Search Results (3,571)

Guided forest bathing is increasingly incorporated into resort wellness portfolios, yet tourism evaluations often report average programme outcomes without examining how guests differ at entry or what the findings imply for product and service design. This prospective one-group pre–post field study examined a three-day guided forest bathing programme delivered as part of the organised wellness offer of a forest-rich Northern Adriatic resort. Of 150 enrolled adults, 132 provided matched questionnaires. Six study-specific experiential and relational dimensions were assessed before and after the programme: nature connectedness, restorative perception, sensory awareness, environmental-ethical sensitivity, planetary health orientation, and intention to continue nature-based self-care. All six post-programme scores were higher after Holm adjustment. The largest standardised differences were observed for sensory awareness (mean difference = 0.91, Cohen’s dz = 1.42), restorative perception (mean difference = 0.61, Cohen’s dz = 1.17), and future nature-based self-care (mean difference = 0.59, Cohen’s dz = 1.04). Change in sensory awareness correlated with change in restorative perception (r = 0.62) and nature connectedness (r = 0.58). Previous nature contact, nature-related hobbies, and spiritual-existential orientation predicted baseline nature connectedness, whereas integrative health orientation, wellness self-care, spirituality, and expectations predicted baseline future self-care. In baseline-adjusted models, lower previous forest contact was associated with higher post-programme sensory awareness. Because the study had no control group, these differences cannot be attributed specifically to guided forest bathing rather than to the wider resort stay, time away from routine, recreation, social interaction, expectancy, or repeated measurement. Co-created restorative value is proposed only as an interpretive framework and was not measured as a construct. For tourism and hospitality management, the findings generate testable decisions concerning guest onboarding, adaptive facilitation, guide training, expectation management, and the development of low-infrastructure nature-based wellness products. Full article

In the campus planning of severe cold regions, indoor pedestrian corridors have emerged as an important strategy for mitigating harsh weather and maintaining pedestrian network continuity. These enclosed systems provide thermal comfort and physical convenience for faculty and students. However, reduced visual contact with outdoor natural environments may be associated with less favorable affective experiences. Existing research primarily emphasizes functional connectivity and spatial efficiency, with limited quantitative evidence on the visual characteristics of indoor corridors and their relationship with emotional perception. Drawing upon environmental psychology, this study examines the associations between corridor visual characteristics and five AI-predicted affective perception dimensions. Using deep-learning-based computer vision, semantic segmentation, and statistical analysis, environmental features and predicted perception scores were analyzed. A local human validation showed moderate overall agreement between human consensus ratings and AI-predicted scores, supporting their use as exploratory perception proxies. The findings indicate that indoor-corridor-related visual features were negatively associated with predicted beauty scores and positively associated with predicted depression scores, while predicted boringness was positively associated with wall enclosure. Predicted safety and liveliness showed weaker associations with individual visual features. These results provide exploratory evidence for balancing thermal protection, circulation efficiency, and psychological comfort in future campus corridor design. Full article

Agro-industrial by-products from beetroot, raspberry, sea buckthorn, and shadbush are valuable sources of natural pigments and renewable filler materials, but their hydrophilic surfaces limit compatibility with hydrophobic polydimethylsiloxane (PDMS) matrices. This study evaluated the effect of catalyst-free vapor-phase hexamethyldisilazane (HMDS) treatment of pigment-rich powders and their incorporation into PDMS composites at 5 and 20 wt.% filler loadings. Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy, and contact angle measurements were used to characterize surface modifications. Mechanical behavior under puncture loading was evaluated using texture analysis. FTIR confirmed successful silylation through the reduction in hydroxyl groups and the emergence of silicon-containing functionalities. Treated fillers exhibited rougher, more irregular surfaces, which suggested improved filler dispersion and interfacial compatibility within the PDMS matrix. Modified composites showed enhanced hydrophobicity, with contact angles up to 102.84°. Composites containing 5 wt.% HMDS-treated raspberry filler demonstrated the highest puncture resistance and elasticity, indicating improved interfacial interactions. In contrast, 20 wt.% filler loading generally reduced puncture resistance and elasticity, possibly due to increased particle agglomeration and reduced matrix continuity, as suggested by the SEM observations, whereas shadbush-filled composites showed decreased performance after treatment. Overall, HMDS surface modification effectively improves the compatibility of pigment-rich agro-waste fillers with PDMS and supports their use as functional fillers and natural colorant sources in silicone-based composites, providing a value-added route for the utilization of agro-industrial by-products. Full article

