Search Results (5,628)

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Shopping mobility accounts for a significant share of total travel, while the growth of e-commerce is reshaping consumer purchasing behaviour and retail dynamics. Comprehending how territorial and sociodemographic factors shape the choice between physical and digital retail channels is therefore a key issue for transport planning and sustainable urban mobility. In this context, it is important to understand how accessibility to different classes of retailers is configured and how it can impact purchasing choices. Through a discrete choice analysis, this study examines the sociodemographic and territorial determinants of purchasing behaviour, focusing on the clothing market. Four purchase alternatives are considered: medium-sized and small urban retail stores, shopping malls, online purchasing, and no purchase. This multi-alternative framework enables the direct estimation of substitution patterns not only between physical and digital retail, but also between distinct forms of physical retail. Data were collected through a survey conducted in Southern Italy, providing empirical evidence from a territorial setting that is structurally underrepresented in the existing literature. A multinomial logit model and a two-level hierarchical logit model incorporating pedestrian accessibility—measured as walking time from residence to the nearest clothing store—alongside sociodemographic and territorial attributes were calibrated to analyse alternative choice behaviour. The calibrated models show interesting results, highlighting the role of pedestrian accessibility in the choice of clothing stores in city centres. Age, income, and territorial variables further differentiate channel preferences across population segments. The findings offer relevant implications for policymakers, governance managers, urban planners, and researchers concerned with retail location, sustainable accessibility, and consumer behaviour. These insights are highly valuable for developing planning that addresses the United Nations 2030 Agenda, particularly Sustainable Development Goal 11. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article

Digital traceability has become an important capability in international trade, especially in high-regulation and high-risk supply chains. However, existing research has not fully explained how institutional, technological, and coordination-related conditions combine to produce successful outcomes. This study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) to 24 trade-corridor/product-chain cases to identify the configurational drivers of Digital Traceability Success (DTS). The findings show that Digital Trade Readiness (DTR), Market Strictness (MKT), Digital Infrastructure (DIF), and Cross-border Coordination (COO) are highly consistent necessary conditions for DTS, whereas Blockchain-enabled Traceability (BCT) is not. The sufficiency analysis identifies one dominant pathway, DTR * PRK * MKT * DIF * COO, with perfect consistency and substantial coverage. These results indicate that traceability success emerges from the alignment of institutional readiness, regulatory pressure, infrastructural capacity, product-related risk, and cross-border coordination rather than from blockchain adoption alone. The study contributes to digital trade and supply-chain governance literature by offering a configurational explanation grounded in conjunctural causation and causal asymmetry. It also clarifies blockchain’s role as a contingent enabling component rather than a universally necessary determinant. Practically, the findings suggest that policymakers and firms should prioritize interoperable infrastructure, institutional readiness, and cross-border governance mechanisms over stand-alone technological solutions. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

Radar is critical for urban security against Unmanned Aerial Vehicles (UAVs), yet signal occlusion caused by dense buildings and complex urban structures remains a major challenge for coverage assessment. Existing approaches commonly rely on 2D maps or 2.5D Digital Surface Models (DSMs), which have difficulty representing vertical facades, vegetation, bridges, overhanging structures, and void spaces. These geometric limitations can introduce errors in radar occlusion determination and direct-path power-density estimation. Full 3D ray-tracing methods offer high fidelity, but their multi-path modeling and material-parameter requirements can be costly for large oblique photogrammetric city meshes. To address this problem, this paper proposes the Visible Radar Power-Density Field (VRPF), a 3D radar power-density field computation framework based on high-resolution oblique photogrammetric models. The method constructs a reusable spatial index for large numbers of triangular facets and performs two-stage occlusion queries: rapid Axis-Aligned Bounding Box (AABB) pruning followed by ray-triangle intersection tests. Together, these components enable efficient direct-path power-density calculation while accounting for line-of-sight occlusion in complex urban scenes. Qualitative and quantitative experiments show that VRPF better preserves occlusion boundaries around building edges, vegetation, and elevated structures than DSM-based baselines. VRPF also requires less time for repeated occlusion queries than a conventional 3D BVH ray-casting baseline while maintaining highly consistent radar-signal occlusion determinations. With 32 threads, VRPF computes power density for ${10}^8$ target points in 5.92 s, about $2.66\times$ faster than the 1 m DSM method. These results indicate that VRPF provides a practical balance between geometric fidelity and computational efficiency for direct-path radar power-density assessment with urban geometric occlusion. Full article

