Distribution of quadratic forms under skew normal settings

AbstractFor a class of multivariate skew normal distributions, the noncentral skew chi-square distribution is studied. The necessary and sufficient conditions under which a sequence of quadratic forms is generalized noncentral skew chi-square distributed random variables are obtained. Several examples are given to illustrate the results

Multi-Objective Thermal Optimization Based on Improved Analytical Thermal Models of a 30 kW IPT System for EVs

Thermal design is particularly important for high-power and compact inductive power transfer (IPT) systems having limited surface area for heat dissipation. This paper presents the thermal design and optimization of a 30 kW IPT system for electric vehicles. An improved analytical thermal model with high accuracy for liquid-cooled magnetic couplers was proposed by using thermal network method (TNM). It considers heating components as well as thermal interface materials. Then multi-objective thermal optimization procedure of the liquid-cooled magnetic coupler was conducted with the presented model. Tradeoffs among temperature rise, weight, and cost were discussed and an optimized solution was selected. The thermal FE models were established and compared with the thermal networks. Subsequently, the thermal performance of the system at different power levels and misaligned conditions was analyzed. The experimental setup based on Fiber Bragg grating sensors was built, and the prototypes were tested with an output power of around 28 kW. The error of stable temperature between the experiment and the prediction was less than 10% at the measurement points, verifying the thermal models. The proposed thermal models and optimization procedure accelerate the thermal design of IPT systems, towards higher power density.Multi-Objective Thermal Optimization Based on Improved Analytical Thermal Models of a 30 kW IPT System for EVsacceptedVersio

CircRNA PDE3B regulates tumorigenicity via the miR-136-5p/MAP3K2 axis of esophageal squamous cell carcinoma

Background. CircRNA has a covalently closed circular conformation and a stable structure. However, the exact role of circRNA in esophageal squamous cell carcinoma (ESCC) remains uncertain. The purpose of this study was to explore the role of hsa_circ_0000277 (circ_PDE3B) in ESCC. Methods. The expression levels of circ_PDE3B, miR-136-5p and mitogen-activated protein kinase kinase kinase 2 (MAP3K2) in ESCC tissues and cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR) or western blot. The proliferation ability of EC9706 and KYSE30 cells was detected by clonal formation, 5-ethynyl-2’-deoxyuridine (EdU) and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-Htetrazolium bromide (MTT) assays. Flow cytometry was used to detect the apoptosis rate of cells. Transwell assay was used to detect the invasion ability of EC9706 and KYSE3 cells. The relationship between miR-136-5p and circ_PDE3B or MAP3K2 was verified by dual-luciferase reporter assay and RNA pull-down, and the effect of circ_PDE3B on tumor growth in vivo was explored through tumor transplantation experiment. Immunohistochemistry (IHC) assay was used to detect MAP3K2 and Ki67 expression in mice tumor tissues. Results. The results showed that circ_PDE3B was highly expressed in ESCC tissues and cells. Downregulated circ_PDE3B expression in ESCC cells significantly reduced cell proliferation, migration and invasion. Circ_PDE3B served as a sponge for miR-136- 5p, and miR-136-5p inhibition reversed the roles of circ_PDE3B knockdown in ESCC cells. MAP3K2 was a direct target of miR-136-5p, and miR-136-5p targeted MAP3K2 to inhibit the malignant behaviors of ESCC cells. Furthermore, circ_PDE3B regulated MAP3K2 expression by sponging miR-136-5p. Importantly, circ_PDE3B knockdown inhibited tumor growth in vivo. Conclusions. In conclusion, circ_PDE3B acted as oncogenic circRNA in ESCC and accelerated ESCC progression by adsorption of miR-136-5p and activation of MAP3K2, supporting circ_PDE3B as a potential therapeutic target for ESCC

Productivity model and experiment of field crop spraying by plant protection unmanned aircraft

Traditional agricultural production requires numerous human and material resources; however, agricultural production efficiency is low. The successful development of plant protection unmanned aerial vehicles (UAVs) has changed the operation mode of traditional agricultural production, saving human, material, and financial resources and significantly improving production efficiency. To summarize the process of improving the productivity of plant protection UAVs, this study established a productivity calculation model of UAVs based on the time composition of the UAV agricultural plant protection process, including spraying, turning, replenishment, and transfer times. The time required for the unmanned aircraft application process was counted through years of tracking the application process of eight different plant protection unmanned aircraft. Plot lengths of 100, 300, 500, 700, 1,000, 1,500, 2,000, 2,500, 3,000, and 3,500 m were established to calculate the theoretical productivity. The results showed that the productivity of different types of plant protection UAVs increased with an increase in plot length in the range of 100 to 1,500 m; however, when the plot length reached a certain value, the productivity growth rate slowed down or even decreased slightly. Simultaneously, based on the working area per 10,000 mu, the recommended plot length and the number of configured models for different models were recommended. If the plant protection UAV was distinguished by electric and oil power, the time utilization rate of electric plant protection UAVs was 72.7%, and the labor productivity was 56.4 mu/person·h. In contrast, the time utilization rate of the heavy load oil-powered plant protection unmanned aircraft was 86%, and the labor productivity was 63.5 mu/person ·h. This study can support plant protection UAV enterprises to optimize equipment efficiency, provide evaluation methods for the operation efficiency assessment of plant protection UAVs, provide a reference for the selection of plant protection UAVs, and provide a basis for field planning

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike.Comment: Preprint; Work in progres

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs

Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despite the promising results, research on anomaly detection has almost exclusively focused on static graphs while the mining of anomalous patterns from dynamic graphs is rarely studied but has significant application value. In addition, anomaly detection is typically tackled from semi-supervised perspectives due to the lack of sufficient labeled data. However, most proposed methods are limited to merely exploiting labeled data, leaving a large number of unlabeled samples unexplored. In this work, we present semi-supervised anomaly detection (SAD), an end-to-end framework for anomaly detection on dynamic graphs. By a combination of a time-equipped memory bank and a pseudo-label contrastive learning module, SAD is able to fully exploit the potential of large unlabeled samples and uncover underlying anomalies on evolving graph streams. Extensive experiments on four real-world datasets demonstrate that SAD efficiently discovers anomalies from dynamic graphs and outperforms existing advanced methods even when provided with only little labeled data.Comment: Accepted to IJCAI'23. Code will be available at https://github.com/D10Andy/SA

Protective Effect of Chlorogenic Acid and Its Analogues on Lead-Induced Developmental Neurotoxicity Through Modulating Oxidative Stress and Autophagy

Lead (Pb) is among the deleterious heavy metal and has caused global health concerns due to its tendency to cause a detrimental effect on the development of the central nervous system (CNS). Despite being a serious health concern, treatment of Pb poisoning is not yet available, reflecting the pressing need for compounds that can relieve Pb-induced toxicity, especially neurotoxicity. In the quest of exploring protective strategies against Pb-induced developmental neurotoxicity, compounds from natural resources have gained increased attention. Chlorogenic acid (CGA) and its analogues neochlorogenic acid (NCGA) and cryptochlorogenic acid (CCGA) are the important phenolic compounds widely distributed in plants. Herein, utilizing zebrafish as a model organism, we modeled Pb-induced developmental neurotoxicity and investigated the protective effect of CGA, NCGA, and CCGA co-treatment. In zebrafish, Pb exposure (1,000 μg/L) for 5 days causes developmental malformation, loss of dopaminergic (DA) neurons, and brain vasculature, as well as disrupted neuron differentiation in the CNS. Additionally, Pb-treated zebrafish exhibited abnormal locomotion. Notably, co-treatment with CGA (100 µM), NCGA (100 µM), and CCGA (50 µM) alleviated these developmental malformation and neurotoxicity induced by Pb. Further underlying mechanism investigation revealed that these dietary phenolic acid compounds may ameliorate Pb-induced oxidative stress and autophagy in zebrafish, therefore protecting against Pb-induced developmental neurotoxicity. In general, our study indicates that CGA, NCGA, and CCGA could be promising agents for treating neurotoxicity induced by Pb, and CCGA shows the strongest detoxifying activity

Hetero2^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin