Punching shear failure in slab-column connections is a brittle collapse mode that threatens the safety of flat reinforced concrete (RC) slabs. Conventional design provisions are generally conservative but exhibit inconsistencies across geometric and material variations. This study develops an eXtreme Gradient Boosting (XGBoost) model to predict the ultimate punching shear capacity of flat RC slabs, using a database of experimental results categorized by four different geometric domains, including square slab with square column, circular slab with circular column, square slab with circular column, and circular slab with square column, covering the geometric, materials strength, and reinforcement properties of input parameters. The model achieved high predictive accuracy across the domains with coefficient of determination (R2) values > 0.930 in unseen testing datasets with minimal bias (0.994–1.006) and reduced scatter. Model interpretability, addressed through the SHapley Additive exPlanations analysis, confirmed slab thickness and average effective depth as the most critical predictors of shear capacity, followed by concrete strength and reinforcement parameters, while boundary condition parameters showed negligible influence due to the predominance of interior column cases. These findings demonstrate that XGBoost provides accurate, reliable, and interpretable predictions of punching shear capacity, offering a data-driven alternative to code-based methods and supporting safer and more consistent design of flat RC slabs.

With the rapid development of artificial intelligence technology, robotics, as its core branch, has attracted extensive attention from researchers. This paper designs and develops a robotic arm learning system based on multi-source sensor information fusion, which investigates the autonomous learning capability of robotic arms by closely integrating deep reinforcement learning (DRL) as the core framework for skill acquisition. By incorporating imitation learning as a source of expert prior and leveraging DRL’s intrinsic ability for policy optimization through environmental exploration, the proposed system achieves both rapid learning and robust generalization. Specifically, we introduce the gradient penalty mechanism from Wasserstein generative adversarial networks (WGANs), a technique that improves the stability of adversarial training by penalizing gradients that deviate from a specified norm. This mechanism is incorporated into the soft actor-critic (SAC) algorithm, a widely used off-policy DRL method known for its sample efficiency and robust performance in continuous control tasks. The resulting SAC-GP (SAC-gradient penalty) algorithm benefits from both SAC’s stable policy learning and WGAN’s improved training regularization, leading to superior convergence speed and system stability. Furthermore, this paper proposes a hybrid learning framework by combining generative adversarial imitation learning (GAIL) with SAC-GP, enabling the agent to benefit from both demonstration-based policy initialization and continuous self-improvement via reinforcement learning. Finally, a door-opening experiment is designed to verify the learning and execution capabilities of the system in both virtual and real environments. Experimental results demonstrate that the proposed learning system possesses excellent learning and motion execution abilities in practical applications. This achievement not only provides new insights for research in robot learning but also lays a solid foundation for the future development of robotic technology.

In a time of mass global e-waste production, a re-evaluation of re-purposing and recycling practices feels particularly relevant not only in life but also in art-making processes, especially in temporarily mounted installations, both sonic and visual. What is junk and what is useful material? Can the use of salvaged materials also encourage creativity and innovation? This paper, weaving in theoretical frameworks from sound studies, media archaeology and eco-sonic aesthetics, suggests that using mismatched ‘garbage’ loudspeakers and unconventional loudspeaker arrays can offer sound artists creative opportunities for the exploration of new aural spaces and spatial and timbral possibilities, through the formation of sounding sculptures. Examining Social Sciences and Humanities Research Council-funded sound and art installation Waste Whisperer (2023) as a case study, which involved a bespoke 40 loudspeaker set-up of salvaged ‘trash’, the article also explores the work of artists such as Benoit Maubrey, John Wynne and Nor Tijan Firdaus, who use discarded e-waste as their primary sculptural materials, as well as Paul Rogers’ research around the concept of ‘sonic junk’. In addition, the concepts of transparency and ‘realism’ in the audio medium are discussed, positing critical reflections on prevailing techno-utopian narratives in contemporary audio communities around ‘matching’ loudspeakers and spatialisation conventions.

Hemiplegia, the paralysis of one side of the body, is a common effect of stroke and provides unique challenges for afflicted individuals, including asymmetric body strength and limited mobility, especially in the sit-to-stand (STS) motion. Reducing weight-bearing asymmetry during STS is important for improving mobility outcomes of hemiplegic patients. To address this concern, a semi-wearable STS assistive robot is proposed to provide assistive force and motion guidance during the STS motion. It is a planar 2-DoF assistive robot attached near the hip, designed to reduce weight-bearing asymmetry and facilitate correct execution of the STS motion by guiding the user along a target STS path and constraining pelvic motion in the frontal plane, controlled using a single worn IMU. The method for generating unique target STS paths and assistive robot design is presented. Experiments on healthy test subjects with the motion of one leg constrained were conducted to determine the changes and correlations in force and motion parameters when using the assistive robot during STS. The assistive robot improved rising STS asymmetry in some test subjects and reduced stabilization weight-bearing asymmetry in all test subjects. Motion data showed that the assistive robot facilitated hip translation and tilt toward the test subjects’ constrained side, while a counter trunk tilt toward the unconstrained side was observed. The results of the experiments suggest that more active control of the hip position and tilt and providing real-time feedback during the STS motion could further improve the function of the robot.