Search Results (10,961)

Understanding the mechanical behavior of implanted neural electrode arrays is crucial for BCI development, which is the foundation for ensuring surgical safety, implantation precision, and evaluating electrode efficacy and long-term stability. Therefore, a reliable FE models are effective in reducing animal experiments and are essential for a deeper understanding of the mechanics of the implantation process. This study established a novel finite element model to simulate neural electrode implantation into brain tissue, specifically characterizing the nonlinear mechanical responses of brain tissue. Synchronized electrode implantation experiments were conducted using ex vivo porcine brain tissue. The results demonstrate that the model accurately reproduces the dynamics of the electrode implantation process. Quantitative analysis reveals that the implantation force exhibits a positive correlation with insertion depth, the average implantation force per electrode within a multi-electrode array decreases with increasing electrode number, and elevation in electrode size, shank spacing, and insertion speed each contribute to a systematic increase in insertion force. This study provides a reliable simulation tool and in-depth mechanistic analysis for predicting the implantation forces of high-density neural electrode arrays and offer theoretical guidance for optimizing BCI implantation device design. Full article

Globally, winery wastewaters (WWWs) are estimated to account for about 62.5 billion L annually (2021), with COD levels up to 300,000 mg O₂/L primarily attributed to residual ethanol, posing serious environmental concerns. Conventional treatments are effective in COD removal, but they often miss opportunities for energy recovery and resource valorization. This study investigates the aqueous phase reforming (APR) of ethanol-rich wastewater as an alternative treatment for both COD reduction and energy generation. Two scenarios were assessed: electricity and heat cogeneration (S1) and hydrogen production (S2). Process simulations in Aspen Plus^® V14, based on lab-scale APR data, provided upscaled material and energy flows for techno-economic analysis, life cycle assessment, and energy sustainability analysis of a 2.5 m³/h plant. At 75% ethanol conversion, the minimum selling price (MSP) was USD0.80/kWh with a carbon footprint of 0.08 kg CO₂-eq/kWh for S1 and USD7.00/kg with 2.57 kg CO₂-eq/kg H₂ for S2. Interestingly, S1 revealed a non-linear trade-off between APR performance and energy integration, with higher ethanol conversion leading to a higher electricity selling price because of the increased heat reactor duty. In both cases, the main contributors to global warming potential (GWP) were platinum extraction/recovery and residual COD treatment. Both scenarios achieved a positive energy balance, with an energy return on investment (EROI) of 1.57 for S1 and 2.71 for S2. This study demonstrates the potential of APR as a strategy for self-sufficient energy valorization and additional revenue generation in wine-producing regions. Full article

In this paper, we propose a deep learning-based surrogate model for Multivariate Empirical Mode Decomposition (MEMD) using Long Short-Term Memory (LSTM) networks, aimed at efficiently extracting Intrinsic Mode Functions (IMFs) from electroencephalographic (EEG) signals. Unlike traditional data-driven methods, our approach leverages temporal sequence modeling to learn the decomposition process in an end-to-end fashion. We further enhance the decomposition targets by employing Noise-Assisted MEMD (NA-MEMD), which stabilizes mode separation and mitigates mode mixing effects, leading to better supervised learning signals. Extensive experiments on synthetic and real EEG data demonstrate the superior performance of the proposed LSTM surrogate over conventional feedforward neural networks and standard MEMD-based targets. Specifically, the LSTM trained on NA-MEMD outputs achieved the lowest mean squared error (MSE) and the highest signal-to-noise ratio (SNR), significantly outperforming the feedforward baseline, even when compared using the Power Spectral Density (PSD). These results confirm the effectiveness of combining LSTM architectures with noise-assisted decomposition strategies to approximate nonlinear signal analysis tasks such as MEMD. The proposed surrogate model offers a fast and accurate alternative to classical empirical methods, enabling real-time and scalable EEG analysis. Full article

