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The purpose of this study is to (1) improve the spectral features of the second-order uniformly non-oscillatory (UNO) slope limiters, and (2) numerical simulation of the unified-form of generalized fully-coupled saturated thermo-poro-elastic systems in the axisymmetric cylindrical coordinate via cell-adaptive Kurganov-Tadmor (KT) central high-resolution scheme using the UNO limiters.

(1) The spectral features of the UNO limiter are improved by compression-adaptive MINMOD (MM) limiters, achieved by blending different types of MM limiters to achieve less numerical dissipation and dispersion. These blended MM limiters preserve the total variation diminishing (TVD) feature over non-uniform non-centered cells. Also, the spectral features of the central schemes using the UNO limiters are investigated. (2) For the thermo-poro-elastic problem, corresponding first-order hyperbolic system is provided, including flux, source, diffusion and nonlinear terms. Where, there are different interacting components in the source and flux terms. The nonlinear terms are also considered by the Picard-like linearization concept.

Compression-adaptive UNO limiters would be stable over adapted cells with centered and non-centered cells. The benchmarks confirm that both spectral features and numerical accuracy are improved. For the generalized thermo-poro-elastic problem, corresponding responses including the shock waves can properly be captured.

Studying heat effects (e.g. hot fluid or freezing) and explosions on tunnels. Also, the UNO limiters could be used for simulations of various systems of conservation laws.

This study aims to efficiently estimate the extremely small failure probability with high-dimensional inputs and multiple failure domains.

This paper proposed an adaptive stratified mixture importance sampling method. The proposed method first constructs an explicit and regular mixture importance sampling probability density function (M-IS-PDF) by taking the clustering centroids as the density centers. Then by the constructed M-IS-PDF, the proposed method explores the rare multiple failure domains by adaptively stratifying, thereby addressing the issue of estimating extremely small failure probability robustly and efficiently.

Compared with the existing cross-entropy based IS method, the constructed M-IS-PDF not only covers the domains significantly contributing to the failure probability through clustering centroids to reduce the variance of failure probability estimation, but also has no undetermined parameter set to optimize, enhancing the adaptability in high-dimensional problems. Compared with the subset simulation method, the adaptive stratified M-IS-PDF constructed is explicit, regular and easy sampling. It not only has high sampling efficiency but also avoids estimating conditional failure probabilities layer by layer, improving the algorithmic robustness for estimating extremely small failure probability.

Both numerical and engineering examples indicate that, under the similar failure probability estimation accuracy, the proposed method requires significantly smaller sample size and lower computational cost than subset simulation and cross-entropy based IS methods, demonstrating higher efficiency and robustness in addressing intractable reliability analysis problems with high-dimensional inputs, multiple failure domains and rare failure.

During the authors’ practical experiments about designing hover controller for unmanned helicopter, they find pendulum motion exists, thus destroying their expected control performance, such as perfect tracking, target detection, accurate attacking etc., so they turn to study how to suppress pendulum motion through designing one additional controller to guarantee pendulum motion sufficiently small. More specifically, the detailed mechanism of hover pendulum motion is analyzed to yield one accurate nonlinear dynamical equation, corresponding to hover pendulum motion. To apply the existed linear optimal control strategy directly, the necessary linearization process is needed to derive one approximated linear state equation, including the explicit form of control input. After describing their idea about pendulum control structure from the point of practice, linear optimal control strategy is proposed to design the ideal controller without external noise. Then its improved robust controller is also given through solving their difference game problem or min-max problem. From their obtained closed forms of the pendulum controller, they are all one linear state feedback form, being convenient to implement in practical engineering. Finally, to achieve the mission of theory for application, they use their established practical platform to do some simulations, so proving the theoretical results. The purpose of this paper aims to design one optimal controller to suppress hover pendulum motion for unmanned helicopter.

First, considered the practical hover motion of unmanned helicopter, its detailed mechanism is analyzed to get one linear state equation after the authors’ complex mathematical derivation. Second, their idea of control structure and control design is applied to design one linear state feedback controller through the derived optimal control strategy. Third, one practical platform is established to prove their control idea.

From the authors’ introduction and knowledge, control exists everywhere. As unmanned helicopter has become increasingly important as one tactical weapon, so they think how to design controller for hover pendulum motion, while suppressing the hover pendulum. Specifically, they give the detailed theoretic derivation and practical platform.

To the best knowledge of the authors’ knowledge, pendulum motion control is to stop the swing as soon as possible and suppress the oscillatory motion of pendulum angle. Then, based on that linear state equation for hover pendulum motion, linear optimal control strategy is applied to achieve above two dual missions, i.e. controlling the motion or pendulum suspension point and eliminating or suppressing the pendulum phenomenon.

In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

This study aims to extend the application of the lattice Boltzmann method (LBM) to solve solid-to-liquid phase transition problems involving low Prandtl number (Pr) materials. It provides insight about the flow instability in a cavity undergoing melting. This work further report interface development and thermal transport against the Boussinesq number.

This study modifies the lattice Bhatnagar–Gross–Krook model by including correction components in the energy and density distribution functions. To prevent numerical instability, a tuning parameter in the flow domain is set in the range of 0.15–0.7 for the range of Rayleigh number and Prandtl number. To the best of the authors’ knowledge, the modified LBM is being used for the first time to examine the low Pr domain melting behavior of liquid metals.

The interaction with complicated flow structure with natural convection, studied in a square enclosure, has a significant impact on the melting of metals in the low Pr range. Results show that the melting rate and the length of the interface between two phases are significantly influenced by the Boussinesq number (Bo), the product of Pr and Rayleigh number (Ra). For changing Ra, the maximum interface length is almost constant in the in the Boussinesq number range up to 100 and beyond this range the interface length increases with Bo.

