Search Results (7,019)

Spatial co-location patterns are used to describe the spatial associations between features, finding wide applications in geographic information systems, urban planning, and other fields. Traditional frameworks for mining spatial features typically consist of two stages: constructing spatial proximity relationships and discovering frequent patterns. However, existing methods have limitations: the construction of proximity relationships relies on fixed distance thresholds or clustering centers, making it difficult to adapt to spatial density heterogeneity; meanwhile, frequency metrics overly depend on participation indices, lacking quantitative analysis of the strength of geometric associations between features. To address these issues, a spatial co-location pattern mining method based on Hausdorff distance is proposed. Drawing on the concept of Hausdorff distance, this method employs Voronoi tessellation to achieve data-adaptive partitioning of the spatial domain. Combined with a K-dimensional tree, it adopts an iterative strategy of direct allocation, proportional allocation, and residual allocation to align instances, generating a spatial proximity relationship graph. Additionally, a new frequency metric based on instance distribution—alignment rate—is introduced, leveraging the decreasing trend of alignment rate in conjunction with a pruning optimization algorithm. Experimental results demonstrate that this method excels in handling noise points, effectively addressing the challenges of uneven data density distribution while enhancing the identification of weakly associated yet potentially valuable patterns. Full article

Human–environment interactions in antiquity were fundamentally shaped by environmental constraints, with spatial patterns of human construction works reflecting strategic resource optimization. This study employed Geographic Information System (GIS) and binary logistic regression (BLR) to analyze the siting principles of ancient postal buildings in Fujian, China, integrating related environmental factors of elevation, slope, relief amplitude, and distance to rivers. The results revealed significant spatial differentiation, with elevation exhibiting the strongest influence on siting preference, followed by slope, relief amplitude, and distance to rivers. Clustering patterns along coasts and rivers indicated a strategic balance between transmission efficiency and military defense needs. The applicability of the integrated GIS–BLR approach in studying the ancient postal system demonstrates its extensibility to other ancient settlement systems while offering insights for contemporary conservation practice and sustainable development. Full article

We present a Bayesian statistical analysis to evaluate the Nexus Paradigm (NP) of quantum gravity, using horizon-scale observations of supermassive black holes (SMBHs) Sagittarius A* (Sgr A*) and M87* from the Event Horizon Telescope (EHT). The NP predicts angular diameters for the dark depression, emission ring, and base diameter, which we compare to EHT measurements. Employing Gaussian likelihoods and priors informed by mass-to-distance ratio uncertainties, we compute the posterior distribution for the angular scale parameter ${\theta }_g$, achieving a combined ${\chi }^2\approx 0.0062$ (four degrees of freedom) corresponding to a 4.37$\sigma$ (99.9972%) confidence level. Individual features show deviations $<0.1 \sigma$ supporting the NP’s claim of 99th percentile agreement. Compared to General Relativity (GR), which predicts a shadow diameter inconsistent with the observed dark depression (${\chi }^2\approx 168, ~12.97 \sigma$) the NP is favored with a Bayes factor of $~{10}^{36}$. These results validate the NP’s predictions and highlight its potential as a quantum gravity framework, though refined uncertainties and broader model comparisons are recommended. Full article

This study aimed to characterize students from the Instituto Tecnológico del Valle de Etla (ITVE), located in Oaxaca, Mexico, within the virtual learning environment (VLE) and to identify the main technological challenges affecting their learning experience. The research adopted a descriptive quantitative approach, using a self-administered questionnaire applied to a sample of 71 students enrolled in distance education programs. The instrument made it possible to analyze variables such as online instructional design, teaching experience, and information technologies. The results evidenced four distinct student profiles identified as follows: demanding, digitally competent, dependent on didactic material, and with technological barriers. These profiles reflect disparities in connectivity conditions, digital competencies, and expectations toward instructional design. The evidence generated by this research contributes to the formulation of more inclusive and resilient educational policies, in line with Sustainable Development Goal 4 (SDG 4), which promotes inclusive, equitable, and quality education for all. Full article

Myriophyllum spicatum is a globally distributed aquatic plant capable of sexual and clonal reproduction. Despite its ecological importance and biochemical potential, studies on its genetic and clonal structure in freshwater systems throughout South Korea remain limited. We investigated the genetic and clonal diversity of M. spicatum using 30 newly developed microsatellite markers across 120 individuals from six freshwater systems in South Korea. Overall, 148 alleles were identified, with an average polymorphism information content value of 0.530. Clonal diversity differed among populations, with the genotypes to individuals (G/N) ratio ranging from 0.200 to 1.000. Bottlenecks and clonal dominance were observed in riverine populations. High genetic differentiation (mean F_ST = 0.556) indicated limited gene flow, and STRUCTURE analysis revealed six distinct genetic clusters. No significant correlation was found between genetic and geographic distance, suggesting possible seed dispersal by waterfowl, particularly between adjacent populations. Genetic structure was shaped by habitat type, disturbance intensity, and reproductive strategy. Stable reservoir habitats favored sexual reproduction and higher genetic diversity, whereas disturbed river systems showed clonal dominance and reduced variation. These findings provide essential genetic insights for conservation planning and sustainable management of aquatic plant resources. Full article

