Search Results (4,962)

Understanding the sustainable management of the complex adaptive tourism systems requires an integrated research approach that combines environmental processes with stakeholder behaviors. Agent-based modelling (ABM) has emerged as a pivotal tool for decoding the resilience, adaptability, and sustainability of tourism systems. However, the current application landscape, methodological limitations, and future research directions of ABM remain insufficiently synthesized, thereby constraining its full potential in advancing sustainable tourism management. This study examines 137 publications on the application of ABM in tourism research between 1989 and 2025, aiming to clarify the application characteristics and evolutionary trajectories. The results show the following: (1) ABM applications in tourism have become increasingly comprehensive and refined, evolving from simplistic simulations based on simplex agents and static spatial representations toward integrated models incorporating heterogeneous agents, fine-grained spatial environments, and multiple contextual factors. (2) Behavioral modeling has progressed from basic human–space interactions to complex, co-evolutionary dynamics among human, social, and ecological systems. (3) ABM applications exhibit context specificity: climate-sensitive scenarios emphasize resource dynamics and adaptation strategies; disaster-prone contexts focus on multi-agent responses and emergency management; conservation-oriented systems support sustainable policy development; and management-centric scenarios prioritize technological innovation and macro-level regulation. Future research should prioritize refining agent interactions through dynamic social network integration, incorporating cross-scale and long-distance system linkages, and strengthening the connection between theoretical modeling and real-world applications. This study would provide a comprehensive knowledge base for advancing the innovative application of ABM in sustainable tourism research and contribute to strengthening resilience, adaptive governance, and long-term sustainability within complex tourism systems. Full article

Energy hydrogen is emerging as a key driver for the deep decarbonization of energy systems in the Americas, particularly in sectors that are difficult to electrify, such as heavy industry, long-distance transportation, and seasonal energy storage. This article presents a comprehensive review of current prospects and long-term planning for hydrogen in North America, Central America, and South America, analyzing its role within energy transition strategies to long term. It examines technological advancements in green hydrogen production from renewable energy sources, projected costs, required infrastructure, and potential integration schemes with existing electricity systems. Furthermore, it assesses emerging regulatory frameworks, public policies, and national and regional initiatives that seek to position hydrogen as a pillar of energy security, economic competitiveness, and emissions reduction. The study identifies differentiated opportunities based on the availability of renewable resources, industrial capacities, and socioeconomic contexts, as well as common challenges related to investment, standardization, and social acceptance. Finally, implications for long-term energy planning are discussed, highlighting the potential of hydrogen to strengthen the resilience and sustainability of the energy system in the Americas. Full article

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

Illyrian helmets represent a key element of Iron Age martial culture in the western Balkans, reflecting technological knowledge, workshop traditions, and long-distance cultural exchange. Based on the currently available archaeological record, Illyrian helmets are first attested in contexts dating to the 8th–7th centuries BC, with finds concentrated in Greece and the central and western Balkans, including Macedonia, Albania, Dalmatia, and the wider interior. Over time, the form developed into several variants (Types I–IIIB). This study presents the elemental characterization of the total set of 27 Illyrian helmets excavated in Albania and currently preserved in local museum collections, a region where the later types are particularly well attested. As the helmets are intact and exhibited in museums, portable in situ XRF analysis was employed. The main research questions addressed how the alloy composition, including minor and trace elements, reflects local metallurgical practices and distinguishes Illyrian helmets from similar helmets in neighboring regions. The results indicate the consistent use of bronze alloys dominated by copper (89–95%) with low- to medium-tin contents (3.5–9.9%), consistent with established alloying practices for durable protective equipment. Minor and trace elements, including iron (up to 1.5%), lead (up to 0.76%), arsenic (up to 0.09%), zinc (up to 1.17%), and antimony (up to 2.36%), likely reflect metallurgical choices, recycling practices, or impurities linked to regional copper deposits. Principal Component Analysis of four retained components, collectively accounting for 88.5% of the total variance, confirms a broadly standardized bronze tradition, with compositional outliers suggesting locally variable ore sources or recycling rather than systematic typological change. These elemental signatures, particularly the association of arsenic, antimony, zinc, and iron, suggest regional metallurgical characteristics consistent with Albanian sulphide ore deposits, while the overall compositional homogeneity supports the hypothesis of centralized production at workshops such as Epidamnus and Apollonia. Full article

Ultra-wideband (UWB) technology has been widely adopted for indoor positioning due to its high temporal resolution. However, the accuracy of UWB-based indoor positioning is fundamentally limited by ranging measurement errors, particularly under non-line-of-sight (NLOS) conditions, where systematic bias and uncertainty are introduced into the measured distances. In this paper, a measurement error mitigation method is proposed to improve UWB ranging reliability in complex indoor environments. The method first identifies NLOS measurements using low-dimensional physical features and a lightweight machine learning classifier. Subsequently, an error compensation strategy is applied to correct biased ranging observations, which are then incorporated into a nonlinear least squares positioning model. Experimental results obtained in typical indoor environments demonstrate that the proposed method significantly reduces ranging errors and improves positioning accuracy compared with conventional approaches. The results indicate that the proposed framework effectively enhances measurement robustness without increasing system complexity. Full article

The rapid development of machine learning, large language models, and related technologies has greatly increased the demand for data storage capacity. Therefore, the role of distributed storage systems in such applications has become more prominent. However, it is inevitable that a single node fails in a distributed storage system during long-term use. Being able to repair failed nodes in a timely manner is extremely important for the stable operation of distributed storage systems, and a specific encoding scheme is required to meet the needs of efficiently repairing failed nodes. This research presents a novel family of binary locally repairable codes (LRCs) developed using multiple disjoint recovery sets constructed based on mutually orthogonal Latin squares (MOLS). The proposed constructions achieve distance optimality under the Singleton-like bound for LRCs with availability. Specifically, the codes are parameterized as $(n=r^2+tr,k=r^2,r,t)$ and $(n=rm+tm,k=rm,r,t)$, where n is the block length, k is the dimension, r is the locality, and t is the availability. These codes achieve minimum distance $d=t+1$, guaranteeing efficient recovery with t disjoint repair sets, each of size r. Compared to existing constructions, the proposed codes offer significant improvements in terms of code rate $\mathcal{R}={\displaystyle \frac{r}{r+t}}$, support for larger block lengths, and reduced finite field size requirements (field size $q=2$). Additionally, a method is introduced to improve the minimum distance of codes with even availability t, constructing codes with parameters $(n+1,k,r,t)$ and $d=t+2$, while preserving optimality. These properties make the proposed codes particularly suitable for distributed storage systems, where efficient and parallel repair of failed nodes is critical. Full article

Background: Global positioning system (GPS) technology is widely used to quantify external training load (ETL) in youth soccer. Despite its extensive application in training and match contexts, considerable heterogeneity is present in the selection, definition, and interpretation of GPS-derived variables, limiting comparability between studies and practical implementation by coaches. Objective: This narrative review aimed to summarize and critically evaluate the current literature on GPS-based ETL monitoring in youth soccer players, with a focus on commonly used variables, methodological considerations, and practical applications in training and match contexts. Methods: A narrative literature search was conducted using PubMed, SPORTDiscus, and Scopus databases. Peer-reviewed studies published in English between the years of 2012 and 2025 were included. Data were extracted on participant characteristics, GPS technology, monitored ETL variables, and contextual settings. Results: The 34 reviewed studies primarily reported total distance (TD; m), high-speed running distance (HSR; m), sprint distance (SD; m), distance per minute (m·min⁻¹), peak speed (km·h⁻¹), and acceleration- and deceleration-based (ACC, DEC; count) ETL variables. Substantial variability was observed in speed thresholds, acceleration definitions, and data processing methods. Positional roles, training formats (e.g., small-sided games), and seasonal phase influenced ETL demands, although methodological inconsistencies limited cross-study comparisons. Conclusion: GPS technology provides valuable insights into the ETL demands of youth soccer. The lack of standardized variable definitions and thresholds remains a major limitation. Greater methodological consistency and clearer reporting standards are required to enhance the practical usefulness of GPS monitoring for coaches in youth soccer. Full article

