First-order Components Research Articles

Multi-scale analysis of the mapping relationship between turnout irregularities and vehicle responses based on cross-wavelet transform

Track irregularities significantly impact the dynamic performance of vehicles passing through turnouts. Despite this, the precise mapping relationship between these irregularities and vehicle responses remains unclear. Utilizing measured data, the inherent irregularities of turnouts were extracted using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The mapping relationship between the wavelength, spatial position, and time lag effect of turnout inherent irregularities and vehicle responses was analyzed using cross-wavelet transform (CWT). The first-order and second-order intrinsic modal components within the original irregularities distinctly reflect the inherent characteristics of the switch and crossing panels. The inherent irregularities in these panels respectively correspond to the vertical and lateral accelerations of the vehicle within the wavelength ranges of 2∼4 m and 4∼8 m. Within the switch panel, the longitudinal levels of the right rail and the twist exert the most significant influence on the vehicle's vertical acceleration, accounting for 17.80% and 22.89% respectively. In the crossing panel, the track gauge and the alignments of the right rail have the greatest impact on the vehicle's lateral acceleration, accounting for 24.72% and 35.18% respectively.

A discretization-free deep neural network-based approach for advection-dispersion-reaction mechanisms

This study aims to provide insights into new areas of artificial intelligence approaches by examining how these techniques can be applied to predict behaviours for difficult physical processes represented by partial differential equations, particularly equations involving nonlinear dispersive behaviours. The current advection-dispersion-reaction equation is one of the key formulas used to depict natural processes with distinct characteristics. It is composed of a first-order advection component, a third-order dispersion term, and a nonlinear response term. Using the deep neural network approach and accounting for physics-informed neural network awareness, the problem has been elaborately discussed. Initial and boundary conditions are added as constraints when the neural networks are trained by minimizing the loss function. In comparison to the existing results, the approach has produced qualitatively correct kink and anti-kink solutions, with losses often remaining around 0.01%. It has also outperformed several traditional discretization-based methods.

A new model for basic needs: comparison of the second order factorial structures of young schema questionnaire and its relationship with symptoms of psychopathology

This study investigates the structure of Young’s early maladaptive schemas, core emotional needs, and their associations with psychopathological symptoms. Early maladaptive schemas and psychopathological symptoms were assessed in three clinical samples from Turkey (N1 = 1312, N2 = 999, N3 = 999). First-order Principal Component Analysis (PCA) identified 17 schemas, eight of which closely mirrored Young's original schemas. A second-order PCA revealed four schema domains: Impaired Autonomy, Disconnection, Impaired Limits, and Overvigilance. Confirmatory Factor Analyses were employed on distinct samples to compare the PCA model with alternative models, and the PCA model exhibited superior fit across various indices. The study reveals varying relationships between schemas and diverse psychopathological symptoms, providing nuanced insights for tailoring psychotherapeutic strategies and interventions. Symptoms related to interpersonal sensitivity had the strongest connections to schemas, and the factor representing vulnerability to harm and pessimism schemas had the most number of strong connections with psychopathological symptoms. While the study sheds light on the schemas in a Turkish context, future research is essential to validate these findings and explore the predictive aspects of schemas. In essence, this research contributes to understanding of schemas within the Turkish cultural context and its implications for clinical practice.

A novel kriging-improved high-dimensional model representation metamodelling technique for approximating high-dimensional problems

Aiming to address the issue of the ‘curse of dimensionality’ encountered in approximating high-dimensional problems using surrogate-based methods, high-dimensional model representation (HDMR), decomposing the high-dimensional problem into summands of different-order component functions, has been widely studied. To reduce the computational demands of the current HDMR metamodelling techniques, an improved high-dimensional model representation framework (iHDMR) is presented, taking full advantage of the relationships between the first-order and second-order component functions in the Cut-HDMR theory. then, a novel metamodelling technique, termed kriging (KRG)-iHDMR, is implemented by integrating the kriging technique and the suggested surface-axes alternating sampling strategy into the iHDMR framework. The accuracy and efficiency of KRG-iHDMR are demonstrated by various numerical and engineering examples with different dimensions. Finally, an optimization problem of the multi-stage solid launch vehicle propulsion system is introduced to verify the engineering feasibility of the KRG-iHDMR metamodelling technique.

