The photopolymerisation of methacrylic acid by colloidal semiconductors

Summary Methacrylic acid was photopolymerised using CdS and composite, CdS/HgS and CdS/TiO₂, colloidal semiconductor particles as initiators. The previously proposed photoinitiation mechanism involving the photogenerated positive hole in the valence band of the CdS colloid was confirmed by the electron scavenging action of TiO₂. The effect of pH on the course of the polymerisation was investigated and is discussed.     

Dependency-based security risk assessment for cyber-physical systems

A cyber-physical attack is a security breach in cyber space that impacts on the physical environment. The number and diversity of such attacks against Cyber-Physical Systems (CPSs) are increasing at impressive rates. In times of Industry 4.0 and Cyber-Physical Systems, providing security against cyber-physical attacks is a serious challenge which calls for cybersecurity risk assessment methods capable of investigating the tight interactions and interdependencies between the cyber and the physical components in such systems. However, existing risk assessment methods do not consider this specific characteristic of CPSs. In this paper, we propose a dependency-based, domain-agnostic cybersecurity risk assessment method that leverages a model of the CPS under study that captures dependencies among the system components. The proposed method identifies possible attack paths against critical components of a CPS by taking an attacker’s viewpoint and prioritizes these paths according to their risk to materialize, thus allowing the defenders to define efficient security controls. We illustrate the workings of the proposed method by applying it to a case study of a CPS in the energy domain, and we highlight the advantages that the proposed method offers when used to assess cybersecurity risks in CPSs.

     

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts.     

Transformer tap setting observability in state estimation

In conventional state estimation, transformer tap settings are treated as fixed network parameters. This may reduce the accuracy of state estimation algorithm, when a tap measurement is in error or an unmeasured tap is unknown. This paper presents a numerical observability analysis algorithm for a state estimation model which treats transformer tap settings as state variables and provides for turns ratio and phase-shift angle measurements. The proposed model is transformed to a conventional node frame of reference, by introducing one fictitious bus and one fictitious branch for each transformer with tap estimation enabled. For an unobservable system, observable islands are determined and additional measurements are directly provided for placement. Test results are presented.     

Passive tracking of a maneuvering target: an adaptive approach

The problem of target tracking using passive sensors is examined. The target is assumed to execute maneuvers at times unknown to the observer. An adaptive algorithm, which takes into account such maneuvers, is derived using the multimodel partitioning approach. Simulation results show the superiority of the proposed algorithm over a tracker employing the extended Kalman filter     

Genetically determined variable structure multiple model estimation

In this paper, the multimodel partitioning theory is combined with genetic algorithms to produce a new generation of multimodel partitioning filters, whose structure varies to conform to a model set being determined dynamically and on-line by using a suitably designed genetic algorithm. The proposed algorithm does not require any knowledge of the model switching law, is practically implementable, and exhibits superior performance compared with a fixed-structure multimodel partitioning filter (MMPF), as indicated by simulation experiments     

A genetic algorithm for active noise control actuator positioning

Active noise control systems (ANCS) have been used extensively in several diverse applications in order to reduce the sound levels within a confined space. Several control schemes have been proposed, all of which rely on the same basic idea, namely that of introducing an artificial secondary sound field in order to partially cancel the primary sound field. This secondary field is created by using a number of actuators (usually speakers), placed in several locations within the space of interest. In our particular case of interest, the ANCS under consideration is an interior one. The overall ANCS comprises a set of sensors, a control system, and the actuators. Clearly, the optimal design of all three components needs to be addressed when designing an ANCS. In this paper, the problem of deciding to which specific locations (out of several possible ones) the actuators will be located for optimal performance of the ANCS is addressed. Specifically, a genetic algorithm is designed for this problem. The algorithm was implemented and tested against a simple greedy approach. Results indicate the efficiency of the Genetic Algorithm for the problem at hand.     

Interconnecting Autonomous Medical Domains

In this article we discuss the technological challenges from a security and interoperability perspective toward enabling the interconnection of different medical domains. The emergence of networked infrastructures and electronic health records (EHRs) has brought new challenges in the field of medical informatics. Healthcare information needs to be accessible by authorized users only, while its fundamental security properties (namely, integrity, availability, and confidentiality) must be retained. It is concluded that international efforts are needed to enable the creation of a widely accepted EHR that will be able to be used between different countries to provide better health services and therefore improve the care of their traveling citizens.     

Securing The Electronic Market: The KEYSTONE Public Key Infrastructure Architecture

In this paper, the unified, abstract KEYSTONE Public Key Infrastructure is presented. This architecture consists of a reference model, a functional architecture specification, and a set of technologies that can be used for implementing the functional units, along with all relevant standards. It was derived within the course of the KEYSTONE project, which was funded by the European Commission under the Electronic Trust Services II Programme. The proposed PKI architecture guarantees openness, scalability, flexibility, extensibility, integration with existing TTP and information infrastructure, transparency and, above all, security. Thus, it enjoys all the desirable characteristics and fulfils all those criteria that are essential for a PKI to constitute a successful framework for the development of inter-domain and international Trusted Services.