Search Results (9,135)

Background: Parental knowledge and perceptions towards Functional Foods (FFs) play a critical role in shaping children’s dietary behaviors. This study aimed to investigate dietary habits, FFs knowledge and perceptions among Greek primary school children and their parents and to evaluate the feasibility of a one-month pilot asynchronous nutrition education program. Methods: A cross-sectional study included 374 children aged 9–11 years and 159 parents from urban (Thessaloniki) and rural (Lemnos) areas. Children completed questionnaires on dietary habits, FFs knowledge and Mediterranean Diet (MD) adherence (KIDMED score), while parents provided sociodemographic information, BMI, dietary habits, FFs knowledge and perceptions. A pilot asynchronous nutrition education intervention was delivered via pre-recorded videos on FFs, the MD, portion sizes and food label interpretation, with participation tracked and program evaluation conducted among parents. Data was analyzed using IBM SPSS Statistics (version 28). Descriptive statistics were calculated, group differences were assessed with t-tests and ANOVA and associations between variables were examined using chi-square tests and Pearson correlations (p < 0.06). Results: Children showed moderate MD adherence, frequent fast-food and soft drinks consumption and low FF knowledge, with a substantial gap between perceived and actual understanding. Parental FF knowledge was uneven, higher among normal-weight participants and largely limited to fortified products. Positive associations were found between children’s and parents’ diet quality and natural FF consumption, as well as between parental and child physical activity. The asynchronous intervention was positively rated; substantial attrition was observed across sessions and follow-up, which limited the ability to assess the intervention’s effects on behavioral change. Conclusions: This study highlights critical gaps in FFs knowledge among families and demonstrates that asynchronous, family-based nutrition education is feasible but challenged by engagement attrition. Targeted interventions are needed to clarify FF concepts and promote healthier family dietary behaviors. Full article

This study employs a constructivist grounded theory approach based on 69 in-depth interviews conducted between March 2022 and December 2023 to examine socio-cognitive dynamics in Korea’s bottled water and household water purifier markets. The study addresses a gap in prior research by explaining how product meanings and stakeholder strategies co-evolve across adjacent “safe-water” markets under regulatory and sustainability pressures. Drawing on qualitative data from 69 stakeholders, including producers (n = 30), consumers (n = 19), and institutional experts (n = 20), we analyze how distrust, risk perception, and health consciousness reshape conceptual systems and market strategies. These shifts drive innovation across markets, including new technologies, service models, and branding strategies. The findings show that socio-cognitive stabilization arises through iterative interactions among institutional shocks, producer reinterpretation, and consumer adaptation. In the bottled water market, the meanings of “natural purity” became materially embedded in packaging, mineral labeling, and brand narratives. In the purifier sector, “technological reliability” was institutionalized through service-based maintenance systems and visible quality control technologies. These processes developed within asymmetric communicative environments shaped by corporate branding capacity and media amplification. This study refines socio-cognitive market theory by specifying boundary conditions under institutional distrust in developed economies. Although Republic of Korea possesses advanced drinking water infrastructure comparable to that of other developed economies, public confidence in tap water has periodically weakened following highly salient contamination incidents and regulatory transitions. This paradox provides a theoretically informative context for examining how product meanings and stakeholder behaviors mutually adapt over time. Although environmental impact metrics were not directly measured, the findings suggest that sustainability policies must address socio-cognitive trust dynamics alongside regulatory instruments such as plastic levies, certification schemes, and transparent risk communication. Full article

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

Background/Objectives Approximately 1 in 10 community-dwelling older adults are affected by or at risk of malnutrition, and this prevalence increases to nearly 1 in 3 among those receiving home care or recently hospitalized, contributing to higher rates of frailty, falls, hospitalization, functional decline, and mortality. Many of these individuals depend on informal or family caregivers for nutritional care, including assistance with grocery shopping meal preparation, feeding, and monitoring dietary intake. Thus, informal caregivers play an increasingly central role in supporting dietary intake and maintaining nutritional status. This narrative review aims at assessing the relationship between informal caregiver involvement and malnutrition in community-dwelling older adults who are dependent for nutritional-related needs, summarizing evidence on caregiver’s role and caregiver-associated determinants of malnutrition, as well as on interventions that incorporate caregivers into nutrition care. We discuss factors associated with malnutrition in later life, with particular emphasis on caregiver knowledge, burden, interventions and outcomes. In addition, caregiver-inclusive models of care and tools, including nutrition education and guidelines/recommendations, medical nutrition therapy, and multidisciplinary care models will be addressed. Methods: A structured review of the literature was conducted (date of last search December 2025), searching multiple databases for pertinent articles. Following identification of eligible articles for inclusion, a narrative synthesis of evidence was completed. Results and Conclusions: Despite the high degree of heterogeneity in methodology, observational studies demonstrate that several caregiver attributes influence the nutritional status of care recipients, including caregiver’s own nutritional status, burden, knowledge and literacy, psychosocial, environmental and economic factors. Intervention studies show that caregiver-focused, -inclusive and -delivered interventions have a positive impact on several outcomes, including improved older care recipient dietary intakes, nutritional status and quality of life without impacting on caregiver burden. Thus, strengthening caregiver support and integrating caregivers into nutrition screening and intervention frameworks may represent a critical opportunity to reduce malnutrition risk and improve health outcomes among older adults. Still, significant gaps remain in caregiver-focused intervention research, particularly in diverse cultural and social contexts. Full article

