Search Results (171)

The aviation industry aims to reduce environmental impact by adopting alternative propulsion systems, including hydrogen-based, hybrid-electric, and all-electric architectures, requiring a new Thermal Management System (TMS). In addition, new design methods are needed for the TMS, at the system and component levels, to handle various fluids and varying fluid properties. Within the TMS, heat exchangers are critical components that may require significant space and must be considered early in the design process. This paper presents a parametric sizing methodology for heat exchangers suitable for early design phases within a Multidisciplinary Design Analysis and Optimization (MDAO) framework, specifically for cryogenic heat transfer. The method combines physical equations with validated empirical relationships, using iterative solver algorithms for sizing. To address multi-variable design challenges, the methodology integrates discretization schemes for fluid properties, temperature, and energy calculations, and constraint-based optimization with a weighted-sum approach for solution selection. The methodology is validated with a commercial heat exchanger, and cross-validated with a cryogenic Heat Exchanger (HX). A case study for an all-electric hydrogen fuel cell aircraft architecture with a 7.6 MW propulsion system is presented to demonstrate the effectiveness of the methodology. The presented heat exchanger performance can be predicted across multiple conditions quickly enough to enable large design space exploration. Overall, the presented model is a crucial element for the design of a TMS for future aircraft with hydrogen-based propulsion systems. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35${}^{\circ }$ on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

Accurate lithologic maps derived from satellite imagery underpin structural interpretation, mineral exploration, and geohazard assessment. However, automated mapping in complex terranes remains challenging because spectrally similar units, narrow anisotropic bodies, and ambiguous contacts can degrade boundary fidelity. In this study, we propose SegNeXt-HFCA, a hierarchical multiscale fusion network with coordinate attention for lithologic segmentation from a Sentinel-2/DEM feature stack. The model builds on SegNeXt and introduces a hierarchical multiscale encoder with coordinate attention to jointly capture fine textures and scene-level structure. It further adopts a class-frequency-aware hybrid loss that combines boundary-weighted online hard-example mining cross-entropy with Lovász-Softmax to better handle long-tailed classes and ambiguous contacts. In addition, we employ a robust training and inference scheme, including entropy-guided patch sampling, exponential moving average of parameters, test-time augmentation, and a DenseCRF-based post-refinement. Two study areas in the Beishan orogen, northwestern China (Huitongshan and Xingxingxia), are used to evaluate the method with a unified 10-channel Sentinel-2/DEM feature stack. Compared with U-NetFormer, PSPNet, DeepLabV3+, DANet, LGMSFNet, SegFormer, BiSeNetV2, and the SegNeXt backbone, SegNeXt-HFCA improves mean intersection-over-union (mIoU) by about 3.8% in Huitongshan and 2.6% in Xingxingxia, respectively, and increases mean pixel accuracy by approximately 3–4%. Qualitative analyses show that the proposed framework better preserves thin-unit continuity, clarifies lithologic contacts, and reduces salt-and-pepper noise, yielding geologically more plausible maps. These results demonstrate that hierarchical multiscale fusion with coordinate attention, together with class- and boundary-aware optimization, provides a practical route to robust lithologic mapping in structurally complex regions. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Unstructured mesh deformation is an effective way to automatically generate mesh after geometric shape changes such as fluid–structure interaction simulation or aerodynamic shape optimization. The radial basis function method is one of the best mesh deformation methods, which takes into account both computational time and deformation ability. However, the current existing methods are confronted by the contradiction between computational efficiency and deformation accuracy. In this paper, a hybrid deformation method combining the radial basis function and distance-weighted function is proposed, which can effectively reduce computing cost and eliminate deformation error. Firstly, based on the radial basis function method with data reduction scheme, an efficient equidistant sampling method for points selection independent of the specific form of deformation is proposed, and a sampling algorithm based on bisection is devised to make the number of sample points quickly approach the expected value. Secondly, a compact distance-weighted function deformation method is developed, which is used to diffuse the deformation errors of boundary mesh points directly to interior mesh points in order to completely eliminate the deformation errors. Finally, two configurations, AGARD 445.6 wing and HIRENASD wing, are used to test the deformation capability of the hybrid method and the computing time of several key processes. The results show that the hybrid method can accurately realize large mesh deformation with a maximum displacement up to 50% span length, and at the same time, the mesh deformation can be completed with a single core in about 100 s for millions of mesh points, which indicates that the hybrid method in this paper has the ability to be applied to complicated configurations in real engineering. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

