Search Results (3,764)

The current focus of urban environmental governance is on the traffic noise pollution caused by road transportation. Elevated complex roads, defined as transportation systems comprising elevated roads and underlying ground-level roads, exhibit unique traffic noise distribution characteristics due to the presence of double-decked roads and viaducts. This study conducted noise measurements at two sections of elevated complex roads in Guangzhou, including assessing noise levels at the road boundaries and examining noise distribution at different distances from roads and building heights. The results show that the horizontal distance attenuation of noise in adjacent areas exhibits no significant difference from that of ground-level roads, but substantial discrepancies exist in vertical height distribution. The under-viaduct space experiences more severe noise pollution than areas above the viaduct height, and the installation of sound barriers alters the spatial distribution trend of traffic noise. Given that installing sound barriers solely on elevated roads is insufficient to improve the acoustic environment, systematic noise mitigation strategies should be developed for elevated composite road systems. Additionally, the study reveals that nighttime noise fluctuations are significantly greater than those during the day, further exacerbating residents’ noise annoyance. Full article

To enhance the fire safety protection level of deeply buried metro stations, this study conducted full-scale fire experiments based on Wulichong Station of Guiyang Metro Line 3. It systematically investigated the laws of smoke movement and temperature distribution under the coupled effects of different fire source powers and smoke extraction system states. Through the set up of multiple sets of comparative test conditions, the study focused on analyzing the influence mechanism of the operation (on/off) of the smoke extraction system on smoke spread characteristics and temperature field distribution. The results indicate that under the condition where the smoke extraction system is turned off, the smoke exhibits typical stratified spread characteristics driven by thermal buoyancy, with the temperature rising significantly as the vertical height increases. When the smoke extraction system is activated, the horizontal airflow generated by mechanical smoke extraction significantly alters the flame morphology (with an inclination angle exceeding 45°), effectively extracting and discharging the hot smoke and leading to a more uniform temperature distribution within the space. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Based on the ionosonde data from stations at different latitudes in high- and low-solar-activity years, the effects of solar activity on the parameters of the Es layer and the foE amplitude spectrum are analyzed. The results show that the influence of solar activity on the intensity of the Es layer at different latitude sites is not consistent, and there is no significant agreement conclusion. And the spectral analysis results show that solar activity has little influence on the amplitude spectrum of foEs. But the incidence of Es layer, the height distribution of Es layer during daytime, and the Es layer traces have a negative correlation with solar activity. The research in the paper has certain significance for the study of influencing factors in the formation of the Es layer. Full article

The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, TreeQSM for structural reconstruction, and 3D alpha-shape spatial quantification, using terrestrial laser scanning (TLS) technology. This framework was applied to three typical farmland shelterbelts in the Ulan Buh Desert oasis, enabling the first precise quantitative characterization of structural components during the leaf-on stage. The results showed the following to be true: (1) The combined three-algorithm method achieved ≥90.774% relative accuracy in extracting structural parameters for all measured traits except leaf surface area. (2) Branch length, diameter, surface area, and volume decreased progressively from first- to fourth-order branches, while branch angles increased with ascending branch order. (3) The trunk, branch, and leaf components exhibited distinct vertical stratification. Trunk volume and surface area decreased linearly with height, while branch and leaf volumes and surface areas followed an inverted U-shaped distribution. (4) Horizontally, both surface area density (Scd) and volume density (Vcd) in each cube unit exhibited pronounced edge effects. Specifically, the Scd and Vcd were greatest between 0.33 and 0.60 times the shelterbelt’s height (H, i.e., mid-canopy). In contrast, the optical porosity (Op) was at a minimum of 0.43 H to 0.67 H, while the volumetric porosity (Vp) was at a minimum at 0.25 H to 0.50 H. (5) The proposed volumetric stratified porosity (Vsp) metric provides a scientific basis for regional farmland shelterbelt management strategies. This three-dimensional structural analytical framework enables precision silviculture, with particular relevance to strengthening ecological barrier efficacy in arid regions. Full article