This study investigates the effect of a nonlinear saturation controller (NSC) on the van der Pol–Mathieu–Duffing oscillator (VMDO). The oscillator is a single degree of freedom (DOF) system. It is driven by an external force. It is described by a nonlinear differential equation (DE). The multiple-scale perturbation method (MSPT) is applied. It gives second-order analytical solutions. The first indirect Lyapunov method is used. It provides the frequency–response equation. It also shows the stability conditions. Internal resonance is included. The analysis considers steady-state responses. It studies simultaneous primary resonance with a 1:2 internal resonance (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

The aim of this study was to investigate microstructure and the mechanical and operational characteristics of thick and thin walls 3D-built by electron beam additive manufacturing (EBAM). In addition, the milling parameters (rotation speed, feed, and cutting width) were optimized based on simultaneous assessments of Ra roughness on the machined surfaces and material removing rate values. The wall dimensions did not exert a noticeable effect on their chemical compositions, as compared with the original wires used for 3D printing. In comparison, the strength characteristics of the wrought steel (cold-rolled plate) were higher due to finer grains, with both ferrite content and dislocation density being greater as well. In the 3D building process, multiple thermal cycles gave rise to the formation of elongated columnar grains, reducing the strength characteristics. The corrosion rate of the wrought steel was almost twice those of the 3D-printed blanks because of the higher content of both ferrite and twins. By assessing the machinability of the EBAM-built blanks using the stationary milling machine, the cutting forces were comparable due to similar mechanical properties (including microhardness). To improve the removing rate values and reduce the cutting forces, it is recommended to enhance the cutting speeds while not increasing the feeds. For the semi-industrial milling machine, both linear multiple regression and nonlinear neural network models were applied. An integrated approach was proposed that rationally determined both additive manufacturing and post-processing parameters based on a combination of express assessment and analysis of the mechanical, operational, and technological characteristics of built products within a single laboratory complex. Full article

The use of regression-based surrogate models to determine design scaling rules for mechanical product families has proven to be a powerful approach for dimensioning complex geometries. However, there is a broad range of surrogate models in the literature, and each model can be configured in multiple ways. The optimal model election is highly conditioned by the case study nature (e.g., non-linearity level), and consequently, it is mandatory to evaluate different surrogate models. This process can be cumbersome and time consuming, but the election of an inaccurate model may lead to several design–analysis iterations that increase the product cost and development time. Therefore, in this paper, a novel surrogate modelling technique to determine representative design scaling rules for product families—named Univariate Regression-Based Multivariate (URBaM)—is presented. The proposed method seeks to minimize design–analysis iterations while suppressing the process involved in evaluating different surrogate models, independent of the non-linearity level nature of the design problem. The URBaM model is evaluated through six oil and gas valve family design case studies, addressing critical mechanical component design problems across different non-linearity levels (two low, two medium, and two high). The results obtained with the URBaM model in these six cases are compared against 14 configurations of eight widely used techniques in the literature. The obtained results demonstrate that the URBaM model is capable of accurately adapting to different non-linearity levels with a single configuration and presents high stability regarding MAPE, NRMSE, and RMAE metrics from case to case. Consequently, the potential of the URBaM surrogate modelling technique to assist the design process of scalable mechanical product families is proven. Full article