This study presents a novel fifth-order unequal-sized trigonometric weighted essentially non-oscillatory (US-TWENO) scheme and a novel hybrid US-TWENO (HUS-TWENO) scheme with a novel troubled cell indicator in a finite difference framework to address hyperbolic conservation laws on structured grids. Firstly, we propose three unequal-degree reconstruction polynomials in the new trigonometric polynomial space to devise a novel fifth-order US-TWENO scheme. Then, we devise a novel troubled cell indicator capable of accurately identifying troubled cells containing strong discontinuities: the existence of extreme points of the trigonometric polynomials within the smallest interval (the target cell itself) is determined by whether the estimated minimum and maximum values of their derivative trigonometric polynomials have opposite signs. To the best of our knowledge, this is the first troubled cell indicator devised specifically within the target cell interval. The HUS-TWENO scheme is improved, offering greater efficiency, lower dissipation, and higher resolution. Numerical experiments demonstrate the effectiveness of the HUS-TWENO scheme. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

The use of wireless sensor networks (WSNs) enables multidimensional and high-precision forest environment monitoring around the clock. However, the limited energy supply of sensor nodes using solely batteries is insufficient to support long-term data collection. Furthermore, since the complex terrain, dense vegetation, and variable weather in forests present unique challenges, relying on a single energy source is insufficient to ensure a stable energy supply for sensor nodes. Combining multiple energy sources is a promising way which has not been well studied. In this paper, to effectively utilize multiple energy sources, we propose a novel dynamic clustering routing protocol which considers the inherent diversity and intermittency of energy sources of the WSN in the forest. First, to address the inconsistency in residual energy caused by uneven energy harvesting among sensor nodes, a cluster head selection weight function is developed, and a dynamic weight-based cluster head election algorithm is proposed. This mechanism effectively prevents low-energy nodes from being selected as cluster heads, thereby maximizing the utilization of harvested energy. Second, a Q-learning-based adaptive hybrid transmission scheme is introduced, integrating both single-hop and multi-hop communication. The scheme dynamically optimizes intra-cluster transmission paths based on the current network state, reducing energy consumption during data transmission. The simulation results show that the proposed routing algorithm significantly outperforms existing methods in total network energy consumption, network lifetime, and energy balance. These advantages make it particularly suitable for forest environments characterized by strong fluctuations in harvested energy. In summary, this work provides an energy-efficient and adaptive routing solution suitable for forest environments with fluctuating energy availability. Full article

Weighted sum-rate (WSR) maximization in downlink massive multi-user multiple-input (MU-MIMO) with per-antenna power constraints (PAPCs) and imperfect channel state information (CSI) is computationally challenging. Classical weighted minimum mean-square error (WMMSE) algorithms, in particular, have per-iteration costs that scale cubically with the number of base-station antennas. This article proposes a robust low-complexity WMMSE-based precoding framework (RLC-WMMSE) tailored for massive MU-MIMO downlink under PAPCs and stochastic CSI mismatch. The algorithm retains the standard WMMSE structure but incorporates three key enhancements: a diagonal dual-regularization scheme that enforces PAPCs via a lightweight projected dual ascent with row-wise safety projection; a Woodbury-based transmit update that replaces the dominant $M\times M$ inversion with an $\left(NK\right)\times \left(NK\right)$ symmetric positive-definite solve, greatly reducing the per-iteration complexity; and a hybrid switching mechanism with adaptive damping that blends classical and low-complexity updates to improve robustness and convergence under channel estimation errors. We also analyze computational complexity and signaling overhead for both TDD and FDD deployments. Simulation results over i.i.d. and spatially correlated channels show that the proposed RLC-WMMSE scheme achieves WSR performance close to benchmark WMMSE-PAPCs designs while providing substantial runtime savings and strictly satisfying the per-antenna power limits. These properties make RLC-WMMSE a practical and scalable precoding solution for large-scale MU-MIMO systems in future wireless sensor and communication networks. Full article

