IntroductionFinite element (FE) models have significantly advanced bioengineering by enabling the characterization of biological tissues, assessment of mechanical responses to loading, and development of prosthetic and biomedical applications. Porcine bone provides a relevant experimental model because of its similarities to human bone tissue. This study investigated the mechanical response of porcine femoral bone by comparing experimental compression data with finite element models derived from medical imaging.MethodsComputed tomography (CT) scans of a porcine femur were segmented to generate FE models with element-wise, piecewise-constant inhomogeneous isotropic material properties assigned from local Hounsfield Unit (HU) averages. Axial compression tests provided experimental load–displacement data, while stress and strain fields were obtained from the numerical models.ResultsThe experimental and numerical displacement values differed by approximately 0.3%, 2%, and 1% for the cortical, cortical–trabecular, and trabecular regional comparisons, respectively. The numerical models additionally enabled characterization of stress and strain distributions and identification of regions of elevated mechanical response.DiscussionThese results demonstrate close specimen-specific agreement between the experimental displacement measurements and the CT-based FE predictions. However, because the HU–density–modulus coefficients were fitted using experimental data from the same femur, the reported agreement represents specimen-specific experimental-numerical consistency rather than independent validation, and transferability to other specimens or imaging protocols remains to be established.
IntroductionConnected and Automated Vehicles face challenges of fixed parameters, poor dynamic adaptability, and the lack of longitudinal and lateral coordination, which affect safe and stable vehicle operations. To solve these problems, this study aims to develop an advanced coordinated control system for intelligent vehicles.MethodsThis study proposes a dynamics modeling technique based on online calibration of connected parameters. This technique integrates the real-time data update patterns of vehicle-infrastructure cooperation to construct an accurate motion model, which combines dynamic parameter inputs to achieve precise evaluation of vehicle driving states. In addition, this study adopts a control technique based on Adaptive Fuzzy Sliding Mode (AFSM) and fuzzy Reinforcement Learning (RL) for coordinated vehicle management and control. This technique takes dynamic states as inputs, enhances the suppression of chattering interference by introducing a fuzzy inference layer, and calibrates the final control results through a dual strategy integrating spatial constraints and adaptive mechanisms.ResultsExperiments are conducted based on a commercial bus. In lateral target tracking control, the model in this study reaches a displacement of 3.78 m at 20 s, and the lateral tracking error drops to −0.02 m, outperforming similar models. In real-vehicle extreme lane-changing control experiments, the root-mean-square lateral error of the model in this study is 0.035 m, while the maximum control chattering rate is only 2.4%. Finally, in longitudinal and lateral coordinated performance analysis, the maximum speed error of the model in this study is 0.25 km/h, and the jerk is 0.12 m/s3, both of which are superior to similar models.DiscussionThe proposed technique demonstrates good application effects in addressing parameter ambiguity and dynamic control imbalance. This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.
IntroductionAccurate road type recognition and pavement information aggregation are essential for reliable vehicle navigation in complex urban conditions.MethodsThis paper integrates a CART decision tree with DBSCAN clustering within an edge-cloud architecture. CART classifies roads using 28-dimensional sensor features via Gini gain optimization and post-pruning. DBSCAN (Eps = 30 m, MinPts = 3) aggregates and denoises pavement information points using Haversine distance.ResultsCART achieved 96.8% accuracy, 97.2% precision, 97.3% recall, and a 97.2% F1 score, outperforming RF, SVM, and LR with minimal model size and inference time. DBSCAN merged 85.7% of redundant points with noise below 5%. Real-vehicle tests showed 98% + recognition accuracy for non-ordinary urban roads, a 92.5% detection rate, 89.2% warning success rate, and a 126 m average advance warning distance.DiscussionThe CART-DBSCAN integration provides an effective closed-loop navigation solution from perception to warning. Future work will incorporate multi-modal sensors and domain adaptation to enhance robustness across diverse scenarios.