Exploring Situational Factors for Female International Students and Their Satisfaction in Higher Education Using CB-SEM

While the topic of attracting and maintaining inbound international students has received much scholarly attention in higher education, attracting female international students has rarely been discussed. This study addresses the issue of attracting degree-seeking female international students. Based on the notion of situational factors and total quality management (TQM), we assumed both institutional design and students’ satisfaction are core concepts for attracting and retaining female international students that will make a difference in higher education. A total of 215 survey responses were analyzed, and a covariance-based structural equation modeling (CB-SEM) approach was developed. The results confirm female international students’ perception of 14 observed variables in the three first-order components and suggest that they can create a second-order construct of push factors, institutional situations, and satisfaction (latent variables). This study demonstrates that higher-order modeling leads to theoretical parsimony and reduces model complexity in the proposed CB-SEM. The findings suggest that the casual relationship works well and both institutional leadership and international strategy can play crucial roles in the process of transforming female international students’ learning satisfaction.

Bayesian inference for long memory term structure models

In this study, we propose a novel adaptation of the Dynamic Nelson–Siegel term structure model, incorporating long memory properties to enhance its forecasting accuracy. Our approach involves modelling the evolution of latent factors using fractional Gaussian noise processes, approximated by a weighted sum of independent first-order autoregressive components. The resulting formulation allows for a Gaussian Markov Random Field representation, facilitating the application of computationally efficient Bayesian techniques through Integrated Nested Laplace Approximations. Extensive simulation and empirical analysis demonstrate that integrating long memory significantly improves the model's forecasting performance, particularly for longer time horizons. By shedding light on the potential benefits of incorporating long memory concepts into traditional term structure models, our research highlights its utility in capturing intricate temporal dependencies and enhancing prediction precision.

Hybrid-order soft trust region-based sequential convex programming for reentry trajectory optimization

Due to the complex aerodynamic effects, the reentry trajectory optimization problem is highly nonlinear. When using sequential convex programming (SCP) methods to solve it, the iteration solution is difficult to converge. To improve this, we propose a hybrid-order soft trust region-based SCP method. We analyze the penalty effect of typical trust regions. Based on the analysis, we develop a hybrid-order soft trust region combining a small-weight first-order component and a higher-order components. To solve the subproblem reliably and effectively, we equivalently reformulate it as a second-order cone programming (SOCP) form through the relaxation technique. Combined with the line search method, we further design an SCP algorithm with guaranteed convergence under some assumptions. In the numerical simulations, the effectiveness and robustness of the proposed method have been verified using a nominal case and 200 Monte Carlo cases.

The factor structure of the Motivated Strategies for Learning Questionnaire (MSLQ): new methodological approaches and evidence

BackgroundThe area of self-regulated learning integrates the fields of metacognition and self-regulation and assumes that the student is an active processor of information capable of self-regulating his learning by putting together the cognitive, metacognitive, and motivational components. The Motivated Strategies for Learning Questionnaire (MSLQ) is a benchmark for the measurement of self-regulated learning. However, the field of study does not show adequate evidence of its structural validity. The vast majority of studies involving this question present serious methodological mistakes, compromising the evidence of validity.ObjectiveOur study investigates the structural validity of MSLQ including all 15 scales and corrects relevant mistakes in the previous studies.MethodWe tested different models through item confirmatory factor analysis in a convenience sample of 670 college students (M = 22.8 years, SD = 5.2) from a public Brazilian university in the technological area. The models with the ML, MLR, MLM and WLMSV estimators.ResultsOnly WLSMV produced models with acceptable fit. The final model has a bi-factor structure with a general factor (self-regulated learning), 15 components as first-order factors, and four broad components as second-order factors. Twelve first-order components, all second-order components and the general factor had acceptable reliability. The components’ elaboration, intrinsic goal orientation and metacognitive self-regulation, did not show acceptable reliability, in terms of McDonald’s omega.ConclusionConsidering the worldwide importance of the MSLQ, we do not recommend the use of the measurement of these components for clinical practice and psychoeducational diagnosis until new studies show that this low reliability only occurs in our sample. Our study shows new evidence, correcting many previous methodological mistakes and producing initial evidence favorable to the factor structure of the MSLQ.

Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China.

A Dynamic Nonlinear VDCOL Control Strategy Based on the Taylor Expansion of DC Voltages for Suppressing the Subsequent Commutation Failure in HVDC Transmission

Subsequent commutation failure in high-voltage DC transmission systems seriously emphasizes the safe and stable operation of power systems. Via analyzing the mechanism of commutation failure and the principle of voltage-dependent current order limiter (VDCOL), this paper proposes a dynamic nonlinear VDCOL control strategy based on the Taylor expansion of DC voltage for suppressing subsequent commutation failure. To solve the problem of fluctuating DC current command value caused by a large drop in DC voltage, this paper constructs a nonlinear VDCOL control that can be dynamically adjusted according to the AC bus voltage level, and Taylor expansion of DC voltage is used to obtain its first-order and second-order differential components. Different scales of differential elements are chosen to predict the DC voltage compensation value while balancing sensitivity and accuracy. The compensated DC voltage, used as the starting voltage of VDCOL, is input to the VDCOL control constructed in this paper to suppress the subsequent commutation failure of the transmission system by reducing the fluctuation of the current command value. Finally, the standard test model of HVDC is established based on the actual parameters, and the simulation results show that the Method proposed in this paper has an effective suppressing effect in the case of single-phase or three-phase faults of different severity and is conducive to the restoration of the system power transmission.

Nonlinear flutter of a narrow truss girder

The nonlinear flutter of a narrow railway truss girder are studied through free vibration and forced vibration wind tunnel tests. Four functions are used to fit the structural amplitude-varying damping ratio under no wind condition, and the fourth-order polynomial has higher goodness-of-fit. The narrow truss girder shows nonlinear flutter of a velocity-restricted type without amplitude hysteresis. At large angles of attack (AoA), the vertical participation and the AoA have little effect on the onset wind speed and amplitude. While at medium AoAs, even a low vertical participation level will increase the onset wind speed and decrease the amplitude. At small AoAs, there is no vibration observed. The flutter derivative A2* is positive only in the limited reduced wind speed and amplitude range. The nonlinearity of self-excited lifting moment is higher only in the limited reduced wind speed and amplitude range, while that of self-excited lift is weaker and becomes stronger with the increasing amplitude and reduced wind speed. The negative aerodynamic damping is mainly provided by the flutter derivative A2* at small or large AoAs. Generally, the nonlinear flutter response is mainly controlled by the first-order component of the self-excited force, and the influence of high-order components can be ignored.

Effects of wave direction on ship turning in regular waves

In this study, we focused on the drift motion of a ship during turning in regular waves and investigated the effects of wave direction on ship turning and drifting. First, turning tests in regular waves were conducted using a bulk carrier model by systematically changing the wave direction and wavelength to capture drifting characteristics. Subsequently, long-duration turning simulations were performed to capture the drifting characteristics with a large heading angle change over 2000° in waves using the two-time scale method (Yasukawa, 2006b; Skejic and Faltinsen, 2008). The simulation results indicated that the ship constantly drifted in a particular direction with respect to the incident wave direction during turning when sufficient time had passed. This phenomenon can be understood analytically using new formulas derived for the drifting indices (drifting distance and drifting direction). According to the formulas, the ship turning trajectory is deformed in waves drifting in proportional to time. The drifting velocity and drifting direction were determined by combining the average velocity components (ship surge velocity, lateral velocity, and yaw rate) and the first-order velocity fluctuation components with turning circular frequency. The formulas for the drifting indices were verified through comparison with the simulation results.