Assessment of cultural ecosystem services (CESs) is a key component in advancing the sustainable development of urban ecosystems. Mapping the spatial distribution of CESs provides spatially explicit insights for urban landscape planning. However, most assessments lack regional adaptability, particularly in cities with pronounced environmental and cultural heterogeneity. To address this gap, this study focused on the central urban area of Lhasa, using communities as units to develop a tailored CES assessment framework. The framework integrated the MaxEnt model with multi-source indicators to analyze the spatial distribution of five CES categories and their relationships with environmental variables. Spatial statistics and classification at community level informed the CES spatial optimization strategies. Results indicated that high-value CES areas were predominantly concentrated in the old city cluster, typified by Barkhor and Jibenggang subdistricts, following an east–west spatial pattern along the Lhasa River. Distance to tourist spot contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services, making it the most influential environmental variable. At the community level, CESs exhibited a distinct spatial gradient, with higher values in the central area and lower values in the eastern and western peripheries. For the ecotourism and aesthetic category, 61.47% of the community area was classified as low service, whereas only 1.48% and 7.33% were identified as excellent and high. Moreover, communities within subdistricts such as Barkhor and Zhaxi demonstrated excellent service across four CES categories, with notably lower performance in the health category. This study presents a quantitative and adaptable framework and planning guidance to support the sustainable development of CESs in cities with similar characteristics. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

We give a variational characterization of the Born rule. For each measurement context, we project a quantum state ρ onto the corresponding abelian algebra by minimizing Umegaki relative entropy; Petz’s Pythagorean identity makes the dephased state the unique local minimizer, so the Born weights $p_C\left(i\right)=\mathrm{Tr}\left(\rho P_i\right)$ arise as a consequence, not an assumption. Globally, we measure contextuality by the minimum classical Kullback–Leibler distance from the bundle $\left\{p_C\left(\rho \right)\right\}$ to the noncontextual polytope, yielding a convex objective $\Phi \left(\rho \right)$. Thus, $\Phi \left(\rho \right)=0$ exactly when a sheaf-theoretic global section exists (noncontextuality), and $\Phi \left(\rho \right)>0$ otherwise; the closest noncontextual model is the classical I-projection of the Born bundle. Assuming finite dimension, full-rank states, and rank-1 projective contexts, the construction is unique and non-circular; it extends to degenerate PVMs and POVMs (via Naimark dilation) without change to the statements. Conceptually, the work unifies information-geometric projection, the presheaf view of contextuality, and categorical classical structure into a single optimization principle. Compared with Gleason-type, decision-theoretic, or envariance approaches, our scope is narrower but more explicit about contextuality and the relational, context-dependent status of quantum probabilities. Full article

Nowadays, the use of technology has become increasingly indispensable, leading to prolonged exposure to computers and other screen devices. This situation is common in work areas related to Information and Communication Technologies (ICTs), where people spend long hours in front of a computer. This exposure has been associated with the development of musculoskeletal disorders, among which nonspecific back pain is particularly prevalent. This observational study presents the design of a telerehabilitation strategy based on 3D animations, which is aimed at enhancing the musculoskeletal health of individuals working or studying in ICT-related fields. The intervention was developed through the Moodle platform and designed using the ADDIE instructional model, incorporating educational content and therapeutic exercises adapted to digital ergonomics. The sample included university students in the field of computer science who were experiencing symptoms associated with prolonged computer use. After a four-week intervention period, the results show favorable changes in pain perception and knowledge of postural hygiene. These findings suggest that a distance-based educational and therapeutic strategy may be a useful approach for the prevention and treatment of back pain in academic settings. Full article

Blood glucose monitoring is crucial for the daily management of diabetic patients. In this study, we developed a differential absorbance and photoplethysmography (PPG)-based non-invasive blood glucose measurement system (NIBGMS) using visible–near-infrared (Vis-NIR) light. Three light-emitting diodes (LEDs) (625 nm, 850 nm, and 940 nm) and three photodetectors (PDs) with different source–detector separation distances were used to detect the differential absorbance of tissues at different depths and PPG signals of the index finger. A spatiotemporal multimodal fused long short-term memory (STMF-LSTM) model was developed to improve the prediction accuracy of blood glucose levels by multimodal fusion of optical spatial information (differential absorbance and PPG signals) and glucose temporal information. The validity of the NIBGMS was preliminarily verified using multilayer perceptron (MLP), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XG Boost) models on datasets collected from 15 non-diabetic subjects and 3 type-2 diabetic subjects, with a total of 805 samples. Additionally, a continuous dataset consisting 272 samples from four non-diabetic subjects was used to validate the developed STMF-LSTM model. The results demonstrate that the STMF-LSTM model indicated improved prediction performance with a root mean square error (RMSE) of 0.811 mmol/L and a percentage of 100% for Parkes error grid analysis (EGA) Zone A and B in 8-fold cross validation. Therefore, the developed NIBGMS and STMF-LSTM model show potential in practical non-invasive blood glucose monitoring. Full article

