Search Results (628)

The primary aim of this study is to validate the utility of Social Media Data (SMD) as a scientifically grounded tool for quantifying the spatial mismatch between objective ecological supply and subjective social demand. Assessing the spatial coupling and mismatch between Habitat Quality (HQ)—representing objective ecological supply—and Ecological Perception (EP)—representing subjective social demand—is essential for developing targeted urban management and development strategies. Focusing on the core urban area of Hangzhou, this study quantified ecological supply using the InVEST HQ model. To reflect social demand, 4958 geolocated Weibo posts were processed using contextual sentiment analysis. A Coupling Coordination Degree model served as a diagnostic tool to evaluate the synergy between these two dimensions. Additionally, a Geodetector model was employed to investigate the factors driving spatial differentiation in this coupling. The findings indicate that: (1) The regional average HQ is 0.56, reflecting a moderate overall level of degradation, while EP shows a preference for natural environments and exhibits a distinct “strip-like” spatial distribution. (2) The overall CCD value is 0.384; high-coupling areas are primarily concentrated in regions with superior natural conditions and dense vegetation, whereas low-coupling areas correspond to zones with intensive urban functions. (3) Driving factor analysis reveals that land-use type exerts the most significant influence on the overall degree of coupling. This study demonstrates that the HQ-EP coupling framework provides a reliable spatial diagnostic tool for urban planners to identify socio-ecological vulnerabilities. The results suggest that an appropriate integration of natural elements enhances coupling outcomes, with the highest synergy observed in environments characterized by high HQ and minimal anthropogenic disturbance. Full article

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions. Full article

The Linpan in western Sichuan is a composite rural landscape of “household-water-forest-field” on the Chengdu Plain. Under the interference of human activities, problems such as landscape fragmentation and ecological function degradation have become increasingly serious, threatening regional ecological security. The specific components involved in the “study on ecological risk sequence” include landscape disturbance degree, landscape vulnerability degree, landscape connectivity, and human activity intensity. Given the lack of long-term ecological risk research on the Linpan landscape in Chongzhou City to support conservation decisions, this study takes it as the object. Based on five phases of land use data from 2003 to 2023, a landscape ecological risk assessment model was constructed. This model is a deterministic and nonlinear comprehensive evaluation model. The determinism is reflected in the fact that, based on specific influencing factors, a unique and definite result can be obtained through a fixed indicator system and calculation method. The nonlinearity is reflected in the fact that the comprehensive risk index does not involve a simple linear superposition of the various factors; instead, the evaluation result is obtained by integrating the factors through nonlinear approaches such as weighted coupling. Using ArcGIS and spatial analysis methods, based on a temporal resolution of 5 years and a spatial resolution of 30 m, the spatiotemporal evolution characteristics were revealed. The results show that: (1) From 2003 to 2023, the Linpan landscape pattern in Chongzhou City underwent significant evolution, characterized by “reduction in agricultural land, expansion of construction land, and slight recovery of ecological land”. Landscape fragmentation intensified, connectivity decreased, but overall aggregation remained stable. (2) The evolution of the landscape pattern drove the ecological risk to show a stable pattern of “low in the northwest and high in the southeast”. The global Moran’s I value decreased from 0.887 to 0.832, indicating that risk aggregation intensified in the early period and was alleviated in the later period. (3) Landscape disturbance degree is the key factor dominating the change in the comprehensive ecological risk index. Compared with similar studies, this research shares the commonality of urbanization-driven fragmentation exacerbation risk, but also exhibits the uniqueness of Linpan structural resilience and conservation policies promoting a reduction in high-risk areas. This study can provide a scientific basis for Linpan protection, land use optimization, and ecological security pattern construction in Chongzhou City. Full article

To address the challenges in intuitive feature discrimination and precise quantitative evaluation of cable defects, this paper proposes a diagnostic methodology utilizing the Symmetrized Dot Pattern (SDP) transform and reflection coefficient spectra. The Dung Beetle Optimizer (DBO) is introduced to adaptively optimize the SDP transform parameters, employing the Structural Similarity Index Measure (SSIM) as a fitness function to maximize discriminability between deterioration states. Three quantitative features, including the number of effective pixels, the degree of red–blue aliasing, and radial dispersion, are extracted to characterize the physical degradation processes of signal energy accumulation, angular evolution, and path divergence. By incorporating a self-reference calibration mechanism for structural differences, features are fused into a Comprehensive Deterioration Index (CDI). Experimental results on coaxial cables simulating shielding damage and thermal aging demonstrate that SDP images reveal continuous evolution patterns corresponding to defect severity. A regression model based on these patterns effectively characterizes deterioration trends. Compared to complex models, this study achieves intuitive fault identification and preliminary quantitative description of degradation trends through image feature fusion. Although the current sample size is limited, the results validate the feasibility of this method in evaluating cable deterioration severity, offering an efficient new data-processing perspective for cable condition monitoring. Full article

