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In this paper, a system security problem is studied based on a two-person non-cooperative dynamic game. Security of the system is regarded in the context of observability. Namely a system is secure if it is not observable. Both the defender and attacker are assumed to have means to modify the dimension of unobservable subspace, which is set as the value function. The strategies and algorithms to minimize or maximize the value function are given respectively. Finally, examples are provided to demonstrate the effectiveness of the proposed algorithms and show some interesting game results, including accumulation mode, loop mode and locked mode., Part of ISBN 9781713872344QC 20240222

In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. Controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system., QC 20240222Part of ISBN 9798350301243

Based on the intertemporal substitution effect, the high inflation expectations can stimulate agents to consume now rather than in the future. However, under the background of global climate policy changes, how inflation expectations affect energy consumption? Using the bootstrap Granger full-sample causality test and sub-sample rolling window tests, this study examines the intertemporal substitution effect of energy consumption in the U.S. The results based on the full-sample data indicate no causality between inflation expectations and energy consumption, which suggests that the intertemporal substitution effect of energy consumption does not exist. Nevertheless, the rolling window method which estimates a time-varying causality identifies a short-lived positive effects of inflation expectations on energy consumption in a distinct sub-period before the global Paris agreement, but disappears since then. Therefore, the intertemporal substitution effect regarding energy consumption does not exist under the background of pressing carbon targets. The effects of energy consumption on inflation expectations can be positive or negative, which tells a cautionary tale about climate policies aiming at engineering lower carbon emissions.

Zhixin Liu,1,2,* Huanyu Zhang,1 Nan Wang,1,* Yajie Feng,1,* Junping Liu,1 Lin Wu,1 Zhaoyue Liu,1 Xinru Liu,1 Libo Liang,1 Jie Liu,3 Qunhong Wu,1 Chaojie Liu4 1Department of Social Medicine, School of Health Management, Harbin Medical University, Harbin, People’s Republic of China; 2Department of Health Policy and Management, School of Public Health, Peking University, Beijing, People’s Republic of China; 3Intensive Care Unit, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 4Department of Public Health, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia*These authors contributed equally to this workCorrespondence: Libo Liang; Jie Liu, Email llbhit@163.com; liujie21814@126.comBackground: This study aimed to test the mediating role of anxiety and insomnia in the association between fear of infection and fatigue.Methods: A cross-sectional questionnaire survey was conducted on the nurses deployed to Heihe. A serial multiple mediation model was established to determine the role of anxiety and insomnia in the association between fear of infection and fatigue.Findings: Over half (53.0%) of the study participants reported experiencing fear of infection despite stringent personal protection measures. The scores of anxiety (11.87± 5.19), insomnia (16.33± 5.95), and fatigue (45.94± 12.93) were moderately correlated, with a Pearson correlation coefficient ranging from 0.501 to 0.579. Anxiety, either alone or in combination with insomnia, mediated the association between fear of infection and fatigue.Conclusion: The findings suggest that anxiety and insomnia play a mediating role in the relationship between fear of infection and fatigue. These results emphasize the importance of implementing targeted mental health interventions and work arrangements to address the well-being of healthcare professionals.Keywords: fear of infection, anxiety, insomnia, fatigue

Mixed linear regression (MLR) is a powerful model for characterizing nonlinear relationships by utilizing a mixture of linear regression sub-models. The identification of MLR is a fundamental problem, where most of the existing results focus on offline algorithms, rely on independent and identically distributed (i.i.d) data assumptions, and provide local convergence results only. This paper investigates the online identification and data clustering problems for two basic classes of MLRs, by introducing two corresponding new online identification algorithms based on the expectation-maximization (EM) principle. It is shown that both algorithms will converge globally without resorting to the traditional i.i.d data assumptions. The main challenge in our investigation lies in the fact that the gradient of the maximum likelihood function does not have a unique zero, and a key step in our analysis is to establish the stability of the corresponding differential equation in order to apply the celebrated Ljung's ODE method. It is also shown that the within-cluster error and the probability that the new data is categorized into the correct cluster are asymptotically the same as those in the case of known parameters. Finally, numerical simulations are provided to verify the effectiveness of our online algorithms.

We generate and transmit 75-GHz-bandwidth OFDM signals over the air using three mutually frequency-locked lasers, achieving minimal frequency gap between the wireless W and D bands using optical-assisted approaches, resulting in 173.5 Gb/s detected capacity., Comment: 3 pages, 2 figures, conference submission

Turing's model has been widely used to explain how simple, uniform structures can give rise to complex, patterned structures during the development of organisms. However, it is very hard to establish rigorous theoretical results for the dynamic evolution behavior of Turing's model since it is described by nonlinear partial differential equations. We focus on controllability of Turing's model by linearization and spatial discretization. This linearized model is a networked system whose agents are second order linear systems and these agents interact with each other by Laplacian dynamics on a graph. A control signal can be added to agents of choice. Under mild conditions on the parameters of the linearized Turing's model, we prove the equivalence between controllability of the linearized Turing's model and controllability of a Laplace dynamic system with agents of first order dynamics. When the graph is a grid graph or a cylinder grid graph, we then give precisely the minimal number of control nodes and a corresponding control node set such that the Laplace dynamic systems on these graphs with agents of first order dynamics are controllable., Comment: 13 pages, 4 figures, submitted to automatica

In this paper, we consider the distributed filtering problem over sensor networks such that all sensors cooperatively track unknown time-varying parameters by using local information. A distributed forgetting factor least squares (FFLS) algorithm is proposed by minimizing a local cost function formulated as a linear combination of accumulative estimation error. Stability analysis of the algorithm is provided under a cooperative excitation condition which contains spatial union information to reflect the cooperative effect of all sensors. Furthermore, we generalize theoretical results to the case of Markovian switching directed graphs. The main difficulties of theoretical analysis lie in how to analyze properties of the product of non-independent and non-stationary random matrices. Some techniques such as stability theory, algebraic graph theory and Markov chain theory are employed to deal with the above issue. Our theoretical results are obtained without relying on the independency or stationarity assumptions of regression vectors which are commonly used in existing literature., Comment: 12 pages

In this paper, we study the distributed adaptive estimation problem of continuous-time stochastic dynamic systems over sensor networks where each agent can only communicate with its local neighbors. A distributed least squares (LS) algorithm based on diffusion strategy is proposed such that the sensors can cooperatively estimate the unknown time-invariant parameter vector from continuous-time noisy signals. By using the martingal estimation theory and Ito formula, we provide upper bounds for the estimation error of the proposed distributed LS algorithm, and further obtain the convergence results under a cooperative excitation condition. Compared with the existing results, our results are established without using the boundedness or persistent excitation (PE) conditions of regression signals. We provide simulation examples to show that multiple sensors can cooperatively accomplish the estimation task even if any individual can not.