The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.

Deregulation on the delivery side of the power market has continuously been moving forward worldwide, which make bidirectional flow and interactions between customers and grids needs to be more refined and in-depth. Large-scale coverage of the advanced metering infrastructure (AMI) brings in skyrocketing of an immense amount of fine-grained, real-time consumption data and causes communication traffic congestion between meters and a cloud computing center. To tackle these two challenges, this paper proposes a modified IP-based non-intrusive load monitoring approach using appliance characteristics extracted by quadratic symbolic aggregate approximation (2-SAX). A 2-SAX algorithm is implemented to carry out dimensionality reduction on equipment load data and extracted the state's transition behavior characteristics and operation probability characteristics of each device. The extracted features can use to modify the disaggregation results of integer programming for overcoming the shortcomings of the previous IP approach. The developed method is tested with AMPds dataset. The results of experiments illustrate the 2-SAX consequences in 38.82%, 52.46%, and 13.41% reduction in MAE, MAPE, and RMSE on the heat pump and achieves similar performance on the other appliances, compared with normal SAX. Meanwhile, the proposed method MIP-AC2S delivers significant accuracy advantage and competitive performance over IP, ALIP, and MIP disaggregation method.INDEX TERMS Non-intrusive load monitoring, appliance characteristics, quadratic symbolic aggregate approximation (2-SAX), load data mining.

Background The evidence of transcatheter arterial chemoembolization (TACE) plus tyrosine kinase inhibitor and immune checkpoint inhibitor in unresectable hepatocellular carcinoma (HCC) was limited. This study aimed to evaluate the role of TACE plus apatinib (TACE + A) and TACE combined with apatinib plus camrelizumab (TACE + AC) in patients with unresectable HCC. Methods This study retrospectively reviewed patients with unresectable HCC who received TACE + A or TACE + AC in 20 centers of China from January 1, 2019 to June 31, 2021. Propensity score matching (PSM) at 1:1 was performed to reduce bias. Treatment-related adverse events (TRAEs), overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR) were collected. Results A total of 960 eligible patients with HCC were included in the final analysis. After PSM, there were 449 patients in each group, and the baseline characteristics were balanced between two groups. At data cutoff, the median follow-up time was 16.3 (range: 11.9–21.4) months. After PSM, the TACE + AC group showed longer median OS (24.5 vs 18.0 months, p < 0.001) and PFS (10.8 vs 7.7 months, p < 0.001) than the TACE + A group; the ORR (49.9% vs 42.5%, p = 0.002) and DCR (88.4% vs 84.0%, p = 0.003) of the TACE + AC group were also higher than those in the TACE + A group. Fever, pain, hypertension and hand-foot syndrome were the more common TRAEs in two groups. Conclusions Both TACE plus apatinib and TACE combined with apatinib plus camrelizumab were feasible in patients with unresectable HCC, with manageable safety profiles. Moreover, TACE combined with apatinib plus camrelizumab showed additional benefit.

The genotypes involving the MTHFR C677T, MS A2756G, and CBS T833C polymorphisms, including combinations of these genotypes, were the most important factors associated with blood FA and Hcy levels of the investigated SNPs in the OCM genes.