Search Results (8,613)

This study introduces the Perception Latency Mitigation Network (PLM-Net), a modular deep learning framework designed to mitigate perception latency in vision-based imitation-learning lane-keeping systems. Perception latency, defined as the delay between visual sensing and steering actuation, can degrade lateral tracking performance and steering stability. While delay compensation has been extensively studied in classical predictive control systems, its treatment within vision-based imitation-learning architectures under constant and time-varying perception latency remains limited. Rather than reducing latency itself, PLM-Net mitigates its effect on control performance through a plug-in architecture that preserves the original control pipeline. The framework consists of a frozen Base Model (BM), representing an existing lane-keeping controller, and a Timed Action Prediction Model (TAPM), which predicts future steering actions corresponding to discrete latency conditions. Real-time mitigation is achieved by interpolating between model outputs according to the measured latency value, enabling adaptation to both constant and time-varying latency. The framework is evaluated in a closed-loop deterministic simulation environment under fixed-speed conditions to isolate the impact of perception latency. Results demonstrate significant reductions in steering error under multiple latency settings, achieving up to $62\%$ and $78\%$ reductions in Mean Absolute Error (MAE) for constant and time-varying latency cases, respectively. These findings demonstrate the architectural feasibility of modular latency mitigation for vision-based lateral control under controlled simulation settings. The project page including video demonstrations, code, and dataset is publicly released. Full article

Laser wire directed energy deposition (LWDED) has garnered significant attention for the fabrication of large metallic components. However, the complex coupling effects among its process parameters pose challenges for porosity control. Optimizing parameter combinations to effectively minimize porosity is therefore critical to the broader adoption of this technology. In this study, systematic experiments and modeling were conducted to optimize the LWDED process parameters and predict porosity. First, single-factor and orthogonal experiments were performed to evaluate the individual effects of laser power, scanning speed, wire feeding speed, and air pressure on porosity. Subsequently, range analysis and analysis of variance were employed to determine the influence of each parameter and the significance of their interactions. Four machine learning models—SVR, RF, GPR, and XGBoost—were then trained and compared. Among them, the SVR model exhibited the best predictive performance, achieving an R² of 0.8960, an RMSE of 0.19, and an MAE of 0.15, outperforming the other three models. Based on this, the SVR model was further utilized to establish the mapping between process parameters and porosity. Contour maps and three-dimensional surface plots were generated to visualize porosity variation patterns under interacting parameters. Validation experiments showed that the maximum relative error between model predictions and experimental measurements was 0.514%, with an average error of 0.251%. This study provides a reliable reference for selecting low-porosity parameter combinations in the LWDED fabrication of 5356 aluminum alloy components. Full article

Within a spatial-economics framework, this paper extends a general-equilibrium model to examine how high-speed rail (HSR) openings reduce migration costs and thereby alleviate regional factor misallocation. The model predicts that improved connectivity lowers labor mobility frictions, facilitates cross-regional reallocation of productive factors, and reduces misallocation. Using a panel of China’s prefecture-level cities from 2006 to 2016 and a difference-in-differences design, we estimate the causal effects of HSR on the misallocation of labor and capital. The results show that HSR openings significantly improve both labor and capital allocation, and the findings remain robust to a range of endogeneity checks and alternative specifications. Heterogeneity analyses indicate that the improvement is concentrated in eastern cities, while the effects are statistically insignificant in central and western regions. We also find that the reduction in misallocation occurs in both provincial capital and non-capital cities. These results imply that HSR can enhance resource-use efficiency and support sustainable regional development by reducing spatial frictions and promoting more balanced factor allocation. From a policy perspective, accelerating HSR network expansion can lower cross-regional mobility costs and enable freer flows of labor and capital, thereby improving allocative efficiency and fostering inclusive and sustainable growth. Full article

