Many-objective bi-level energy scheduling method for integrated energy stations based on power allocation strategy.

The integrated energy station of new energy vehicle hydrogenation/charging/power exchange is proposed, which also includes hydrogen production, hydrogen storage, electricity sales to users and the grid (WPIES). To address the efficiency of renewable energy use, this paper proposes a future value competition strategy for wind and photovoltaic (PV) allocation based on goal optimization (FVCS). In order to better realize the distribution of wind power/PV in the integrated energy station and improve the energy utilization efficiency of the integrated energy station, a two-layer optimization model of FVCS-WPIES is proposed, in which the upper layer model aims to maximize the expected income. The goals of the lower-level model are to maximize total profit, minimize battery losses, and minimize pollutant emissions. The model also considers the hydrogen power constraint and the upper-level model penalty. The comparison results show that the Pareto solution set is superior to the traditional model.

Multi-view and multi-scale behavior recognition algorithm based on attention mechanism.

Human behavior recognition plays a crucial role in the field of smart education. It offers a nuanced understanding of teaching and learning dynamics by revealing the behaviors of both teachers and students. In this study, to address the exigencies of teaching behavior analysis in smart education, we first constructed a teaching behavior analysis dataset called EuClass. EuClass contains 13 types of teacher/student behavior categories and provides multi-view, multi-scale video data for the research and practical applications of teacher/student behavior recognition. We also provide a teaching behavior analysis network containing an attention-based network and an intra-class differential representation learning module. The attention mechanism uses a two-level attention module encompassing spatial and channel dimensions. The intra-class differential representation learning module utilized a unified loss function to reduce the distance between features. Experiments conducted on the EuClass dataset and a widely used action/gesture recognition dataset, IsoGD, demonstrate the effectiveness of our method in comparison to current state-of-the-art methods, with the recognition accuracy increased by 1-2% on average.

Characteristics of Shisa Family Genes in Zebrafish.

Shisa represents a type of single-transmembrane adaptor protein containing an N-terminal cysteine-rich domain and a proline-rich C-terminal region. Nine shisa subfamily genes have been proposed in most vertebrates; however, some might be species-specific. The number of shisa genes present in zebrafish remains unclear. This study aimed to investigate the evolutionary relationships among shisa family genes in zebrafish (TU strain) using phylogenetic and syntenic analyses. The function of shisa-2 was preliminarily examined via CRISPR/Cas13d-mediated knockdown. Following identification in zebrafish, 10 shisa family genes, namely shisa-1, 2, 3, 4, 5, 6, 7, 8, 9a, and 9b, were classified into three main clades and six subclades. Their encoding proteins contained a cysteine-rich N-terminal domain and a proline-rich C-terminal region containing different motifs. A specific syntenic block containing atp8a2 and shisa-2 was observed to be conserved across all species. Furthermore, all these genes were expressed during embryogenesis. Shisa-2 was expressed in the presomitic mesoderm, somites, and so on. Shisa-2 was identified as a regulator of the expression of the somite formation marker mesp-ab. Overall, our study provides new insights into the evolution of shisa family genes and the control of shisa-2 over the convergent extension cells of somitic precursors in zebrafish.

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

BACKGROUND: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT. METHODS: We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs. RESULTS: In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8. CONCLUSION: The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.

Biprism distortion modeling and calibration for a single-lens stereovision system.

Biprism-based single-lens stereovision systems have several advantages over conventional two-camera systems, which has made them popular in recent years. However, employing a biprism in front of the camera will induce additional unique distortion in the image, which cannot be adequately represented by the existing distortion models, making it is difficult to calibrate or correct. In this work, a parametric biprism distortion model aiming to correct this biprism distortion is developed and evaluated. The estimation of the model starts with a general bivariate polynomial and is then refined based on distortion properties. These properties are determined by the geometrical analysis of the distortion formation in the system. This refined model is evaluated in two ways: distortion map data fitting and virtual camera calibration. Both simulation and actual experiments are carried out to show the feasibility of the proposed distortion model and the improvements in calibration accuracy and depth recovery compared with the existing distortion models.