In the forest ecosystems of Central European Russia, the influence of forest edges on the spatial distribution of Drosophilidae was studied for the first time. The research was conducted during the period of 2021–2022 in the Republic of Mordovia. Beer traps baited with fermented beer and sugar were used to collect Drosophilidae. Two study plots were selected, differing in their forest edges, tree stands, and adjacent open ecosystems. In both cases, the forest directly bordered an open ecosystem. Edges serve as transitional biotopes, where both forest and meadow (open area) faunas coexist. Knowing that many drosophilid species prefer forest habitats, we designated forest interior sites as control points. Traps were set at heights of 1.5 m (lower) and 7.5 m (upper) on trees. A total of 936 specimens representing 27 species were collected. Nine species were common across all traps, while ten species were recorded only once. At the forest edges, 23 species were captured across both heights, compared to 19 species in the forest interiors. However, the total abundance at the forest edges was 370 specimens, while it was 1.5 times higher in the forest interiors. Both abundance and species richness varied between plots. Margalef’s index was higher at the forest edges than in the forest interiors, particularly at 1.5 m height at the edge and at 7.5 m height in the forest interior. Shannon and Simpson indices showed minimal variation across traps at different horizontal and vertical positions. The highest species diversity was observed among xylosaprobionts (9 species) and mycetophages (8 species). All ecological groups were represented at the forest edges, whereas only four groups were recorded in the forest interiors, with the phytosaprophagous species Scaptomyza pallida being absent. In general, both species richness and drosophilid abundance increased in the lower strata, both at the forest edge and within the interior. Using the R package Indicspecies, we identified Gitona distigma as an indicator species for the forest edge and Scaptodrosophila rufifrons as an indicator for the forest interior in the lower tier for both plots. In addition, Drosophila testacea, D. phalerata, and Phortica semivirgo were found to be indicator species for the lower tier in both plots, while Leucophenga quinquemaculata was identified as an indicator species for the upper tier at the second plot. Full article

To compensate for the instability of renewable energy sources during China’s energy transition, large thermal power plants must provide critical operational flexibility, primarily through deep peaking. To investigate the combustion performance and wear and tear of a 600 MW pulverized coal boiler under deep peaking, the gas–solid flow characteristics and distributions of flue gas temperature, wall heat flux, and wall wear rate in a 600 MW tangentially fired pulverized coal boiler under variable loads (353 MW, 431 MW, 519 MW, and 600 MW) are investigated in this study employing computational fluid dynamics numerical simulation method. Results demonstrate that increasing the boiler load significantly amplifies gas velocity, wall heat flux, and wall wear rate. The maximum gas velocity in the furnace rises from 20.9 m·s⁻¹ (353 MW) to 37.6 m·s⁻¹ (600 MW), with tangential airflow forming a low-velocity central zone and high-velocity peripheral regions. Meanwhile, the tangential circle diameter expands by ~15% as the load increases. The flue gas temperature distribution exhibits a “low-high-low” profile along the furnace height. As the load increases from 353 MW to 600 MW, the primary combustion zone’s peak temperature rises from 1750 K to 1980 K, accompanied by a ~30% expansion in the coverage area of the high-temperature zone. Wall heat flux correlates strongly with temperature distribution, peaking at 2.29 × 10⁵ W·m⁻² (353 MW) and 2.75 × 10⁵ W·m⁻² (600 MW) in the primary combustion zone. Wear analysis highlights severe erosion in the economizer due to elevated flue gas velocities, with wall wear rates escalating from 3.29 × 10⁻⁷ kg·m⁻²·s⁻¹ (353 MW) to 1.23 × 10⁻⁵ kg·m⁻²·s⁻¹ (600 MW), representing a 40-fold increase under full-load conditions. Mitigation strategies, including ash removal optimization, anti-wear covers, and thermal spray coatings, are proposed to enhance operational safety. This work provides critical insights into flow field optimization and wear management for large-scale coal-fired boilers under flexible load operation. Full article

