Lu Sun

I. As a multiphase porous material (i.e., solid phase, two gas phases, and three water phases), concrete is used in nuclear reactors and nuclear waste repositories for spent fuel storage as a radia...

This study presents a PRISMA-based systematic review of 343 publications focused on tracking how AI-based methods have evolved within robot manipulator control from 2015 to 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according to functional roles, including perception and estimation, planning, learning-based control, interaction and safety, and learning and adaptation. In addition to this functional taxonomy, the study analyzes publication growth, application domains, robot types, evaluation settings, and methodological patterns to characterize the evolution of the field over the past decade. The results show that research activity has been concentrated primarily in learning control, while other functional roles have received comparatively less attention. The data also reveals an uneven distribution across application areas and robot platforms in the literature, with a strong reliance on simulation and limited evidence of integrated real-world deployment. These patterns indicate that, despite rapid growth and methodological diversity, the field remains imbalanced in both research focus and validation maturity. Rather than summarizing individual studies in isolation, this review provides a high-level perspective on where effort has been concentrated, where major gaps persist, and which directions are most critical for advancing AI-based robot manipulator control toward reliable and scalable real-world use.

This study provides a comprehensive review on monocular metrology and fuzzy random-set portfolio management of financial assets. The findings and conclusions are elaborated as follows. Soft computing and AI have already enhanced and will further empower a variety of applications of monocular metrology and fuzzy random-set portfolio management of financial assets through progressive quantification and capturing of domain situations. The single most significant limitation of monocular metrology lies in its intrinsic incapability of direct measurement of 3D geometry through 2D imagery. The future of monocular metrology lies in deep learning and end-to-end solutions, multi-sensor data fusion, algorithmic optimization and real-time performance, self-supervised learning and generalization, and standardization and practical deployment. Neither statistical validation nor performance optimization alone is sufficient to support decision-makers in making portfolio decisions that are reliably and trust-worthy. A promising portfolio management decision-making framework should integrate the statistical rigor of fuzzy statistics with fuzzy random portfolio optimization techniques to quantitatively account for fuzziness and uncertainty while better balancing computational efficiency, statistical reliability, interpretability, and practical credibility.

This study presents a structured analysis of 343 publications on artificial intelligence-based methods for robot manipulator control published between 2015 and 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according to functional roles, including perception and estimation, planning, learning based control, interaction and safety, and learning and adaptation. In addition to this functional taxonomy, the study analyzes publication growth, application do-mains, robot types, evaluation settings, and methodological patterns to characterize the evolution of the field over the past decade. The results show that research activity has been concentrated primarily in learning control, while other functional roles have received comparatively less attention. The literature also reveals an uneven distribution across ap-plication areas and robot platforms, with a strong reliance on simulation and limited evidence of integrated real-world deployment. These patterns indicate that, despite rapid growth and methodological diversity, the field remains imbalanced in both research focus and validation maturity. Rather than summarizing individual studies in isolation, this review provides a high-level perspective on where effort has been concentrated, where major gaps persist, and which directions are most critical for advancing AI-based robot manipulator control toward reliable and scalable real-world use.

This paper reviews constitutive models used to predict concrete spalling under elevated temperatures, with emphasis on fire exposure and concrete linings in deep geological repositories for spent fuel and nuclear waste. The review synthesizes (1) how material composition (ordinary Portland cement concrete, geopolymer concrete, and fiber-reinforced systems using polypropylene and steel fibers) affects spalling resistance; (2) how coupled environmental and mechanical actions (temperature, moisture, stress state, chloride ingress, and radiation) drive damage initiation and spalling; and (3) how constituent-scale characteristics (microstructure, porosity, permeability, elastic modulus, and water content) govern thermal–hydro–mechanical–chemical (THMC) transport and damage evolution. We compare major constitutive modeling frameworks, including plasticity–damage models (e.g., concrete damage plasticity), statistical damage approaches, and fully coupled THM/THMC formulations, and highlight how key parameters (e.g., water-to-binder ratio, temperature-driven pore-pressure gradients, and crack evolution laws) control predicted spalling onset, depth, and timing. Several overarching challenges emerge: lack of standardized experimental protocols for spalling tests and assessments, which limits cross-study benchmarking; continued debate on whether spalling is dominated by pore pressure, thermo-mechanical stress, or their interaction; limited integration of multiscale and constituent-level material characteristics; and high data and computational demands associated with advanced multi-physics models. The paper concludes with targeted research directions to improve model calibration, validation, and performance-based design of concrete systems for high-temperature and repository applications.

