A bead sequence-driven deposition pattern evaluation criterion for lowering residual stresses in additive manufacturing

Deposition patterns can significantly influence the distribution and magnitude of residual stress in additively manufactured parts. Time-consuming thermal-mechanical simulations and costly experimental studies are often required to identify the optimal patterns. A simple and generic method to evaluate and optimize the deposition pattern for the purpose of minimizing residual stress is in urgent need. To overcome the shortcomings of the current practice, here we propose a novel pattern evaluation criterion. Starting from the discretization of the deposition pattern by a series of sequence numbers, we introduce two interconnected concepts. The first is called “equivalent bead sequence number” which can be physically interpreted as an index of the localized heat accumulation induced by the deposition process. Based on this point-wise “equivalent bead sequence number”, the second concept called “bead sequence number dispersion index” which can be considered as a representation of the global heat accumulation gradient, is proposed as a criterion for assessing the resulting residual stress. The temperature fields and residual stresses of a square part with six typical deposition patterns predicted by thermo-mechanical finite element simulations are used to develop and verify the proposed criterion. It is found that the “equivalent bead sequence number” of a given pattern is closely correlated to the distribution of the associated temperature and residual stress. More interestingly, both the highest equivalent and highest maximum principal residual stress of a pattern linearly increase with its corresponding value of “bead sequence number dispersion index”. Guided by this relation, two new patterns with lower residual stress are developed and evaluated. Among all the patterns considered, the so-called S pattern shows the lowest value of the “bead sequence number dispersion index” which corresponds to the lowest residual stress. The proposed sequence-driven approach provides a new candidate for real-time evaluation and optimization of the deposition pattern in additive manufacturing.publishedVersio

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). Functional data analysis (FDA) deals with random variables (or process) with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50). Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). Functional data analysis (FDA) deals with random variables (or process) with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50). Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors

espite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box adversarial attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, an Explainable Transfer-based Black-Box Adversarial Attack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML models and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints

Toll-like receptor activation by helminths or helminth products to alleviate inflammatory bowel disease

Helminth infection may modulate the expression of Toll like receptors (TLR) in dendritic cells (DCs) and modify the responsiveness of DCs to TLR ligands. This may regulate aberrant intestinal inflammation in humans with helminthes and may thus help alleviate inflammation associated with human inflammatory bowel disease (IBD). Epidemiological and experimental data provide further evidence that reducing helminth infections increases the incidence rate of such autoimmune diseases. Fine control of inflammation in the TLR pathway is highly desirable for effective host defense. Thus, the use of antagonists of TLR-signaling and agonists of their negative regulators from helminths or helminth products should be considered for the treatment of IBD

LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender Algorithm

According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA