Search Results (335)

CoCrNiTiAl_x high-entropy alloy films with varied Al contents were fabricated on 42CrMo steel substrates via magnetron sputtering. By adjusting the sputtering power of the Al target, an investigation was systematically carried out to explore the effect of different Al contents on the microstructural evolution, mechanical properties, and corrosion resistance of the film, with the underlying synergistic mechanism governing these properties being elucidated. With increasing Al content, the film microstructure gradually transforms from an amorphous phase at low Al contents to a nanocrystalline–amorphous composite structure, until it is converted into the BCC phase, and the film’s crystallinity exhibits a trend of first increasing and then decreasing. In terms of mechanical properties, the film hardness is significantly enhanced from 7.6 ± 1.3 GPa to 18.9 ± 1.1 GPa with increasing Al content, while the toughness gradually declines. Wear tests show that the film wear rate first decreases and then increases with rising Al content, reaching a minimum of 2.06 × 10⁻⁵ mm³/N·m. The superior protective state, characterized by a corrosion potential reaching −361.2 mV and corrosion current density dropping to 1.12 μA/cm², arises from the generation of an integrated, consistently structured composite passivation barrier in 3.5 wt.% solution. This study confirms that appropriate Al doping can synergistically optimize the microstructure, mechanical properties, and corrosion resistance of CoCrNiTiAl_x films, providing experimental and theoretical support for the compositional design and engineering applications of high-performance high-entropy alloy protective films. Full article

Accurate quantification of ecological losses in estuarine environments is often hindered by the mismatch between coarse-resolution biological surveys and fine-scale physical disturbances from engineering activities. While numerical models can simulate high-resolution environmental shifts, the inherent sparsity of ecological monitoring points limits the precision of spatial impact assessments. This study develops an integrated spatial-downscaling framework to transform sparse monitoring data into a high-resolution spatial continuum. A three-tiered modeling approach was used: first, the estuarine domain was partitioned into five eco-hydrodynamic zones using an entropy-weighted Support Vector Machine (SVM); second, localized chained Generalized Additive Models (GAMs) were established within each zone using MIKE-simulated hydrodynamic and water-quality data as proxy drivers; and third, these localized response functions were propagated across the study area to quantify multi-trophic biomass and economic losses. The framework revealed substantial spatial non-stationarity. Dredging operations locally altered the estuarine hydrodynamic regime. In northern channels, decreases in flow velocity were statistically associated with phytoplankton biomass to decline by 5.0% to 23.42%. Conversely, southern velocity increases enhanced water exchange and plankton growth. Using silt curtains as a mitigation strategy reduced the loss of phytoplankton by 11.4% and zooplankton by 9.6%. As a result, the total economic loss decreased from 26.54 million CNY to 25.34 million CNY, equivalent to a 4.5% reduction in economic loss. These results indicate that the proposed downscaling method can generate spatially explicit biological estimates. By offering a systematic pathway for impact evaluation and compensation in data-limited coastal regions, this framework supports more ecologically sustainable dredging operations. Nevertheless, the framework remains dependent on the representativeness of sparse monitoring stations, and future applications should integrate cross-estuary validation to improve transferability and uncertainty control. Full article

High-temperature corrosion severely impairs the service life of boiler heating tubes and threatens the safe and economical operation of thermal power units. With diversified fuels (coal, biomass and refuse-derived fuels) and continuously elevated operating parameters (steam temperature exceeding 620 °C for ultra-supercritical units), boiler heating surfaces are exposed to increasingly complex corrosive environments. High-temperature oxidation, sulfidation, chlorination, molten salt hot corrosion and deposit-induced multi-factor coupled corrosion coexist and exacerbate each other. This paper adopts a four-dimensional analytical framework of “mechanisms–technologies–materials–evaluation” to systematically summarize relevant research progress. From the perspective of corrosion mechanisms, the evolution of understandings from single high-temperature oxidation to multi-factor coupled corrosion is reviewed. In terms of surface coating technologies, seven mainstream processes including HVOF/HVAF spraying, plasma spraying, cold spraying, laser cladding and weld overlay are compared in terms of preparation characteristics and engineering applicability. For coating materials, twelve material systems such as NiCr alloys, MCrAlY, cermets, Fe-based amorphous/nanocrystalline alloys and high-entropy alloys are evaluated for their corrosion resistance under diverse service conditions. As for monitoring and evaluation, this work introduces full-range corrosion management technologies covering electrochemical monitoring, non-destructive testing, numerical simulation and life assessment. Finally, the paper discusses the application prospects of gradient coating design, AI-assisted material screening and digital twin technology, and points out key research gaps including long-term service reliability verification of coatings and quantitative prediction models for multi-factor coupled corrosion. Full article