Graphene oxide (GO)-based nanomaterials functionalized with targeting ligands are promising platforms for selective cancer drug delivery. Among relevant targets, integrin α_Vβ₃ is a highly overexpressed receptor in several solid tumors and is commonly targeted using cyclic Arg-Gly-Asp (cRGD) peptides. However, the molecular details governing the interaction between cRGD-functionalized GO dots and integrins remain poorly understood. In this work, all-atom molecular dynamics simulations are employed to investigate the interaction between integrin α_Vβ₃ and a nanocarrier composed of a GO dot coated with polyethylene glycol (PEG) and functionalized with cRGD ligands. Multiple 1 μs simulation replicas are used to characterize both specific ligand recognition and non-specific nanocarrier/receptor interactions. The simulations show that cRGD binding within the integrin-binding pocket is stable, indicating that the nanocarrier does not impair receptor recognition. Beyond cRGD-mediated binding, both PEG-cRGD chains and GO itself establish additional contacts with the protein, whose nature and distribution are modulated by the relative orientation of the GO plane. Overall, the structural dynamics of integrin α_Vβ₃ remains preserved upon nanocarrier binding. These findings provide atomistic insights into the interplay between ligand-mediated and multivalent surface-mediated interactions of GO-based nanocarriers with integrins for the rational design of selective nanocarriers for cancer therapy. Full article

Fake social media profiles increasingly resemble ordinary accounts, combining believable images, fluent biographies, copied posts, and selective engagement, which makes single signal detection unreliable. This study develops and evaluates a forensic natural language processing (NLP) framework that treats profile text as digital evidence rather than raw model input. Profiles were collected from Facebook, Instagram, X, Truth Social, and LinkedIn through network expansion and vocabulary guided scraping, then anonymized, merged, cleaned, and organized into structured text and profile records. Ground truth came from human eye annotation (referred to throughout as human eye review, human review, or manual review) supported by three LLM reviewers (referred to throughout as LLM assisted review), OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and Google Gemini 1.5 Pro, with strict agreement logic producing confidence-tiered labels across platform-specific label spaces covering scam, fraud, harmful, and legitimate behavior. Each model received a defined set of label categories, was instructed to return a single label with a one sentence rationale, and worked independently with no cross-model communication. Multidimensional features, including TF-IDF representations, sentence embeddings, scam lexicon counts, sentiment and emotion scores, and behavioral indicators, were extracted through a pipeline built to keep links, hashtags, contact markers, and repeated phrasing as forensic signals rather than noise. Traditional classifiers, transformer models, and ensembles were trained on verified labels, and the best model per platform was applied to the full dataset of over 309,108 records under confidence filtered classification, with predictions below a 0.70 probability threshold flagged for human review. The framework links classification outputs to textual indicators through explainable AI, achieving weighted F1 scores between 0.86 and 0.96 across five platforms. These results show that profile text alone provides strong, production-relevant evidence for detecting fake and harmful accounts, even though the framework treats this as one evidence stream to be weighed alongside image and behavioral signals rather than as a final determination on its own. The accompanying evidence trail means an investigator or a trust and safety team can see why a profile was flagged rather than being handed a bare score, which is what turns a classifier into a tool that can be checked, challenged, and used directly in an investigation. Full article

With increasing rail traffic intensity, reliable detection of shallow subsurface rail damage is essential for operational safety. This critical narrative review evaluates non-destructive testing technologies relevant to defects whose active crack front or principal scattering zone lies within the upper approximately 0.5–10 mm of the rail, while treating the 10–15 mm range as a transition to deeper-defect verification. Magnetic flux leakage, magnetic particle inspection, visual inspection, eddy current testing, and conventional ultrasonic testing are first examined as screening or confirmatory comparators. The review then focuses on four ultrasonic surface-wave excitation routes—contact piezoelectric, active air-coupled, electromagnetic acoustic, and laser ultrasonic—and distinguishes source-specific laboratory capability from demonstrated field evidence. Because the cited studies use different defect geometries, rail conditions, sensor configurations, speeds, and decision criteria, their numerical values are reported as source-conditioned evidence rather than as a normalized ranking. An engineering decision matrix links defect depth and size, inspection speed, surface condition, and noise environment to a recommended screening–confirmation workflow. The synthesis identifies contact piezoelectric UT/PAUT as the most mature quantitative confirmation route, while EMAT, air-coupled UT, and laser UT retain method-specific advantages but require stronger natural-defect and in-service validation. Full article

Existing platform-monitoring methods for sport communities commonly rely on isolated descriptive text-mining outputs or general sentiment scores; they rarely integrate interpretable ESG issue coding with class-sensitive risk detection and provide limited support for auditable, privacy-conscious analysis of safety, ethics, and institutional trust. These limitations motivate a multi-stage framework that converts heterogeneous platform discourse into complementary structural and evaluative signals. Conceptually, digital trust is treated as the focal governance outcome; ethics and safety are substantive domains of concern; ESG provides the bounded classification and response ontology; and early warning denotes a prototype, human-reviewed weak-signal triage concept rather than incident prediction or a deployed security-monitoring system. Using 377,700 cleaned Korean-language comments on judo and Brazilian Jiu-Jitsu (BJJ) collected from Naver News and YouTube between 2010 and 2025, the framework combines n-gram analysis, LDA topic modeling, CONCOR network analysis, bounded ESG discourse classification, and three-class sentiment prediction. The individual analytical algorithms are established; the methodological contribution lies in their governance-oriented orchestration through a bounded ESG/non-ESG coding gate, a study-specific index layer, and a human-reviewed pathway from aggregate discourse signals to proportionate review. The analytical workflow identifies issue salience, relational topic structures, ESG dimensions, sentiment risk, legitimacy balance, and platform-specific risk concentration while excluding personally identifiable information. Empirically, social and governance concerns dominate the corpus, and governance-related negative sentiment consistently exceeds social-risk sentiment, highlighting rule transparency, coach ethics, misinformation, platform reputation, and institutional response as central trust-risk domains. Cell-weighted sensitivity checks preserved the governance-over-social and YouTube-over-Naver risk ordering, although the pooled salience estimate remained sensitive to the rapid expansion of BJJ discourse on YouTube. The fine-tuned KLUE-BERT model achieved a Macro-F1 of 0.838 and a negative-class F1 of 0.862, outperforming the strongest baseline, Text-CNN (Macro-F1 = 0.791), by 0.047 absolute Macro-F1 points (approximately 6.0% relative improvement). These findings support the feasibility of a batch-oriented, human-reviewed prototype for prioritizing aggregate discourse patterns. They do not establish the effectiveness of a real-time security-monitoring, incident-detection, or operational early-warning system. Full article

This study developed an eco-friendly mine backfill material using coal gangue, fly ash, desert sand, and natural rice husk. The mechanical properties and damage evolution characteristics were investigated through uniaxial compression, Brazilian splitting, acoustic emission (AE), digital image correlation (DIC), and scanning electron microscopy (SEM) tests. Polyvinyl alcohol (PVA) fibers provided the highest reinforcement efficiency, followed by polypropylene (PP) fibers. At a natural rice husk dosage of 5%, the backfill reached a compressive strength of 5.6 MPa and a splitting tensile strength of 0.376 MPa, representing an 83.4% increase in tensile strength compared with the fiber-free group and approaching the performance of the PP fiber group. Increasing the cement replacement ratio from 50% to 70% reduced strength, whereas longer curing improved strength development. The coupled AE–DIC observations indicate that natural rice husk incorporation delayed crack localization and changed the failure process from sudden brittle cracking to more progressive damage. SEM observations show that natural rice husk formed local contact and partial embedding with the cementitious matrix, with hydration products around the rice husk and visible pull-out voids. Overall, natural rice husk can be used as a low-cost and sustainable reinforcing material for coal gangue–desert sand–fly ash-based mine backfill. Full article

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review offers a thorough analysis of the shift from custom 3D-printed platforms to adaptive 4D-printed wearable biosensors that include time-sensitive, stimuli-responsive materials. We carefully investigate how nanomaterial-engineered transducers, including carbon nanomaterials, MXenes, and metallic nanostructures, improve electrochemical sensitivity, signal stability, and mechanical durability in sweat-based and electrophysiological sensing. Additionally, we examine the integration of thermoresponsive polymers, moisture-activated hydrogels, shape-memory materials, and self-healing networks that support autonomous control of skin–sensor contact, microfluidic sweat management, and structural stability under high mechanical strain. Case studies focused on sports monitoring demonstrate how these innovations enable multimodal measurement of mechanical, chemical, and molecular biomarkers in real time. Lastly, we address manufacturing scalability, regulatory issues, and translational challenges necessary to move from proof-of-concept to clinical deployment. By combining nanomaterial-driven electrochemical precision with 4D-printed mechanical adaptability, this review maps a path toward adaptive, self-regulating wearable platforms that sustain analytical accuracy even under extreme physiological demands. Full article

