Search Results (1,281)

The United Nations Sustainable Development Goal (SDG) 11.1 emphasizes improving well-being, ensuring housing security, and promoting social equity. Informal settlements, one of the most vulnerable groups, require significant attention due to their dynamic changes and habitat quality. These areas limit the ability to comprehensively capture spatial heterogeneity and dynamic shifts in regional sustainable development. This study proposes an integrated approach using multi-source remote sensing data to extract the spatial distribution of informal settlements in Mumbai and assess their habitat environment quality. Specifically, seasonal spectral indices and texture features were constructed using Sentinel and SAR data, combined with the mean decrease impurity (MDI) indicator and hierarchical clustering to optimize feature selection, ultimately using a random forest (RF) model to extract the spatial distribution of informal settlements in Mumbai. Additionally, an innovative habitat environment index was developed through a Gaussian fuzzy evaluation model based on entropy weighting, providing a more robust assessment of habitat quality for informal settlements. The study demonstrates that: (1) texture features from the gray level co-occurrence matrix (GLCM) significantly improved the classification of informal settlements, with the random forest classification model achieving a kappa coefficient above 0.77, an overall accuracy exceeding 0.89, and F1 scores above 0.90; (2) informal settlements exhibited two primary development patterns: gradual expansion near formal residential areas and dependence on natural resources such as farmland, forests, and water bodies; (3) economic vitality emerged as a critical factor in improving the living environment, while social, natural, and residential conditions remained relatively stable; (4) the proportion of highly suitable and moderately suitable areas increased from 65.62% to 65.92%, although the overall improvement in informal settlements remained slow. This study highlights the novel integration of multi-source remote sensing data with machine learning for precise spatial extraction and comprehensive habitat quality assessment, providing valuable insights into urban planning and sustainable development strategies. Full article

Recent single-cell experiments that measure copy numbers of over 40 proteins in thousands of individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the statistical time-evolution of protein abundances in single cells, information that could yield insights into the mechanisms influencing the biochemical signaling kinetics of a cell. However, when multiple candidate models (i.e., mechanistic models applied to initial protein abundances) can potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. For example, popular approaches like Kullback–Leibler divergence and Akaike’s Information Criterion are often difficult to implement largely because mathematical expressions for the likelihoods of candidate models are typically not available. To perform model selection, we introduce an entropy-based approach that uses split-sample techniques to exploit the availability of large data sets and uses (1) existing generalized method of moments (GMM) software to estimate model parameters, and (2) standard kernel density estimators and a Gaussian copula to estimate candidate models. Using simulated data, we show that our approach can select the ”ground truth” from a set of competing mechanistic models. Then, to assess the relative support for a candidate model, we compute model selection probabilities using a bootstrap procedure. Full article

The area law obeyed by the thermodynamic entropy of black holes is one of the fundamental results relating gravity to statistical mechanics. In this work, we provide a derivation of the area law for the quantum relative entropy of the Schwarzschild black hole for an arbitrary Schwarzschild radius. The quantum relative entropy between the metric of the manifold and the metric induced by the geometry and the matter field has been proposed in G. Bianconi as the action for entropic quantum gravity leading to modified Einstein equations. The quantum relative entropy generalizes Araki’s entropy and treats the metrics between zero-forms, one-forms, and two-forms as quantum operators. Although the Schwarzschild metric is not an exact solution of the modified Einstein equations of the entropic quantum gravity, it is an approximate solution valid in the low-coupling, small-curvature limit. Here, we show that the quantum relative entropy associated to the Schwarzschild metric obeys the area law for a large Schwarzschild radius. We provide a full statistical mechanics interpretation of the results. Full article

This study investigates systemic risk, return patterns, and diversification within the Johannesburg Stock Exchange (JSE) during the COVID-19 pandemic, utilizing data-centric approaches and the ARMA-GARCH vine copula-based conditional value-at-risk (CoVaR) model. By comparing three investment strategies—industry sector-based, asset risk–return plot-based, and clustering-based—this research reveals that the industrial and technology sectors show no ARCH effects and remain isolated from other sectors, indicating potential diversification opportunities. Furthermore, the analysis employs C-vine and R-vine copulas, which uncover weak tail dependence among JSE sectors. This finding suggests that significant fluctuations in one sector minimally impact others, thereby highlighting the resilience of the South African economy. Additionally, entropy measures, including Shannon and Tsallis entropy, provide insights into the dynamics and predictability of various portfolios, with results indicating higher volatility in the energy sector and certain clusters. These findings offer valuable guidance for investors and policymakers, emphasizing the need for adaptable risk management strategies, particularly during turbulent periods. Notably, the industrial sector’s low CoVaR values signal stability, encouraging risk-tolerant investors to consider increasing their exposure. In contrast, others may explore diversification and hedging strategies to mitigate risk. Interestingly, the industry sector-based portfolio demonstrates better diversification during the COVID-19 crisis than the other two data-centric portfolios. This portfolio exhibits the highest Tsallis entropy, suggesting it offers the best diversity among the types analyzed, albeit said diversity is still relatively low overall. However, the portfolios based on groups and clusters of sectors show similar levels of diversity and concentration, as indicated by their identical entropy values. Full article

