crises of the twenty-first century, disproportionately affecting adolescent girls. Syrian girls have been primarily portrayed as victims of war or “the lost generation”, neglecting the plurality of their experiences. Building on Bandura’s social cognitive theory, Giddens’ structuration theory, Kabeer’s empowerment framework, and Mahmood’s modalities of agency, this study examines how Syrian refugee adolescent girls in Lebanon enact agency within contexts of forced displacement and how structural factors shape these processes. We conducted a secondary analysis of 293 first-person narratives from Syrian girls and mothers collected in 2016 using Cognitive Edge’s SenseMaker^®. Thematic analysis revealed seven structural barriers—restricted access to education, economic insecurity, inadequate infrastructure/living conditions, limited healthcare, gender and social norms, xenophobia, and lack of legal status—as well as key enablers including community services, parental support, and peer networks. Girls expressed agency through seven interconnected processes: awareness/acknowledgement of barriers, emotional navigation, resource identification, decision-making, future planning, reflection, and action execution. These processes were adaptive and recursive, highlighting that agency during displacement is dynamic, relational, and conditioned by structural forces. These findings inform approaches that both reduce structural barriers and enable refugee girls’ agency. Full article

The COVID-19 pandemic profoundly disrupted the lives of older adults, yet their experiences have remained underexplored. This paper draws on empirical evidence from a two-wave (W1 April–July 2020, W2 January–April 2021) qualitative study in the UK (n = 62) and a companion study in Colombia (n = 32), focusing on participants aged 60 and above. Data was analysed using constructivist grounded theory principles, leading to the development of an ecological theory of older adults’ well-being within the context of a health crisis at three interconnected levels: individual, community, and societal. Individual resources, such as adaptability and support systems, contributed to enhancing and maintaining their well-being. Community support and a sense of belonging were essential to meet the needs of people in later life, whilst necessary social health protection measures during the pandemic restricted social activities, further impacting well-being, mostly perceived as challenging. Cultural differences and societal support systems shaped participants’ experiences. The study emphasises the interdependence of the different levels in impacting older adults’ well-being, offers strategies for policy and practice, and advocates and contributes for the development of gerontological theories in the context of health crises. Full article