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Multilayer composite liner structures are the primary structural form of hydraulic tunnels. However, the bearing mechanism of multilayer composite liners has not been investigated thoroughly. Many existing design schemes do not properly achieve a balance between structural safety, anti-seepage capacity, and cost effectiveness. Thus, a new composite liner structure type and its theoretical model was proposed.

A novel hydraulic tunnel composite liner structure with a polyurea spray coating interlayer was proposed in this study. A theoretical model based on the state-space method was developed and verified using FEM models and existing theoretical models. Parametric analysis was conducted based on the theoretical model to investigate the influence of various variables, including interfacial shear stiffness, inner liner thickness, and outer liner elastic modulus.

It was concluded that the proposed theoretical model can be used successfully to calculate multilayer composite liner structures with high calculation efficiency. The overall deformation stiffness of the composite liner system increased with the interfacial shear stiffness. The sprayed coating interlayer significantly affects the residual force distribution between the outer and inner liners, which can also be affected by the adjustment of the thickness of the outer and inner liners. Thus, attention should be paid to these factors in the rational design of the proposed composite liner system.

With the development of China’s water conservancy projects, complex geological conditions, high surrounding rock stress, high internal and external water pressures, and other unique application scenarios have gradually increased. This places higher requirements on the bearing performance and impermeability of hydraulic tunnel lining structures. On the other hand, conventional hydraulic tunnel lining structures can hardly achieve a satisfactory balance between economy, structural safety, and impermeability. Thus, the proposed structure has the potential to be used in a wide range of applications.

This study aims to discuss the simultaneous longitudinal and lateral flight control of the octorotor, a rotary wing unmanned aerial vehicle (UAV), for the first time under the effect of morphing and to improve autonomous flight performance.

This study aims to design and control the octorotor flight control system with stochastic optimal tuning under morphnig effect. For this purpose, models of different arm lengths of the octorotor were drawn in the Solidworks program. The morphing was carried out by simultaneously lengthening or shortening the arm lengths of the octorotor. The morphing rate was estimated by using simultaneous perturbation stochastic approximation (SPSA). The stochastic gradient descent algorithm, which is frequently used in machine learning, was used to estimate the changing moments of inertia with the change of arm lengths. The proportional integral derivative (PID) controller has been preferred as an octorotor control algorithm because of its simplicity of structure. The PID gains required to control both longitudinal and lateral flight were also estimated with SPSA.

With SPSA, three longitudinal flight PID gains, three lateral flight PID gains and one morphing ratio were estimated. PID gains remained within the limits set for SPSA, giving satisfactory results. In addition, the cost index created was 93% successful. The gradient descent algorithm used for the moment of inertia estimation achieved the optimum result in 1,570 iterations. However, in the simulations made with the obtained data, longitudinal and lateral flight was successfully carried out.

Octorotor longitudinal and lateral flight control was performed quickly and effectively with the proposed method. In addition, the desired parameters were obtained with the optimization methods used, and the longitudinal and lateral flight of the octorotor was successfully carried out in the desired trajectory.

Conventional isolated dc–dc converters offer an efficient solution for performing voltage conversion with a large improved voltage gain. However, the small-signal analysis of these converters shows that a right-half-plane (RHP) zero appears in their control-to-output transfer function, exhibiting a nonminimum-phase stability. This RHP zero can limit the frequency response and dynamic specifications of the converters; therefore, the output voltage response is sluggish. To overcome these problems, the purpose of this study is to analyze, model and design a new isolated forward single-ended primary-inductor converter (IFSEPIC) through RHP zero alleviation.

At first, the normal operation of the suggested IFSEPIC is studied. Then, its average model and control-to-output transfer function are derived. Based on the obtained model and Routh–Hurwitz criterion, the components are suitably designed for the proposed IFSEPIC, such that the derived dynamic model can eliminate the RHP zero.

The advantages of the proposed IFSEPIC can be summarized as: This converter can provide conditions to achieve fast dynamic behavior and minimum-phase stability, owing to the RHP zero cancellation; with respect to conventional isolated converters, a larger gain can be realized using the proposed topology; thus, it is possible to attain a smaller operating duty cycle; for conventional isolated converters, transformer core saturation is a major concern, owing to a large magnetizing current. However, the average value of the magnetizing current becomes zero for the proposed IFSEPIC, thereby avoiding core saturation, particularly at high frequencies; and the input current of the proposed converter is continuous, reducing input current ripple.

The key benefits of the proposed IFSEPIC are shown via comparisons. To validate the design method and theoretical findings, a practical implementation is presented.

Throughout the 21st century, US households have experienced unprecedented levels of leverage. This dynamic has been exacerbated by income shortfalls during the COVID-19 crisis. Leveraging and deleveraging decisions affect household consumption. This study investigates the effect of the dynamics of household leverage and consumption on the stock market.

The authors explore the relation between household leverage and consumption in the context of the consumption capital asset pricing model (CCAPM). The authors test the model's implication that leverage has a negative risk premium by transforming the asset pricing restriction into an unconditional linear factor model and estimate the model using the general method of moments procedure. The authors run time-series regressions to estimate individual stocks' exposures to leverage, and cross-sectional regressions to investigate the leverage risk premium.

