Search Results (191)

To quickly predict the fatigue strength of welded joints in high-cycle fatigue tests and fit the S-N curve, this paper proposes a new model based on infrared thermal imaging technology. High-cycle fatigue tests were conducted on laser-welded joints of weathering steel Q450NQR1 and separately, on joints made of stainless steel T4003, while local temperature variations in the joints were monitored. Based on the experimentally observed temperature drop behavior, a novel Temperature-Drop-Curve-Based Fatigue Damage Entropy (TDC-FDE) model was developed to rapidly estimate the fatigue life and fatigue limit of welded joints. The model quantifies the entropy generated during fatigue damage evolution based on the temperature-decrease slope and establishes a direct relationship between entropy and the fatigue performance of the joint using this slope as the linking parameter. Experimental results indicate that a material’s specific heat capacity, density, elastic modulus, and applied stress level directly influence fatigue damage entropy generation. The entropy increase associated with purely elastic deformation does not contribute to fatigue damage in high-cycle fatigue; therefore, this portion should be excluded from the fatigue damage entropy calculation. The fatigue damage entropy of a given weld joint tends to remain nearly constant under different stress levels and loading frequencies. Finally, traditional fatigue tests demonstrated that the maximum deviation between the fatigue strength predicted by the proposed model and the experimentally measured values does not exceed 3.4%, thereby verifying the model’s accuracy and effectiveness in evaluating fatigue performance. Full article

In the context of sustainable development, the concept of active mobility plays a key role in modern urban areas. To evaluate active mobility in these areas, we formulate a framework for characterising active mobility by calculating street network indices using global, free, and open data. This framework comprises the download and processing of pedestrian, cycling, driving, and public transport street networks from OpenStreetMap, the selection of street network indices from the academic literature, and their implementation and calculation. A total of 50 indicators are reported for each urban area distributed in eight index types, including thematic variables, proximity to Points of Interest (POIs), proximity to public transport, intersection density, street density, street length, link–node ratio, circuity, slope, and orientation entropy. To test the framework, we calculate street network indices for pedestrian and cycling networks for the urban areas of 176 cities from around the world. The resulting dataset is published as open data. An analysis of the calculated indices indicates that cities in higher-income economies generally exhibit better conditions for active mobility, especially in Europe, attributed to better map completeness, and to more compact and connected urban areas where it is easier to access amenities and public transport. Full article

Entropy calculation provides meaningful insight into the dynamics and complexity of temporal signals, playing a crucial role in classification tasks. These measures are able to describe intrinsic characteristics of temporal series, such as regularity, complexity or predictability. Depending on the characteristics of the signal under study, the performance of entropy as a feature for classification may vary, and not any kind of entropy calculation technique may be suitable for that specific signal. Therefore, we aim to increase entropy’s classification accuracy performance, specially in the case of Slope Entropy (SlpEn), by enhancing the information content of the patterns present in the data before calculating the entropy, with downsampling techniques. More specifically, we will be using both uniform downsampling (UDS) and non-uniform downsampling techniques. In the case of non-uniform downsapling, the technique used is known as Trace Segmentation (TS), which is a non-uniform downsampling scheme that is able to enhance the most prominent patterns present in a temporal series while discarding the less relevant ones. SlpEn is a novel method recently proposed in the field of time series entropy estimation that in general outperforms other methods in classification tasks. We will combine it both with TS or UDS. In addition, since both techniques reduce the number of samples that the entropy will be calculated on, it can significantly decrease the computation time. In this work, we apply TS or UDS to the data before calculating SlpEn to assess how downsampling can impact the behaviour of SlpEn in terms of performance and computational cost, experimenting on different kinds of datasets. In addition, we carry out a comparison between SlpEn and one of the most commonly used entropy calculation methods: Permutation Entropy (PE). Results show that both uniform and non-uniform downsampling are able to enhance the performance of both SlpEn and PE when used as the only features in classification tasks, gaining up to 13% and 22% in terms of accuracy, respectively, when using TS and up to 10% and 21% when using UDS. In addition, when downsampling to 50% of the original data, we obtain a speedup around ×2 with individual entropy calculations, while, when incorporating the downsampling algorithms into time count, speedups with UDS are between ×1.2 and ×1.7, depending on the dataset. With TS, these speedups are above ×2, while maintaining accuracy levels similar to those obtained when using the 100% of the original data. Our findings suggest that most temporal series, specially medical ones, have been measured using a sampling frequency above the optimal threshold, thus obtaining unnecessary information for classification tasks, which is then discarded when performing downsampling. Downsampling techniques are potentially beneficial to any kind of entropy calculation technique, not only those used in the paper. It is able to enhance entropy’s performance in classification tasks while reducing its computation time, thus resulting in a win-win situation. We recommend to downsample to percentages between 20% and 45% of the original data to obtain the best results in terms of accuracy in classification tasks. Full article

