Microstructure and mechanical properties of NZ30K alloy by semi-continuous direct chill and sand mould casting processes

The Mg-3.0Nd-0.2Zn-0.4Zr (NZ30K) alloys were prepared by direct-chill casting (DCC) and sand mould casting (SMC) processes, respectively and their microstructures and mechanical properties were investigated. The results indicate that casting method plays a remarkable influence on the microstructure and mechanical properties of as-cast NZ30K alloy. The grain size increases from 35-40 μm in the billets made by the DCC to about 100-120 μm in the billets by the SMC. The aggregation of Mg12Nd usually found at the triple joints of grain boundaries in the billets prepared by SMC while is not observable from the billets by DCC. The tensile strengths and elongations of the billets are 195.2 MPa and 15.5% by DCC, and 162.5 MPa and 3.2% by SMC, respectively. The tensile strength of the alloy by DCC is remarkably enhanced by T6 heat treatment, which reached 308.5 MPa. Fracture surfaces of NZ30K alloy have been characterized as intergranular fracture by SMC and quasi-cleavage fracture by DCC, respectively

MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces

Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge. More concretely, we transform it into a supervised learning task by integrating multimodal and pre-trained language models. Our approach incorporates state information derived from images and action-related data obtained from text, thereby bolstering RL training performance and promoting long-term strategic thinking. We emphasize the contextual understanding of language and demonstrate how decision-making in RL can benefit from aligning states' and actions' representation with languages' representation. Our method significantly outperforms current baselines as evidenced by evaluations conducted on Atari and OpenAI Gym environments. This contributes to advancing offline RL performance and efficiency while providing a novel perspective on offline RL.Our code and data are available at https://github.com/Zheng0428/MORE_

Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators

Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules. At the same time, it is desirable to perform simulations of a collection of particles under various conditions in which the molecules can fluctuate. In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of-distribution scenarios with limited training data. While our work focuses on temperature as a test case, the versatility of our approach allows for efficient simulation through any continuous dynamic conditions, such as pressure and volumes. Our framework has two stages: 1) Pre-trains with data mixing technique, augments molecular structure data and temperature prompts, then applies a curriculum learning method by increasing the ratio of them smoothly. 2) Meta-learning-based fine-tuning framework improves sample-efficiency of fine-tuning process and gives the soft prompt-tuning better initialization points. Comprehensive experiments reveal that our framework excels in accuracy for in-domain data and demonstrates strong generalization capabilities for unseen and out-of-distribution samples

Chronic jet lag alters gut microbiome and mycobiome and promotes the progression of MAFLD in HFHFD-fed mice

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most common chronic liver disease worldwide. Circadian disruptors, such as chronic jet lag (CJ), may be new risk factors for MAFLD development. However, the roles of CJ on MAFLD are insufficiently understood, with mechanisms remaining elusive. Studies suggest a link between gut microbiome dysbiosis and MAFLD, but most of the studies are mainly focused on gut bacteria, ignoring other components of gut microbes, such as gut fungi (mycobiome), and few studies have addressed the rhythm of the gut fungi. This study explored the effects of CJ on MAFLD and its related microbiotic and mycobiotic mechanisms in mice fed a high fat and high fructose diet (HFHFD). Forty-eight C57BL6J male mice were divided into four groups: mice on a normal diet exposed to a normal circadian cycle (ND-NC), mice on a normal diet subjected to CJ (ND-CJ), mice on a HFHFD exposed to a normal circadian cycle (HFHFD-NC), and mice on a HFHFD subjected to CJ (HFHFD-CJ). After 16 weeks, the composition and rhythm of microbiota and mycobiome in colon contents were compared among groups. The results showed that CJ exacerbated hepatic steatohepatitis in the HFHFD-fed mice. Compared with HFHFD-NC mice, HFHFD-CJ mice had increases in Aspergillus, Blumeria and lower abundances of Akkermansia, Lactococcus, Prevotella, Clostridium, Bifidobacterium, Wickerhamomyces, and Saccharomycopsis genera. The fungi-bacterial interaction network became more complex after HFHFD and/or CJ interventions. The study revealed that CJ altered the composition and structure of the gut bacteria and fungi, disrupted the rhythmic oscillation of the gut microbiota and mycobiome, affected interactions among the gut microbiome, and promoted the progression of MAFLD in HFHFD mice

