This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.
Many autoimmune diseases exhibit an alternating pattern of relapses and remissions in which the apparent self-tolerance phase is interrupted by periodic autoimmune episodes. In this paper, we introduce a class of terminally differentiated effector T cells to an existing model of autoimmune disease and investigate the stability and Hopf branching phenomenon in a model of multiple sclerosis with a saturable functional response. First, we explore the local asymptotic stability of the equilibrium point and propose conditions for the existence of Hopf branching. Finally, with the help of canonical type theory and the central manifold theorem, we analyze the direction of Hopf branching and the stability of branching periodic solutions.
Multiple autoimmune diseases often exhibit a cyclic pattern of relapse and remission, with significant periods of loss of self-tolerance being interrupted by recurrent autoimmune events. In this article, we explore a specific type of terminally differentiated regulatory T cell HLA−DR+TRegcells, and their application in existing autoimmune disease models. We also conduct an in-depth study on a multiple sclerosis model. This model incorporates a Holling-II type functional response mechanism. The focus of the study is to analyze whether the equilibrium points of the system have local asymptotic stability and determine the conditions for the existence of Hopf bifurcation. Furthermore, the direction of Hopf bifurcations and the stability of its periodic solutions can be analyzed through normal form theory and center manifold theorem.
目的:探讨生活满意度、日常生活活动能力和社交活动在慢性病共病(multiple chronic conditions,MCCs)与抑郁关联中的多重平行中介作用。方法:数据来源于2020年中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)数据库,选取45~99岁调查对象进行横断面研究。结果:共纳入17318例研究对象,中老年人MCCs患病率为58.10%,抑郁患病率为37.56%,88.88%的中老年人对生活满意,日常生活活动能力(activities of daily living,ADL)评分为(6.53±1.50)分,社交活动评分的中位数为0(0,2.00)分。多重平行中介效应分析显示,在全部研究对象和各亚组中MCCs与抑郁症状关联的直接效应均有统计学意义(P<0.05),生活满意度和ADL评分在该关联中的中介效应也均有统计学意义(P<0.05),而社交活动的中介效应仅在老年人中有统计学意义(P<0.05)。结论:MCCs在直接影响抑郁症状的同时,还可通过生活满意度和ADL间接影响抑郁症状。因此,应关注中老年慢性病群体,预防与延缓慢性病进展的同时要增强日常生活自理能力,提高生活满意度,鼓励和帮助慢性病较多的老年人积极参与社交活动,以减少抑郁症状的发生。
This study reviewed a combination of health care programs in the metropolitan area of Syracuse, New York. They were designed to improve care, however a major purpose was to support efficiency. The study described a number of individual programs that were developed in order to improve the quality and the efficiency of care. These programs were implemented by a combination of local providers and payors. They included the development of outpatient services such as ambulatory surgery, as well as preventive care, case management, telemedicine, and mental health. The impact of these programs was a combination of these services, rather than individual efforts. The impact of these efforts was the product of a range of individual services, especially care management. Additional efforts should make it possible to extend these efforts among providers and payors in the Syracuse area. This approach should make it possible to extend the impact of health care efficiency further.
Dear Editor,Accurately tracking multiple individuals in insect groups provides crucial information for insect collective behavior,population activity and interactions with the environment(Price et al.,201l).
Chengshi WuJin GeBin HanHengjing LanXian ZhouZhuxi GeWeichan CuiXiaofeng LiuXianhui Wang
As an extension of linear regression in functional data analysis, functional linear regression has been studied by many researchers and applied in various fields. However, in many cases, data is collected sequentially over time, for example the financial series, so it is necessary to consider the autocorrelated structure of errors in functional regression background. To this end, this paper considers a multiple functional linear model with autoregressive errors. Based on the functional principal component analysis, we apply the least square procedure to estimate the functional coefficients and autoregression coefficients. Under some regular conditions, we establish the asymptotic properties of the proposed estimators. A simulation study is conducted to investigate the finite sample performance of our estimators. A real example on China's weather data is applied to illustrate the validity of our model.
Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.
