Neural Network-Based Classification of Single-Phase Distribution Transformer Fault Data

The ultimate goal of this research is to develop an online, non-destructive, incipient fault detection system that is able to detect incipient faults in transformers and other electric equipment before the faults become catastrophic. With the condition assessment capability of the detection system, operators are equipped with better information during their decision-making process. Corrective actions are taken prior to transformer and equipment failures to prevent down-time and reduce operating and maintenance costs. Diagnosis of data associated with incipient failures is essential to develop an efficient, non-destructive, and online system. Field testing data were collected from controlled experiment and simulation data from mathematical models are studied. This thesis presents a data-mining approach to analyze field recorded and simulation data to characterize incipient fault data and study its properties. A supervised classifier using neural network (NN) toolbox in Matlab provides an efficient and accurate classification method to separate monitoring signal data into clusters base on their properties. However, raw data collected from the field and simulations will create too many dimensions and inputs to the neural network and make it a complex and over-generalized classification. Therefore, features are extracted from the data set, and these features are formed into feature clusters in order to identify patterns in signals as they are related to various physical behaviors of the system. The similarity between recognized patterns and patterns shown in future monitoring signals will trigger the warning of initializing or developing faults in transformers or equipment. This thesis demonstrates how different features were extracted from the raw data using various analysis techniques in both time domain and time-frequency domain, and the design and implementation of a neural network-based classification method. The classifier outputs are classes of data being separated into groups based on their characteristics and behaviors. Meaning of different classes is also explained in this thesis.Texas A&M University Honors and Academic Scholarships Office, Power System Automation Lab at Texas A&M Universit

Enriched Physics-informed Neural Networks for Dynamic Poisson-Nernst-Planck Systems

This paper proposes a meshless deep learning algorithm, enriched physics-informed neural networks (EPINNs), to solve dynamic Poisson-Nernst-Planck (PNP) equations with strong coupling and nonlinear characteristics. The EPINNs takes the traditional physics-informed neural networks as the foundation framework, and adds the adaptive loss weight to balance the loss functions, which automatically assigns the weights of losses by updating the parameters in each iteration based on the maximum likelihood estimate. The resampling strategy is employed in the EPINNs to accelerate the convergence of loss function. Meanwhile, the GPU parallel computing technique is adopted to accelerate the solving process. Four examples are provided to demonstrate the validity and effectiveness of the proposed method. Numerical results indicate that the new method has better applicability than traditional numerical methods in solving such coupled nonlinear systems. More importantly, the EPINNs is more accurate, stable, and fast than the traditional physics-informed neural networks. This work provides a simple and high-performance numerical tool for addressing PNPs with arbitrary boundary shapes and boundary conditions.Comment: 24 pages, 16 figures, 6 table

A Single-Cell Atlas of Bovine Skeletal Muscle Reveals Mechanisms Regulating Intramuscular Adipogenesis and Fibrogenesis

Background Intramuscular fat (IMF) and intramuscular connective tissue (IMC) are often seen in human myopathies and are central to beef quality. The mechanisms regulating their accumulation remain poorly understood. Here, we explored the possibility of using beef cattle as a novel model for mechanistic studies of intramuscular adipogenesis and fibrogenesis. Methods Skeletal muscle single-cell RNAseq was performed on three cattle breeds, including Wagyu (high IMF), Brahman (abundant IMC but scarce IMF), and Wagyu/Brahman cross. Sophisticated bioinformatics analyses, including clustering analysis, gene set enrichment analyses, gene regulatory network construction, RNA velocity, pseudotime analysis, and cell-cell communication analysis, were performed to elucidate heterogeneities and differentiation processes of individual cell types and differences between cattle breeds. Experiments were conducted to validate the function and specificity of identified key regulatory and marker genes. Integrated analysis with multiple published human and non-human primate datasets was performed to identify common mechanisms. Results A total of 32 708 cells and 21 clusters were identified, including fibro/adipogenic progenitor (FAP) and other resident and infiltrating cell types. We identified an endomysial adipogenic FAP subpopulation enriched for COL4A1 and CFD (log2FC = 3.19 and 1.92, respectively; P \u3c 0.0001) and a perimysial fibrogenic FAP subpopulation enriched for COL1A1 and POSTN (log2FC = 1.83 and 0.87, respectively; P \u3c 0.0001), both of which were likely derived from an unspecified subpopulation. Further analysis revealed more progressed adipogenic programming of Wagyu FAPs and more advanced fibrogenic programming of Brahman FAPs. Mechanistically, NAB2 drives CFD expression, which in turn promotes adipogenesis. CFD expression in FAPs of young cattle before the onset of intramuscular adipogenesis was predictive of IMF contents in adulthood (R2 = 0.885, P \u3c 0.01). Similar adipogenic and fibrogenic FAPs were identified in humans and monkeys. In aged humans with metabolic syndrome and progressed Duchenne muscular dystrophy (DMD) patients, increased CFD expression was observed (P \u3c 0.05 and P \u3c 0.0001, respectively), which was positively correlated with adipogenic marker expression, including ADIPOQ (R2 = 0.303, P \u3c 0.01; and R2 = 0.348, P \u3c 0.01, respectively). The specificity of Postn/POSTN as a fibrogenic FAP marker was validated using a lineage-tracing mouse line. POSTN expression was elevated in Brahman FAPs (P \u3c 0.0001) and DMD patients (P \u3c 0.01) but not in aged humans. Strong interactions between vascular cells and FAPs were also identified. Conclusions Our study demonstrates the feasibility of beef cattle as a model for studying IMF and IMC. We illustrate the FAP programming during intramuscular adipogenesis and fibrogenesis and reveal the reliability of CFD as a predictor and biomarker of IMF accumulation in cattle and humans

Progenitor Cell Isolation From Mouse Epididymal Adipose Tissue and Sequencing Library Construction

Here, we present a protocol to isolate progenitor cells from mouse epididymal visceral adipose tissue and construct bulk RNA and assay for transposase-accessible chromatin with sequencing (ATAC-seq) libraries. We describe steps for adipose tissue collection, cell isolation, and cell staining and sorting. We then detail procedures for both ATAC-seq and RNA sequencing library construction. This protocol can also be applied to other tissues and cell types directly or with minor modifications. For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).1 *1 Liu, Q., Li, C., Deng, B., Gao, P., Wang, L., Li, Y., ... & Fu, X. (2023). Tcf21 marks visceral adipose mesenchymal progenitors and functions as a rate-limiting factor during visceral adipose tissue development. Cell reports, 42(3) 112166. https://doi.org/10.1016/j.celrep.2023.11216

Loss of \u3ci\u3eActa2\u3c/i\u3e in Cardiac Fibroblasts Does Not Prevent the Myofibroblast Differentiation or Affect the Cardiac Repair After Myocardial Infarction

In response to myocardial infarction (MI), quiescent cardiac fibroblasts differentiate into myofibroblasts mediating tissue repair. One of the most widely accepted markers of myofibroblast differentiation is the expression of Acta2 which encodes smooth muscle alpha-actin (SMαA) that is assembled into stress fibers. However, the requirement of Acta2/SMαA in the myofibroblast differentiation of cardiac fibroblasts and its role in post-MI cardiac repair remained unknown. To answer these questions, we generated a tamoxifen-inducible cardiac fibroblast-specific Acta2 knockout mouse line. Surprisingly, mice that lacked Acta2 in cardiac fibroblasts had a normal post-MI survival rate. Moreover, Acta2 deletion did not affect the function or histology of infarcted hearts. No difference was detected in the proliferation, migration, or contractility between WT and Acta2-null cardiac myofibroblasts. Acta2-null cardiac myofibroblasts had a normal total filamentous actin level and total actin level. Acta2 deletion caused a significant compensatory increase in the transcription level of non-Acta2 actin isoforms, especially Actg2 and Acta1. Moreover, in myofibroblasts, the transcription levels of cytoplasmic actin isoforms were significantly higher than those of muscle actin isoforms. In addition, we found that myocardin-related transcription factor-A is critical for myofibroblast differentiation but is not required for the compensatory effects of non-Acta2 isoforms. In conclusion, the Acta2 deletion does not prevent the myofibroblast differentiation of cardiac fibroblasts or affect the post-MI cardiac repair, and the increased expression and stress fiber formation of non-SMαA actin isoforms and the functional redundancy between actin isoforms are able to compensate for the loss of Acta2 in cardiac myofibroblasts

Global climate forcing of aerosols embodied in international trade

International trade separates regions consuming goods and services from regions where goods and related aerosol pollution are produced. Yet the role of trade in aerosol climate forcing attributed to different regions has never been quantified. Here, we contrast the direct radiative forcing of aerosols related to regions’ consumption of goods and services against the forcing due to emissions produced in each region. Aerosols assessed include black carbon, primary organic aerosol, and secondary inorganic aerosols, including sulfate, nitrate and ammonium. We find that global aerosol radiative forcing due to emissions produced in East Asia is much stronger than the forcing related to goods and services ultimately consumed in that region because of its large net export of emissions-intensive goods. The opposite is true for net importers such as Western Europe and North America: global radiative forcing related to consumption is much greater than the forcing due to emissions produced in these regions. Overall, trade is associated with a shift of radiative forcing from net importing to net exporting regions. Compared to greenhouse gases such as carbon dioxide, the short atmospheric lifetimes of aerosols cause large localized differences between consumption- and production-related radiative forcing. International efforts to reduce emissions in the exporting countries will help alleviate trade-related climate and health impacts of aerosols while lowering global emissions

Transboundary health impacts of transported global air pollution and international trade

Millions of people die every year from diseases caused by exposure to outdoor air pollution1, 2, 3, 4, 5. Some studies have estimated premature mortality related to local sources of air pollution6, 7, but local air quality can also be affected by atmospheric transport of pollution from distant sources8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18. International trade is contributing to the globalization of emission and pollution as a result of the production of goods (and their associated emissions) in one region for consumption in another region14, 19, 20, 21, 22. The effects of international trade on air pollutant emissions23, air quality14 and health24 have been investigated regionally, but a combined, global assessment of the health impacts related to international trade and the transport of atmospheric air pollution is lacking. Here we combine four global models to estimate premature mortality caused by fine particulate matter (PM2.5) pollution as a result of atmospheric transport and the production and consumption of goods and services in different world regions. We find that, of the 3.45 million premature deaths related to PM2.5 pollution in 2007 worldwide, about 12 per cent (411,100 deaths) were related to air pollutants emitted in a region of the world other than that in which the death occurred, and about 22 per cent (762,400 deaths) were associated with goods and services produced in one region for consumption in another. For example, PM2.5 pollution produced in China in 2007 is linked to more than 64,800 premature deaths in regions other than China, including more than 3,100 premature deaths in western Europe and the USA; on the other hand, consumption in western Europe and the USA is linked to more than 108,600 premature deaths in China. Our results reveal that the transboundary health impacts of PM2.5 pollution associated with international trade are greater than those associated with long-distance atmospheric pollutant transport

To what extent can China’s near-term air pollution control policy protect air quality and human health? A case study of the Pearl River Delta region

Following a series of extreme air pollution events, the Chinese government released the Air Pollution Prevention and Control Action Plan in 2013 (China's State Council 2013). The Action Plan sets clear goals for key regions (i.e. cities above the prefecture level, Beijing-Tianjin-Hebei Province, the Yangtze River Delta and the Pearl River Delta) and establishes near-term control efforts for the next five years. However, the extent to which the Action Plan can direct local governments' activities on air pollution control remains unknown. Here we seek to evaluate the air quality improvement and associated health benefits achievable under the Action Plan in the Pearl River Delta (PRD) area from 2012 to 2017. Measure-by-measure quantification results show that the Action Plan would promise effective emissions reductions of 34% of SO2, 28% of NOx, 26% of PM2.5 (particulate matter less than 2.5 μm in diameter), and 10% of VOCs (volatile organic compounds). These emissions abatements would lower the PM2.5 concentration by 17%, surpassing the 15% target established in the Action Plan, thereby avoiding more than 2900 deaths and 4300 hospital admissions annually. We expect the implementation of the Action Plan in the PRD would be productive; the anticipated impacts, however, fall short of the goal of protecting the health of local residents, as there are still more than 33 million people living in places where the annual mean ambient PM2.5 concentrations are greater than 35 μg m−3, the interim target-3 of the World Health Organization (WHO). We therefore propose the next steps for air pollution control that are important not only for the PRD but also for all other regions of China as they develop and implement effective air pollution control policies

Multi-Site and Multi-Scale Unbalanced Ship Detection Based on CenterNet

Object detection plays an essential role in the computer vision domain, especially the machine learning-based approach, which has developed rapidly in the past decades. However, the development of convolutional neural networks in the marine field is relatively slow, such as in ship classification and tracking. In this paper, ship detection is considered as a central point classification and regression task but discards the non-maximum suppression operation. We first improved the deep layer aggregation network to enhance the feature extraction capability of tiny targets, then reduced the number of parameters through the lightweight convolution module, and finally employed a unique activation function to enhance the nonlinearity of the model. By doing this, the improved network not only suits unbalanced sample ratios in classifying, but is more robust in scenarios where both the number and resolution of samples are unstable. Experimental results demonstrate that the proposed approach obtains outstanding performance and especially suits tiny object detection compared with current advanced methods. Furthermore, in contrast to the original CenterNet framework, the mAP of the proposed approach increased by 5.6%