Search Results (177)

Road-surface distress poses a serious threat to traffic safety and imposes a growing burden on urban maintenance budgets. While modern detectors based on convolutional networks and Vision Transformers achieve strong frame-level performance, they often overlook an essential property of road environments—structural symmetry within road networks and damage patterns. We present Graph-MambaRoadDet (GMRD), a symmetry-aware and lightweight framework that integrates dynamic graph reasoning with state–space modeling for accurate, topology-informed, and real-time road damage detection. Specifically, GMRD employs an EfficientViM-T1 backbone and two DefMamba blocks, whose deformable scanning paths capture sub-pixel crack patterns while preserving geometric symmetry. A superpixel-based graph is constructed by projecting image regions onto OpenStreetMap road segments, encoding both spatial structure and symmetric topological layout. We introduce a Graph-Generating State–Space Model (GG-SSM) that synthesizes sparse sample-specific adjacency in $\mathcal{O}\left(M\right)$ time, further refined by a fusion module that combines detector self-attention with prior symmetry constraints. A consistency loss promotes smooth predictions across symmetric or adjacent segments. The full INT8 model contains only 1.8 M parameters and 1.5 GFLOPs, sustaining 45 FPS at 7 W on a Jetson Orin Nano—eight times lighter and 1.7× faster than YOLOv8-s. On RDD2022, TD-RD, and RoadBench-100K, GMRD surpasses strong baselines by up to +6.1 mAP_50:95 and, on the new RoadGraph-RDD benchmark, achieves +5.3 G-mAP and +0.05 consistency gain. Qualitative results demonstrate robustness under shadows, reflections, back-lighting, and occlusion. By explicitly modeling spatial and topological symmetry, GMRD offers a principled solution for city-scale road infrastructure monitoring under real-time and edge-computing constraints. Full article

Hydraulic fracturing technology serves as the primary method for efficiently developing deep shale resources. During hydraulic fracturing, the thermal stress caused by the injection of fracturing fluid, which has low temperature, has a significant effect on the propagation of multiple hydraulic fractures in deep shale reservoirs. Due to the unclear mechanisms governing multi-fracture propagation in deep shale reservoirs, this study proposed a hydraulic fracturing model for multi-fracture propagation based on the principles of linear elastic fracture mechanics. The model was employed to investigate how formation properties and operational parameters influenced the expansion of multiple hydraulic fractures. The findings revealed that thermal stress fracturing caused by low-temperature fluid injection significantly affected the rock breakdown pressure and fracture initiation timing. Specifically, when the reservoir temperature exceeded 180 °C, the breakdown pressure decreased substantially, and the fracture initiation occurred much earlier. Moreover, an increase in rock thermal conductivity further reduced both the breakdown pressure and the propagation pressure, alleviating the “stress shadow” effect on intermediate fractures and promoting more uniform fracture growth. Furthermore, when the reservoir temperature surpassed 180 °C and the thermal conductivity exceeded 1.3 W/(m K), the influence of horizontal stress difference and cluster spacing on multi-fracture propagation diminished sharply—by more than 40%. This condition facilitated tight containment of the deep shale reservoir and significantly expanded the stimulated reservoir volume. These findings not only enriched and refined the theoretical understanding of hydraulic fracturing in deep shale reservoirs but also provided a valuable reference for optimizing fracturing parameters in the development of deep oil and gas reservoirs. Full article

Chinese coalbed methane (CBM) reservoirs exhibit characteristically low recovery rates due to adsorbed gas dominance and “three-low” properties (low permeability, low pressure, and low saturation). CO₂ thermal drive (CTD) technology addresses this challenge by leveraging dual mechanisms—thermal desorption and displacement to enhance production; however, its effectiveness necessitates uniform fracture networks for temperature field homogeneity—a requirement unmet by conventional long-fracture fracturing. To bridge this gap, a coupled seepage–heat–stress–fracture model was developed, and the temperature field evolution during CTD in coal under non-uniform fracture networks was determined. Integrating multi-cluster fracture propagation with stress barrier and intra-stage stress differential characteristics, a stress-barrier-responsive diverting fracturing technology meeting CTD requirements was established. Results demonstrate that high in situ stress and significant stress differentials induce asymmetric fracture propagation, generating detrimental CO₂ channeling pathways and localized temperature cold islands that drastically reduce CTD efficiency. Further examination of multi-cluster fracture dynamics identifies stress shadow effects and intra-stage stress differentials as primary controlling factors. To overcome these constraints, an innovative fracture network uniformity control technique is proposed, leveraging synergistic interactions between diverting parameters and stress barriers through precise particle size gradation (16–18 mm targeting toe obstruction versus 19–21 mm sealing heel), optimized pumping displacements modulation (6 m³/min enhancing heel efficiency contrasted with 10 m³/min improving toe coverage), and calibrated diverting concentrations (34.6–46.2% ensuring uniform cluster intake). This methodology incorporates dynamic intra-stage adjustments where large-particle/low-rate combinations suppress toe flow in heel-dominant high-stress zones, small-particle/high-rate approaches control heel migration in toe-dominant high-stress zones, and elevated concentrations (57.7–69.2%) activate mid-cluster fractures in central high-stress zones—collectively establishing a tailored framework that facilitates precise flow regulation, enhances thermal conformance, and achieves dual thermal conduction and adsorption displacement objectives for CTD applications. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