Examining the mechanism of two-way interaction between the air turbine and generator is essential for accurately predicting the performance of oscillating water column (OWC) devices. This study developed a fully integrated model for a back-bent duct buoy device, which incorporated the chamber, impulse turbine, permanent magnet synchronous generator, PI controller, and speed control strategies. The models of chamber–turbine and turbine-control systems were validated separately against wave-flume experimental results under regular and irregular wave conditions. In addition, a comparative study of two control strategies based on Best Efficiency Point Tracking was conducted by analysing key performance parameters at each energy conversion. The mechanism of two-way interaction between the turbine and the generator was elucidated. The integrated model demonstrated a great potential in predicting the conversion performance of wave energy to electrical energy under real sea conditions, as well as testing control strategies and algorithms before physical deployment. Full article

The paper proposes an adaptive architecture of an electromechanical drive with internally controlled energy exchange, implemented through the integration of an inertial flywheel and a controlled clutch into the structure of a planetary transmission. A multi-mass dynamic and energy model of the system is developed, and the power balance is verified. Based on the energy formulation, adaptive energy and predictive energy control strategies are implemented. The results of numerical simulation confirm that the use of the internal energy exchange loop increases system stability, reduces peak motor torque by 30–40%, decreases maximum output speed deviations by 35–45% under step load conditions, and reduces the root-mean-square tracking error by 20–30% compared with reactive energy-based control, demonstrating improved tracking performance and reduced actuator load compared to the classical drive architecture. Full article

This paper examines income convergence in Europe by jointly analyzing European Union member states and Western Balkan economies over the period 2004–2023. While classical growth theory predicts that poorer economies should grow faster than richer ones, empirical evidence for Europe remains mixed, particularly when institutional and structural heterogeneity is taken into account. Using panel data techniques, the study tests for absolute and conditional β-convergence and complements this analysis with an assessment of σ-convergence. The results provide strong evidence of absolute income convergence across the sample, indicating that economies with lower initial income levels tend to grow faster. Conditional convergence is also confirmed, although the direct effect of institutional quality weakens once structural factors such as foreign direct investment and human capital are included, suggesting that institutions operate primarily through indirect channels. An interaction analysis shows no systematic evidence that institutional quality alters the speed of convergence. Finally, σ-convergence analysis reveals pronounced regional heterogeneity, with strong convergence among new EU member states, stable but low dispersion within the Western Balkans, and more modest convergence patterns in the EU core. Overall, the findings highlight that European convergence remains uneven and highly conditional on institutional and structural characteristics. Full article

Climate change poses significant threats to the performance of photovoltaic (PV) systems through higher operating temperatures, yet most impact assessments rely on steady-state thermal models that neglect the effects of thermal inertia. This study applies a validated transient thermal model based on an ordinary differential equation to assess the evolution of PV cell temperature under future climate scenarios for Bucharest, Romania, a site representative for the continental climate prevailing across Central and Eastern Europe, thus providing a validated methodological framework transferable to the broader region. High-resolution meteorological predictions from EURO-CORDEX (0.11° resolution) provide temperature, solar irradiance, and wind speed data for RCP 4.5 and RCP 8.5 scenarios across 2030s and 2050s time horizons. The dynamic modeling approach reveals moderate increases in thermal stress on PV systems under both climate scenarios, with increases in mean cell temperature of 0.6 to 1.2 °C. Also, an intensification of the inter-annual variability was observed, with implications for long-term reliability and uncertainty of the systems and their efficiency. The results quantify both thermal resilience and scenario-variable performance degradation and identify the temporal evolution of thermal vulnerability, providing essential guidance for climate-resilient solar energy planning and long-term investment strategies in the region. Full article