The paper aims at rationalizing the distribution of grain structure parameters across a gas turbine engine (GTE) disk in order to minimize the disk’s mass while ensuring its safe operational conditions. For this purpose, advanced methods of digital design were improved and applied, including state-of-the-art approaches based on multilevel modeling of material structure and properties. The problem was solved using a combined approach. Macro-phenomenological models were employed to determine, during the flight cycle, the evolving fields of stress-strain state and temperature of the entire part. These results were then transferred into a multilevel model to analyze specific regions for the study of strength characteristics, namely, high-temperature strength (resistance to creep and long-term strength), fracture toughness, low-cycle fatigue strength, and thermal stability (resistance to recrystallization and grain boundary migration). Multilevel modeling was based on a comprehensive analysis of the literature data on the structure of the nickel alloy VV751P, as well as on the mechanisms of its deformation and failure. The results of digital design were obtained and analyzed, and recommendations were proposed for rationalizing the material’s grain structure to reduce the disk’s mass while maintaining strength characteristics. The developed approach proved to be effective in digital multilevel design for functionally critical components.
IntroductionThe outsourcing of manufacturing activities results in operational uncertainties such as supplier delay, instabilities of raw materials, inconsistencies of product quality, cultural difference, regulatory pressure and digital integration issues. There is inadequate empirical research that explores the interrelationship of these operational risks driven by suppliers in the Indian context of outsourcing. The current study focuses on identifying, measuring and categorizing the major operational risks of manufacturing operations driven by suppliers.MethodsA quantitative research approach was utilized for the research. Data were collected from manufacturing professionals involved in supplier coordination, quality control, logistics, production planning and operational management. The responses were analyzed using SPSS through frequency analysis, descriptive statistics, correlation analysis, KMO and Bartlett’s test and exploratory factor analysis. Parallel analysis and Velicer’s MAP test were also utilized.ResultsThe results show that raw material availability was the most critical risk factor with the highest mean value of 4.58, followed by supplier-side disruption at 4.53 and inadequate real-time data sharing at 4.42. Product reliability linked with supplier performance recorded a mean of 4.33 while customer satisfaction decline due to supplier delays recorded a mean of 4.25. Correlation analysis showed strong associations between communication gaps and business ethics misalignment (r = 0.687) and between product specification variation and supplier work practice differences (r = 0.675). The KMO value of 0.861 and Bartlett’s test significance at p < 0.001 confirmed suitability for factor analysis. Parallel analysis and Velicer’s MAP test supported a final three-component solution explaining 52.449% of total variance. The three components were labelled supplier coordination, compliance and performance risk operational quality, regulatory and process-control risk and supply and production-continuity with digital-readiness risk.Discussion and conclusionThe study concludes that supplier-driven operational risk is multidimensional and requires integrated supplier monitoring, digital coordination, quality alignment, compliance tracking and production-continuity planning. The study is limited to questionnaire-based responses. Future research may use longitudinal data, SEM or machine-learning models to validate causal risk pathways.
One of the major challenges faced by marine ecosystem and the environment in general is oil spills especially in oil producing areas or areas with crude oil infrastructure. This threatens aquatic life, render the water body and the environment polluted and unsafe. However, accurate and detection could minimise the impact through a timely and effective response. Though the deployment of deep learning for oil spills detection using synthetic aperture radar (SAR) images, have proved effective, nevertheless, lack of interpretability of artificial intelligence models makes it a black-box which reduces the stakeholders’ trust especially in crucial applications such as environmental monitoring. This study demonstrates the application of explainable artificial intelligence (XAI) specifically the deep learning model for oil spill detection. The model integrates the SpillNet, a customised Convolutional Neural Network (CNN) architecture with five XAI techniques and unique evaluation metrics suitable for marine environmental monitoring were introduced. These include the Marine Domain Relevance (MDR) for the quantification of oil spill, False Positive Analysis (FPA) for look-alike discrimination and Domain Alignment Score (DAS); an expert-based checklist with composite metric. Our comprehensive evaluation of 20 representative samples from 1002 SAR images shows that Gradient-Weighted Class Activation Mapping (Grad-CAM) achieves the highest domain alignment score (0.608 ± 0.074). The proposed SpillNet model also achieved segmentation accuracy (in terms of IoU) of 0.830 (83%) and validation accuracy of 90.5%. Thus, making it the most suitable XAI method for operational oil spill detection systems especially in open-ocean scenarios. The system directly supports several United Nations (UN) Sustainable Development Goals, including the Sustainable Development Goal (SDG) 6 (Clean Water), SDG 7 (Clean Energy), and SDG 14 (Life Underwater), by improving environmental protection through reliable AI-based monitoring systems.
