This note addresses the problem of synchronized output regulation of linear networked system where all the nodes have their outputs track signals produced by the same exosystem and the state of exosystem is accessible only to leader nodes, while follower nodes regulate their outputs via a distributed synchronous protocol. This problem can be decoupled into two: one is the output regulation problem on the synchronous manifold and the other is the stability problem of synchronous manifold. The proposed synchronous protocol is independent of the network topology. The stability of synchronous manifold is analyzed through the permissible eigenvalue region and the requirements of information graph. Finally, a numerical example illustrates the efficacy of the presented results.

An important task of public health officials is to keep track of health issues, such as spreading epidemics. In this paper, we are addressing the issue of spreading public concern about epidemics. Public concern about a communicable disease can be seen as a problem of its own. Keeping track of trends in concern about public health and identifying peaks of public concern are therefore crucial tasks. However, monitoring public health concerns is not only expensive with traditional surveillance systems, but also suffers from limited coverage and significant delays. To address these problems, we are using Twitter messages, which are available free of cost, are generated world-wide, and are posted in real time. We are measuring public concern using a two-step sentiment classification approach. In the first step, we distinguish Personal tweets from News (i.e., Non-Personal) tweets. In the second step, we further separate Personal Negative from Personal Non-Negative tweets. Both these steps consist themselves of two substeps. In the first sub-step (of both steps), our programs automatically generate training data using an emotionoriented, clue-based method. In the second sub-step, we are training and testing three different Machine Learning (ML) models with the training data from the first sub-step; this allows us to determine the best ML model for different datasets. Furthermore, we are testing the already trained ML models with a human annotated, disjoint dataset.Based on the number of tweets classified as Personal Negative, we compute a Measure of Concern (MOC) and a timeline of the MOC. We attempt to correlate peaks of the MOC timeline to peaks of the News (Non-Personal) timeline. Our best accuracy results are achieved using the two-step method with a Naïve Bayes classifier for the Epidemic domain (six datasets) and the Mental Health domain (three datasets).

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In this paper, we develop a simple algorithm to determine the required number of generating units of wind-turbine generator and photovoltaic array, and the associated storage capacity for stand-alone hybrid microgrid. The algorithm is based on the observation that the state of charge of battery should be periodically invariant. The optimal sizing of hybrid microgrid is given in the sense that the life cycle cost of system is minimized while the given load power demand can be satisfied without load rejection. We also report a case study to show the efficacy of the developed algorithm.

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Direct drive wave energy converters have been proposed in view of the disadvantage of mechanical complexity and low conversion efficiencies in conventional wave energy converters. By directly coupling a linear generator to a reciprocating wave energy device it is suggested that direct drive power takeoff could be a viable alternative to hydraulic and pneumatic based systems. To further realise the benefits of a direct drive system this paper presents a control scheme based on reaction force control to maximise energy extraction. It focuses predominantly on the theoretical analysis of the linear generator reaction force. The modelling, simulation and control of direct drive wave energy conversion are systematically investigated by computer-aided analysis via Matlab/Simulink.