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According to relevant research, non-uniform speed has a significant impact on the vehicle-track systems. Up to now, research work on it is still very limited. In this paper, the PEM is adopted to further transform it into a deterministic process to solve the vehicle’s problem of running at a non-uniform speed.

The multi-body vehicle model has 10 degrees of freedom and the track is regarded as a finite long beam supported by lumped sleepers and ballast blocks. They are connected via linear Hertz springs. The vertical track irregularity is a Gaussian stationary process in the space domain. It is transformed into a uniformly modulated nonstationary random process in the time domain with respect to the non-uniform vehicle speed. By solving the equation of motion of the coupled vehicle-track system with the pseudo-excitation method, the pseudo-response and consequently the power spectral density and the standard deviation of the structural response can be obtained.

Two kinds of vehicle braking programs are taken in the numerical example and some beneficial conclusions are drawn.

The pseudo-excitation method (PEM) was used to perform the random vibration analysis of a coupled non-uniform speed vehicle-track system. Transforming the track irregularity into a uniformly modulated nonstationary random process in time domain with respect to the non-uniform vehicle speed was undertaken. The pseudo-response of the coupled system is solved by applying the Newmark algorithm with constant space integral steps. The random vibration transfer mechanism of the coupled system is fully discussed.

This paper aims to propose an effective dynamic calibration and compensation method to solve the problem that the statically calibrated force sensor would produce large dynamic errors when measuring dynamic signals.

The dynamic characteristics of the force sensor are analyzed by modal analysis and negative step dynamic force calibration test, and the dynamic mathematical model of the force sensor is identified based on a generalized least squares method with a special whitening filter. Then, a compensation unit is constructed to compensate the dynamic characteristics of the force measurement system, and the compensation effect is verified based on the step and knock excitation signals.

The dynamic characteristics of the force sensor obtained by modal analysis and dynamic calibration test are consistent, and the time and frequency domain characteristics of the identified dynamic mathematical model agree well with the actual measurement results. After dynamic compensation, the dynamic characteristics of the force sensor in the frequency domain are obviously improved, and the effective operating frequency band is widened from 500 Hz to 1,560 Hz. In addition, in the time domain, the rise time of the step response signal is reduced from 0.29 ms to 0.17 ms, and the overshoot decreases from 26.6% to 9.8%.

An effective dynamic calibration and compensation method is proposed in this paper, which can be used to improve the dynamic performance of the strain-gauge-type force sensor and reduce the dynamic measurement error of the force measurement system.

This paper aims to focus on the finite element method in the frequency domain (FD-FEM) for the transient electric field in the non-sinusoidal steady state under the non-sinusoidal periodic voltage excitation.

Firstly, the boundary value problem of the transient electric field in the frequency domain is described, and the finite element equation of the FD-FEM is derived by Galerkin’s method. Secondly, the constrained electric field equation on the boundary in the frequency domain (FD-CEFEB) is also derived, which can solve the electric field intensity on the boundary and the dielectric interface with high accuracy. Thirdly, the calculation procedures of the FD-FEM with FD-CEFEB are introduced in detail. Finally, a numerical example of the press-packed insulated gate bipolar transistor under the working condition of the repetitive turn-on and turn-off is given.

The FD-CEFEB improves numerical accuracy of electric field intensity on the boundary and interfacial charge density, which can be achieved by modifying the existing FD-FEMs’ code in appropriate steps. Moreover, the proposed FD-FEM and the FD-CEFEB will only increase calculation costs by a little compared with the traditional FD-FEMs.

The FD-CEFEB can directly solve the electric field intensity on the boundary and the dielectric interface with high accuracy. This paper provides a new FD-FEM for the transient electric field in the non-sinusoidal steady state with high accuracy, which is suitable for combined insulation structure with a long time constant.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The purpose of the study is to adopt Morlet’s wavelet method to examine the differences in the level of volatility (i.e. riskiness) between the conventional and Shari’ah indexes during the COVID-19 pandemic (February 4 to June 19, 2020) on selected Association of South East Asian Nation (ASEAN) and Gulf Cooperation Council (GCC) countries. As a comparison, the equivalent time period of relative tranquillity is used; February 4 to June 19, 2019.

Morlet’s wavelet method is used in analyzing the volatility levels for both the conventional and Shari’ah indexes before and during the COVID-19 pandemic for the selected ASEAN and GCC countries.

