Search Results (7,045)

Effective communication between construction sites and engineering or architectural offices is critical to the success of construction projects, particularly in the maintenance of critical infrastructure such as bridges. In scenarios where distance limits the physical presence of specialists, Requests for Information (RFIs) are the primary formal exchange tool. However, issues such as incomplete data, poor quality, or delayed responses often lead to significant project delays. This study proposes a multi-user Virtual Reality (VR) platform to optimize communication workflows in these contexts. Using the Design Science Research Methodology (DSRM), an immersive environment was developed to connect up to 20 users simultaneously, integrating BIM models with support for technical details, language, and contextual factors. The tool was validated through a case study focused on the maintenance of a railway bridge, where five real RFIs were simulated. Results show that the immersive experience enhances spatial understanding, improves remote collaboration, and accelerates decision-making. Users highlighted the sense of presence and perceived usefulness, positioning this tool as an effective alternative to overcome communication barriers in geographically distributed infrastructure maintenance. Full article

The present research investigates the fine-scale spatial genetic structure (SGS) and gene flow dynamics in the endangered relict tree Tetracentron sinense, a keystone species in China’s montane ecosystems facing severe habitat fragmentation and genetic erosion. Utilizing genome-wide SNPs from 378 individuals across four natural populations (BMXS, MGFD, GLGS, SXFP), derived from ddRAD-seq, we quantified genetic diversity, SGS (Sp statistic), and dispersal patterns through spatial autocorrelation, parentage analysis, and age-class stratification. Results indicated critically low heterozygosity (observed heterozygosity, H_O = 0.019–0.022) and high inbreeding coefficient (Fis = 0.147–0.304), with moderate SGS (Sp = 0.0076–0.021) suggesting restricted gene flow (effective dispersal radius: 11–32 m). Seed-mediated dispersal was predominant, but topography and rainfall constrained dispersal (<5% beyond 50 m). Saplings exhibited stronger SGS, and the SXFP population experienced 100% sapling mortality due to inbreeding depression. Conservation efforts should prioritize assisted gene flow, habitat restoration, and ex situ sampling at distances greater than 115 m to preserve genetic diversity and adaptive potential. This study highlights the urgent need for genomics-informed conservation strategies in fragmented montane ecosystems. Full article

Graph Neural Networks (GNNs) face significant challenges in node classification across diverse graph structures. Traditional message passing mechanisms often fail to adaptively weight node relationships, thereby limiting performance in both homophilic and heterophilic graph settings. We propose the Eigenvector Distance-Modulated Graph Neural Network (EDM-GNN), which enhances message passing by incorporating spectral information from the graph’s eigenvectors. Our method introduces a novel weighting scheme that modulates information flow based on a combined similarity measure. This measure balances feature-based similarity with structural similarity derived from eigenvector distances. This approach creates a more discriminative aggregation process that adapts to the underlying graph topology. It does not require prior knowledge of homophily characteristics. We implement a hierarchical neighborhood aggregation framework that utilizes these spectral weights across multiple powers of the adjacency matrix. Experimental results on benchmark datasets demonstrate that EDM-GNN achieves competitive performance with state-of-the-art methods across both homophilic and heterophilic settings. Our approach provides a unified solution for node classification problems with strong theoretical foundations in spectral graph theory and significant empirical improvements in classification accuracy. Full article

Background: Rural fires, worsened by climate factors such as drought, biomass buildup, and ignition sources, threaten sustainability. Recovering residual biomass (RB) presents a promising way to lower fire risk by reducing fuel loads and generating renewable energy; however, logistical costs in the RB supply chain—due to poor stakeholder coordination—limit its feasibility. App-based models can help solve these issues by improving information sharing, but their economic viability remains largely unexplored. This study suggests that such models work well when large amounts of biomass are involved and moisture content is low. Still, they might need external incentives for widespread use and fire risk reduction. Methods: The study modeled recovery scenarios by comparing costs (harvesting, retrieval, transport, and pre-processing) with biomass market value, using expert inputs and sensitivity analysis on variables like fuel prices and wages. Results: The economic feasibility is possible for large volumes (e.g., 10-ton loads) with low moisture (<30%), allowing transportation distances up to 459 km; however, small-scale or high-moisture situations often are not viable without support. Conclusions: App-based models need external support, like subsidies, to overcome owner and RB challenges, ensuring effective fire mitigation and sustainability benefits. Full article

Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic research. However, as current models always concatenate dynamic information with distinct meanings (such as position, ship speed, and heading) into a single integrated input when processing trajectory point information as input, it becomes difficult for the models to grasp the correlations between different types of dynamic information of trajectory points and the specific information contained in each type of dynamic information itself. Aiming at the problem of insufficient modeling of the relationships among dynamic information in ship trajectory prediction, we propose the Multi-dimensional Attribute Relationship Transformer (MART) model. This model introduces a simulated trajectory training strategy to obtain the Association Loss (AssLoss) for learning the associations among different types of dynamic information; and it uses the Distance Loss (DisLoss) to integrate the relative distance information of the attribute embedding encoding to assist the model in understanding the relationships among different values in the dynamic information. We test the model on two AIS datasets, and the experiments show this model outperforms existing models. In the 15 h long-term prediction task, compared with other models, the MART model improves the prediction accuracy by 9.5% on the Danish Waters Dataset and by 15.4% on the Northern European Dataset. This study reveals the importance of the relationship between attributes and the relative distance of attribute values in spatiotemporal sequence modeling. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

