COVID-19 has continuously influenced energy security and caused an enormous impact on human life and social activities due to the stay-at-home orders. After the Omicron wave, the economy and the energy system are gradually recovering, but uncertainty remains due to the virus mutations that could arise. Accurate forecasting of the energy consumed by the residential and commercial sectors is challenging for efficient emergency management and policy-making. Affected by geographical location and long-term evolution, the time series of the energy consumed by the residential and commercial sectors has prominent temporal and spatial characteristics. A hybrid model (CNN-BiLSTM) based on a convolution neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to extract the time series features, where the spatial features of the time series are captured by the CNN layer, and the temporal features are extracted by the BiLSTM layer. Then, the recursive multi-step ahead forecasting strategy is designed for multi-step ahead forecasting, and the grid search is employed to tune the model hyperparameters. Four cases of 24-step ahead forecasting of the energy consumed by the residential and commercial sectors in the United States are given to evaluate the performance of the proposed model, in comparison with 4 deep learning models and 6 popular machine learning models based on 12 evaluation metrics. Results show that CNN-BiLSTM outperforms all other models in four cases, with MAPEs ranging from 4.0034% to 5.4774%, improved from 0.1252% to 49.1410%, compared with other models, which is also about 5 times lower than that of the CNN and 5.9559% lower than the BiLSTM on average. It is evident that the proposed CNN-BiLSTM has improved the prediction accuracy of the CNN and BiLSTM and has great potential in forecasting the energy consumed by the residential and commercial sectors.
Objective The prenatal diagnosis of chromosomal mosaicism is fraught with uncertainty. Karyotyping, chromosomal microarray analysis (CMA), and fluorescence in situ hybridization (FISH) are three commonly used techniques. In this study, we evaluated these techniques for the prenatal diagnosis of chromosomal mosaicism and its clinical outcome. Study Design A retrospective review of mosaicism was conducted in 18,369 pregnant women from January 2016 to November 2021. The subjects underwent amniocentesis to obtain amniotic fluid for G-band karyotyping with or without CMA/FISH. Cases diagnosed with chromosomal mosaicism were selected for further analysis. Results In total, 101 cases of chromosomal mosaicism were detected in 100 pregnant women (0.54%, 100/18,369). Four were lost during follow-up, 61 opted to terminate their pregnancy, and 35 gave birth to a healthy singleton or twins. Among these 35 cases, postnatal cytogenetic testing was performed on eight and two exhibited mosaicism; however, nothing abnormal was observed in the postnatal phenotype follow-up. Karyotyping identified 96 incidents of chromosomal mosaicism including 13 with level II mosaicism and 83 with level III mosaicism, FISH identified 37 cases of mosaicism, and CMA identified 17. The most common form of chromosomal mosaicism involved monosomy X, of which the mosaic fraction in cultured karyotyping appeared higher or comparable to uncultured FISH/CMA (p < 0.05). Discordant mosaic results were observed in 34 of 101 cases (33.7%), most of which resulted from the detection limit of different techniques and/or the dominant growth of a certain cell line. Conclusion Based on the postnatal follow-up results from the babies born, we obtained a more hopeful result for the prognosis of chromosomal mosaicism. Although karyotyping was the most sensitive method for detecting chromosomal mosaicism, artifacts and bias resulting from culture should be considered, particularly for sex chromosomal abnormalities involving X monosomy, in which the combination with uncultured FISH was necessary. Key Points
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