Search Results (5,589)

The performance of the turbine in a variable geometry turbocharger (VGT) may be affected by changes in the vane operating angle and heat transfer loss during operation. However, existing studies have been conducted under the assumption of an adiabatic process. In this study, we investigated the effect of heat transfer between all working fluids and a VGT structure when using computational fluid dynamics to evaluate turbine performance. Through this study, we confirmed that when heat transfer was considered, the turbine efficiency decreased by approximately 2–6%, depending on the vane position angle change, compared to when heat transfer was not considered. In addition, the total entropy production ratio, which represented the flow loss in the turbine during operation, increased by approximately 0.2–0.5% when heat transfer was considered. In conclusion, the findings confirmed that the heat transfer phenomenon directly affected the efficiency and flow loss during the turbine performance evaluation process. Full article

The value realization of forest ecological products (VRF) is crucial for rural revitalization, while the rural digital economy (RDE) plays a central role in enhancing farmers’ income (FI). This study constructs index systems to evaluate the RDE and VRF using the entropy weight method and the input–output model. Based on panel data from 31 Chinese provinces (2011–2021), we employ a comprehensive analytical framework that includes spatiotemporal evolution analysis, benchmark regression models, mediation effect analysis, and heterogeneity analysis. The results of the benchmark regression models show that the RDE significantly boosts FI, with each unit of increase in the RDE leading to a 2579-unit rise in income. Spatiotemporal evolution analysis reveals that the positive effect of the RDE weakens from the Eastern coastal regions to the less developed Western regions. Furthermore, mediation effect analysis indicates that VRF mediates the relationship between the RDE and FI. Heterogeneity analysis demonstrates that the impact of the RDE varies across regions and income levels. These findings provide strong evidence of the role of the RDE in promoting FI and highlight VRF as a mediating mechanism, offering policy insights for integrating digital and ecological strategies to foster inclusive rural growth. Full article

The erosion resistance of materials against solid particles is a very important property, especially in the transportation of powders or in aeronautics (dust inside jet engines). There is a strong need to introduce new materials that have higher solid particle erosion resistance than state-of-the-art materials. Thus, in the present work, the solid erosion particles of high entropy alloys (HEAs) based on the Al0.7CoCrFeNi matrix were studied compared to the state-of-the-art stainless steel AISI 304. Furthermore, the effect of the addition of Ti to HEAs on hardness and erosion resistance was investigated. Current research included the development of the chemical composition of a new kind of HEA designed on the basis of thermodynamical calculations performed in CALPHAD, its manufacturing, full characterization involving microstructural and phase analyses, hardness measurements, solid particle erosion tests, and finally, the elucidation of erosion mechanisms. It was found that HEAs showed higher hardness as well as erosion resistance than AISI 304. Moreover, it was found that the increase in Ti content in an HEA resulted in an increase in the hardness and resistance to the erosion of the studied HEA. As the main reason for this phenomenon, the stabilization of the β-BCC phase, suppression of the α-FCC phase, and the appearance of the Ni₃Ti phase in the studied HEA were claimed. Full article

: The design of small-molecule inhibitors targeting proprotein convertase subtilisin/Kein type 9 (PCSK9) remains a forefront challenge in combating atherosclerosis. While various monoclonal antibodies have achieved clinical success, small-molecule inhibitors are hindered by the unique structural features of the PCSK9 binding interface. In this study, a potential small-molecule inhibitor was identified through virtual screening, followed by molecular dynamics (MD) simulations to explore the binding mechanisms between the inhibitor and the PCSK9 protein. Binding free energies were calculated using molecular mechanics/Generalized Born surface area (MM/GBSA) with the interaction entropy (IE) method, and critical hot-spot residues were identified via alanine scanning analysis. Key residues, including ARG237, ILE369, ARG194 and PHE379, were revealed to form critical interactions with inhibitor and play dominant roles during the inhibitor’s binding. In addition, the polarization effect was shown to significantly influence PCSK9–ligand binding. The identified inhibitor exhibited highly similar binding patterns with two known active compounds, providing valuable insights for the rational design and optimization of small-molecule inhibitors targeting PCSK9. This work contributes to the development of more effective treatments for hyperlipidemia and associated cardiovascular diseases. Full article