3D electrodeposition printing is an emerging process for fabricating metallic parts with controllable geometry, yet the coupled influences of electrochemical kinetics, ion transport, and tool motion on layer height remain difficult to interpret. This work presents a physics-based process model that links key process inputs, current density, electrolyte concentration, the inter-electrode gap, and tool scanning speed, to the resulting layer height in 3D electrodeposition printing of nickel-based structures. The model combines species transport in the inter-electrode gap with Butler–Volmer kinetics, under carefully stated assumptions regarding current efficiency, overpotential, and lateral spreading. Model predictions are validated against experimentally reported layer heights over a range of process conditions, yielding average errors (9–15%) and root-mean-square errors (0.13–0.28 µm) that demonstrate good agreement and highlight the impact of simplifying assumptions. Systematic parametric studies reveal how each process input monotonically influences layer height in ways consistent with Faraday’s law and diffusion-controlled growth, while also quantifying the relative sensitivity to different parameters. Building on these results, we introduce a dimensionless 3D Electrodeposition Printing Index that consolidates the key process and material parameters into a single scalar describing the geometric growth regime. The index enables construction of process maps that capture how combinations of current density, scan speed, concentration, and gap affect achievable layer height within the validated operating window. The scope and limitations of the proposed modeling framework and the index, particularly regarding other materials, more complex geometries, and pulsed or strongly convective regimes, are explicitly discussed, providing a basis for future model extensions and experimental validation. Full article

This work used machine learning to forecast product density and optimize the laser powder bed fusion (LPBF) process for parts made of pure zinc (Zn). A relative density of 90–97% (6.42–6.95 g/cm³) was obtained by varying combinations of key process parameters, including laser power, scanning speed, track overlapping, hatch spacing, and layer thickness. Machine learning provided models for density prediction and better comprehension of the impact of input parameters. A SHapley Additive exPlanation (SHAP) analysis quantified the contributions of specific features, enhancing model interpretability. Fifty-one experimental runs were used to test several methods, including Bayesian ridge, CatBoost, elastic net, lasso, linear regression, random forest, ridge regression, and XGBoost. CatBoost performed best, with a test coefficient of determination (R²) of 0.893, a mean absolute error (MAPE) of 0.010 and a root mean square error (RMSE) of 0.015. A feature importance analysis showed that laser power (49%) and scanning speed (42%) had the greatest influence, while hatch spacing (5%) and layer thickness (4%) had minimal impacts on product density. Therefore, selecting the correct optimized set of process parameters determines the resulting density and can support more efficient LPBF process development. Full article

To optimize the structural parameters of a peanut digging shovel and enhance its operational performance, the forces exerted on the digging shovel were examined through a graphical mechanics approach. This analysis identified the primary structural and operational parameters of the shovel’s design. A numerical simulation model for the working resistance of the shovel was established adopting EDEM (2018) discrete element analysis software and subsequently validated through comparative analysis with field experiment results. Employing the Box–Behnken response surface method, quadratic regression models were constructed with digging resistance and soil non-breakage ratio as the response variables, while forward speed, soil entry angle, and blade tilt angle were taken as the influencing factors. Optimization analysis of these parameters was conducted. The optimization results indicate that with a forward speed of 0.8 m/s, a soil entry angle of 20°, and a blade tilt angle of 40°, the working resistance of the shovel is 1667 N, and the soil non-breakage ratio is 20.56%. The error between the field test results and the predictions from the optimized model was less than 2%, illustrating the feasibility of the model and the optimization outcomes. This study offers a technical reference for future simulation-based optimization of peanut digging shovels. Full article

A cross-construction method is proposed to establish a wind turbine wake dataset with significantly reduced computational fluid dynamics (CFD) costs. This method involves adjusting one operating parameter, such as the tip speed ratio (TSR), while maintaining the others at their optimal values. This procedure is repeated across another parameter (inflow velocity) to generate a sparse but informative dataset. CFD simulations were performed using large eddy simulation (LES) coupled with an actuator line model (ALM) to generate data. A pre-training and fine-tuning network based on error classification (PFNEC) was developed, achieving high prediction accuracy with coefficients of determination of 0.9750 and 0.9851 for two validation conditions. Two models based on a softmax function and a residual block were designed, and they achieved the best performance, with coefficients of determination of 0.9921 and 0.9891 under different conditions. The Fourier embedding was applied to enhance input features of neural networks. Four samples added to the original dataset improved the prediction accuracy for extreme operating conditions, from coefficient of determination values of 0.7143 and 0.7034 to 0.9939 and 0.9886 with Fourier embedding. This cross-construction method can significantly reduce the cost of dataset establishment. The models exhibited reliable generalization and prediction accuracy. Full article