This paper presents a rapid approach for the dynamic separation of radial polarization (R-pol) and azimuthal polarization (A-pol) components in circular Airy vortex beams (CAVBs) by utilizing the linear electro-optic (EO) effect in uniaxial crystals. By applying an external electric field along the z-axis of a strontium barium niobate (SBN) crystal, tunable spatial separation of the R-pol and A-pol components is achieved. Under positive electric fields, the crystal maintains negative uniaxial properties with increased birefringence, extending the focal separation distance. Conversely, negative electric fields initially reduce the birefringence of the crystal; further increases in negative field strength will transition the crystal to a positive uniaxial state, subsequently enhancing birefringence and restoring focal separation. Experimental simulations demonstrate a focal separation of 1.4 mm at ±15 kV/mm, with R-pol focusing first at +15 kV/mm and A-pol preceding at −15 kV/mm. The polarization distributions at the foci confirm the successful separation of the two components. This approach overcomes the static limitation of conventional polarization splitters in separating R-pol and A-pol components, showing significant potential for optical manipulation, high-resolution imaging, and quantum information processing. Full article

To address the demand for low-cost deployment in quantum key distribution (QKD) networks, this study explores the implementation of unidimensional (UD) modulation continuous-variable quantum key distribution (CV-QKD) protocols within downstream access networks. The UD CV-QKD protocol employs a single modulator for information encoding, offering benefits such as reduced implementation cost and lower random number consumption, which collectively decrease the overall setup expense of QKD systems. Through systematic performance analysis, it is demonstrated that the proposed UD modulation downstream access network scheme exhibits strong scalability and practical applicability. When supporting 32 users, the system maintains secure communication over transmission distances of up to 50 km. As the number of users increases to 64, performance declines slightly; however, the system still achieves a 35 km transmission distance, which remains suitable for many metropolitan access applications. Even in high-density access scenarios involving 128 users, the system sustains a positive key rate within a transmission range of 20 km. Furthermore, this study evaluates the protocol’s practical security under source intensity errors and finite-size effects. These results provide meaningful guidance for deploying low-cost, high-security quantum communication access networks and contribute to advancing QKD technologies toward scalable, real-world implementations. Full article

Predicting tunnel seepage field is a critical challenge in the construction of underground engineering projects. While traditional analytical solutions and numerical methods struggle with complex geometric boundaries, standard Physics-Informed Neural Networks (PINNs) encounter additional challenges in tunnel seepage problems, including training instability, boundary handling difficulties, and low sampling efficiency. This paper develops an enhanced PINN framework designed specifically for predicting tunnel seepage field by integrating Exponential Moving Average (EMA) weight stabilization, Residual Adaptive Refinement with Distribution (RAR-D) sampling, and Bayesian optimization for collaborative training. The framework introduces a trial function method based on an approximate distance function (ADF) to address the precise handling of circular tunnel boundaries. The results demonstrate that the enhanced PINN framework achieves an exceptional prediction accuracy with an overall average relative error of 5 × 10⁻⁵. More importantly, the method demonstrates excellent practical applicability in data-scarce scenarios, maintaining acceptable prediction accuracy even when monitoring points are severely limited. Bayesian optimization reveals the critical insight that loss weight configuration is more important than network architecture in physics-constrained problems. This study is a systematic application of PINNs to prediction of tunnel seepage field and holds significant value for tunnel construction monitoring and risk assessment. Full article

Light Electric Vehicles (LEVs) offer a promising response to environmental and urban mobility challenges. This study is among the first to exploratorily examine their use, user characteristics, and owner evaluations. A qualitative pre-study with four LEV owners was conducted and informed a subsequent quantitative phase involving 23 owners. Over two weeks, participants recorded all LEV trips using GPS tracking and completed two questionnaires. Findings show that LEVs are regularly used for commuting, shopping, and work-related trips. Notably, many users live outside urban centers, indicating strong potential for short-distance travel in rural and small-town contexts for our sample. This challenges the view of LEVs as primarily urban or recreational vehicles. Within our sample, usage patterns were diverse, indicating that even among early adopters there is no single typical usage profile. While cars were perceived as slightly safer, no participant reported feeling unsafe in their LEV. User satisfaction was high: 24 of 27 respondents would choose the same vehicle again. Overall, LEVs emerge as a versatile and satisfying mobility option, relevant beyond city limits. Given their wide range of uses and positive user feedback, LEVs should be more strongly considered in transport policy to promote more sustainable and needs-based mobility. Full article