High-flow combined valves are critical regulating components in steam turbine systems; their flow capacity, pressure loss characteristics and flow stability across a wide range of operating conditions directly affect the economic efficiency and reliability of the unit. However, the integrated structure of combined valves complicates the throttling jet, separation recirculation and local secondary flow between the upper and lower valves, and the underlying flow mechanisms still require further elucidation. This paper employs a combined approach of numerical simulation and experimental testing to investigate high-flow combined valves under various valve opening and pressure ratio conditions. Given the complex nature of the actual filter screen structure and the difficulty of performing high-precision discretisation directly, a porous medium equivalent model is used to simulate the filter screen. The numerical model was validated using scaled experimental data and total pressure loss characteristics. On this basis, a systematic analysis was conducted of the internal flow patterns, flow regulation characteristics, vortex structure evolution, and energy dissipation patterns within the combined valve. The results indicate that, over a wide range of operating conditions, the Realizable k-ε turbulence model combined with the porous media model can predict the total pressure loss characteristics of the filter screen more accurately than the SST k-ω model. Furthermore, although the filter screen increases the total pressure loss to some extent, it improves the uniformity of the incoming flow, attenuates downstream unsteady fluctuations, and reduces flow entropy generation. This study provides a basis for optimising flow control and designing filter mesh structures in high-flow combined valves.
To provide a comprehensive and balanced perspective, the review also acknowledges well-established mainstream alternatives, including induction motors (IMs), which have demonstrated practical viability in commercial EV applications such as Tesla’s early production models. Variable flux motors (VFMs) have emerged as a transformative technology for new energy vehicle propulsion systems, addressing the fundamental trade-off between low-speed torque capability and high-speed efficiency that constrains conventional permanent magnet synchronous motors (PMSMs). This review systematically examines the structural design aspects of variable flux motors, encompassing hybrid permanent magnet topologies, magnetization state control mechanisms, flux regulation strategies, and electromagnetic optimization methodologies. Particular emphasis is placed on variable flux memory machines (VFMMs) employing low-coercive-force (LCF) magnets such as AlNiCo in combination with high-coercive-force (HCF) neodymium-iron-boron (NdFeB) magnets, as well as novel rotor shifting mechanisms and variable leakage flux designs. The paper synthesizes recent advances in series and parallel magnetic circuit configurations, swiveling magnetization techniques, and multi-objective design optimization frameworks. By analyzing comparative performance metrics across different VFM architectures and identifying persistent technical barriers including magnetization state control precision, demagnetization resistance, and manufacturing complexity, this review aims to provide a comprehensive reference for researchers and engineers engaged in next-generation wide-speed-range electric propulsion system development.
While Artificial Intelligence (AI) and Machine Learning (ML) hold significant promise for Engineering Design Optimization (EDO), traditional optimization approaches frequently suffer from excessive computational and time expenses. To overcome these barriers, this study introduces a high-performance optimization framework built upon three core contributions. First, a cost-effective data acquisition strategy is proposed, utilizing existing manufacturer catalogs alongside data augmentation methods to produce high-quality datasets with minimal resource expenditure. Second, an automated, Genetic Algorithm (GA)-driven approach is designed to optimize the hyperparameters of a Deep Neural Network (DNN), successfully eliminating the reliance on manual expert calibration. Third, a Surrogate-Assisted Genetic Algorithm (SAGA) is deployed, leveraging the highly accurate DNN as a surrogate model to rapidly navigate discrete design spaces and circumvent computationally exhaustive simulations. The practical viability of the framework was rigorously evaluated through an industrial steel grating design application. Empirical outcomes indicate substantial real-world utility, yielding an average mass reduction of 23.21±0.65% across 44 standardized configurations without violating structural or serviceability constraints. The optimization pipeline exhibited remarkable computational efficiency, completing the task in just 3.2 min per model. This acceleration is directly facilitated by the high-fidelity surrogate model, which delivered a classification accuracy of 97.678±0.472% and robust mass prediction metrics (R2=0.999, MAE = 0.719±0.101 kg/m2). Additionally, the methodology demonstrated excellent adaptability by successfully resolving the classical 200-bar truss benchmark subject to strict displacement and frequency constraints. Demonstrating superior performance over existing baseline approaches in both solution quality and execution time, this study substantiates the framework as a highly flexible, scalable, and robust approach for resolving complex engineering design challenges.
