Search Results (380)

As a key component for maintaining the efficient and stable operation of flexible DC transmission systems, the arm reactor often suffers from uneven loss distribution and localized overheating in its windings due to the superimposed AC and DC currents, which adversely affects its operational lifespan. Furthermore, arm reactors are frequently deployed in offshore environments for long-distance, high-capacity power transmission, imposing additional requirements on energy utilization efficiency and seismic resistance. To address these challenges, this study proposes an optimization design method for arm reactors based on a triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise,” aiming to achieve “low loss–low temperature rise–low weight.” First, an equivalent electromagnetic model of the arm reactor under combined AC and DC operating conditions is established to analytically calculate the self- and mutual-inductance-distribution characteristics between winding layers and the loss distribution across windings. The calculated losses are then applied as heat sources in a fluid–thermal coupling method to compute the temperature field of the arm reactor. Next, leveraging a Kriging surrogate model to capture the relationship between the winding temperature rise in the bridge-arm reactor and the loss density, encapsulation width, encapsulation height, and air duct width, the revised analytical expression reduces the temperature rise error from 43.74% to 11.47% compared with the traditional empirical formula. Finally, the triple-constraint mechanism of “equal resistive voltage–equal loss density–equal encapsulation temperature rise” is proposed to balance interlayer current distribution, suppress total loss generation, and limit localized hotspot formation. A prototype constructed based on the optimized design demonstrates a 44.51% reduction in total loss, a 39.66% decrease in hotspot temperature rise, and a 24.83% reduction in mass while maintaining rated inductance, validating the effectiveness of the proposed design algorithm. Full article

Urban agglomerations underpin regional economic growth and sustainability transitions, yet the spatial heterogeneity and drivers of land use intensity (LUI) remain insufficiently resolved in inland settings. This study develops a high-resolution framework—combining a 1 km hexagonal grid with XGBoost-SHAP—to (i) map subsystem-specific LUI evolution, (ii) identify dominant drivers and nonlinear thresholds, and (iii) inform differentiated, sustainable land governance in the Guanzhong Plain Urban Agglomeration (GPUA) over 2000–2020. Composite LUI indices were constructed for human settlement (HS), cropland (CS), and forest (FS) subsystems; eleven natural, socioeconomic, urban–rural, and locational variables served as candidate drivers. The results show marked redistributions across subsystems. In HS, the share of low-intensity cells declined (86.54% to 83.18%) as that of medium- (12.10% to 14.26%) and high-intensity ones (1.22% to 2.56%) increased, forming a continuous high-intensity corridor between Xi’an and Xianyang by 2020. CS shifted toward medium-intensity (32.53% to 50.57%) with the contraction of high-intensity cells (26.62% to 14.53%), evidencing strong dynamism (55.1% net intensification; 38.5% net decline). FS transitioned to low-intensity dominance by 2020 (59.12%), with stability and delayed growth concentrated in conserved mountainous zones. Urban–rural gradients were distinct: HS rose by >20% (relative to 2000) in cores but remained low and stable in rural areas (mean < 0.20); CS peaked and stayed stable at fringes (mean ≈ 0.60); FS shifted from an inverse gradient (2000–2010) to core-area recovery by 2020. Explainable machine learning revealed inverted U-shaped relationships for HS (per capita GDP) and CS (population density) and a unimodal peak for FS with respect to distance to urban centers; model performance was strong (HS R² up to 0.82) with robust validation. Policy recommendations are subsystem-specific: enforce growth boundaries and prioritize infill/polycentric networks (HS); pair farmland redlines with precision agriculture (CS); and maintain ecological redlines with differentiated conservation and afforestation (FS). The framework offers transferable, data-driven evidence for calibrating thresholds and sequencing interventions to reconcile land use intensification with ecological integrity in rapidly urbanizing contexts. Full article

The vibration signal of rotating machinery is usually nonlinear and non-stationary, and the feature set has information redundancy. Therefore, a high-dimensional feature reduction method based on multi-manifold learning is proposed for rotating machinery fault diagnosis. Firstly, considering the non-uniformity of multi-fault feature distribution and the sensitivity of domain selection in traditional manifold learning methods, the neighborhood size of each data point is selected adaptively by using the relationship between neighborhood size and sample density. Then, the between-manifold graph and within-manifold graph are constructed adaptively by the class information, and the divergence matrix and edge distance corresponding to the manifold graph are calculated. Feature fusion reduction is achieved by maximizing edge distance and minimizing within-class differences. Finally, the multi-manifold theoretical dataset and several rotating machinery fault datasets are selected for testing. The results show that the proposed algorithm has higher fault identification accuracy than traditional manifold learning methods. Full article

