Multistage Infrastructure System Design: An Integrated Biofuel Supply Chain against Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production

Exploration of the two-dimensional Ising magnetic materials in the triangular prismatic crystal field

Magnetic anisotropy is essential for stabilizing two-dimensional (2D) magnetism, which has significant applications in spintronics and the advancement of fundamental physics. In this work, we examine the electronic structure and magnetic properties of triangular prismatic MSi$_2$N$_4$ (M = V, Cr) monolayers, using crystal field theory, spin-orbital state analyses, and density functional calculations. Our results reveal that the pristine VSi$_2$N$_4$ monolayer exhibits magnetism with a V$^{4+}$ 3$d^1$ $S$ = 1/2 charge-spin state within the triangular prismatic crystal field. However, the strong $d$ orbital hybridization between adjacent V$^{4+}$ ions disrupts the $d$ orbital splitting in this crystal field, resulting in a relatively small in-plane magnetic anisotropy of approximately 2 $\mu$eV per V atom.In contrast, the pristine CrSi$_2$N$_4$ monolayer is nonmagnetic, characterized by the Cr$^{4+}$ 3$d^2$ $S$ = 0 state. Upon substituting nonmagnetic Cr$^{4+}$ with Si$^{4+}$, Cr$_\frac{1}{3}$Si$_\frac{8}{3}$N$_4$ transforms into an antiferromagnetic insulator with Cr$^{4+}$ 3$d^2$ $S$ = 1 state, featuring a large orbital moment of -1.06 $\mu_{\rm B}$ oriented along the $z$-axis and huge perpendicular magnetic anisotropy of 18.63 meV per Cr atom. These findings highlight the potential for further exploration of 2D Ising magnetic materials within a unique triangular prismatic crystal field

Hyperin up-regulates miR-7031-5P to promote osteogenic differentiation of MC3T3-E1 cells

Objective. To investigate the effects of Hyperin (Hyp) on osteogenic differentiation of MC3T3E1 cells. Methods. Differentially expressed miRNA was screened by miRNA Microarray. miR-7031-5P overexpression and knockdown MC3T3-E1 cell models were constructed by transfecting miR-7031-5P mimics and inhibitor. Alizarin red staining (ARS) assay was used to observe the formation of mineralized nodules in MC3T3-E1 cells. ALP activity was detected by using ALP detection kit. Western blot assay was used to examine the changes in osteogenic differentiation-related proteins. The relationship between miR-7031-5P and Wnt7a was revealed by dual luciferase report experiments. Results. We found that miR-7031-5P was upregulated in MC3T3-E1 cells after Hyp treatment. The results indicated that compared with the untreated group, Hyp promoted the formation of mineralized nodules and the alkaline phosphatase (ALP) activity of MC3T3-E1 cells via overexpressing miR-7031-5P. Besides, elevated miR-7031-5P increased OPN, COL1A1, and Runx2 mRNA expression. More importantly, Wnt7a was identified as the downstream target gene of miR-70315P promoting osteogenic differentiation of MC3T3-E1 cells. Conclusions. Hyp up-regulated miR-7031-5P to promote osteogenic differentiation of MC3T3-E1 cells by targeting Wnt7

Turning Social Capital into Economic Capital: the Sales Effect of Friendship Group Participation in Social Commerce Websites

Friendship groups have been widely adopted in social commerce platforms because of the powerful and pervasive influence of groups on decision making. Despite their widespread use, the sales effects of seller participation in friendship groups (FGP) have received limited research attention. Using a quasi-experimental design with 373,964 products from 8,250 sellers on a leading social commerce platform, we find that FGP increase sellers\u27 product sales performance through the formation of relational and cognitive capital. In addition, we find that seller guarantee, product guarantee and product rating strengthen the sales effect of FGP, while the number of seller followers weakens the sales effect of FGP. Our study contributes to the literature by examining how, why, and when FGP affect sales performance in social commerce. We also provides guidance for sellers and platforms to use friendship groups and group marketing to improve sales performance in social commerce

SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval

Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in ad-hoc retrieval tasks, effective pre-training strategies for legal case retrieval remain to be explored. Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. However, most existing language models have difficulty understanding the long-distance dependencies between different structures. Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements. Even subtle differences in key legal elements can significantly affect the judgement of relevance. However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements. To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. It is highlighted in the following three aspects: (1) SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. (2) SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. (3) SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. Extensive experiments over publicly available legal benchmarks demonstrate that our approach can significantly outperform previous state-of-the-art methods in legal case retrieval.Comment: 10 pages, accepted by SIGIR 202

Identification and analysis of the stigma and embryo sac-preferential/specific genes in rice pistils

The secretion-related genes. (XLS 40 kb

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics

Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance, but rely on plenty of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM). The method redesigns the way of back-propagating gradients and non-linearly activates the multi-scale fused heatmaps to generate more fine-grained class activation maps with appropriate activation degree for different sizes of damage sites. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms

Chemoresistance to gemcitabine in hepatoma cells induces epithelial-mesenchymal transition and involves activation of PDGF-D pathway

Hepatocellular carcinoma (HCC) is one of the common malignances in the world and has high mortality in part due to development of acquired drug resistance. Therefore, it is urgent to investigate the molecular mechanism of drug resistance in HCC. To explore the underlying mechanism of drug resistance in HCC, we developed gemcitabine-resistant (GR) HCC cells. We used multiple methods to achieve our goal including RT-PCR, Western blotting analysis, transfection, Wound-healing assay, migration and invasion assay. We observed that gemcitabine-resistant cells acquired epithelial-mesenchymal transition (EMT) phenotype. Moreover, we found that PDGF-D is highly expressed in GR cells. Furthermore, down-regulation of PDGF-D in GR cells led to partial reversal of the EMT phenotype. Our findings demonstrated that targeting PDGF-D could be a novel strategy to overcome gemcitabine resistance in HCC