Explore the vast range of titles available.

Considering the high computational cost caused by solving multi-objective optimization (MOO) problems, an efficient multi-objective optimization method based on the adaptive approximation model is developed. Firstly, the Latin hypercube design (LHD) is employed for obtaining the initial sample points. Secondly, initial approximation models of objective functions and constraints are established by using the radial basis function (RBF). For ensuring the accuracy of the approximation models, the reverse shape parameter analysis method (RSPAM) is proposed to obtain improved approximation models. Thirdly, the micro multi-objective genetic algorithm (μMOGA) is adopted to solve the Pareto optimal set and the local-densifying approximation method is also applied to strengthen the ability of solving accurate Pareto optimal sets. Finally, the effectiveness and practicability of the proposed method is demonstrated by two numerical examples and two engineering examples.

The increasing demand for monoclonal antibody (mAb) therapeutics has intensified the need for more efficient and consistent biomanufacturing processes. We present a data-driven, machine-learning (ML) approach to exploring and predicting upstream yield behavior. Drawing on industrial-scale batch records for a single mAb product from a contract development and manufacturing organization, we applied regression models to identify key process parameters and estimate production outcomes. Random forest regression, gradient boosting machine, and support vector regression (SVR) were evaluated to predict three yield indicators: bioreactor final weight (BFW), harvest titer (HT), and packed cell volume (PCV). SVR outperformed other models for BFW prediction (R2 = 0.978), while HT and PCV were difficult to model accurately with the available data. Exploratory analysis using sequential least-squares programming suggested parameter combinations associated with improved yield estimates relative to historical data. Sensitivity analysis highlighted the most influential process parameters. While the findings demonstrate the potential of ML for predictive, data-driven yield improvement, the results should be interpreted as an exploratory proof of concept rather than a fully validated optimization framework. This study highlights the need to incorporate process constraints and control logic, along with interpretable or hybrid modeling frameworks, to enable practical deployment in regulated biomanufacturing environments.

Sports characteristics and physiological signals serve as critical benchmarks for analyzing athletic performance and enhancing training methodologies. This study explores the design and development of an advanced smart wearable device system tailored for monitoring sports training. This system is engineered to track athletic performance in real-time by integrating embedded wearable devices with sophisticated software. It primarily encompasses the recognition of human movement states, detection of electrocardiogram (ECG) signals, and monitoring of respiratory signals, thereby facilitating comprehensive analysis of human physiological parameters and movement metrics. This, in turn, supports athletes in optimizing their training routines. Empirical results from the study indicate that the mean-square error for both ECG and respiratory signals recorded during testing approximated ±0.8Hz, falling within the predetermined error tolerance range. Additionally, analyses of joint angle variations during running activities confirmed the efficacy of the proposed smart wearable system in improving sports performance.

The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

While steady-state visual evoked potentials (SSVEPs) are widely used in brain–computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory–inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain–computer interfaces.

The Hopf bifurcation behavior is an important issue for the nonlinear dynamic analysis of gas foil bearing (GFB)-rotor systems. However, there is a lack of detailed study on different types of Hopf bifurcation and their corresponding characteristics for GFB-rotor systems. This paper is intended to provide a clear and systematic insight into the nonlinear dynamic characteristics of GFB-rotor systems with a supercritical or a subcritical Hopf bifurcation. The onset speed (OS) of instability (i.e., the bifurcation point) for the system is calculated by the linear stability analysis. The periodic solution of the system before or after the bifurcation point is obtained by the shooting method, and its stability is assessed by the Floquet multipliers. The shock stability and the unbalanced response characteristics of the GFB-rotor system with a supercritical or a subcritical Hopf bifurcation are presented. A GFB-rotor system with a supercritical Hopf bifurcation shows better dynamic characteristics than a system with a subcritical Hopf bifurcation. The parameter analysis reveals that the aspect ratio and the foil stiffness of the GFBs have obvious effects on the Hopf bifurcation type, while the loss factor has a relatively small effect. It is remarkable that although a lower foil stiffness increases the OS of instability, the actual speed limit would probably decrease as the Hopf bifurcation changes from a supercritical to a subcritical type. This can contribute to an understanding of the necessity of studies on actual available operating speed based on nonlinear analysis rather than conventional linear analysis for the bearing design.

The shear failure of concrete is a sudden brittle failure, which is difficult to be forewarned. To investigate the shear crack mechanisms in concrete, this study first systematic compared acoustic emission (AE) behavior during direct shear tests, compression shear tests (Z-shaped specimens), and three point bending shear tests. AE parameters (amplitude, cumulative count and energy), average frequency (AF)-rise time/amplitude (RA) analysis, K-means clustering, and b -value analysis were integrated to classify cracks and characterize damage progression. The correlation between the shear crack propagation mechanism of concrete and AE parameters was revealed. The AE activity during concrete shear failure was successfully characterized, providing valuable insights into the damage development and evolution processes. The research findings establish a quantitative framework for using AE technology to detect shear cracks and monitor real-time damage evolution in concrete structures.

Micro CT technology can provide three-dimensional high-precision digital images with micrometer (or smaller) resolution, offering robust technical support for the study of the internal structure of rocks. Based on the data obtained from the CT scanning of Leopard sandstone, a series of image processes were carried out using the Avizo software to extract pore distribution. The watershed algorithm was employed to independently segment each pore, and the largest sphere algorithm was used to establish a sphere-tube model. Based on the constructed pore network model, quantitative analyses of the porosity, fractal dimensions, permeability, and other parameters of the sandstone sample were conducted. Moreover, the connectivity of the pores was analyzed using an automatic skeleton model. Results show: ① CT scanning digital core technology can not only visualize the three-dimensional connectivity and isolated pores of sandstone but also quantitatively characterize the pore structure parameters; ② The analyzed Leopard sandstone has a connected porosity of 13.76%, an isolated porosity of 0.53%, and the pore radius is mainly concentrated in the 0-70μm small pore range, occupying 76.1% of the total pore volume; ③ The lower the sample porosity, the higher the fractal dimension, indicating a more complex pore structure.

Combining the advantages of cast-in-place hollow slabs and prefabricated reinforced truss composite concrete slabs, a novel hollow composite slab is proposed, characterized by the inclusion of hollow thin-walled boxes without reinforcement at the edges, referred to as the hollow composite slab. To further investigate the flexural performance and critical design parameters of the hollow composite slab, numerical simulations were conducted using the finite element software ABAQUS. Based on the actual specimen fabrication and test results, the rationality of the finite element modeling was validated. Using the finite element model, a parametric analysis of key parameters for the specimens was conducted. The results showed that the finite element model could effectively simulate the crack distribution, flexural performance, and deformation characteristics of hollow composite slabs. The influence of concrete strength and the longitudinal dimension of hollow thin-walled boxes on the flexural performance of hollow composite slabs was minimal, with ultimate bearing capacities changing by only 4.63% and 0.91%, respectively. In contrast, changes in slab thickness and span had a significant impact on the flexural performance, with ultimate bearing capacities changing by 20.46% and 42.09%, respectively. The bearing capacity of hollow composite slabs increased significantly with increasing slab thickness but decreased markedly with increasing span.