Natural climate variations and human activities have significantly altered the river–lake hydrological regimes in the middle and lower reaches of the Yangtze River, leading to substantial changes in the inundation patterns of the Dongting Lake wetland, which in turn profoundly affect the spatial distribution and landscape patterns of wetland vegetation. Determining the response mechanisms and appropriate thresholds of wetland landscape patterns to hydrological rhythm changes is of great importance for maintaining the health of wetland ecosystems and optimizing the ecological operation of water conservancy projects. Based on long-term measured water level data (1992–2023) and multi-temporal Landsat remote sensing images (1997–2022), combined with a digital elevation model (DEM), this study systematically analyzed the spatiotemporal evolution characteristics of the inundation processes in Dongting Lake before and after the operation of the Three Gorges Project (TGP) and their driving mechanisms on the plant landscape patterns of the floodplain wetland. The results show that after the TGP operation, the inundation pattern of Dongting Lake exhibited a drying trend, with a significant decline in annual mean water level (the largest drop of approximately 0.7 m in East Dongting Lake) and a marked reduction in the lake-wide average inundation duration (T) and inundation frequency (F). From 1997 to 2022, the total area of wetland vegetation in Dongting Lake showed a significant expansion trend, and the succession of the landscape pattern experienced a nonlinear process of stability, fragmentation, and recovery. The stepwise regression model revealed that the three elements of the inundation process explained more than 80% of the landscape pattern variation, among which inundation frequency (F) and inundation duration (T) were the core driving factors. Specifically, inundation frequency primarily regulated landscape diversity (SHDI) and contagion (CONTAG) through an environmental filtering effect, while maximum inundation depth (H) mainly maintained the physical connectivity (COHESION) of the landscape. Furthermore, the study quantified the stable hydrological range of the Dongting Lake wetland ecosystem: when the inundation frequency is maintained at 0.40–0.50 and the annual inundation duration is controlled at 4–5 months, the wetland landscape is in an optimal structural state. Once the warning thresholds are breached (e.g., F < 0.35 or T < 90 days), it may trigger the rapid expansion of cultivated poplar forests under combined hydrological and anthropogenic influences, leading to severe habitat fragmentation. These findings deepen the understanding of the response mechanisms of vegetation landscape patterns in large lake wetlands under altered hydrological rhythms. Full article

This research investigates the factors influencing the behavioral intention and actual use of e-wallets among Generation Z by extending the UTAUT3 model to include personal innovativeness and perceived security. The study employs a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 535 Generation Z e-wallet users between 15 January and 28 February 2026. The results reveal that traditional determinants such as performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation do not significantly influence behavioral intention in a mature digital environment. In contrast, social influence, price value, habit, personal innovativeness, and perceived security significantly shape users’ intentions. Furthermore, the findings indicate that behavioral intention fully mediates the relationship between personal innovativeness and perceived security with actual usage behavior. This suggests that although users may possess innovative tendencies and perceive strong security, these factors influence usage only through the formation of intention. The study also shows that Generation Z demonstrates a strong ability to manage financial activities independently within digital platforms, reflecting high levels of digital and financial literacy. At the same time, users remain highly aware of potential risks, particularly regarding data privacy and transaction security, which significantly affect their intention to adopt e-wallet services. Additionally, actual usage behavior is primarily driven by habit and behavioral intention, indicating routinized usage patterns. Overall, this study highlights the critical roles of trust, social influence, and behavioral reinforcement in explaining technology adoption among Generation Z. Full article

Objectives: To evaluate the buccolingual inclination of maxillary and mandibular first molars using 3D digital models in adolescents and adults with near-normal occlusion. Material and Methods: Forty-one untreated adults (mean: 41.7 years) and sixteen adolescents (mean: 15.2 years) with near-normal occlusion were selected. Each subject’s 3D digital models were imported into Dolphin Imaging software. The coronal cross-section was attained in no more than a 1.2 mm slice, using a section that best included both right and left first molars while both arches were in occlusion. The long axis of each molar was determined by drawing a line from the midpoint between the buccal and lingual cusp tips to the midpoint of the buccolingual width at the cervical region. The inclination angle of each first molar was then measured. Results: Adolescents and adults showed a similar directional trend in maxillary buccal inclination and mandibular lingual inclination of the first molars. The mean buccal inclination for the maxillary molar in adolescents was 9.0° and 9.1° in adults. The mean lingual inclination for the mandibular first molar in adolescents was 14.5° and 15.6° in adults. Conclusions: (1) In near-normal occlusion, measured from 3D digital models, maxillary first molars showed approximately 9° buccal inclination, and the mandibular first molars showed about 15° lingual inclination. (2) Within the limitations of the present sample and measurement protocol, 3D digital models provided reproducible crown-based measurements of first molar buccolingual inclination, with values similar to those previously reported in CBCT-based studies. Direct validation against CBCT measurements in the same subjects and evaluation in patients with transverse discrepancies or post-treatment records are needed before broader diagnostic or outcome-related claims can be made. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

