Search Results (4,770)

Overtourism management increasingly uses public communication to frame how visitors are expected to move, behave, and understand their responsibilities in pressured destination spaces. Yet, in heritage cities, such communication is often examined through isolated signs, etiquette campaigns, visitor advisories, or congestion notices rather than as part of a wider visitor-management communication system. This paper examines Kyoto as a case of overtourism communication in an urban heritage setting. Using document-based content analysis supported by exploratory statistical tests, it maps 64 public-facing communication artifacts published between 2016 and 2025 and codes them by message orientation, artifact type, source environment, and spatial focus. The findings show that exclusionary messaging does not dominate Kyoto’s public-facing overtourism communication within the analyzed corpus. Dispersal and behavioral guidance is the most common orientation, while restriction is targeted and secondary, and invitational place guidance remains comparatively limited. Message orientation is significantly associated with artifact type, with restriction concentrated in signs and posters and invitational place guidance appearing mainly in guides and brochures. Semi-institutional actors account for most artifacts, indicating the central role of destination-management organizations and other visitor-facing tourism bodies in translating management concerns into public guidance. Spatially, guidance-oriented communication appears broadly across the city, while restriction has its clearest place-based concentration in Eastern Kyoto, especially Gion-related areas. A smaller set of messages invites visitors to explore wider Kyoto or less conventional places beyond standard guidebook routes. Drawing on the lens of soft urban governance, the paper argues that public-facing overtourism communication can be understood as one visible layer through which visitor-management expectations are framed, spatially differentiated, and made publicly legible. As a document-based analysis, the study does not measure visitor reception, compliance, or behavioral effects, which remain priorities for future research. Full article

Thin sections are fundamental tools in mineralogy, petrography, archaeometry, paleontology, and heritage science, allowing for the microscopic study of mineral assemblages, rock textures, ceramic fabrics, and fossil microstructures. Although thin-section preparation is often presented as a standardized technical procedure, the quality and interpretative reliability of the final section depend strongly on material behavior, laboratory equipment, bonding and thinning procedures, thickness-control criteria, and operator decisions made during preparation. This paper examines thin-section preparation as a process of methodological calibration, understood as the material-specific adjustment of preparation decisions in order to preserve the microstructural features required for subsequent interpretation. The study combines an overview of current preparation practice with documented hands-on workflows from academic and heritage-oriented thin-section laboratories. Two case studies are used to develop the calibration framework. The first concerns silicified fossil wood and marly carbonate samples prepared at the Department of Geology, University of Patras, Greece, using water-cooled cutting, epoxy bonding under heat and pressure, machine-assisted and manual thinning, micrometer-based thickness monitoring, and optical assessment adapted to carbonate-rich samples. The second concerns archaeological ceramics, fossiliferous limestone, oolitic limestone, and coherent lithic/sedimentary samples prepared at the INSTAP Study Center for East Crete, Greece, where preparation decisions included mounting-face selection, cleaning, vacuum impregnation where required, controlled lapping, and transmitted/polarized-light quality assessment. Together, the case studies show that equivalent preparation stages require different operational decisions according to hardness, brittleness, porosity, cohesion, fossil content, ceramic fabric, and intended analytical purpose. Preparation-induced features such as microcracks, smearing, surface relief, detachment, or loss of weak fabrics may be misread as primary geological, technological, taphonomic, or conservation-related features if preparation choices are not properly considered. Framed in this way, thin-section preparation is positioned as a foundational first step in heritage material characterization, on which the reliability of subsequent optical, electron-optical, and microanalytical methods directly depends. Full article

Generative AI is reshaping how workers form digital commerce entrepreneurial intentions, yet how AI-related fear of missing out (AI-FoMO) converts into proactive entrepreneurial behavior remains unclear. Drawing on social cognitive theory, this study tests a model in which AI-FoMO drives AI reliance, which in turn shapes two distinct cognitive routes: a tool-referenced route through perceived task automation (PTA) and an agent-referenced route through borrowed entrepreneurial competence (BEC), a newly proposed construct capturing how human–AI coupling reshapes workers’ self-perceived capability for solo digital entrepreneurship. We further test whether digital self-efficacy (DSE) moderates the conversion of these perceptions into intention. Survey data from 328 Korean workers were analyzed using PLS-SEM. AI-FoMO predicted AI reliance (β = 0.391), which shaped both PTA (β = 0.531) and BEC (β = 0.291); both routes predicted entrepreneurial intention, and both serial mediation paths were significant, with the PTA route approximately 2.6 times stronger than the BEC route. DSE moderated only the BEC route (β = 0.105, p = 0.025), not the PTA route (β = −0.077, p = 0.109), an asymmetry confirmed by a formal coefficient-difference test. The findings reframe AI-FoMO as a potential driver of approach-oriented entrepreneurial behavior, introduce BEC as an extension of vicarious experience to the human–AI context, and show that digital self-efficacy selectively regulates how AI-induced perceptions convert into entrepreneurial intention. Full article

