Search Results (2,844)

High-resolution remote sensing imagery (HRRSI) presents significant challenges for building extraction tasks due to its complex terrain structures, multi-scale features, and rich spectral and geometric information. Traditional methods often face limitations in effectively integrating multi-scale features while maintaining a balance between detailed and global semantic information. To address these challenges, this paper proposes an innovative deep learning network, Multi-Source Multi-Scale Residual Attention Network (MMRAD-Net). This model is built upon the classical encoder–decoder framework and introduces two key components: the GCN OA-SWinT Dense Module (GSTDM) and the Res DualAttention Dense Fusion Block (R-DDFB). Additionally, it incorporates Digital Surface Model (DSM) data, presenting a novel feature extraction and fusion strategy. Specifically, the model enhances building extraction accuracy and robustness through hierarchical feature modeling and a refined cross-scale fusion mechanism, while effectively preserving both detail information and global semantic relationships. Furthermore, we propose a Hybrid Loss, which combines Binary Cross-Entropy Loss (BCE Loss), Dice Loss, and an edge-sensitive term to further improve the precision of building edges and foreground reconstruction capabilities. Experiments conducted on the GF-7 and WHU datasets validate the performance of MMRAD-Net, demonstrating its superiority over traditional methods in boundary handling, detail recovery, and adaptability to complex scenes. On the GF-7 Dataset, MMRAD-Net achieved an F1-score of 91.12% and an IoU of 83.01%. On the WHU Building Dataset, the F1-score and IoU were 94.04% and 88.99%, respectively. Ablation studies and transfer learning experiments further confirm the rationality of the model design and its strong generalization ability. These results highlight that innovations in multi-source data fusion, multi-scale feature modeling, and detailed feature fusion mechanisms have enhanced the accuracy and robustness of building extraction. Full article

This study designed Al_xCrFeNi (x = 0.8, 1.0, 1.2) medium-entropy alloys featuring a BCC + B2 dual-phase structure to systematically investigate the effects of Al content variation and heat treatment on microstructure evolution and corrosion behavior. Microstructural characterization revealed that all investigated alloys maintained the BCC + B2 dual-phase labyrinth structure. Electrochemical tests showed that as the Al content increased, the corrosion current density and corrosion rate in a 3.5 wt% NaCl solution increased. Synergistic analysis of post-corrosion morphology (through electrochemical testing and in-situ immersion) combined with XPS analysis of the passive films revealed that the initial stage of corrosion was primarily pitting. Subsequently, due to the loose and porous Al₂O₃ passive layer formed by the NiAl-rich phase, which was easily attacked by Cl⁻ ions, the corrosion progressed into selective corrosion of the NiAl phase. Notably, heat treatment at 1000 °C induced microstructural refinement with enhanced coupling between chunky and labyrinth structures, resulting in improved corrosion resistance despite a 4–6% reduction in Vickers hardness due to elemental homogenization. Among the investigated alloys, the heat-treated Al_0.8CrFeNi exhibited the most promising corrosion resistance. Full article

This paper investigates the effect of strain rate on the mechanical deformation and microstructural development of dual-phase AlCrFe₂Ni₂ high-entropy alloy during quasi-static and dynamic compression processes. It is revealed that the as-cast AlCrFe₂Ni₂ alloy is composed of a mixture of FCC, disordered BCC, and ordered B2 crystal structure phases. The alloy shows excellent compressive properties under quasi-static and dynamic deformation. The yield strength exceeds 600 MPa while the compressive strength is more than 3000 MPa at the compression rates of 30% under quasi-static conditions. Under dynamic compression conditions, the ultimate compression stresses are 1522 MPa, 1816 MPa, and 1925 MPa with compression strains about 12.8%, 14.7%, and 18.2% at strain rates of 1300 s⁻¹, 1700 s⁻¹ and 2100 s⁻¹, respectively. The dynamic yield strength is approximately linear with strain rate within the specified range and exhibit great sensitivity. The strong localized deformation regions (i.e., adiabatic shear bands (ASBs)) appear in dynamically deformed samples by dynamic recrystallization due to the conflicting processes of strain rate hardening and heat softening. Full article

Data heterogeneity is the result of increasing data volumes, technological advances, and growing business requirements in the IT environment. It means that data comes from different sources, may be dispersed in terms of location, and may be stored in different structures and formats. As a result, the management of distributed data requires special integration and analysis techniques to ensure coherent processing and a global view. Distributed learning systems often use entropy-based measures to assess the quality of local data and its impact on the global model. One important aspect of data processing is feature selection. This paper proposes a research methodology for multi-level attribute ranking construction for distributed data. The research was conducted on a publicly available dataset from the UCI Machine Learning Repository. In order to disperse the data, a table division into subtables was applied using reducts, which is a very well-known method from the rough sets theory. So-called local rankings were constructed for local data sources using an approach based on machine learning models, i.e., the greedy algorithm for the induction of decision rules. Two types of classifiers relating to explicit and implicit knowledge representation, i.e., gradient boosting and neural networks, were used to verify the research methodology. Extensive experiments, comparisons, and analysis of the obtained results show the merit of the proposed approach. Full article