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

Understanding the multi-crack coupling fracture behavior in brittle materials is particularly critical for aging dam infrastructure, where 78% of structural failures originate from crack network coalescence. In this study, we introduce the concepts of crack distance ratio (DR) and size ratio (SR) to describe the relationship between crack position and length and employ the discrete element method (DEM) for extensive numerical simulations. Specifically, a crack density function is introduced to assess microscale damage evolution, and the study systematically examines the macroscopic mechanical properties, failure modes, and microscale damage evolution of rock-like materials under varying DR and SR conditions. The results show that increasing the crack distance ratio and crack angle can inhibit the crack formation at the same tip of the prefabricated crack. The increase in the size ratio will promote the formation of prefabricated cracks on the same side. The increase in the distance ratio and size ratio significantly accelerate the rapid increase in crack density in the second stage. The crack angle provides the opposite effect. In the middle stage of loading, the growth rate of crack density decreases with the increase in crack angle. Overall, the size ratio has a greater influence on the evolution of microscopic damage. This research provides new insights into understanding and predicting the behavior of materials under complex stress conditions, thus contributing to the optimization of structural design and the improvement of engineering safety. Full article

Understanding how elevation influences forest structure and species composition is crucial for effective conservation in mountainous regions like Nepal, where ecosystems change dramatically over short distances. This study assessed forest dynamics along an elevational gradient (600–3200 m) in Nepal’s mid-hills, incorporating elevational zonation (Tropical, Subtropical, Lower Temperate, and Upper Temperate) and aspect-driven variations. We established 27 square plots (20 × 20 m) at 100 m elevation intervals along a trekking route from Tallo Dungeshwor near the Karnali River to Mahabu Lek, recording all tree species with a diameter at breast height (DBH) ≥ 5 cm. Tree density across the elevational gradient ranged from 250 to 800 trees/ha. Basal area varied between 7.46 and 82.43 m²/ha, while mean tree height ranged from 6.89 to 16.62 m. Species diversity was assessed using the Shannon diversity index, and species dominance was evaluated through the Importance Value Index (IVI). Diversity peaked at mid-elevations, with Shorea robusta and Quercus semicarpifolia identified as dominant species. While minor variations occurred across topographic aspects, statistical analysis confirmed elevation as the dominant driver of forest structure and composition. Correlation analysis revealed a significant positive relationship between elevation and Simpson’s diversity index (r = 0.45, p < 0.05), indicating increased dominance diversity at higher elevations. These findings highlight the critical role of elevation and aspect in shaping forest ecosystems and offer valuable baseline data for climate-resilient management. We recommend conservation planning that is sensitive to topographic gradients, integrates long-term, climate-adaptive monitoring, and engages local communities to anticipate ecological shifts and address mounting anthropogenic pressures in vulnerable montane zones. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most engineering and industrial measurement applications, the accuracy and density of LiDAR point clouds are highly dependent on laser scanners, leading to significant variability that critically affects registration quality. Key factors influencing point cloud accuracy include scanning distance, incidence angle, and the surface characteristics of the target. Notably, in short-range scanning scenarios, incidence angle emerges as the dominant error source. Building on this insight, this study systematically investigates the relationship between scanning incidence angles and point cloud quality. We propose an incident-angle-dependent weighting function for point cloud observations, and further develop an improved weighted Iterative Closest Point (ICP) registration algorithm. Experimental results demonstrate that the proposed method achieves approximately 30% higher registration accuracy compared to traditional ICP algorithms and a 10% improvement over Faro SCENE’s proprietary solution. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