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions; and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities

Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation

In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations. Adapting a self-training algorithm based on instruction back-translation and answer polishment, Kun leverages unlabelled data from diverse sources such as Wudao, Wanjuan, and SkyPile to generate a substantial dataset of over a million Chinese instructional data points. This approach significantly deviates from traditional methods by using a self-curation process to refine and select the most effective instruction-output pairs. Our experiments with the 6B-parameter Yi model across various benchmarks demonstrate Kun's robustness and scalability. Our method's core contributions lie in its algorithmic advancement, which enhances data retention and clarity, and its innovative data generation approach that substantially reduces the reliance on costly and time-consuming manual annotations. This methodology presents a scalable and efficient solution for improving the instruction-following capabilities of LLMs, with significant implications for their application across diverse fields. The code and dataset can be found at https://github.com/Zheng0428/COIG-KunComment: 12 pages, 12 figure

Impact of recycler information sharing on supply chain performance of construction and demolition waste resource utilization

In recent years, the generation of a large amount of construction and demolition waste (CDW) has threatened the public environment and human health. The inefficient supply chain of CDW resource utilization hinders the green development of countries around the world, including China. This study aims to reveal the impact of information sharing regarding recyclers’ market demand forecast on the performance of CDW resource utilization supply chains. Therefore, this paper uses the incomplete information dynamic game method to establish and solve the decision-making model of the construction and demolition waste resource utilization supply chain under the conditions of recyclers sharing and not sharing their information. The paper then obtains the Bayesian equilibrium solution and the optimal expected profit for each party. Finally, a numerical simulation was used in order to verify the validity of the model and conclusions. The main conclusions are as follows. In the CDW resource utilization supply chain, if the recycler is more pessimistic about the market’s demand forecast, their information sharing makes the remanufacturer more motivated to improve their level of environmental responsibility. In addition, information sharing by recyclers is always beneficial in increasing the profit of the remanufacturer, but it also may make the recycler lose profit. When the efficiency of the environmental responsibility investment of remanufacturers is in a high range, information sharing increases the profits of recyclers. Conversely, information sharing has no significant effect on the profits of recyclers. The impact on the profits of the entire CDW resource utilization supply chain depends on the intensity of competition among channels, the market share of offline recycling channels and the efficiency of environmental responsibility investments

Solvated inverse vulcanisation by photopolymerisation

Inverse Vulcanisation (IV) under neat reaction conditions (without solvent) has enabled the research and development of the fundamental chemistry as well as the generation of unique sulfur-rich polymers with unprecedented properties. However, such bulk polymerisation can be problematic, especially with high molecular weight. The energetics of the thermal polymerisation process, combined with poor heat control of solvent-free polymerisation, cause risks of dangerous auto-acceleration if the process is scaled up. The required high temperatures (>160 °C or 135 °C even with catalysts), exceed the boiling point of most commonplace organic solvents, preventing implementation of solvents for IV under thermal conditions. We report here a photo-induced IV polymerisation in solvent at room temperature. The reactions proceed smoothly and efficiently with excellent yields, despite the potential negative factors of reflection, refraction, and low absorption intensity of light by these organic solvents, opening an attractive avenue for the preparation of functional sulfur-rich polymers as well as their potential applications. The extension of crosslinkers to the value-added C5 fraction of industrial byproduct and β-carotene showcase the benefit of this low temperature protocol. Mechanistic study reveals that the moisture in both substrates and solvents might play a key role for the generation of toxic H2S by-product in IV reaction under thermal conditions, with photopolymerisation remaining un-affected. This protocol not only extensively expands the scope of crosslinkers for the IV reaction together with resultant polymers, but also provides a potential scale-up route for industrial application by avoiding the generation of toxic H2S by-product and possible explosion risk with high temperature