This study investigates the dynamic interdependencies among key sectors of Borsa Istanbul—industrial, services, technology, banking, and electricity—using a novel network-geometric framework. Daily closure prices from 2022 to 2024 are transformed into logarithmic returns and analyzed via a sliding window approach. In each window, mutual information is computed to construct weighted networks that are filtered using Triangulated Maximally Filtered Graphs (TMFG) to isolate the most significant links. Forman–Ricci curvature is then calculated at the node level, and entropy measures over k-neighborhoods ($k=1,2,3$) capture the complexity of both local and global network structures. Cross-correlation, Granger causality, and transfer entropy analyses reveal that sector responses to macroeconomic shocks—such as inflation surges, interest rate hikes, and currency depreciation—vary considerably. The services sector emerges as a critical intermediary, transmitting shocks between the banking and both the industrial and technology sectors, while the electricity sector displays robust, stable interconnections. These findings demonstrate that curvature-based metrics capture nuanced network characteristics beyond traditional measures. Future work could incorporate high-frequency data to capture finer interactions and empirically compare curvature metrics with conventional indicators. Full article

Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs. Full article

Lossless image compression is vital for missions with limited data transmission bandwidth. Reducing file sizes enables faster transmission and increased scientific gains from transient events. This study compares two wavelet-based image compression algorithms, CCSDS 122.0 and JPEG 2000, used in the European Space Agency Comet Interceptor and Hera missions, respectively, in varying scenarios. The JPEG 2000 implementation is sourced from the JasPer library, whereas a custom implementation was written for CCSDS 122.0. The performance analysis for both algorithms consists of compressing simulated asteroid images in the visible and near-infrared spectral ranges. In addition, all test images were noise-filtered to study the effect of the amount of noise on both compression ratio and speed. The study finds that JPEG 2000 achieves consistently higher compression ratios and benefits from decreased noise more than CCSDS 122.0. However, CCSDS 122.0 produces comparable results faster than JPEG 2000 and is substantially less computationally complex. On the contrary, JPEG 2000 allows dynamic (entropy-permitting) reduction in the bit depth of internal data structures to 8 bits, halving the memory allocation, while CCSDS 122.0 always works in 16-bit mode. These results contribute valuable knowledge to the behavioral characteristics of both algorithms and provide insight for entities planning on using either algorithm on board planetary missions. Full article

Achieving green, low-carbon, and sustainable development in the Yangtze River Basin is an important part of promoting the modernization of agriculture and rural areas. Based on the panel data of 19 provinces in the Yangtze River Basin from 2002 to 2022, this article constructed a comprehensive evaluation system for the agricultural economy–population development–ecological environment system. By using the entropy-weighted TOPSIS method and the coupling coordination degree model, the comprehensive development level and the coupling coordination status of the agricultural economy, population development, and ecological environment system in the Yangtze River Basin were quantitatively analyzed. The results show the following: (1) The comprehensive index of the agricultural economy–population development–ecological environment system in the Yangtze River Basin shows a fluctuating upward trend, with obvious regional differences, and the comprehensive level showed a trend of gradual improvement from west to east. (2) The coupling degree of the agricultural economy–population development–ecological environment system in the Yangtze River Basin exhibits a volatile characteristic, initially increasing, then decreasing, and subsequently increasing again. Overall, the trend is moving toward a tighter coupling state. (3) The coupling degree of the agricultural economy–population development–ecological environment system in the provinces of the Yangtze River Basin shows a steadily increasing trend, yet the overall coupling coordination degree is not high and remains in a barely coordinated state. Accordingly, suggestions are put forward to optimize the economic structure, improve the population quality, adhere to ecological protection, and accelerate regional linkage so as to promote the coordinated development of economic development, population growth, and ecological protection in the basin. Full article

The cavitation phenomenon significantly impacts the performance of pump turbines, necessitating in-depth research on their cavitation characteristics. This study investigates the performance characteristics of a pump turbine through experimental and numerical simulation methods, with consistent results verifying the accuracy of the numerical simulations. The cavitation flow field is numerically analyzed to compare the cavitation distribution and velocity streamlines at different stages of cavitation development. The Q criterion and entropy production method are employed to identify vortex structures and energy loss regions, respectively, exploring the correlation between vortices and energy losses in the cavitation flow field under low-flow pump conditions. The results demonstrate that intensified cavitation generates more multi-scale vortices in the flow field, leading to increased entropy production and reduced energy efficiency. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) methods were subsequently applied to extract vorticity characteristics from transient cavitation flow fields, revealing primary energy loss regions and elucidating the evolution and distribution patterns of vortices. The POD analysis shows that low-order modes represent dominant vortex structures, while intensified cavitation increases both the quantity of vortices and their complexity in scale, distribution, and evolutionary frequency. The DMD results further indicate distinct evolutionary patterns for vortices of different scales. This research provides insights into the instability characteristics of cavitation flow fields in pump turbines under low-flow pump conditions and offers theoretical support for optimizing the design of pump turbines to expand their high-efficiency operational range. Full article

