Characterisation of the relationship between surface texture and surface integrity of superalloy components machined by grinding

The surface texture of a machined component is influenced largely by the processing parameters used during machining and hence, there is a relationship between both the formation of the surface texture and surface integrity of the machined component. In the study to be reported in this paper, GH4169, a hard-to-cut superalloy, widely used in aero-engines, was selected for a detailed investigation into the relationship between the surface texture and the component-performance (surface integrity) of the machined components for which a series of grinding experiments with different grinding-wheels and grinding parameter-values was carried out in order to quantitatively analyze variations of the surface roughness with processing parameters. Further, considering that the features of the ground-surfaces measured are of a random nature, statistic properties of the produced surfaces were revealed and characterised with power spectral density function (PSD) and auto-covariance function(ACV) method respectively

The cytoplasmic adaptor protein Caskin mediates Lar signal transduction during Drosophila motor axon guidance

The multiprotein complexes that receive and transmit axon pathfinding cues during development are essential to circuit generation. Here, we identify and characterize the Drosophila sterile α-motif (SAM) domain-containing protein Caskin, which shares homology with vertebrate Caskin, a CASK [calcium/calmodulin-(CaM)-activated serine-threonine kinase]-interacting protein. Drosophila caskin (ckn) is necessary for embryonic motor axon pathfinding and interacts genetically and physically with the leukocyte common antigen-related (Lar) receptor protein tyrosine phosphatase. In vivo and in vitro analyses of a panel of ckn loss-of-function alleles indicate that the N-terminal SAM domain of Ckn mediates its interaction with Lar. Like Caskin, Liprin-α is a neuronal adaptor protein that interacts with Lar via a SAM domain-mediated interaction. We present evidence that Lar does not bind Caskin and Liprin-α concurrently, suggesting they may assemble functionally distinct signaling complexes on Lar. Furthermore, a vertebrate Caskin homolog interacts with LAR family members, arguing that the role of ckn in Lar signal transduction is evolutionarily conserved. Last, we characterize several ckn mutants that retain Lar binding yet display guidance defects, implying the existence of additional Ckn binding partners. Indeed, we identify the SH2/SH3 adaptor protein Dock as a second Caskin-binding protein and find that Caskin binds Lar and Dock through distinct domains. Furthermore, whereas ckn has a nonredundant function in Lar-dependent signaling during motor axon targeting, ckn and dock have overlapping roles in axon outgrowth in the CNS. Together, these studies identify caskin as a neuronal adaptor protein required for axon growth and guidance

The Key Artificial Intelligence Technologies in Early Childhood Education: A Review

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discusses the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.Comment: 39 pages, 9 figures, 4 table

Multi-level head-wise match and aggregation in transformer for textual sequence matching

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vector-representation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.Comment: AAAI 2020, 8 page

Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN

Daily electricity consumption forecasting is a classical problem. Existing forecasting algorithms tend to have decreased accuracy on special dates like holidays. This study decomposes the daily electricity consumption series into three components: trend, seasonal, and residual, and constructs a two-stage prediction method using piecewise linear regression as a filter and Dilated Causal CNN as a predictor. The specific steps involve setting breakpoints on the time axis and fitting the piecewise linear regression model with one-hot encoded information such as month, weekday, and holidays. For the challenging prediction of the Spring Festival, distance is introduced as a variable using a third-degree polynomial form in the model. The residual sequence obtained in the previous step is modeled using Dilated Causal CNN, and the final prediction of daily electricity consumption is the sum of the two-stage predictions. Experimental results demonstrate that this method achieves higher accuracy compared to existing approaches.Comment: Key words: Daily electricity consumption forecasting; time series decomposition; piecewise linear regression; Dilated Causal CN