How Does Suppliers’ Fairness Affect the Relationship Quality of Agricultural Product Supply Chains?

Although many studies have suggested that the relationship between different supply chain members significantly affects agricultural product quality, suppliers’ perceptions of fairness, which greatly influence their decisions on building the relationship quality, are often overlooked. Particularly, the empirical evidence to investigate the impacts of suppliers’ fairness on the relationship quality and the factors that affect the suppliers’ fairness is missing, and therefore this knowledge gap needs to be filled by new research. Herein, we conducted a survey of 450 agricultural product suppliers and systemically analyzed the impact of antecedents on fairness perception and the impact of fairness perception on relationship quality. In addition, we developed a structural equation model and found that information sharing and price satisfaction had significantly positive effects on procedural fairness and distributive fairness, respectively. Furthermore, our studies demonstrated that procedural fairness is more important in improving the relationship quality than distributive fairness. However, supplier dependence is another important impact factor, and it greatly decreases the positive effects of suppliers’ fairness on relationship quality. In summary, the study results provide several managerial implications and extend our understanding of the importance of suppliers’ fairness in the relationship quality, which involves product development with respect to the supplier’s performance

An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, which leads to redundant information in sentence embedding. To address this problem, we present an information minimization based contrastive learning (InforMin-CL) model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning. Specifically, we find that information minimization can be achieved by simple contrast and reconstruction objectives. The reconstruction operation reconstitutes the positive instance via the other positive instance to minimize the information entropy between positive instances. We evaluate our model on fourteen downstream tasks, including both supervised and unsupervised (semantic textual similarity) tasks. Extensive experimental results show that our InforMin-CL obtains a state-of-the-art performance.Comment: 11 pages, 3 figures, published to COLING 202

Directional diffusion models for graph representation learning

In recent years, diffusion models have achieved remarkable success in various domains of artificial intelligence, such as image synthesis, super-resolution, and 3D molecule generation. However, the application of diffusion models in graph learning has received relatively little attention. In this paper, we address this gap by investigating the use of diffusion models for unsupervised graph representation learning. We begin by identifying the anisotropic structures of graphs and a crucial limitation of the vanilla forward diffusion process in learning anisotropic structures. This process relies on continuously adding an isotropic Gaussian noise to the data, which may convert the anisotropic signals to noise too quickly. This rapid conversion hampers the training of denoising neural networks and impedes the acquisition of semantically meaningful representations in the reverse process. To address this challenge, we propose a new class of models called {\it directional diffusion models}. These models incorporate data-dependent, anisotropic, and directional noises in the forward diffusion process. To assess the efficacy of our proposed models, we conduct extensive experiments on 12 publicly available datasets, focusing on two distinct graph representation learning tasks. The experimental results demonstrate the superiority of our models over state-of-the-art baselines, indicating their effectiveness in capturing meaningful graph representations. Our studies not only provide valuable insights into the forward process of diffusion models but also highlight the wide-ranging potential of these models for various graph-related tasks

MetaViewer: Towards A Unified Multi-View Representation

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in features potentially degrade the quality of the derived representation. To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner. Specifically, we train a meta-learner, namely MetaViewer, to learn fusion and model the view-shared meta representation in outer-level optimization. Start with this meta representation, view-specific base-learners are then required to rapidly reconstruct the corresponding view in inner-level. MetaViewer eventually updates by observing reconstruction processes from uniform to specific over all views, and learns an optimal fusion scheme that separates and filters out view-private information. Extensive experimental results in downstream tasks such as classification and clustering demonstrate the effectiveness of our method.Comment: 8 pages, 5 figures, conferenc

Guanxintai Exerts Protective Effects on Ischemic Cardiomyocytes by Mitigating Oxidative Stress

Oxidative stress participates in numerous myocardial pathophysiological processes and is considered a therapeutic target for myocardial ischemia and heart failure. Guanxintai (GXT), a traditional Chinese medicine, is commonly used to treat cardiovascular disease on account of its numerous beneficial physiological activities, such as dilating coronary arteries, inhibiting platelet aggregation, and reducing the serum lipid content. However, the antioxidative properties of GXT and potential underlying mechanisms remain to be established. In the present study, we investigated the protective effects of GXT on ischemic cardiomyocytes and the associated antioxidative mechanisms, both in vivo and in vitro. Notably, GXT treatment reduced the degree of cardiomyocyte injury, myocardial apoptosis, and fibrosis and partially improved cardiac function after myocardial infarction. Furthermore, GXT suppressed the level of ROS as well as expression of NADPH oxidase (NOX) and phospho-p38 mitogen-activated protein kinase (MAPK) proteins. Our results collectively suggest that the protective effects of GXT on ischemic cardiomyocytes are exerted through its antioxidative activity of NOX inhibition

Mechanisms of Ferroptosis and Relations With Regulated Cell Death: A Review

Ferroptosis is a newly identified form of nonapoptotic regulated cell death (RCD) characterized by iron-dependent accumulation of lipid peroxides. It is morphologically and biochemically different from known types of cell death. Ferroptosis plays a vital role in the treatment of tumors, renal failure, and ischemia reperfusion injury (IRI). Inhibition of glutathione peroxidase 4 (GPX4), starvation of cysteine, and peroxidation of arachidonoyl (AA) trigger ferroptosis in the cells. Iron chelators, lipophilic antioxidants, and specific inhibitor prevent ferroptosis. Although massive researches have demonstrated the importance of ferroptosis in human, its mechanism is not really clear. In this review, we distanced ourselves from this confusion by dividing the mechanisms of ferroptosis into two aspects: processes that facilitate the formation of lipid peroxides and processes that suppress the reduction of lipid peroxides. At the same time, we summarize the relations between ferroptosis and several types of cell death

Study on the Effect of Nano-SiO 2

Both process and mechanical of silicon substrate chemical mechanical polishing (CMP) are studied in detail, and the effects of experiments designed indicate that nano-SiO2 grinding particles seem to be acted as catalyzer besides the grinding action during the CMP process. This is different from the traditional function. As a result, in the condition of low pH, the nano-SiO2 slurry can be recycled. In the meanwhile, the removal rate can gain stability and pH value does not change obviously

A Survey of Source Code Search: A 3-Dimensional Perspective

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this paper, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.Comment: submitted to ACM Transactions on Software Engineering and Methodolog