Strangeness production in two-particle azimuthal correlations on the near and away side measured with ALICE in pp collisions at 7 TeV

Two-particle azimuthal correlations allow one to study high-$p_{\rm T}$ parton fragmentation without full jet reconstruction. Enhancements of the azimuthal correlations are seen at $\Delta \varphi \approx 0$ and $\Delta \varphi \approx \pi$, resulting from back-to-back jet fragmentation in the parton center-of-mass system. We present the current status of the study of correlations between charged trigger particles and associated strange baryons ($\Lambda$) and mesons (K$_{S}^{0}$) in pp collisions at $\sqrt{s}$ = 7 TeV. A data-driven feeddown correction for $\Lambda$ is also presented, which could allow a more accurate calculation of the primary $\Lambda/$K$_{S}^{0}$ ratio in jets and the underlying event.Comment: 5 pages, 4 figures, Proceedings of the Second Annual Conference on Large Hadron Collider Physics (LHCP 2014), June 2-7, 2014, New Yor

Time-frequency and point process algorithms for cardiac arrhythmia analysis and cardiorespiratory control

Cardiovascular diseases are major causes of disability and premature death globally. In particular, atrial fibrillation is the most common cardiac arrhythmia condition found in clinical practice, and is associated with an increased risk of stroke. Heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) are important indicators of cardiovascular health, and provide useful information on autonomic nervous system inputs to cardiac cycle and cardiorespiratory coupling, respectively. New methods to support the treatment of cardiovascular diseases and identifying efficient ways of measuring cardiovascular health could yield significant benefits. In this thesis, we present a number of advanced algorithms for cardiorespiratory signal processing. We present algorithms for analyzing atrial fibrillation arrhythmia from electrocardiograms (ECG). We propose an orthonormal basis function based representation for fibrillatory waveforms, and use a regularized least square solution for atrial activity extraction from ECG, suppressing more dominant ventricular components. Time-frequency analysis of atrial activity is used to identify and track fibrillatory frequencies from extracted atrial activity, which provides possible guidance to tailored treatments. In addressing the problem of tracking fibrillatory frequencies, we have developed a framework for generating new classes of time-frequency distributions with many desirable properties. This framework is based on multi-dimensional Fourier transform of a radially symmetric function, and can be used to generate new distributions with unique characteristics. A realization of this framework on a high-dimensional radial delta function results in a new class of time-frequency distributions, which we call radial-delta distributions. The class of radial-delta distributions unifies number of well known distributions, and further provides methods for high resolution time-frequency analysis of multi-component signals with low interference terms. We present a maximum likelihood inverse Gaussian point process model for dynamic and instantaneous HRV and RSA estimation from heart beat interval series and respiration recordings. Unlike previous methods, we perform time-frequency analysis of heart beat interval series, respiration, as well as the coherence between the two, and dynamically evaluate RSA transfer function based on instantaneous respiration and maximum coherence frequencies. The point process algorithm and dynamic respiration based RSA estimation methods are applied on two experimental protocols, a meditation experiment and a pain experiment. These applications demonstrate the robustness of the point process model in estimating HRV and RSA under different psychophysiological states. Regardless of the significant variations in respiration during meditation practice, goodness-of-fit tests are still found to be well within the desired confidence bounds, which validate the proposed models. Results indicate a sign! ificant increase in RSA during meditation practice, which suggest positive influence of meditation on the cardiovascular health. In the second experiment, reduced RSA during pain indicates the ability of the method to differentiate between different acute pain levels. Novel time-frequency distributions and orthonormal basis atrial activity representation based analysis provide accurate tracking of fibrillatory frequencies of atrial fibrillation arrhythmia from ECG. The point process model with time-frequency analysis provides accurate estimations of HRV and RSA, and is robust to dynamic changes in respiration and autonomic inputs. These algorithms provide useful tools for monitoring cardiovascular health and particular arrhythmia conditions

Enhancement of Charging Resource Utilization of Electric Vehicle Fast Charging Station with Heterogeneous EV Users

This thesis presents innovative charging resource allocation and coordination strategies that maximize the limited charging resources at FCS with heterogeneous EV users. It allows opportunistic EV users (OEVs) to exploit available charging resources with dynamic event-driven charging resource allocation and coordination strategies apart from primary EV users (PEVs) (registered or scheduled EV users). Moreover, developed strategies focus on the limited charging resources that are allocated for primary/ registered EV users (PEVs) of the FCS who access the FCS with specific privileges according to prior agreements. But the available resources are not optimally utilized due to various uncertainties associated with the EV charging process such as EV mobility-related uncertainties, EVSE failures, energy price uncertainties, etc. Developed strategies consider that idle chargers and vacant space for EVs at the FCS is an opportunity for further utilizing them with OEVs using innovative charging resource coordination strategies. This thesis develops an FCS-centric performance assessment framework that evaluates the performance of developed strategies in terms of charging resource utilization, charging completion and the quality of service (QoS) aspects of EV users. To evaluate QoS of EV charging process, various parameters such as EV blockage, charging process preemptage, mean waiting time, mean charging time, availability of FCS, charging reliability, etc are derived and analyzed. In addition, the developed innovative charging resource allocation and coordination strategies with resource aggregation and demand elasticity further enhance the charging resource utilization while providing a high QoS in EV charging for both PEVs and OEVs.publishedVersio

Disaster Recovery Framework for Commercial Banks in Sri Lanka

The banking sector is the backbone of the entire financial economy of a country. In today's globalized world, most organizations use online transaction processing systems for transferring money and doing business. Natural or man-made disasters can lead to data loss which in turn can cause millions of dollars of money lost. This study focuses on disaster recovery practices in commercial banks in Sri Lanka. From our preliminary findings, it was concluded that commercial banks only have ad-hoc disaster recovery standards and practices, as there is no standard framework available. Fourteen (14) banks were selected for data collection and relevant authorities were interviewed. The results were translated as qualitative observations to understand the best practices. Similarly, international standards, compliance requirements of the central bank, and existing researches were used to develop a disaster recovery practice framework. The proposed framework was then validated for its efficiency and usefulness among commercial banks and found to be acceptable by the banking industry.