Flash floods arise from the interaction of rugged topography, short-duration intense rainfall, and rapid flow concentration. Conventional risk mapping often builds empirical indices with expert-assigned weights or trains supervised models on historical event inventories—approaches that degrade in data-scarce regions. We propose a fully data-driven, unsupervised Geographic Information System (GIS) framework based on fractional order k-means, which clusters multi-dimensional geospatial features without labeled flood records. Five raster layers—elevation, slope, aspect, 24 h maximum rainfall, and distance to the nearest stream—are normalized into a feature vector for each 30 m × 30 m grid cell. In a province-scale case study of Zhejiang, China, the resulting risk map aligns strongly with the observations: 95% of 1643 documented flash flood sites over the past 60 years fall within the combined high- and medium-risk zones, and 65% lie inside the high-risk class. These outcomes indicate that the fractional order distance metric captures physically realistic hazard gradients while remaining label-free. Because the workflow uses commonly available GIS inputs and open-source tooling, it is computationally efficient, reproducible, and readily transferable to other mountainous, data-poor settings. Beyond reducing subjective weighting inherent in index methods and the data demands of supervised learning, the framework offers a pragmatic baseline for regional planning and early-stage screening. Full article

The rapid urban growth in Saudi Arabia has intensified challenges in sustainable solid waste management, particularly in selecting suitable landfill sites that minimize environmental risks and protect public health. Tabuk Province, located in the northwest of the Kingdom, represents a region where arid climatic conditions, fragile ecosystems, and increasing urbanization make landfill sitting highly complex. Traditional decision-making approaches often struggle to capture uncertainties in expert opinions and spatial data, leading to less reliable outcomes. While Geographic Information Systems and Multicriteria Decision-Making have been applied to this field, the explicit integration of fuzzy logic remains limited, especially in arid regions. This study addresses this gap by combining the Fuzzy Analytic Hierarchy Process with Geographic Information Systems to establish a more robust framework for landfill site selection in Tabuk. Seven critical criteria were considered, including distance from major roads, airports, urban centers, coastlines, wetlands, and protected areas, with expert assessments analyzed through fuzzy reasoning to improve decision reliability. The results generated a spatial suitability map highlighting priority zones for landfill development, particularly in the western and southwestern areas of the province, where environmental sensitivity is lower and accessibility to infrastructure is greater. The findings emphasize that proximity to urban areas and road networks are dominant factors influencing suitability. The novelty of this study lies in its methodological integration, which enhances transparency, adaptability, and objectivity in landfill sitting. By promoting environmentally responsible waste management, the framework directly supports the Sustainable Development Goal of Good Health and Well-Being and the Sustainable Development Goal of Sustainable Cities and Communities, ensuring safer urban development and healthier living conditions. Moreover, the approach is transferable to other arid and semi-arid regions, offering valuable insights for countries facing similar challenges in sustainable urban planning. Full article

Scarturus williamsi (Williams’ jerboa) is a medium-sized, semi-fossorial rodent endemic to steppe ecosystems across Anatolia, Iran, and Azerbaijan, with specialized habitat requirements in semi-arid continental environments. This study integrates a mitochondrial DNA analysis with species distribution modeling to assess the species’ evolutionary structure and vulnerability to future climate change. The phylogeographic analysis and species distribution modeling reveal the evolutionary history and climate vulnerability of Scarturus williamsi across Anatolia and adjacent regions. The mitochondrial DNA analysis of 98 individuals demonstrates exceptional haplotype diversity (Hd = 0.9896), with 90 unique haplotypes and complete regional isolation, indicating pronounced population structuring across five evolutionary lineages: Central Anatolia, Eastern Anatolia, Aegean, Black Sea, and Azerbaijan–Iran. The Iran–Azerbaijan lineage exhibits the deepest evolutionary divergence, while Eastern Anatolia functions as the primary Anatolian refugium and Central Anatolia as the secondary refugial center. The strong isolation by distance (r = 0.735, p < 0.001) across ~2500 km explains 54.0% of the genetic variation, with the hierarchical structure reflecting greater Iran–Turkey isolation than intra-Turkish differentiation. The species distribution modeling identifies the Mean Temperature of Driest Quarter (bio9) and the Mean Diurnal Range (bio2) as primary habitat determinants, with bimodal preferences reflecting highland versus steppe adaptations. Climate projections reveal severe vulnerability with habitat losses of 63.69–98.41% by 2081–2100 across emission scenarios. SSP3-7.0 represents the most catastrophic scenario, with a severe habitat reduction (98.41% loss), while even optimistic scenarios (SSP1-2.6) project a 60–70% habitat loss. All scenarios show accelerating degradation through mid-century, with the steepest losses occurring between 2041 and 2080. Projected eastward shifts face constraints from the Anatolian Diagonal, limiting the climate tracking capacity. Despite occupying open landscapes, S. williamsi exhibits exceptional sensitivity to climate change, with Anatolian refugial areas representing critical diversity centers facing substantial degradation. Results provide baseline genetic structure and climate vulnerability information for understanding climate impacts on S. williamsi and Irano–Anatolian steppe fauna. Full article

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen–Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science. Full article