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Mô phỏng khí động học đóng vai trò quan trọng trong quá trình nghiên cứu, thiết kế ô tô, bên cạnh các thí nghiệm được thực hiện trong ống khí động. Trong đó, độ chính xác của kết quả mô phỏng và yêu cầu về tài nguyên tính toán phụ thuộc rất lớn vào quá trình chia lưới. Chất lượng lưới bị ảnh hưởng bởi nhiều yếu tố, trong đó có loại phần tử lưới được sử dụng. Trong nghiên cứu này, bốn loại lưới phi cấu trúc, bao gồm Poly-Hexcore, Hexcore, Polyhedral và Tetrahedral được sử dụng để đánh giá đặc tính khí động học của mô hình xe DrivAer Fastback thông qua phương pháp mô phỏng số. Các thông số của quá trình chia lưới khi sử dụng bốn loại lưới trên được phân tích và đánh giá. Kết quả mô phỏng số cũng được so sánh với kết quả thực nghiệm để đánh giá độ chính xác giữa các loại lưới được sử dụng. Kết quả cho thấy, loại lưới Poly-Hexcore là phù hợp nhất để sử dụng cho quá trình mô phỏng khí động học ô tô.

Research examining the antecedents of recalls, instead of their consequences, is relatively sparse and has not considered whether firms' likelihood to recall products is influenced by legal changes that could induce managerial opportunism, such as those reducing shareholder litigation risk. To examine this question, the authors exploit the staggered adoption of universal demand (UD) laws across several U.S. states as a quasi-natural experiment. UD laws aim to prevent frivolous litigation from disrupting a firm's normal business operations by making it more difficult for shareholders to sue managers for neglecting their fiduciary duties and hold them personally liable. Although UD laws are well-intended, the reduced threat of shareholder litigation disciplining a firm's managers could have unintended negative consequences. Indeed, using a difference-in-differences analysis, the authors find that following the adoption of UD laws, affected firms become less likely to recall products. This effect is weaker in the presence of organizational mechanisms constraining managers' self-interest-seeking behavior, such as a corporate culture focused on customer needs and interests or the exercise of normative control through monitoring by institutional investors. The authors do not find support for a potential alternative explanation of operational improvement and therefore higher product quality driving their findings. [ABSTRACT FROM AUTHOR]

This study explores the identical synchronization of drive-response neural networks consisting of n different reaction-diffusion systems with arbitrary structures. Specifically, the drive network is made up of n reaction-diffusion systems of FitzHugh-Nagumo type, while the response network contains n reaction-diffusion systems of Hindmarsh-Rose type. Despite the arbitrary topological structures of these two complex networks, we develop a nonlinear adaptive controller to achieve the desired synchronization. Additionally, the paper presents numerical results to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a way to create fuzzy controller from LQR method by using ANFIS toolbox of Matlab. First, after proving the ability of stability of this SIMO system under LQR method in Matlab/Simulink, we create a fuzzy controller through ANFIS toolbox of Matlab. The data, which is used to train, is collected from responses of system under LQR controller. Also, we present a hardware platform of ball and beam system. Under this fuzzy controller, control quality of ball and beam is better than under LQR controller in both simulation and experiment.

Atom-to-atom maps (AAMs) are bijections that establish the correspondence of reactant and product atoms across chemical reactions. They capture crucial features of the reaction mechanism and thus play a central role in modeling chemistry at the level of graph transformations. AAMs are equivalent to so-called "imaginary transition state" (ITS) graphs, making it possible to reduce tasks such as the computational comparison of AAMs to testing graph isomorphisms. In many application scenarios, nonetheless, only partial information is available, i.e., only partial maps or, equivalently, only subgraphs of the ITS graphs, are known. Here, we investigate whether and how, and to what extent, such partial chemical data can be completed and compared. The focus of this contribution is entirely on the development of a solid mathematical foundation for the analysis of partial AAMs and their associated partial ITS graphs. [ABSTRACT FROM AUTHOR]

Purpose: Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need. Methods: The SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities. Results: The rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively. Conclusion: The SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning. Scientific Contribution: SynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem. [ABSTRACT FROM AUTHOR]