Michelin Green Star restaurants combine sustainability with high-end dining, yet it remains unclear whether motivation to visit and willingness to pay reflect the same psychological process. Across two studies, we examined differentiated consumer responses to this hybrid context and the role of sustainability communication. Study 1 used survey data from 526 participants and structural equation modeling. Visit intention was positively associated with descriptive norms (β = 0.254), perceived behavioral control (β = 0.356), and positive anticipated emotions (β = 0.302). Green consumer orientation, luxury restaurant hedonism, and perceived sustainability were indirectly associated with intention, mainly through positive anticipated emotions. Willingness to pay showed a narrower pattern, being positively associated with perceived behavioral control (β = 0.171) and luxury restaurant hedonism (β = 0.359), whereas sustainability-related distal antecedents showed no significant direct or indirect associations with this outcome. Study 2 experimentally compared practice-based and experience-based messages among 102 participants. The messages did not differ in their average effects, although intention increased overall following exposure. The interaction between message condition and green consumer orientation predicted post-message positive anticipated emotions (b = 0.447), and the index of moderated mediation through positive anticipated emotions was significant (index = 0.200, 95% bootstrap CI [0.069, 0.418]). Overall, motivational readiness and stated monetary allocation appear to reflect partly distinct processes, while the preliminary experimental findings suggest that communication effects depend on the alignment between communication orientation and consumers’ environmental orientation. Full article

As smart agriculture gradually shifts from single-yield monitoring toward the coordinated optimization of input efficiency, resource conservation, and farm profitability, the use of multi-source agricultural data to support precise water, fertilizer, and pesticide inputs has become an important issue. To address the inconsistent sampling frequencies, heterogeneous semantic scales, and difficulty in directly modeling input–profit relationships among field images, meteorological environments, soil states, and agricultural management records, a Multimodal Agricultural Input–Output Optimization Network, termed MAION, is proposed. In this framework, a unified plot–time-window agricultural state representation is constructed through a cross-modal spatiotemporal alignment module. The potential effects of different input behaviors on yield, cost, and net profit are estimated through an input–output causal profit modeling module, and profit-driven reinforcement learning is further used to generate input strategies oriented toward long-term net profit maximization. Since the task belongs to yield, cost, and profit regression prediction and continuous agricultural decision optimization rather than classification or recognition, classification metrics such as accuracy, precision, and recall were not adopted. Instead, RMSE, MAE, $R^2$, Net Profit Improvement, Input–Output Ratio, Cumulative Reward, Policy Stability, and Regret were used for evaluation. Experimental results show that MAION achieves the best performance in yield, cost, and net profit prediction, with RMSE values of $0.587$, $0.531$, and $0.648$, respectively, and corresponding $R^2$ values of $0.914$, $0.891$, and $0.883$. These results are markedly superior to those of Random Forest, XGBoost, LSTM, GRU, Transformer, Multimodal Transformer, and reinforcement learning baseline models. In the economic decision-making experiment, MAION achieves a Net Profit Improvement of $17.68\%$, an Input–Output Ratio of $1.71$, a Cost Efficiency Gain of $15.46\%$, and a Cumulative Reward of $301.27$, while obtaining the lowest policy fluctuation and regret. The results indicate that the proposed framework can provide effective data-driven decision support for precision input, cost control, and profit optimization in smart agriculture. Full article

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary. Full article

Dam displacement forecasting is important for assessing whether long-term structural responses remain consistent with established operational behavior. In multi-point monitoring systems, however, irregular survey-line layouts and unequal numbers of monitoring points make it difficult to organize long-term records while preserving their engineering meaning. This study develops a field-oriented forecasting framework by reconstructing daily observations into a structured displacement-field object defined by survey-line order, monitoring-point alignment, and three displacement components. A valid-position-aware protocol is introduced to distinguish actual monitoring locations from structural padding, ensuring that model training and evaluation remain restricted to the same physical monitoring definition. Using long-term operational records from the Tianshengqiao First Dam, four representative models, namely SimVP, SimVPv2, PatchTST, and TimesNet, are evaluated under the same chronological split, causal forward-fill-only preprocessing, input window, prediction horizon, and evaluation boundary. All four models achieve strong predictive performance, with R² values above 0.97 in the X direction and above 0.99 in the Y and Z directions. No single trained model or forecasting route exhibits a consistent advantage across all displacement components and evaluation metrics. Under the present single-dam, case-specific setting, the relative ranking varies with displacement direction and forecasting horizon and should not be interpreted as evidence of general direction-specific suitability for any particular architecture. At the route level, the field-based route retains a slight advantage in Y-direction forecasting and overall MAE, whereas the sequence-based route remains competitive for Z-direction displacement and longer-horizon X-direction prediction. The proposed framework provides a practical and physically consistent digital representation for organizing irregular monitoring records, comparing forecasting routes, and supporting deployment-oriented model selection and subsequent model adaptation. Full article

