Comparing numerical and machine learning algorithms for optimized operation points of an electrical machine

Abstract This work compares the Lookup Table (LuT) based numerical method with neural network (NN) based, and reinforcement learning (RL) based methods, for finding the optimal operating point of an Interior Permanent Magnet Synchronous Machine. Commonly, numerical methods are used to search for Maximum Torque Per Ampere (MTPA) points, which, although relatively accurate, often require significant computation time and generate large amounts of output data to obtain precise operating points. In this thesis, a simple approach was employed to establish a three-dimensional LuT based on nonlinear data, which is used as a baseline for comparing machine learning models. By comparing multiple metrics, it was verified that the presented NN-based method can quickly, efficiently, and accurately fit the LuT data, making it suitable for data reduction and addressing the issue of large output data of LuT. The RL-based method offers a simple model that is not dependent on data and can essentially achieve MTPA control, providing new inspiration for finding operating points. Finally, based on the comparative results, the advantages and challenges of the proposed different models are presente

Dynamic spatio-temporal graph neural networks for hot topic prediction in scientific literature

With information explosion occurring in past decades, the rapid growth of papers published results in the rapid change of hot topics, especially in the biomedical domain. It turns out very hard for researchers who are interested in biomedical domain to track hot topics over time, as well as to predict the trends of them in the near future. Based on the above demand, it is important to have a model which is able to follow and predict the trend of hot topics continuously. Deep learning has been proven to be an efficient method to extract information from texts and use the information to predict the future trends. Under the thriving background of Deep Learning, Graph Neural Network (GNN) is able to capture the information from graph structures. There are various applications using GNN models, such as traffic flow prediction, chemical structure discovering, etc. In this research project, a dynamic spatio-temporal graph neural network is presented to keep track of the selected hot keywords and topics in the biomedical domain and predict the possible frequencies in the near future. The input of the model is obtained by extracting the monthly frequency information of selected keywords and topics from paper abstracts in PubMed, the largest biomedical literature collection. After training with data over a decade, the model is able to predict trends of selected hot keywords and topics in next 5 months. Thus, the presented model can help follow the trend of hot topics in the biomedical domain.Includes bibliographical reference

Heat transfer analysis of cellular heat exchanger based on a fresh fractal Vicsek model by Galerkin finite element method

The energy density is increasing in modern industry, and the lack of heat transfer efficiency is one of the fatal problems of current equipment, which seriously affects the improvement of production level. The cellular heat exchanger is a kind of heat exchanger used in different fields. It plays an irreplaceable role in energy saving, efficiency improvement and reducing pressure drop. However, when the geometric shape of the fractal heat exchanger is very complex, the heat transfer analysis is impossible or difficult to show. Therefore, the objective of this project is to solve the difficulty of analysis on complex fractal heat exchangers and have a better representation of the physics. A totally fresh tessellation method, which can be used to analyze complex fractal heat exchangers, is introduced in this essay. By the research process, surjective hole-fill maps for Vicsek fractal were established, and Vicsek fractal was represented by continuous tessellations, and the fractal Vicsek model was expressed by continuous subdivision. The transfer theory on fractals and Galerkin finite element method were used to analyze the heat transfer of fractals and tessellations. The results of two analyses, the fractal model and the tesselation model, were almost the same, and confirm the function of the tessellation method in the analysis of complex heat exchangers. Furthermore, it provides a new way to solve the difficult problem of heat transfer analysis of complex geometric heat exchangers.The energy density is increasing in modern industry, and the lack of heat transfer efficiency is one of the fatal problems of current equipment, which seriously affects the improvement of production level. The cellular heat exchanger is a kind of heat exchanger used in different fields. It plays an irreplaceable role in energy saving, efficiency improvement and reducing pressure drop. However, when the geometric shape of the fractal heat exchanger is very complex, the heat transfer analysis is impossible or difficult to show. Therefore, the objective of this project is to solve the difficulty of analysis on complex fractal heat exchangers and have a better representation of the physics. A totally fresh tessellation method, which can be used to analyze complex fractal heat exchangers, is introduced in this essay. By the research process, surjective hole-fill maps for Vicsek fractal were established, and Vicsek fractal was represented by continuous tessellations, and the fractal Vicsek model was expressed by continuous subdivision. The transfer theory on fractals and Galerkin finite element method were used to analyze the heat transfer of fractals and tessellations. The results of two analyses, the fractal model and the tesselation model, were almost the same, and confirm the function of the tessellation method in the analysis of complex heat exchangers. Furthermore, it provides a new way to solve the difficult problem of heat transfer analysis of complex geometric heat exchangers

Regularization Method for the Approximate Split Equality Problem in Infinite-Dimensional Hilbert Spaces

We studied the approximate split equality problem (ASEP) in the framework of infinite-dimensional Hilbert spaces. Let , , and   be infinite-dimensional real Hilbert spaces, let and   be two nonempty closed convex sets, and let and   be two bounded linear operators. The ASEP in infinite-dimensional Hilbert spaces is to minimize the function over and . Recently, Moudafi and Byrne had proposed several algorithms for solving the split equality problem and proved their convergence. Note that their algorithms have only weak convergence in infinite-dimensional Hilbert spaces. In this paper, we used the regularization method to establish a single-step iterative for solving the ASEP in infinite-dimensional Hilbert spaces and showed that the sequence generated by such algorithm strongly converges to the minimum-norm solution of the ASEP. Note that, by taking in the ASEP, we recover the approximate split feasibility problem (ASFP)

Autonomous self-evolving research on biomedical data: the DREAM paradigm

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate in-depth questions or solutions, all without human intervention. Here we developed DREAM, the first biomedical Data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. In clinical dataset, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. This paradigm may also have a revolutionary impact on other data-driven scientific research fields.11 pages, 4 figures, content added, typos in figure corrected, references revised and font change

Propagation-invariant strongly longitudinally polarized toroidal pulses

Recent advancements in optical, terahertz, and microwave systems have unveiled non-transverse optical toroidal pulses characterized by skyrmionic topologies, fractal-like singularities, space-time nonseparability, and anapole-exciting ability. Despite this, the longitudinally polarized fields of canonical toroidal pulses notably lag behind their transverse counterparts in magnitude. Interestingly, although mushroom-cloud-like toroidal vortices with strong longitudinal fields are common in nature, they remain unexplored in the realm of electromagnetics. Here, we present strongly longitudinally polarized toroidal pulses (SLPTPs) which boast a longitudinal component amplitude exceeding that of the transverse component by over tenfold. This unique polarization property endows SLPTPs with robust propagation characteristics, showcasing nondiffracting behavior. The propagation-invariant strongly longitudinally polarized field holds promise for pioneering light-matter interactions, far-field superresolution microscopy, and high-capacity wireless communication utilizing three polarizations

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation. This DRL approach has the potential to lead to a large management model based on certain transformer neural network structures, resulting in an artificial general intelligence paradigm for various management tasks. Traditional methods have limitations for solving complex real-world problems, and we demonstrate how DRL can surpass existing heuristic approaches for solving management tasks. We aim to solve the problems in a unified framework, considering the interconnections between different tasks. Central to our methodology is the development of a foundational decision model coordinating decisions across the different domains through generative decision-making. Our experimental results affirm the effectiveness of our DRL-based framework in complex and dynamic business environments. This work opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management

NAC1 confines virus‐specific memory formation of CD4+ T cells through the ROCK1‐mediated pathway

Nucleus accumbens‐associated protein 1 (NAC1), a transcriptional cofactor, has been found to play important roles in regulating regulatory T cells, CD8 + T cells, and antitumor immunity, but little is known about its effects on T‐cell memory. In this study, we found that NAC1 expression restricts memory formation of CD4 + T cells during viral infection. Analysis of CD4 + T cells from wild‐type (WT) and NAC1‐ deficient (−/−) mice showed that NAC1 is essential for T‐cell metabolism, including glycolysis and oxidative phosphorylation, and supports CD4 + T‐cell survival in vitro. We further demonstrated that a deficiency of NAC1 downregulates glycolysis and correlates with the AMPK‐mTOR pathway and causes autophagy defective in CD4 + T cells. Loss of NAC1 reduced the expression of ROCK1 and the phosphorylation and stabilization of BECLIN1. However, a forced expression of ROCK1 in NAC1−/− CD4 + T cells restored autophagy and the activity of the AMPK‐mTOR pathway. In animal experiments, adoptively transferred NAC1−/− CD4 + T cells or NAC1−/− mice challenged with VACV showed enhanced formation of VACV‐specific CD4 + memory T cells compared to adoptively transferred WT CD4 + T cells or WT mice. This memory T‐cell formation enhancement was abrogated by forcing expression of ROCK1. Our study reveals a novel role for NAC1 as a suppressor of CD4 + T‐cell memory formation and suggests that targeting NAC1 could be a new approach to promoting memory CD4 + T‐cell development, which is critical for an effective immune response against pathogens

Control of CD4+ T cells to restrain inflammatory diseases via eukaryotic elongation factor 2 kinase

CD4+ T cells, particularly IL-17-secreting helper CD4+ T cells, play a central role in the inflammatory processes underlying autoimmune disorders. Eukaryotic Elongation Factor 2 Kinase (eEF2K) is pivotal in CD8+ T cells and has important implications in vascular dysfunction and inflammation-related diseases such as hypertension. However, its specific immunological role in CD4+ T cell activities and related inflammatory diseases remains elusive. Our investigation has uncovered that the deficiency of eEF2K disrupts the survival and proliferation of CD4+ T cells, impairs their ability to secrete cytokines. Notably, this dysregulation leads to heightened production of pro-inflammatory cytokine IL-17, fosters a pro-inflammatory microenvironment in the absence of eEF2K in CD4+ T cells. Furthermore, the absence of eEF2K in CD4+ T cells is linked to increased metabolic activity and mitochondrial bioenergetics. We have shown that eEF2K regulates mitochondrial function and CD4+ T cell activity through the upregulation of the transcription factor, signal transducer and activator of transcription 3 (STAT3). Crucially, the deficiency of eEF2K exacerbates the severity of inflammation-related diseases, including rheumatoid arthritis, multiple sclerosis, and ulcerative colitis. Strikingly, the use of C188-9, a small molecule targeting STAT3, mitigates colitis in a murine immunodeficiency model receiving eEF2K knockout (KO) CD4+ T cells. These findings emphasize the pivotal role of eEF2K in controlling the function and metabolism of CD4+ T cells and its indispensable involvement in inflammation-related diseases. Manipulating eEF2K represents a promising avenue for novel therapeutic approaches in the treatment of inflammation-related disorders