This study examines the coupling coordination between digital inclusive finance (DIF) and rural industrial integration development (RIID) in China from 2011 to 2021, using panel data from 282 prefecture-level cities. By employing the coupling coordination model and entropy method, the research analyzes the spatiotemporal evolution and regional disparities of DIF and RIID. Key findings include the following: (1) The coupling coordination degree between DIF and RIID shows a consistent upward trend, transitioning from mild imbalance to primary coordination, though RIID lags behind DIF. (2) Significant regional disparities exist, with an “N-shaped” spatial distribution pattern from south to north, where eastern and northeastern regions exhibit higher coordination levels compared to central and western regions. (3) Regional differences are narrowing, driven primarily by inter-regional disparities, with strong spatial spillover effects observed in “high–high” and “low–low” agglomerations. (4) The overall spatial network tightness and stability have improved, with eastern regions playing a central role in the network, while northeastern and western regions remain relatively marginal. Policy recommendations include expanding DIF applications in rural industries, reducing regional disparities through resource allocation, promoting rural industrial integration in underdeveloped areas, and strengthening regional coordination to facilitate resource flow. Full article

High-entropy alloys are novel metallic materials distinguished by very special mechanical and chemical properties that are superior to classical alloys, attracting high global interest for the study and development thereof for different applications. This work presents the creation and characterisation of an FeMoTaTiZr high-entropy alloy composed of chemical constituents with relatively low biotoxicity for human use, suitable for medical tools such as surgical scissors, blades, or other cutting tools. The alloy microstructure is dendritic in an as-cast state. The chemical composition of the FeMoTaTiZr alloy micro-zone revealed that the dendrites especially contain Mo and Ta, while the inter-dendritic matrix contains a mixture of Ti, Fe, and Zr. The structural characterisation of the alloy, carried out via X-ray diffraction, shows that the main phases formed in the FeMoTaTiZr matrix are fcc (Ti₇Zr₃)_0.2 and hcp Ti₂Fe after annealing at 900 °C for 2 h, followed by water quenching. After a second heat treatment performed at 900 °C for 15 h in an argon atmosphere followed by argon flow quenching, the homogeneity of the alloy was improved, and a new compound like Fe_3.2Mo_2.1, Mo_0.93Zr_0.07, and Zr(MoO₄)₂ appeared. The microhardness increased over 6% after this heat treatment, from 694 to 800 HV_0.5, but after the second annealing and quenching, the hardness decreased to 730 HV_0.5. Additionally, a Lactate Dehydrogenase (LDH) cytotoxicity assay was performed. Mesenchymal stem cells proliferated on the new FeMoTaTiZr alloy to a confluence of 80–90% within 10 days of analysis in wells where the cells were cultured on and in the presence of the alloy. When using normal human fibroblasts (NHF), both in wells with cells cultured on metal alloys and in those without alloys, an increase in LDH activity was observed. Therefore, it can be considered that certain cytolysis phenomena (cytotoxicity) occurred because of the more intense proliferation of this cell line due to the overcrowding of the culture surface with cells. Full article

Existing methods for calculating terrain entropy in grid digital elevation models (DEMs) often face computational anomalies in specific topographies within small windows. To address this issue, an improved method was developed based on the Euclidean distance approach. This method was inspired by Claramunt’s technique of weighting information entropy by the average distance between points with the same value and different values. Specifically, vectors were formed between grid points and categorized by value consistency and relative positions. Those formed between points of different values were classified by the value of the starting point as well as parallel and adjacent relationships. This comprehensive grouping strategy was integrated into distance calculations, becoming a new probability operator that accurately reflects terrain spatial characteristics. Experimental verification confirms that the method proposed aligns with the fundamental concept of entropy, yielding a regression equation of $y=0.011\ln x+0.463$ with a coefficient of determination of 94.73%, a reliability of 44.015, and a measurement ability of 0.757. For the mixed iterative images with gradually increasing spatial disorder, their entropy values should follow a logarithmic trend. Therefore, a logarithmic function is used for fitting. A determination coefficient greater than 50% indicates that the method adheres to the original definition of entropy and is effective in capturing the increasing spatial disorder of the grid DEM. A lower reliability value suggests smoother data computation between the two iterations. A lower measurement ability value indicates slower convergence for grid DEMs with gradually increasing spatial disorder. The improved method was also tested on simulated and real DEMs, and the results showed a strong correlation between calculated terrain entropy values and terrain complexity. By effectively capturing spatial information changes, this approach overcomes the shortcoming of computational anomalies and demonstrates high reliability in terrain entropy calculation in grid DEMs. Full article

