The Research of Product Graphical Information Sharing Technology of Virtual Manufacturing Enterprise in E-Commerce Environment

This paper has built a product model by UML and corresponding Product Schema. Then we have illuminated transmit mechanism of the product information by a dumbbell XML document. At last, we have pointed out the direction of the research. This research will provide a significative explore to the product data interchange between the members of virtual manufacturing enterprise in e-commerce environmen

MC^2: A Multilingual Corpus of Minority Languages in China

Large-scale corpora play a vital role in the construction of large language models (LLMs). However, existing LLMs exhibit limited abilities in understanding low-resource languages, including the minority languages in China, due to a lack of training data. To improve the accessibility of these languages, we present MC^2, a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus so far. It encompasses four underrepresented languages, i.e., Tibetan, Uyghur, Kazakh in the Kazakh Arabic script, and Mongolian in the traditional Mongolian script. Notably, two writing systems in MC^2 are long neglected in previous corpora. As we identify serious contamination in the low-resource language split in the existing multilingual corpora, we propose a quality-centric solution for collecting MC^2, prioritizing quality and accuracy while enhancing representativeness and diversity. By in-depth analysis, we demonstrate the new research challenges MC^2 brings, such as long-text modeling and multiplicity of writing systems. We hope MC^2 can help enhance the equity of the underrepresented languages in China and provide a reliable data foundation for further research on low-resource languages.Comment: Work in progres

Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles

The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions. We introduce cryoPROS, an AI-based approach designed to address the above issue. By generating the auxiliary particles with a conditional deep generative model, cryoPROS addresses the intrinsic bias in orientation estimation for the observed particles. We effectively employed cryoPROS in the cryo-EM single particle analysis of the hemagglutinin trimer, showing the ability to restore the near-atomic resolution structure on non-tilt data. Moreover, the enhanced version named cryoPROS-MP significantly improves the resolution of the membrane protein NaX using the no-tilted data that contains the effects of micelles. Compared to the classical approaches, cryoPROS does not need special experimental or image acquisition techniques, providing a purely computational yet effective solution for the preferred orientation problem. Finally, we conduct extensive experiments that establish the low risk of model bias and the high robustness of cryoPROS

Lawyer LLaMA Technical Report

Large Language Models (LLMs), like LLaMA, have exhibited remarkable performances across various tasks. Nevertheless, when deployed to specific domains such as law or medicine, the models still confront the challenge of a deficiency in domain-specific knowledge and an inadequate capability to leverage that knowledge to resolve domain-related problems. In this paper, we focus on the legal domain and explore how to inject domain knowledge during the continual training stage and how to design proper supervised finetune tasks to help the model tackle practical issues. Moreover, to alleviate the hallucination problem during model's generation, we add a retrieval module and extract relevant articles before the model answers any queries. Augmenting with the extracted evidence, our model could generate more reliable responses. We release our data and model at https://github.com/AndrewZhe/lawyer-llama.Comment: Work in progres

Dietary supplementation of exopolysaccharides from Lactobacillus rhamnosus GCC-3 improved the resistance of zebrafish against spring viremia of carp virus infection

Spring viremia of carp virus (SVCV) can cause high mortality of fish. The aim of this study was to investigate the effects of Lactobacillus rhamnosus GCC-3 exopolysaccharides (GCC-3 EPS) on zebrafish (Danio rerio) infected with SVCV and elucidate the underlying mechanisms. Zebrafish were fed with a control diet or diet supplemented with 0.5% and 1% of GCC-3 EPS for 2 weeks. The results showed that supplementation of GCC-3 EPS significantly improved the survival rate of zebrafish compared with the control group. In addition, dietary 0.5% and 1% GCC-3 EPS significantly up-regulated the expression of genes related to type I interferon (IFN) antiviral immunity. Consistent with in vivo results, GCC-3 EPS significantly inhibited SVCV replication in zebrafish embryonic fibroblast (ZF4) cells while significantly increased the expression of type I IFN signaling pathway related genes. Furthermore, knocking down TANK-binding kinase 1 significantly blocked the antiviral effect of GCC-3 EPS. Dietary GCC-3 EPS improved gut microbiota, and the culture supernatant of GCC-3 EPS-associated microbiota significantly inhibited SVCV replication in ZF4 cells compared with the control-microbiota counterpart. In conclusion, our results indicate that dietary GCC-3 EPS can improve the resistance of zebrafish against SVCV infection, and the mechanism may involve enhanced type I interferon signaling. KEYW

Design and implementation of an auxiliary calculation system for structural parameters of the rotor system of a roller huller

