With the rapid development of smart manufacturing, the joint optimization of production, equipment maintenance, and product quality control has become key to improving manufacturing system performance. To formulate maintenance strategies for manufacturing systems, most studies use equipment degradation level as an indicator of whether maintenance actions should be initiated. However, product quality often deteriorates before equipment degradation is detected. This study develops a new joint optimization model based on quality information. First, we quantify a production quality risk indicator based on the deviation of key quality characteristics and potential risk and formulate a preventive maintenance strategy on that basis. Then, we construct a dual-objective optimization model using Pareto frontier analysis that accounts for both cost minimization and equipment availability in cycle maximization, aiming to improve the accuracy of maintenance strategies. Finally, the effectiveness of the proposed method is demonstrated through specific examples and sensitivity analysis, as well as a comparative study. The results show that the proposed method achieves the lowest total cost among the considered policies, while keeping a high availability comparable to existing policies. This outcome can provide decision support for the high-reliability, low-cost operation and maintenance of smart manufacturing systems.

With the rapid advancement of large language models (LLMs) and vision-language models (VLMs), significant progress has been made in developing open-vocabulary robotic manipulation systems. However, many existing approaches overlook the importance of object dynamics, limiting their applicability to more complex, dynamic tasks. In this work, we introduce KUDA, an open-vocabulary manipulation system that integrates dynamics learning and visual prompting through keypoints, leveraging both VLMs and learning-based neural dynamics models. Our key insight is that a keypoint-based target specification is simultaneously interpretable by VLMs and can be efficiently translated into cost functions for model-based planning. Given language instructions and visual observations, KUDA first assigns keypoints to the RGB image and queries the VLM to generate target specifications. These abstract keypoint-based representations are then converted into cost functions, which are optimized using a learned dynamics model to produce robotic trajectories. We evaluate KUDA on a range of manipulation tasks, including free-form language instructions across diverse object categories, multi-object interactions, and deformable or granular objects, demonstrating the effectiveness of our framework. The project page is available at http://kuda-dynamics.github.io.

With the rapid adoption of multimodal large language models (MLMs) in autonomous agents, cross-platform task execution capabilities in educational settings have garnered significant attention. However, existing benchmark frameworks still exhibit notable deficiencies in supporting cross-platform tasks in educational contexts, especially when dealing with school-specific software (such as XiaoYa Intelligent Assistant, HuaShi XiaZi, etc.), where the efficiency of agents often significantly decreases due to a lack of understanding of the structural specifics of these private-domain software. Additionally, current evaluation methods heavily rely on coarse-grained metrics like goal orientation or trajectory matching, making it challenging to capture the detailed execution and efficiency of agents in complex tasks. To address these issues, we propose KGCE (Knowledge-Augmented Dual-Graph Evaluator for Cross-Platform Educational Agent Benchmarking with Multimodal Language Models), a novel benchmarking platform that integrates knowledge base enhancement and a dual-graph evaluation framework. We first constructed a dataset comprising 104 education-related tasks, covering Windows, Android, and cross-platform collaborative tasks. KGCE introduces a dual-graph evaluation framework that decomposes tasks into multiple sub-goals and verifies their completion status, providing fine-grained evaluation metrics. To overcome the execution bottlenecks of existing agents in private-domain tasks, we developed an enhanced agent system incorporating a knowledge base specific to school-specific software. The code can be found at https://github.com/Kinginlife/KGCE.

Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. Existing ridesharing systems (e.g., lyft, uberPOOL) often offer each traveler only one choice that aims to minimize system-wide vehicle travel distance or time. In this demonstration, we present a price-and-time-aware ridesharing system, termed as PTRider, which provides more options. It considers both pick-up time and price, so that travellers are able to choose the vehicle matching their preferences best. To answer the ridesharing request in real time, PTRider builds indexes on the road network and vehicles separately, and utilizes corresponding efficient matching methods. A real-life dataset that contains 432,327 trips extracted from 17,000 Shanghai taxis for one day (May 29, 2009) is used to demonstrate that PTRider can return various options for every ridesharing request in real time.

With the development of collaborative design and its extensive application, collaborative design efficiency is becoming one of the hottest research topics. However, it is difficult for some common evaluation methods to analyze and evaluate collaborative design efficiency directly. Aiming at these problems, a GA-BP algorithm for collaborative design efficiency evaluation is proposed in this paper. Firstly, genetic algorithm (GA) and Back Propagation neural network (BP) are analyzed; secondly, the main principle and workflow of GA-BP algorithm are deep researched; finally, a practical collaborative design efficiency case is evaluated by GA-BP algorithm and Fuzzy AHP method individually as experimental studies. The result shows the validity and advantages of GA-BP algorithm in collaborative design efficiency evaluation.