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While large-scale literature reviews are nowadays becoming a staple element of modern research practice, there are many challenges in taking on such an endeavour, yet little evidence of previous studies addressing these challenges exists. This paper introduces a practical and efficient review framework for extremely large corpora of literature, refined by five parallel implementations within a multi-disciplinary project aiming to map out the research and practice landscape of Modelling, Simulation, and Management methods, spanning a variety of sectors of application where such methods have made a significant impact. Centred on searching and screening techniques along with the use of some emerging IT-assisted analytic and visualisation tools, the proposed framework consists of four key methodological elements to deal with the scale of the reviews, namely: a) an incremental and iterative review structure, b) a 3-stage screening phase including filtering, sampling and sifting, c) use of visualisation tools, and d) reference chasing (both forward and backward). Five parallel implementations of systematically conducted literature search and screening yielded a total initial search result of 146 087 papers., ultimately narrowed down to a final set of 1383 papers which was manageable within the limited time and other constraints of this research work. Keywords AbstractWhile large-scale literature reviews are nowadays becoming a staple element of modern research practice, there are many challenges in taking on such an endeavour, yet little evidence of previous studies addressing these challenges exists. The paper introduces a practical and efficient review framework for extremely large corpora of literature, refined by

In this paper, the extended Kalman filtering problem is investigated for a class of nonlinear systems with multiple missing measurements over a finite horizon. Both deterministic and stochastic nonlinearities are included in the system model, where the stochastic nonlinearities are described by statistical means that could reflect the multiplicative stochastic disturbances. The phenomenon of measurement missing occurs in a random way and the missing probability for each sensor is governed by an individual random variable satisfying a certain probability distribution over the interval [0,1]. Such a probability distribution is allowed to be any commonly used distribution over the interval [0, 1] with known conditional probability. The aim of the addressed filtering problem is to design a filter such that, in the presence of both the stochastic nonlinearities and multiple missing measurements, there exists an upper bound for the filtering error covariance. Subsequently, such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati-like difference equations that are of a form suitable for recursive computation in online applications. An illustrative example is given to demonstrate the effectiveness of the proposed filter design scheme.

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Abstract-This paper investigates the robust sliding mode control (SMC) problem for a class of uncertain nonlinear stochastic systems with mixed time-delays. Both the sector-like nonlinearities and the norm-bounded uncertainties enter into the system in randomly ways, and such randomly occurring uncertainties (ROUs) and randomly occurring nonlinearities (RONs) obey certain mutually uncorrelated Bernoulli distributed white noise sequences. The mixed time-delays consist of both the discrete and the distributed delays. The time-varying delays are allowed in state. By employing the idea of delay-fractioning and constructing a new Lyapunov-Krasovskii functional, sufficient conditions are established to ensure the stability of the system dynamics in the specified sliding surface by solving certain semidefinite programming problem. A full-state feedback SMC law is designed to guarantee the reaching condition. A simulation example is given to demonstrate the effectiveness of the proposed SMC scheme.