With economic growth, the demand for power systems is increasingly large. Short-term load forecasting (STLF) becomes an indispensable factor to enhance the application of a smart grid (SG). Other than forecasting aggregated residential loads in a large scale, it is still an urgent problem to improve the accuracy of power load forecasting for individual energy users due to high volatility and uncertainty. However, as an important variable that affects the power consumption pattern, the influence of weather factors on residential load prediction is rarely studied. In this paper, we review the related research of power load forecasting and introduce a short-term residential load forecasting model based on a long short-term memory (LSTM) recurrent neural network with weather features as an input.
With the increasing demand for advanced steel is increasing year by year, and the internal cleanness content of steel inclusions becomesis an important evaluation indicator for the evaluation of material material quality. Sub-macroscopicInclusions defects are randomly distributed inside the steel materials, which has a great impact on the performance and quality safety of the steel. In especial, sub-macroscopic inclusions with sizes ranging from 50μm to 400μm have seriously affected material stability and fatigue life because they are not covered by existing testing standards. In addition,Besides, the existing current detection methods for inclusions in steel generally have problems such as low efficiency and complexity process. In this paper, we propose a non-destructive inclusion testing and classification framework basing on ultrasonic testing experiments, signal feature extraction and machine learning methods. Under the optimal experimental detection conditions we found through experiments, a large-scale sub-macroscopic inclusion signal data set is established to realize the classification of defects. Moreover, Empirical Mode Decomposition (EMD) and other feature extraction algorithms are applied to further boost the model performance. We propose a Catboost-based stacking fused model named Stacked-CBT, which obtains state-of-the-art experimental result with accuracy rate of 86.65% and demonstrates that the proposed framework is feasible to classify the sub-macroscopic inclusion signals. To the best of our knowledge, there is no previous study in this field has acquire such large amount of experimental sub-macroscopic signal data while taking into consideration classification-specific designs.
Background: Endometriosis(EM) is a major cause of infertility, but the pathogenesis and mechanisms have not been fully elucidated. MiR-19b-3p is involved in many diseases, but its functional role in EM-associated infertility has not been investigated. In this study, we aimed to examine miR-19b-3p abundance and IGF1 concentration in cumulus cells (CCs) and follicular fluid in EM-associated infertility patients and to reveal the potential role of miR-19b-3p in KGN cells by identifying its target and elucidating the underlying mechanisms. Results: The results showed that compared to the control group (patients with tubal infertility), EM-associated infertility patients had a lower percentage of mature oocytes. Abundance of miR-19b-3p was increased in CCs in EM-associated infertility patients. IGF1 was a direct target of miR-19b-3p and was negatively regulated by miR-19b-3p in KGN cells. Overexpression of miR-19b-3p significantly inhibited viability and proliferation, promoted apoptosis, and arrested cell cycle at G0/G1 phase in KGN cells. The effects of miR-19b-3p could be reversed by co-transfection of IGF1 and the biological effects of miR-19b-3p in KGN cells were mediated by IGF1. In addition, miR-19b-3p targeted IGF1 to downregulate AKT phosphorylation and to participate in apoptotic pathway in KGN cells. Conclusions: This study demonstrates that miR-19b-3p abundance is increased in CCs and IGF1 concentration is decreased in follicular fluid in EM-associated infertility patients, and miR-19b-3p participates in the regulation of biological effects of KGN cells by targeting IGF1.
With the improvement of science and technology, the demand for advanced steel with excellent performance has gradually increased. Therefore, the evaluation of steel internal cleanness is an important indicator for the evaluation of material quality. Sub-macroscopic inclusions, which size from 50um to 400um and cannot be detected under the domestic and international bearing steel testing standard, are bound to affect the quality, stability and service life of bearing steel seriously. Hence, the researches of inclusion control technology has gradually attracted attention in the academia and industrial manufacture field. In this paper, we propose an end-to-end LFCN classification model based on LSTM unit and 1DFCN, and verify the effectiveness on the large-scale sub-macroscopic inclusion signal data set collected by ultrasonic experiments. To the best of our knowledge, this study is the first one in this field that has acquire such large amount of experimental sub-macroscopic signal data and solve the classification task by FCN. Especially, our framework can accurately detect the features of sub-macroscopic inclusions, which meets the urgent need of the metallurgical industry. The accuracy rate of proposed model is 88.97%, which is state-of-the-art experimental result among other strong time series classifiers.
Continuous-time long-term event prediction plays an important role in many application scenarios. Most existing works rely on autoregressive frameworks to predict event sequences, which suffer from error accumulation, thus compromising prediction quality. Inspired by the success of denoising diffusion probabilistic models, we propose a diffusion-based nonautoregressive temporal point process model for long-term event prediction in continuous time. Instead of generating one event at a time in an autoregressive way, our model predicts the future event sequence entirely as a whole. In order to perform diffusion processes on event sequences, we develop a bidirectional map between target event sequences and the Euclidean vector space. Furthermore, we design a novel denoising network to capture both sequential and contextual features for better sample quality. Extensive experiments are conducted to prove the superiority of our proposed model over state-of-the-art methods on long-term event prediction in continuous time. To the best of our knowledge, this is the first work to apply diffusion methods to long-term event prediction problems.
This study was designed to reveal the molecular differences between granulosa cells (GCs) from patients with endometriosis and normal controls. Methods: RNA sequencing was performed on GCs from patients with EM-related infertility (n = 3) and controls (n = 3). Differentially expressed long noncoding RNAs [differentially expressed lncRNAs (DELs), jlog2 FCj>4, false discovery rate (FDR) <0.05] and genes [differentially expressed genes (DEGs), jlog2 FCj>1.4, FDR <0.05] in patients with EM-related infertility and controls were screened. Protein-protein interaction (PPI) networks of the DEGs were constructed. Then, mRNA-miRNA-lncRNA pairs based on DEGs and DELs were constructed by comprehensive bioinformatics analyses. In addition, overlapping genes identified from both the PPI and the mRNA-miRNA-lncRNA pairs were selected. Finally, a competing endogenous RNA (ceRNA) network incorporating transcription factors (TFs) was constructed. Results: A total of 25,806 lncRNAs and 19,684 mRNAs were detected, and 7 DELs and 46 DEGs were identified. Five hub genes from the PPI network were also identified. A single overlapping gene, NR4A2, from both the PPI network and mRNA-miRNA-lncRNA pairs was identified. Finally, a ceRNA network incorporating TFs, including one mRNA (NR4A2), one miRNA (hsa-miR-217), three lncRNAs (XIST, MCM3AP-AS1, and C17orf51), and five TFs (SRF, POLR2A, NRF1, MNT, and TCF7L2), was successfully constructed. Conclusions: The proposed ceRNA network and the prediction of TFs in GCs from EM-related infertility revealed differences in GCs from patients with EM. Importantly, the novel TFs, lncRNAs, miRNAs, and mRNAs involved in the ceRNA network might provide new insights into the underlying molecular mechanisms of EM-related infertility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.