Search Results (31,196)

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address the limitations of the conventional Harris hawk optimization (HHO) algorithm in solving complex optimization problems. HHO often faces challenges such as susceptibility to local optima, slow convergence, and inadequate precision in global solution-seeking. MSHHO integrates four innovative strategies to bolster HHO’s effectiveness in both local exploitation and global exploration. These include a positive charge repulsion strategy for diverse population initialization, a nonlinear decreasing parameter to heighten competitiveness, the introduction of Gaussian random walk, and mutual benefit-based position updates to enhance mobility and escape local optima. Empirical validation on 12 benchmark functions from CEC2005 and comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications to three real-world engineering problems and UAV flight trajectory optimization further demonstrate MSHHO’s efficacy in overcoming complex optimization challenges. This study underscores MSHHO as a robust framework with enhanced global exploration capabilities, significantly improving convergence accuracy and speed in engineering applications. Full article

Blast furnace slags are formed by CaO-SiO₂-Al₂O₃-MgO systems and have several physical characteristics, one of which is viscosity. Viscosity is an important variable for the operation and blast furnace performance. This work aimed to model viscosity through linear and non-linear models in order to obtain a model with precision and accuracy. The best model constructed was a non-linear model by artificial neural networks that presented 23 nodes in the first hidden layer and 24 nodes in the second hidden layer with 6 input variables and 1 output variable named ANN 23-24. ANN 23-24 obtained better statistical evaluations in relation to 11 different literature equations for predicting viscosity in CaO-SiO₂-Al₂O₃-MgO systems. ANN 23-24 was also subjected to numerical simulations in order to demonstrate the validation of the non-linear model and presented applications such as viscosity prediction, calculation of the inflection point in the viscosity curve by temperature, the construction of ternary diagrams with viscosity data, and the construction of iso-viscosity curves. Full article

High-harmonic generation from solid films is an attractive method for converting infrared laser pulses to ultraviolet and vacuum ultraviolet wavelengths and for examining the films using the generation process. In this work, AlN thin films grown on a sapphire substrate are studied. Below-band-gap third harmonics and above-band-gap fifth harmonics were generated using a Ti:sapphire oscillator running at 800 nm. A strong enhancement of the fifth-harmonic signal in the forward direction was observed from thicker 39 nm and 100 nm films compared to thinner 8 nm and 17 nm films. For the fifth harmonic generated in the backward direction, and also for the third harmonic in both the forward and backward directions, only a weak dependence of the harmonic signal on the film thickness was measured. Using both X-ray diffraction and dependence of the fifth harmonic on the laser polarization measurements, these behaviors are attributed to the crystallization and the grain size of the films, promising fifth-harmonic generation as a suitable tool to study AlN film properties. Full article

Understanding the complex variations in water use efficiency (WUE) is critical for optimizing agricultural productivity and resource management. Traditional analytical methods often fail to capture the nonlinear and multiscale variations inherent in WUE, where multifractal theory offers distinct advantages. Given its limited application in WUE studies, this paper analyzes the spatiotemporal characteristics and influencing factors of the WUE in Anhui Province from 2001 to 2022 using a multifractal, multiscale approach. The results indicated that the WUE exhibited significant interannual variation, peaking in summer, especially in August (2.4552 gC·mm⁻¹·m⁻²), with the monthly average showing an inverted “V” shape. Across different spatial and temporal scales, the WUE displayed clear multifractal characteristics. Temporally, the variation in fractal features between years was not prominent, while inter-seasonal variation was most complex in August during summer. Spatially, the most distinct multifractal patterns were observed in hilly and mountainous areas, particularly in regions with brown soil distribution. Rainfall was identified as the primary natural driver influencing regional WUE changes. This study aims to promote the sustainable use of water resources while ensuring the stability of agricultural production within protected farmlands. Full article

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc. Full article

With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model. Full article

There has been extensive research on the partial differential equations governing the theory of gas flow in coal mines. However, the traditional Proper Orthogonal Decomposition–Radial Basis Function (POD-RBF) reduced-order algorithm requires significant computational resources and is inefficient when calculating high-dimensional data for coal mine gas pressure fields. To achieve the rapid computation of gas extraction pressure fields, this paper proposes a model reduction method based on deep neural networks (DNNs) and convolutional autoencoders (CAEs). The CAE is used to compress and reconstruct full-order numerical solutions for coal mine gas extraction, while the DNN is employed to establish the nonlinear mapping between the physical parameters of gas extraction and the latent space parameters of the reduced-order model. The DNN-CAE model is applied to the reduced-order modeling of gas extraction flow–solid coupling mathematical models in coal mines. A full-order model pressure field numerical dataset for gas extraction was constructed, and optimal hyperparameters for the pressure field reconstruction model and latent space parameter prediction model were determined through hyperparameter testing. The performance of the DNN-CAE model order reduction algorithm was compared to the POD-RBF model order reduction algorithm. The results indicate that the DNN-CAE method has certain advantages over the traditional POD-RBF method in terms of pressure field reconstruction accuracy, overall structure retention, extremum capture, and computational efficiency. Full article

Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensation, reference signal generation, filter control optimization, and fault detection and diagnosis. This paper reviews the most recent applications of ML in APFs, highlighting their abilities to adapt to nonlinear load conditions, improve fault detection and classification accuracy, and optimize system performance in real time. However, this paper also highlights several limitations of these methods, such as the high computational complexity, the need for extensive training data, and challenges with real-time deployment in distributed power systems. For example, the marginal improvements in total harmonic distortion (THD) achieved by ML-based methods often do not justify the increased computational overhead compared to traditional control methods. This review then suggests future research directions to overcome these limitations, including lightweight ML models for faster and more efficient control, federated learning for decentralized optimization, and digital twins for real-time system monitoring. While traditional methods remain effective, ML-based solutions have the potential to significantly enhance APF performance in future power systems. Full article

