Search Results (15,869)

This study developed a comprehensive framework integrating ecosystem services (ESs), ecological adaptability, and ornamental value to guide tree species selection in historic cities constrained by soil salinization and subsurface heritage conservation. Taking Kaifeng, Henan Province, as a case study, we employed field surveys, i-Tree Eco, the Analytic Hierarchy Process, and K-means clustering to evaluate trees across protective, park, attached, and square green spaces. Results showed that carbon-related services dominated Kaifeng’s urban ES profile, with carbon storage (CS) and sequestration (CSE) value densities of 9.09 ¥·m⁻² and 0.84 ¥·m⁻²·y⁻¹, respectively. Air pollutant removal (AR) (0.21 ¥·m⁻²·y⁻¹) and P (0.009 ¥·m⁻²·y⁻¹) values remained comparatively low. Camphora officinarum Nees ex Wall delivered the highest annual ES value per tree (33.24 ¥·y⁻¹). Plaza greenery outperformed other space types in overall service provision, and deciduous broadleaf species generated greater ES value than evergreen conifers. Cluster analysis identified four functional groups: stress-tolerant pioneers, balanced adapters, high-efficiency carbon sinks, and ornamental specialists—each suited to specific green space contexts. This integrated framework offers a transferable approach for evidence-based tree selection in saline historic cities, supporting nature-based solutions in urban green space (UGS) planning. Full article

Accurate measurement of the 2D soot spatial distribution is vital for optimizing combustion efficiency and reducing pollutant emissions. While 1D laser extinction (LE) is robust and cost-effective, it provides only line-of-sight integrated information, lacking the spatial resolution required to resolve complex soot topologies. We propose Soot-MixFormer, a hybrid deep learning model designed for the high-fidelity inversion of 2D soot distributions from 1D extinction data. The architecture integrates CNN-based local feature extraction with Transformer-based global dependency modeling. Key innovations include a dynamic decoupled generation head and a Dual-Axial Gated Refinement (DAGR) module coupled with a physical hard constraint layer to ensure mass conservation and physical consistency. Experimental results demonstrate that Soot-MixFormer significantly outperforms baseline MLP and CNN models, achieving a Structural Similarity Index (SSIM) of 0.800 and a Pearson Correlation Coefficient (PCC) of 0.915, and a highly suppressed Root Mean Square Error (RMSE) representing less than 10% relative error in high-concentration zones. Furthermore, the model exhibits exceptional robustness, maintaining a cosine similarity above 0.72 even under 10% simulated measurement noise. The model is highly efficient, with only 0.97 M parameters and a real-time inference speed of ~246 FPS. This study provides a novel, low-cost diagnostic paradigm for real-time, high-accuracy monitoring of soot fields in industrial combustion environments, effectively bridging the gap between simple 1D sensing and complex 2D spatial reconstruction. Full article

Deep learning-based data-driven methods have gained significant attention in underwater acoustic source localization. However, their performance is often constrained by environmental disturbances and the scarcity of real-world underwater acoustic data. To address these issues, this paper presents a novel network termed MTCL-Net, a multi-task learning network that incorporates contrastive learning as an auxiliary task for underwater acoustic source ranging. A standard dataset and a perturbed dataset to simulate real underwater interferences are constructed based on known environmental parameters in this method. A Siamese dual-branch architecture is employed, where a contrastive learning task enables the automatic extraction of position-related features. The network jointly optimizes three tasks: source localization in perturbed environments, localization on the standard dataset, and position similarity discrimination, which improves the robustness and generalization ability. The experimental results on simulated and sea trial data demonstrate that MTCL-Net outperforms traditional matched field processing (MFP), single-task learning (STL), and multi-task learning based on depth–range (MTL-DR) methods in terms of mean absolute error (MAE) and probability of credible localization (PCL-10%). Specifically, on SWellEx-96 sea trial data, MTCL-Net achieves an MAE of 0.17 km and a PCL-10% of 90.36%. Moreover, the proposed method only needs a few samples for fine-tuning and shows strong practicality in uncertain marine environments. Full article