Automated electroencephalography (EEG) seizure detection is meaningful in clinical medicine. However, current approaches often lack comprehensive feature extraction and are limited by generic classifier architectures, which limit their effectiveness in complex real-world scenarios. To overcome this traditional coupling between feature representation and classifier development, this study proposes DySC-MDE, an end-to-end co-designed framework for seizure detection. A novel multi-domain entropy (MDE) representation is constructed at the feature level based on amplitude-sensitive permutation entropy (ASPE), which adopts entropy-based quantifiers to characterize the nonlinear dynamics of EEG signals across diverse domains. Specifically, ASPE is extended into three distinct variants, refined composite multiscale ASPE (RCMASPE), discrete wavelet transform-based hierarchical ASPE (HASPE-DWT), and time-shift multiscale ASPE (TSMASPE), to represent various temporal and spectral dynamics of EEG signals. At the classifier level, a dynamic synapse classifier (DySC) is proposed to align with the structure of the MDE features. Particularly, DySC includes three parallel and specialized processing pathways, each tailored to a specific entropy variant. These outputs are then adaptively fused through a dynamic synaptic gating mechanism, which can enhance the model’s ability to integrate heterogeneous information sources. To fully evaluate the effectiveness of the proposed method, extensive experiments are conducted on two public datasets using cross-validation. For the binary classification task, DySC-MDE achieves an accuracy of 97.50% and 98.93% and an F1-score of 97.58% and 98.87% in the Bonn and CHB-MIT datasets, respectively. Moreover, in the three-class task, the proposed method maintains a high F1-score of 96.83%, revealing its strong discriminative performance and generalization ability across different categories. Consequently, these impressive results demonstrate that the joint optimization of nonlinear dynamic feature representations and structure-aware classifiers can further improve the analysis of complex epileptic EEG signals, which opens a novel direction for robust seizure detection. Full article

The inerter is a device that produces a force proportional to the relative acceleration of both inerter terminals. When combined with springs and dampers in a vehicle suspension system, it forms an inerter–spring–damper (ISD) suspension. This structure shows significant advantages in improving vehicle ride comfort and road friendliness. This paper systematically reviews research progress on ISD suspension. First, the working principle and structural types of the inerter are introduced. Then, an overview of the breakdown phenomena and nonlinear characteristics of ISD suspension is provided, followed by a systematic analysis of ISD suspension structure designs. Next, the control strategies for ISD suspension are discussed, along with their applications in the automotive field. Finally, the paper outlines the main challenges in current inerter research and explores its potential applications in vehicle suspensions. This work can provide a reference for the development of inerter and ISD suspension technologies. Full article

Hilly areas serve as critical ecological barriers yet face developmental challenges, drawing increasing attention to how land use transformation affects eco-environmental quality (EEQ). Systematic studies on EEQ drivers in complex terrains remain limited, particularly regarding nonlinear and interactive effects. This study examines Zhejiang’s hilly area—typical of southern China’s hills—using land use data from 2000, 2010, and 2020. Methods including land use transfer matrix, EEQI, hotspot analysis, and XGBoost-SHAP were applied to assess impacts and quantify drivers. Results show a slight but consistent decline in EEQ index (EEQI) (0.7635 to 0.7472), driven primarily by rapid built-up land (BL) expansion (276.41% increase). NDVI was the most influential factor (SHAP: 0.1226, >59%), followed by GDP per unit area and temperature. NDVI showed a threshold effect (>0.65 strengthens benefit), and strong interaction with per capita GDP. A slope-vegetation coupling mechanism was identified: on slopes > 30°, high NDVI significantly amplifies EEQ improvement, highlighting the importance of vegetation conservation on steep slopes. These findings provide a scientific basis for targeted land management in hilly regions of southern China and similar areas. Full article

Background: Novel and accessible biomarkers may add to the existing risk stratification schemes in patients with acute coronary syndrome (ACS). The platelet-to-lymphocyte ratio (PLR) and glucose-to-lymphocyte ratio (GLR) have emerged as potential indicators of systemic inflammation and metabolic stress, both of which are pivotal in ACS pathophysiology. The aim of this study was to investigate the prognostic significance of the PLR and GLR in patients with ACS. Methods: We performed a retrospective cohort study of patients hospitalized with ACS between 2017 and 2023 at Hippokration Hospital of Thessaloniki, Greece. PLR and GLR were calculated from admission blood samples. The primary endpoint was all-cause mortality. Logistic and Cox regression models were used to investigate the associations of PLR and GLR with all-cause mortality. Receiver operating characteristic (ROC) analysis, Kaplan–Meier survival curves, and restricted cubic spline (RCS) modeling were also applied. Results: In total, 853 patients (median age: 65 years, 72.3% males) were included. Higher PLR and GLR were independently associated with increased risk of long-term mortality [adjusted Odds Ratio (aOR) for PLR: 1.007, 95% CI: 1.005–1.008; and for GLR: aOR = 1.006, 95% CI: 1.003–1.008]. The optimal cut-off values were 191.92 for PLR and 66.80 for GLR. Kaplan–Meier and Cox regression analyses confirmed significantly reduced survival in patients with GLR and PLR values exceeding these thresholds. RCS analysis revealed non-linear relationships, with mortality risk rising sharply at higher levels of both markers. PLR showed superior prognostic performance (AUC: 0.673, 95% CI: 0.614–0.723) compared to GLR (AUC: 0.602, 95% CI: 0.551–0.653). Conclusions: While PLR demonstrated greater predictive accuracy, both PLR and GLR were consistently associated with mortality and may provide complementary prognostic information. Incorporating those ratios into routine clinical assessment may improve risk stratification, particularly in resource-limited settings or for patients without traditional risk factors. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

