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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.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

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.

This chapter reports a generative AI (GenAI)-supported short-term IELTS writing course for Chinese university students. A six-step human–AI hybrid argumentative writing framework was proposed to guide the course and enhance students’ prompting skills, including the steps of claim, structure, arguments and counterarguments, credibility and reliability, extend arguments, and revisions. An I-A-I (Initial-Assisted-Independent) approach was adopted to facilitate students’ critical thinking skills and the independent analysis of the content generated by GenAI. Ten students participated in the course, submitted their pre- and post-writing samples, and accepted the interviews. T-tests on participants’ writing samples showed that they performed significantly better in IELTS academic writing, proposing main claims, and organising the structure of the essay. The interview data further revealed that the course helped participants structure their essay, provide evidence for arguments, and revise their essay independently. The results underscored two crucial components of AI literacy in using ChatGPT for academic writing, including the prompt skills to ensure the synergistic interactions with GenAI, and the critical thinking skills to critically assess the responses of ChatGPT. In summary, the chapter provides theoretical contributions and pedagogical implications on the application of GenAI for university students to enhance their academic writing abilities and equip them with the literacy of using GenAI for academic writing.

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.

A densely distributed network radar system compensates for the disadvantages of sparse radars and poses a significant threat to low-altitude penetration by an unmanned combat aerial vehicle (UCAV). Unlike previous studies, this paper aims to consider radar blind areas and proposes a rapid online method for planning low-altitude penetration paths.

First, the optimization problem coupling digital elevation map (DEM), radar detection probability model and nonholonomic UCAV kinematic model is established. Second, an online solution framework of penetration path planning is constructed. An intervisibility method and map scaling are proposed to generate a detection probability map (DPM). Through completeness and consistency analysis, an adaptive hybrid A* algorithm with fast local replanning strategy is proposed to search a path that takes into account time-consuming, detection probability under nonholonomic constraints. Finally, three scenarios of multiple known, pop-up and vanished static radars are simulated using C++. The computational performance is compared and analyzed.

The results showed that the proposed online method can generate low-detection-probability penetration paths within subseconds.

This paper provides a new online method to plan UCAV penetration trajectory in military and academic contexts.