Despite recent advances in automation, the AECO industry still faces inefficiencies, high costs, and safety risks. To address these, we present MMCR, a mixed-reality (MR) and machine-learning (ML) guided cable-robotic system for prefabricated construction. A HoloLens 2 MR interface delivers spatially anchored guidance and risk alerts, while a Unity ML agent enables autonomous navigation, obstacle avoidance, grasping, and placement. In real-world tests, the ML-enabled cable robot manipulated precast components in real time, reducing manual intervention and cognitive load. Compared with manual and MR-only baselines, MMCR achieved a 4 × reduction in positioning error, 2.8 × faster task completion, and 88.9% fewer operator interventions. These results indicate that MR-assisted, learning-based cable robotics can streamline onsite assembly and enhance safety, advancing practical pathways toward smart, efficient construction.

This paper addresses the challenge of distributed adaptive platooning control of connected vehicles with randomly switching topologies. A linearized longitudinal vehicle platoon dynamic model is investigated, which simultaneously considers the bounded external disturbances and leader control input. By representing the switching topologies model in terms of a continuous-time Markov process, a distributed adaptive control scheme is developed such that follower vehicles can synchronize their velocities and accelerations with the leader while preserving the intended distance. The outstanding feature of the proposed distributed adaptive control scheme is that it avoids acquiring global information associated with communication topology. Besides, the design procedure is scalable since the control gain can be derived by only solving a linear matrix inequality offline related to the vehicle dynamics parameters, which means that the method remains applicable when the platoon size changes. Finally, the efficacy of theoretical findings is substantiated through the utilization of simulations.

Recently, the emerging trend in automatic program repair is to apply deep neural networks to generate fixed code from buggy ones, called NPR (Neural Program Repair). However, the existing NPR systems are trained and evaluated under very different settings (e.g., different training data, inconsistent evaluation data, wide-ranged candidate numbers), which makes it hard to draw fair-enough conclusions when comparing them. Motivated by this, we first build a standard benchmark dataset and an extensive framework tool to mitigate threats for the comparison. The dataset consists of a training set, a validation set and an evaluation set with 144,641, 13,739 and 13,706 bug-fix pairs of Java respectively. The tool supports selecting specific training, validation, and evaluation datasets and automatically conducting the pipeline of training and evaluating NPR models, as well as easily integrating new NPR models by implementing well-defined interfaces. Then, based on the benchmark and tool, we conduct a comprehensive empirical comparison of six SOTA NPR systems w.r.t the repairability, inclination and generalizability. The experimental results reveal deeper characteristics of compared NPR systems and subvert some existing comparative conclusions, which further verify the necessity of unifying the experimental setups in exploring the progresses of NPR systems. Meanwhile, we reveal some common features of NPR systems (e.g., they are good at dealing with code-delete bugs). Finally, we identify some promising research directions derived from our findings.