IntroductionAn ideal flight mechanism model reasonably approximates the efficiency of aerodynamic flight, similar to how ideal heat engines approximate what is possible with different engine designs. It is useful for modeling ground effect flight. The ideal model provides a benchmark against which vehicle prototype performances may be compared to rapidly assess design effectiveness; similar benchmarks have been absent to date.MethodsThe ideal flight mechanism model equation was derived from force and energy balances on aircraft in flight to preserve reversible losses. Data for the paper was calculated through computational fluid dynamics (CFD) to reasonable estimate aircraft performance.ResultsCFD performances of better performing airfoils and digital prototypes approach the lift-to-drag ratio (L/D) of the ideal mechanism model. The prominent operational parameter of the ideal equation model in ground effect flight is the ratio of the vertical perimeter area below the vehicle to the planform area. The variable is applicable for unifying two- and three-dimensional comparisons. Digital prototypes with aspect ratios less than 0.4 have lower L/D estimates than model projections, a finding identified for further study to better understand how to improve performance at low aspect ratios.DiscussionDigital prototype performances were evaluated in three phases of flight: (a) takeoff, (b) cruising velocities, and (c) higher speed travel. For takeoff, hovercraft functionality may be used, but analysis indicates that wheeled suspension is more efficient. Cruising velocities operate most efficiently with ram effect lift generated in the lower cavity of the lower ground effect flight transit (GEFT) vehicle, which is capable of approaching model predictions. Vertical ducts with fans passing through the fuselage are analyzed to extend cruising travel conditions over a greater velocity range to maintain cavity pressures and sufficient lift. At higher speeds, lower lift coefficients are needed to maintain ground effect flight. Trailing-edge stagnation plates (i.e., spoilers) may be used to reduce drag under these conditions. Although jet aircraft travel at higher altitudes to reduce drag, ground effect vehicles may use stagnation plates to achieve higher flight efficiency.
In deep-sea mining operations, the presence of sediment has a significant impact on hydraulic extraction performance. However, existing studies generally assume a rigid substrate, failing to reflect the effects of real sedimentary environments. To evaluate the adaptability of different hydraulic extraction technologies under sediment-laden boundary conditions, this study coupled VOF interface tracking, discrete element particle dynamics, and a non-Newtonian rheological model to establish a jet-particle-sediment multiphase numerical method. It systematically compared the extraction performance of three mainstream extraction heads: suction-lift, wall-following jet, and jet-flushing. The results indicate that sediment significantly inhibits extraction efficiency. Among the methods, the suction-lift method is most severely affected. The wall-following jet method causes the least disturbance to the environment. However, its extraction capacity is limited. The jet-flushing method is least affected by sediment. It also exhibits the best overall performance. Furthermore, particle dynamics analysis reveals two typical modes of collection failure: escape and retention. Escape failure stems from a lack of lift, causing particles to escape the collection zone without being effectively retained; retention failure results from a spatial lag in the onset of lift, where particles acquire lift but have already missed the optimal lifting position. This study clarifies the significance of sediment in the analysis of hydraulic mineral collection for deep-sea mining and reveals the mechanical mechanisms underlying particle collection failure, thereby providing a theoretical basis for the selection and optimized design of deep-sea mining equipment.
Lower-limb rehabilitation exoskeletons are often discussed in terms of mechanics, sensing, and control, yet their rehabilitation value depends on how these elements work together during human–robot interaction. This review focuses on the integration of sensing, compliant actuation, and assist-as-needed control in lower-limb rehabilitation exoskeletons. Recent research suggests that effective assistance depends not only on actuator output, but also on reliable gait-state detection, intention-related sensing, mechanical transparency, and real-time adaptation. Current progress in sensing based on inertial measurement units (IMUs), force and pressure measurements, and electromyography (EMG) is reviewed, followed by discussion of how actuation choice and mechanical compliance influence safe and effective assistance. Major control strategies, including trajectory tracking, impedance control, hierarchical control, learning-based methods, and assist-as-needed approaches, are then compared. Remaining barriers to clinical translation include signal instability, safety and certification requirements, and the persistent gap between laboratory performance and patient-specific rehabilitation needs. Future progress will likely depend on tighter co-design of sensing, hardware compliance, and cooperative control.
