Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem

RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit

Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to user requests. This problem can be alleviated by augmenting LLMs with information retrieval (IR) systems (also known as retrieval-augmented LLMs). Applying this strategy, LLMs can generate more factual texts in response to user input according to the relevant content retrieved by IR systems from external corpora as references. In addition, by incorporating external knowledge, retrieval-augmented LLMs can answer in-domain questions that cannot be answered by solely relying on the world knowledge stored in parameters. To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit. In RETA-LLM, we create a complete pipeline to help researchers and users build their customized in-domain LLM-based systems. Compared with previous retrieval-augmented LLM systems, RETA-LLM provides more plug-and-play modules to support better interaction between IR systems and LLMs, including {request rewriting, document retrieval, passage extraction, answer generation, and fact checking} modules. Our toolkit is publicly available at https://github.com/RUC-GSAI/YuLan-IR/tree/main/RETA-LLM.Comment: Technical Report for RETA-LL

Soluble epoxide hydrolase inhibitor promotes the healing of oral ulcers

Objective: Oral ulcers are a lesion in the oral mucosa that impacts chewing or drinking. Epoxyeicosatrienoic Acids (EETs) have enhanced angiogenic, regenerative, anti-inflammatory, and analgesic effects. The present study aims to evaluate the effects of 1-Trifluoromethoxyphenyl-3-(1-Propionylpiperidin-4-yl) Urea (TPPU), a soluble epoxide hydrolase inhibitor for increasing EETs level, on the healing of oral ulcers. Methods: The chemically-induced oral ulcers were established in Sprague Dawley rats. The ulcer area was treated with TPPU to evaluate the healing time and pain threshold of ulcers. The expression of angiogenesis and cell proliferation-related protein in the ulcer area was detected using immunohistochemical staining. The effects of TPPU on migration and angiogenesis capability were measured with scratch assay and tube formation. Results: Compared with the control group, TPPU promoted wound healing of oral ulcers with a shorter healing time, and raised pain thresholds. Immunohistochemical staining showed that TPPU increased the expression of angiogenesis and cell proliferation-related protein with reduced inflammatory cell infiltration in the ulcer area. TPPU enhanced cell migration and tube-forming potential in vitro. Conclusions: The present results support the potential of TPPU with multiple biological effects for the treatment of oral ulcers by targeting soluble epoxide hydrolase

SiMaN: Sign-to-Magnitude Network Binarization

Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in handling discrete constraints while ensuring bit entropy maximization, which typically makes their weight optimization very difficult. Existing methods relax the learning using the sign function, which simply encodes positive weights into +1s, and -1s otherwise. Alternatively, we formulate an angle alignment objective to constrain the weight binarization to {0,+1} to solve the challenge. In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise. Therefore, a high-quality discrete solution is established in a computationally efficient manner without the sign function. We prove that the learned weights of binarized networks roughly follow a Laplacian distribution that does not allow entropy maximization, and further demonstrate that it can be effectively solved by simply removing the $\ell_2$ regularization during network training. Our method, dubbed sign-to-magnitude network binarization (SiMaN), is evaluated on CIFAR-10 and ImageNet, demonstrating its superiority over the sign-based state-of-the-arts. Our source code, experimental settings, training logs and binary models are available at https://github.com/lmbxmu/SiMaN

Market Stakeholder Analysis of the Practical Implementation of Carbonation Curing on Steel Slag for Urban Sustainable Governance

Carbonation curing on steel slag is one of the most promising technologies for the iron and steel industry to manage its solid waste and carbon emissions. However, the technology is still in its demonstration stage. This paper investigates the market stakeholders of carbonation curing on steel slag for construction materials for its effective application by taking China as a case study. A holistic analysis of the competition, market size, and stakeholders of carbonation curing on steel slag was carried out through a literature review, a survey, a questionnaire, and interviews. The results showed that carbonation curing on steel slag had the advantages of high quality, high efficiency, low cost, and carbon reduction compared with other technologies. Shandong province was the most suitable province for the large-scale primary application of the technology. Stakeholder involvement to establish information platforms, enhance economic incentives, and promote adequate R&D activities would promote carbonation curing of steel slag into practice. This paper provides a reference for the commercialization of carbonation curing on similar calcium- and magnesium-based solid waste materials

Inducing Effect of Dihydroartemisinic Acid in the Biosynthesis of Artemisinins with Cultured Cells of Artemisia annua

Artemisinin has been used in the production of “artemisinin combination therapies” for the treatment of malaria. Feeding of precursors has been proven to be one of the most effective methods to enhance artemisinin production in plant cultured cells. At the current paper, the biosynthesis of artemisinin (ART) and its four analogs from dihydroartemisinic acid (DHAA) in suspension-cultured cells of Artemisia annua were investigated. ARTs were detected by HPLC/GC-MS and isolated by various chromatography methods. The structures of four DHAA metabolites, namely, dihydro-epi-deoxyarteannuin B, arteannuin I, arteannuin K, and 3-β-hydroxy-dihydro-epi-deoxyarteannuin B, were elucidated by physicochemical and spectroscopic analyses. The correlation between gene expression and ART content was investigated. The results of RT-PCR showed that DHAA could up-regulate expression of amorpha-4,11-diene synthase gene (ADS), amorpha-4,11-diene C-12 oxidase gene (CYP71AV1), and farnesyl diphosphate synthase gene (FPS) (3.19-, 7.21-, and 2.04-fold higher than those of control group, resp.), which indicated that biosynthesis processes from DHAA to ART were enzyme-mediated

Construction and experimental validation of a signature for predicting prognosis and immune infiltration analysis of glioma based on disulfidptosis-related lncRNAs

BackgroundsDisulfidptosis, a newly discovered mechanism of programmed cell death, is believed to have a unique role in elucidating cancer progression and guiding cancer therapy strategies. However, no studies have yet explored this mechanism in glioma.MethodsWe downloaded data on glioma patients from online databases to address this gap. Subsequently, we identified disulfidptosis-related genes from published literature and verified the associated lncRNAs.ResultsThrough univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) regression algorithms analyses, we identified 10 lncRNAs. These were then utilized to construct prognostic prediction models, culminating in a risk-scoring signature. Reliability and validity tests demonstrated that the model effectively discerns glioma patients’ prognosis outcomes. We also analyzed the relationship between the risk score and immune characteristics, and identified several drugs that may be effective for high-risk patients. In vitro experiments revealed that LINC02525 could enhances glioma cells’ migration and invasion capacities. Additionally, knocking down LINC02525 was observed to promote glioma cell disulfidptosis.ConclusionThis study delves into disulfidptosis-related lncRNAs in glioma, offering novel insights into glioma therapeutic strategies