Considering the challenges currently faced in the civil air traffic management (ATM) domain, this study investigates an aircraft safety situation awareness method based on ADS-B surveillance data. First, a sector safety situation assessment framework is constructed, incorporating six-dimensional indicators such as peak-hour traffic volume and speed standard deviation. The information entropy method is employed to determine objective weights, and an improved fuzzy C-means clustering algorithm is used to classify safety situations into three levels: “good,” “moderate,” and “attention required.” On this basis, multiple prediction models are compared, among which the random forest model achieves the best performance with an accuracy of 90%. Experimental results indicate that, under the experimental conditions of this study, speed standard deviation and approach rate contribute most significantly to safety situation assessment. The proposed method provides air traffic management authorities with an objective and quantifiable technical solution for safety situation awareness. Full article

Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions between the ego vehicle (EV) and target vehicle (TV) in pairwise scenarios. First, the study defines an intention representation method that characterizes intentions using spatial area boundaries, feasible speed ranges, and a set of goal points (speed goal points, position-orientation goal points). Second, a spatial motion planning approach is adopted to evaluate the intention, which optimizes the driving scheme using a multi-objective cost function (incorporating pursuit precision, comfort, energy efficiency, and travel efficiency). Finally, the game-theoretic decision-making model is constructed. The Social Value Orientation (SVO) is introduced to quantify drivers’ social preferences, and the payoff function, which integrates safety rewards (based on inter-vehicle distance) and performance rewards (based on motion planning indices), is established. Simulation results verify that the proposed model can effectively address the interactive intention decision-making problem between the AV and other road users and handle different scenarios. Full article

Introduction: Advanced-stage high-grade serous ovarian cancer (HGSC) is a disease that is difficult to manage due to its heterogeneous clinical behavior. No reliable prediction of response to chemotherapy is currently available and the overall survival rate remains poor. Herein, we sought to determine the molecular mechanisms by which microRNAs (miRNAs) confer chemoresistance in ovarian cancer and demonstrate the efficacy of targeting miRNAs to sensitize HGSC to cisplatin treatment. Methods: Next-generation miRNA sequencing was performed using microdissected HGSC specimens to identify an miRNA signature for intrinsic chemoresistance, and miR-625-3p was selected for further study. The effects of miR-625-3p on cisplatin sensitivity were evaluated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays and cell death enzyme-linked immunosorbent assay. Transcriptome profiling analysis, online prediction algorithms, and reporter assays were used to demonstrate SSX2IP as the direct gene target of miR-625-3p. Cell death enzyme-linked immunosorbent assays, mass spectrometry, and high-speed confocal microscopy were used to determine the roles of SSX2IP in mediating the effects of miR-625-3p in cisplatin sensitivity via the extracellular vesicle (EV) secretion of cisplatin. Results: An miRNA signature for intrinsic chemoresistance was identified. Amongst all the downregulated miRNAs in the chemo-refractory samples, only miR-625-3p was associated with poorer overall survival and progression-free survival rates. Further functional studies showed that the overexpression of miR-625-3p significantly decreased cisplatin resistance in ovarian cancer cells both in vitro and in vivo. SSX2IP (Synovial Sarcoma, X Breakpoint 2 Interacting Protein) was confirmed to be the direct gene target of miR-625-3p and its upregulation abrogated miR-625-3p-mediated cisplatin resistance by enhancing the EV export of cisplatin in ovarian cancer cells. Conclusions: These findings provide a new paradigm for intrinsic cisplatin resistance acquisition by HGSC cells, which will be crucial for developing new treatment strategies for ovarian cancer based on the upregulation of miR-625-3p or downregulation of SSX2IP to enhance cisplatin sensitivity and improve patient survival rates. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS^® platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article