Accurate detection of individual tree crowns is a critical prerequisite for precisely extracting forest structural parameters, which is vital for forestry resources monitoring. While unmanned aerial vehicle (UAV)-acquired RGB imagery, combined with deep learning-based networks, has demonstrated considerable potential, existing methods often rely exclusively on RGB data, rendering them susceptible to shadows caused by varying illumination and suboptimal performance in dense forest stands. In this paper, we propose integrating LiDAR-derived Canopy Height Model (CHM) with RGB imagery as complementary cues, shifting the paradigm of tree crown detection from unimodal to multimodal. To fully leverage the complementary properties of RGB and CHM, we present a novel Multimodal learning-based Tree Crown Detection Network (MTCDNet). Specifically, a transformer-based multimodal feature fusion strategy is proposed to adaptively learn correlations among multilevel features from diverse modalities, which enhances the model’s ability to represent tree crown structures by leveraging complementary information. In addition, a learnable positional encoding scheme is introduced to facilitate the fused features in capturing the complex, densely distributed tree crown structures by explicitly incorporating spatial information. A hybrid loss function is further designed to enhance the model’s capability in handling occluded crowns and crowns of varying sizes. Experiments conducted on two challenging datasets with diverse stand structures demonstrate that MTCDNet significantly outperforms existing state-of-the-art single-modality methods, achieving AP50 scores of 93.12% and 94.58%, respectively. Ablation studies further confirm the superior performance of the proposed fusion network compared to simple fusion strategies. This research indicates that effectively integrating RGB and CHM data offers a robust solution for enhancing individual tree crown detection. Full article

This research introduces an innovative fractal–fractional synergy framework for multiscale analysis of stress field dynamics in geo-energy systems. By integrating fractional calculus with multiscale fractal dimension analysis, we develop a coupled approach examining stress redistribution patterns across different geological scales. The methodology combines fractal characterization of rock mechanical parameters with fractional-order stress gradient modeling, validated through integrated analysis of core testing, well logging, and seismic inversion data. Our fractal–fractional operators enable simultaneous characterization of stress memory effects and scale-invariant fracture propagation patterns. Key insights reveal the following: (1) Non-monotonic variations in rock mechanical properties (fractal dimension D = 2.31–2.67) correlate with oil–water ratio changes, exhibiting fractional-order transitional behavior. (2) Critical stress thresholds (12.19–25 MPa) for fracture activation follow fractional power-law relationships with fracture orientation deviations. (3) Fracture network evolution demonstrates dual-scale dynamics—microscale tip propagation governed by fractional stress singularities (order α = 0.63–0.78) and macroscale expansion obeying fractal growth patterns (Hurst exponent H = 0.71 ± 0.05). (4) Multiscale modeling reveals anisotropic development with fractal dimension increasing by 18–22% during multi-well fracturing operations. The fractal–fractional formalism successfully resolves the stress-shadow paradox while quantifying water channeling risks through fractional connectivity metrics. This work establishes a novel paradigm for coupled geomechanical–fluid dynamics analysis in complex reservoir systems. Full article

Shadow in remote sensing images can obscure important details of land features, making shadow detection crucial for enhancing the accuracy of subsequent analyses and applications. Current shadow detection methods primarily rely on the spectral information of images, which can often result in shadow misdetection due to the phenomenon of spectral confusion of different objects. To mitigate this issue, we propose a method that combines topography and spectra (CTS). Firstly, we introduce a new DEM-based shadow coarse detection method to obtain the DEM rough shadow mask, which uses a relationship between the magnitude of terrain height angle and solar elevation angle to determine shadow properties. Then, we use the MC3 (modified C3 component) index-based shadow fine detection method to obtain an MC3 mean map, which includes image enhancement with a stretching process and multi-scale superpixel segmentation. We then derive the Shadow pixel Proportion Map (SPM) by counting the DEM rough shadow mask in terms of superpixels. The Joint Shadow probability Map (JSM) is obtained by combining the SPM and the MC3 mean map with specific weights. Finally, a multi-level Otsu threshold method is applied to the JSM to generate the shadow mask. We compare the proposed CTS method against several state-of-the-art algorithms through both qualitative assessments and quantitative metrics. The results show that the CTS method demonstrates superior accuracy and consistency in detecting true shadows, achieving an average overall accuracy of 95.81% on mountainous remote sensing images. Full article