We conducted an exploratory evaluation of short-term changes following participation in a two-day intensive training programme aimed at addressing sustainability-oriented and project-management-related psychological outcomes, focusing on self-efficacy and behavioral intentions among university staff and researchers. A repeated-measures pre-post design was adopted. A total of 28 participants took part in the training, which covered the fundamentals of project management (including its main phases and processes) and the integration of sustainability principles into project management practices. Participants were assessed at two time points: prior to the training (T1) and immediately after its completion (T2). Primary outcomes included project sustainability self-efficacy, project management self-efficacy, pro-sustainability behavioral intentions, and behavioral intentions to adopt new project management practices. Cognitive fatigue and perceived training utility were assessed at T2 to capture participants’ immediate post-training experiences. Changes over time were examined using paired-samples t-tests and regression models controlling for baseline (T1) levels of each outcome variable. Participants showed significant increases from T1 to T2 across all outcome variables, with a moderate effect for project sustainability self-efficacy (Cohen’s d = 0.42) and large to very large effects for project management self-efficacy (d = 1.35), pro-sustainability behavioral intentions (d = 2.08), and behavioral intentions to adopt new project management practices (d = 1.38). Baseline levels generally predicted post-training scores, suggesting partial temporal stability, whereas perceived training utility showed weak and non-significant associations with post-training outcomes. Cognitive fatigue did not emerge as a significant predictor of any outcome. Overall, these exploratory findings provide preliminary evidence of short-term changes observed following participation in the training programme in sustainability- and project-management-related psychological outcomes. However, given the small convenience sample, single-group pre-post design, and immediate post-training assessment, the findings should be interpreted cautiously and do not allow causal conclusions regarding the effects of the training programme. Larger controlled and longitudinal studies are needed to determine whether these changes can be replicated and sustained over time. Full article

The effectiveness of machine learning and deep learning methods for network anomaly detection depends strongly on the quality and representativeness of the datasets used for training and evaluation. Despite recent advances, many publicly available benchmarks rely on synthetic traffic, outdated attack scenarios, or limited representation of encrypted communications. This work presents a network traffic dataset derived from operational firewall logs collected in a heterogeneous institutional environment dominated by HTTPS/TLS traffic. A structured data-centric pipeline was implemented, including preprocessing, behavioral feature engineering, unsupervised pseudo-labeling through the EFMS–KMeans algorithm, class balancing using SMOTE, and the generation of model-oriented sequential representations for deep learning analysis. The resulting dataset contains large-scale flow-level records describing volumetric, behavioral, and temporal traffic characteristics while preserving privacy through anonymization procedures. Technical validation was conducted using statistical analysis, entropy-based measurements, clustering quality metrics, and dimensionality reduction techniques, confirming data consistency, structural diversity, and class separability. The dataset is publicly available through the Mendeley Data repository together with metadata and documentation supporting anomaly detection research, encrypted traffic analysis, and the evaluation of machine learning and deep learning approaches in realistic cybersecurity environments. Full article

Background and Objectives: Cardiovascular disease remains the leading cause of premature mortality worldwide. Subclinical glucose dysregulation, a contributor to accelerated vascular aging, is undetectable by conventional screening in apparently healthy individuals; even within the normal glycemic range, postprandial glucose excursions promote endothelial injury and inflammatory pathways independently of mean glucose levels. Hypothesis: Continuous glucose monitoring (CGM)-guided metabolic phenotyping, combined with personalized dietary optimization, structured fasting protocols, and selective longevity-oriented pharmacotherapy, constitutes a mechanistically coherent, hypothesis-generating preventive strategy that may attenuate cardiovascular risk and biological aging in apparently healthy non-diabetic adults, pending confirmation in prospective outcome trials. Materials and Methods: This narrative review synthesizes evidence from prospective cohort studies, randomized controlled trials, and mechanistic investigations connecting CGM-guided metabolic assessment with preventive cardiology and the emerging field of longevity medicine, focusing on glycemic variability biology, nutrient-sensing pathways, and the cardiovascular and longevity profiles of low-dose metformin and acarbose. Results: CGM-derived metrics capture inter-individual glycemic variability invisible to standard assessments and provide behavioral feedback for dietary personalization. Structured fasting and low-dose metformin converge on shared nutrient-sensing pathways implicated in both vascular aging and longevity, with CGM enabling objective confirmation of metabolic adaptation. Acarbose has shown cardiovascular and lifespan benefit signals in secondary trial analyses and preclinical longevity models, though these findings require replication and are not yet established in non-diabetic populations. Conclusions: We propose a four-phase research framework integrating CGM metabolic phenotyping, dietary optimization, fasting titration, and selective pharmacological augmentation for apparently healthy adults at cardiovascular risk. Prospective hard-endpoint trials are lacking, and this framework should be regarded as hypothesis-generating rather than an established clinical strategy, warranting rigorous outcome-based evaluation before clinical adoption. Full article