Gene regulatory networks differ substantially across individual cell lines, and population-level network inferences often fail to capture the underlying biological heterogeneity. To better capture this heterogeneity, cell line-specific gene network analysis is required. However, interpreting such high-dimensional cell line-specific networks remains a major challenge in the field of network biology. One interpretative approach is to identify differentially regulated gene networks (DGNs) between phenotypes because these networks can highlight phenotype-specific regulatory mechanisms. Although several methods have been proposed for DGN analysis, they are not suitable for cell line-specific gene network analysis, which is characterized by pronounced heterogeneity across individual networks. To address this problem, we proposed a novel statistical method for identifying DGNs in a cell line-specific scenario. The proposed framework integrates cell line-specific network estimation, a Kullback–Leibler divergence-based comparison of multivariate distributions, and a DKL-ratio statistic to quantify between-phenotype heterogeneity relative to within-phenotype homogeneity. Our method evaluates both between-phenotype heterogeneity and within-phenotype homogeneity, ensuring the robust detection of phenotype-specific network structures. Through Monte Carlo simulation studies, we systematically evaluated the performance of the proposed method and demonstrated that our strategy consistently outperformed existing methods in terms of accuracy, precision, true positive rate (TPR), true negative rate (TNR), and F-measure across diverse network structures and mean shift scenarios. Statistical significance was assessed using a permutation-based framework, confirming that the identified networks are unlikely to arise from random variation. We further applied our strategy to Quizartinib sensitivity-specific gene network analysis and identified immune-related subnetworks enriched in antigen processing and presentation pathways. These subnetworks included hub genes such as IFIT1, PSMB9, and HLA-B, which are known to be associated with immune evasion and drug resistance in acute myeloid leukemia. Our findings demonstrate that the proposed method enables statistically reliable and biologically interpretable identification of phenotype-specific gene regulatory mechanisms, providing insights into potential therapeutic targets. Full article

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces. Full article

Inherent defects in engineering rock masses inevitably lead to randomness in mechanical parameters and uncertainty in tunnel deformation and failure. To address these challenges, this study proposes a novel coupled analysis method that integrates complex function theory, physical model testing, and Monte Carlo simulation (MCS) for the deformation estimation and failure probability analysis of non-circular tunnels. Theoretically, this method provides a high-speed, high-accuracy analytical framework that overcomes the limitations of purely numerical approaches, particularly in handling continuous–discontinuous failure processes. Practically, it enables a more reliable and efficient stability assessment of tunnel systems under uncertain geological conditions. The proposed method is applied to a traffic tunnel at the Baihetan Hydropower Station. A series of uniaxial compression tests on 40 rock specimens are conducted to obtain statistical distributions of rock deformation parameters. An analytical solution for tunnel displacement is derived using plane elastic complex function theory, and the random displacement field is estimated via MCS. Physical model tests reveal that the elastic stage accounts for 83% of the overload failure process, based on which an elastic limit displacement function is established for tunnel arch settlement and surrounding rock convergence. The failure probability of the tunnel is then calculated, and the effects of the mean, coefficient of variation, and cross-correlation coefficient of rock deformation parameters on failure probability are discussed. The entire computational process is characterized by high speed and precision, offering a new and practical tool for tunnel stability evaluation and reliability-based design. Full article

Energy-intensive refrigeration systems require predictive models that remain informative under counterfactual control trajectories, not only on archived operation. This paper develops a control-oriented multi-step state model and a nonlinear model predictive control framework for an indoor ice-rink refrigeration system. Historical state, control, and exogenous variables are encoded jointly with an admissible future control trajectory, and a normalized thermal-balance residual is added to the training objective. A lightweight conditioned transformer predicts ice temperature, return-glycol temperature, supply-glycol temperature, and compressor power over a 30 min horizon. The selected weakly regularized model with regularization coefficient λ_phys = 0.001 decreases the normalized thermal-balance root-mean-square error on the horizon tail by 30.29% relative to the base model while increasing the average ice-temperature root-mean-square error by only 1.90%. In a surrogate-based counterfactual four-day evaluation, the resulting nonlinear model predictive controller reduces predicted daily energy by 4.84%, terminal violation share by 17.32%, mean absolute terminal ice-temperature deviation by 18.74%, and the mean objective value by 30.82% relative to historical admissible setpoint tracking. The mean full control cycle time is 0.0311 s, confirming real-time feasibility for a 5 min supervisory update interval. All controller results are surrogate-based rather than field-deployed and therefore represent receding-horizon benchmark results under learned-model evaluation, not realized field savings. Full article

