Damage characteristics and constitutive modeling of the 2D C/SiC composite: Part I – Experiment and analysis

AbstractThis paper reports an experimental investigation on the macroscopic mechanical behaviors and damage mechanisms of the plain-woven (2D) C/SiC composite under in-plane on- and off-axis loading conditions. Specimens with 15°, 30°, and 45° off-axis angles were prepared and tested under monotonic and incremental cyclic tension and compression loads. The obtained results were compared with those of uniaxial tension, compression, and shear specimens. The relationships between the damage modes and the stress state were analyzed based on scanning electronic microscopy (SEM) observations and acoustic emission (AE) data. The test results reveal the remarkable axial anisotropy and unilateral behavior of the material. The off-axis tension test results show that the material is fiber-dominant and the evolution rate of damage and inelastic strain is accelerated under the corresponding combined biaxial tension and shear loads. Due to the damage impediment effect of compression stress, compression specimens show higher mechanical properties and lower damage evolution rates than tension specimens with the same off-axis angle. Under cyclic tension–compression loadings, both on-axis and off-axis specimens exhibit progressive damage deactivation behaviors in the compression range, but with different deactivation rates

Verifying Data Constraint Equivalence in FinTech Systems

Data constraints are widely used in FinTech systems for monitoring data consistency and diagnosing anomalous data manipulations. However, many equivalent data constraints are created redundantly during the development cycle, slowing down the FinTech systems and causing unnecessary alerts. We present EqDAC, an efficient decision procedure to determine the data constraint equivalence. We first propose the symbolic representation for semantic encoding and then introduce two light-weighted analyses to refute and prove the equivalence, respectively, which are proved to achieve in polynomial time. We evaluate EqDAC upon 30,801 data constraints in a FinTech system. It is shown that EqDAC detects 11,538 equivalent data constraints in three hours. It also supports efficient equivalence searching with an average time cost of 1.22 seconds, enabling the system to check new data constraints upon submission.Comment: 14 pages, 11 figures, accepted by ICSE 202

Game-based Platforms for Artificial Intelligence Research

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-sourced games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the game-based platforms for artificial intelligence research, discusses the research trend induced by the evolution of those platforms, and gives an outlook

Correlation analysis of separation shock oscillation and wall pressure fluctuation in unstarted hypersonic inlet flow

The flow field in a hypersonic inlet model at a design point of M = 6 has been studied experimentally. The focus of the current study is to present the time-resolved flow characteristics of separation shock around the cowl and the correlation between the separation shock oscillation induced by the unstart flow and the wall pressure fluctuation when the inlet is in a state of unstart. High-speed Schlieren flow visualization is used to capture the transient shock structure. High-frequency pressure transducers are installed on the wall around both the cowl and isolator areas to detect the dynamic pressure distribution. A schlieren image quantization method based on gray level detection and calculation is developed to analyze the time-resolved spatial structure of separation shock. Results indicate that the induced separation shock oscillation and the wall pressure fluctuation are closely connected, and they show the same frequency variation characteristics. The unsteady flow pattern of the “little buzz” and “big buzz” modes are clarified based on time-resolved Schlieren images of separation shock. Furthermore, the appropriate location of the pressure transducers is determined on the basis of the combined analysis of fluctuating wall-pressure and oscillating separation shock data

Synthesizing Conjunctive Queries for Code Search

This paper presents Squid, a new conjunctive query synthesis algorithm for searching code with target patterns. Given positive and negative examples along with a natural language description, Squid analyzes the relations derived from the examples by a Datalog-based program analyzer and synthesizes a conjunctive query expressing the search intent. The synthesized query can be further used to search for desired grammatical constructs in the editor. To achieve high efficiency, we prune the huge search space by removing unnecessary relations and enumerating query candidates via refinement. We also introduce two quantitative metrics for query prioritization to select the queries from multiple candidates, yielding desired queries for code search. We have evaluated Squid on over thirty code search tasks. It is shown that Squid successfully synthesizes the conjunctive queries for all the tasks, taking only 2.56 seconds on average

Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation

Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, recommendation can be formulated as a Markov decision process (MDP), where recommendation system (agent) can interact with users (environment) and acquire feedback (reward signals).However, it is impractical to conduct online interactions with the concern on user experience and implementation complexity, and we can only train RL recommenders with offline datasets containing limited reward signals and state transitions. Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders.Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender. To address these challenges, we propose a novel RL recommender named model-enhanced contrastive reinforcement learning (MCRL). On the one hand, we learn a value function to estimate the long-term engagement of users, together with a conservative value learning mechanism to alleviate the overestimation problem.On the other hand, we construct some positive and negative state-action pairs to model the reward function and state transition function with contrastive learning to exploit the internal structure information of MDP. Experiments demonstrate that the proposed method significantly outperforms existing offline RL and self-supervised RL methods with different representative backbone networks on two real-world datasets.Comment: 11 pages, 7 figure

ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback

Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversational LLMs on chemical reaction and retrosynthesis. While drug editing, a critical task in the drug discovery pipeline, remains largely unexplored. To bridge this gap, we propose ChatDrug, a framework to facilitate the systematic investigation of drug editing using LLMs. ChatDrug jointly leverages a prompt module, a retrieval and domain feedback (ReDF) module, and a conversation module to streamline effective drug editing. We empirically show that ChatDrug reaches the best performance on 33 out of 39 drug editing tasks, encompassing small molecules, peptides, and proteins. We further demonstrate, through 10 case studies, that ChatDrug can successfully identify the key substructures (e.g., the molecule functional groups, peptide motifs, and protein structures) for manipulation, generating diverse and valid suggestions for drug editing. Promisingly, we also show that ChatDrug can offer insightful explanations from a domain-specific perspective, enhancing interpretability and enabling informed decision-making. This research sheds light on the potential of ChatGPT and conversational LLMs for drug editing. It paves the way for a more efficient and collaborative drug discovery pipeline, contributing to the advancement of pharmaceutical research and development

Constrained Reinforcement Learning for Dynamic Material Handling

As one of the core parts of flexible manufacturing systems, material handling involves storage and transportation of materials between workstations with automated vehicles. The improvement in material handling can impulse the overall efficiency of the manufacturing system. However, the occurrence of dynamic events during the optimisation of task arrangements poses a challenge that requires adaptability and effectiveness. In this paper, we aim at the scheduling of automated guided vehicles for dynamic material handling. Motivated by some real-world scenarios, unknown new tasks and unexpected vehicle breakdowns are regarded as dynamic events in our problem. We formulate the problem as a constrained Markov decision process which takes into account tardiness and available vehicles as cumulative and instantaneous constraints, respectively. An adaptive constrained reinforcement learning algorithm that combines Lagrangian relaxation and invalid action masking, named RCPOM, is proposed to address the problem with two hybrid constraints. Moreover, a gym-like dynamic material handling simulator, named DMH-GYM, is developed and equipped with diverse problem instances, which can be used as benchmarks for dynamic material handling. Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.Comment: accepted by the 2023 International Joint Conference on Neural Networks (IJCNN

1,1′-(2,5-Dimethyl­thio­phene-3,4-di­yl)diethanone

The title compound, C10H12O2S, crystallizes with four mol­ecules in the asymmetric unit. The main conformational difference between these mol­ecules is the orientation of the acetyl groups with respect to the ring. Whereas one acetyl group is only slightly twisted with respect to the thio­phene ring [C—C—C—O torsion angles = 165.7 (4), −164.6 (4), 164.3 (4) and −163.6 (4)°], the other acetyl group is markly twisted out of the ring plane [C—C—C—O torsion angles = −61.2 (6), 61.3 (7), −59.7 (7) and 59.9 (6)°]. In the crystal, mol­ecules are linked by weak C—H⋯O inter­actions into infinite chains along the c axis