[Objective] To improve the design efficiency of the mechanical structure of the rotor system of a rubber roller husker. [Methods] An auxiliary calculation system for the rotor system design parameters of a rubber roller husker （RRHRSPD） that can be independently installed and operated under the Windows system was developed using VB and Matlab platforms. Based on the equivalent design method and the traditional mechanical design method, the coupling relationships among the technological parameters, dynamic parameters and structural parameters of the husker were established, and the calculation principle of the structural parameters of the rotor system was also explored. VB was used to develop the software operation interface and to quickly call the Matlab dynamic link library, which formed the core computing mechanism of the system, and the system was packaged and released. [Results] A set of auxiliary calculation system for structural parameters of the rotor system of rubber roller husker was developed. [Conclusion] The calculation results of the system are accurate and reliable, which can be used not only for the design and calculation of the rotor system structure of the rubber roller husker, but also for the virtual test platform for the performance analysis of the rubber roller husker

A methodology for building generation trajectories to balance continuous-time load profiles

In power systems, maintaining a balance between generation and load is crucial. Traditional discrete-time dispatch methods often fall short, as they do not account for continuous-time changes in the load profiles throughout the time span. This oversight can lead to inaccuracies in tracing load profiles and even cause ramping resource shortages. In this paper, we propose the idea of continuous-time generation trajectories as dispatch results, to align with continuous-time load profiles. To ensure the solvability of the continuous-time dispatch, we propose an iterative dispatch methodology, which includes two stages: trajectory construction and constraint verification. In the trajectory construction stage, we use a parametric programming model to divide the continuous-time load profiles into multiple segments. Subsequently, we build the generation trajectories for each segment using parametric solutions. In the constraint verification stage, we specifically check the continuous-time ramping constraints. This stage identifies the infeasible trajectories, which will be updated during the next iteration. We repeat this iterative process until each unit has a feasible continuous-time generation trajectory throughout the time span. The effectiveness of our methodology is demonstrated in an illustrative 5-bus system and an actual 661-bus system

Effects of nuclease-treated fermentation product of Lactobacillus rhamnosus GCC-3 on growth, hepatic health and gut microbiota of zebrafish (Danio rerio) fed a high-fat diet

Probiotics are reported to improve the nutrition, immunity, and health of fish. Nuclease can hydrolyze nucleic acids of probiotics to produce nucleotides. The present study investigated the effect of stabilized fermentation product of nuclease-treated Lactobacillus rhamnosus GCC-3 (GCC-3 NT) on growth, non-specific immunity, liver health, and gut microbiota of zebrafish (Danio rerio). Compared to the high-fat diet (HFD) group, GCC-3 NT did not affect the growth performance of zebrafish. However, GCC-3 NT treatment can significantly increase the lysozyme activity and the total antioxidant capacity of body surface mucus. In addition, dietary GCC-3 NT significantly reduced the content of hepatic triglycerides (TAG) in zebrafish while significantly increased the expression of acyl-coenzyme A oxidases 3 (ACOX3) and proliferator-activated receptor γ coactivator 1α (PGC1α) compared with the HFD group. The 16S rRNA gene sequencing showed that GCC-3 NT reduced the relative abundance of Actinobacteria while increased Firmicutes at the phylum level. The relative abundance of Rhodococcus was significantly decreased and Lactobacillus and Staphylococcus abundance were significantly increased in the GCC-3 NT group compared to the HFD group. Furthermore, PCoA analysis showed GCC-3 NT diet had a significant effect on the autochthonous microbiota compared to the HFD diet. Together, our results showed that nuclease-treated L. rhamnosus fermentation product can improve the immunity, liver health and gut microbiota of zebrafish, suggesting that it can be potentially used as a functional feed additive for aquaculture

Harder Tasks Need More Experts: Dynamic Routing in MoE Models

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike traditional MoE approaches that rely on fixed Top-K routing, which activates a predetermined number of experts regardless of the input's complexity, our method dynamically selects experts based on the confidence level in expert selection for each input. This allows for a more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over conventional Top-2 routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input's complexity. Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://github.com/ZhenweiAn/Dynamic_MoE

Unveiling precipitation behavior in Mg-Y based alloys

Mg-Y based alloys exhibit a promising combination of strength and deformability through tuning precipitation and solute strengthening mechanisms and tailoring the activity of non-basal dislocations. Understanding the precipitation sequence of Mg-Y based alloys and its dependence on Yttrium concentration in the matrix will provide a guideline for fine tuning structure, morphology and distribution of precipitates in Mg-Y based alloys. In this paper, we explore the precipitation behaviors of Mg-11Y (wt%) and Mg-11Y-1Al (wt%) alloys using aberration-corrected scanning transmission electron microscopy, and rationalize the experimental observations based on first-principles density functional theory calculations. The precipitation sequence during ageing at 225 °C is identified to be SSSS → clusters/G.P. Zones →β′ (Mg7Y) → β′′/βt′′ (Mg3Y). A novel βt′′ phase forms through in-situ transformation from the β′ phase, which shares the same Mg3Y composition with D019-β′′ phase and exhibits the same cbco-structure as β′ phase in Mg-Y based alloys