This is of significant engineering importance for enhancing the operation stability and reliability of aeroengines. To ensure the precise identification of aerodynamic instability, it proposes a deep learning model for multi-source fusion based on cross-attention and bidirectional Long Short-Term Memory (CA_BiLSTM) network. From a high-speed multistage compressor, multi-dimensional feature extraction is performed in the time domain, frequency domain, and entropy value range. Based on dispersion entropy, feature cross-identification is constructed with a multi-level early warning method. In response to the nonlinear aerodynamic parameters, Variational Mode Decomposition (VMD) and Dung Beetle Optimizer (DBO) for global optimization are integrated to construct a VMD_DBO_LSTM-coupled prediction model for aerodynamic stability. To address the limitation of single-point detection, this paper proposes a dual-channel fusion model based on cross-attention mechanism. Through shared convolution and dynamic weighting mechanism, the CA_BiLSTM model can precisely characterize the nonlinear features of the complex flow. It can fully integrate the complementary information of inlet and outlet signals, achieving the collaborative signal characterization. Its anti-interference capability is significantly superior to that of the original single-point signal. Combined with the dispersion entropy threshold, it can detect instability 1580 r in advance, effectively overcoming the problems of information deficiency and incomplete representation caused by traditional single-point monitoring. Full article

In traditional mechanical de-icing technologies, the time-frequency evolution and spatial propagation mechanisms of transient high-frequency impact signals in flexible transmission lines remain unclear. To address this issue, transient impact responses were experimentally investigated using a full-scale transmission line model. An active de-icing exciter, featuring controllable impact energy and the potential for sustained online operation, was independently developed. High-frequency transient acceleration signals were acquired at multiple measurement points on a 20 m single-span line. The spatial distribution and time-frequency attenuation characteristics of the impact energy were quantitatively evaluated by extracting high-order time-domain statistical features, including root mean square, kurtosis, and crest factor, together with frequency-domain analyses based on Fast Fourier Transform (FFT) and wavelet entropy. The results indicate that: (1) The exciter generated highly impulsive transient responses, with a kurtosis up to 795.3 and a crest factor approaching 40. This suggests a strong local concentration of impact energy at the excitation source, which provides a dynamic basis for analyzing potential localized stress concentration and dynamic responses of the conductor system. (2) The transmission line structure exhibited a significant low-pass filtering effect on transient high-frequency shock waves. As the shock wave propagated towards the distal end, its high-frequency components above 30 Hz were substantially attenuated, likely due to internal dry friction within the stranded conductor. Consequently, the dominant frequency decreased to a low-frequency macroscopic sway of approximately 12 Hz, indicating a reduced risk of transmitting high-frequency shock loads to distal fittings and towers. (3) Under geometric nonlinear coupling, the vertical impact energy was partially transferred to the longitudinal and lateral directions during propagation, leading to sustained out-of-plane swaying. This study reveals the signal evolution characteristics of transient impacts in overhead transmission lines and provides experimental evidence for optimizing excitation parameters and assessing the engineering safety of active impact de-icing technologies. Full article

To address the dilemma of ‘non-grain use of cultivated land’ and support China’s requisition–compensation balance policy, this study developed a multi-dimensional assessment framework integrating the production, ecological, and economic dimensions (3D evaluation model), using Shenyang City as a case study to demonstrate the framework’s operational application and policy relevance. Based on 34,704 Third National Land Survey (TNLS) parcels (27,408.39 ha), we applied the constraint factor assessment method and entropy-weighted composite index model. The results show that non-cultivated agricultural land (NCAL) is generally marginally suitable (citywide average score: 2.50/4), with highly suitable areas accounting for only 4.04% (1106.30 ha). These areas exhibit a triangular spatial pattern distributed across northeastern Faku County, central Sujiatun District, and southern Xinmin City. Sensitivity tests using equal weights and ±20% dimension-weight perturbations confirm that high-suitability area remains limited (3.37–5.63% under entropy-weight scenarios; 8.54% under equal weights). Primary limiting factors include severe organic matter deficiency (average 19 g/kg), shallow soil depth, unfavorable pH, land requiring engineering restoration (94%), and punctiform heavy metal contamination (7.53% of plots, 2065.05 ha as spatially excluded areas). Consequently, we propose a five-tier sequential restoration framework: (1) near-term priority recultivation of highly suitable areas; (2) mid-term topsoil reconstruction for moderately suitable areas; (3) medium-to-long-term topsoil stripping and thickening for low-suitability areas; (4) long-term soil amelioration and slope-to-terrace conversion for marginally suitable areas; and (5) strict prohibition of restoration in unsuitable areas. This study establishes a spatially explicit decision-making system integrating “evaluation–classification–sequencing”, and distinguishes technical suitability from economic, institutional, and policy feasibility, providing a decision-support framework for scientifically implementing the cultivated land requisition–compensation balance policy. Future empirical studies using post-restoration monitoring data are needed to test its predictive accuracy against observed restoration outcomes. Full article

