Topological analysis of the power grid and mitigation strategies against cascading failures

This paper presents a complex systems overview of a power grid network. In recent years, concerns about the robustness of the power grid have grown because of several cascading outages in different parts of the world. In this paper, cascading effect has been simulated on three different networks, the IEEE 300 bus test system, the IEEE 118 bus test system, and the WSCC 179 bus equivalent model, using the DC Power Flow Model. Power Degradation has been discussed as a measure to estimate the damage to the network, in terms of load loss and node loss. A network generator has been developed to generate graphs with characteristics similar to the IEEE standard networks and the generated graphs are then compared with the standard networks to show the effect of topology in determining the robustness of a power grid. Three mitigation strategies, Homogeneous Load Reduction, Targeted Range-Based Load Reduction, and Use of Distributed Renewable Sources in combination with Islanding, have been suggested. The Homogeneous Load Reduction is the simplest to implement but the Targeted Range-Based Load Reduction is the most effective strategy.Comment: 5 pages, 8 figures, 1 table. This is a limited version of the work due to space limitations of the conference paper. A detailed version is submitted to the IEEE Systems Journal and is currently under revie

A quantitative content analysis of UK newsprint coverage of proposed legislation to prohibit smoking in private vehicles carrying children

This project was funded by Cancer Research UK (MC_U130085862) and the Scottish School of Public Health Research. Cancer Research UK and the Scottish School of Public Health Research were not involved in the collection, analysis, and interpretation of data, writing of the manuscript or the decision to submit the manuscript for publication. Shona Hilton, Karen Wood and Chris Patterson were funded by the UK Medical Research Council as part of the Understandings and Uses of Public Health Research programme (MC_UU_12017/6) at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. Thanks to Josh Bain and Alan Pollock for coding assistance.Peer reviewedPublisher PD

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.We study the application of unsupervised matrix decomposition algorithms such as Non-negative Matrix Factorization (NMF) to frequency domain representations of music audio signals. These algorithms, driven by a given reconstruction error function, learn a set of basis functions and a set of corresponding coefficients that approximate the input signal. We compare the use of three reconstruction error functions when NMF is applied to monophonic and harmonized musical scales: least squares, Kullback-Leibler divergence, and a recently introduced “phase-aware” divergence measure. Novel supervised methods for interpreting the resulting decompositions are presented and compared to previously used methods that rely on domain knowledge. Finally, the ability of the learned basis functions to generalize across musical parameter values including note amplitude, note duration and instrument type, are analyzed. To do so, we introduce two basis function labeling algorithms that outperform the previous labeling approach in the majority of our tests, instrument type with monophonic audio being the only notable exception

An empirical study of memory sharing in virtual machines

Content-based page sharing is a technique often used in virtualized environments to reduce server memory requirements. Many systems have been proposed to capture the benefits of page sharing. However, there have been few analyses of page sharing in general, both considering its real-world utility and typical sources of sharing potential. We provide insight into this issue through an exploration and analysis of memory traces captured from real user machines and controlled virtual machines. First, we observe that absolute sharing levels (excluding zero pages) generally remain under 15%, contrasting with prior work that has often reported savings of 30% or more. Second, we find that sharing within individual machines often accounts for nearly all (\u3e90%) of the sharing potential within a set of machines, with inter-machine sharing contributing only a small amount. Moreover, even small differences between machines significantly reduce what little inter-machine sharing might otherwise be possible. Third, we find that OS features like address space layout randomization can further diminish sharing potential. These findings both temper expectations of real-world sharing gains and suggest that sharing efforts may be equally effective if employed within the operating system of a single machine, rather than exclusively targeting groups of virtual machines

Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons

Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware. The most widely used spiking neuron in deep learning is the Leaky Integrate and Fire (LIF) neuron. LIF neurons operate sequentially, however, since the computation of state at time t relies on the state at time t-1 being computed. This limitation is shared with Recurrent Neural Networks (RNN) and results in slow training on Graphics Processing Units (GPU). In this paper, we propose the Stochastic Parallelizable Spiking Neuron (SPSN) to overcome the sequential training limitation of LIF neurons. By separating the linear integration component from the non-linear spiking function, SPSN can be run in parallel over time. The proposed approach results in performance comparable with the state-of-the-art for feedforward neural networks on the Spiking Heidelberg Digits (SHD) dataset, outperforming LIF networks while training 10 times faster and outperforming non-spiking networks with the same network architecture. For longer input sequences of 10000 time-steps, we show that the proposed approach results in 4000 times faster training, thus demonstrating the potential of the proposed approach to accelerate SNN training for very large datasets

Studies of potential anode materials for lithium-ion batteries

Lithium-ion batteries (LIBs) are currently used in nearly all consumer electronics, including cellular phones, laptop computers, and wearable devices such as smart watches. In the future, these batteries will also be used in electric vehicles and to store excess energy on a grid scale from intermittent sources such as wind and solar. On the anode side of LIBs, graphite has been the state-of-the-art material for the last 25 years and is reaching its technological limits, so research into new anode materials is needed in order to meet the increasing consumer demands for smaller, longer lasting batteries. The use of lead would be an incremental improvement over graphite since it has a higher capacity and is also cheap and abundant. A series of lead chalcogenides (PbO, PbS, PbSe, and PbTe) was synthesized, and their electrochemical properties were tested to determine their usefulness as potential LIB anode materials. PbO and PbS were found to perform poorly. PbSe performed better, although exhibited side reactions that rendered it unusable in actual LIBs. PbTe performed extremely well over the given testing window, able to be charged and discharged in only 30 minutes without suffering capacity fade. However, this material would be too expensive to use on a large scale due to tellurium’s rarity. Additionally, the 30 year old lithium-lead reaction mechanism in the literature was updated using a series of ex situ X-ray diffraction experiments. A step change improvement over graphite could come from using lithium metal, which would increase the anode capacity by a factor of 10. However, lithium metal suffers from uncontrollable dendritic growths which pose extreme safety hazards. An electrolyte additive was developed using potassium ions to overcome this dendrite issue. Dendritic growth was completely halted when this additive was used, and the cells cycled stably over the entire 18 day test period. The corrosion layer that forms on the surface of the lithium metal was characterized (via electrochemical impedance spectroscopy, X-ray photoelectron spectroscopy, and time of flight – secondary ion mass spectrometry) and found to be altered by the presence of potassium, leading to the improved performance.Chemical Engineerin

Enterprise gamification systems and employment legislation: a systematic literature review

A recent innovation in employee motivation systems is the introduction of ‘gamification’, which refers to the use of game design mechanics and principles to influence behaviour to enhance staff motivation and engagement. Enterprise gamification systems aggravate the differences in information availability between employers and employees, and employees who may be forced to adopt such systems may be placed under stress, worsening employment relationships in the workplace. Therefore, this research examines the potential legal implications of gamified employee motivation systems. This study undertook a systematic review of enterprise gamification and then used thematic analysis coupled with a review of legislation to examine whether gamification in workplaces meets the legal obligations of employers under their ‘duty of good faith’ in the New Zealand context. We find that carefully designed enterprise gamification systems should provide sufficient information and clarity for employees and support positive employment relationships. Deployments of enterprise gamification systems should be carefully planned with employee consultation and feedback supporting the introduction of an enterprise gamification system. Future research should look beyond the ‘good faith’ obligation and examine the relationship between gamification systems and the law on personal grievances