Here we present a high-resolution landslide susceptibility model for Guiyang County, China, developed to support sustainable disaster risk management. Our approach couples optimized positive and negative training samples with an ensemble of machine-learning algorithms to maximize predictive fidelity. We compiled a georeferenced inventory of 146 landslides by integrating historical records with systematic field validation. Sample optimization was central to our methodology: landslide presence points were refined via buffer-based dilution, and four classifiers—SVM, LDA, RF, and ET—were trained with identical covariate sets to ensure comparability. Three strategies for selecting pseudo-absences—buffering, low-slope filtering, and coupling with the IOE—were benchmarked. The Slope-IOE-O model, which synergizes low-gradient screening with entropy-weighted sampling, yielded the highest predictive capacity (AUC = 0.965). SHAP-based interpretability revealed that slope, monthly maximum rainfall, surface roughness, and elevation collectively dominate susceptibility, with pronounced non-linearities and interactions. Slope contribution peaks at 20–30°, monthly maximum rainfall exhibits a critical threshold near 225 mm, and the synergy between high roughness and road density amplifies landslide risk. Spatially, susceptibility follows a pronounced north–south gradient, with high-hazard corridors aligned along northern and southern mountain belts and the urban core of southern Guiyang County. By integrating rigorously curated training data with robust machine-learning workflows, this study provides a transferable framework for proactive landslide risk assessment, offering scientific support for sustainable land-use planning and resilient development in mountainous regions. Full article

Fe₄₂Mn₂₈Cr₁₅Co₁₀Si₅ is a highly strain-hardenable high-entropy alloy (HEA) that is challenging to machine with traditional metal cutting tools. The electrochemical behavior of this HEA was examined in nitrate- and chloride-based electrolytes to understand the electrochemical machining (ECM) process. Potentiodynamic and potentiostatic tests were conducted on this alloy in 1 M and 2.35 M NaNO₃ solutions, with and without additions of 0.01 M nitric acid and 0.01 M citric acid. A 20% NaCl solution was also tested as an electrolyte. Nitrate solutions caused passivation of the HEA, while no passivation was observed in chloride solutions. Surface analysis with X-ray photoelectron spectrometry (XPS) indicated that adding citric acid helped reduce surface passivation. The Faradaic efficiency of ECM increased with higher applied voltage. The chloride solution showed higher Faradaic efficiency than nitrate-based solutions. Specifically, the Faradaic efficiency of 20% NaCl at 10 V is 57.4%, compared to 21.9% for 20% NaNO₃ + 0.01 M citric acid at 10 V. Electrochemical parameters, including anodic and cathodic exchange current densities, Tafel slopes, and corrosion current densities, were calculated from the experimental data. The corrosion current densities in the 20% nitrate solutions ranged from 2.35 to 3.2 × 10⁻⁵ A/cm², while the 20% chloride solution had a lower corrosion rate at 1.45 × 10⁻⁵ A/cm². These electrochemical parameters can help predict the dissolution behavior of the HEA in nitrate and chloride solutions and aid in optimizing the ECM process. Full article

We present a quantitative theory of contraction and expansion cycles within the Quantum Memory Matrix (QMM) cosmology. In this framework, spacetime consists of finite-capacity Hilbert cells that store quantum information. Each non-singular bounce adds a fixed increment of imprint entropy, defined as the cumulative quantum information written irreversibly into the matrix and distinct from coarse-grained thermodynamic entropy, thereby providing an intrinsic, monotonic cycle counter. By calibrating the geometry–information duality, inferring today’s cumulative imprint from CMB, BAO, chronometer, and large-scale-structure constraints, and integrating the modified Friedmann equations with imprint back-reaction, we find that the Universe has already completed $N_{\mathrm{past}}=3.6\pm 0.4$ cycles. The finite Hilbert capacity enforces an absolute ceiling: propagating the holographic write rate and accounting for instability channels implies only $N_{\mathrm{future}}=7.8\pm 1.6$ additional cycles before saturation halts further bounces. Integrating Kodama-vector proper time across all completed cycles yields a total cumulative age $t_{\mathrm{QMM}}=62.0\pm 2.5\mathrm{Gyr}$, compared to the $13.8\pm 0.2\mathrm{Gyr}$ of the current expansion usually described by $\mathrm{\Lambda }$CDM. The framework makes concrete, testable predictions: an enhanced faint-end UV luminosity function at $z\gtrsim 12$ observable with JWST, a stochastic gravitational-wave background with $f^{2/3}$ scaling in the LISA band from primordial black-hole mergers, and a nanohertz background with slope $\alpha \simeq 2/3$ accessible to pulsar-timing arrays. These signatures provide near-term opportunities to confirm, refine, or falsify the cyclical QMM chronology. Full article