This extensive study examines the solar rooftop photovoltaic potential (RTP) over polluted cities in major geographic and economic zones of India. The study examines the climatology of solar radiation attenuation due to aerosol, clouds, architectural effects, etc. The study exploits earth observations from ground, satellite, and radiative transfer modeling (RTM) in conjunction with geographic information systems tools. The study exploits long-term observations of cloud properties from the Meteosat Second Generation (MSG) satellites operated by EUMETSAT and aerosol properties data gathered from ground-based measurements provided by AERONET. The innovation in the study is defined in two steps. Firstly, we estimated the RTP using the current state of the art in the field, which involved using suitability factors and energy output based on the PVGIS simulations and extrapolating these for effective rooftop areas of the cities. Secondly, we advanced beyond the current state of the art by incorporating roof morphological characteristics and various area share factors to assess the RTP in more realistic terms. These two steps were applied under two different scenarios. The study determined that the optimum tilt angle is equal to the cities’ latitude for installing solar PV systems. In addition, the research emphasizes the advantages for the environment while offering energy and economic losses. According to our findings, the RTP in the rural city examined in this study is 31% greater than the urban city of India under both scenarios. The research has found that the metropolitan city, which boasts a maximum rooftop area of approximately 167 km², could host a significant RTP of around 13,005 ± 1210.71 (6970 ± 751.38) MWh per year under scenario 1 (scenario 2). Overall, solar radiation losses due to aerosol effects dominate radiation losses due to cloud effects on the city scale. Amongst all polluted cities, estimated financial losses due to aerosols, clouds, and shadows are 11,241.70 million, 4439 million, and 1167.65 million rupees, respectively. Our findings emphasize the necessity of accounting for air pollution for accurate solar potential assessments in thoughtful city planning. The creative approach that utilizes publicly available data establishes a strong foundation for penetrating solar photovoltaic (PV) technology into society. This integration could significantly contribute to climate change mitigation and adaptation efforts, promoting environmentally sustainable urban development and prevention strategies. Full article

In this paper, we discussed the cofinite sensitivity, shadowing property (SP), P-chaos, and chain mixing of a system induced by symmetric maps (Cournot maps) $D(a,b)=\left(t\right(b),s(a\left)\right)$ over a product space $G\times H$, where $s:G\to H$, $t:H\to G$, $a\in G$, $b\in H$, G and H are closed subintervals with $G,H\subset \mathbb{R}$. The following hold: (1) D is cofinitely sensitive and equivalent to $D^2{|}_{{\Gamma }_1}$ or $D^2{|}_{{\Gamma }_2}$ being sensitive, where ${\Gamma }_1=\{(t\left(b\right),b):b\in H\}$, ${\Gamma }_2=\{(a,s\left(a\right)):a\in G\}$. (2) D possessing an SP is equivalent to both $s\circ t$ and $t\circ s$ having an SP. (3) $t\circ s$ possesses an SP if and only if $s\circ t$ does as well. (4) D is P-chaotic and equivalent to the maps $s\circ t$ and $t\circ s$ being P-chaotic. (5) If D is chain mixing, then both $D^2{|}_{{\Gamma }_1}$ and $D^2{|}_{{\Gamma }_2}$ are chain mixing. (6) If $D^2{|}_{{\Gamma }_1}$ and $D^2{|}_{{\Gamma }_2}$ are chain mixing, then D is chain transitive. Moreover, we extended (1)–(4) to three-dimensional cases. Full article

In this paper, we investigate the shadow properties of a rotating black hole with a weakly coupled global monopole charge using a modified Newman–Janis algorithm. This study explores how these charge and rotational effects shape the black hole’s shadow, causal structure, and ergoregions, with implications for distinguishing it from Kerr-like solutions. Analysis of null geodesics reveals observable features that may constrain the global monopole charge and weak coupling parameters within nonminimal gravity frameworks. Observational data from M87* and Sgr A* constrain the global monopole charge and coupling constant to $0\le \gamma \lesssim 0.036$ and $-0.2\lesssim \alpha \le 0$, respectively. Full article