CO₂ transport is a crucial part of CCUS. Nonetheless, due to the physical property differences between CO₂ and natural gas and oil, CO₂ pipeline transport is distinct from natural gas and oil transport. Gaseous CO₂ transportation has become the preferred scheme for transporting impurity-containing CO₂ tail gas in purification plants due to its advantages of simple technology, low cost, and high safety, which are well suited to the scenarios of low transportation volume and short distance in purification plants. The research on its physical property and state parameters is precisely aimed at optimizing the process design of gaseous transportation so as to further improve transportation efficiency and safety. Therefore, it has important engineering practical significance. Firstly, this paper collected and analyzed the research cases of CO₂ transport both domestically and internationally, revealing that phase state and physical property testing of CO₂ gas containing impurities is the basic condition for studying CO₂ transport. Subsequently, the exhaust gas captured by the purification plant was captured after hydrodesulfurization treatment, and the characteristics of the exhaust gas components were obtained by comparing before and after treatment. By preparing fluid samples with varied CO₂ content and conducting the flash evaporation test and PV relationship test, the compression factor and density of natural gas under different temperatures and pressures were obtained. It is concluded that under the same pressure in general, the higher the CO₂ content, the smaller the compression factor. Except for pure CO_2, the higher the CO₂ content, the higher the density under constant pressure, which is related to the content of C₂ and heavier hydrocarbon components. At the same temperature, the higher the CO₂ content, the higher the viscosity under the same pressure; the lower the pressure, the slower the viscosity growth slows down. The higher the CO₂ content at the same temperature, the higher the specific heat at constant pressure. With the decrease in temperature, the CO₂ content reaching the highest specific heat at the identical pressure gradually decreases. Finally, BWRS, PR, and SRK equations of state were utilized to calculate the compression factor and density of the gas mixture with a molar composition of 50% CO₂ and the gas mixture with a molar composition of 100% CO₂. Compared with the experimental results, the most suitable equation of state is selected as the PR equation, which refers to the parameter setting of critical nodes of CO₂ gas transport. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Background/Objectives: The tumor microenvironment (TME) plays a critical role in cancer progression by shaping immune responses and influencing patient outcomes. We hypothesized that the relative proximity of specific immune cell pairs to cancer cells within the TME could help predict their pro- or anti-tumor functions and reflect clinically relevant immune dynamics. Methods: We analyzed imaging mass cytometry (IMC) data from lung adenocarcinoma (LUAD) and triple-negative breast cancer (TNBC) cohorts. For each immune cell pair, we calculated a relative distance (RD) score, which quantifies the spatial difference in proximity to cancer cells. We assessed the prognostic and predictive significance of these RD-scores by comparing them with conventional features such as cell fractions, densities, and individual cell distances. To account for variations in cell abundance, we also derived normalized RD-scores (NRD-scores). Results: RD-scores were more strongly associated with overall patient survival than standard immunological metrics. Among all immune cell pairs, the RD-score comparing the proximity of B cells to that of intermediate monocytes showed the most significant association with improved survival. In TNBC, RD-scores also improved the distinction between responders and non-responders to immunochemotherapy and chemotherapy. Normalized RD-scores reinforced these findings by minimizing the influence of cell density and further highlighting the importance of immune cell spatial relationships. Conclusions: RD-scores offer a spatially informed biomarker that outperforms traditional metrics in predicting survival and treatment response. This approach provides a new perspective on immune cell behavior in the TME and has potential utility in guiding personalized cancer therapies and patient stratification. Full article

The sustainable development of eco-tourism is significantly influenced by multiple conditions within spatiotemporally continuous geographic scenarios. However, existing evaluations of the development value of eco-tourism resources (Eco-TRDVs) are non-spatial and do not sensitively represent their complex relationships. This study proposed a GIS approach for evaluating regional Eco-TRDVs by mapping the complex interconnections with spatial distances. Inherent and external conditions for evaluating Eco-TRDVs were classified under three indicators and digitized using GIS and remote sensing technologies. Then, the analytic hierarchy process and GIS cost distance analysis were introduced to define the initial values and cumulate Eco-TRDVs with distances. Taking the Taihang Honggu National Forest Park, China, as the case area, the Eco-TRDVs over the entire area in 2017 and 2020 were mapped. The results present a continuous spatial variability of Eco-TRDVs and comprehensively reflect the complex interconnections of constraint elements with spatial distances. The evaluation is sensitive to the intrinsic value of poles, as evidenced by the high development values and high-density distribution of their contours. Source additions improve the evaluation considerably, with transportation networks having a greater impact than economic development zones and urban elements. Furthermore, aggravated fragmentation of the price flow field increases spatial heterogeneity. The development value shows a negative linear correlation with distance. The proposed approach handles the spatially oriented relationships of the multi-conditions, and supports future planning and monitoring of spatial-temporal changes in eco-tourism development. Full article

Port green spaces are essential protective barriers, enhancing safety and environmental resilience in high-activity port regions. Given the intensity of human activities in these areas, understanding the factors influencing the carbon sequestration capacity and ecological benefits of port green spaces is crucial for developing sustainable green ports. This study integrated field investigations and remote sensing data to estimate carbon density and carbon sequestration capacity in the Dongjiakou Port area, examining their relationship with port green space planning. The results indicated that carbon density in green spaces showed a significant negative correlation with the number of lanes in adjacent roads, where an increase in lane numbers corresponded to lower carbon density. Additionally, carbon density decreased significantly with increasing distance from the shipping center. In contrast, a significant positive correlation was observed between carbon density and distance from large water bodies, indicating that green spaces closer to large water bodies exhibited smaller carbon density. Infrastructure development in Dongjiakou substantially negatively impacted vegetation carbon sequestration capacity, with effects not reversible in the short term. However, green space enhancement efforts provided additional ecological benefits, leading to a 50.9 ha increase in green space area. When assessing carbon density in urbanizing areas, geographical influences should be prioritized. Furthermore, the long-term environmental impacts of urban expansion must be considered at the early planning stages, ensuring the implementation of proactive protective measures to mitigate potential ecological disruptions. Full article