The article presents an integrated RACI–AHP–BIM methodology that supports responsibility management, decision-making, and information management in complex construction projects delivered under the design–build model, with particular emphasis on conservation-orientated investments. The approach combines three complementary components: the RACI responsibility matrix, the analytic hierarchy process (AHP), and building information modeling (BIM). The methodology is validated on a higher-education conservation project using a BIM execution plan (BEP), scan-to-BIM procedures, and structured decision-making. The integration of RACI with BIM reduced accountability gaps and improved stakeholder coordination, while linking AHP with BIM data enabled data-driven design decisions using the BOCR model. The findings demonstrate measurable benefits, including clearer responsibility allocation, improved interdisciplinary coordination, and more transparent decision-making. The application of laser scanning and scan-to-BIM supported the creation of a digital model of historic elements for both design and future facility management. The main contribution is a holistic integration of RACI, AHP, and BIM into a unified methodology for conservation-orientated projects with high functional complexity, providing a reference framework for public-sector investment management. Full article

Bessera elegans (Asparagaceae) is an endemic Mexican species that is traditionally valued for ornamental purposes and locally reported medicinal uses, yet it remains largely underexplored from phytochemical and biological perspectives. The identification of bioactive secondary metabolites from under-investigated plant species is a key step toward developing plant-derived compounds with potential biotechnological applications. Therefore, in this context, we compile and critically analyze the available information on the botany, phytochemistry, and reported cytotoxic and insecticidal activities of B. elegans. Phytochemical studies, mainly focused on the bulbs, have led to the isolation of steroidal glycosides, homoisoflavonoids, flavonoids, and norlignans. Several of these compounds exhibit cytotoxicity against human cancer cell lines, including leukemia and lung adenocarcinoma models. More recent investigations of flower extracts have revealed additional classes of secondary metabolites and preliminary insecticidal activity, highlighting the species’ chemical diversity. Although the current biological evidence remains limited, the reported cytotoxic and insecticidal effects provide a biochemical basis supporting the relevance of B. elegans as a potential source of plant-derived bioactive compounds. This review highlights existing knowledge gaps and emphasizes the need for further phytochemical and biological studies to support future biotechnological applications of metabolites from underexplored endemic plant species. Full article

Side-scan sonar (SSS) serves as the primary perceptual instrument for Autonomous Underwater Vehicles (AUVs) in large-scale marine search and rescue (SAR) operations. However, the detection of critical targets is frequently hindered by severe hydro-acoustic noise, the spatial discontinuity of wreckage, and the weak visual signatures of small targets. To surmount these challenges, this paper presents WPG-DetNet. First, we introduce a Wavelet-Embedded Residual Backbone (WERB) to reconstruct the conventional downsampling paradigm. By substituting standard pooling with the Discrete Wavelet Transform (DWT), this architecture explicitly disentangles high-frequency noise from structural information in the frequency domain, thereby achieving the adaptive preservation of edge fidelity for large human-made targets while filtering out speckle interference. Then, addressing the distinct challenge of discontinuous aircraft wreckage, the framework further incorporates a Debris Graph Reasoning Module (D-GRM). This module models scattered fragments as nodes in a topological graph to capture long-range semantic dependencies, transforming isolated instance recognition into context-aware scene understanding. Finally, to bridge the gap between AI and underwater physics, we design a Shadow-Aided Decoupling Head (SADH) equipped with a physics-informed geometric loss. By enforcing mathematical consistency between target height and acoustic shadow length, this mechanism establishes a rigorous discriminative criterion capable of distinguishing weak-echo human bodies from seabed rocks based on shadow geometry. Experiments on the SCTD dataset demonstrate that WPG-DetNet achieves a mean Average Precision ($mA{P}_{50}$) of 97.5% and a Recall of 96.9%. Quantitative analysis reveals that our framework outperforms the classic Faster R-CNN by a margin of 12.8% in $mA{P}_{50}$ and surpasses the Transformer-based RT-DETR-R18 by 5.6% in high-precision localization metrics ($mA{P}_{50:95}$). Simultaneously, WPG-DetNet maintains superior efficiency with an inference speed of 62.5 FPS and a lightweight parameter count of 16.8 M, striking an optimal balance between robust perception and the real-time constraints of AUV operations. Full article