Developmental dyslexia is a common neurodevelopmental disorder characterized by persistent difficulties in accurate and fluent word reading despite adequate intelligence, educational opportunity, and intact sensory function. Contemporary neurobiological models have progressively shifted from localization-based explanations toward network-oriented perspectives emphasizing large-scale brain connectivity, developmental maturation, and neuroplasticity. Nevertheless, the developmental mechanisms underlying early right-hemisphere recruitment remain incompletely understood. Although increased right-hemisphere activation has traditionally been interpreted as a compensatory response to left-hemisphere dysfunction, accumulating evidence from longitudinal neuroimaging, intervention, and developmental studies indicates that this explanation alone does not adequately account for the heterogeneity of neurobiological findings observed across individuals with developmental dyslexia. This narrative review synthesizes evidence from developmental neurobiology, network neuroscience, longitudinal neuroimaging, and intervention research to examine the biological processes underlying early right-hemisphere recruitment. Across the reviewed literature, developmental dyslexia is increasingly characterized by atypical maturation of distributed reading networks involving alterations in white-matter development, functional connectivity, hemispheric lateralization, and experience-dependent neuroplasticity. Collectively, these findings suggest that reading networks remain dynamically modifiable throughout literacy acquisition and that multiple developmental pathways may contribute to diverse neurobiological and behavioral outcomes. Building on this evidence, we propose a Developmental Neuroplasticity Framework for Reading Network Reorganization that integrates existing neurobiological models within a unified developmental perspective. Rather than proposing a new neurobiological mechanism, the framework seeks to explain the heterogeneous patterns of early right-hemisphere recruitment that are not fully accounted for by compensation-based interpretations alone. Specifically, it conceptualizes early right-hemisphere recruitment as one possible adaptive developmental outcome emerging from interactions among early neurodevelopmental vulnerability, distributed network connectivity, developmental neuroplasticity, and environmental experience. By integrating evidence that has largely been considered within separate theoretical perspectives, the framework generates empirically testable predictions regarding developmental trajectories, network reorganization, and intervention-related variability, while providing a conceptual basis for earlier identification of children at risk and the development of more targeted, developmentally informed intervention strategies. Full article

As piston rods for excavator boom hydraulic cylinders shift from integral forging to welded assemblies of separately manufactured rod bodies and rod heads, joint reliability becomes critical to load-bearing performance. In this study, 60 mm diameter quenched-and-tempered/as-rolled 1045 steel joints were fabricated by continuous-drive friction welding under three coupled secondary-friction pressure–time conditions. Joints made with two quenched-and-tempered base metals were compared under the same high-pressure/short-time condition. Optical microscopy, electron backscatter diffraction, microhardness testing, tensile testing, and scanning electron microscopy were used to characterize microstructure and failure behavior. Fine, multi-oriented reconstructed microstructures formed in all weld zones, while a hardness valley developed in the heat-affected zone on the quenched-and-tempered side. As the machine-displayed secondary-friction pressure increased from 1.43 to 2.14 MPa, the actual secondary-friction time decreased from 57.3 to 37.5 s. Under the corresponding high-pressure/short-time condition, heat-affected-zone softening was slightly mitigated and tensile strength increased from 743.37 to 755.78 MPa. Different elongations were observed between the two joint types. All specimens fractured in the softened heat-affected zone on the quenched-and-tempered side and exhibited dimple-dominated fracture surfaces. Under the present tensile-testing conditions, the weld zone was not the fracture-controlling region. Full article

