Welding-induced Residual Stresses in U-Ribs of a Steel Bridge Deck

To calculate the welding-induced residual stresses in U-ribs of the steel deck plate and conduct quantitative analysis of influential factors, the U-ribs of steel deck plate of Xinghai Bay Bridge was taken as the research object. In the ABAQUS finite element software, the local models of U-ribs of steel deck plate were established. Nodal body force loads, i.e., heat generation rate, of the double ellipsoidal heat source models were applied via the compiled subsidiary Dflux program. The welding process of the v-groove welds was simulated, to obtain the residual stresses distribution in the top plate and U-rib plates. The influence of thickness of top plate and angle of welding groove on the residual stresses in the U-ribs were studied. The results show that the welding-induced residual stresses calculated by the numerical method proposed in this paper agree well with the experimental data. The maximum residual stresses in the top plates and the U-rib plates all occur near the welds, which exceeds the yielding limitation of the material. As the thickness of top plate increases, the maximum values of residual stresses in the top plates and U-ribs increase. However, with the increase of groove angle, the maximum values of residual stresses in the top plates and U-rib plates decrease

Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets

Machine Reading Comprehension (MRC) is a challenging NLP research field with wide real world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed the human performance on many datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need of improving existing datasets, evaluation metrics and models to move the MRC models toward 'real' understanding. To address this lack of comprehensive survey of existing MRC tasks, evaluation metrics and datasets, herein, (1) we analyzed 57 MRC tasks and datasets; proposed a more precise classification method of MRC tasks with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets; (4) We also discussed some open issues in MRC research and highlight some future research directions. In addition, to help the community, we have collected, organized, and published our data on a companion website(https://mrc-datasets.github.io/) where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard.Comment: 59 page

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years

A multifunctional tripodal fluorescent probe for the recognition of Cr3+, Al3+, Zn2+ and F− with controllable ESIPT processes

Three 4-(benzo[d]thiazol-2-yl)-2,5-dihydroxybenzaldehyde fluorophores were introduced to construct a tripodal multifunctional ESPIT fluorescence probe L. The fluorescent analysis revealed that probe L exhibited excellent recognition capabilities towards Cr3+, Al3+, Zn2+ and F− ions with large Stokes shifts. Furthermore, under optimal conditions, the detection limit of probe L towards Cr3+, Al3+, Zn2+ and F− were low, of the order of 10−8 M, which indicated that probe L was sensitive to these four ions. Interestingly, the fluorescent and 1H NMR titration experiments revealed that the recognition mechanism of probe L towards the ions Cr3+, Al3+, Zn2+ and F− were different. The presence of Cr3+ and Al3+ recovered the ESIPT, but the presence of Zn2+ trigger a moderate deprotonation of the phenolic OH and induced an ESIPT red-shifted (60 nm) emission wavelength. Finally, the presence of F− completely deprotonated the free phenolic OH and a remarkable red-shifted (130 nm) ESIPT emission was observed. In other words, the ESIPT process of probe L is controllable. Furthermore, the utility of probe L as a biosensor in living cells (PC3 cells) towards Cr3+, Al3+ and Zn2+ ions has been demonstrated

Effects and mechanism of Ganoderma lucidum polysaccharides in the treatment of diabetic nephropathy in streptozotocin-induced diabetic rats

Ganoderma lucidum polysaccharides (GLP) have renal protection effect but there was no study on the diabetic nephropathy. This study was designed to investigate its effect and mechanism using a diabetic rat model induced by streptozotocin (50 mg/kg, i.p.). The diabetic rats were treated with GLP (300 mg/kg/day) for 10 weeks. The blood glucose, glycated hemoglobin, body weight, and the levels of blood creatinine, urea nitrogen, and urine protein were assessed. And renal pathologies were assessed by the tissue sections stained with hematoxylin-eosin, Masson’s trichome, and periodic acid-Schiff. The expression of phosphorylated phosphoinositide 3 kinase (p-PI3K), phosphorylated protein kinase B (p-Akt), and phosphorylated mammalian target of rapamycin (p-mTOR), the autophagy proteins beclin-1, LC3-II, LC3-I, and P62; the apoptosis-related proteins caspase-3 and caspase-9; and the inflammation markers IL-6, IL-1β, and TNF-ɑ were assessed. Results showed that GLP alleviated the impairment of renal function by reducing urinary protein excretion and the blood creatinine level and ameliorated diabetic nephropathy. The expression of p-PI3K, p-Akt, and p-mTOR in the diabetic kidney were significantly reduced in the GLP treatment group compared to the without treatment group. GLP treatment activated the autophagy indicators of beclin-1 and the ratio of LC3-II/LC3-I but reduced p62 and also inhibited the expression of caspase-3, caspase-9 and IL-6, IL-1β, and TNF-ɑ. In conclusion, the effect of GLP amelioration diabetic nephropathy may be via the PI3k/Akt/mTOR signaling pathway by inhibition of the apoptosis and inflammation and activation of the autophagy process

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Financing Constraints, Carbon Emissions and High-Quality Urban Development—Empirical Evidence from 290 Cities in China

The tightening of the financing environment and global climate change have become urgent problems for high-quality economic development all over the world. Facing these challenges, the Chinese government is committed to alleviating regional financing constraints and setting carbon-emission reduction targets. However, are these measures effective for high-quality urban development? This paper attempts to use unbalanced panel data from 290 cities on the Chinese mainland from 2004–2017 to provide an answer to the problem using a scatter plot and the mediator effect model. Results show that: (1) financing constraints limit the funds required for urban development, which is not conducive to high-quality urban development, but high-quality urban development has the characteristics of “path dependence”; (2) In the context of environmental regulation, financing constraints are mainly enacted through reducing carbon emissions, which is inconducive to high-quality urban development. Carbon emissions are the transmission mechanism whereby financing constraints affect high-quality urban development; (3) Cities with large financing constraints have insufficient capital investment for high-quality urban development, and the aggravation of financing constraints has an increasingly obvious inhibitory effect on high-quality urban development. Moreover, due to the effect of the global economic crisis in 2008, the negative effect of financing constraints on high-quality urban development had the characteristics of U-shaped fluctuation. Thus, this paper believes that the implementation of China’s double carbon policy is at the expense of high-quality urban development, and there is a long way to go before high-quality urban development reaches later stages. Other countries should carefully weigh up the relationship between environmental pollution and economic development when facing financing constraints