Nonlinear computation for focused wave groups on abrupt depth transitions

Focused wave groups in intermediate water depth propagating over varying abrupt depth transitions are studied numerically and experimentally. The experiments well validate the fully nonlinear model by a conformal mapping method. The generation of second-order and third-order harmonics is observed near the top of the submerged step, as well as the decrease of the main (first-order) components propagating along with the step. In addition, an enhancement of kurtosis occurs just after the step with a high-trailed profile, revealing a considerable asymmetry of the surface elevations. In addition, a wave group passing two or three steps is also investigated numerically. More submerged obstacles effectively strengthen the reflection capability in terms of the reflection coefficients computed with the amplitude of wave envelopes. Although the harmonics manifest a higher risk of extreme waves on abrupt depth transitions, the increase of water depth changes might not further enhance the maximum skewness and kurtosis over the varying bottom.

Early prediction of atherosclerosis diagnosis with medical ambient intelligence.

Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the "SEcond-order Classifier (SEC)" is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method's diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

Developing and testing scales for home support service continuity (HSSC): cross-sectional studies in Canada and UK

ObjectivesEnsuring the continuity of home support services has become increasingly important due to challenges arising from ageing demographics and healthcare staffing shortages. However, there is a lack of validated measurements...

Conceptualisation, measurement, and prediction of foreign language learning psychological capital among Chinese EFL students

ABSTRACT Psychological capital (PsyCap), the resource that responds to developing an individual’s positive psychological capacities, is positively related to academic behaviours, achievement emotions, and academic resource. However, the domain-specificity of the PsyCap scale and the prediction of PsyCap resources have not been examined in an English as a Foreign Language (EFL) context, especially in China. This study aims to conceptualise EFL PsyCap, develops its corresponding measurement, and explore how EFL PsyCap resources were associated with EFL-related achievement emotions, academic engagement, and EFL performance. Item-level analyses, internal consistency analyses, and confirmatory factor analyses were conducted to assess the reliability and validity of the EFL PsyCap scale in a sample of 1,396 Chinese EFL learners. Furthermore, structural equation modelling analyses were conducted to examine the relationship between EFL PsyCap resources, achievement emotions, academic engagement, and EFL performance after controlling for gender and age. Results showed that the 16-item EFL PsyCap scale retains excellent psychometric properties and EFL PsyCap is a hierarchical construct underpinned by its four first-order components of optimism, hope, resilience, and self-efficacy. Additionally, EFL PsyCap resources positively predicted academic enjoyment, behavioural engagement, and EFL performance and negatively predicted academic boredom and anxiety.

Scale Factor Self-Calibration of MEMS Gyroscopes Based on the High-Order Harmonic Extraction in Nonlinear Detection

This article presents a novel self-calibration method to improve the scale factor (SF) stability by the synchronous control on the first-order and third-order harmonic components of nonlinear detection. On the conventional scheme of a drive loop, the readout amplitude of the first-order harmonic component is controlled at a constant value. However, the actual vibration amplitude of drive-mode motion would drift due to the change of circuit gains between the readout amplitude and actual amplitude, thereby resulting in the deterioration of the SF stability. The third-order harmonic component of nonlinear detection shares the same circuit with the vibration signal, indicating that its readout amplitude can track the change in circuit gains in real time. Therefore, this new approach replaces the original fixed reference with the varying readout amplitude of the third-order harmonic component to eliminate the drive-mode vibration error and eventually calibrate the drift of SF. Experiments performed on a honeycomb disk resonator gyroscope (HDRG) have demonstrated the feasibility of the calibration method. The calibration based on the harmonic extraction yields a short-term SF repeatability of 24.9 ppm and a long-term SF repeatability of 30.8 ppm. Allan deviation analysis of the calibrated SF demonstrates a 3.5-ppm precision upon an average of 200 s. The temperature sensitivity of the SF is reduced from 717.3 ppm/°C to 5.02 ppm/°C and the SF repeatability is improved from 15643 ppm to 94.9 ppm over the temperature range of −30 °C to 30 °C.