Slope aspect is the primary topographic factor controlling the surface thermal state in mountainous cold regions. By modulating the magnitude and timing of solar radiation on slopes, it systematically affects soil temperature, maximum frost depth, and freeze–thaw timing, and it drives differentiation of the coupled hydrothermal process between sunny and shady slopes. However, the quantitative patterns of slope aspect freeze–thaw dynamics in high-latitude seasonally frozen soils and their response mechanisms to climate warming have not been systematically revealed. Therefore, based on field monitoring, this study used the SHAW model to simulate the soil freeze–thaw process and designed multiple warming scenarios to evaluate the evolving trend of the aspect effect. The results showed that: (1) the SHAW model effectively simulated soil temperature dynamics (R² = 0.939, NSE = 0.913, RMSE = 1.71 °C); (2) the profile-mean soil temperature on sunny slopes was 3.10 °C higher than on shady slopes, with a maximum frost depth approximately 61.2 cm shallower, freezing onset about 18 days later, complete thawing 59–77 days earlier, and freezing and thawing rates approximately 28% and 50% higher, respectively; and (3) under the SSP2-4.5 scenario, various freeze–thaw differentiation metrics did not exhibit a systematic convergence trend, and the aspect effect remained robust against climate warming. These findings offer a quantitative basis for ecological and hydrological assessment, water-resource scheduling, and foundation-stability design in cold regions, thereby supporting ecosystem conservation, sustainable water-resource use, and climate-resilient infrastructure development, and informing sustainable development planning and policy-making in high-latitude regions under a warming climate. Full article

This study assessed photovoltaic (PV) soiling losses at Alamogordo, New Mexico, USA, located within the Chihuahuan Desert and near the White Sands gypsum dune field, a region with frequent dust events. Soiling material collected from PV module surfaces showed seasonal variations in mineral composition, with quartz being the main component during the fall season and calcite predominating during the spring. All samples collected during the following spring season contained large amounts of gypsum, indicating transport from White Sands, supported by HYSPLIT back-trajectories and surface wind data. Soiling materials collected from PV module surfaces generally had a mineral composition similar to that of the surrounding local soils. The mean particle size of collected soiling material samples ranged from 8 to 21 µm, with ~90% of particles being dust (<50 µm) and ~10% of the soiling particles being sand (>50 µm). Despite Alamogordo experiencing 22 dust events during this study, soiling-related power losses were relatively low, about 2% to 3%, much lower than reported for Global Dust Belt locations. The prevailing south-to-southwest winds and their gusts acted as a passive cleaning mechanism, as they were aligned with the front of the PV modules and likely resuspended particles off panel surfaces. Additionally, relatively low rainfall (about 2.2 mm per hour) was effective in restoring PV performance. These findings suggest that, due to the relatively low soiling losses observed, frequent cleaning may not be necessary at this location, resulting in potential savings in maintenance costs over the long-term operation of the PV system. Full article

The cessation of recurrent anthropogenic activities can promote vegetation succession. In this paper, we report a case study of passive restoration of the herbaceous plant vegetation associated with cork oaks carried out by citizens in collaboration with local farmers in a suburban area of Rome (Italy). A sampling design has been carried out in two comparable patches using replicated plots: (i) a first patch corresponding to the passive restored area, evolving from an uncultivated field towards a cork oak forest, where the mowing activity was stopped in 2017, and (ii) a second patch corresponding to an uncultivated land periodically mowed as a control. We recorded 24 plant species in the restored patch and 9 in the control patch. The Shannon-Wiener diversity index was significantly higher in the restored patch when compared to the control. Whittaker diagrams, graphically representing evenness, showed significant differences among plotted values. The Chao 2 richness estimators evidence the differences between patches (52.17 species vs. 9), graphically observed in the sample rarefaction curves. An analysis in the 2017–2025 period showed a substantial increase in NDVI values in the restored patch (from 0.18 in 2017 to 0.28 in 2025; approximately +54% relative to 2017; mean NDVI increased from 0.181 in 2017 to 0.29 in 2025), indicating an increase in cover/biomass associated with the post-2017 restoration of the area. Suspending mowing, both humidity (due to the reduction in grass cover) and nutrients increase, and the pH is reduced (Ellenberg indices): it is possible that the young oak trees are comparatively more effective cation exchangers. Therefore, only a few years after mowing was suspended, we observed a marked recovery not only of the dominant cork oak component but also of the herbaceous species (Vulpio-Dasypyretum villosi association). Even young, isolated cork oak trees can act as nurse plants (or keystone structures), supporting many species and creating microhabitats for shade-tolerant plants. This passive restoration began when local citizens and a school asked landowners to stop mowing in an area where cork oaks were naturally regenerating, making it an example of autonomous citizen-led environmental management. Full article