The integration of physics-based modeling and data-driven prediction is creating new opportunities for predictive design, optimization, and the deployment of digital twins in advanced manufacturing systems. In compliant mechanisms, particularly double-bridge configurations used in precision positioning and surface engineering applications, accurate prediction and optimization of amplification ratio remain challenging due to coupled geometric interactions and nonlinear design trade-offs. This study presents a Physics-Guided Digital-Twin-Ready Framework for the predictive design and multi-objective optimization of double-bridge compliant mechanisms. A physics-consistent dataset comprising 8,000 design samples was generated using Latin Hypercube Sampling, analytical compliance modeling, constraint-based filtering, and response-space stratified sampling. The resulting dataset provides balanced coverage of amplification ratios within the range of 5–50, enabling robust learning across diverse design regimes. Machine-learning models, including Random Forest and Extreme Gradient Boosting (XGBoost), were developed to predict amplification ratio from geometric and material parameters. The models achieved excellent predictive performance, with coefficients of determination (R2) exceeding 0.99, mean absolute errors below 0.93, and root mean square errors below 0.65. Uncertainty quantification was incorporated through ensemble variance estimation, yielding prediction intervals with less than 5% relative uncertainty in well-sampled regions. SHAP-based explainability and sensitivity analyses revealed that amplification behavior is primarily governed by geometric parameters, particularly beam lengths and flexure thickness, whereas material stiffness has comparatively lower influence. NSGA-II-based multi-objective optimization identified Pareto-optimal solutions that balance amplification ratio and equivalent stiffness, highlighting the inherent trade-off between displacement amplification and structural rigidity. The developed surrogate models enable rapid design exploration, uncertainty assessment, and optimization, while achieving computational speed-ups of approximately 103–107 times compared with finite-element-based evaluation workflows, depending on the evaluation method. The primary contribution of this work is the integration of analytical compliance modeling, physics-consistent dataset generation, uncertainty-aware machine learning, explainable artificial intelligence, and multi-objective optimization within a unified predictive framework. The proposed methodology should be interpreted as a digital-twin-ready surrogate architecture rather than a fully implemented digital twin, as real-time sensing, and online model updating are beyond the scope of the present study. Nevertheless, the framework provides a scalable foundation for future integration with experimental measurements, multi-fidelity datasets, and digital-twin-enabled manufacturing environments.
The increasing need for energy security and the development of clean energy sources to mitigate the impact of climate change necessitate the development of multisource energy generation systems comprising solar panels, generators, and grids. Four hybrid deep learning models were developed to predict the solar power generation (SPG) from historical solar panel data. These include a time convolutional network with enhanced multilayer perceptron (TCN-EMLP), a convolutional neural network with long short-term memory (CNN-LSTM), an LSTM with AutoEncoder (LSTM-AE), and a transformer model. These models were deployed for the predictions of solar power generation (SPG). All models were applied to a dataset containing 378 observations of solar energy data, allowing for a direct comparison between the hybrid deep learning methods employed. The input variables used include battery level (BL), ambient temperature (Temp), solar Irradiance (Ir). The results indicated that LSTM-AE showed superior performance relative to TCN-EMLP, LSTM-AE, and the transformer model with a strong R2 of 0.8359, a summary R2 of 0.8059, a root mean square error (RMSE) of 7.4304 W, and a mean absolute error (MAE) of 5.9322 W. CNN-LSTM achieved a significantly high performance comparable to TCN-EMLP and the transformer model. The utilization of deep learning to build intelligent automated multisource energy systems could lead to enhanced prediction accuracy, better performance, and higher sustainability by lessening reliance on non-renewable backup systems.
IntroductionPredictive current control (PCC) for permanent magnet synchronous motors (PMSM) exhibits slow response and obvious chattering under parameter variation and load shock, while existing schemes cannot coordinate anti-disturbance performance, dynamic speed and battery power constraints.MethodsThis paper designs an improved dynamic double-power reaching law (DPRL) with finite-time convergence and low chattering, embeds it into nonlinear active disturbance control (NADRC) coupled with an extended sliding mode disturbance observer, and adds a battery power limiting module. Simulations and dual-motor bench tests are implemented with multiple contrast algorithms and ablation groups.ResultsThe proposed strategy achieves zero overshoot across all test conditions. During sudden 10 N·m load, the speed drop is only 285 r/min with 1.5 s recovery; acceleration and reversal response time are reduced by 40% and 70% respectively, and d/q‐axis current ripples are significantly weakened. DiscussionThe integrated DPRL‐NADRC PCC enhances PMSM robustness and dynamic performance under complex disturbances and power constraints. Future work will develop automatic gain tuning algorithms and validate the method under high‐speed demagnetization and multi-motor operating scenarios.
