A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through $k$-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.Comment: Accepted by AAAI-201

Learning Contextualized Document Representations for Healthcare Answer Retrieval

We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical taxonomies. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We use our continuous representations to resolve queries with short latency using approximate nearest neighbor search on sentence level. We apply the CDV model for retrieving coherent answer passages from nine English public health resources from the Web, addressing both patients and medical professionals. Because there is no end-to-end training data available for all application scenarios, we train our model with self-supervised data from Wikipedia. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking and is able to adapt to heterogeneous domains without additional fine-tuning.Comment: The Web Conference 2020 (WWW '20

Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader

The development of a battery-type loader is an important research direction in the field of industrial mining equipment. In the energy system, the battery will inevitably encounter the problem of heat dissipation when using high-power electricity. In this study, we took the power battery pack of a 3 m3 battery-type underground loader as the research object. The influence of single factors, such as the position of the air outlet of the battery pack, the size of the air outlet, the width of the separator, and the reverse plate, on the heat dissipation characteristics of the battery pack were studied. Then, a prediction model between the structural parameters and temperature was established using a radial basis function (RBF) neural network. This prediction model was then used as an adaptation evaluation model for global optimization through the multiobjective particle swarm optimization (PSO) algorithm, using which the optimal combination of structural parameters was obtained. The maximum temperature of the battery pack after optimization was reduced by 22%, compared to that before optimization, and the average temperature was reduced by 12.5%. Overall, the heat dissipation effect significantly improved. The optimization results indicate that the method proposed in this paper is feasible for use in optimizing battery heat dissipation systems in electric vehicles, thus providing a reference for research related to battery pack heat dissipation

Digital Technologies in Offsite and Prefabricated Construction: Theories and Applications

Due to its similarity to industrialized products, the offsite construction industry is seen as a focus for the transformation of Construction 4.0. Many digital technologies have been applied or have the potential to be applied to realize the integration of design, manufacturing, and assembly. The main objective of this review was to identify the current stage of applying digital technologies in offsite construction. In this review, 171 related papers from the last 10 years (i.e., 2013–2022) were obtained by collecting and filtering them. They were classified and analyzed according to the digital twin concept, application areas, and specific application directions. The results indicated that there are apparent differences in the utilization and development level of different technologies in different years. Meanwhile, the introduction, current stages, and benefits of different digital technologies are also discussed. Finally, this review summarizes the current popular fields and speculates on future research directions by analyzing article publication trends, which sheds light on future research

Text Matching as Image Recognition

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines

Mineralogy and Geochemistry of the Upper Ordovician and Lower Silurian Wufeng-Longmaxi Shale on the Yangtze Platform, South China: Implications for Provenance Analysis and Shale Gas Sweet-Spot Interval

The sediment provenance influences the formation of the shale gas sweet-spot interval of the Upper Ordovician–Lower Silurian Wufeng–Longmaxi shale from the Yangtze Platform, South China. To identify the provenance, the mineralogy and geochemistry of the shale were investigated. The methods included optical microscopy analysis, X-ray diffraction testing, field-emission scanning electron imaging, and major and trace element analysis. The Wufeng–Longmaxi shale is mainly composed of quartz (avg. 39.94%), calcite (avg. 12.29%), dolomite (avg. 11.75%), and clay minerals (avg. 28.31%). The LM1 interval is the shale gas sweet-spot and has the highest contents of total quartz (avg. 62.1%, among which microcrystalline quartz accounts for 52.8% on average) and total organic carbon (avg. 4.6%). The relatively narrow range of TiO2–Zr variation and the close correlation between Th/Sc and Zr/Sc signify no obvious sorting and recycling of the sediment source rocks. Sedimentary sorting has a limited impact on the geochemical features of the shale. The relatively high value of ICV (index of compositional variability) (1.03–3.86) and the low value of CIA (chemical index of alteration values) (50.62–74.48) indicate immature sediment source rocks, probably undergoing weak to moderate chemical weathering. All samples have patterns of moderately enriched light rare-earth elements and flat heavy rare-earth elements with negative Eu anomalies (Eu/Eu* = 0.35–0.92) in chondrite-normalized diagrams. According to Th/Sc, Zr/Sc, La/Th, Zr/Al2O3, TiO2/Zr, Co/Th, SiO2/Al2O3, K2O/Na2O, and La/Sc, it can be inferred that the major sediment source rocks were acidic igneous rocks derived from the active continental margin and continental island arc. A limited terrigenous supply caused by the inactive tectonic setting is an alternative interpretation of the formation of the sweet-spot interval

A Robust, Porous and Solution-Processable Molecular Crystal Containing Multiple Donor-Acceptor Units

