Placement is a critical step in the physical design, as it largely determines the potential for subsequent optimization. In this work, we propose a timing-driven detailed placement framework: first, a simplified RC-tree model is employed for flip-flop–buffer compensation; then, a gradient-augmented global heuristic algorithm is incorporated; and finally, timing improvement is achieved through local collaborative optimization. A comprehensive evaluation on eight ICCAD 2015 benchmarks demonstrates the effectiveness of our approach. Compared to DREAMPlace4.0-DP, a state-of-the-art timing-driven placer, our framework achieves an average improvement of 19.60% in WNS and 55.74% in TNS, while introducing less disturbance to the global placement. Moreover, it delivers a 0.80% reduction in HPWL and reduces runtime by 20.24%.

Timing optimization is critical to the entire design flow of the very-large-scale integrated (VLSI) circuit, and Global Placement is pivotal in achieving timing closure within the design flow of very-large-scale integration circuits. However, most global placement algorithms focus on optimizing wirelength rather than timing. Therefore, we propose a novel timing-driven global placement algorithm to address this gap. This paper proposes a timing-driven global placement algorithm utilizing a Nadam-based net-weighting strategy. Additionally, we employ a hybrid heuristic approach for adaptive dynamic adjustment of net weights. The experimental results on the ICCAD 2015 contest benchmarks show that compared to the RePlAce, our algorithm significantly improves WNS and TNS by 40.7% and 56.5%, respectively.

Previous research on user payment behaviour in online community has predominantly focused on the pre-paid mode, with limited understanding of the antecedents influencing user payment behaviour in the post-paid mode. Drawing upon the dual-process theory and social identity theory, this study uses the text mining method based on the attention mechanism and the social network method to explore the influence of the quality of knowledge products and the credibility of contributors, focusing on the role of knowledge expertise and social identity. Using 247,013 replies to 32,373 questions from 3,631 seekers on Xueqiu.com (one of the most popular post-paid online Q&A communities in China), our empirical results found that informativeness, relevance of knowledge products, contributor honour, and experience significantly positively influence user payment behaviour, while timeliness of knowledge products exhibits a significant negative impact on user payment behaviour. Furthermore, contributor social interaction has a significant negative impact on user payment behaviour, contrary to the direction observed in the pre-paid mode. Moreover, the findings show that knowledge expertise and social identity have significant moderating effects in this process. This study provides assistance in developing diverse payment modes for knowledge platforms, and suggests insights for users to contribute knowledge more effectively.

Large Language Models (LLMs) are widely used in natural language processing tasks due to their powerful semantic understanding and knowledge integration capabilities. Numerous existing recommendation studies consider recommendation tasks as a type of natural language processing, and thus LLMs have consequently brought new changes to the recommendation system paradigm. Existing research on recommendations using LLMs partly utilizes their rich data information, fine-grained user profiling, and expanded recommendation content to improve recommendation effectiveness. Additionally, some and partly studies directly uses LLMs to implement a generative recommendation paradigm. This paper adopts the literature review method to systematically sort out the current research status of news recommendation based on LLMs and classifies and summarizes the relevant research. To comprehensively understand the research in the field of news recommendation using LLMs, this paper introduces the current major work in the field of news recommendation from the two categories of generative LLM-assisted recommendation and direct generative recommendation and summarizes the current work as well as the potential future research directions and challenges.

Abstract Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.

Information overload is one of the major challenges for online shoppers. One possible solution to this aforementioned problem is to take advantage of the interactive decision aids (IDAs). Prior studies on IDAs have mainly focused on information overload problems caused by the products (e.g., recommendation agents) and hence, have overlooked the information overload problems related to online reviews. However, online reviews are becoming more popular and turning into a major information source in consumer purchase decisions. To bridge this gap, this study investigates the effect of message grouping, an IDA approach supporting review browsing, onto the consumer's information processing and system usage intention. An experiment with a one-factor, three-level design was conducted to test the proposed research model. It is noted that grouping customer reviews into task-related categories significantly increases users' perceived usefulness, system satisfaction and intention to use the system next time. However, grouping customer reviews into task-distracting categories may significantly decrease users' perceived usefulness, system satisfaction and intention to use the system.