This study systematically investigates the impact of Cu²⁺ doping on the structural, magnetic, and magnetocaloric properties of Cu_xCo_1−xCr₂O₄ nanoparticles synthesized via a solution combustion method. Cu incorporation up to x = 20% induces a progressive structural transformation from a cubic spinel to a trigonal corundum phase, as confirmed by X-ray diffraction and Raman spectroscopy. The doping process also leads to increased particle size, improved crystallinity, and reduced agglomeration. Magnetic measurements reveal a transition from hard to soft ferrimagnetic behavior with increasing Cu content, accompanied by a notable rise in the Curie temperature from 97.7 K (x = 0) to 140.2 K (x = 20%). The magnetocaloric effect (MCE) is significantly enhanced at higher doping levels, with the 20% Cu-doped sample exhibiting a maximum magnetic entropy change (−ΔS_M) of 2.015 J/kg-K and a relative cooling power (RCP) of 58.87 J/kg under a 60 kOe field. Arrott plot analysis confirms that the magnetic phase transitions remain second-order in nature across all compositions. These results demonstrate that Cu doping is an effective strategy for tuning the magnetostructural response of CoCr₂O₄ nanoparticles, making them promising candidates for low-temperature magnetic refrigeration applications. Full article

As China shifts from rapid to high-quality development, urban growth has exhibited allometric patterns. This study evaluated land use efficiency from the perspective of architectural space, focusing on 41 cities in the Yangtze River Delta urban agglomeration from 2010 to 2020. A land development intensity index was constructed at both the provincial and municipal levels using the entropy weight method, integrating floor area ratio, building density, and functional mix. The spatiotemporal characteristics of land development intensity and population density were analyzed, and a coordination coupling model was applied to identify mismatches between land and population. The results reveal: (1) Temporally, the imbalance of “more people, less land” in the Yangtze River Delta diminished. Spatially, leading regions exhibit a diffusion effect. Shanghai showed a decline in both population density and development intensity; Zhejiang maintained balanced development; Jiangsu experienced accelerated growth; and Anhui showed signs of catching up. (2) Although the two indicators showed a high coupling degree and strong correlation, the coordination degree remained low, indicating poor quality of correlation. The land-population relationship demonstrated a fluctuating pattern of “strengthening–weakening” over time. Shanghai exhibited the highest coordination, while more than half of the cities in Jiangsu, Zhejiang, and Anhui still needed optimization. (3) Unlike previous findings that linked such patterns to shrinking cities, in this transformation stage, the number of cities where land development intensity exceeded population density continued to grow in advanced regions. This study first applied 3D building data at the macro scale to support differentiated spatial policies. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

Land use mix is important for urban planning, and existing land use mix metrics frameworks have been developed comprehensively in terms of categories, distances, and attributes. However, most existing indices focus solely on the spatial dimension of land use mixing, neglecting the inherent temporal variation of land use within short time scales, which results in difficulties in comprehensively and accurately capturing the cyclical dynamic characteristics of land use. In response to this problem, this study introduces innovative modifications to the diversity indicator from the perspective of the temporal availability of land use, based on the business time characteristics of land use. Specifically, three time-sensitive indexes were proposed, including the temporal diversity index (TDI), the daily temporal diversity index (DTDI), and the temporal entropy index (TEI). With these indexes, this paper measures and analyzes the functional mix of street blocks in Xi’an City. The results of the study show that the indexes are effective in reflecting changes in the temporal dimension of the land use mix. Meanwhile, Xi’an’s land use mix pattern is more reasonable in terms of setting business hours, but the type of functional mix needs to be optimized. The proposed indicator system offers a novel perspective on the spatiotemporal mixing of land use and delivers more precise decision-making support for urban planning and management. Full article

Gearbox failures are a major cause of unplanned downtime and increased maintenance costs, making accurate diagnosis crucial in ensuring wind turbine reliability and cost-efficiency. However, most existing diagnostic methods fail to fully extract the spatiotemporal features in SCADA data and neglect the impact of class imbalance, thereby limiting diagnostic accuracy. To address these challenges, this paper proposes a fault diagnosis model for wind turbine gearboxes based on CNN-SA-GRU and Focal Loss. Specifically, a CNN-SA-GRU network is constructed to extract both spatial and temporal features, in which CNN is employed to extract local spatial features from SCADA data, Shuffle Attention is integrated to efficiently fuse channel and spatial information and enhance spatial representation, and GRU is utilized to capture long-term spatiotemporal dependencies. To mitigate the adverse effects of class imbalance, the conventional cross-entropy loss is replaced with Focal Loss, which assigns higher weights to hard-to-classify fault samples. Finally, the model is validated using real wind farm data. The results show that, compared with the cross-entropy loss, using Focal Loss improves the accuracy and F1 score by an average of 0.24% and 1.03%, respectively. Furthermore, the proposed model outperforms other baseline models with average gains of 0.703% in accuracy and 4.65% in F1 score. Full article