The splitting mechanism of the second-order rogue wave—Interaction between two component first-order Akhmediev breathers

Based on the nonlinear Schrödinger equation, it's illustrated that the peak intensity of Akhmediev breather is negatively correlated with the modulation frequency firstly. Then, two splitting modes of the second-order rogue wave have been studied in detail through the second-order rogue wave train formed by the nonlinear superposition of two first-order Akhmediev breathers whose modulation frequency ratio is 1:2. It is revealed that the process of the mode B splitting can be divided into three stages: the restructuring stage where the intensity of the latter sub-peaks of the main peak decreases first and then increases, the competition stage where the fine structure forms and the relaxation stage. The process of the mode A splitting can be divided into two stages: the restructuring stage and the relaxation stage. And both are due to the interaction between the two first-order component Akhmediev breathers. The results can be used to explain the different characteristics of the two splitting modes of the second-order rogue wave. In addition, the inevitability of the fine structure appearance in the process of the mode B splitting of the second-order rogue wave, which causes the transformation point on the peak intensity curve of the second order rogue wave, is also revealed. We anticipate that it will be a meaningful and novel way to study the characteristics of the high-order rogue wave through interaction between breathers with certain modulation frequency ratio.

Neuroretinal dysfunction in patients affected by neurofibromatosis type 1.

To examine neuroretinal function by using the multifocal electroretinography (mfERG) test in patients with neurofibromatosis type 1 (NF1) without optic pathway gliomas (OPGs). This study was conducted on 35 patients (35 eyes) with NF1 and 30 healthy subjects (30 eyes) for the control group. Each subject underwent a complete ophthalmological examination including spectral domain-optical coherence tomography (SD-OCT) and mfERG. The 1.5-Tesla magnetic resonance imaging (MRI) scan of the brain was performed in NF1 patients to assess the presence of OPGs. All participants were recruited having a best corrected visual acuity (BCVA) of no less than 20/20 in each eye. The amplitude and implicit time of the P1 wave (first-order Kernel component) were evaluated on mfERG. Data analysis was carried out in the two central degrees and in the four quadrants from two to 25 degrees of visual field. Statistically significant results were obtained for the P1 wave amplitudes in the 4 quadrants in NF1 patients compared to healthy controls, while the reduction was not significant in the 2 central degrees between the groups. A statistically significant difference was observed among the P1 wave amplitudes as recorded in the 4 quadrants within the NF1 group, with lower amplitudes detected in the nasal quadrants. No differences in the implicit times were recorded in the 2 central degrees and in the 4 quadrants as compared between NF1 patients and controls. Impaired neuroretinal function in NF1 patients is expressed in a decreased amplitude of the P1-wave between 2 and 25 central retinal degrees on mfERG. Altered intracellular signal transduction due to abnormal neurofibromin-mediated cyclic adenosine monophosphate (cAMP) generation, can be involved. The possible use of mfERG as subclinical retinal damage indicator has a potential utility in clinical practice for the follow-up of NF1 patients.

Machined surface defects monitoring through VMD of acoustic emission signals

Machined surface defects influence functional properties of the component, such as reducing carrying capacity and shortening fatigue life. This study collected the cutting force and the acoustic emission (AE) signals, analyzed the effect of shear and ploughing effect on the frequency of the AE signals in machining, built the relationship between the first-order mode components (IMF1) and the second-order mode components (IMF2) of AE signals and the shearing and ploughing process respectively through variational mode decomposition (VMD). A new parameter Er = (RMS2/RMS1)2 was proposed as the signal feature to monitor machined surface defects, which represented the ratio of specific ploughing energy and specific shear energy. The results showed that when Er was in the range of 0.03–0.05, the machined surface quality was good, once it was higher than 0.05, the ploughing effect between the flank face and the machined surface was severe, thereby causing machining-induced surface defects.