Current simulation training for pilots wearing night vision goggles (NVGs) (e.g., night landings and tactical reconnaissance) faces fidelity limitations from conventional displays. This study proposed a novel dynamic NIR-LED direct-view display system for authentic nighttime scene simulation. Through comparative characterization of NVG response across LED wavelengths under ultra-low-current conditions, 940 nm was identified as the optimal wavelength. Quantification of inherent nonlinear response in NVG observation enabled derivation of a mathematical model that provides the foundation for inverse gamma correction compensation. A prototype NIR-LED display was engineered with 1.25 mm pixel pitch and 1280 × 1024 resolution at 60 Hz refresh rate, achieving >90% uniformity and >2000:1 contrast. Subjective evaluations confirmed exceptional simulation fidelity. This system enables high-contrast, low-power NVG simulation for both full-flight simulators and urban low-altitude reconnaissance training systems, providing the first quantified analysis of NVG-LED nonlinear interactions and establishing the technical foundation for next-generation LED-based all-weather visual displays. Full article

Chemical vapor deposition (CVD) is a high-precision thin-film fabrication technique that is widely applied in semiconductor manufacturing, optical component manufacturing, and materials science. The performance of the deposition process plays a critical role in determining the quality of the final product. However, multiple variables in CVD processes have a highly nonlinear nature that involves complex interactions. Therefore, conventional experimental methods exhibit limitations in quality control and process optimization for CVD. In this study, we developed a predictive model based on process parameters and quality indicators using machine learning techniques to analyze and optimize the CVD processes. Through data collection, feature selection, model training, and model validation, the developed machine-learning algorithms were tested and evaluated. The adopted machine learning algorithms effectively captured the nonlinear relationships between multiple variables in CVD processes, accurately predicted thin-film quality indicators, and provided data for optimizing process parameters. In addition, the analysis results of feature importance revealed the effect of each key parameter on product quality, offering a basis for process improvement. Overall, the results of this study highlight the capability of machine learning algorithms for quality control and optimization in CVD processes for future advancements in smart manufacturing. Full article

Corrosion is a phenomenon that significantly impacts the durability of reinforced concrete (RC) structures, particularly in highly corrosive environments like coastal regions. The existing numerical modelling often relies on complex approaches that are impractical for structural assessment. For this reason, this study proposes a simplified numerical modelling approach to simulate the cyclic behaviour of existing RC framed structures with corrosion levels (η) below 25%. The proposed modelling employs concentrated plasticity hinges for beams and fiber sections for columns, incorporating corrosion-induced degradation through modified backbone curves and material properties based on the corrosion level of the structural element. The modelling approach was validated against experimental results from the literature; the proposed model adequately captures hysteretic energy, lateral load, and deformation capacities, with maximum errors of 11% for maximum lateral load, 12% for ultimate load, and 33% for dissipated energy in RC frames. For isolated columns, the errors were 11, 12, and 22%, respectively. In addition, a maximum difference of 7% was found in the lateral load capacity of the corroded frames associated with the Life Safety limit state. Finally, it was concluded that the proposed methodology is suitable for representing the cyclic behaviour of corroded RC columns and frames and provides engineers with a tool to evaluate the behaviour of corroded structures without resorting to complex models. Full article