Greenhouse gas emissions from sanitation systems remain underquantified, particularly when considering the entire service chain. Previous studies have largely focused on emissions from containment, with limited attention to later stages such as collection, transport, treatment and disposal. To address this gap, this research comprehensively estimates greenhouse gas (GHG) emissions from sanitation systems in Lahan municipality, Nepal. We used an extended version of the IPCC-based Tier-1 approach. Data collection included a household survey and key informant interviews. In scenario A, the baseline total annual emissions are 8.7 Gg CO₂e, mostly from the digestion of faecal sludge in the containment (7.3 Gg CO₂e). In scenario B, when a projected faecal sludge treatment plant (FSTP) is built and in operation, annual emissions reach 10.0 Gg CO₂e, driven by methane emitted by the anaerobic digester in the plant. Scenario C considers climate mitigation strategies: increasing the share of households emptying their containments, increased emptying frequency and adding of methane capture in the FSTP. This can reduce annual emissions to 7.9 Gg CO₂e per year, which is 21% less than in scenario B. Our results suggest that methane capture in the FSTP is the most critical mitigation strategy. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article

This paper presents a standard-oriented architecture for automating information security risk management (ISRM) using artificial intelligence. The study first evaluates eight international frameworks (including COBIT 2019, NIST SP 800-53, and ISO 31000) for automation suitability, identifying ISO/IEC 27005 as the optimal structural foundation. Based on these findings, an architecture integrating Natural Language Processing and machine learning to automate risk identification, assessment, and treatment is proposed. A core component is a decision-making module that combines expert reasoning with a Multi-LLM consensus mechanism to ensure reliability. To provide exploratory support for the proposed architecture, a comparative study using five state-of-the-art Large Language Models (ChatGPT, Gemini Advanced, Grok, Microsoft Copilot, and DeepSeek Chat) was conducted on a standardized risk identification task. The results highlight strong cross-model consensus patterns, providing exploratory evidence that LLMs may support expert-informed risk identification and reasoning tasks while acknowledging the current limitations in complex reasoning. This approach proposes a transparent architectural foundation for AI-driven ISRM whose scalability must be established through future prototype-based evaluation, thereby bridging the gap between rigid compliance standards and generative AI capabilities. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

Micro- and nanoplastics (MNPs) are pervasive environmental contaminants with growing relevance for human health across the lifespan. Older adults may be especially vulnerable to their effects due to cumulative lifetime exposure, age-related physiological changes, and a higher burden of chronic disease. Adopting a One Health perspective, this review synthesizes current evidence on the sources, exposure pathways, and biological effects of MNPs, integrating findings from environmental, animal, and human studies with a specific focus on aging populations. Experimental studies consistently show that MNP exposure triggers oxidative stress, inflammation, mitochondrial dysfunction, and cellular senescence, mechanisms central to biological aging. These processes are linked to dysfunction of the cardiovascular, nervous, gastrointestinal, and immune systems, suggesting that MNPs may contribute to the development or progression of age-related diseases. Within the One Health framework, MNPs also act as carriers of chemical additives and environmental pollutants, potentially amplifying health risks through combined and cumulative exposures along food chains and ecosystems. Despite increasing mechanistic evidence, direct epidemiological data in older adults remain limited. This review highlights key knowledge gaps and emphasizes the need for integrative, longitudinal research to clarify the role of MNPs in aging and to inform public health and environmental policy. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

The aim of this study was to explore and describe how a recent public health emergency affected the management of community sport organisations (CSOs) in favelas and how the crisis management strategy shaped community resilience. We relied upon the stakeholder theory of crisis management, which posits that during crises, organisations should adopt management practices that address the needs of multiple stakeholders rather than merely focusing on organisational survival. We conducted 13 interviews with sport managers of CSOs located in favelas in four different regions of the city of Rio de Janeiro, Brazil. The findings show that managers respond to the crisis by focusing on community support and resilience. Three factors informed the relationship between management practices and community resilience: sense of leadership and responsibility, filling the gaps of the public sector, and equality, diversity, and inclusion practices. Our study extends the application of stakeholder theory of crisis management to suggest the importance of considering the inclusion of stakeholders (e.g., government, sport managers) who have been ignored in the proposition of the theory and in the sport management literature. Theoretical and practical implications are discussed. Full article

The proliferation of Resident Space Objects (RSOs), including satellites, rocket bodies, and debris, poses escalating challenges for Space Situational Awareness (SSA). Optical light curves capture temporal brightness variations influenced by factors such as attitude variation, viewing geometry, and surface properties. When appropriately processed and analyzed, these data can support RSO characterization and classification. This paper presents a scoping review of machine learning (ML) and deep learning (DL) methods for RSO classification using light-curve data. From 297 peer-reviewed studies published between 2014 and 2025, a screened subset of 29 works is selected for detailed methodological comparison. We trace the methodological evolution from handcrafted feature engineering toward convolutional, recurrent, and self-supervised models that learn representations directly from photometric time series. An analysis of three publicly accessible databases, Mini Mega TORTORA, Space Debris Light-Curve Database, and Ukrainian Database, reveals pronounced class imbalance, with payloads comprising over 80% of observations. While models trained on simulated data routinely achieve 95 to 99% accuracy, performance on measured light curves degrades to 75 to 92%, exposing a persistent gap between simulation and observation. We further identify data scarcity, repeated observations of the same objects, and inconsistent evaluation protocols as key barriers to reproducible benchmarking. Future progress will require benchmark-ready, sensor-aware datasets spanning diverse orbital regimes and viewing geometries, alongside physics-informed and transfer-learning approaches that improve robustness across sensors and between synthetic and observational domains. Full article