Numerical Simulation and Experimental Validation of Residual Stresses in Water-Quenched Aluminum Alloy Castings

Aluminum alloy castings are normally water quenched after solution treatment to improve mechanical properties. Rapid water quenching can result in high-residual stress and severe distortion which significantly affect functionality and performance of the products. To optimize product design and durability, one needs to model and predict residual stress and distortion produced in the water-quenched components. In this article, a finite element-based approach was developed to simulate the transient heat transfer and residual stress development during water quenching. In this approach, an iterative zone-based heat transfer algorithm was coupled with material constitutive model called mechanical threshold stress (MTS). With the integrated models, a good agreement was achieved between the numerically predicted and the experimentally measured residual stresses in the aluminum alloy frame-shape casting. The integrated FEA-based heat transfer and residual stress models were also applied to a water-quenched cast aluminum cylinder head with a great success

QQ~Q\widetilde{Q}-systems for twisted quantum affine algebras

We establish the $Q \widetilde{Q}$-systems for the twisted quantum affine algebras that were conjectured in arXiv:1606.05301. We develop the representation theory of Borel subalgebra of twisted quantum affine algebras and we construct their prefundamental representations. We also propose a general conjecture on the relations between twisted and non-twisted types. We prove this conjecture for some particular classes of representations, including prefundamental representations.Comment: 39 page

Weyl group twists and representations of quantum affine Borel algebras

We define categories $\mathcal{O}^w$ of representations of Borel subalgebras $\mathcal{U}_q\mathfrak{b}$ of quantum affine algebras $\mathcal{U}_q\hat{\mathfrak{g}}$, which come from the category $\mathcal{O}$ twisted by Weyl group elements $w$. We construct inductive systems of finite-dimensional $\mathcal{U}_q\mathfrak{b}$-modules twisted by $w$, which provide representations in the category $\mathcal{O}^w$. We also establish a classification of simple modules in these categories $\mathcal{O}^w$. We explore convergent phenomenon of $q$-characters of representations of quantum affine algebras, which conjecturally give the $q$-characters of representations in $\mathcal{O}^w$. Furthermore, we propose a conjecture concerning the relationship between the category $\mathcal{O}$ and the twisted category $\mathcal{O}^w$, and we propose a possible connection with shifted quantum affine algebras.Comment: 30 page

Factors for Chinese students choosing Australian higher education and motivation for returning: a systematic review

Under the third wave of international student mobility, Australia has become the third largest country receiving international students. Compared with the United States and the United Kingdom, Australia can still maintain a stable increase in terms of hosting Chinese students. For Australia, attracting international students becomes an important part of Australian universities’ business and cultural diversity. This paper reports the Chinese students’ initiations of choosing Australian higher education and motivations for returning, aiming at contributing to a more accurate and comprehensive understanding of Chinese students’ international flows. By retrieving all relevant literature published from 2000 to 2017, this paper engages with a systematic review to provide an overview of what exactly motivates Chinese students choosing Australian higher education and returning. Based on the robust assessment criteria, we selected 68 articles for analysis, and according to the coding results, we developed four themes influencing Chinese students’ choice of Australia, including academic requirement and attainment, employment and future career prospects, host country environment, and social connections and three themes for returning: emotional needs, culture and integration in Australia, and career opportunities in China. The research results contribute to policy implications for Australian international higher education development

Error Estimation in the Mean-Field Limit of Kinetic Flocking Models with Local Alignments

In this paper, we present an innovative particle system characterized by moderate interactions, designed to accurately approximate kinetic flocking models that incorporate singular interaction forces and local alignment mechanisms. We establish the existence of weak solutions to the corresponding flocking equations and provide an error estimate for the mean-field limit. This is achieved through the regularization of singular forces and a nonlocal approximation strategy for local alignments. We show that, by selecting the regularization and localization parameters logarithmically with respect to the number of particles, the particle system effectively approximates the mean-field equation

Probabilistic Contrastive Learning for Domain Adaptation

The standard contrastive learning acts on the extracted features with $\ell_{2}$ normalization. For domain adaptation tasks, however, we find that contrastive learning with the standard paradigm does not perform well. The reason is mainly that the class weights (weights of the final fully connected layer) are not involved during optimization, which does not guarantee the produced features to be clustered around the class weights learned from source data. To tackle this issue, we propose a simple yet powerful probabilistic contrastive learning (PCL) in this paper, which not only produces compact features but also enforces them to be distributed around the class weights. Specifically, we break the traditional contrastive learning paradigm (feature+$\ell_{2}$ normalization) by replacing the features with probabilities and removing $\ell_{2}$ normalization. In this way, we can enforce the probability to approximate the one-hot form, thereby narrowing the distance between the features and the class weights. PCL is generic due to conciseness, which can be used for different tasks. In this paper, we conduct extensive experiments on five tasks, \textit{i.e.}, unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA), semi-supervised learning (SSL), UDA detection, and UDA semantic segmentation. The results demonstrate that our PCL can bring significant gains for these tasks. In particular, for segmentation tasks, with the blessing of PCL, our method achieves or even surpasses CPSL-D with a smaller training cost (1*3090, 5 days vs 4*V100, 11 days). Code is available at https://github.com/ljjcoder/Probabilistic-Contrastive-Learning.Comment: 12 pages,4 figure

A Short Review for Ontology Learning: Stride to Large Language Models Trend

Ontologies provide formal representation of knowledge shared within Semantic Web applications. Ontology learning involves the construction of ontologies from a given corpus. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge extraction and representation. A new trend of these approaches is relying on large language models (LLMs) to enhance ontology learning. This paper gives a review in approaches and challenges of ontology learning. It analyzes the methodologies and limitations of shallow-learning-based and deep-learning-based techniques for ontology learning, and provides comprehensive knowledge for the frontier work of using LLMs to enhance ontology learning. In addition, it proposes several noteworthy future directions for further exploration into the integration of LLMs with ontology learning tasks