The effects of Pr on melting rate, Ra and Pr together on the length of the solid–liquid interface and the thermofluidic behavior in the melt zone are explained. This work also includes mapping the maximum melt interface size with Bo.

In many real-world problems, this paper faces some production processes that have internal structures that should be taken into account to have a comprehensive analysis. This situation is settled in the network data envelopment analysis (DEA) literature. Therefore, in this article, this paper aims to develop two types of covering location problems joint with network data envelopment analysis. This paper propose biobjective mixed integer programming models associated with the aforementioned development. In the first model, the goal is to select locations of facilities such that the total efficiency score is maximized and the total establishing cost for covering all demands is minimized. In the second model, this paper considers the location of facilities for maximizing the total efficiency score and covering the demands.

Covering location problems is considered a very important issue in the decision-making of organizations and companies. The purpose of these problems is to assign a set of demand points to a set of candidate locations so that optimal covering is provided for the demand points. Considering the efficiency of facility location with the help of DEA helps the decision-maker to reach more effective information and better analysis of the problem.

This paper applied the proposed models in the health centers of Shahrood City, so that each of the centers is considered a decision-making unit, and each of the decision-making units consists of three subunits that are connected in a series network. The primary results highlight the importance of the internal units beside the overall performance of healthcare centers.

Therefore, in this article, this paper develops two novel types of covering location problems joint with network data envelopment analysis. This paper proposes biobjective mixed integer programming models associated with the aforementioned development.

Opportunistic and delayed maintenances are increasingly becoming important strategies for sustainable maintenance practices since they increase the lifetime of complex systems like aircrafts and heavy equipment. The objective of the current study is to quantify the optimal time window for adopting these strategies.

The current study considers the trade-offs between different costs involved in the opportunistic and delayed maintenances (of equipment) like the fixed cost of scheduled maintenances, the opportunistic rewards that may be earned and the cost of premature parts replacement. The probability of the opportunistic maintenance has been quantified under two different scenarios – Mission Reliability and Renewal Process. In the case of delayed maintenance, the cost of the delayed maintenance is also considered. The study uses optimization techniques to find the optimal maintenance time windows and also derive useful insights.

Apart from finding the optimal time window for the maintenance activities the study also shows that opportunistic maintenance is beneficial provided the opportunistic reward is significantly large; the cost of conducting scheduled maintenance in the pre-determined slot is significantly large. Similarly, the opportunistic maintenance may not be beneficial if the pre-mature equipment parts replacement cost is significantly high. The optimal opportunistic maintenance time is increasing function of Weibull failure rate parameter “beta” and decreasing function of Weibull failure rate parameter “theta.” In the case of optimal delayed maintenance time, these relationships reverse.

To the best of our knowledge, very few studies exist that have used mission reliability to study opportunistic maintenance or considered the different cost trade-offs comprehensively.

This study aims to tackle the primary challenges in human–robot–environment interaction (HREI) within unknown environments. The key issues include recognizing human motion intention and managing force impacts during transitions from free space to constrained space. Addressing these challenges is critical for improving compliance, enhancing force control accuracy, and ensuring the safety and performance of HREI systems.

First, the energy equation of the second-order system is presented, and variable admittance control laws are designed for both free space and constraint space based on the energy equation. Then, a smooth switching method based on selection matrix is developed. Subsequently, the admittance-based overall control system is discussed. Finally, comparative simulations and experiments are conducted to verify the efficacy of the variable admittance control method using the 7-degree-of-freedom (7-DOF) manipulator Panda arm.

The simulation and experiment results demonstrate that the proposed variable admittance control method outperforms the traditional method in terms of force overshoot and accuracy.

This study does not account for the shape of unknown surfaces in the formulation of the variable admittance control law.

This paper proposes an energy-based variable admittance control method that uses energy considerations and uses a smooth switching technique to deduce human intentions and mitigate the effects of impact force.

The heat transport phenomenon in which energy transfers due to temperature differences is an important topic of interest for scientists in recent times. It is because of its wide range of applications in numerous domains such as electronics, heat dispersion, thermoregulation, cooling mechanism, the managing temperature in automotive mobile engines, climate engineering, magnetoresistance devices, etc. On account of such considerations, the magnetohydrodynamic (MHD) entropy rate for nanomaterial (CoFe₂O₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is analyzed. The Darcy–Forchheimer relation is utilized to describe the impact of a porous medium on a stretched sheet. Two nanoparticles molybdenum (MoS₄) and cobalt ferrite (CoFe₂O₄) are combined to make hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂). Heat flux corresponds to the Cattaneo–Christov model executed through heat transfer analysis. The influence of dissipation and heat absorption/generation on energy expression for nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is described.

Nonlinear partial differential expressions have been exchanged into dimensionless ordinary differential expressions using relevant transformations. Newton’s built-in shooting method is employed to achieve the required results.

Concepts of fluid flow, energy transport and entropy optimization are discussed. Computational analysis of local skin friction and Nusselt number against sundry parameters for nanomaterial (CoFe₂O₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is engrossed. Larger magnetic field parameters decay fluid flow and entropy generation, while an opposite behavior is observed for temperature. Variation in magnetic field variables and volume fractions causes the resistive force to boost up. Intensification in entropy generation can be seen for higher porosity parameters, whereas a reverse trend follows for fluid flow. Heat and local Nusselt numbers rise with an increase in thermal relaxation time parameters.

No such work is yet published in the literature.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.