In engineer-to-order (ETO) manufacturing environments, the high variability of final product configurations makes it difficult to consistently estimate material consumption and, consequently, to define appropriate inventory control policies. This paper proposes a data-driven framework based on unsupervised learning to identify product typologies from historical manufacturing orders in a real industrial context. The approach employs a fuzzy k-prototypes algorithm to cluster mixed-type data, allowing the simultaneous treatment of numerical and categorical variables. In the case study, the proposed crisp-BOM-based scenario achieved a 28.67% reduction in line-side WIP and a 10.79% reduction in linear storage space, corresponding to the release of approximately two to three assembly stations. From the resulting fuzzy memberships, probabilistic bill of materials (BOM) structures are constructed, capturing the inherent variability of material consumption across different product configurations. A defuzzification procedure is then applied to obtain a crisp BOM representation suitable for operational decision-making. Additionally, a material versatility indicator based on entropy is introduced to quantify the dispersion of each material across product typologies. This indicator, together with the estimated consumption per cluster, is used as input for an analytical inventory model that supports the classification of materials into kanban or kitting policies. The methodology is validated using real data from a high- and medium-voltage switchgear manufacturing plant, comprising over 60,000 order–material observations. The results show that the proposed framework enables a more structured characterization of material behavior, reducing reliance on planner experience and improving the consistency of inventory policy decisions. From an industrial perspective, the approach provides a practical and scalable tool for aligning inventory strategies with the actual consumption patterns of ETO systems. Full article

Bioprospecting for extremozymes from unique ecological niches is crucial for developing robust biocatalysts for green chemistry. Here, we report the de novo hybrid genome assembly of Penicillium crustosum CTM10622, isolated from the humid montane forest of El Feïdja National Park, Tunisia. Using Illumina NextSeq™ 500 and Nanopore PromethION 2 Solo, a highly contiguous 31.38 Mb assembly (N50 = 1.94 Mb; 98.3% BUSCOs) was achieved. This robust genomic foundation enabled the identification of an extensive hydrolase repertoire, leading to the discovery of a novel alkaline serine lipase, PCLIP, subsequently heterologously expressed in Pichia pastoris. Recombinant rPCLIP exhibited a high specific activity (15,000 U/mg at pH 10, 65 °C) and exceptional thermostability, with half-lives of 14 and 8 h at 80 and 90 °C, respectively. The enzyme’s identity as a serine lipase was confirmed by its complete inhibition by Orlistat or tetrahydrolipstatin (THL) (51 µM), PMSF (5 mM), and diisopropylfluorophosphate (DIFP) (2 mM). To determine its substrate specificity, advanced computational approaches, including convolutional neural network-based docking and explicitly solvated molecular dynamics, were employed to compare rPCLIP with its homologue PCrL, a recombinant serine alkaline lipase from Penicillium crustosum Thom P22. While rPCLIP showed optimal experimental activity toward short-chain glyceryl tributyrate, simulations revealed that long-chain trioctanoin acts as a ‘thermodynamic trap’ due to over-stabilization. Conversely, the rigid rPCrL favors tricaprylin, driven by a ‘hydrophobic engine’ effect where the solvated environment forces chain burial with minimal entropic penalty. The findings demonstrate that rPCLIP specificity is driven by a delicate interplay of geometric complementarity, Van der Waals enthalpy, and conformational entropy. Full article

