Search Results (18,160)

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

This manuscript develops and demonstrates a practical framework for evaluating automated classifiers used in communication research, using harmful language detection as an illustrative case. We combine (a) a structured review of documentation practices for 27 publicly available classifiers and their associated annotation processes with (b) a cross-dataset evaluation that re-tests each model beyond its original training context. Across 27 datasets, we extract and compare reporting on construct definitions, annotator instructions, and inter-annotator agreement, and we quantify generalization by applying each model to multiple out-of-domain test sets. We also benchmark a contemporary large language model (GPT-5) under a consistent prompting protocol to illustrate how LLM-based classification compares to fine-tuned classifiers. Results show that documentation is uneven and often insufficient for theory-driven measurement, inter-annotator agreement varies widely across datasets, and cross-dataset performance frequently drops substantially relative to within-dataset evaluations. Building on these findings and existing validation guidance, we provide a reusable checklist and decision flow to help researchers select, justify, and report classifier-based measures in ways that support transparency and cumulative science. Recommendations for researchers, reviewers, and journal editors stress aligning model selection with standards of validity, reliability, and transparency. Full article

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management. Full article

An updated understanding of the U.S. oral health care system is essential for addressing the burden of oral disease, high dental expenditures, and persistent inequities in access. This narrative review synthesizes current evidence on the prevalence of major oral diseases, dental care delivery, financing, dental workforce, and public health initiatives, and highlights the challenges and future opportunities in the U.S. A comprehensive search of PubMed, Google Scholar, and reports from U.S. federal agencies and professional organizations was conducted between September 2025 and March 2026. Following the latest National Health and Nutrition Examination Survey, untreated caries remains widespread, affecting 11% of children (ages 2–5), 10% of adolescents (ages 12–19), 21% of adults (ages 35–49), and 12% of older adults (ages 65–74). Periodontal diseases are common, with 42% of adults aged 30 years or older having periodontitis. Oral cancer incidence stands at 11.5 per 100,000 and increases sharply with advancing age. Edentulism among older adults (ages 65–74) was approximately 11%. The U.S. dental workforce includes over 200,000 dentists, yet shortages affect rural and low-income areas, with 62 million Americans living in Dental Health Professional Shortage Areas. Dental care is primarily delivered through private practices, supplemented by community health centers. Financing relies mostly on private insurance and out-of-pocket payments, while the coverage of public programs like Medicaid varies across states, and Medicare generally excludes routine dental care for older adults. Water fluoridation remains widespread, yet ongoing debates highlight persistent challenges. School-based dental sealants and topical fluoride programs are widely recognized as cost-effective and scalable, offering substantial benefits at the population level. Nevertheless, community-based preventive measures are often hindered by resource constraints, inequitable access, and in some cases political conflicts. In summary, oral diseases remain prevalent in the U.S. Limited public coverage, workforce shortages in rural or underserved areas, and uneven access to dental care highlight the need for systemic reforms to improve oral health equity. These findings point to the importance of strengthening dental public health research and coordinated policy action to reduce structural barriers and expand access to dental care. Full article

Writing-intensive courses help undergraduate students develop disciplinary knowledge and communication skills, yet many students, particularly first-generation college students and those writing in a second language, enter these courses with low confidence and high writing anxiety. Writing self-efficacy, or students’ beliefs about their ability to succeed as writers, is associated with motivation and academic success, but less is known about how instructional practices shape its development. This qualitative study examined how students experienced instructional practices in a writing-intensive public health course and how these experiences influenced writing self-efficacy. Data were collected through a focus group with six undergraduate students and analyzed using a deductive thematic approach guided by Bandura’s four sources of self-efficacy. Students identified scaffolded assignments, opportunities for revision, and explanatory feedback as key facilitators of writing self-efficacy. Supportive classroom relationships, including proactive instructor outreach and consistent feedback, also appeared to foster confidence. Barriers included linguistic challenges, limited academic role models, and negative experiences with writing support services. These findings suggest writing self-efficacy may develop through the interaction of structured instructional practices and supportive classroom environments. Full article