Fine particulate matter (PM)-induced pollution is one of the major causes of indoor air quality deterioration. Passive air purification technologies offer advantages of structural simplicity and low energy consumption, yet their spatiotemporal mass transfer characteristics remain poorly understood. This study presents a theoretical and experimental investigation of PM spatiotemporal mass transfer under the sink effect induced by an electro-convective passive air purifier. The apparent mass transfer coefficient (D_app) and PM concentration prediction models based on Fick’s second law were established, and then the space-and-time-dependent mass transfer coefficient (D_st) was determined by using the Boltzmann–Matano method. The results revealed that the absolute values of D_st quantified local migration intensity, while its sign provided directional information unattainable from conventional averaged parameters. The logarithmic values of D_app showed a consistent logarithmic relationship with distance at fixed time windows, and the validated prediction model maintained errors within ±15%, enabling accurate reconstruction of full-field concentration distributions from limited measurement points. The complementary nature of these two coefficients offers a comprehensive evaluation framework. This work advances both the theoretical understanding and practical application of passive air purification technology, offering new tools for indoor PM exposure control and purifier performance optimization. Full article

The stability of retained roadways in closely spaced coal seams beneath a goaf is strongly affected by complex stress redistribution and the deterioration of roof structures under downward mining conditions. To address this issue, a combined approach involving theoretical analysis, numerical simulation, and field monitoring was adopted to investigate the deformation characteristics and stability control of gob-side retained roadways in short-distance coal seam groups. The movement characteristics of the roof and the deformation law of surrounding rock of the retained roadway under downward mining were revealed. An embedded short-arm beam structural model for a roof cutting retained roadway was established, and a calculation method for determining the required support resistance of the retained roadway was proposed. Based on this model, design criteria for the passive support system of the retained roadway were developed. A surrounding rock control technology with hollow grouting anchor cable support and low-disturbance directional roof cutting as the core was proposed, and the support resistance of a one-beam–four-column support system was determined to effectively limit roof subsidence. Field application results show that the surrounding rock displacement was controlled within 350 mm, and the roadway section shrinkage rate was maintained at 16.4%, indicating good stability of the retained roadway and satisfying the requirements of ventilation and transportation. This study provides a mechanical basis and practical guidance for stability control and support design of roof cutting retained roadways in closely spaced coal seams beneath goaf. Full article

We introduce the concept of a one-way, broadband information package, the Cosmic Illuminating Gift, intended to provide distant intelligences with fundamental empirical data about the Universe. Unlike previous messaging to extraterrestrial intelligences (METI) that emphasized greetings or cultural identity, the Gift aims to transmit unbiased, universally interpretable information that recipients could not otherwise obtain due to their distinct spacetime position and epoch. By emphasizing raw observations, rather than human interpretations or cosmological models, the Gift aspires to serve as a neutral and enduring resource. A central assumption of the project is that any potential recipients are likely to possess a level of intelligence and technological sophistication far beyond our own. Accordingly, the content and encoding of the Gift are not designed to “teach” fundamentals, but to deliver compact, logically structured packets that such civilizations could decode even at extremely low signal-to-noise levels. This perspective shifts the challenge from brute-force transmission to ensuring that photons arrive in spectrally quiet windows and that the format is unmistakably artificial and distinguishable from astrophysical backgrounds. We outline strategies for content selection, encoding, and transmission that reflect this assumption. Practical implementation is feasible with current or near-term infrastructure, and future advances will only improve the quality of subsequent Gifts. Ultimately, the endeavor is unique among scientific projects in that it anticipates no feedback or measurable result within the span of our civilization’s timeline. Its significance lies instead in the act of contribution itself: offering a durable, universal dataset as a gesture of intellectual solidarity across cosmic distances. Full article

To meet the stringent requirements of broadband operations, high stability and low dispersion in high-power microwave systems, this paper proposes a broadband linearly polarized quasi-non-diffractive wave generation method based on a filter-equivalent circuit model. A metasurface structure centered at 75 GHz is designed for this method, which enables the generation of linearly polarized quasi-non-diffractive beams in the 60–90 GHz band with a 40% relative bandwidth and a maximum non-diffractive distance of 120 mm. By optimizing the metasurface’s phase response through the equivalent circuit model, the proposed method effectively suppresses the dispersion effects that limit bandwidth utilization in existing designs. Simulation results confirm that the generated beams maintain excellent stability across the entire operating band, with minimal variations in the maximum non-diffractive distance. This work fills the research gap in broadband achromatic linearly polarized quasi-non-diffractive beams, and its relative bandwidth outperforms all previous linear polarization designs. It thus provides a reliable technical solution for long-distance, high-efficiency electromagnetic wave transmission in defense, industrial and medical applications that rely on high-power microwave technology. Full article