Climate adaptation in high-density cities requires equitable access to cooling and habitat benefits from blue–green infrastructure (BGI). However, the critical thresholds where human activity overwhelms these services remain unknown. Taking Shenzhen as a case study, we quantified habitat quality using the InVEST model and divided the urban landscape into low-, medium-, and high-quality zones. After screening significant drivers via multiple linear regression, we compared six machine learning algorithms and selected XGBoost (for low- and medium-quality zones) and LightGBM (for high-quality zones) with Optuna-tuned hyperparameters, then applied SHAP dependence analysis to identify nonlinear, threshold-driven responses. We identify a systemic thermal threshold of 23.7 °C. Above this temperature, BGI cooling efficiency decouples from habitat quality. We also quantify actionable intervention windows. Low-quality habitats—dominated by dense residential areas—collapse when building density exceeds 0.7 or road density exceeds 0.009. Medium-quality habitats offer a balance window (core area > 2844 m², building density < 0.361) that enables incidental nature contact during daily travel. High-quality refugia require a core area > 8600 m² with near-zero disturbance. These thresholds expose a stark green inequity: socioeconomically vulnerable groups in low-quality zones are systematically disconnected from cooling services. We translate these findings into a tiered spatial intervention framework—restoration, accessibility enhancement, and strict protection—to advance climate-resilient and socially equitable urban planning. Full article

Outdoor learning has been used in different European contexts to support personal and social development. However, the ways in which these initiatives are organised and implemented can vary according to the target group, the educational purpose, and the local context. The aim of this study was to comparatively analyze selected Outdoor Learning initiatives for young people and adults in Europe, focusing on their main characteristics and on the forms of development they promote. A qualitative exploratory review was carried out using websites of educational networks, NGOs, training centers, and institutional projects. Seven initiatives were selected from an initial set of 27 cases identified through the search process. The selected initiatives were Venture Trust, Venture Out, Forest School, KOA Aventura, Moc Warmii, Die Seminarschneider, and LIFE Trekking. The results show a diversity of program durations, pedagogical approaches, and participant profiles, but also some common elements, such as experiential learning, reflection, cooperation, and contact with nature. The analysis suggests that Outdoor Learning initiatives may support different forms of personal and social development, although their design and aims are strongly influenced by the context in which they are developed. Full article

Water-in-water (W/W) Pickering emulsions based on aqueous two-phase systems (ATPS) are promising for food applications, but their stabilization is challenged by the ultra-low interfacial tension inherent to ATPSs. This study developed food-grade W/W Pickering emulsions stabilized by bacterial cellulose nanocrystals (BCNCs) and evaluated their ability to regulate starch digestibility. The effects of dextran (Dex) concentration, maltodextrin (MD) concentration, and BCNC content on the microstructure, rheological properties, and stability of the emulsions were systematically investigated. The partitioning behavior of amylopectin (AMP) between the two phases and the in vitro digestion behavior of AMP-loaded W/W Pickering emulsions in the presence of α-amylase were further examined. Fourier-transform infrared spectroscopy confirmed the successful preparation of BCNCs. The optimal formulation (24 wt% Dex, 14 wt% MD, and 0.24 wt% BCNCs) exhibited a uniform droplet distribution and enhanced storage stability. Contact angle analysis indicated preferential wettability of BCNCs toward the continuous Dex-rich phase, promoting interfacial adsorption and droplet stabilization. The emulsions remained stable at pH 3.0–7.0 but were sensitive to ionic strength above 7 mM sodium chloride. Importantly, in vitro digestion showed that BCNC-stabilized emulsions significantly inhibited α-amylase-mediated hydrolysis of AMP, with the hydrolysis extent reducing from ~54% to 14%. These results indicate that BCNCs act as effective natural stabilizers for W/W Pickering emulsions, forming a physical barrier that retards starch digestion. This work provides a sustainable strategy for designing slow-digestible, low-glycemic-index food systems with potential applications in functional foods and pharmaceuticals. Full article