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

In this paper, based on all the data from September 2021 to June 2024 collected by a 30 m meteorological tower and a differential image motion monitor (DIMM) at the Muztagh-Ata site located on the Pamir Plateau in western Xinjiang, China, we study the characteristics of the surface temperature inversion and its effect on astronomical seeing at the site. The results show the following: The temperature inversion at the Muztagh-Ata site is highly pronounced at night; it is typically distributed below a height of about 18 m; it weakens and disappears gradually after sunrise, while it forms gradually after sunset and remains stable during the night; and it is weaker in spring and summer but stronger in autumn and winter. Correlation studies with meteorological parameters show the following: increases in both cloud coverage and humidity weaken temperature inversion; the distribution of inversion with wind speed exhibits a bimodal distribution; southwesterly winds prevail at a frequency of 73.76% and are typically accompanied by strong temperature inversions. Finally, by statistical patterns, we found that strong temperature inversion at the Muztagh-Ata site usually bring better seeing by suppressing atmospheric optical turbulence. Full article

A salient feature of modern material surfaces used in cutting-edge technologies is their structural and spatial complexity, which endows them with novel properties and multifunctionality. The quantitative characterization of material complexity is a challenge that must be addressed to optimize their production and performance. While numerous metrics exist to quantify the complexity of spatial structures in various scientific domains, methods specifically tailored for characterizing the spatial complexity of material surface morphologies at the micro- and nanoscale are relatively scarce. In this paper, we utilize the concept of multiscale entropy to quantify the complexity of surface morphologies of rough surfaces across different scales and investigate the effects of amplitude fluctuations (i.e., surface height distribution) in both stepwise and smooth self-affine rough surfaces. The crucial role of the binning scheme in regulating amplitude effects on entropy and complexity measurements is highlighted and explained. Furthermore, by selecting an appropriate binning strategy, we analyze the impact of 2D imaging on the complexity of a rough surface and demonstrate that imaging can artificially introduce peaks in the relationship between complexity and surface amplitude. The results demonstrate that entropy-based spatial complexity effectively captures the scale-dependent heterogeneity of stepwise rough surfaces, providing valuable insights into their structural properties. Full article

With growing reliance on hydraulic fracturing to develop tight oil and gas reservoirs characterized by low porosity and permeability, optimizing proppant transport and placement has become critical to sustaining fracture conductivity and production. However, how fracture geometry influences proppant distribution under varying field conditions remains insufficiently understood. This study employed computational fluid dynamics to investigate proppant transport and placement in hydraulic fractures of which the aperture tapers linearly along their length. Four taper rate models (δ = 0, 1/1500, 1/750, and 1/500) were analyzed under a range of operational parameters: injection velocities (1.38–3.24 m/s), sand concentrations (2–8%), proppant particle sizes (0.21–0.85 mm), and proppant densities (1760–3200 kg/m³). Equilibrium proppant pack height was adopted as the key metric for pack morphology. The results show that increasing injection rate and taper rate both serve to lower pack heights and enhance downstream transport, while a higher sand concentration, larger particle size, and greater density tend to raise pack heights and promote more stable pack geometries. In tapering fractures, higher δ values amplify flow acceleration and turbulence, yielding flatter, “table-top” proppant distributions and extended placement lengths. Fine, low-density proppants more readily penetrate to the fracture tip, whereas coarse or dense particles form taller inlet packs but can still be carried farther under high taper conditions. These findings offer quantitative guidance for optimizing fracture geometry, injection parameters, and proppant design to improve conductivity and reduce sand-plugging risk in tight formations. These insights address the challenge of achieving effective proppant placement in complex fractures and provide quantitative guidance for tailoring fracture geometry, injection parameters, and proppant properties to improve conductivity and mitigate sand plugging risks in tight formations. Full article