The notion of the usual environment is a key factor in distinguishing tourism activities from a demand-side perspective, yet applying it in practice presents persistent difficulties when estimating tourism’s physical and monetary scale. These challenges are particularly pronounced in nautical tourism, and especially in the case of domestic same-day boat trips. Focusing on Croatia, a country where yachting makes up a substantial share of overall tourism flows, this study examines criteria for classifying domestic nautical same-day trips from the demand perspective. Qualitative research on the population of residents who are recreational boat owners was conducted. The aim of the research was to assess residents’ perception of the usual environment when on a same-day boat trip from the criteria of trip frequency, distance, motives, and activities. Seventeen in-depth interviews were conducted, providing insight into subjective and objective determinants of trip classification. Although the analysis revealed a blurred understanding of the distinction between boating as a lifestyle and as a tourism activity, the results indicate that official statistics likely underestimate the number of recreational same-day boat trips. This finding underscores the need for more precise measurement of total physical flows in nautical tourism as a prerequisite for effective sustainability assessment and informed management policies. Full article

Forest fires not only destroy vegetation and directly decrease forested areas, but they also significantly impair forest stand structures and habitat conditions, ultimately leading to imbalances within the entire forest ecosystem. Therefore, accurate forest fire detection is critical for ecological safety and for protecting lives and property. However, existing algorithms often struggle with detecting flames and smoke in complex scenarios like sparse smoke, weak flames, or vegetation occlusion, and their high computational costs hinder practical deployment. To cope with it, this paper introduces F3-YOLO, a robust and fast forest fire detection model based on YOLOv12. F3-YOLO introduces conditionally parameterized convolution (CondConv) to enhance representational capacity without incurring a substantial increase in computational cost, improving fire detection in complex backgrounds. Additionally, a frequency domain-based self-attention solver (FSAS) is integrated to combine high-frequency and high-contrast information, thus better handling real-world detection scenarios involving both small distant targets in aerial imagery and large nearby targets on the ground. To provide more stable structural cues, we propose the Focaler Minimum Point Distance Intersection over Union Loss (FMPDIoU), which helps the model capture irregular and blurred boundaries caused by vegetation occlusion or flame jitter and smoke dispersion. To enable efficient deployment on edge devices, we also apply structured pruning to reduce computational overhead. Compared to YOLOv12 and other mainstream methods, F3-YOLO achieves superior accuracy and robustness, attaining the highest mAP@50 of 68.5% among all compared methods on the dataset while requiring only 5.4 GFLOPs of computational cost and maintaining a compact parameter count of 2.6 M, demonstrating exceptional efficiency and effectiveness. These attributes make it a reliable, low-latency solution well-suited for real-time forest fire early warning systems. Full article

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. Full article

Fraxinus spp. is one of the most important salt-alkali resistant tree species in the Yellow River region of China. However, the limited number of superior families and individuals, as well as the lack of a well-established parent selection system for hybrid breeding, have seriously constrained the improvement of seed orchards and the construction of advanced breeding populations. To address these issues, this study investigated 22 full-sib families of Fraxinus spp., using SSR molecular markers to calculate the genetic distance (GD) between parents. Combined with combining ability analysis, the study aimed to predict heterosis in offspring growth traits and select superior families and individuals through multi-trait comprehensive evaluation. The results showed the following: (1) Tree height (TH), diameter at breast height (DBH), and volume index (VI) exhibited extremely significant differences among families, indicating rich variation and strong selection potential. (2) The phenotypic and genotypic coefficients of variation for TH, DBH, and VI ranged from 4.34% to 16.04% and 5.10% to 17.73%, respectively. Family heritability was relatively high, ranging from 0.724 to 0.818, suggesting that growth is under strong genetic control. (3) The observed and expected heterozygosity of 15 parents were 0.557 and 0.410, respectively, indicating a moderate level of heterozygosity. Nei’s genetic diversity index and Shannon’s information index were 0.488 and 0.670, respectively, indicating relatively high genetic diversity. GD between parents ranged from 0.155 to 0.723. (4) Correlation analysis revealed significant or highly significant positive correlations between family heterosis and growth traits, combining ability, and GD, with specific combining ability (SCA) showing the strongest predictive power. Regression analysis further demonstrated significant linear correlations between GD and heterosis of TH and VI, and between SCA and heterosis of TH, DBH, and VI, establishing a GD threshold (≤0.723) and SCA-based co-selection strategy. In addition, four superior Fraxinus families and 11 elite individuals were selected. Their genetic gains for TH, DBH, and VI reached 2.28%, 3.30%, and 9.96% (family selection), and 1.98%, 2.11%, and 4.00% (individual selection), respectively. By integrating genetic distance (GD) and quantitative genetic combining ability (SCA), this study established a quantifiable prediction model and proposed the “GD–SCA dual-index parent selection method”, offering a new paradigm for genetic improvement in tree breeding. Full article