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Because of the extensive use of long‐span structures in modern engineering, much attention has been given to the extent to which ground motion phase‐lags affect the internal forces of such structures. In this paper, this problem is studied from the aspect of random seismic analysis, i.e. the random seismic responses of long‐span structures are explored with the phase‐lags of the ground joints of the structures taken into account. The earthquake is regarded as a stationary random process. Formulae for calculating the random responses of the structural displacements and internal forces are derived. Numerical examples are presented which illustrate some basic features of such random response, and also show that the ground motion phase‐lags have considerable effects on structural safety analysis.

The purpose of this paper is to present a new random vibration-based assessment method for coupled vehicle-track systems with uncertain parameters when subjected to random track irregularity.

The uncertain parameters of vehicle are described as bounded random variables. The track is regarded as an infinite periodic structure, and the dynamic equations of the coupled vehicle-track system, under mixed physical coordinates and symplectic dual coordinates, are established through wheel-rail coupling relationships. The random track irregularities at the wheel-rail contact points are converted to a series of deterministic harmonic excitations with phase lag by using the pseudo excitation method. Based on the polynomial chaos expansion of the pseudo response, a chaos expanded pseudo equation is derived, leading to the combined hybrid pseudo excitation method - polynomial chaos expansion method

The impact of uncertainty propagation on the random vibration analysis is assessed efficiently. According to GB5599-85, the reliability analysis for the stability index is implemented, which can grade the comfort level by the probability. Comparing to the deterministic analysis, it turns out that neglect of the parameter uncertainty will lead to potentially risky analysis results.

The proposed method is compared with Monte Carlo simulations, achieving good agreement. It is an effective means for random vibration analysis of uncertain coupled vehicle-track systems and has good engineering practicality

Proposes a new approach for analysing the stationary random response of complex structures located in a non‐homogeneous stochastic field. The approach is a kind of complete CQC method because the cross‐correlation terms between both the participant modes and the ground joint excitations are included in the response calculations. Also takes into account the effect of the loss of coherency between ground joints.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

In regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.

The cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.

The computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.

This paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

Traditional nondestructive failure localization techniques are increasingly difficult to meet the requirements of high density and integration of system in package (SIP) devices in terms of resolution and accuracy. Time domain reflection (TDR) is recognized as a novel positioning analysis technology gradually being used in the electronics industry because of the good compatibility, high accuracy and high efficiency. However, there are limited reports focus on the application of TDR technology to SiP devices.

In this study, the authors used the TDR technique to locate the failure of SiP devices, and the results showed that the TDR technique can accurately locate the cracking of internal solder joints of SiP devices.

The measured transmission rate of electromagnetic wave signal was 9.56 × 107 m/s in the experimental SiP devices. In addition, the TDR technique successfully located the failure point, which was mainly caused by the cracking of the solder joint at the edge of the SiP device after 1,500 thermal cycles.

TDR technology is creatively applied to SiP device failure location, and quantitative analysis is realized.

This study aims to explore how the “new normal” induces the dynamics in the asymmetric relationship between service quality attributes and customer satisfaction.

This study analyzes online reviews for hotels in New York City. The authors use multi-attribute models to examine how a situational factor – the COVID-19 outbreak – creates dynamics in the asymmetric effect of service quality attributes on customer satisfaction. Then, the authors examine the change in these dynamics over time after adjusting to the “new normal.”

The COVID-19 pandemic has introduced dynamics into the asymmetrical relationship between hotel service attribute performances and customer satisfaction. The pandemic magnified the asymmetric influences of particular attributes on satisfaction in the hospitality industry. In addition, the findings indicate the changes in such dynamics over time.

The findings emphasize that hotel managers should consider situational factors when understanding customer satisfaction. Particularly, this study suggests developing tailored strategies for responses during the COVID-19 pandemic. Hotel managers need to address changing customer expectations of service attributes to overcome unprecedented difficulties because of the limitations and new needs imposed during the pandemic.

This study contributes to the hospitality literature with an understanding of the significance of situational factors in asymmetric analysis.

This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work.

A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate.

In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model.

Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

This paper aims to investigate whether single-name options trading prior to earnings announcements is more informative when there exist real activity manipulations.

Using 5,419 earnings announcements during 2004–2018 made by 208 public US companies with relatively high options volumes ranked by the CBOE, the authors uncover two regularities using predictive regressions for stock return.

First, the total options volume up to twenty days pre-announcement is significantly higher than that in other periods only for earnings management firms; moreover, after detailing options characteristics, the authors find these intensive pre-announcement trading to be concentrated in transactions of in-the-money call and long-term maturity put options. Second, an increase in the single-name call minus put options volume can positively predict the underlying stock’s next-day excess return much better in real earnings management firms, with a larger magnitude of effect in periods right before regular earnings announcement dates.

This paper makes a marginal and novel contribution by showing that real earnings management can serve as a proxy for the potential profit from informed trading in options as the return predictability of options volume becomes stronger for firms that have the manipulation motive and indeed perform manipulative actions.