IntroductionAssembly line productivity is a critical performance factor in passenger car manufacturing because it directly affects output stability, production cost and delivery efficiency. Existing studies discuss machine, manpower, supply chain and quality issues separately, but limited empirical work examines these barriers together in passenger car manufacturing units of the Pune region. To address this gap, the present study provides an integrated empirical assessment of productivity constraints by combining barrier ranking, interrelationship analysis and factor-based classification within a single quantitative framework. This study aims to identify, evaluate and classify the major barriers affecting assembly line productivity using quantitative industry responses.MethodsA structured questionnaire was used to collect responses from 535 respondents associated with passenger car manufacturing operations. The data were analyzed using frequency analysis, descriptive statistics, correlation analysis and exploratory factor analysis.ResultsThe results showed that lack of modern tools or outdated machinery was the most critical barrier with 97.0% total agreement and the highest mean score of 4.60. Inadequate maintenance followed with 95.5% agreement and a mean score of 4.54. Frequent product defects recorded 93.7% agreement and a mean score of 4.42. Equipment breakdowns recorded 89.3% agreement while customer complaints due to defects recorded 85.3% agreement. Correlation analysis showed strong association between absenteeism or operator delays and poor worker coordination with r = 0.685. Factor analysis extracted four major productivity dimensions with a KMO value of 0.863 and 58.223% total variance explained.Discussion and ConclusionThe main contribution of the study is the identification of statistical associations and shared dimensions among technical, workforce, supply-logistics and quality-related barriers. The study concludes that productivity improvement needs integrated action through technology upgradation, preventive maintenance, defect control, logistics improvement and workforce coordination. Beyond the Pune automotive cluster, the findings provide useful guidance for passenger-car assembly units and similar manufacturing systems in emerging industrial regions where perceived productivity constraints are associated with machine, workforce, material-flow and quality-control limitations. The study is limited to questionnaire-based responses from selected units and therefore the results should be generalized cautiously without plant-level longitudinal validation. Future work may apply regression, SEM or machine-learning models for predictive validation.
Off-highway machines, agricultural harvesters, construction excavators, and mining haul trucks operate under extreme load variability, harsh unstructured environments, and constrained sensor instrumentation, creating prognostic conditions fundamentally different from on-road vehicles. While AI-enabled predictive maintenance has matured for passenger vehicles and well-instrumented industrial assets, and off-highway telematics adoption is expanding rapidly, its translation to these software-defined field machines remains insufficiently addressed. This systematic review synthesizes AI-driven prognostic methods, data challenges, and deployment considerations specific to off-highway operation. Following a PRISMA 2020 protocol, the 2014–2025 literature is screened across seven databases, with the 51 studies retained for synthesis additionally quantified by method family, equipment sector, and publication year to expose the relative scarcity of off-highway-specific evidence, and a wide range of methodologies is synthesized, from foundational supervised learning (SVMs, Random Forests) and advanced deep learning (CNNs, LSTMs for RUL prediction) to unsupervised (Autoencoders), ensemble, and transfer-learning techniques. The review contrasts the primary prognostic frameworks—data-driven, physics-based, and hybrid—and the role of knowledge-based expert systems in delivering interpretable alerts. A significant focus is placed on the data pipeline, including sensor selection strategies, data quality, feature engineering, severe class imbalance, and labeling complexity. Implementation hurdles such as operating-condition variability, model validation, the computational constraints of edge devices, and Explainable AI (XAI) are further examined, with a critical analysis of where each method degrades under field variability. Finally, emerging directions are explored, including Digital Twins and Edge Computing, closing with reformulated, off-highway-specific research gaps for real-world deployment.
