Job-dependent tool switching is necessary in many batch processing systems (BPSs). Heterogeneous tool demand and extra time consumption for tool switch bring great challenge for high-performance production scheduling in BPSs. In this study, we present a novel batch scheduling problem derived from a satellite vibration test system. Different from basic scheduling problems in BPSs with tool switching, it has complications caused by many-to-many task-tool matching and job release time constraints. Specifically, each job has predetermined candidate tools. One of them should be selected and equipped to process a given job. Unlike sequence-dependent setup time that is only related to two adjacent jobs, tool switching time is additionally dependent on the tools selected by both jobs from their respective candidate tools. Both job sequence and tool selections need to be determined to minimize makespan. Mixed-integer linear programming (MILP) and constraint programming (CP) models are developed for formulating the problem. We prove that the problem isNP-hard. To obtain exact solutions, we propose a novel branch-andbound algorithm (B&B) based on the analysis and extraction of problem properties. It integrates a domain reduction mechanism, branching strategies, and dominance rules. Among them, the domain reduction mechanism reduces the number of jobs to be scheduled; the branching strategies accelerate solving speed; and the dominance rules save the memory consumption. Ablation studies and key influencing factor experiments are performed. The results show that the proposed B&B can optimally solve much larger instances than state-of-the-art methods (including some heuristics). It can optimally solve industrial-size problems in a short time, implying that it can well satisfy the performance demand of a satellite vibration test system and thus advancing the filed of optimal scheduling for BPSs.

Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.

One of the crucial challenges in human-robot interaction is how to enable robots to predict human intentions. In this study, we explore how data augmentation technique can contribute to human intention prediction when only limited training data is available. Specifically, we conduct experiments of predicting the intentions of a human throwing a ball towards designated targets. Prediction performances with various data augmentation methods are presented and compared. The experiment results show that prediction accuracy can be improved from 50% to 75%.

We propose a new rule, loop reversal, in block diagram and signal flow graph manipulation (or transformation) that leads to equivalent block diagrams or signal flow graphs from existing ones. It is known that “algebraic loops” can exist in some systems, where different choices of causality end up with different yet equivalent block diagrams or signal flow graphs, in which some are loop reversed versions of others. However, to the author's knowledge, loop reversal has not been discussed in the context of block diagram or signal flow graph manipulation, and is still missing from textbooks teaching this topic.

A two-dimensional space-time pilot-symbol assisted demodulation system is proposed in this paper. Since complex scattering environments can produce nontrivial space-time correlation characteristics, this paper proposes coherent demodulation by estimating the fading channel distortion using pilot observations across both space and time. The optimum interpolation filter takes in multiple signals from receiving antennas and exploiting the space-time channel correlation to produce multiple channel-estimate outputs corresponding to every antenna. The optimal channel-estimation filter, its mean-square error performance, and the pairwise symbol-error probability are derived for realistic scattering models to characterize the system performance. A design criterion is proposed for the pilot-symbol spacing and the filter length. Since the filter is a function of the statistical description of the channel, we investigate if this statistical description and the resulting optimal filters can be estimated simply from the pilot observations.