Health chatbots have been growing into a necessary tool for dealing with risky and important contexts, such as medical and health information seeking. Meanwhile, trust towards chatbots influences people’s willingness to embrace technology and use it consistently. Thus, it is important to explore the mechanism of forming trust towards the health chatbots. The TAM has been introduced to explain the mechanism. This study extends the TAM framework by incorporating perceived risk and self-efficacy to develop an expanded model that explains the mechanisms underlying trust formation in health chatbots, applying a survey and investigating 480 Chinese chatbot users on the Credamo. The findings show that perceived risk reduces trust both directly and indirectly through perceived usefulness, perceived ease of use, and self-efficacy. Both parallel and serial mediation pathways were supported. These results offer a more complete insight into trust formation in high-risk AI contexts and provide practical guidance for chatbot design and governance in health communication. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

Cryptocurrency adoption remains difficult to explain when treated as a single decision or static outcome. Addressing this limitation, the present study develops a qualitative, process-oriented account of cryptocurrency adoption among users in Central and Eastern Europe, with particular attention to how engagement emerges, changes, and stabilizes over time. Semi-structured individual in-depth interviews were conducted with 25 cryptocurrency users, and the material was analyzed using reflexive thematic analysis within an interpretivist framework. The findings show that adoption unfolds as a multi-phase process embedded in users’ biographies, financial practices, and socio-technical environments. Across accounts, cryptocurrencies were described not only as speculative assets but also as tools of financial autonomy, learning, and optionality under conditions of institutional uncertainty and constrained access to conventional financial pathways, making the CEE context particularly revealing for a process-oriented understanding of adoption. The analysis identified six interrelated themes: adoption as a project of financial autonomy; the “conscious investor” identity; the market as a school of cost and irreversibility; platforms and communities as adoption infrastructures; the relational politics of visibility; and practice stabilization. Together, these themes show that factors already highlighted in prior adoption research—such as trust, risk, autonomy, and knowledge—do not function as stable predictors, but change their meaning across different phases of engagement. The study contributes to FinTech adoption research by proposing a processual model that reconceptualizes cryptocurrency adoption as a phased, experience-dependent pattern of participation rather than a static outcome of parallel determinants. In doing so, it extends existing variable-centered frameworks toward a more dynamic and interpretive understanding of financial technology use. Full article

Convergence education promotes learning experiences that integrate science, technology, engineering, and mathematics (STEM) to address complex real-world problems. However, secondary teachers often report limited access to professional development (PD) and curricular resources that support transdisciplinary instruction. This exploratory case study examines how four secondary teachers (three chemistry; one engineering) made sense of a transdisciplinary PD model, Convergence Research in STEM and Tradition (CReST), that leverages cultural heritage artifacts (Renaissance frescoes) as boundary objects to connect chemistry, engineering, world history, and technology. Teachers participated in a four-day immersive PD experience in Firenze (Florence) and Pisa, Italy, that included site-based learning, interaction with conservation scientists, and structured reflection. Data included daily reflective journals during the PD and semi-structured interviews following the experience, focused on teachers’ reflections on CReST and its implications for their instructional thinking. Using inductive thematic analysis, we identified patterns in teachers’ meaning-making about convergence instruction and the pedagogical possibilities the artifact opened for their classrooms. Findings indicate that (a) the fresco and associated conservation practices functioned as shared reference points for cross-disciplinary connections; (b) teachers reported shifts toward problem-centered, artifact-anchored pedagogy; and (c) sustained collaboration and shared tools were viewed as necessary for extending learning beyond the immersive experience. These findings indicate early, self-reported shifts in instructional planning, including artifact-based entry tasks, problem-centered instruction, and integration of real-world conservation practices. Implications are offered for designing science teacher PD that uses boundary objects to support coherent, culturally grounded STEM integration. Full article