Catalytic nanozymes have emerged as a versatile class of engineered nanomaterials that combine enzyme-like catalytic activity with exceptional physicochemical stability, tunable composition, and scalable fabrication, offering significant advantages over natural enzymes for antimicrobial applications in food systems. Recent advances in materials engineering have enabled the development of nanozymes with enhanced catalytic efficiency, broad-spectrum antimicrobial activity, and improved resistance to harsh food-processing environments. Nevertheless, current research remains fragmented across diverse material platforms and application scenarios, while a comprehensive understanding of how engineering strategies govern catalytic performance, antimicrobial efficacy, and translational potential is still lacking. This review provides a critical and systematic analysis of catalytic nanozymes for antimicrobial food systems from a structure–activity relationship perspective. Emphasis is placed on the engineering principles that regulate enzyme-mimicking activities, including compositional tuning, crystal phase and facet engineering, defect creation, heterostructure construction, pore architecture, surface functionalization, and single-atom engineering. The relationships between these structural features and catalytic mechanisms, including peroxidase-, oxidase-, catalase-, and multi-enzyme-like activities, are discussed in relation to the generation of reactive oxygen species, membrane disruption, extracellular polymeric substance degradation, biofilm eradication, and pathogen inactivation. Representative applications in food-contact surface decontamination, antimicrobial packaging, fresh produce preservation, and intelligent food processing are critically evaluated using recent experimental evidence. Beyond antimicrobial performance, this review introduces a safe-by-design framework that integrates material engineering with toxicological assessment, nanoparticle migration, environmental fate, regulatory considerations, and scalable manufacturing. Emerging opportunities for artificial intelligence-assisted nanozyme design, high-throughput materials discovery, and data-driven optimization are also discussed as transformative approaches for accelerating industrial translation. By integrating materials science, catalytic mechanisms, food microbiology, and safety assessment, this review establishes a comprehensive framework for the rational development of next-generation catalytic nanozymes toward sustainable, effective, and industrially applicable antimicrobial food systems. Full article

Clarifying combustion-space behavior is essential for operating large-tonnage natural gas-fired float glass furnaces with complex single-furnace dual-cooler layouts. In this study, a three-dimensional computational fluid dynamics model of the gas-phase combustion space of a 1300 t/d natural gas-fired float glass furnace was developed and validated using crown-temperature measurements, with a maximum relative error of 3.3%. The effects of burner inclination angle (β = 5°, 10°, and 15°) and excess air ratio (α = 1.0–1.20) on temperature distribution, flame morphology, and flue-gas recirculation were investigated. The results show that β = 5° produces a more horizontally extended natural-gas jet, enhances contact with preheated air, and forms a wider high-temperature region, with a maximum temperature of 2512 K. Increasing the excess air ratio improves combustion completeness and enlarges the high-temperature region; however, further increasing α from 1.15 to 1.20 provides only marginal thermal benefits while increasing sensible heat loss through the exhaust gas. Among the investigated operating conditions, β = 5° and α = 1.15 achieve the lowest outlet flue-gas specific enthalpy of 755 KJ/Kg. Full article

Bismuth hexacyanoferrate (BiHCF), a Prussian blue analogue containing redox-active Fe–CN–Bi coordination motifs, was investigated as a model electrode for cathodic processes in chloride-rich saline and hypersaline electrolytes. Rather than evaluating BiHCF solely as a hydrogen evolution catalyst, this work focuses on the coupled electrochemical and interfacial processes that govern its response in NaCl solutions and natural brines from seawater, reverse osmosis (RO) reject, and high-altitude brine environments. Structural characterization by SEM–EDS, XRD and FTIR confirmed the formation of crystalline BiHCF with rod-like micrometric morphology and preserved cyanide coordination. Linear sweep voltammetry under controlled hydrodynamic conditions revealed a progressive cathodic displacement of the mixed potential with increasing NaCl concentration, together with a marked suppression of oxygen reduction kinetics at high chloride activity. Mixed-potential analysis showed that HER kinetics remain comparatively less sensitive to salinity than ORR, whereas the anodic contribution associated with BiHCF oxidation becomes strongly affected by chloride-induced surface transformation. Post-electrochemical characterization indicates the formation of a BiOCl-rich surface layer when the BiHCF is in contact with a hypersaline electrolyte during the cathodic subprocess (close to 0 mV_SHE), which accounts for the transition from active mixed-control behaviour to a passivated interfacial regime. Density functional theory calculations suggest that elementary water activation and hydrogen-forming steps at Bi sites are intrinsically feasible, implying that the experimentally observed overpotentials originate primarily from transport, interfacial resistance and chloride-driven passivation rather than from an unfavourable molecular reaction pathway. These findings provide a mechanistic framework for understanding Bi-based Prussian blue analogue electrodes in non-purified saline electrochemical systems and highlight the dual role of chloride as both a charge-compensating electrolyte species and a passivating reactant. Full article