Network dismantling—the targeted removal of nodes to degrade large-scale connectivity—plays a central role in resilience analysis, epidemic containment, and systemic-risk mitigation. Recent work shows that dismantling performance depends strongly on mesoscale modular structure, suggesting that community-aware strategies may offer advantages over classical centrality-based heuristics. In this work, we perform a large-scale, systematic evaluation of dismantling strategies and introduce gateways as a new mesoscale dismantling concept. While similar experiments exist using degree- and betweenness-based dismantling strategies, we check a new strategy based on gateways, which capture asymmetric entry points into communities and generalize the notion of inter-community connectors. Furthermore, we process a massive dataset of 568,584 LFR benchmark graphs, covering a wide range of degree distributions, community sizes, and mixing parameters. For evaluation, we use both extrinsic (ARI, NMI, FMI, VI) and intrinsic (Modularity, Coverage, Performance, Average Conductance, Average Internal Density) metrics. We find that across parameter regimes and evaluation metrics, classical strategies (degree, betweenness, community connections) and gateway-based dismantling exhibit broadly similar performance. Our results also corroborate recent findings that dismantling effectiveness is robust to the specific partitioning algorithm and that inter-community connectivity plays a dominant role in global fragmentation. The evaluation provides large-scale evidence that gateway-aware dismantling captures an operationally relevant mesoscale mechanism as good as previous approaches and motivates further empirical studies on real networks and cost-aware settings. Full article

To overcome polymer-based plugging materials’ disadvantage of being prone to degradation and failure under hydrothermal conditions, an epoxy resin plugging particle with a high-pressure-bearing capacity under high temperatures was prepared by optimizing the curing process. Bisphenol A Epoxy Resin E51 and Diethyltoluenediamine (DETDA) were selected as raw materials for sample preparation. Due to the high viscosity of the system, 1,2-cyclohexanediol diglycidyl ether was introduced as a diluent, and an optimal concentration of 20% was determined through experimental optimization. Non-isothermal differential scanning calorimetry, bottle testing, and infrared spectroscopy were employed to investigate the variation laws of curing temperature, curing time and curing degree during the epoxy resin curing process via one-step and multi-step methods. The compressive strength of the epoxy resin prepared using the two processes was evaluated. After comprehensively comparing the preparation time, process complexity, and compressive strength of the final samples of the one-step and two-step curing methods, the one-step process (90 °C/5 h) was determined to be superior. In addition, the results of the fracture plugging experiment showed that after the bulk epoxy resin prepared using the optimized process was made into particles through a mechanical method and treated under hydrothermal conditions at 120 °C, the maximum breakthrough pressure reached 4.2 MPa, which was 950% and 135.96% higher than that of Particle 1 (Poly(2-acrylamido-2-methylpropanesulfonic acid)/acrylamide (PAMPS/AM) gel) and Particle 2 (PAMPS/AM gel treated with Polyethylene glycol (PEG)), respectively, which were used as control groups. This result indicates that epoxy resin can be used as a high-temperature-resistant plugging material and should be further researched. Full article

Shelf life of frozen foods is intrinsically linked to consumer sensory acceptability. However, quantifying the synergistic impact of extended storage and variable thermal cycles on perception remains challenging. This study proposes a non-destructive image-based approach for estimating the acceptability of frozen pizza using a machine learning model and identifying tomato sauce degradation as indicator of product quality decay. Qualitative consumer feedback (90%) identified tomato sauce saturation as the primary driver of visual rejection. Image processing pipeline was developed to isolate the sauce region from each sample for further color extraction (saturation in the HSV color space). A second-degree polynomial regression model was used to describe the saturation trend over time and, in parallel, a logistic regression classifier was trained to predict binary consumer acceptability based on both saturation and storage duration. The models were evaluated using frozen pizzas (−12 and −18 °C) for up to 200 days. The regression model achieved an R² of 0.68 and an RMSE of 12.8, while the classifier attained an accuracy of 88.2% and an AUC of 0.93. The resulting framework enables early, non-invasive estimation of product acceptability and shows strong potential for practical application in shelf life studies within the frozen food industry. Full article

