With growing emphasis on sustainable manufacturing, we address the energy-aware Simple Assembly Line Balancing Problem with Power Peak Minimization (SALB3PM). We propose a metaheuristic to solve the SALB3PM. Our metaheuristic combines (1) a multi-start framework with three neighborhood operators (insertion, swap, delay incrementing) and (2) a dynamic penalty mechanism for handling cycle-time infeasibility. We designed and compared four algorithm variants to evaluate the impact of components. We conducted computational experiments on benchmark instances. The results show that our best variant achieves an average optimality gap of 2.38% when considering known optima, and optimal solutions were achieved in all runs for over 50% of instances. The approach contributes to sustainable manufacturing through energy-efficient production line design.
One intriguing but difficult route to scalable quantum information processing (QIP) is linear optical quantum computing (LOQC). The systematic growth of LOQC is examined in this research. It focuses on significant experimental developments, fault-tolerant methods, and the enduring difficulties influencing the field area. In addition, it highlights the incorporation of Gottesman-Kitaev-Preskill (GKP) encoding which is an essential step towards fault-tolerant quantum computation. We analyze the importance of the Knill-Laflamme-Milburn (KLM) protocol in addressing the probabilistic limits of two-qubit gates. Strong quantum structures are made possible by this synergy, which increases resistance to phase errors and photon loss. Notwithstanding these developments, problems with state preparation, measurement accuracy, and error correction still exist, requiring multidisciplinary efforts to improve methods and investigate fresh ideas.Beyond its scope, the knowledge gathered from LOQC may have an impact on secure quantum communication networks and hybrid quantum systems. Enhancing error correction codes, creating new quantum optical technologies, and encouraging cooperation within quantum computing paradigms should be the main goals of future research. The present work intends to stimulate more developments and push QIP towards scalable and useful quantum computation by reviewing the state of LOQC today and defining strategic directions.
This work develops PF-4DGS, an novel framework for 4D Gaussian splatting that addresses the challenges associated with the reliance on accurate prior knowledge of camera poses in dynamic scene modeling. Our approach employs a pose-free optimization strategy that simultaneously estimates camera parameters and reconstructs the scene within a unified framework. We introduce a stable initialization technique and an efficient joint optimization loop that simultaneously improves scene reconstruction and camera tracking. Comprehensive evaluations on real-world datasets demonstrate that PF-4DGS achieves accuracy comparable to leading methods, even without prior camera pose information. This advancement marks a huge breakthrough in Gaussian splatting and promotes the application of this technique in dynamic environments.
Artificial intelligence applications are used across various domains, including education, commerce, science, language, law, and healthcare. With the rapid advancement of technology, new artificical applications continue to emerge. Among these, chatbots are the most widely discussed, with ChatGPT being the most prominent one. Although several studies have evaluated ChatGPT, few offer specific recommendations regarding its usability, particularly in terms of its user interface. In this study, the design of ChatGPT’s web user interface is evaluated through expert review and system usability assessment. The findings indicate that the most significant issue is the “match between system and the real world,” based on Nielsen’s heuristics. In contrast, “aesthetic and minimalist design” received the most positive feedback from participants. While the system achieved a usability score of 80, the results also revealed a few inadequately integrated functions.
Explainable AI Planning (XAIP) is a pivotal research area focused on enhancing the transparency, interpretability, and trustworthiness of automated planning systems. This paper provides a comprehensive review of XAIP, emphasizing key techniques for plan explanation, such as contrastive explanations, hierarchical decomposition, and argumentative reasoning frameworks. We explore the critical role of argumentation in justifying planning decisions and address the challenges of replanning in dynamic and uncertain environments, particularly in high-stakes domains like healthcare, autonomous systems, and logistics. Additionally, we discuss the ethical and practical implications of deploying XAIP, highlighting the importance of human-AI collaboration, regulatory compliance, and uncertainty handling. By examining these aspects, this paper aims to provide a detailed understanding of how XAIP can improve the transparency, interpretability, and usability of AI planning systems across various domains.