This study has several findings; first, the markets in the ASEAN region appear to be more volatile during the pandemic than in the GCC region. Second, most of the Shari’ah indexes were more volatile during the COVID-19 pandemic than their conventional counterparts. Nevertheless, the GCC index pairs appear to show more similarities between both the Shari’ah and conventional index.

The findings from this study indicate that investors, government, regulators and all other stakeholders should stay vigilant during a pandemic or health threat period as it has become a pertinent source of volatility spillovers. As such, investors should devise optimal asset allocation strategies, portfolio diversification and portfolio rebalancing measures, taking into consideration not only financial adversity but also public health gravity as a potential source of turbulent markets.

This study uses the wavelet method to examine the volatility level of both the Shari’ah and conventional indexes during the COVID-19 pandemic and its equivalent time frame in 2019. It has further added to the Islamic literature by comparing the volatility between selected ASEAN and GCC countries. The wavelet method is most appropriate for short-duration studies as it captures both the time and frequency domains of the time-series behavior.

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

This paper aims to identify the values antecedents of women’s social entrepreneurship. It explores where and how these values emerge and how they underpin the perceived desirability and feasibility of social venture creation.

Values development across the life-course is interrogated through retrospective sense-making by thirty UK-based women social entrepreneurs.

The findings express values related to empathy, social justice and action-taking, developed, consolidated and challenged in a variety of experiential domains over time. The cumulative effects of these processes result in the perceived desirability and feasibility of social entrepreneurial venture creation as a means of effecting social change and achieving coherence between personal values and paid work, prompting social entrepreneurial action-taking.

This paper offers novel, contextualised insights into the role that personal values play as antecedents to social entrepreneurship. It contributes to the sparse literature focussed on both women’s experiences of social entrepreneurship generally, and on their personal values specifically.

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

The purpose of this paper is to design a suitable guard trace to reduce the electromagentic interference between two closely spaced high frequency transmission lines. A novel cross-shaped resonator combined via fence is passed down to alleviate far-end and near-end crosstalk (NEXT) in tightly coupled high-speed transmission lines. The distance between the adjacent transmission lines is increased stepwise as a function of trace width.

A rectangular-shaped resonator via fence is connected by a guard trace has been proposed to overcome the coupling between the traces that is separated by 2 W. Similarly, by creating a cross-shaped resonator via fence connected by guard trace that reduces the spacing further by 1.5 W.

A tightly coupled transmission line structure that needs separation by a designed unit cell structure. Further research needs to be conducted to improve the NEXT, far-end crosstalk (FEXT) and spacing between the transmission lines.

This study portrays a novel method that combines the resonators via fence with a minimum spacing between the tightly coupled transmission lines which reduce the NEXT and FEXT; thereby reducing the size of the routing area. The resultant test structures are characterized at high frequencies using time domain and frequency domain analysis. The following scattering parameters such as insertion loss, NEXT and FEXT of the proposed method are measured as 1.504 dB, >30 dB and >20 dB, respectively.

Fuzzy-set qualitative comparative analysis (fsQCA) is explored as a transformative tool rooted in complexity theory, shedding light on uncertainties shaping real-world decisions in tourism, with a focus on its application in the hospitality domain.

This study systematically evaluates fsQCA’s application in hospitality and tourism research, employing bibliometric analysis to scrutinize the published literature since its induction in 2011. The research seeks to understand the evolving usage by qualitatively reviewing impactful studies based on total citations.

The study reveals the ascendancy of fsQCA as a predominant approach in hospitality and tourism studies, particularly in illuminating decision-making paradigms in key sectors like destination and hotel selections and entrepreneurial orientations. However, an absence of fsQCA applications in gastronomy and wine tourism is identified, signaling uncharted territories for future inquiry.

Theoretical implications include paradigm shifts to complexity theory, configural analysis and asymmetric algorithms. Practical implications involve improved decision-making and tailored marketing, benefiting industry practitioners. Limitations include potential academic bias, while future research suggests exploring sub-sectors, sustainability and emerging technologies.

This study identifies gaps in the fsQCA application and pioneers its examination within the hospitality domain, offering a unique perspective on understanding intricate relationships and configurations among variables. The study emphasizes the efficacy of asymmetric methodologies in elucidating behavioral nuances in hospitality and tourism, providing a foundation for future inquiries to expand horizons and unravel the nuanced applications of fsQCA in this research domain.