The authors show that shocks to household debt have strong and lasting effects on consumption growth. The authors extend the CCAPM to accommodate this effect and find, using various test assets, a negative risk premium associated with household deleveraging. Looking at individual stocks the authors show that the deleveraging risk premium is not explained by well-known risk factors.

This paper contributes to the literature on the role of leverage in economics and finance by establishing a relation between household leverage and spending decisions. The authors provide novel evidence that households' leveraging and deleveraging decisions can be a fundamental and influential force in determining asset prices. Further, this paper argues that household leverage might explain the small, persistent, and predictable component in consumption growth hypothesised in the long-run risk asset pricing literature.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

This paper aims to propose a robust kinematic controller based on sliding mode theory designed to solve the trajectory tracking problem and also the formation control using the leader–follower strategy for nonholonomic differential-drive wheeled mobile robots with a PD dynamic controller.

To deal with classical sliding mode control shortcomings, such as the chattering and the requirement of a priori knowledge of the limits of the effects of disturbances, an immune regulation mechanism-inspired approach is proposed to adjust the control effort magnitude adaptively. A simple fuzzy boundary layer method and an adaptation law for the immune portion gain online adjustment are also considered. An obstacle avoidance reactive strategy is proposed for the leader robot, given the importance of the leader in the formation control structure.

To verify the adaptability of the controller, obstacles are distributed along the reference trajectory, and the simulation and experimental results show the effectiveness of the proposed controller, which was capable of generating control signals avoiding chattering, compensating for disturbances and avoiding the obstacles.

The proposed design stands out for the ability to adapt in a case involving obstacle avoidance, trajectory tracking and leader–follower formation control by nonholonomic robots under the incidence of uncertainties and disturbances and also considering that the immune-based control provided chattering mitigation by adjusting the magnitude of the control effort, with adaptability improved by a simple integral-type adaptive law derived by Lyapunov stability analysis.

This study aims to simultaneously and stochastically maximize autonomous flight performance of a variable wing incidence angle having an unmanned aerial vehicle (UAV) and its flight control system (FCS) design.

A small UAV is produced in Iskenderun Technical University Drone Laboratory. Its wing incidence angle is able to change before UAV flight. FCS parameters and wing incidence angle are simultaneously and stochastically designed to maximize autonomous flight performance using an optimization method named simultaneous perturbation stochastic approximation. Obtained results are also benefitted during UAV flight simulations.

Applying simultaneous and stochastic design approach for a UAV having passively morphing wing incidence angle and its flight control system, autonomous flight performance is maximized.

Permission of the Directorate General of Civil Aviation in Turkish Republic is necessary for real-time flights.

Simultaneous stochastic variable wing incidence angle having UAV and its flight control system design approach is so useful for maximizing UAV autonomous flight performance.

Simultaneous stochastic variable wing incidence angle having UAV and its flight control system design methodology succeeds confidence, excellent autonomous performance index and practical service interests of UAV users.

Creating an innovative method to recover autonomous flight performance of a UAV and generating an innovative procedure carrying out simultaneous stochastic variable wing incidence angle having UAV and its flight control system design idea.

The spatial–temporal conflicts in the construction process of concrete arch dams are related to the construction quality and duration, especially for pouring blocks with a continuous high-strength and high-density construction process. Furthermore, the complicated construction technology and limited space resources aggravate the spatial–temporal conflicts in the process of space resource allocation and utilization, directly affecting the pouring quality and progress of concrete. To promote the high-strength, quality-preserving and rapid construction of dams and to clarify the explosion moment and influence degree of the spatial–temporal conflicts of construction machinery during the pouring process, a quantification method and algorithm for a “Conflict Bubble” (CB) between construction machines is proposed based on the “Time–Space Microelement” (TSM).

First, the concept of a CB is proposed, which is defined as the spatial overlap of different entities in the movement process. The subsidiary space of the entity is divided into three layered spaces: the physical space, safe space and efficiency space from the inside to the outside. Second, the processes of “creation,” “transition” and “disappearance” of the CB at different levels with the movement of the entity are defined as the evolution of the spatial–temporal state of the entity. The mapping relationship between the spatial variation and the running time of the layered space during the movement process is defined as “Time–Space” (TS), which is intended to be processed by a microelement.

The quantification method and algorithm of the CB between construction machinery are proposed based on the TSM, which realizes the quantification of the physical collision accident rate, security risk rate and efficiency loss rate of the construction machinery at any time point or time period. The risk rate of spatial–temporal conflicts in the construction process was calculated, and the outbreak condition of spatial–temporal conflict in the pouring process was simulated and rehearsed. The quantitative calculation results show that the physical collision accident rate, security risk rate and efficiency loss rate of construction machinery at any time point or time period can be quantified.

This study provides theoretical support for the quantitative evaluation and analysis of the spatial–temporal conflict risk in the pouring construction process. It also serves as a reference for the rational organization and scientific decision-making for pouring blocks and provides new ideas and methods for the safe and efficient construction and the scientific and refined management of dams.