In January 2025, a magnitude 6.8 earthquake struck Dingri County, Shigatse, Tibet, causing severe damage. Rapid and precise extraction of damaged buildings is essential for emergency relief and rebuilding efforts. This study proposes an approach integrating YOLO-E (Real-Time Seeing Anything) and the Segment Anything Model 2 (SAM2) to extract damaged buildings with multi-source remote sensing images, including post-earthquake Gaofen-7 imagery (0.80 m), Beijing-3 imagery (0.30 m), and pre-earthquake Google satellite imagery (0.15 m), over the affected region. In this hybrid approach, YOLO-E functions as the preliminary segmentation module for initial segmentation. It leverages its real-time detection and segmentation capability to locate potential damaged building regions and generate coarse segmentation masks rapidly. Subsequently, SAM2 follows as a refinement step, incorporating shapefile information from pre-disaster sources to apply precise, pixel-level segmentation. The dataset used for training contained labeled examples of damaged buildings, and the model optimization was carried out using stochastic gradient descent (SGD), with cross-entropy and mean squared error as the selected loss functions. Upon evaluation, the model reached a precision of 0.840, a recall of 0.855, an F1-score of 0.847, and an IoU of 0.735. It successfully extracted 492 suspected damaged building patches within a radius of 20 km from the earthquake epicenter, clearly showing the distribution characteristics of damaged buildings concentrated in the earthquake fault zone. In summary, this hybrid YOLO-E and SAM2 approach, leveraging multi-source remote sensing imagery, delivers precise and rapid extraction of damaged buildings with a precision of 0.840, recall of 0.855, and IoU of 0.735, effectively supporting targeted earthquake rescue and post-disaster reconstruction efforts in the Dingri County fault zone. Full article

Optimization of grain size distribution in high-entropy alloys (HEAs) is a promising design strategy to overcome wear and corrosion resistance. In this study, a (FeCoNi)₈₆Al₇Ti₇ high-entropy alloy with customized isometric and heterogeneous structure, as well as fine-crystal isometric design by SPS, is investigated for microstructure, surface morphology, hardness, frictional wear, and corrosion resistance. The effects of the SPS process on the microstructure and mechanical behavior are elucidated, and the frictional wear and corrosion resistance of the alloys are improved with heterogeneous structural fine-grain strengthening and uniform fine-grain strengthening. The wear mechanisms and corrosion behavior mechanisms of (FeCoNi)₈₆Al₇Ti₇ HEAs with different phase structure designs are elaborated. This work highlights the potential of using powder metallurgy to efficiently and precisely control and optimize the multi-scale microstructure of high-entropy alloys, thereby improving their frictional wear and corrosion properties in demanding applications. Full article

Increasing frequency and severity of urban flooding, driven by climate change and urban population growth, present major challenges. Traditional flood control infrastructure alone cannot fully prevent flood damage, highlighting the need for a comprehensive and multi-dimensional disaster management approach. This study proposes the Flood Risk Index for Building (FRIB)—a building-level assessment framework that integrates vulnerability, hazard, and exposure. FRIB assigns customized risk levels to individual buildings and evaluates the effectiveness of a multi-dimensional method. Compared to traditional indicators like flood depth, FRIB more accurately identifies high-risk areas by incorporating diverse risk factors. It also enables efficient resource allocation by excluding low-risk buildings, focusing efforts on high-risk zones. For example, in a case where 5124 buildings were targeted based on 1 m flood depth, applying FRIB excluded 24 buildings with “low” risk and up to 530 with “high” risk, reducing unnecessary interventions. Moreover, quantitative metrics like entropy and variance showed that as FRIB levels rise, flood depth distributions become more balanced—demonstrating that depth alone does not determine risk. In conclusion, while qualitative labels such as “very low” to “very high” aid intuitive understanding, FRIB’s quantitative, multi-dimensional approach enhances precision in urban flood management. Future research may expand FRIB’s application to varied regions, supporting tailored flood response strategies. Full article

