Mechanisms of sensorineural cell damage, death and survival in the cochlea.

The majority of acquired hearing loss, including presbycusis, is caused by irreversible damage to the sensorineural tissues of the cochlea. This article reviews the intracellular mechanisms that contribute to sensorineural damage in the cochlea, as well as the survival signaling pathways that can provide endogenous protection and tissue rescue. These data have primarily been generated in hearing loss not directly related to age. However, there is evidence that similar mechanisms operate in presbycusis. Moreover, accumulation of damage from other causes can contribute to age-related hearing loss (ARHL). Potential therapeutic interventions to balance opposing but interconnected cell damage and survival pathways, such as antioxidants, anti-apoptotics, and pro-inflammatory cytokine inhibitors, are also discussed

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Enantioselective reactions of crotyl silane with ortho-quinone methide intermediates and progress toward an asymmetric cyclopropanation of allenylsilanes

The unique ability of allyl and crotyl silane reagents to act as competent nucleophiles as well as electron-rich dienophiles prompted an investigation into utilizing allyl and crotyl silanes in reactions with ortho-quinone methides (oQMs). In the presence of anhydrous FeCl3 and 2,6-lutidine, reaction of allyltrimethylsilane and the oQM intermediate generated from 2-(hydroxy(phenyl)methyl) phenol was found to produce both the cycloaddition and 1,4-addition product in 92% combined overall yield. This method was successfully extended to a stereoselective reaction between an enantioenriched (S,E)-crotyl silane and a variety of oQMs generated from electronically diverse ortho-hydroxybenzyl alcohol precursors. Both the chiral chroman and crotylation products were isolated in ratios reflective of the electronic nature of the parent oQM with overall combined yields of up to 96% and >99:1 er. A titanium tetrachloride-mediated ring-opening and elimination sequence was subsequently developed to provide direct access to the crotylation products, containing a unique vicinal tertiary carbon stereocenter bond construction, in good yields and enantioselectivities. An enantioselective cyclopropanation of di- and tri-substituted allenylsilanes was investigated to expand the relatively limited scope of asymmetric allene cyclopropanation reactions and to provide access to functionalized, chiral alkylidenecyclopropanes (ACPs). In the presence of 2 mol% of the Ru(S-Pheox) catalyst, 1,1-di-substituted allenylsilanes reacted with the metal carbenoid generated from benzyl diazoacetate ester to give the corresponding ACP products in up to 85% yield and 99:1 er. Increasing the catalyst loading to 10 mol% enabled the first reported asymmetric cyclopropanation of chiral, tri-substituted allenylsilanes, which gave optimal yields for the (R)-allenylsilanes over the corresponding (S)-isomers. A proposed mechanistic model was devised to rationalize the observed double stereodifferentiation event in the asymmetric cyclopropanation, which predicted the (R)-allenylsilanes and (S)-pheox ligand to be a matched pair. The reactivity of the densely functionalized ACP products was tested and led to the preparation of an unexpected 3-oxabicyclo[3.1.0]hexan-2-one product via iodolactonization

Egocentric Perception using a Biologically Inspired Software Retina Integrated with a Deep CNN

We presented the concept of of a software retina, capable of significant visual data reduction in combination with scale and rotation invariance, for applications in egocentric and robot vision at the first EPIC workshop in Amsterdam [9]. Our method is based on the mammalian retino-cortical transform: a mapping between a pseudo-randomly tessellated retina model (used to sample an input image) and a CNN. The aim of this first pilot study is to demonstrate a functional retina-integrated CNN implementation and this produced the following results: a network using the full retino-cortical transform yielded an F1 score of 0.80 on a test set during a 4-way classification task, while an identical network not using the proposed method yielded an F1 score of 0.86 on the same task. On a 40K node retina the method reduced the visual data bye×7, the input data to the CNN by 40% and the number of CNN training epochs by 36%. These results demonstrate the viability of our method and hint at the potential of exploiting functional traits of natural vision systems in CNNs. In addition, to the above study, we present further recent developments in porting the retina to an Apple iPhone, an implementation in CUDA C for NVIDIA GPU platforms and extensions of the retina model we have adopted

Risk Assessment Tool for Liquid Overfill of Process and Storage Vessels

PresentationThe paper describes the process that a large company uses to analyze the risk associated with liquid overfill from pressure vessels and atmospheric storage in both petrochemical and refining operations. Due to learnings from recent overfill events, a tool was developed to assess the risk of liquid overfill. This paper covers methodology and industry learnings that were used to develop a tool that is able to consistently assess liquid overfill risks across various operation types. The hope is that in sharing this information other companies may incorporate parts of the methodology or develop similar tools to identify overfill risks

Piloting a workflow for extracting author citations from Samuel Johnson's Dictionary of the English Language

Since the 18th century, English-language dictionaries have used quo- tations from written works to illustrate a word's use in context. These quotations form a link between language authority and literary authority. In this paper we pilot a workflow for identifying, extracting, and counting author citations in Samuel Johnson's Dictionary of the English Language to investigate how au- thors in a defined corpus are represented. We consider how these authors are distributed across the text and compare our results to past studies that used dif- ferent methodologies. We find a consistency that encourages the broader appli- cation of our workflow on other dictionary texts, enabling further study of au- thor citations in dictionaries across time

Don\u27t Trip! A Quicker Way to Plan Your Trip

Currently, Google Maps does not provide users with a way to find the optimal path for a user to travel given a list of points. Given a list of destinations you may want to visit, our web application will do all of the difficult planning for you and ultimately find the most optimal path for you to take to visit all of your destinations in a timely manner. The people who will benefit most from this application are tourists or people who travel often and want to explore an unfamiliar city. Even locals running their weekly errands could save time driving around back and forth between destinations by using the application to efficiently organize the order in which they should visit their desired stores