Rare-earth high-entropy oxides are a new promising class of multifunctional materials characterized by their ability to stabilize complex, multi-cationic compositions into single-phase structures through configurational entropy. This feature enables fine-tuning structural properties such as oxygen vacancies, lattice distortions, and defect chemistry, making them promising for advanced technological applications. While initial research primarily focused on their catalytic performance in energy and environmental applications, recent research demonstrated their potential in optoelectronics, photoluminescent materials, and aerospace technologies. Progress in synthesis techniques has provided control over particle morphology, composition, and defect engineering, enhancing their electronic, thermal, and mechanical properties. Rare-earth high-entropy oxides exhibit tunable bandgaps, exceptional thermal stability, and superior resistance to phase degradation, which positions them as next-generation materials. Despite these advances, challenges remain in scaling up production, optimizing compositions for specific applications, and understanding the fundamental mechanisms governing their multifunctionality. This review provides a comprehensive analysis of the recent developments in rare-earth high-entropy oxides as relatively new and still underrated material of the future. Full article

Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia. Full article

Existing image fusion algorithms involve extensive models and high computational demands when processing source images that require non-rigid registration, which may not align with the practical needs of engineering applications. To tackle this challenge, this study proposes a comprehensive framework for convolutional sparse fusion in the context of non-rigid registration of visible–infrared images. Our approach begins with an attention-based convolutional sparse encoder to extract cross-modal feature encodings from source images. To enhance feature extraction, we introduce a feature-guided loss and an information entropy loss to guide the extraction of homogeneous and isolated features, resulting in a feature decomposition network. Next, we create a registration module that estimates the registration parameters based on homogeneous feature pairs. Finally, we develop an image fusion module by applying homogeneous and isolated feature filtering to the feature groups, resulting in high-quality fused images with maximized information retention. Experimental results on multiple datasets indicate that, compared with similar studies, the proposed algorithm achieves an average improvement of 8.3% in image registration and 30.6% in fusion performance in mutual information. In addition, in downstream target recognition tasks, the fusion images generated by the proposed algorithm show a maximum improvement of 20.1% in average relative accuracy compared with the original images. Importantly, our algorithm maintains a relatively lightweight computational and parameter load. Full article

Physiological indicators are increasingly employed in sports training. However, studies on surface electromyography (sEMG) primarily focus on the analysis of isometric contraction. Research on sEMG related to isotonic contraction, which is more relevant to athletic performance, remains relatively limited. This paper examines the changes in the isotonic contraction performance of the male upper arm muscles resulting from long-term basketball training using the sEMG metrics. We recruited basketball physical education (B-PE) and non-PE majors to conduct a controlled isotonic contraction experiment to collect and analyze sEMG signals. The sample entropy event detection method was utilized to extract the epochs of active segments of data. Subsequently, statistical analysis methods were applied to extract the key sEMG time domain (TD) and frequency domain (FD) features of isotonic contraction that can differentiate between professional and amateur athletes. Machine learning methods were employed to perform ten-fold cross-validation and repeated experiments to verify the effectiveness of the features across the different groups. This paper investigates the key features and channels of interest for categorizing male participants from non-PE and B-PE backgrounds. The experimental results show that the F12B feature group consistently achieved an accuracy of between 80% and 90% with the SVM2 model, balancing both accuracy and efficiency, which can serve as evaluation indices for isotonic contraction performance of upper limb muscles during basketball training. This has practical significance for monitoring isotonic sEMG features in sports and training, as well as for providing individualized training regimens. Full article

