Effective cutting of a quantum spin chain by bond impurities

Spin chains are promising media for short-haul quantum communication. Their usefulness is manifested in all those situations where stationary information carriers are involved. In the majority of the communication schemes relying on quantum spin chains, the latter are assumed to be finite in length, with well-addressable end-chain spins. In this paper we propose that such a configuration could actually be achieved by a mechanism that is able to effectively cut a spin ring through the insertion of bond defects. We then show how suitable physical quantities can be identified as figures of merit for the effectiveness of the cut. We find that, even for modest strengths of the bond defect, a ring is effectively cut at the defect site. In turn, this has important effects on the amount of correlations shared by the spins across the resulting chain, which we study by means of a scattering-based mechanism of a clear physical interpretation. © 2013 American Physical Society.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Enabling ultra high precision on hard steels using surface defect machining

This paper is an extension to an idea coined during the 13th EUSPEN Conference (P6.23) named "surface defect machining" (SDM). The objective of this work was to demonstrate how a conventional CNC turret lathe can be used to obtain ultra high precision machined surface finish on hard steels without recourse to a sophisticated ultra precision machine tool. An AISI 4340 hard steel (69 HRC) workpiece was machined using a CBN cutting tool with and without SDM. Post-machining measurements by a Form Talysurf and a Scanning Electron Microscope (FEI Quanta 3D) revealed that SDM culminates to several key advantages (i) provides better quality of the machined surface integrity and offers (ii) lowering feed rate to 5μm/rev to obtain a machined surface roughness of 30 nm (optical quality).

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Rough Waters, Dark Shadows and Cross-Cutting Winds:the impact of European Union enlargement on Turkey’s EU accession negotiations

For a multiplicity of socio-economic, geo-political, strategic and identity-based reasons, Turkey’s progress towards EU membership is often treated as a sui generis case. Yet although Turkey’s accession negotiations with the European Union (EU) are essentially a bilateral – and often stormy – affair, they take place within a wider and dynamic process of enlargement in which not only can the gloomy – sometimes dark – shadows of past and prospective enlargements be clearly detected, but so too can the often chill winds from ongoing, parallel negotiations with other candidates. How the EU negotiates accession and what it expects from candidates has continued to evolve since the EU began drawing up its framework for negotiations with Turkey ten years ago. This paper charts this evolution by first identifying changes in the light of Croatia’s negotiating experience, the ‘lessons learnt’ by the EU in meeting the challenges of Bulgarian and Romanian accession, the EU’s handling of Iceland’s membership bid and accession negotiations, and the revised approach to negotiating accession evident in the more recent frameworks for accession negotiations with Montenegro and Serbia. The paper then explores the extent to which these changes have impacted on the approach the EU has adopted in framing and progressing accession negotiations with Turkey. In doing so, it questions both the consistency with which the EU’s negotiates accession and the extent to which Turkey’s progress towards EU membership is conditioned by the broader dynamics of EU enlargement as opposed to simply the dynamics within EU-Turkey relations and domestic Turkish reform efforts.

COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.