What is the impact of geopolitics on the geometry of global trade? What is the key structural role that led to the emergence of the US–China trade bipolarity? Here, we study the geometry of international trade, taking into account not only the direct but also the indirect trade relations. We consider the self-weight of each country as an indicator of its intrinsic robustness to exogenous shocks. We assess the vulnerability of a country to potential demand or supply shocks based on the entropy (diversification) of its trade flows. By considering the indirect trade relations, we found that the key structural role that led to the emergence of the US–China trade bipolarity is that of the intermediary hub that acts as a “bridge” between different trade clusters. The US and China occupied key network positions of high betweenness centrality as early as 2010. As international trade was increasingly dependent on only these two intermediary trade hubs, this fact led to geopolitical tensions such as the US–China trade war. Therefore, betweenness centrality could serve as a structural indicator, forewarning of possible upcoming geopolitical tensions. The US–China trade bipolarity is also strongly present in self-weights, where a race in terms of their intrinsic robustness to exogenous shocks is more than evident. It is also interesting that the US and China are not only the top shock spreaders but also the most susceptible to shocks. However, China can act more as a shock spreader than a shock receiver, while for the USA, the opposite is true. Regarding the impact of geopolitics, we found that the Russia–Ukraine conflict forced Ukraine to diversify both its exports and imports, aiming to lower its vulnerability to possible shocks. Finally, we found that international trade is becoming increasingly oligopolistic, even when indirect trade relationships are taken into account, thus indicating that a “Deep Oligopoly” has formed. Full article

This study investigates the level of sustainable development, evolution patterns, and obstacles in Qinghai Province. Considering the province’s unique characteristics and ecological significance, we have established an evaluation indicator system based on the DPSIR model. The entropy weight–TOPSIS model is used to assess the overall sustainability of Qinghai from 2008 to 2022. The grey GM(1,1) model is used to predict future sustainability trends, while the coupling coordination model quantifies the degree of coordination among subsystems. Furthermore, the barrier degree model is used to explore the factors hindering the improvement of Qinghai’s sustainable development. (1) The study finds that Qinghai’s overall sustainable development has shown a fluctuating upward trend, increasing from a weaker phase in 2008 to a stronger phase in 2022. All five subsystems in the sustainability evaluation system have shown gradual improvements in their index scores. This suggests that Qinghai’s sustainability level is expected to continue improving in the future. (2) From 2008 to 2022, the highest barrier degrees were observed in the pressure and state systems, with the barrier degrees of other systems gradually decreasing. Nine main factors, including the number of students in higher education, urban unemployment rate at year-end, and input–output ratio, have been identified as the obstacles to improving the province’s sustainable development level. (3) The coupling coordination degree of the five subsystems has shown a positive development trend, progressing through three stages: mild imbalance, basic coordination, and good coordination. The coordination type has shifted from deterioration to improvement. To achieve high-level sustainable development in Qinghai, leveraging the province’s advantageous environmental resources is crucial. Strengthening ecological protection, optimizing the industrial structure, accelerating urbanization, and emphasizing science and education are key pathways for Qinghai’s future development. Full article

Small and micro enterprises (SMEs) make important contributions to economic development, innovation, and employment in every country. The increasingly strict environmental regulations have become a global trend, but the empirical literature that evaluates the impacts of environmental regulations on the SMEs’ growth based on their observational data is extremely rare. This study aims to investigate how city-level environmental regulations in China affect the SMEs’ growth, with a focus on identifying lag effects, heterogeneous impacts across regions/enterprise types, and the mediating roles of technological innovation and policy support, using unbalanced panel data from 2007 to 2016. Using a dynamic panel model and entropy-weighted assessment, the results show the following: (1) Stricter environmental regulations significantly impede SMEs’ growth, with this effect persisting for up to two years. Robustness tests confirm the stability of these findings. (2) Despite the overall negative impact, our analysis reveals that environmental regulations can stimulate SMEs’ growth by promoting technological innovation and increasing policy support. (3) Heterogeneity analysis shows that the regulatory effects vary by region, ownership structure, and tax status, with the most adverse impacts observed in private firms, small-scale taxpayers, and businesses outside the Yangtze River Economic Belt. These findings highlight the need for differentiated regulatory approaches to balance environmental objectives with SMEs’ growth. The study is limited by its focus on data from 2007 to 2016, not considering recent policy shifts, and may have limited generalizability to economies with decentralized environmental governance. 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