Deep mining in metal mines faces more and more complex geological conditions, such as “three highs and one disturbance” (high ground stress, high permeability, high temperature, and mining-induced disturbance), which can easily trigger water inrush disasters and seriously affect the safety and efficiency of deep mining. This paper focuses on the deep hydrogeological structural characteristics of the Huize lead–zinc mine. Firstly, two main factors affecting the production safety of the mining area, namely the water source and water channel of the mine, were analyzed. Based on this analysis, nine factors were determined as indicators for the risk assessment of water inrush, including the water head difference, water-bearing capacity, permeability coefficient, aquifer thickness, water pressure, fault type, fault scale, fault water conductivity, and karst zoning characteristics. Then, a water inrush risk assessment model for the deep mine was constructed, and the weights of the individual factors were determined using the analytic hierarchy process (AHP) and entropy weight method (EWM). Combined with the multi-factor spatial fitting function of the GIS, a zoning map of the risk assessment of water inrush was developed. The results showed that the aquifer groups of the Permian Liangshan Formation and the Carboniferous Maping Formation (P₁l + C₃m) were relatively safe, whereas the karst fissure aquifer of the Qixia–Maokou Formation (P₁q + m) posed a high risk of water inrush, necessitating advanced exploration and water drainage in the area. These findings provide guidance for water control measures in the Huize lead–zinc mine and offer valuable insights into the prediction and prevention of mine water hazards associated with ore body mining in karst aquifers. Full article

The corrosion characteristics and passive behavior of as-cast Ni₄₀Fe₃₀Co₂₀Al₁₀ medium-entropy alloy (MEA) fabricated by the vacuum arc melting technique were investigated in 3.5 wt.% NaCl, 0.5 M HCl, and 0.5 M H₂SO₄ solutions. Although the impact of different solutions on the corrosion current density was not pronounced, the corrosion potential values of MEAs in H₂SO₄, HCl and NaCl solutions were −0.37, −0.58 and −1.16 V, respectively, indicating that the resistance to general corrosion in acidic solutions becomes strengthened. Through electrochemical passive region tests, surface morphology analysis and ICP testing, it was found that, due to the high-entropy effect and uniform single-phase structure, an optimized and stable passive film formed specifically in the Cl⁻-containing solution. The ion concentrations in the passive region of MEA in NaCl solution were an order of magnitude lower than those of other two samples, suggesting that its passive film formed exhibits a more prominent capacity to inhibit metal dissolution. Compared with electrochemical reactions in H₂SO₄ and HCl solutions, MEA shows enhanced pitting resistance in NaCl solution, which could be attributed to the presence of abundant unoxidized metal atoms (51.9 at.%). Al is identified as the primary component in the formation of the passive film, which plays a protective role for the Co-rich interior of the MEA. Although MEA has a relatively high passivation current in the H₂SO₄ solution, it has the widest passivation zone (1.87 V), indicating the optimized stability of the formed passive film. Moreover, it displays a high level of resistance to pitting corrosion in the solution containing only H⁺- and free of Cl⁻. Both the MEAs show significant grain-boundary corrosion in H₂SO₄ and HCl solutions. Among them, the MEA in HCl experiences more severe intragranular corrosion. Notably, MEA withstands the erosion of a single Cl⁻- or H⁺-containing solution, but it is unable to resist the synergistic effect of a solution containing both H⁺ and Cl⁻. Full article

Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (${\mathrm{smooth}}_{\mathrm{RE}}$) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains. Full article

To more intuitively demonstrate the locational distribution of spatial agglomeration of HQD (high-quality development) in China’s coal cities, this study uses the entropy value method, standard deviation ellipse, and geographic detector to investigate the law of dynamic evolution and driving factors of HQD in China’s coal cities from 2011 to 2020. The findings are as follows: (1) The HQD level of China’s coal cities is experiencing a positive trajectory, with the highest level of development in the east, followed by the regions located in the center and west of the country, and relatively low in the northeast. Throughout the “Twelfth Five-Year Plan” period, Suzhou made the greatest progress, while Fuxin had the greatest decline. Throughout the “13th Five-Year Plan” period, Xingtai and Handan made the greatest progress, while Qitaihe had the greatest decline. (2) The HQD level of China’s coal cities as a whole shows a northeast–southwest direction, the center of gravity shifts southward, indicating a concentration pattern. The eastern and central areas are oriented in a northwest–southeast direction; the center of gravity in the east shifts to the northwest, and the center of gravity in the middle shifts to the southeast; and both regions have a higher level of HQD in the east–west direction. The western and northeastern regions are in a northeast–southwest direction, with the center of gravity moving to the northeast: the western region shows a tendency toward diffusion, and the northeastern region shows an agglomeration trend. (3) Patent authorization per 10,000 people, foreign trade dependence, R&D investment intensity, and GDP per capita were important drivers for the HQD of China’s coal cities; The degree of government intervention is the best interaction factor, and the degree of opening to the outside world and the forest coverage rate are the best interaction objects. Full article