TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.Comment: Accepted by ACL2023 main conferenc

Dmbg-Net: Dilated multiresidual boundary guidance network for COVID-19 infection segmentation

Accurate segmentation of infected regions in lung computed tomography (CT) images is essential for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, lung lesion segmentation has some challenges, such as obscure boundaries, low contrast and scattered infection areas. In this paper, the dilated multiresidual boundary guidance network (Dmbg-Net) is proposed for COVID-19 infection segmentation in CT images of the lungs. This method focuses on semantic relationship modelling and boundary detail guidance. First, to effectively minimize the loss of significant features, a dilated residual block is substituted for a convolutional operation, and dilated convolutions are employed to expand the receptive field of the convolution kernel. Second, an edge-attention guidance preservation block is designed to incorporate boundary guidance of low-level features into feature integration, which is conducive to extracting the boundaries of the region of interest. Third, the various depths of features are used to generate the final prediction, and the utilization of a progressive multi-scale supervision strategy facilitates enhanced representations and highly accurate saliency maps. The proposed method is used to analyze COVID-19 datasets, and the experimental results reveal that the proposed method has a Dice similarity coefficient of 85.6% and a sensitivity of 84.2%. Extensive experimental results and ablation studies have shown the effectiveness of Dmbg-Net. Therefore, the proposed method has a potential application in the detection, labeling and segmentation of other lesion areas

Research on dynamic robust planning method for active distribution network considering correlation

The universality of load subjects in distribution network brings challenges to the reliability of distribution network planning results. In this paper, a two-stage dynamic robust distribution network planning method considering correlation is proposed. The method evaluates the correlation between random variables using the Spearman rank correlation coefficient, and converts the correlated random variables into mutually independent random variables by Cholesky decomposition and independent transformation; expresses the source-load uncertainty by a bounded interval without distribution, and describes the active distribution network planning as a dynamic zero-sum game problem by combining with the two-phase dynamic robust planning; use the Benders decomposition approach to tackle the issue; mathematical simulation is used to confirm the accuracy and efficacy of the method. The results show that the dynamic robustness planning method of active distribution network taking into account the correlation can accurately simulate the operation of active distribution network with uncertain boundaries, which enhances the reliability and economy of the active distribution network planning results

Remote sensing and environmental assessment of wetland ecological degradation in the Small Sanjiang Plain, Northeast China

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions

Fracture behavior and self-sharpening mechanisms of polycrystalline cubic boron nitride in grinding based on cohesive element method

Unlike monocrystalline cubic boron nitride (CBN), polycrystalline CBN (PCBN) shows not only higher fracture resistance induced by tool-workpiece interaction but also better self-sharpening capability; therefore, efforts have been devoted to the study of PCBN applications in manufacturing engineering. Most of the studies, however, remain qualitative due to difficulties in experimental observations and theoretical modeling and provide limited in-depth understanding of the self-sharpening behavior/mechanism. To fill this research gap, the present study investigates the self-sharpening process of PCBN abrasives in grinding and analyzes the macro-scale fracture behavior and highly localized micro-scale crack propagation in detail. The widely employed finite element (FE) method, together with the classic Voronoi diagram and cohesive element technique, is used considering the pronounced success of FE applications in polycrystalline material modeling. Grinding trials with careful observation of the PCBN abrasive morphologies are performed to validate the proposed method. The self-sharpening details, including fracture morphology, grinding force, strain energy, and damage dissipation energy, are studied. The effects of maximum grain cut depths (MGCDs) and grinding speeds on the PCBN fracture behavior are discussed, and their optimum ranges for preferable PCBN self-sharpening performance are suggested

In Silico Insights into the Symbiotic Nitrogen Fixation in Sinorhizobium meliloti via Metabolic Reconstruction

BACKGROUND: Sinorhizobium meliloti is a soil bacterium, known for its capability to establish symbiotic nitrogen fixation (SNF) with leguminous plants such as alfalfa. S. meliloti 1021 is the most extensively studied strain to understand the mechanism of SNF and further to study the legume-microbe interaction. In order to provide insight into the metabolic characteristics underlying the SNF mechanism of S. meliloti 1021, there is an increasing demand to reconstruct a metabolic network for the stage of SNF in S. meliloti 1021. RESULTS: Through an iterative reconstruction process, a metabolic network during the stage of SNF in S. meliloti 1021 was presented, named as iHZ565, which accounts for 565 genes, 503 internal reactions, and 522 metabolites. Subjected to a novelly defined objective function, the in silico predicted flux distribution was highly consistent with the in vivo evidences reported previously, which proves the robustness of the model. Based on the model, refinement of genome annotation of S. meliloti 1021 was performed and 15 genes were re-annotated properly. There were 19.8% (112) of the 565 metabolic genes included in iHZ565 predicted to be essential for efficient SNF in bacteroids under the in silico microaerobic and nutrient sharing condition. CONCLUSIONS: As the first metabolic network during the stage of SNF in S. meliloti 1021, the manually curated model iHZ565 provides an overview of the major metabolic properties of the SNF bioprocess in S. meliloti 1021. The predicted SNF-required essential genes will facilitate understanding of the key functions in SNF and help identify key genes and design experiments for further validation. The model iHZ565 can be used as a knowledge-based framework for better understanding the symbiotic relationship between rhizobia and legumes, ultimately, uncovering the mechanism of nitrogen fixation in bacteroids and providing new strategies to efficiently improve biological nitrogen fixation

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Characterization and Gene Mapping of an Open-Glume <i>Oryza sativa</i> L. Mutant

Floral organ development determines agricultural productivity by affecting seed development, seed quality, and final yield. In this study, we described the novel ogl mutant in rice (Oryza sativa L.), which is characterized by an open-glume phenotype, increased pistil number, reduced stamen number, decreased seed setting rate, and smaller rice grains. Genetic analysis showed that the open-glume phenotype might be controlled by a recessive qualitative trait locus. Employing bulked segregant analysis (BSA), one candidate region was identified on rice chromosome 1. The glume opening phenotype cosegregated with SNP (Chr1:1522703), which was located at the start codon of one transcript of OsJAG, resulting in partial loss of OsJAG function. cDNA analysis revealed that OsJAG encodes two transcript variants. Compared to normal plants, the expression of OsJAG.1 was upregulated in open-glume plants. When investigating the glume phenotype, we found that the expression of genes related to floral development changed greatly in open-glume plants. Taken together, this work increases our understanding of the developmental role of OsJAG in rice floral development

Valine and isoleucine supplementation improve performance and serum biochemical concentrations in growing gilts fed low-protein diets

This study evaluated the effects of valine and isoleucine supplementation in low-crude-protein (CP) diets on performance, serum parameters, and carcass traits in growing gilts. Two-hundred gilts (29.1 ± 1.7 kg) were allotted randomly to one of five diets that included a control CP (177 g kg−1) or four low-CP (135 g kg−1) diets for 45 d. The low-CP diets were added with lysine + threonine + methionine (LCM), LCM+ tryptophan (LCT), LCT + valine (LCV), or LCV + isoleucine (LCI), respectively. Non significant difference in average daily gain was obtained in gilts receiving the control, LCV, or LCI diets, which was higher than that of pigs fed the LCM or LCT diets (P < 0.05). The supplementation of crystalline tryptophan, valine, and isoleucine improved the average daily feed intake and serum levels of total protein, tryptophan, and isoleucine (linear and quadratic effects, P < 0.05) and serum valine concentration (linear effect, P < 0.05). The results indicated that the valine supplementation, or the both combination of valine and isoleucine, could further improve the performance in 29–62 kg gilts fed the 135 g kg−1 CP diet.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author