Entropy estimation is widely used in time series analysis, particularly in the field of Biomedical Engineering. It plays a key role in analyzing a wide range of physiological signals and serves as a measure of signal complexity, which reflects the complexity of the underlying system. The widespread adoption of entropy in research has led to numerous entropy definitions, with Approximate Entropy and Sample Entropy being among the most widely used. Over the past decade, the field has remained highly active, with a significant number of new entropy definitions being proposed, some inspired by Approximate and Sample Entropy, some by Permutation entropy, while others followed their own course of thought. In this paper, we review and compare the most prominent entropy definitions that have appeared in the last decade (2015–2024). We performed the search on 20 December 2024. We adopt the PRISMA methodology for this purpose, a widely accepted standard for conducting systematic literature reviews. With the included articles, we present statistical results on the number of citations for each method and the application domains in which they have been used. We also conducted a thorough review of the selected articles, documenting for each paper which definition has been employed and on which physiological signal it has been applied. Full article

This study aimed to investigate the physical factors influencing the occurrence of forest fires and to create a fire risk map of Phrae Province. Remote sensing and geographic information system (GIS) technology were applied for the analysis, focusing on seven factors: the digital elevation model (DEM); slope; Normalized Difference Vegetation Index (NDVI); aspect; and distances from people, water, and roads. All of these geographical factors can affect forest fires. This resulted in a MaxEnt (Maximum Entropy) model with an AUC (area under the curve) of 0.849, indicating its great prediction ability. The findings revealed that the variables influencing forest fire incidence were the DEM, NDVI, slope, distance from roads, distance from water, distance from communities, and aspect, in that order. Subsequently, a fire risk map for wildfires was developed by reclassifying the data into five levels—very low risk, low risk, medium risk, high risk, and very high risk—accounting for 341,395.54, 88,132.64, 76,162.41, 81,157.55, and 57,384.10 hectares or 52.99, 13.68, 11.82, 12.60, and 8.91% of the total area, respectively. The areas classified as very high risk, high risk, medium risk, and low risk included the Song, Long, and Rong Kwang Districts. The area with the lowest risk was Nong Muang Khai District. Full article

This research presents the development of spinel-type high-entropy oxide (HEO) catalysts with the general composition (MnFeNiCoX)₃O₄, where X represents Cr, Cu, Zn, and Cd, synthesized through a solution combustion method. The impact of the fifth metal element on the oxygen evolution reaction (OER) was systematically explored using structural, morphological, and electrochemical characterization techniques. Among the various compositions, the Cr-containing catalyst, (MnFeNiCoCr)₃O₄, displayed outstanding electrocatalytic behavior, delivering a notably low overpotential of 323 mV at a current density of 10 mA/cm² in 1.0 M KOH—surpassing the performance of benchmark RuO₂. Additionally, this material exhibited the smallest Tafel slope (56 mV/dec), the greatest double-layer capacitance (3.35 mF/cm²), and the most extensive electrochemically active surface area, all indicating enhanced charge transfer capability and high catalytic proficiency. The findings highlight the potential of element tailoring in HEOs as a promising strategy for optimizing water oxidation catalysis. Full article

In this study, the synergistic tuning mechanism of heat treatment (600, 800, and 1000 °C) and dealloying (40, 60, and 80 °C) on the microstructure and electrocatalytic performance of an FCC + BCC-type CoCrNi0.5Ti0.3V0.2Al0.4 eutectic high-entropy alloy (EHEA) was systematically investigated. The findings indicate that with an increase in heat treatment temperature, there is a gradual increase in grain size and a change in the fraction of the two phases. Notably, heat treatment at 800 °C resulted in an FCC-dominated dual-phase structure with uniformly refined grains. As the dealloying temperature increased, the pore size also increased, leading to a uniform distribution of the internal FCC and BCC phases. The sample subjected to heat treatment at 800 °C and dealloying at 80 °C exhibited an OER overpotential of only 265 mV and a Tafel slope of 67.84 mV/dec, significantly enhancing the electrocatalytic activity and stability of the alloy. This study elucidates the mechanism by which the combination of heat treatment and dealloying processes optimizes the electrocatalytic performance of eutectic high-entropy alloys, providing a novel strategy for the design of non-precious metal electrocatalysts. Full article