To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration. Full article

While the impact of long-term climate change on economic systems has received substantial attention, the influence of short-term weather variations on economic growth has been comparatively neglected. This study utilizes county-level panel data from 2001 to 2020 to investigate the impact of weather on regional economic growth in China. The findings indicate that average temperature significantly reduces economic growth, whereas average precipitation does not have a significant overall effect. Notably, the adverse impact of temperature on economic growth is nonlinear and is exacerbated at higher temperatures, particularly in wealthier counties. The channel analysis shows that weather influences regional economic growth by affecting sectoral economies, factor inputs and economic productivity. While the primary industry’s overall growth rate remains unaffected by weather, sub-sectors such as grain production and animal husbandry are impacted. The secondary industry, especially large-scale industrial enterprises, is adversely affected by both temperature and precipitation. Conversely, higher average temperatures positively correlate with growth in the tertiary sector, promoting retail sales of consumer goods. The study also finds limited evidence for weather’s impact on investment growth, primarily in real estate development, and no significant effect on labor input growth. Additionally, weather conditions, particularly temperature, negatively affect total factor productivity, labor productivity and capital productivity, with precipitation adversely impacting capital productivity alone. These findings underscore the importance of tailored strategies to mitigate the negative effects of adverse weather conditions on sustaining sustainable regional economic growth. Full article

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm’s global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian–Cauchy mutation strategy was employed to improve the algorithm’s ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms. Full article

Microtubules are the basic components of the eukaryotic cytoskeleton. We discuss a class of nonlinear waves traveling in microtubules. The waves are obtained on the basis of a kind of z-model. The model used is extended to account for (i) the possibility for nonlinear interaction between neighboring dimers and (ii) the possibility of asymmetry in the double-well potential connected to the external electric field caused by the interaction of a dimer with all the other dimers. The model equation obtained is solved by means of the specific case of the Simple Equations Method. This specific case is denoted by SEsM(1,1), and the equation of Riccati is used as a simple equation. We obtain three kinds of waves with respect to the relation of their velocity with the specific wave velocity $v_c$ determined by the parameters of the dimer: (i) waves with $v>v_c$, which occur when there is nonlinearity in the interaction between neighboring dimers; (ii) waves with $v<v_c$ (they occur when the interaction between neighboring dimers is described by Hooke’s law); and (iii) waves with $v=v_c$. We devote special attention to the last kind of waves. In addition, we discuss several waves which travel in the case of the absence of friction in a microtubule system. Full article

This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two versions of the classical Lorenz model with a small time-scale separation factor, is treated as an atmosphere–ocean research simulator. In numerical experiments, the number of hidden layers and the number of nodes in each hidden layer varied from 1 to 5 and from 16 to 256, respectively. The basic configuration of the surrogate model, determined experimentally, has three hidden layers, each comprising between 16 and 128 nodes. The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy. As the forecast horizon increases, the accuracy of forecasts deteriorates, which was quite expected, primarily due to the chaotic behaviour of the fast subsystem. All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy. The obtained results indicate that the quality of short-term forecasts with a lead time of up to 0.75 model time units (MTU) improves most significantly. The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time. Full article

Engine fault diagnosis is a critical task in automotive aftermarket management. Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. Based on these faults, a hybrid approach is applied to classify different conditions that help the planning and decision-making of the automobile industry. The proposed hybrid approach combines the wavelet packet transform (WPT), Markov blanket feature selection, random convolutional kernel transform (ROCKET), tree-structured Parzen estimator (TPE) for hyperparameters tuning, and ten machine learning (ML) classifiers, such as ridge regression, quadratic discriminant analysis (QDA), naive Bayes, k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extra trees (ET), gradient boosting machine (GBM), and LightGBM. The audio data are broken down into sub-time series with various frequencies and resolutions using the WPT. These data are subsequently utilized as input for obtaining an informative feature subset using a Markov blanket-based selection method. This feature subset is then fed into the ROCKET method, which is paired with ML classifiers, and tuned using Optuna using the TPE approach. The generalization performance applying the proposed hybrid approach outperforms other standard ML classifiers. Full article

This research paper attempts to describe the transmission dynamic of zoonotic visceral leishmaniasis with the aid of a mathematical model by considering the asymptomatic stages in humans and animals. The disease is endemic in several countries. Data used in the research are obtained from the literature while some are assumed based on the disease dynamic. The consideration of both asymptomatic and the symptomatic infected individuals is incorporated in both humans and animals (reservoir), as well as lines of treatment for the human population. It is found that the model has two fixed points; the VL-free fixed point and the VL-endemic fixed point. Stability analysis of the fixed points shows that the VL-free fixed point is globally asymptotically stable whenever the basic reproduction number is less than one and the VL-endemic fixed point is globally asymptotically stable whenever the basic reproduction number is greater than one. Sensitivity analysis is conducted for the parameters in the basic reproduction number, and the profile of each state variable is also depicted using the data obtained from the literature and those assumed. The transmission probability from infected sandflies to animals, transmission probability from infected animals to sandflies, per capita biting rate of sandflies of animals, and rate of transfer from symptomatic infected animals to the recovered class are among the most sensitive parameters that have the greatest influence on the basic reproduction number. Moreover, the value of the basic reproduction number is obtained to be 0.98951, which may require further study, as the margin between potential disease control and outbreak is thin. Full article