Concrete Pavement Mixture Design and Analysis (MDA): Development and Evaluation of Vibrating Kelly Ball Test (VKelly Test) for the Workability of Concrete

Due to the low workability of slipform concrete mixtures, the science of rheology is not strictly applicable for such concrete. However, the concept of rheological behavior may still be considered useful. A novel workability test method (Vibrating Kelly Ball or VKelly test) that would quantitatively assess the responsiveness of a dry concrete mixture to vibration, as is desired of a mixture suitable for slipform paving, was developed and evaluated. The objectives of this test method are for it to be cost-effective, portable, and repeatable while reporting the suitability of a mixture for use in slipform paving. The work to evaluate and refine the test was conducted in three phases: 1. Assess whether the VKelly test can signal variations in laboratory mixtures with a range of materials and proportions 2. Run the VKelly test in the field at a number of construction sites 3. Validate the VKelly test results using the Box Test developed at Oklahoma State University for slipform paving concrete The data collected to date indicate that the VKelly test appears to be suitable for assessing a mixture’s response to vibration (workability) with a low multiple operator variability. A unique parameter, VKelly Index, is introduced and defined that seems to indicate that a mixture is suitable for slipform paving when it falls in the range of 0.8 to 1.2 in./√s

Quantifying Repeatability Reproducibility Sources of Error and Capacity of a Measurement: Demonstrated Using Laboratory Soil Plasticity Tests

The repeatability, reproducibility, and sources of error inherent in a given measurement are important considerations for potential users. To quantify errors arising from a single operator or multiple laboratories, most testing standards uses a one-way analysis of variance- (ANOVA-) based method, which utilizes a simple standard deviation across all measurements. However, this method does not allow users to quantify the sources of error and capacity (i.e., the precision to tolerance ratio). In this study, an innovative two-way ANOVA-based analysis method is selected to quantify the relative contributions of different sources of error and determine whether a measurement can be used to check conformance of a measured characteristic to engineering specifications. In this study, the standardized Atterberg limits tests, fall-cone device Atterberg limits tests, and bar linear shrinkage tests widely used for determining the soil plasticity were selected for evaluation and demonstration. Comparisons between results of the various testing methods are presented, and the error sources contributing to the overall variations between tests are discussed. Based on the findings of this study, the authors suggest use of two-way ANOVA-based R&R analysis to quantify the sources of measurement error and capacity and also recommend using the fall cone device and ASTM standardized thread rolling device for determining liquid and plastic limits of soils, respectively

Loss of ATF3 exacerbates liver damage through the activation of mTOR/p70S6K/ HIF-1α signaling pathway in liver inflammatory injury.

Activating transcription factor 3 (ATF3) is a stress-induced transcription factor that plays important roles in regulating immune and metabolic homeostasis. Activation of the mechanistic target of rapamycin (mTOR) and hypoxia-inducible factor (HIF) transcription factors are crucial for the regulation of immune cell function. Here, we investigated the mechanism by which the ATF3/mTOR/HIF-1 axis regulates immune responses in a liver ischemia/reperfusion injury (IRI) model. Deletion of ATF3 exacerbated liver damage, as evidenced by increased levels of serum ALT, intrahepatic macrophage/neutrophil trafficking, hepatocellular apoptosis, and the upregulation of pro-inflammatory mediators. ATF3 deficiency promoted mTOR and p70S6K phosphorylation, activated high mobility group box 1 (HMGB1) and TLR4, inhibited prolyl-hydroxylase 1 (PHD1), and increased HIF-1α activity, leading to Foxp3 downregulation and RORγt and IL-17A upregulation in IRI livers. Blocking mTOR or p70S6K in ATF3 knockout (KO) mice or bone marrow-derived macrophages (BMMs) downregulated HMGB1, TLR4, and HIF-1α and upregulated PHD1, increasing Foxp3 and decreasing IL-17A levels in vitro. Silencing of HIF-1α in ATF3 KO mice ameliorated IRI-induced liver damage in parallel with the downregulation of IL-17A in ATF3-deficient mice. These findings demonstrated that ATF3 deficiency activated mTOR/p70S6K/HIF-1α signaling, which was crucial for the modulation of TLR4-driven inflammatory responses and T cell development. The present study provides potential therapeutic targets for the treatment of liver IRI followed by liver transplantation

RGAT: A Deeper Look into Syntactic Dependency Information for Coreference Resolution

Although syntactic information is beneficial for many NLP tasks, combining it with contextual information between words to solve the coreference resolution problem needs to be further explored. In this paper, we propose an end-to-end parser that combines pre-trained BERT with a Syntactic Relation Graph Attention Network (RGAT) to take a deeper look into the role of syntactic dependency information for the coreference resolution task. In particular, the RGAT model is first proposed, then used to understand the syntactic dependency graph and learn better task-specific syntactic embeddings. An integrated architecture incorporating BERT embeddings and syntactic embeddings is constructed to generate blending representations for the downstream task. Our experiments on a public Gendered Ambiguous Pronouns (GAP) dataset show that with the supervision learning of the syntactic dependency graph and without fine-tuning the entire BERT, we increased the F1-score of the previous best model (RGCN-with-BERT) from 80.3% to 82.5%, compared to the F1-score by single BERT embeddings from 78.5% to 82.5%. Experimental results on another public dataset - OntoNotes 5.0 demonstrate that the performance of the model is also improved by incorporating syntactic dependency information learned from RGAT.Comment: 8 pages, 5 figure