This study aimed to establish the first national diagnostic reference levels (DRLs) for coronary angiography (CAG) and interventional cardiology procedures in Korea, based on a nationwide patient-dose survey conducted in 2024. Radiation dose data were collected from 20 cardiovascular centers between April and December 2024 using a dedicated server system for radiation dose-structured reports, namely, Digital Imaging and Communications in Medicine. We classified 1980 procedures into the following seven procedural groups: CAG, CAG with percutaneous coronary intervention (CAG + PCI), CAG with percutaneous transluminal coronary angioplasty (CAG + PTCA), coronary spasm provocation, acute myocardial infarction (AMI), chronic total occlusion (CTO), and PCI alone. The DRLs were defined as the 75th percentile of the cumulative kerma–area product (KAP) and fluoroscopy time (FT). The established DRLs for KAP (Gy·cm²) were: CAG, 18.68; CAG + PCI, 63.40; AMI, 58.52; and CTO, 106.83. The corresponding DRLs for FT (s) were: CAG, 440.00; CAG + PCI, 1201.50; AMI, 947.64; and CTO, 2819.00. This study established the first official national DRLs for CAG and interventional cardiology procedures in Korea, using real-world clinical data. These reference levels provide a practical framework for institutions to benchmark radiation exposure, evaluate practice patterns, and optimize patient radiation safety. Full article

Using International Waterbird Census data spanning 1993–2022, we analysed temporal trends in the abundance and community composition of wintering waterbirds in the Venice Lagoon (NE Italy). We examined total numbers, major lagoon macro-areas (fish farms, open lagoon, coastal littoral zone, minor wetlands), species-level and guild-level trends and assessed climate-related community changes through the Community Temperature Index (CTI). Total wintering waterbird abundance increased markedly over the study period, from 74,348 birds in 1993 to 445,350 in 2022. Fish farms (about 20% of the total area) hosted the largest number of individuals (about 83%) and accounted for most of the lagoon-wide increase, while open lagoon (15%) and coastal littoral (<2%) areas showed weaker and more variable dynamics. Species-level analyses revealed pronounced heterogeneity, with strong increases in several Anatidae, contrasted by stable or declining trends in other species. The CTI exhibited a significant long-term increase, indicating a progressive shift towards communities dominated by warm-affinity species. CTI decomposition nevertheless showed this signal was disproportionately driven by a limited number of highly abundant species. Our results indicate that wintering waterbird dynamics in the Venice Lagoon are shaped by the interaction between large-scale climatic processes and local habitat management, particularly within fish farms. While management practices can likely sustain exceptionally high wintering numbers and potentially buffer climate-driven redistribution, they may also promote strong species dominance and associated ecological risks. Integrating long-term census data with climate and functional indicators provides a robust framework for understanding and managing Mediterranean wetlands under ongoing climate change. Full article

Indigenous peoples in Amazonia face major mental health inequities, including high rates of suicidal behaviour among adolescents and young adults in some settings. We conducted a scoping review of the peer-reviewed literature on community health workers (CHWs) and equivalent cadres involved in Indigenous and remote contexts, with a focus on their roles in relation to mental health, psychosocial support, and suicide prevention among Indigenous populations in Amazonia and the Guiana Shield. We reported this review in line with PRISMA-ScR. Searches (September–November 2025) were conducted in PubMed/MEDLINE, Scopus, Web of Science and SciELO, complemented by targeted searches in major publisher platforms and JSTOR. We included English, French, Spanish and Portuguese publications that (i) described CHWs or functionally equivalent cadres in Indigenous/remote contexts and/or (ii) reported CHW-related roles, models, or experiences relevant to mental health, psychosocial support or suicide prevention in Amazonian settings. Global documentation of CHW designations used in Indigenous/remote contexts was compiled; we compiled evidence from Amazonia and the Guiana Shield on CHW roles, programme models, implementation conditions and reported outcomes. Data were charted into a structured template (cadre designation, setting, population, study type, functions, programme features and reported mental health/suicide-related outcomes) and synthesised descriptively and thematically. CHWs commonly function as cultural and linguistic brokers between Indigenous communities and biomedical systems, supporting early detection of distress, psychosocial accompaniment, referral navigation and dialogue with local healing practices. Reported programme models differ markedly: Brazil’s institutionalised Indigenous Health Agents (AIS) offer stability and formal recognition, whereas French Guiana relies more heavily on project-based mediation with innovative practices but greater funding fragility. The available literature remains heterogeneous and uneven across countries, with limited evaluative designs and substantial reliance on descriptive reports. Future work should prioritise stronger implementation and impact evaluation, alongside Indigenous-led governance and sustainable support for CHW cadres. Full article

