An effective multi-model based nonlinear control for USC power plant.

Energy efficiency optimization for the ultra supercritical (USC) boiler-turbine unit is a major concern in the field of power generation. In order to deal with the nonlinearity and slow dynamic response problems, a new nonlinear control method is proposed which integrates internal model control (IMC) and generalized predictive control (GPC) into a unified framework. Specifically, through a long short-term memory (LSTM) neural network based IMC, the system achieves rapid convergence to the vicinity of the desired setpoint, significantly enhancing the response speed. Then, by a composite weighted human learning optimization network based nonlinear generalized predictive control (CWHLO-GPC), high-accuracy tracking performance is achieved. Finally, an example on a 1000MW USC power plant demonstrates the proposed method can achieve fast and stable dynamic response under large load variation.

Effects of Group Prenatal Health Care Combined with Happiness Training on Delivery Mode and Maternal Role Adaptation in Elderly Primiparous Women: A Study for Improvements in Patients Health Behavior.

Objective: This study aimed to explore the effects of group prenatal health care combined with happiness training on delivery mode and maternal role adaptation in elderly primiparous women. Methods: A total of 110 elderly primiparous women who were expected to deliver in hospital from January 2020 to December 2021 were selected and assigned to two equal size groups: Group A and Group B. Results:After the nursing intervention, the natural delivery rate of Group A was 85.45%, significantly higher than that of Group B at 52.73% (P<0.05). The initial feeding time and first lactation time of Group A were significantly shorter than those of Group B, and the 48-hour lactation volume was higher than that of Group B (P<0.05). The RAQ scores of Group A, including maternal role happiness score, the baby's impact on the mother's life score, baby's daily living care ability score, and maternal role belief score, were all higher than those of Group B (P<0.05). The GWB score of Group A was significantly higher than that of Group B, while the EPDS score was significantly lower than that of Group B (P<0.05). Conclusion: Group prenatal health care combined with happiness training can improve the delivery mode of elderly primiparous women, help them adapt better to their maternal role, and enhance their subjective sense of well-being.

An optimized nonlinear generalized predictive control for steam temperature in an ultra supercritical unit.

The optimize control of the ultra supercritical (USC) unit has been a major concern in power industry. The intermediate point temperature process is a multi-variable system with strong nonlinearity, large scale and great delay, which greatly affects the safety and economy of the USC unit. Generally, it is difficult to realize effective control by using conventional methods. This paper presents a nonlinear generalized predictive control based on a composite weighted human learning optimization network (CWHLO-GPC) to improve the control performance of intermediate point temperature. Based on the characteristics of the onsite measurement data, the heuristic information is incorporated into the CWHLO network, and expressed by different local linear models. Then, global controller is elaborately constituted based on a scheduling program inferred from the network. Compared with classical generalized predictive control (GPC), the non-convex problem is effectively solved by introducing CWHLO models into the convex quadratic program (QP) routine of local linear GPC. Finally, detailed analysis on set point tracking and interference resisting via simulation is addressed to illustrate the efficiency of the proposed strategy.

Advancements and application prospects of three-dimensional models for primary liver cancer: a comprehensive review.

Primary liver cancer (PLC) is one of the most commonly diagnosed cancers worldwide and a leading cause of cancer-related deaths. However, traditional liver cancer models fail to replicate tumor heterogeneity and the tumor microenvironment, limiting the study and personalized treatment of liver cancer. To overcome these limitations, scientists have introduced three-dimensional (3D) culture models as an emerging research tool. These 3D models, utilizing biofabrication technologies such as 3D bioprinting and microfluidics, enable more accurate simulation of the in vivo tumor microenvironment, replicating cell morphology, tissue stiffness, and cell-cell interactions. Compared to traditional two-dimensional (2D) models, 3D culture models better mimic tumor heterogeneity, revealing differential sensitivity of tumor cell subpopulations to targeted therapies or immunotherapies. Additionally, these models can be used to assess the efficacy of potential treatments, providing guidance for personalized therapy. 3D liver cancer models hold significant value in tumor biology, understanding the mechanisms of disease progression, and drug screening. Researchers can gain deeper insights into the impact of the tumor microenvironment on tumor cells and their interactions with the surrounding milieu. Furthermore, these models allow for the evaluation of treatment responses, offering more accurate guidance for clinical interventions. In summary, 3D models provide a realistic and reliable tool for advancing PLC research. By simulating tumor heterogeneity and the microenvironment, these models contribute to a better understanding of the disease mechanisms and offer new strategies for personalized treatment. Therefore, 3D models hold promising prospects for future PLC research.

Immunosuppressive landscape in hepatocellular carcinoma revealed by single-cell sequencing.

Background/Aims: Hepatocellular carcinoma (HCC), accounting for 75-85% of primary liver cancer cases, is the third leading cause of cancer-related death worldwide. The purpose of this research was to examine the tumor immune microenvironment (TIME) in HCC. Methods: We investigated the HCC TIME by integrated analysis of single-cell and bulk-tissue sequencing data to reveal the landscape of major immune cell types. Results: Regulatory T(Treg) cells were found to be specifically distributed in the TIME of HCC. Several immune checkpoints, including TNFRSF4, TIGIT and CTLA4, were found to be uniquely overexpressed in Treg cells, and the glycolysis/gluconeogenesis pathway was enriched in Treg cells. We also discovered the presence of two NK-cell subsets with different cytotoxic capacities, one in an activated state with antitumor effects and another with an exhausted status. In addition, memory B cells in HCC were found to exist in a unique state, with high proliferation, low differentiation, and low activity, which was induced by overexpression of PRAP1 and activation of the MIF-CD74 axis. Conclusions: We revealed the TIME landscape in HCC, highlighting the heterogeneity of major immune cell types and their potential mechanisms in the formation of an immunosuppressive environment. Hence, blocking the formation of the TIME could be a useful therapeutic strategy for HCC.