The growing need for secure image transmission across public networks requires robust encryption algorithms. Traditional chaos-based image ciphers typically have a small key space, weak avalanche behavior, or are susceptible to differential cryptanalysis. To overcome such inadequacies, this paper suggests a new adaptive image cryptosystem that combines a fractional-order memristive chaotic engine and a non-linear hybrid encryption kernel. The system uses piano-inspired feedback; the keystream generator dynamically adapts to the previously encrypted pixel, enabling powerful Cipher Block Chaining (CBC)-style chaining and content-dependent diffusion. A four-dimensional memristive system is solved by the use of fractional-order calculus, which gives an ultra-large key space (>10⁸⁰) and very high sensitivity to initial conditions—confirmed by a positive largest Lyapunov exponent (1.7199). The encryption kernel maps the traditional Exclusive OR (XOR) with the reversible two-step operation: the modular addition of the plaintext with the first keystream byte and the XOR with the second keystream one, both of which increase non-linearity and confusion. Large-scale experiments with six standard 256 × 256 colour images indicate almost ideal entropy (7.9994), Number of Pixel Change Rate (NPCR) which is 99.62, Unified Average Changing Intensity (UACI) which is 33.43, correlation coefficients are near to zero, very low Gray-Level Co-occurrence Matrix (GLCM) homogeneity (≈0.017) and high contrast (≈4843) and low energy (≈0.006 The ciphertext passes seven National Institute of Standards and Technology (NIST) SP-800-22 statistical tests, is extremely sensitive to keys (a perturbation of 1 × 10⁻¹⁴ alters >99.6% of ciphertext) and resists chosen-plaintext and known-plaintext attacks. Decryption has linear time complexity O(N), and average encryption and decryption times are 3.40 s and 2.75 s for 256 × 256 images. The proposed cryptosystem provides an attractive security–performance trade-off that can be used in high-security systems like medical image protection, privacy-preserving multimedia transmission, and secure cloud storage. Full article

Physical activity is a key lifestyle factor for mental health prevention, yet the influence of accelerometer placement on mood prediction remains unclear. We merged high-resolution acceleration data and Ecological Momentary Assessment (EMA) mood reports from 259 healthy participants across three ambulatory studies (SedMood, 24 hrCog, HO). Additionally, 15 min pre-assessment movement windows consisting of raw triaxial acceleration (64 Hz) from hip, thigh, chest, and wrist sensors were paired with six-item mood EMA queries. Features (e.g., mean, entropy, spectral power) were extracted and fed into gradient-boosted decision tree models (XGBoost), trained separately for energetic arousal, valence, and calmness. Performance was measured using the metrics MAE, RMSE and R². Within individual studies, chest and hip sensors achieved the highest performance, followed by wrist and thigh. In the combined dataset, hip sensors again outperformed thigh (R² 0.38 vs. 0.20). Multi-sensor models rarely surpassed the best single-sensor configuration and sometimes reduced accuracy. These results suggest that sensor location modestly impacts mood-prediction performance, with hip and chest offering the most reliable signals, while adding sensors does not reliably enhance predictive power. Future work should explore larger, homogenous datasets and location-specific feature engineering to refine wearable-based mental health monitoring. Full article

Promoting the high-quality development of farmers’ specialized cooperatives and narrowing regional development gaps is critical for advancing China’s rural revitalization strategy. Based on provincial panel data covering 30 Chinese regions from 2015 to 2023, this paper constructs a five-dimensional evaluation index system including standardized operation, operational performance, service scope, driving effect, and industrial upgrading, and adopts the entropy weight method to quantify the comprehensive development level of cooperatives. By combining spatial autocorrelation, kernel density estimation, the Dagum Gini coefficient and the Geodetector model, this paper explores the spatio-temporal evolution, regional disparities and multi-factor coupled driving mechanism of cooperative development. The main findings are as follows: (1) While the total quantity of cooperatives keeps expanding nationwide, their overall development level presents an evolutionary feature of declining first and then rising; industrial upgrading gradually becomes a new growth engine, whereas operational performance and driving effect slip downward. (2) The spatial layout of cooperatives maintains a typical pyramid structure; high-value agglomeration shifts from the Yangtze River Delta to southeast coastal regions, and low-value clusters are persistently concentrated in Northeast China. (3) The overall Dagum Gini coefficient reflects widening-then-shrinking regional gaps, and intra-eastern provincial differences constitute the primary source of nationwide spatial divergence. (4) Household consumption and rural labor force stock serve as core driving factors; regional economic development, agricultural production efficiency, rural human capital and land resource allocation form a coupled driving system, and all explanatory variables show mutual enhancement effects without offsetting interactions. Targeted policy suggestions are put forward to realize balanced and high-quality development of farmers’ specialized cooperatives across China. Full article

