Sudarshan Kriya Yoga and Alteration in Depression Severity and Default Mode Network Connectivity

Major depressive disorder is one of the most common and burdensome psychiatric disorders. Many individuals still experience symptoms despite pharmacological treatment. Breathing techniques have shown potential for stress, depression, and anxiety reduction. A second promising treatment are mindfulness-based interventions, which have been shown to effectively decrease depression with fewer side effects than pharmacological agents. Both interventions are accessible via remote instruction. This study will investigate the effect of Sudarshan Kriya Yoga breathwork on depression symptoms. Using a prospective, randomized controlled trial, subjects will receive Sudarshan Kriya Yoga, Mindfulness Based Stress Reduction, or standard care for 8-weeks. We will measure changes in depressive symptoms using two validated depression inventories and will examine their effects on selected brain regions using resting-state functional magnetic resonance imaging. These results may guide an integrative approach for depression, expanding current options through a low-cost, low-risk, and accessible behavioral intervention adjunctive to concurrent pharmacological treatment

Scenario Analysis of Cost-Effectiveness of Maintenance Strategies for Fixed Tidal Stream Turbines in the Atlantic Ocean

This paper has developed an operation and maintenance (O&M) model for projected 20 MW tidal stream farm case studies at two sites in the northeast Atlantic in France and at EMEC’s Fall of Warness site in the UK. The annual energy production, number of incidents, and downtimes of the farms for corrective and planned (preventive) maintenance strategies are estimated using Monte Carlo simulations that vary weather windows, repair vessel availabilities, and mean annual failure rates modelled by Weibull distributions. The trade-offs between the mean annual failure rates, time availability, O&M costs, and energy income minus the variable O&M costs were analysed. For all scenarios, a 5-year planned maintenance strategy could considerably decrease the mean annual failure rates by 37% at both sites and increase the net energy income. Based on a detailed sensitivity analysis, the study has suggested a simple decision-making method that examines how the variation in the mean annual failure rate and changes in spare-part costs would reduce the effectiveness of a preventive maintenance strategy. This work provides insights into the most important parameters that affect the O&M cost of tidal stream turbines and their effect on tidal energy management. The output of the study will contribute to decision-making concerning maintenance strategies

A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).Comment: 15 pages, 3 figures, 13th IEEE Colloquium on Signal Processing & its Applications (CSPA 2017

A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities. This paper presents a novel approach which exploits rich mid-level convolutional features to categorize indoor scenes. Traditionally used convolutional features preserve the global spatial structure, which is a desirable property for general object recognition. However, we argue that this structuredness is not much helpful when we have large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target dataset, but it also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over previous state of the art approaches on five major scene classification datasets