ObjectiveThis study evaluates POI-based spatial accessibility to candidate electric-vehicle charging facilities in central Ningbo and develops a transparent screening framework for identifying urban destinations with comparatively weak road-network access.MethodsCandidate charging-facility POIs and urban functional POIs were collected through the Amap Web Service API from 20 to 30 June 2026. A directed OpenStreetMap motor-vehicle network was used to calculate nearest-facility road-network distance. Network and Euclidean distances were compared directly; fixed-rate energy use was reported only as an illustrative conversion of access distance. Non-parametric group comparisons, alternative priority-weighting schemes, and outlier-exclusion tests were used to assess robustness.ResultsThe final inventory contained 1,306 candidate charging-facility POIs and 9,426 urban functional POIs; 32 functional POIs were unreachable on the constructed network. The median network distance was 0.626 km, compared with a median Euclidean distance of 0.365 km, and the median detour ratio was 1.577. Network distance exceeded Euclidean distance for 93.59% of valid pairs (Wilcoxon p < 0.001). District and POI-category differences were significant, with Beilun and tourism/park POIs showing the greatest mean access distances. The baseline priority model classified 1,409 POIs as high priority and 940 as very high priority; alternative weighting schemes retained 76.5%–93.8% of the baseline top-quartile set.ConclusionThe proposed contribution is a reproducible, data-light diagnostic workflow that integrates network topology, destination context, direct Euclidean comparison, and uncertainty testing. The results identify areas for further operational investigation rather than verified demand shortfalls or construction sites. No user-behaviour, charging-transaction, station-capacity, queueing, or route-specific vehicle-energy data were available.
MXenes have been widely reviewed as biomedical nanomaterials for sensing, therapy, imaging, drug delivery and tissue engineering. However, most reviews organize MXene studies by biomedical application categories rather than mechanical conditions under which MXene-containing interfaces operate. This leaves an important gap for biomechanical engineering: how MXene-based soft interfaces maintain signal transduction, transport and biological contact when bent, stretched, compressed, hydrated or attached to moving tissues. This mini review addresses that gap by evaluating MXenes as biomechanical interface materials rather than isolated conductive nanofillers or biomedical additives. The rapid growth of MXene-based soft sensors, bioelectronic devices and tissue-contacting systems highlights the need to assess whether these materials can meet translational requirements for stable, deformable, reproducible and biologically compatible interfaces. The review is structured around systems in which mechanical deformation, hydrated transport, electron–ion conduction and biological contact directly affect device function, with representative examples including wearable deformation sensors, electronic skins, hydrogel electrodes, deformable biosensors, wound-contact interfaces and regenerative scaffolds. Applications dominated by drug delivery, cancer therapy, bioimaging, implant coatings or static antibacterial activity are not comprehensively reviewed unless they clarify deformation-dependent transport, tissue contact or interface reliability. We critically compare how surface terminations, oxidation state, flake size, percolation networks, polymer bonding, swelling, modulus matching and biological boundary conditions regulate biomechanical functions. Key challenges include aqueous instability, storage-related degradation, calibration drift, motion artifacts, fatigue, sterilization tolerance and incomplete biological testing. We propose a four-layer interface framework and validation priorities that pair biomechanical testing with cytotoxicity, irritation, inflammatory response and reproducibility assessment.
IntroductionAccurate determination of aircraft engine compressor characteristics is vital for stability and performance optimization. However, experimental data acquisition is often hindered by high costs, resulting in a strong reliance on limited test data.MethodsTo address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network (GAT) encoder, a Multi-scale Feature Enhancement (MSFE) module, and a cross-attention decoder based on the inverted Transformer architecture. The GAT encoder explicitly models relational dependencies among neighboring operating points along each speed line, capturing local continuity while reducing computational complexity. The MSFE module employs parallel convolutional kernels of sizes 1, 3, and 5 to extract multi-granularity features, effectively broadening the receptive field. Furthermore, the cross-attention decoder enables explicit knowledge transfer across different speed lines, leveraging structural similarities between well-sampled and sparsely sampled speeds—a critical advantage when data is scarce.ResultsComprehensive evaluations demonstrate that GAESA-iFormer achieves optimal performance with a feature dimension of 64 and four encoder layers. When trained on transformed secondary data, the proposed model significantly outperforms state-of-the-art baselines, reducing the RMSE by 27.03% and MAE by 31.78%.DiscussionThe results indicate that the proposed GAESA-iFormer model achieves improved prediction accuracy on the current compressor dataset under limited experimental data.