A multi-parameter optimization-based design method for soil-mixing blades was proposed to address the issue of excessive straw residue in the seeding layer after maize straw incorporation. A discrete element model simulating the interaction between the soil-mixing blades, soil, and corn straw was established. The key structural parameters included the bending line angle (α), bending angle (β), side angle (δ), tangential edge height (h), and bending radius (r); the straw burial rate (Y₁) and straw percentage in the seeding layer (Y₂) were selected as evaluation indicators. Single-factor experiments determined the significance level (p < 0.05) and the parameter range. A Box–Behnken response surface design, combined with analysis of variance (ANOVA), was employed to elucidate the influence patterns of the structural parameters and their interactions regarding straw burial performance. Multi-objective optimization yielded an optimal parameter combination: α = 55°, β = 100.01°, δ = 130°, h = 40.05 mm, and r = 28.67 mm. The simulation results demonstrated that this configuration achieved a Y₁ of 96.04% and reduced Y₂ to 35.25%. Field validation tests recorded Y₁ and Y₂ values of 96.54% and 34.13%, respectively. This study quantitatively elucidated the relationship between soil-mixing blade parameters and straw spatial distribution, providing a theoretical foundation for optimizing straw incorporation equipment. Full article

Recent advancements in Earth observation technologies have accelerated remote sensing (RS) data acquisition, yet cross-domain semantic segmentation remains challenged by domain shifts. Traditional unsupervised domain adaptation (UDA) methods often rely on computationally intensive and unstable generative adversarial networks (GANs). This study introduces elevation-aware domain adaptation (EADA), a multi-task framework that integrates elevation estimation (via digital surface models) with semantic segmentation to address distribution discrepancies. EADA employs a shared encoder and task-specific decoders, enhanced by a spatial attention-based feature fusion module. Experiments on Potsdam and Vaihingen datasets under cross-domain settings (e.g., Potsdam IRRG → Vaihingen IRRG) show that EADA achieves state-of-the-art performance, with a mean IoU of 54.62% and an F1-score of 65.47%, outperforming single-stage baselines. Elevation awareness significantly improves the segmentation of height-sensitive classes, such as buildings, while maintaining computational efficiency. Compared to multi-stage approaches, EADA’s end-to-end design reduces training complexity without sacrificing accuracy. These results demonstrate that incorporating elevation data effectively mitigates domain shifts in RS imagery. However, lower accuracy for elevation-insensitive classes suggests the need for further refinement to enhance overall generalizability. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

The wind energy resources in the northwestern desert and semi-desert grassland regions of China are abundant. However, the ramifications of large-scale centralized wind farm operations on terrestrial rodents remain incompletely understood. In May and September 2024, we employed a grid sampling method combined with burrow counting and kernel density analysis to investigate the spatial distribution of Alashan ground squirrel (Spermophilus alashanicus) burrows in different wind turbine power zones (control, 750 kW, 1500 kW, 2000 kW, and 2500 kW) at the Taiyangshan wind farm in China. Using generalized additive models and structural equation models, we analysed the relationship between burrow spatial distribution and environmental factors. The results revealed no significant linear correlation between burrow density and turbine layout density, but was significantly positively correlated with turbine power (p < 0.05). The highest burrow density was observed in the 2500 kW zone, with values of 24.43 ± 7.18 burrows/hm² in May and 21.29 ± 3.38 burrows/hm² in September (p < 0.05). The squirrels exhibited a tendency to avoid constructing burrows within the rotor sweeping areas of the turbines. The burrow density distribution exhibited a multinuclear clustering pattern in both May and September, with a northwest–southeast spatial orientation. Turbine power, aspect, and plan convexity had significant positive effects on burrow density, whereas vegetation height had a significant negative effect. Moreover, vegetation height indirectly influenced burrow density through its interactions with turbine power and relief degree. Under the combined influence of turbine power, topography, and vegetation, Alashan ground squirrels preferred habitats in low-density, high-power turbine zones with shorter vegetation, sunny slopes, convex landforms, and minimal disturbance. Full article