Idiopathic Fascicular Ventricular Tachycardia

Idiopathic fascicular ventricular tachycardia is an important cardiac arrhythmia with specific electrocardiographic features and therapeutic options. It is characterized by relatively narrow QRS complex and right bundle branch block pattern. The QRS axis depends on which fascicle is involved in the re-entry. Left axis deviation is noted with left posterior fascicular tachycardia and right axis deviation with left anterior fascicular tachycardia. A left septal fascicular tachycardia with normal axis has also been described. Fascicular tachycardia is usually seen in individuals without structural heart disease. Response to verapamil is an important feature of fascicular tachycardia. Rare instances of termination with intravenous adenosine have also been noted. A presystolic or diastolic potential preceding the QRS, presumed to originate from the Purkinje fibers can be recorded during sinus rhythm and ventricular tachycardia in many patients with fascicular tachycardia. This potential (P potential) has been used as a guide to catheter ablation. Prompt recognition of fascicular tachycardia especially in the emergency department is very important. It is one of the eminently ablatable ventricular tachycardias. Primary ablation has been reported to have a higher success, lesser procedure time and fluoroscopy time

Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

Dynamic Analysis and Design of Motorcycle Mounting System Subjected to Road Loads

This paper presents a comprehensive model of a motorcycle mounting system. The model presented herein consists of two main assemblies. The powertrain assembly and the swing-arm assembly are modeled as a six degree of freedom rigid bodies. The two assemblies are connected to each other using a shaft that is usually referred to as the coupler. The connection points on both assemblies are known.Unlike automobiles, motorcycle performance and handling is highly affected by the external disturbance. In addition to minimizing the shaking loads, the mounting system must be set up such that it also minimizes the external disturbance from the environment such as irregularities in the road profile and road bumps. This disturbance can be transmitted through the tire patch to the engine causing it to hit nearby components. The engine movement needs to be minimized due to space limitations surrounding the engine. In order to do so, these transmitted external loads must be minimized by the use of the mounting system. The load minimization process is achieved by selecting the optimum stiffness parameters, location and orientation of the mounting system that are supporting the engine. This goal is achieved by an optimization scheme that guarantees that the transmitted loads are minimized. An investigation will be done to explore the effect of different road profiles on the mount final geometrical shape

Modernity and Disenchantment: Charles Taylor on the Identity of the Modern Self

In his magnum opus, Sources of the Self: The Making of Modern Identity (1989), Charles Taylor gives an exhaustive and teleologically interpretive history of the modern self. He, in fact, is in search of the core of the modern identity. By ‘identity’ Taylor means the ensemble of the understanding of what is to be a ‘human agent’, a ‘person’, a ‘self’. Taylor in generating the ontology of the self is greatly inspired by the understanding of Dasein in Heidegger. This paper also focuses on how Taylor uses Heidegger’s hermeneutics of the self in several ways to give to modernity a base that is not Cartesian. Taylor’s central argument is ‘how the assertion of the modern individual has spawned an erroneous understanding (identity) of the self’, where one experiences a loss of horizon. He has turned our attention, more than anyone else, towards the communitarian constitution of the self, and pointed out the limitations of insights within liberal individualism. For Taylor, as for early Heidegger, the self is not neutral or atomic. The self exists only in terms of questions and constitutive concerns, and it is not amenable to arbitrary determination, but can be made sense of only in terms of its life as a whole at any moment

Partial purification and characterisation of some low molecular weight á - amylases from Dolichos biflorus

Dolichos biflorus, a commonly used legume in Uttarakhand, produces alpha amylase enzyme for conversion of starch present in its cotyledons to glucose, so that this glucose can be further utilized for the life controlling processes, glycolysis and Kreb’s cycle. Yield of this á - amylase isolated from the germinating legume comes out to be 27.7 IUml-1. Maximal amylase production occurs at pH 6.1 at 45O C. The enzyme was purified two fold, first with ultra-filtration and then with Ion-exchange chromatography. Ultra-filtration revealed size of amylase to be between 10 kDa and 30 kDa, against larger sizes of other bacterial amylases. The pH and temperature optima for purified enzyme were 6.1 and 45OC respectively. The Km for starch came out to be 1.95 mgml-1. This finding of generating one more new and low-price source of á - amylase is a great advancement in biotechnology

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address  large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably