Decision-making techniques for smart grid energy management

This thesis has contributed to the design of suitable decision-making techniques for energy management in the smart grid with emphasis on energy efficiency and uncertainty analysis in two smart grid applications. First, an energy trading model among distributed microgrids (MG) is investigated, aiming to improve energy efficiency by forming coalitions to allow local power transfer within each coalition. Then, a more specific scenario is considered that is how to optimally schedule Electric Vehicles (EV) charging in a MG-like charging station, aiming to match as many as EV charging requirements with the uncertain solar energy generation. The solutions proposed in this thesis can give optimal coalition formation patterns for reduced power losses and achieve optimal performance for the charging station. First, several algorithms based on game theory are investigated for the coalition formation of distributed MGs to alleviate the power losses dissipated on the cables due to power transfer. The seller and buyer MGs can make distributed decisions whether to form a coalition with others for energy trading. The simulation results show that game theory based methods that enable cooperation yield a better performance in terms of lower power losses than a non-cooperative approach. This is because by forming local coalitions, power is transferred within a shorter distance and at a lower voltage. Thus, the power losses dissipated on the transmission lines and caused by power conversion at the transformer are both reduced. However, the merge-and-split based cooperative games have an inherent high computational complexity for a large number of players. Then, an efficient framework is established for the power loss minimization problem as a college admissions game that has a much lower computational complexity than the merge-and-split based cooperative games. The seller and buyer MGs take the role of colleges and students in turn and apply for a place in the opposite set following their preference lists and the college MGs’ energy quotas. The simulation results show that the proposed framework demonstrates a comparable power losses reduction to the merge-and-split based algorithms, but runs 700 and 18000 times faster for a network of 10 MGs and 20 MGs, respectively. Finally, the problem of EV charging using various energy sources is studied along with their impact on the charging station’s performance. A multiplier k is introduced to measure the effect of solar prediction uncertainty on the decision-making process of the station. A composite performance index (the Figure of Merit, FoM) is also developed to measure the charging station’s utility, EV users charging requirements and the penalties for turning away new arrivals and for missing charging deadlines. A two-stage admission and scheduling mechanism is further proposed to find the optimal trade-off between accepting EVs and missing charging deadlines by determining the best value of the parameter k under various energy supply scenarios. The numerical evaluations give the solution to the optimization problem and show that some of the key factors such as shorter and longer deadline urgencies of EVs charging requirements, stronger uncertainty of the prediction error, storage capacity and its initial state will not affect significantly the optimal value of the parameter k

Improving the detection of Helicobacter pylori in biopsies of chronic gastritis: a comparative analysis of H&E, methylene blue, Warthin-Starry, immunohistochemistry, and quantum dots immunohistochemistry

ObjectivesThe objective of the study was to compare the consistency of various staining methods, including H&E, Methylene Blue, Warthin-Starry (W-S), Immunohistochemistry (IHC) and Quantum dots immunohistochemistry (QDs-IHC), in detecting Helicobacter pylori (HP) in cases of mild, moderate and severe chronic gastritis.MethodsBiopsy samples were obtained from 225 patients with chronic gastritis at the Department of Pathology, Yichang Central People’s Hospital between January 2019 and October 2019. The presence of HP was detected using H&E, Methylene Blue, W-S, IHC, and QDs-IHC.ResultsThe positive rates for HP detection using H&E, Methylene Blue, W-S, IHC, and QDs-IHC were 42.22%, 51.11%, 53.78%, 59.11%, and 58.67%, respectively. In cases of mild chronic gastritis, the consistency of test results between H&E, Methylene Blue, W-S, and QDs-IHC with IHC were Kappa=0.196, P=0.033, Kappa=0.706, P<0.001, Kappa=0.717, P<0.001, and Kappa=0.968, P<0.001, respectively. Similarly, in cases of moderate chronic gastritis, Kappa values between H&E, Methylene Blue, W-S, and QDs-IHC with IHC were 0.356, P<0.001, 0.655, P<0.001, 0.741, P<0.001, and 0.946, P<0.001, respectively. For cases of severe chronic gastritis, the Kappa values between the staining methods and IHC were 0.271, P=0.037, 0.421, P=0.002, 0.621, P<0.001, and 1, P< 0.001, respectively.ConclusionThe study showed that the positivity rate of IHC was significantly higher than that of H&E, Methylene Blue, and W-S in detecting HP infection in chronic gastritis cases. In terms of consistency with IHC, QDs-IHC was the most reliable staining method across all severity grades, while the agreement between H&E and IHC was poor, and that between Methylene Blue and W-S with IHC was average. Pathology departments may choose the most appropriate staining method based on their specific needs, considering the staining time, contrast, and cost of each method

Role of extrathyroidal TSHR expression in adipocyte differentiation and its association with obesity

<p>Abstract</p> <p>Background</p> <p>Obesity is known to be associated with higher risks of cardiovascular disease, metabolic syndrome, and diabetes mellitus. Thyroid-stimulating hormone (TSHR) is the receptor for thyroid-stimulating hormone (TSH, or thyrotropin), the key regulator of thyroid functions. The expression of TSHR, once considered to be limited to thyrocytes, has been so far detected in many extrathyroidal tissues including liver and fat. Previous studies have shown that TSHR expression is upregulated when preadipocytes differentiate into mature adipocytes, suggestive of a possible role of TSHR in adipogenesis. However, it remains unclear whether TSHR expression in adipocytes is implicated in the pathogenesis of obesity.</p> <p>Methods</p> <p>In the present study, TSHR expression in adipose tissues from both mice and human was analyzed, and its association with obesity was evaluated.</p> <p>Results</p> <p>We here showed that TSHR expression was increased at both mRNA and protein levels when 3T3-L1 preadipocytes were induced to differentiate. Knockdown of TSHR blocked the adipocyte differentiation of 3T3-L1 preadipocytes as evaluated by Oil-red-O staining for lipid accumulation and by RT-PCR analyses of PPAR-γ and ALBP mRNA expression. We generated obesity mice (C57/BL6) by high-fat diet feeding and found that the TSHR protein expression in visceral adipose tissues from obesity mice was significantly higher in comparison with the non-obesity control mice (<it>P </it>< 0.05). Finally, the TSHR expression in adipose tissues was determined in 120 patients. The results showed that TSHR expression in subcutaneous adipose tissue is correlated with BMI (body mass index).</p> <p>Conclusion</p> <p>Taken together, these results suggested that TSHR is an important regulator of adipocyte differentiation. Dysregulated expression of TSHR in adipose tissues is associated with obesity, which may involve a mechanism of excess adipogenesis.</p

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

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

Over-expression of a gamma-tocopherol methyltransferase gene in vitamin E pathway confers PEG-simulated drought tolerance in alfalfa

α-Tocopherol is one of the most important vitamin E components present in plant. α-Tocopherol is a potent antioxidant, which can deactivate photoproduced reactive oxygen species (ROS) and prevent lipids from oxidation when plants suffer drought stress. γ-Tocopherol methyltransferase (γ-TMT) catalyzes the formation of α-tocopherol in the tocopherol biosynthetic pathway. Our previous studies showed that over-expression of γ-TMT gene can increase the accumulation of α-tocopherol in alfalfa (Medicago sativa). However, whether these transgenic plants confer increased drought tolerance and the underlying mechanism are still unknown.This work was financially supported by Earmarked Fund for China Agriculture Research System (CARS-34), the National Natural Science Foundation of China (31872410), National Crop Germplasm Resources Center (NICGR-78), and the Agricultural Science and Technology Innovation Program (ASTIPIAS10)

The complete chloroplast genome and phylogenetic analysis of Manglietia ventii (Magnoliaceae)

Manglietia ventii is a highly ornamental timber tree species that is listed as endangered on the IUCN Red List. This study was conducted on the complete chloroplast genome of M. ventii. The length of the chloroplast genome is 159,950 bp with GC content as 39.3%. One hundred and thirty-one functional genes were identified in the genome, which included 86 protein-coding genes (PCGs), 37 tRNA genes, and eight rRNA genes. The phylogenetic analysis indicated that M. ventii is most closely related to M. megaphylla and M. aromatica, and the study provides new insights into the evolution of the Magnoliaceae

Transcriptomic and Metabolomic Analyses Providing Insights into the Coloring Mechanism of Docynia delavayi

The metabolome and transcriptome profiles of three different variations of mature Docynia delavayi fruit were synthesized to reveal their fruit color formation mechanism. A total of 787 secondary metabolites containing 149 flavonoid metabolites, most of which were flavonoids and flavonols, were identified in the three variations using ultra performance liquid chromatography- tandem mass spectrometry (UPLC-MS/MS), and we found that the secondary metabolites cyanidin-3-O-galactoside and cyanidin-3-O-glucoside were the major coloring substances in D. delavayi. This was associated with the significant upregulation of the structural genes F3H and F3&prime;H in the anthocyanin synthesis pathway and the control genes WRKY, MYB, bZIP, bHLH, and NAC in RP. F3&prime;H expression may play a significant role in the selection of components for anthocyanin synthesis. Our results contribute to breeding and nutritional research in D. delavayi and provide insight into metabolite studies of the anthocyanin biosynthetic pathway