A Dynamic Magnetic Equivalent Circuit Model For Design And Control Of Wound Rotor Synchronous Machines

Recently, a new magnetic equivalent circuit (MEC) model was developed to support automated multi-objective design of wound-rotor synchronous machines (WRSMs). In this research, the MEC model and its application have been enhanced. Initial enhancement has focused on using the MEC model to explore machine design and control as a unified problem. Excitation strategies for optimal steady-state performance have been developed. The optimization is implemented in two phases. First, stator and field excitation at rated power is obtained as part of a WRSM design in which the objectives are to minimize machine mass and loss. Second, a map between current and torque is generated using a single-objective optimization in which core, resistive, and switch conduction loss are minimized. Optimal as well as sub-optimal and traditional controls are studied and compared. An interesting result is that a relatively straightforward field-oriented control is consistent with a desire for mass/loss reduction and control simplicity. The applicability of the excitation to systems in which prime mover angular velocity varies and is (un)controllable is considered, as well as its impact on machine design. A second contribution has been the derivation of a mesh-based dynamic MEC model for WRSMs. As part of this effort, a reluctance network has been derived to model flux distribution around damper bar openings. The reluctance network is applicable to a user-defined damper bar pattern, which enables the study of optimal damper bar placement. In addition, Faraday\u27s law is applied to establish a state model in which stator, field, and damper winding flux linkages are selected as state variables. The resulting coupled MEC/state model is solved to obtain transient machine dynamics, including damper bar currents. In addition, skew of the rotor pole is incorporated using a multi-slices model. The proposed dynamic model opens new paths for exploration. Perhaps most significantly, it enables rigorous design of coupled synchronous machine/diode rectifier systems, which are used in numerous applications, but are often designed using rules of tradition created prior to the availability of efficient numerical simulation

Epigenetic Regulation of Prostate Cancer

Prostate cancer is (PCa) the second leading cause of cancer death in males in the United State, with 174,650 new cases and 31,620 deaths estimated in 2019. It has been documented that epigenetic deregulation such as histone modification and DNA methylation contributes to PCa initiation and progression. EZH2 (enhancer of zeste homolog 2), the catalytic subunit of the Polycomb Repressive Complex (PRC2) responsible for H3K27me3 and gene repression, has been identified as a promising target in PCa. In addition, overexpression of other epigenetic regulators such as DNA methyltransferases (DNMT) is also observed in PCa. These epigenetic regulators undergo extensive post-translational modifications, in particular, phosphorylation. AKT, CDKs, PLK1, PKA, ATR and DNA-PK are the established kinases responsible for phosphorylation of various epigenetic regulators

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields, such as biomedicine, where making wrong decisions can have severe consequences, it is crucial to interpret the inner working mechanisms of GNNs before applying them. In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, GNNInterpreter learns a probabilistic generative graph distribution that produces the most discriminative graph pattern the GNN tries to detect when making a certain prediction by optimizing a novel objective function specifically designed for the model-level explanation for GNNs. Compared with the existing work, GNNInterpreter is more computationally efficient and more flexible in generating explanation graphs with different types of node features and edge features, without introducing another blackbox to explain the GNN and without requiring manually specified domain-specific knowledge. Additionally, the experimental studies conducted on four different datasets demonstrate that the explanation graph generated by GNNInterpreter can match the desired graph pattern when the model is ideal and reveal potential model pitfalls if there exist any

Studies on the Spatial Distribution of Aphis-eating Ladybirds in Soybean Fields

- Seven-spot ladybird (Coccinella septempunctata Linnaeus), Akebia leaflike moth (Adonia variegate goeze), Ash bark beetle (Leis axyridis Pallas) and bifid tongued bees (Propylaea japonica Thunberg) are principal predators of soybean aphids (Aphis glycineOriginating text in Chinese.Citation: Wang, Xiaoqi, Ding, Xiuyun, Huang, Feng. (1991). Studies on the Spatial Distribution of Aphis-eating Ladybirds in Soybean Fields. Journal of Shenyang Agricultural University, 22(1), 13-16