Clarke Binderup (corkflood1)

This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects 1) the quadratic term and 2) the single-summation term. When the variation of the discrete-time delay is further considered, the constant matrix involved in the quadratic term is extended to be a delay-dependent one. All these innovations make a contribution to a quadratic function with respect to the delay from the forward differences of L-K functionals. Consequently, tractable stability criteria are derived that are shown to be more relaxed than existing results via numerical examples.This article presents a finite-time heterogeneous cyclic pursuit scheme that ensures consensus among agents modeled as integrators. It is shown that for the proposed consensus scheme, even when the gains are nonidentical, consensus results within a finite time, provided all the gains are positive or even if one gain is negative, subject to a lower bound. An algorithm is presented to compute the consensus value and consensus time for a given set of positive gains and initial states of the agents. The set of values, where consensus can occur, by varying the gains, has been derived and a second algorithm aids in determining the positive gains that enable consensus at any point in the aforementioned set, at a given finite time. As an application, the finite-time consensus in line-of-sight rates, over a cycle digraph, for a group of interceptors is shown to be effective in ensuring co-operative collision-free interception of a target, for both constant speed as well as realistic time-varying-speed models of the interceptors. Simulations validate the theoretical results.Determinants of user mental health are diverse, interrelated, and often multifaceted. This study explores how internet use, perceived care quality, patient education, and patient centered communication influence mental health, using structural equation modeling. Findings suggest that increased internet use even for health purposes negatively impacts mental health (= -0087; = -0065; P less then 0001). On the other hand, education level, patient centered-communication (PC-Com) and perception of care quality impact mental health positively (= 0082; = 0146; = 0077; P less then 0001; respectively). Moreover, we also explored the changes across various demographics. The influence of patient education on PC-Com was only significant for Hispanic respondents (= -0160; P less then 0001). Internet use for health purposes influenced P C-Com negatively for White American respondents (= -0047; P = 0015). The study reinstated that the internet use, patient centered communication, patient education, and perceived care quality might influence mental health. The society will increasingly seek health information from online sources, so our study provides recommendations to make online health information sources more user friendly and trustworthy, ultimately to minimize negative impact on mental health.Twin support vector machine (TWSVM), which constructs two nonparallel classifying hyperplanes, is widely applied to various fields. However, TWSVM solves two quadratic programming problems (QPPs) separately such that the final classifiers lack consistency and enough prediction accuracy. Moreover, by reason of only considering the 1-norm penalty for slack variables, TWSVM is not well defined in the geometrical view. In this article, we propose a novel elastic net nonparallel hyperplane support vector machine (ENNHSVM), which adopts elastic net penalty for slack variables and constructs two nonparallel separating hyperplanes simultaneously. We further discuss the properties of ENNHSVM theoretically and derive the violation tolerance upper bound to better demonstrate the relative violations of training samples in the same class. In particular, we design a safe screening