This study presents an application of Boruta-SHapley Additive ExPlanations (Boruta-SHAP) for geotechnical characterization using Measure-While-Drilling (MWD) data, enabling a more interpretable and statistically rigorous assessment of feature importance. Measure-While-Drilling data collected at the scale of an open-pit mine was used to characterize geotechnical properties using regression-based machine learning models. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, the Boruta-SHAP method quantifies the individual contribution of each Measure-While-Drilling variable. This method ensures interpretable and reliable geotechnical characterization as well as robust feature selection by comparing predictors against randomized ‘shadow’ features. The Boruta-SHAP analysis revealed that bit air pressure and torque-to-penetration ratio were the most significant predictors of rock strength, contradicting previous assumptions that rate of penetration was the dominant factor. Moreover, feature importance was conducted for fracture frequency and Geological Strength Index (GSI), a rock mass classification system. A comparative analysis of prediction performance was also performed using a range of different machine learning algorithms that resulted in strong coefficient of determinations of actual field or laboratory results versus predicted values. The results are plausible, confirming that MWD data could provide a high-resolution description of geotechnical conditions prior to mining, leading to a more confident prediction of subsurface geotechnical properties. Therefore, the fragmentation from blasting as well as downstream operational phases, such as digging, hauling, and crushing, could be improved effectively. Full article

Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. Full article

The excellent mechanical properties of high-entropy alloys, especially under harsh service environments, have attracted increasing attention in the last decade. FCC-based and refractory high-entropy alloys (HEAs) are the most extensively used series. However, the strength of FCC-base HEAs is insufficient, although they possess a great ductility and fracture toughness at both room and low temperatures. With regard to the BCC-based refractory HEAs, the unsatisfactory ductility at room temperature shadows their ultrahigh strength at room and high temperatures, as well as their excellent thermal stability. In order to strike a balance between strength and toughness, strengthening mechanisms should be first clarified. Therefore, typical mechanical performance and corresponding strengthening factors are systemically summarized, including the solid solution strengthening, second phase, interface, and synergistic effects for FCC-base HEAs, along with the optimization of principal elements, construction of multi-phase, the doping of non-metallic interstitial elements, and the introduction of kink bands for refractory HEAs. Among which the design of meta-stable structures, such as chemical short-range order, and kink bands has been shown to be a promising strategy to further improve the mechanical properties of HEAs. Full article

Anthocyanins (ANTHs) are polyphenolic compounds with health promoting properties, being known for their strong antioxidant effects as well as for their antimicrobial properties, obesity and cardiovascular disease prevention, and anticarcinogenic activity. Being main dietary components, it is important to know the content of anthocyanins in various dietary sources and their stability in time. The total anthocyanin content (TAC) of various fresh fruits has been spectrophotometrically determined using the pH differential method. The results showed that in the analyzed samples, the TAC increased in the order: blackcurrants > blackberries > blueberries > raspberries > strawberries > plums. The degradation degree of anthocyanins extracted from blueberries (BBEs) in an ethanol/water solution in four experimental conditions was studied. Kinetic studies have been approached, fitting the experimental data recorded by UV–Vis spectrophotometric analysis in agreement with some kinetic models verified for the ANTH degradation reaction. Therefore, zero-order kinetics for BBE extract degradation exposed to sunlight were identified, while for the other storage conditions (shadow, dark, cold), the first-order kinetics were respected. The results indicate that the stability decreased as follows: (ANTH stability)_{sunlight test} << (ANTH stability)_{shadow test} ≈ (ANTH stability)_{dark test} < (ANTH stability)_{cold test}. A mechanism for BBE anthocyanin degradation was proposed and the impact on human health of the degradation products is discussed. Full article

Floods have significant impacts on economic development and cause the loss of both lives and property, posing a serious threat to social stability. Effectively identifying the evolution patterns of floods could enhance the role of flood monitoring in disaster prevention and mitigation. Firstly, in this study, we utilized low-cost multi-source multi-temporal remote sensing to construct an HIS-NSCT fusion model based on SAR and optical remote sensing in order to obtain the best fusion image. Secondly, we constructed a regional growth model to accurately identify floods. Finally, we extracted and analyzed the extent, depth, and area of the farmland submerged by the flood. The results indicated that the HIS-NSCT fusion model maintained the spatial characteristics and spectral information of the remote sensing images well, as determined through subjective and objective multi-index evaluations. Moreover, the regional growth model could preserve the detailed features of water body edges, eliminate misclassifications caused by terrain shadows, and enable the effective extraction of water bodies. Based on multi-temporal remote sensing fusion images of Poyang Lake, and incorporating precipitation, elevation, cultivated land, and other data, the accurate identification of the flood inundation range, inundation depth, and inundated cultivated land area can be achieved. This study provides data and technical support for regional flood identification, flood control, and disaster relief decision-making, among other aspects. Full article