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.

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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.