This study provides a comprehensive review of advancements in the field of train–track–subway system interaction dynamics and suggests future directions for research and development. Mathematical modeling of train–track–subway interaction system is addressed, including wheel–track contact mechanics and wear, train multibody dynamics, train–track system coupling dynamics, track slab subsystem dynamics, subway tunnel–ground interaction models, building vibration excited by ground-borne seismic waves, and noise. Advanced computing and simulation techniques used for numerical studies of the dynamics of train–track–subway system interaction in the past two decades are also addressed, including high-performance computing with efficient algorithms, multi-physics and multi-scale simulation, real-time hardware-in-the-loop simulation, and laboratory and field validation. The study extends the applications of train–track–subway interaction dynamics to subway route planning, structural and material design, subway maintenance, operations safety and reliability, and passenger comfort. Emerging technologies and future perspectives are also reviewed and discussed, including artificial intelligence, smart sensing and real-time monitoring, digital twin technology, and sustainable design integration.

This paper presents the results of real-time experiments conducted to evaluate the performance of a developed adaptive control scheme applied to control the motion of a real closed-kinematic chain mechanism (CKCM) robot manipulator with two degrees of freedom (DOFs). The developed control scheme, referred to as the decentralized adaptive synchronized control scheme (DASCS), was the result of the combination of model reference adaptive control (MRAC) based on the Lyapunov direct method and the synchronization technique. CKCM manipulators were considered in the experimental study due to their advantages over their open-kinematic chain mechanism (OKCM) manipulator counterparts, such as higher stiffness, better stability, and greater payload. The conducted computer simulation study showed that the DASCS was able to asymptotically converge tracking errors to zero, with all the active joints moving synchronously in a prescribed way. One of the important properties of the DASCS is the independence of robot manipulator dynamics, making it computationally efficient and therefore suitable for real-time applications. The present paper reports findings from experiments in which the DASCS was applied to control the above manipulator and carry out various paths. The DASCS’s performance was compared with that of a traditional adaptive control scheme, namely the SMRACS, when both schemes were applied to track the same paths.

This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based on a closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive Synchronized Control scheme (DASCS) is developed. The DASCS can ensure global asymptotic convergence of tracking errors while forcing all active joints to move in a predefined synchronous manner in the presence of uncertainties and sudden changes in payload. Furthermore, the control scheme has a simple structure, independent of the manipulator’s dynamic model, ensuring computational efficiency. Results of computer simulations conducted to evaluate the performance of the control scheme applied to controlling the motion of a CKCM manipulator with six degrees of freedom are reported and discussed.

This paper presents a new decentralized adaptive control scheme for motion control of robot manipulators built based closed-kinematic chain mechanism (CKCM). By employing the synchronization technique and model reference adaptive control (MRAC) based on the Lyapunov direct method, the Decentralized Adaptive Synchronized Control (DASC) scheme is developed. The DASC scheme can ensure global asymptotic convergence of tracking errors while forcing all active joints to move in a predefined synchronous manner in the presence of uncertainties and sudden changes in payload. In addition, the control scheme has a simple structure that does not depend on the knowledge of the dynamic mathematical model of a robot manipulator resulting in computational efficiency of control scheme implementation. Results of computer simulation conducted to evaluate the performance of the control scheme applied to control the motion of a CKCM manipulator with 6 degrees of freedom are reported and discussed.

Nanomaterials has been widely adopted in asphalt binder modification in pavement engineering. Considering the advantage of nano-Al₂O₃ in high-temperature resistance and chemical stability, this study investigated the feasibility of the nano-Al₂O₃ used in asphalt modification in terms of performances and mechanisms. The impact of the nano-Al₂O₃ content on modified asphalt binder’s pavement performance was analyzed via aging resistance, low temperature performance, high temperature performance, and bonding performance using various tests (e.g. DSR test, BBR test, RTFOT test, and BBS test). Results showed that the nano-Al₂O₃ is capable of improving base asphalt’s aging resistance, high temperature performance and bonding performance, while bringing an insignificant adverse impact on performance at low temperatures simultaneously. Moreover, modification mechanisms of the asphalt binder under the modification of nano-Al₂O₃ were revealed from a view of functional groups and molecular characteristics via Gel permeation chromatography (GPC) tests and Fourier transform infrared spectroscopy (FTIR) tests. According to the result, modification of base asphalt binder by nano-Al₂O₃ belongs to physical blended and inhibits the growth of sulfoxide groups during the aging process. Meanwhile, the nano-Al₂O₃ increases the base asphalt binder’s molecular weight based on LMS percentage, number-average and weight-average.