The recent seismic activity in southeastern Turkey in February 2023 again emphasized the critical need to promptly evaluate structural damage to assist in emergency response operations. This study introduces a comprehensive ensemble deep learning approach to structural damage classification following earthquake events, based on a dataset containing 13,270 high-resolution images with 15 different damage classes. Six different state-of-the-art convolutional neural network models (VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2) are combined using a weighted voting approach to handle extreme class imbalance using weighted categorical cross-entropy loss. An integrated explainability component is incorporated into the trained convolutional neural network models to highlight the image regions that contribute to the predicted damage class, thereby improving the interpretability of deep learning decisions in safety-critical post-disaster assessment scenarios. The performance evaluation results show that the ensemble model achieves a test accuracy of 93.77%, with an increase of 2.67% compared to the best performing model individually. Notably, the ensemble model improves performance in minority classes like collapsed buildings. The proposed framework can be used to provide a powerful approach to structural damage evaluation, balancing accuracy with interpretability, to assist structural engineers in post-earthquake evaluation procedures. Full article

With the continuous advancement of thrust-to-weight ratios in modern aero-engines, turbine inlet temperatures have reached levels that far exceed the thermal endurance limits of conventional superalloys and emerging ceramic matrix composites (CMCs). Consequently, thermal barrier coatings (TBCs) and environmental barrier coatings (EBCs) have become indispensable multifunctional systems for hot-section component protection. This review systematically delineates the evolutionary trajectory of TBC/EBC systems, transitioning from traditional yttria-stabilized zirconia (YSZ) and simple silicates to advanced multi-rare-earth-doped oxides, A₂B₂O₇ pyrochlore structures, and high-entropy ceramic systems. A critical comparative assessment is provided regarding their phase stability, thermal-physical properties, and durability challenges above 1200 °C. Furthermore, this paper provides an in-depth analysis of high-temperature degradation mechanisms, focusing on the thermochemical and thermomechanical interactions under calcium-magnesium-alumino-silicate (CMAS) attack, water-oxygen corrosion, and molten salt infiltration. By synthesizing current research gaps, we highlight the trade-offs between low thermal conductivity, high toughness, and environmental resistance. Finally, a strategic roadmap for next-generation coatings is proposed, emphasizing the integration of high-entropy material design, multi-scale structural optimization, and AI-driven life prediction models to meet the stringent reliability requirements of future propulsion systems. Full article

The surging need for public security risk mitigation has placed stricter demands on the modernization of emergency response capacities. Unmanned aircraft systems (UASs) offer a promising solution for liquid anti-riot agent dispersion, yet the complex interaction between rotor-induced downwash and droplet trajectories makes nozzle layout optimization a significant challenge. To address the prohibitive computational costs of traditional Computational Fluid Dynamics (CFD) and the limitations of single-objective optimization, this study proposes an integrated “simulation–modeling–optimization–decision” framework. First, a linear nozzle layout was identified as superior to the traditional circular arrangement, achieving a 44.8% increase in deposition rate. Subsequently, Optimal Latin Hypercube Sampling (OLHS) and CFD simulations were combined to construct high-precision Kriging surrogate models for three key indicators: deposition rate, uniformity, and coverage rate. The NSGA-II algorithm was then employed to solve the multi-objective trade-off, followed by the entropy-weighted TOPSIS method to identify the optimal engineering solution. Results indicate that nozzle count is the dominant system-level variable under the constant per-nozzle flow-rate condition, showing strong positive correlations with all performance indicators. The identified optimal configuration (6 nozzles with a 1.88 m boom length) achieved a 66.1% increase in deposition rate and an 18.7% increase in coverage rate compared to the original circular layout. Furthermore, the surrogate-based framework improved optimization efficiency to 296% compared to full factorial methods. This study provides a scientific theoretical basis and a highly efficient technical pathway for the structural design of high-performance UAV spray systems. Full article

The electrochemical co-conversion of CO₂ and H₂O into valuable products is a promising approach toward carbon-neutral energy systems. Alloy exsolution from perovskite lattices has emerged as an effective strategy to engineer catalytic interfaces, yet the mechanistic influence of exsolved bimetallic species on CO₂/H₂O co-electrolysis remains insufficiently clarified. To address this gap, density functional theory (DFT) calculations were performed in this study to systematically examine how NiFe alloy clusters exsolved from the LSFNO (La_0.7Sr_0.3Fe_0.9Ni_0.1O_3-δ) (111) surface modify the electronic structure of the interfacial region and promote the RWGS reaction in CO₂/H₂O co-electrolysis. Our work highlights bimetallic alloy exsolution as a powerful strategy for improving co-electrolysis catalysts and offers valuable guidance for the rational design of next-generation high-entropy oxide systems. Full article