A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE)

A Comparative Study of AutoML Approaches for Short-Term Electric Load Forecasting

Deep learning is increasingly used in short-term load forecasting. However, deep learning models are difficult to train, and adjusting training hyper-parameters takes time and effort. Automated machine learning (AutoML) can reduce human participation in machine learning process and improve the efficiency of modelling while ensuring the accuracy of prediction. In this paper, we compare the usage of three AutoML approaches in short-term load forecasting. The experiments on a real-world dataset show that the predictive performance of AutoGluon outperforms that of AutoPytorch and Auto-Keras, according to three performance metrics: MAE, RMSE and MAPE. AutoPytorch and Auto-Keras have similar performance and are not easy to compare

Oxygen Defect-Mediated Magnetism in Fe-C Codoped TiO2

The magnetic properties of the C doped and C-Fe codoped TiO2 films fabricated by sol-gel and spin coating have been investigated combining experiments and first-principles calculations. All the samples exhibit the anatase crystal phase and the room temperature ferromagnetism. The values of the saturation magnetizations are in the order of Fe-C codoped TiO2 > Fe-C codoped TiO2 (annealed in O2) > C doped TiO2 > C doped TiO2 (annealed in O2). The calculated net moment values are in the order of Fe-C codoped TiO2 > C doped TiO2 with oxygen vacancies existing, in accord with the experimental results. The hybridization of Fe 3d, C 2p, and O 2p (nearest to the Fe defect) led to the spin split of Fe 3d, C 2p, and O 2p which contributed to the ferromagnetism

Probe-based confocal laser endomicroscopy for evaluating high-altitude pulmonary edema in canines

Background: Real-time assessment of high-altitude pulmonary edema (HAPE) remains a challenge. Probe-based confocal laser microscopy (pCLE) allows a real-time in vivo visualization of the alveoli. This study aimed to develop a new non-invasive method for analyzing microscopic images in a canine model of HAPE using pCLE. Materials and methods: This was a prospective, controlled animal study in adult male beagle dogs randomized to control and HAPE groups. The HAPE group was exposed to a high altitude of 6000 m for 48 h. The blood gas levels, lung morphological changes, infectious factors, and lung wet-to-dry ratio were analyzed in different groups. The pCLE images were described based on the volume air index (VAI), which applies an integral over specific signal intensities. Results: The lung wet-to-dry weight ratio and injury scores in the HAPE group were significantly increased compared with those of the control group. The levels of infectious factors interleukin-1 beta, tumor necrosis factor-alpha, and interleukin-6 were significantly increased in the HAPE group compared with those in the control group. VAI was significantly decreased in the HAPE group. Conclusion: pCLE is a potential adjudicative bronchoscopic imaging technique for assessing HAPE. VAI may be acquired from quantitative parameters in the analysis of images

Upregulated Expression of a Unique Gene by Hepatitis B x Antigen Promotes Hepatocellular Growth and Tumorigenesis

Hepatitis B x antigen (HBxAg) is a trans-activating protein that may be involved in hepatocarcinogenesis, although few natural effectors of HBxAg that participate in this process have been identified. To identify additional effectors, whole cell RNA isolated from HBxAg-positive and HBxAg-negative HepG2 cells were compared by polymerase chain reaction select cDNA subtraction, and one clone, upregulated gene, clone 11 (URG11), was chosen for further characterization. Elevated levels of URG11 mRNA and protein were observed in HBxAg-positive compared to HBxAg-negative HepG2 cells. Costaining was observed in infected liver (P < .01). URG11 stimulated cell growth in culture (P < .01), anchorage-independent growth in soft agar (P < .001), and accelerated tumor formation (P < .01), and yielded larger tumors (P < .02) in SCID mice injected subcutaneously with HepG2 cells. These data suggest that URG11 is a natural effector of HBxAg that may promote the development of hepatocellular carcinoma