We report a curious porous molecular crystal that is devoid of the common traits of related systems. Namely, the molecule does not rely on directional hydrogen bonds to enforce open packing; and it offers neither large concave faces (i.e., high internal free volume) to frustrate close packing, nor any inherently built-in cavity like in the class of organic cages. Instead, the permanent porosity (as unveiled by the X-ray crystal structure and CO2 sorption studies) arises from the strong push-pull units built into a Sierpinski-like molecule that features four symmetrically backfolded (SBF) side arms. Each side arm consists of the 1,1,4,4-tetracyanobuta-1,3-diene acceptor (TCBD) coupled with the dimethylaminophenyl donor, which is conveniently installed by a cycloaddition-retroelectrocyclization (CA-RE) reaction. Unlike the poor/fragile crystalline order of many porous molecular solids, the molecule here readily crystallizes and the crystalline phase can be easily deposited into thin films from solutions. Moreover, both the bulk sample and thin film exhibit excellent thermal stability with the porous crystalline order maintained even at 200 °C. The intermolecular forces underlying this robust porous molecular crystal likely include the strong dipole interactions and the multiple C···N and C···O short contacts afforded by the strongly donating and accepting groups integrated within the rigid molecular scaffold

Mineralogy and Geochemistry of the Upper Ordovician and Lower Silurian Wufeng-Longmaxi Shale on the Yangtze Platform, South China: Implications for Provenance Analysis and Shale Gas Sweet-Spot Interval

The sediment provenance influences the formation of the shale gas sweet-spot interval of the Upper Ordovician–Lower Silurian Wufeng–Longmaxi shale from the Yangtze Platform, South China. To identify the provenance, the mineralogy and geochemistry of the shale were investigated. The methods included optical microscopy analysis, X-ray diffraction testing, field-emission scanning electron imaging, and major and trace element analysis. The Wufeng–Longmaxi shale is mainly composed of quartz (avg. 39.94%), calcite (avg. 12.29%), dolomite (avg. 11.75%), and clay minerals (avg. 28.31%). The LM1 interval is the shale gas sweet-spot and has the highest contents of total quartz (avg. 62.1%, among which microcrystalline quartz accounts for 52.8% on average) and total organic carbon (avg. 4.6%). The relatively narrow range of TiO2–Zr variation and the close correlation between Th/Sc and Zr/Sc signify no obvious sorting and recycling of the sediment source rocks. Sedimentary sorting has a limited impact on the geochemical features of the shale. The relatively high value of ICV (index of compositional variability) (1.03–3.86) and the low value of CIA (chemical index of alteration values) (50.62–74.48) indicate immature sediment source rocks, probably undergoing weak to moderate chemical weathering. All samples have patterns of moderately enriched light rare-earth elements and flat heavy rare-earth elements with negative Eu anomalies (Eu/Eu* = 0.35–0.92) in chondrite-normalized diagrams. According to Th/Sc, Zr/Sc, La/Th, Zr/Al2O3, TiO2/Zr, Co/Th, SiO2/Al2O3, K2O/Na2O, and La/Sc, it can be inferred that the major sediment source rocks were acidic igneous rocks derived from the active continental margin and continental island arc. A limited terrigenous supply caused by the inactive tectonic setting is an alternative interpretation of the formation of the sweet-spot interval

Therapeutic potential of an intestinotrophic hormone, glucagon-like peptide 2, for treatment of type 2 short bowel syndrome rats with intestinal bacterial and fungal dysbiosis

Abstract Background Previous studies showed that type 2 short bowel syndrome (SBS) rats were accompanied by severe intestinal bacterial dysbiosis. Limited data are available for intestinal fungal dysbiosis. Moreover, no effective therapeutic drugs are available for these microbiota dysbiosis. The aims of our study were to investigate the therapeutic potential of glucagon-like peptide 2 (GLP-2) for these microbiota dysbiosis in type 2 SBS rats. Methods 8-week-old male SD rats which underwent 80% small bowel resection, ileocecum resection, partial colon resection and jejunocolostomy, were treated with saline (SBS group, n = 5) or GLP-2 (GLP2.SBS group, n = 5). The Sham group rats which underwent transection and re-anastomosis were given a saline placebo (Sham group, n = 5). 16S rRNA and ITS sequencing were applied to evaluate the colonic bacterial and fungal composition at 22 days after surgery, respectively. Results The relative abundance of Actinobacteria, Firmicutes and proinflammatory Proteobacteria increased significantly in SBS group rats, while the relative abundance of Bacteroidetes, Verrucomicrobia and Tenericutes decreased remarkably. GLP-2 treatment significantly decreased Proteus and increased Clostridium relative to the saline treated SBS rats. The diversity of intestinal fungi was significantly increased in SBS rats, accompanied with some fungi abnormally increased and some resident fungi (e.g., Penicillium) significantly decreased. GLP-2 treatment significantly decreased Debaryomyces and Meyerozyma, and increased Penicillium. Moreover, GLP-2 partially restored the bacteria-fungi interkingdom interaction network of SBS rats. Conclusion Our study confirms the bacterial and fungal dysbiosis in type 2 SBS rats, and GLP-2 partially ameliorated these microbiota dysbiosis