Glow-discharge plasma (GDP) poling is revisited as an electrode-free method for activating piezoelectricity in poly(vinylidene fluoride) (PVDF) films. Although this method was proposed several decades ago, its effect on the properties of PVDF films has remained poorly understood. In this work, we demonstrate that GDP enables efficient poling of oriented PVDF films without pre-deposited electrodes and investigate the relationship between plasma treatment time, structural evolution, and piezoelectric response. Commercially available 25 μm-thick oriented PVDF films (PolyK) were treated in a DC glow discharge for 15 s to 15 min and characterized using FTIR, DSC, piezoresponse force microscopy, UV–Vis–NIR spectrophotometry, quasi-static d₃₃ measurements and water contact-angle measurements. GDP poling produced a side-averaged piezoelectric coefficient d₃₃ of up to ~25 pC/N within 1–5 min, with local maxima at approximately 1, 2.5, and 5 min. This behavior was accompanied by pronounced changes in the domain structure, including an increase in the ferroelectric domain size from 86 to 552 nm, while the crystallinity and electroactive phase fraction changed only moderately. Plasma treatment also increased the wettability of the plasma-facing surface, reducing the water contact angle from about 85° to 42° within 3 min. At longer treatment times (>5 min), however, the piezoelectric response decreased and the optical transparency deteriorated because of increased haze and turbidity, most likely associated with plasma-induced chemical modification of the surface layers. These results indicate that GDP poling has an effective processing window of 1–5 min. The proposed approach provides a vacuum-compatible and electrode-free route for preparing PVDF films with increased surface wettability for flexible piezoelectric sensors, wearable electronics, and integrated polymer-based devices, because it is compatible with electrode deposition on an already activated polymer surface within a single vacuum cycle. Full article

Modern communication networks may provide several heterogeneous links between the same pair of devices, including Wi-Fi, 5G, Bluetooth, and SparkLink. Existing multicast schemes often use simple-graph abstractions and therefore cannot distinguish these parallel links. We present ESMP, a multi-graph-based heuristic framework for efficient and stable multicast construction over heterogeneous parallel communication links. ESMP represents parallel channels as edges with delay and stability attributes. We show that an aggregate-edge-delay-constrained decision variant of the formulation is NP-hard. The framework includes six polynomial-time heuristics: delay-based DMA and DSMA, stability-based SMA and SDMA, and stability-delay-ratio-based RMA and MRMA. Each algorithm derives a metric-specific graph from the original multi-graph and constructs a tree according to its delay-stability preference. We also develop local adjustment strategies for vertex joins, vertex exits, and link dynamics. Experiments on connected synthetic multi-graphs reveal distinct metric preferences. Delay-oriented methods reduce delay, stability-oriented methods improve stability, and ratio-based methods provide stability-aware trade-offs at relatively low delay. In particular, RMA favors low delay, whereas MRMA uses pair-level average stability-delay information and shows comparatively favorable stability preservation and tree compactness in the evaluated scenarios. These findings characterize heuristic behavior in the evaluated synthetic settings and do not establish general optimality. Full article

This study presents a numerical comparison of dimensional and non-dimensional implementations of the classical benchmark problem of steady natural convection in a two-dimensional differentially heated square cavity over the Rayleigh-number range 10³ ≤ Ra ≤ 10¹². The novelty of this work lies in the systematic comparison of both formulations under identical numerical conditions, providing an implementation-oriented assessment of their benchmark accuracy, mesh sensitivity, continuation strategy, and computational efficiency. The governing equations of mass, momentum, and energy conservation were solved under the Boussinesq approximation using primitive variables and the finite-element method. Since both formulations are theoretically equivalent descriptions of the same physical problem, the purpose of this work is not to reassess their physical validity, but to examine their numerical behavior under identical benchmark conditions in terms of mesh sensitivity, continuation strategy, benchmark accuracy, and computational efficiency. In the dimensional implementation, temperature differences of 1, 10, 25, and 50 K were considered, and the corresponding cavity lengths were determined from the Rayleigh-number definition. The main calculations were performed with ΔT = 25 K, while ΔT = 50 K was retained only as an exploratory sensitivity case. Boundary-layer refinement was applied along the vertical walls over the range 10³ ≤ Ra ≤ 10¹². The results show that, once the near-wall gradients are properly resolved, both implementations predict essentially identical average Nusselt numbers, with a maximum relative difference of 0.022%. Temperature contours, stream-function distributions, and centerline profiles also exhibited the same structural behavior in both cases. For the reference mesh, the dimensional implementation exhibited a lower computational cost than the non-dimensional implementation, resulting in an approximately 31% reduction in computation time. These results demonstrate that solving the governing equations directly in dimensional variables provides a numerically efficient and physically interpretable alternative while preserving the benchmark accuracy of the classical non-dimensional formulation. Full article