Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

J-Block Triassic Well Performance & Reservoir Heterogeneity

Imperial Users onl

Reducing the burden of depression in youth: what are the implications of neuroscience and genetics on policies and programs?

Mood disorders are a leading cause of the burden of disease in youth. Three critical lessons emerge from the reviews in this issue that are relevant to our understanding of these common mental disorders: first, that the brain is in a highly dynamic stage of its development during youth; second, that environmental factors interact with genetic factors to influence the probability of risk behaviors and dysphoric states; and third, that shared developmental and genetic factors may account for the bulk of emotional and behavioral outcomes in youth, and that environmental influences may affect the specific expression of the phenotypes associated with these pathways. Although this evidence does not immediately indicate the potential for new interventions, it is consistent with current policy and practice recommendations. Interventions should focus on both improving the early detection and management of depressive disorders as well as preventive strategies that aim to train children and youth to improve cognitive control and manage stress more effectively. Limiting access to harmful risk-taking situations and providing opportunities to engage are less harmful, but equally exciting, alternatives in a pragmatic universal prevention policy option. Key research priorities and paradigms emerge from this evidence, particularly in the context of the grand challenges in global mental health

A Transverse Lattice QCD Model for Mesons

QCD is analysed with two light-front continuum dimensions and two transverse lattice dimensions. In the limit of large number of colours and strong transverse gauge coupling, the contributions of light-front and transverse directions factorise in the dynamics, and the theory can be analytically solved in a closed form. An integral equation is obtained, describing the properties of mesons, which generalises the 't Hooft equation by including spin degrees of freedom. The meson spectrum, light-front wavefunctions and form factors can be obtained by solving this equation numerically. These results would be a good starting point to model QCD observables which only weakly depend on transverse directions, e.g. deep inelastic scattering structure functions.Comment: Lattice 2003 (theory), 3 page

Influenza A nucleoprotein binding sites for antivirals: current research and future potential

This document is the Accepted Manuscript version of the following article: Andreas Kukol and Hershna Patel, ‘Influenza A nucleoprotein binding sites for antivirals: current research and future potential’, Future Biology, Vol 9(7): 625-627, July 2014. The version of record is available online at doi: 10.2217/fvl.14.45Peer reviewedFinal Accepted Versio

Evaluation of a novel virtual screening strategy using receptor decoy binding sites

Virtual screening is used in biomedical research to predict the binding affinity of a large set of small organic molecules to protein receptor targets. This report shows the development and evaluation of a novel yet straightforward attempt to improve this ranking in receptor-based molecular docking using a receptor-decoy strategy. This strategy includes defining a decoy binding site on the receptor and adjusting the ranking of the true binding-site virtual screen based on the decoy-site screen. The results show that by docking against a receptor-decoy site with Autodock Vina, improved Receiver Operator Characteristic Enrichment (ROCE) was achieved for 5 out of fifteen receptor targets investigated, when up to 15 % of a decoy site rank list was considered. No improved enrichment was seen for 7 targets, while for 3 targets the ROCE was reduced. The extent to which this strategy can effectively improve ligand prediction is dependent on the target receptor investigated

Evolutionary conservation of influenza A PB2 sequences reveals potential target sites for small molecule inhibitors.

The influenza A basic polymerase protein 2 (PB2) functions as part of a heterotrimer to replicate the viral RNA genome. To investigate novel PB2 antiviral target sites, this work identified evolutionary conserved regions across the PB2 protein sequence amongst all sub-types and hosts, as well as ligand binding hot spots which overlap with highly conserved areas. Fifteen binding sites were predicted in different PB2 domains; some of which reside in areas of unknown function. Virtual screening of ~50,000 drug-like compounds showed binding affinities of up to 10.3 kcal/mol. The highest affinity molecules were found to interact with conserved residues including Gln138, Gly222, Ile529, Asn540 and Thr530. A library containing 1738 FDA approved drugs were screened additionally and revealed Paliperidone as a top hit with a binding affinity of -10 kcal/mol. Predicted ligands are ideal leads for new antivirals as they were targeted to evolutionary conserved binding sites

Optimal Database Search: Waves and Catalysis

Grover's database search algorithm, although discovered in the context of quantum computation, can be implemented using any system that allows superposition of states. A physical realization of this algorithm is described using coupled simple harmonic oscillators, which can be exactly solved in both classical and quantum domains. Classical wave algorithms are far more stable against decoherence compared to their quantum counterparts. In addition to providing convenient demonstration models, they may have a role in practical situations, such as catalysis.Comment: 4 pages (v2) 6 pages, RevTeX4. Title changed. Substantially expanded to include stability considerations, quantum domain analysis, application to catalysis. (v3) Version accepted for publication. (v4) Error in Eqs.(10,11) corrected by replacing \omega by \omega^2. No change in implementation and applicatio