Thermal hazards in industrial environments often remain undetected until critical failure or injury occurs. Conventional handheld infrared cameras require manual operation and limit continuous situational awareness. This study presents the design and field validation of a wearable helmet-mounted real-time thermal system based on the MLX90640 infrared array sensor and a Raspberry Pi 5 edge computing platform. Experimental validation was performed across multiple scenarios of 400 measurements based on industrial distances of 100 cm and 150 cm. The performance of the system was tested against a pre-calibrated hotspot infrared thermometer using linear regression analysis and standard error metrics to determine proportional agreement. The results indicate a strong proportional relationship between the two systems at both industrial distances, with R² values ranging from 0.9885 to 0.9973 at 100 cm and from 0.9586 to 0.9867 at 150 cm. A moderate increase in mean absolute error (MAE) was observed as the measurement distance increased. Statistically significant increases in error were identified in mechanically dynamic scenarios where statistically significant increases in measurement error were observed (p-value < 0.05), indicating distance-dependent sensitivity under moving mechanical conditions. The higher absolute errors at longer distances mainly result from field-of-view expansion, reduced target occupancy, and mixed-pixel hotspot effects rather than weakened proportional trend stability. An industrial distance-aware linear regression model was developed to describe behavior and support calibrations under different deployment conditions. Despite minor absolute deviations during dynamic operations, the system maintained strong trend-tracking performance, suggesting suitability for daily preliminary hazard monitoring in industrial safety maintenance. Full article

This study presents an application-oriented CFD–FEM integrated workflow for analyzing chamber-side field non-uniformity and kelp-side thermal response during infrared drying. A three-dimensional steady-state CFD model was first established to reconstruct the chamber temperature, airflow, and incident radiation fields under certain operating conditions. Numerical consistency was checked through residual convergence; monitored variables; and global mass balance, for which the net mass imbalance was 0.004077 kg s⁻¹. The reconstructed mid-plane fields were then processed in MATLAB to extract the mean values, extrema, and coefficients of variation, and a composite objective function was used to screen the tested operating conditions in terms of field uniformity, temperature band compliance, and overheating risk. The thermal loads obtained via CFD were subsequently mapped onto a kelp finite element model to simulate the transient surface temperature evolution. Among the tested cases, case01 yielded the lowest composite objective value (J = 0.4535); its mapped kelp response showed a mean surface temperature of 62.23 °C and a maximum temperature of 63.57 °C at the exported time step. The proposed framework is therefore suitable for thermal response assessment and operating condition screening, although determining the full drying behavior still requires coupling of moisture transfer and improved experimental validation. Full article

Volatile halogenated hydrocarbons (VHHs) are critical air toxic pollutants, with some ozone-depleting substances (ODSs) strictly regulated by the Montreal Protocol. However, current understanding of the pollution characteristics, sources, and health risks of atmospheric VHHs in Southwest China remains insufficient. This study performed field observations of atmospheric VHHs in summer in Mianyang, a medium-sized industrial city in the Sichuan Basin. Freon-12 (563 ± 20 ppt) and Freon-11 (264 ± 15 ppt) were the most abundant chlorofluorocarbons (CFCs); chloromethane (785 ± 261 ppt) and methylene chloride (563 ± 505 ppt) dominated among VSLSs. The mean concentration of regulated ODSs (1037 ± 33 pptv) was notably lower than unregulated very short-lived chlorinated substances (1887 ± 745 pptv), reflecting effective ODSs phase-out locally, yet enhancements relative to Northern Hemisphere background implied potential leakage from residual tanks. Methylene chloride and trichloroethylene concentrations exceeded global background levels by over 10 times, indicating strong anthropogenic industrial influences. Phased-out CFCs displayed negligible diurnal variation due to stringent emission controls, whereas unregulated VSLSs exhibited a distinct U-shaped diurnal cycle, with peaks driven by morning boundary layer dynamics and evening accumulation. Positive matrix factorization revealed that industrial sources, including electronic solvents (28.6%), industrial processes (27.8%), and solvent usage (23.7%), accounted for 80.1% of total VHHs. The total carcinogenic risk (2.3 × 10⁻⁵) surpassed the acceptable threshold (1 × 10⁻⁶), dominated by 1,2-dichloroethane, chloroform, carbon tetrachloride, and 1,2-dichloropropane. All individual compounds exhibited mean hazard quotients (HQs) below the non-carcinogenic risk threshold. The cumulative hazard index reached 1.5, suggesting combined non-carcinogenic risks to the local population. These results support VHHs health risk management and ODSs control in Southwest Chinese industrial cities. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

Compact stars serve as natural systems where matter exists at densities far beyond those achievable in laboratory experiments. Among them, magnetars are expected to possess interior magnetic fields that may reach values of the order of ${10}^{17}$–${10}^{18}$ G. These extreme conditions are expected to alter the microscopic and macroscopic properties of dense matter. In this review, we examine how strong magnetic fields affect fermionic matter through mechanisms such as Landau quantization and anomalous magnetic moment interactions. We further discuss the behavior of magnetized hadronic matter within relativistic mean-field approaches and consider the possible emergence of additional degrees of freedom, including hyperons, $\Delta$ resonances, meson condensates, and quark matter. The consequences of these effects for neutron star structure and observational constraints are also briefly outlined. Full article