Under continuous erosion of dynamic water pressure generated by vehicle–water–pavement coupling interaction, asphalt mixture will gradually deteriorate and severe moisture damage finally emerges. The fine aggregate mixture (FAM) component is notably eroded and stripped, while the aggregate component even cracks sometimes. Sufficient attention has not been paid to these critical phenomena. This study employed the 3D laser scanning technique to detect changes in surface roughness of the asphalt mixture before and after it was eroded by dynamic water pressure. The degree of erosion of the asphalt mixture, FAM component, and aggregate component were thereby evaluated. The influences of experimental parameters such as water temperature and pore water pressure magnitude, as well as variable parameters including lithology and asphalt type, were also taken into account. By integrating the detection of physical and mechanical properties evolution of aggregates, the mechanism of moisture damage was comprehensively illustrated from the perspectives of both components of FAM and aggregate. The findings revealed that the 3D laser scanning technique could clearly detect and quantitatively assess the morphological changes on the asphalt mixture surface after been eroded in dynamic water pressure. Both types of asphalt mixtures exhibited varying degrees of erosion and wear, and obvious increases in surface unevenness were observed in each case. Variations in either temperature or pore water pressure magnitude showed limited influence on moisture damage in basalt-based asphalt mixture. In contrast, moisture damage sustained by limestone-based asphalt mixture was notably sensitive to temperature changes but remained largely insensitive to fluctuations in pore water pressure magnitude. The increase in surface roughness of asphalt mixture was primarily attributed to the scouring action of dynamic water pressure, which removed the FAM component surrounding coarse aggregate particles. Degradation in coarse aggregate particles would lead to the deterioration of the entire asphalt mixture. The compatibility between the stripping rate of FAM component and the deterioration rate of coarse aggregate governed the macroscopic manifestation of overall moisture damage in the asphalt mixture. Full article

The dominant pores governing methane adsorption in coal are micropores (pore size < 2 nm). Their spatial heterogeneity can be quantitatively characterized using multifractal theory; however, the evolution patterns and mechanisms of microporous structures across different coalification degrees remain unclear. This research selected a series of coal samples from different ranks and identified the coalification degree using the maximum vitrinite reflectance (Rₒ_,max). By comprehensively employing low-temperature CO₂ adsorption experiments and multifractal analysis, the evolution patterns of the microporous structures and their multifractal spectral parameters were systematically revealed, and the underlying control mechanisms were explored. Results indicate that micropore volume (PV) and specific surface area (SSA) first exhibit a decrease and then increase as Rₒ_,max increases, with the trough occurring during the second coalification jump at Rₒ_,max = 1.2–1.4%. The pore sizes exhibit bimodal distributions, with the primary peak occurring in the range of 0.45–0.65 nm and the secondary peak occurring in the range of 0.8–0.9 nm. All microporous structures possess pronounced multifractal characteristics. The generalized dimension spectrum width (ΔD) and singularity spectrum width (Δα) exhibit an increasing–decreasing–increasing trend with Rₒ_,max, whereas the Hurst exponent (H) follows an inverted parabolic curve, first increases then decreases. This contrasts with the trends in PV and SSA, indicating that the evolution of pore-space heterogeneity and connectivity is independent of and lags the changes in micropore quantity. These patterns are governed by a structural phase transition within the coal macromolecular network. Marked by the second coalification jump, the microporous system shifts from a flexible degradation–polycondensation paradigm to a rigid ordering–construction paradigm. This transition drives the asynchronous, synergistic evolutions of pore quantity, spatial heterogeneity (ΔD and Δα), and topological connectivity (H). This research provides a theoretical basis for quantitatively evaluating pore heterogeneity in coal reservoirs. Full article

The excessive use of chemical fertilizers in rice cultivation has contributed to soil degradation, creating a need for sustainable biological alternatives. This study examined the functional diversity of three indigenous nostocalean cyanobacterial strains (UP1, UP2, and UP3) isolated from forest and paddy field ecosystems by comparing the effects of their cellular biomass and extracellular polymeric substances (EPS) on rice seedling growth and soil properties. Morphological observations and partial 16S rRNA sequence analysis indicated that strains UP1 and UP2 were affiliated with the genus Ahomia, whereas UP3 was placed within the genus Nostoc. Together, these results placed all three isolates within the heterocystous cyanobacterial order Nostocales. The strains were further characterized based on EPS production and its degree of polymerization. Seed germination and seedling vigor assays were conducted to select the most effective biomass and EPS treatments, which were subsequently evaluated in 21-day pot experiments. Fresh biomass from strain UP2 most effectively enhanced rice growth, whereas EPS from strain UP3 promoted root development. EPS application from strain UP3 significantly increased root elongation to 13.44 cm, while high biomass levels of UP2 increased total sugar and free amino acid contents, indicating distinct plant response patterns. Soil analyses revealed differential responses between biomass- and EPS-based applications, with biomass generally producing stronger effects. Biomass from all strains was associated with higher physical soil function index (PSFI) values (up to 1.35). In contrast, improvements in chemical soil function index (CSFI) were observed across treatments, with variable responses and relatively higher values recorded in biomass from strain UP3 (up to 1.24). These findings suggest strain- and form-dependent response patterns of nostocalean cyanobacteria with potential for enhancing rice growth and improving soil functionality under the controlled conditions. Full article