Vehicular ad hoc networks (VANETs) must simultaneously satisfy stringent reliability, latency, and sustainability targets under highly dynamic urban and highway mobility. Existing solutions typically optimise one or two dimensions (link stability, clustering, or energy) but lack an integrated, adaptive mechanism that fuses heterogeneous metrics while remaining lightweight and deployable. This paper introduces a VANET routing protocol named SYMPHONY (Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield) that operates in three coordinated layers: (i) a compact neighbourhood filtering stage that reduces forwarding scope and eliminates transient relays, (ii) a cluster layer that elects resilient cluster heads using fuzzy energy-aware metrics and backup leadership, and (iii) a global inter-cluster optimizer that blends a GA-reseeded swarm metaheuristic with a stability-aware pheromone scheme to produce multi-objective routes. Crucially, SYMPHONY employs an ultra-lightweight online weight-adaptation module (contextual linear bandit) to tune metric fusion weights in response to observed rewards (packet delivery ratio, end-to-end delay, and Green Performance Index). We evaluated the proposed routing protocol SYMPHONY versus strong modern baselines across urban and highway scenarios with varying density and resource constraints. The results demonstrate that SYMPHONY improves packet delivery ratio by up to 12–18%, reduces latency by 20–35%, and increases the Green Performance Index by 22–45% relative to the best baseline, while keeping control overhead and per-node computation within practical bounds. Full article

In marine and coastal climate prediction, the integration of multiple imperfect models can improve accuracy by leveraging their complementary strengths. This study investigates this potential by developing a hybrid data assimilation framework that couples a biased physical model with a deep learning model. A neural network learns an optimal fitting coefficient to weight the contributions of both models throughout the assimilation process. We evaluated the framework in twin experiments based on a five-variable coupled climate model and a trained LSTM. Evaluations using root-mean-square error, frequency histograms, and probability density functions consistently demonstrated that the multi-model synthesis achieves superior assimilation performance compared to the single-model approach. Furthermore, when employing different analysis values for prediction, the overall prediction error of the multi-model coupled scheme is reduced to approximately 50% of that from single-model predictions. The promising results from this conceptual model study preliminarily validate the potential of the multi-model coupling approach, offering valuable insights into its potential application to more realistic oceanographic models. Full article

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. Full article

This study conducts an Academic Literature Analysis of 75 maturity models to clarify how Industry 4.0 and Industry 5.0 are being conceptualized and assessed. We map model scope, level structures, evaluated dimensions, and enabling technologies and complement descriptive statistics with exploratory non-parametric tests on the relationship between level structure and dimensional breadth. Results show a persistent dominance of Industry 4.0 models (≈92%), alongside a recent but steady emergence of Industry 5.0 and hybrid approaches in the latest models. Structurally, five-level schemes prevail, balancing diagnostic granularity and comparability. Content-wise, Technology and Digitalization, Processes and Operations, and Management and Strategy remain core, while People and Competencies and Innovation gain relevance; Sustainability and Social Responsibility and Human–Machine Interaction appear with the rise of Industry 5.0. We contribute (i) an operational definition of “hybrid” maturity models to make the I4.0→I5.0 transition measurable, (ii) a meta-typology of maturity levels explaining the five-level preference, and (iii) an evidence-based technology cartography across models. The findings suggest that future designs should retain the digital backbone of I4.0 while integrating explicit indicators for human-centricity, sustainability, and resilience with transparent weighting and scenario-based validation. Full article

In U-shaped automated container terminals (U-shaped ACTs), automated guided vehicles (AGVs) need to frequently interact with yard cranes (YCs), and separate scheduling of the two devices will affect terminal efficiency. Therefore, this study explores the coordinated scheduling problem between the two devices. To solve this problem, a high-precision simulation model of the U-shaped ACTs is established, which incorporates real operational logic. Second, an Improved Non-dominated Sorting Genetic Algorithm II based on Proximal Policy Optimization (INSGAII-PPO) is proposed. The algorithm uses PPO to realize dynamic genetic operator selection and makes related improvements, which improve the multi-objective optimization ability of NSGAII, and solve the collaborative scheduling problem by combining simulation. Finally, a hybrid weighted Technique for Order Preference by Similarity to Ideal Solution with preferences is proposed to select the final solution. The experimental results show that the scheme obtained by INSGAII-PPO exhibits better convergence and diversity, and offers significant advantages compared with the comparison algorithms. Moreover, the energy consumption and waiting time of the final solution selected by the proposed method are reduced by 3.42% and 4.87% on average. The proposed method has the capability of providing a theoretical reference for the AGVs and YCs collaborative scheduling of U-shaped ACTs. Full article