Digital small and medium-sized enterprises (digital SMEs) in emerging market economies operate in disruption-biased environments where interruptions can quickly deteriorate operational reliability and long-term performance. Existing studies insufficiently integrate business continuity management (BCM) into capability-based performance models in the digital SME context, especially when focusing on operational rather than strategic perspectives in emerging market economies. Moreover, empirical evidence on how multiple organisational capabilities interact under disruption remains fragmented. This study therefore aims to prioritise the most influential capability-based determinants of sustainable performance in Thai digital SMEs using expert consensuses analysed via Fuzzy TOPSIS. This study adopted the following two-stage research design. Stage 1: A three-round e-Delphi panel (n = 21) refined and prioritised the most influential determinant; the expert group included SME business owners (with more than 20 years of SME management experience) and relevant specialists. The consensuses were then analysed using Fuzzy TOPSIS to rank the determinants by relative importance. Stage 2: Structural Equation Modelling (SEM) using survey data from 817 Thai digital SMEs was utilised to validate the proposed capability transmission pathways, and a strong fit was demonstrated (χ²/df = 1.672, CFI = 0.984, RMSEA = 0.029). The study findings highlight continuity-oriented routines as a practical leverage point for SME leaders and policymakers seeking resilient and sustainable performance in digital markets, and positions BCM as an actionable strategy toward achieving these goals. Full article

Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually—channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff’s current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal–polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of $6.8\%$ and a tensile linearity $R^2>0.99$. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves $87.0\%\pm 8.4\%$ accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than $100\mathsf{\mu }\mathrm{s}$. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring. Full article

Digital games are becoming increasingly important as promising tools to foster Education for Sustainable Development (ESD), aiming to combine engagement, systems thinking, and transformative learning. This narrative review synthesizes evidence from 40 studies on serious games, game-based learning, and gamification to assess both the potential and limitations of digital games in ESD contexts. This review thus contributes to the field by integrating theoretical frameworks, empirical evidence, and design principles to provide a coherent understanding of how digital games support ESD learning processes. The findings reveal positive effects on cognitive and motivational outcomes, particularly regarding knowledge acquisition, systems understanding, and learner engagement. In addition, digital games can foster social learning processes such as collaboration, participation, and perspective-taking. These effects are grounded in established theoretical frameworks, including self-determination theory, flow theory, and experiential learning, and are supported by design features such as adaptive feedback, meaningful narratives, social interaction, and authentic decision-making. Across the reviewed studies, cognitive outcomes are most consistently documented, while evidence for long-term behavioral change and real-world transfer remains limited. This reflects both structural challenges of ESD and methodological constraints, including difficulties in measuring behavior, short-term study designs, and heterogeneous implementations. Overall, digital games can support key ESD competencies by enabling learners to engage with complex socio-ecological systems and multi-perspectivity. Their effectiveness and educational value depend less on gameplay itself than on four overarching design principles: encouraging the exploration of systems, linking experience and reflection, balancing between autonomy and guidance, and embedding within broader social and pedagogical processes. Full article

While social media platforms are primary vectors for misinformation, automated detection systems remain largely confined to English. This paper presents a transferable, three-stage framework for fine-tuning transformer models to detect domain-specific deceptive content in Spanish. The pipeline comprises: (1) corpus unification, merging fragmented datasets into a 61,674-article resource mapped into three classes (Real, Fake, Satire) to prevent stylistic confounding; (2) systematic model optimization, extensively benchmarking classical metaheuristics against eight transformer architectures (including mBERT, XLM-RoBERTa, and BETO) using strong regularization to mitigate overfitting; and (3) production deployment, encapsulating the optimized model as a containerized web application for real-time inference. Through rigorous experimentation, the Spanish-specific BETO encoder emerged as the strongest model for this task, achieving 89.18% overall accuracy. The model attains a near-perfect in-source F1-score on the satire class; however, a strict source-held-out test reveals that this performance is highly source-dependent—recall on satire from an unseen outlet drops to 0.08—indicating that single-source class construction leads the model to recognize the source rather than a generalizable category. We report this finding as a central methodological result: corpus design, and in particular the source diversity of each class, is the primary determinant of whether the framework generalizes. Adversarial robustness tests using named-entity masking and typo injection provide complementary evidence on the model’s reliance on semantic versus surface cues. The methodology is designed to be adaptable across domains: by substituting the training corpus, the same framework may in principle be retargeted to other digital threats, such as investment scams and phishing, provided that suitable labeled corpora are constructed and validated for each new domain. The complete framework, dataset, and application are released as open-source resources to support reproducible research and practical countermeasures against online misinformation. Full article