Geological environment evaluation for urban underground space (UGEE) is a critical foundation for optimizing the utilization of urban underground space (UUS) and mitigating exploitation risks. With recent advancements in 3D geological modeling technology, 3D UGEE has emerged as a transformative approach, offering innovative perspectives and technical solutions for rational 3D spatial development and geological risk reduction in subsurface engineering. A core component of the 3D UGEE workflow is the integration of diverse 3D spatial analysis methods, which enable comprehensive extraction of evaluation indices from multidimensional datasets—forming the essential basis for scientifically informed development planning. Focusing on quantitative 3D UGEE, this study systematically investigates the implementation of 3D spatial analysis methods across four key stages: (1) geological condition analysis, (2) evaluation information extraction, (3) 3D comprehensive evaluation, and (4) result analysis. Specifically, five core methodologies are highlighted: (1) 3D spatial statistical analysis, (2) 3D mathematical morphological analysis, (3) 3D surface morphology analysis, (4) 3D spatial distance field analysis, and (5) 3D spatial interpolation analysis. To improve the reliability and objectivity of 3D comprehensive evaluation results, we integrate game theory-based combination weighting with an improved TOPSIS model, which balances the subjectivity of expert judgment and the objectivity of data characteristics while adapting to the 3D block unit data structure, effectively avoiding the bias of single weighting or evaluation models. To validate these techniques, a case study in Hangzhou, Zhejiang Province, is conducted, demonstrating their practical effectiveness in evaluating UUS resources. The findings underscore that advanced 3D spatial analysis methods significantly enhance decision-making precision in UUS planning and risk management, providing a replicable framework for sustainable subsurface development. Full article

Material and texture decisions in bionic machinery installation art often remain intuition-dependent, limiting the reusability of empirical evidence for experience design. Building on the biomimetic content logic in biomimetic design theory, this study proposes a targeted framework—Texture Bionics—and operationalizes texture into four quantifiable perceptual dimensions: transparency, hardness, roughness, and surface texture, forming a controllable sample space of 12 plastic texture conditions. A case database encompassing 56 representative works (2000–present) was constructed to justify material selection; plastics were chosen for their tunable properties and feasibility for parameterized modulation. In a standardized viewing setup (≈500 lx illumination; 60 cm viewing distance), participants viewed a dynamic biomimetic mechanical wing module with interchangeable textured plastic surfaces. Subjective affect responses were captured using PAD ratings, and objective attention was assessed via wearable eye-tracking technology. Repeated-measures analyses showed robust main effects of texture on total fixation duration across all four dimensions, and selective effects on time to first fixation (significant/marginal for transparency, roughness, and surface texture but not hardness); pupillary response metrics provided no stable discrimination. PAD mappings further revealed functional “role types” (e.g., Key driver, Explore guide, Stable base), and a strong association between Arousal and inter-participant variability in fixation distribution, suggesting that high-arousal textures act as strategy amplifiers rather than uniformly increasing attention. Finally, findings were translated into an actionable Texture Design Toolkit using a three-question workflow—function label → attention goal → differentiation risk—to support evidence-based orchestration of installation narratives. Full article

Skeleton-based action recognition is a critical technology for intelligent sports analysis. Although the human skeletal structure exhibits inherent bilateral symmetry, sensor noise on resource-constrained edge devices frequently induces geometric distortion and topological asymmetry. Consequently, achieving a balance between high accuracy and real-time performance remains a significant challenge. To this end, we propose EMS-GCN, an Efficient Multi-scale Shift Graph Convolutional Network that integrates geometric priors. Specifically, we design a Gaussian kernel-driven topology refinement module to mitigate structural noise inherent in sensor data. By leveraging geometric symmetry and Gaussian distances among nodes, this module dynamically constrains graph topology learning, thereby effectively rectifying the structural asymmetry and ambiguity induced by noise. Furthermore, we construct a Multi-scale Shift Linear Attention (MSLA) module to replace computationally intensive temporal convolutions. Leveraging temporal shift invariance, this module captures multi-scale contexts via parameter-free shift operations. Furthermore, we introduce a linear temporal attention mechanism to model global temporal dependencies with linear complexity, effectively resolving the information asymmetry inherent in long-range interactions. Finally, EMS-GCN incorporates a dual-branch attention structure to adaptively calibrate feature responses. Extensive experiments demonstrate that our model maintains high recognition accuracy with only 0.56M parameters, representing a reduction of over 60% compared to mainstream baselines. These results validate the efficacy of leveraging geometric and temporal symmetries to enhance real-time sports analysis. Full article