Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature

RPS-Net: Indoor scene point cloud completion using RBF-point sparse convolution

We introduce a novel approach to the completion of 3D scenes, which is a practically important task as captured point clouds of 3D scenes tend to be incomplete due to limited sensor range and occlusion. We address this problem by utilising sparse convolutions, commonly used for recognition tasks, to this content generation task, which can well capture the spatial relationships while ensuring high efficiency, as only samples near the surface need to be processed. Moreover, traditional sparse convolutions only consider grid occupancies, which cannot accurately locate surface points, with unavoidable quantisation errors. Observing that local surface patches have common patterns, we propose to sample a Radial Basis Function (RBF) field within each grid which is then compactly represented using a Point Encoder-Decoder (PED) network. This further provides a compact and effective representation for 3D completion, and the decoded latent feature includes important information of the local area of the point cloud for more accurate, sub-voxel level completion. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margi

Feasibility and Efficacy of S-Adenosyl-L-methionine in Patients with HBV-Related HCC with Different BCLC Stages

Aims. To understand the feasibility and efficacy of treatment with SAMe in patients with hepatitis B-related HCC with different Barcelona Clinic Liver Cancer (BCLC) stages. Methods. We retrospectively enrolled 697 patients with BCLC early-stage (stages 0-A) and advanced-stage (stages B-C) HCC who underwent SAMe therapy (354 cases) or no SAMe therapy (343 cases). The baseline characteristics, postoperative recoveries, and 24-month overall survival rates of the patients in the 2 groups were compared. Cox regression model analysis was performed to confirm the independent variables influencing the survival rate. Results. For patients in the early-stage (BCLC stages A1–A4) group, little benefit of SAMe therapy was observed. For advanced-stage (BCLC B-C) patients, SAMe therapy reduced alanine aminotransferase (ALT) and aspartate transaminase (AST) levels and effectively delayed the recurrence time and enhanced the 24-month survival rate. Cox regression model analysis in the advanced-stage group revealed that treatment with SAMe, preoperative viral load, and Child-Pugh grade were independent variables influencing survival time. Conclusion. SAMe therapy exhibited protective and therapeutic efficacy for BCLC advanced-stage HBV-related HCC patients. And the efficacy of SAMe therapy should be further explored in randomized prospective clinical trials