Hydrologically vulnerable areas should be identified for sustainable urban watershed management, flood mitigation, and climate-resilient infrastructure planning. However, assessing hydrological vulnerability in complex urban environments requires a comprehensive framework that integrates hydrological components and considers spatial heterogeneity. Thus, this study proposes an objective, data-driven method for identifying hydrologically vulnerable areas in urban regions using multicriteria decision-making (MCDM). The MCDM technique is used to rank the hydrological health of subwatersheds in an urbanizing watershed. Entropy-based weights are assigned to key hydrological indicators, which are computed using the soil and water assessment tool. Entropy-based weighting reveals that groundwater-related components contribute more to overall vulnerability than surface runoff. According to initial MCDM analysis, the most vulnerable areas are those in the upper reaches of the watershed, where steep slopes accelerate runoff and limit infiltration. This confounding influence of elevation is addressed by implementing topographic normalization and reevaluating subwatershed vulnerability while controlling for elevation bias. The findings underscore the importance of incorporating both hydrological and topographical factors into urban watershed vulnerability assessment and demonstrate the applicability of entropy-weighted MCDM to complex, data-scarce urban environments. The proposed framework is a replicable decision support tool for prioritizing hydrologically sensitive areas in intervention planning. Full article

Xi’an, China, has a complex geological environment, with geological hazards seriously hindering urban development and safety. This study analyzed the conditions leading to disaster formation and screened 12 evaluation factors (e.g., slope and slope direction) using Spearman’s correlation. Furthermore, it also introduced an innovative combined weighting method, integrating subjective weights from the hierarchical analysis method and objective weights from the entropy method, as well as an information value model for susceptibility assessment. The main results are as follows: (1) There are 787 hazard points—landslides/collapses are concentrated in loess areas and Qinling foothills, while subsidence/fissures are concentrated in plains. (2) The combined weighting method effectively overcame the limitations of single methods. (3) Validation using hazard density and ROC curves showed that the combined weighting information value model achieved the highest accuracy (AUC = 0.872). (4) The model was applied to classify the disaster susceptibility of Xi’an into high (12.31%), medium (18.68%), low (7.88%), and non-susceptible (61.14%) zones. The results are consistent with the actual distribution of disasters, thus providing a scientific basis for disaster prevention. Full article

Habitat fragmentation restricts the movement of large mammals across broad landscapes, leading to isolation of individuals or groups, reduced interaction with other species, and limited access to vital resources in surrounding habitats. In this study, we aimed to determine the wildlife ecological corridors for five large mammals (Ursus arctos, Cervus elaphus, Capreolus capreolus, Sus scrofa, and Canis lupus) between Kastamonu Ilgaz Mountain Wildlife Refuge and Gavurdağı Wildlife Refuge. In the field studies, we used the transect, indirect observation, and camera-trap methods to collect presence data. Maximum Entropy (MaxEnt) (v. 3.4.1) software was used to create habitat suitability models of the target species, which are based on the presence-only data approach. The results indicated that AUC values varied between 0.808 and 0.835, with water sources, stand type, and slope contributing most significantly to model performance. In order to determine wildlife ecological corridors, resistance surface maps were created using the species distribution models (SDMs), and bottleneck areas were determined. The Circuit Theory approach was used to model the connections between ecological corridors. As a result of this study, we developed connectivity models for five large mammals based on Circuit Theory, identified priority wildlife ecological corridors, and evaluated critical connection points between two protected areas, Ilgaz Mountain Wildlife Refuge and Gavurdağı Wildlife Refuge. These findings highlight the essential role of ecological corridors in sustaining landscape-level connectivity and supporting the long-term conservation of wide-ranging species. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

(1) Background: Human walking involves adapting to diverse terrains, influencing gait biomechanics. This study examined how seven outdoor surfaces—flat–even, banked-right/-left, cobblestone, grass, sloped-down, and sloped-up—affect nonlinear gait dynamics in 30 healthy adults (14 females and 15 males). (2) Methods: Trunk and shank accelerations were analyzed for movement predictability (sample entropy, SE), smoothness (log dimensionless jerk, LDLJ), symmetry (step/stride regularity), and stability (short-/long-term Lyapunov exponents, LyE_s, LyE_l). (3) Results: Surface type significantly influenced all gait metrics, regardless of sex. Banked-right and sloped-down walking reduced SE, indicating less predictable movements. All surfaces except flat–even increased LDLJ, suggesting reduced smoothness. Cobblestone and sloped-down surfaces impaired step symmetry, while banked surfaces enhanced stride symmetry. LyE_s decreased on cobblestones (lower variability), while sloped-up increased it. LyE_l rose on all surfaces except cobblestones, indicating a more chaotic gait. No significant sex differences were found, though males showed a non-significant trend toward lower LyE_s. Notably, sex–surface interactions emerged for SE and stride symmetry on banked-right surfaces, with females showing decreased SE and increased symmetry. (4) Conclusions: These findings underscore the importance of terrain and sex in gait dynamics research. Full article