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

Sustainable spatial development requires land-use allocation that aligns with reflects the environment’s biophysical capacity. However, rapid urbanization and agricultural expansion often result to spatial mismatches between land utilization and land capability, the reby increasing environmental degradation and disaster vulnerability. East Java Province, one of Indonesia’s most densely populated regions, has experienced significant land-use transformation driven by demographic pressure and economic development. This study aims to evaluate the environmental carrying capacity by assessing the spatial compatibility among land capability, existing land use, and the Provincial Spatial Plan (RTRWP) using a Geographic Information System (GIS)-based analytical approach. Land capability was determined based on key biophysical parameters, including slope gradient, soil texture, drainage conditions, erosion susceptibility, effective soil depth, and flood hazard. Spatial overlay analysis was employed to identify areas of conformity and mismatch between land capability and both current and planned land uses. The results indicate that only approximately 52% of the provincial area is utilised in accordance with its land capability. In comparison, the remaining 48% exhibits varying degrees of spatial mismatch. Erosion is identified as the dominant limiting factor, affecting more than 43% of the region, particularly in mountainous and hilly landscapes. Furthermore, over 60% of East Java falls within Land Capability Classes III–VII, indicating moderate to severe environmental constraints on limitations intensive land use. High levels of spatial mismatch are concentrated in the southern upland districts—such as Pacitan, Trenggalek, southern Malang, and Lumajang, which are highly susceptible to landslides, as well as in the northern lowland corridor, including the Surabaya–Gresik–Sidoarjo metropolitan region, which faces a significantly flood risk. These findings suggest that land-use practices exceeding environmental carrying capacity substantially amplify disaster risk. Therefore, integrating land capability assessment into spatial planning and zoning regulations is essential and for promoting ecosystem-based disaster risk reduction and achieving sustainable spatial development in East Java Province. Full article

Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region. Full article

Tornadoes pose severe threats, yet their low frequency in China creates a labeled data scarcity that hinders training robust detection models. Leveraging abundant U.S. data offers a solution, though cross-domain generalization remains challenging due to distinct climatic environments and heterogeneous radar systems. This study systematically evaluates the generalization capability of three representative models—TORP, TORP-XGB, and TDA-CNN—trained on the U.S. TorNet dataset and applied to Chinese CINRAD observations (2020–2024) via a zero-shot transfer strategy. The results indicate that while all models demonstrated robust performance in the source domain (with POD values of 0.75, 0.72, and 0.71 for TORP, TORP-XGB, and TDA-CNN, respectively), they experienced varying degrees of performance attenuation in the target domain (with POD values dropping to 0.56, 0.48, and 0.41, respectively). Notably, the TORP model exhibited superior robustness with minimal performance degradation. Further analysis primarily attributes this cross-domain degradation to three factors: disparities in radar systems, magnitude differences in tornado rotational features, and data quality issues. Crucially, sensitivity experiments confirm that linear feature enhancement substantially improves the detection rate and effectively mitigates the cross-domain performance gap, albeit at the cost of increased false alarms. These findings provide a reference for the cross-domain deployment of tornado identification models and future improvements in transfer learning strategies. Full article

To elucidate the degradation behavior of elastic modulus in normal-strength ordinary concrete under flexural fatigue loading, this study systematically examines its evolution in C50 concrete, which is widely used in engineering applications. Based on four-point bending fatigue test data of plain concrete (PC) and reinforced concrete (RC) beams, degradation curves of the relative residual elastic modulus as a function of the cycle ratio were established. To quantitatively characterize the fatigue degradation process, two integrated indicators—the area under the curve (AUC) and the stable-stage degradation slope (|$K_{mid}$|)—were introduced to represent the degree of cumulative damage and the degradation rate of elastic modulus, respectively. These indicators were subsequently employed to evaluate the effects of maximum stress level, stress ratio, and reinforcement on elastic modulus degradation. The results show that failed PC specimens exhibited a typical three-stage S-shaped degradation pattern, whereas RC specimens primarily exhibited a two-stage degradation behavior. However, the elastic modulus of runout PC specimens remained above 93% of its initial value throughout the entire loading process. For PC specimens, under the same maximum stress level, increasing the minimum stress level from 0.10 to 0.25 resulted in a 24% decrease in |$K_{mid}$| from 0.2505 to 0.1912. At the same minimum stress level, increasing the maximum stress level from 0.75 to 0.90 led to a 94% increase in |$K_{mid}$| from 0.1912 to 0.3705. The presence of reinforcement increased AUC by 3~15% and reduced |$K_{mid}$| by 54~74%, indicating that reinforcement not only mitigated overall damage accumulation but also significantly slowed the degradation rate of the elastic modulus during the stable fatigue stage. The degradation characterization approach proposed in this study provides a simplified and practical framework for fatigue analysis of concrete components based on damage mechanics. Full article