The signal-processing architecture of passive surface acoustic wave (SAW) sensors presents significant implementation challenges due to its radar-like operational principle and the inherent complexity of discrete component-based hardware design. While System-in-Package (SiP) has demonstrated remarkable success in miniaturizing electronic systems for smartphones, automotive electronics, and IoT applications, its potential for revolutionizing SAW sensor interrogator design remains underexplored. This paper presents a novel architecture that synergistically combines time-domain ADC design with SiP-based miniaturization to achieve unprecedented simplification of SAW sensor readout systems. The proposed time-domain ADC incorporates an innovative delay chain calibration methodology that integrates physical unclonable function (PUF) principles during time-to-digital converter (TDC) characterization, enabling the simultaneous generation of unique system IDs. The experimental results demonstrate that the integrated security mechanism provides variable-length bit entropy for device authentication, and has a reliability of 97.56 and uniqueness of 49.43, with 53.28 uniformity, effectively addressing vulnerability concerns in distributed sensor networks. The proposed SiP is especially suitable for space-constrained IoT applications requiring robust physical-layer security. This work advances the state-of-the-art wireless sensor interfaces by demonstrating how time-domain signal processing and advanced packaging technologies can be co-optimized to address performance and security challenges in next-generation sensor systems. Full article

High-quality growth of the construction industry and an improved human settlements environment are essential to sustainable urbanization. Existing studies have paid limited systematic attention to the spatial and temporal dynamics of the coordinated development between the construction industry and human settlements, as well as the underlying factors driving regional disparities. This gap restricts the formulation of precise, differentiated sustainable policies tailored to regions at different development stages and with varying resource endowments. Southern China, characterized by pronounced spatial heterogeneity and unique development trends, offers a natural laboratory for examining the spatio-temporal interaction between these two dimensions. Using panel data for 15 southern provinces (2013–2022), we applied the entropy method, coupling coordination model, Dagum Gini coefficient, spatial trend surface analysis, gravity model, and grey forecasting to evaluate current conditions and predict future trends. The main findings are as follows. (1) The coupling coordination degree rose steadily, forming a stepped spatial pattern from the southwest through the center to the southeast. (2) The coupling coordination degree appears obvious polarization effect, presenting a spatial linkage pattern with Jiangsu-Shanghai-Zhejiang, Hubei-Hunan-Jiangxi, and Sichuan-Chongqing as the core of the three major clusters. (3) The overall Dagum Gini coefficient declined, but intra-regional disparities persisted: values were highest in the southeast, moderate in the center, and lowest in the southwest; inter-regional differences dominated the total inequality. (4) Forecasts for 2023–2027 suggest further improvement in the coupling coordination degree, yet spatial divergence will widen, creating a configuration of “eastern leadership, central catch-up acceleration, and differentiated southwestern development.” This study provides an evidence base for policies that foster high-quality construction sector growth and enhance the living environment. The findings of this study indicate that policymaking should prioritize promoting synergistic regional development, enhancing the radiating and driving role of core regions, and establishing a multi-level coordinated governance mechanism to bridge regional disparities and foster more balanced and sustainable development. Full article

Harmonics are a common phenomenon widely present in power systems. The presence of harmonics not only increases the energy consumption of equipment but also poses hidden risks to the safety and stealth performance of large ships. Thus, there is an urgent need for a detection method for the harmonic characteristics of time series. We propose a novel harmonic feature estimation method, termed Harmonic Aggregation Entropy (HaAgEn), which effectively discriminates against background noise. The method is based on bispectrum analysis; utilizing the distribution characteristics of harmonic signals in the bispectrum matrix, a new Diagonal Bi-directional Integral Bispectrum (DBIB) method is employed to effectively extract harmonic features within the bispectrum matrix. This approach addresses the issues associated with traditional time–frequency analysis methods, such as the large computational burden and lack of specificity in feature extraction. The integration results, $I_x$ and $I_y$, of DBIB on different frequency axes are calculated using cross-entropy to derive HaAgEn. It is verified that HaAgEn is significantly more sensitive to harmonic components in the signal compared to other types of entropy, thereby better addressing harmonic detection issues and reducing feature redundancy. The detection accuracy of harmonic components in the shaft-rate electromagnetic field signal, as evidenced by sea trial data, reaches 96.8%, which is significantly higher than that of other detection methods. This provides a novel technical approach for addressing the issue of harmonic detection in industrial applications. Full article