Techno-mathematical fluency (TmF) is the ability to coordinate mathematical knowledge with technological means—digital and non-digital—to solve mathematical problems and express solutions, by recognising affordances, selecting appropriate tools and data, and integrating them with mathematical ideas in iterative cycles of exploration and integration. It goes beyond instrumental tool use to encompass reasoning, modelling, representation, and communication mediated by technologies, and functions as a form of expertise important for both students’ learning and teachers’ professional practice. Full article

As multi-view datasets expand across diverse practical fields, feature selection (FS) has become an indispensable preparatory stage for machine learning models. Nevertheless, real-world multi-view data is often unlabeled and distributed among isolated clients, posing significant challenges to traditional centralized methods due to privacy concerns and communication constraints. Furthermore, existing centralized and federated approaches frequently suffer from entrapment in local optima and lack robust convergence guarantees. To address these issues, we propose Fed-MUFSHT, a federated framework for multi-view unsupervised FS (MUFS) that integrates tensor learning with a novel metaheuristic optimizer, Hierarchical-Cognitive Tianji’s Horse Racing Optimization (HC-THRO). Within the federated learning paradigm, Fed-MUFSHT follows a dual-stage local optimization process. Stage 1 applies HC-THRO, which integrates Hierarchical Competitive Learning and Adaptive Cognitive Mapping to simulate multi-level strategic competition and cognitive adaptation among individuals. This design enhances global exploration, adaptive learning, and fine-grained feature selection in high-dimensional spaces. Stage 2 employs a TL module based on canonical polyadic (CP) decomposition to perform missing-view imputation and refine latent representation learning. At the global level, a privacy-preserving aggregation strategy based on Normalized Mutual Information (NMI) and feature weights enables efficient model coordination without exposing raw data. Comparative experiments on several public benchmark datasets reveal that Fed-MUFSHT maintains clear advantages over strong competing methods, showing better optimization results together with more dependable convergence characteristics. The overall evidence suggests that the proposed approach is both robust and effective for distributed optimization tasks involving privacy protection. Full article

This paper investigates federated multi-centre liver tumour classification from contrast-enhanced CT under realistic data heterogeneity and domain shift. To address the practical constraint that medical data are often siloed across institutions, we develop a FedProx-based federated learning pipeline that enables collaborative training without exchanging raw patient data. Using the LiTS dataset as the training domain, we construct a slice-level binary classification task based on voxel-level annotations, while rigorously assessing out-of-distribution generalisation on an external held-out dataset, 3D-IRCADb. We conduct comprehensive experiments across multiple backbone architectures, including ResNet-50, EfficientNet-B3, ViT-B/16, and MobileNetV3-Small, comparing FedProx and FedAvg under three heterogeneity intensities (IID, mild non-IID, and severe non-IID). Furthermore, we evaluate transfer learning strategies, ranging from frozen backbones to partial fine-tuning of the last stage, and perform ablations on the proximal coefficient $\mu$ and local epochs E to characterise optimisation behaviour. Our results show that FedProx is generally comparable to FedAvg, with slightly more stable behaviour in some heterogeneous settings. We also observe a clear validation-to-external gap, indicating that external-domain robustness remains challenging and requires cautious interpretation for deployment. ImageNet pretraining yields consistent gains, particularly for data-sparse clients, while partial fine-tuning enhances adaptation to CT-specific features. Finally, MobileNetV3-Small offers a favourable performance–efficiency trade-off by reducing communication payload and computation cost, supporting practical deployment on resource-constrained clinical edge devices. Full article