Advances in the diagnosis and management of pulmonary aspergillosis

Aspergillus is a mould that is ubiquitous in nature and may lead to a variety of infectious and allergic diseases depending on the host's immune status or pulmonary structure. Invasive pulmonary aspergillosis occurs primarily in patients with severe immunodeficiency. The significance of this infection has dramatically increased with growing numbers of patients with impaired immune state associated with the management of malignancy, organ transplantation, autoimmune and inflammatory conditions; critically ill patients appear to be at an increased risk as well. The introduction of new noninvasive tests, combined with more effective and better-tolerated antifungal agents, has resulted in lower mortality rates associated with this infection. Chronic pulmonary aspergillosis is a locally invasive disease described in patients with chronic lung disease or mild immunodeficiency. Recently, the European Society for Clinical Microbiology and Infectious Diseases provided a more robust sub-classification of this entity that allows for a straightforward approach to diagnosis and management. Allergic bronchopulmonary aspergillosis, a hypersensitivity reaction to Aspergillus antigens, is generally seen in patients with atopy, asthma or cystic fibrosis. This review provides an update on the evolving epidemiology and risk factors of the major manifestations of Aspergillus lung disease and the clinical manifestations that should prompt the clinician to consider these conditions. It also details the role of noninvasive tests in the diagnosis of Aspergillus related lung diseases and advances in the management of these disorders

Modeling Crack Propagation in Bituminous Binders under a Rotational Shear Fatigue Load using Pseudo J-Integral Paris’ Law

Fatigue resistance of bituminous binders plays a critical role in determining the fatigue performance of asphalt pavements. It is reported in the literature that, under a rotational shear fatigue load like a dynamic shear rheometer (DSR) test, the crack grows in the cylindrical bitumen sample as a circumferential crack that is initiated at the periphery of the sample and propagates toward the center of the sample. This study aims to model this crack propagation in bituminous binders under rotational shear fatigue load by time sweep (TS) fatigue test using the DSR. The crack length in the TS test is determined using a damage mechanics-based DSR-C model which is a function of the shear moduli and phase angles under undamaged and damaged conditions. The crack evolution is modeled by a pseudo J-integral based Paris’ law. Samples of virgin bitumen 40/60 and polymer-modified bitumen X-70 under unaged and aged conditions are tested by the TS tests at different temperatures, frequencies, and strain levels. Results show that the pseudo J-integral Paris’ law is able to predict accurately the crack propagation in bituminous binders under the rotational shear fatigue load. The crack grows faster in aged bitumen or at lower temperatures. The Paris’ law model parameters (A and n) are independent of loading frequency or load amplitude. They are fundamental material properties and can be determined at one loading frequency and amplitude, then can be implemented to predict the growth of cracks in bituminous binders at different loading frequencies or amplitudes

Rapid Language Model Development Using External Resources for New Spoken Dialog Domains

This paper addresses a critical problem in deploying a spoken dialog system (SDS). One of the main bottlenecks of SDS deployment for a new domain is data sparseness in building a statistical language model. Our goal is to devise a method to efficiently build a reliable language model for a new SDS. We consider the worst yet quite common scenario where only a small amount (∼1.7K utterances) of domain specific data is available for the target domain. We present a new method that exploits external static text resources that are collected for other speech recognition tasks as well as dynamic text resources acquired from World Wide Web (WWW). We show that language models built using external resources can jointly be used with limited in–domain (baseline) language model to obtain significant improvements in speech recognition accuracy. Combining language models built using external resources with the in–domain language model provides over 20 % reduction in WER over the baseline in–domain language model. Equivalently, we achieve almost the same level of performance by having ten times as much in–domain data (17K utterances)

Processing and Properties of Multifunctional Polylactide/Graphene Composites

PhDThis thesis aims to utilize graphene nanoplatelets (GNPs) in biobased and biodegradable thermoplastic polylactide (PLA) matrix for improved properties and multifunctionalities. A comprehensive comparative study was carried out on the effect of the addition of GNPs with different sizes. The mechanical, electrical, thermal and barrier properties of resulting PLA/GNP nanocomposites and their inter-relationship with the microstructure of the composites is revealed. The effect of annealing on dynamic percolation and conductive network formation of PLA/GNP composites including the effect of hybrid GNP fillers of different size is reported, indicating the underlying mechanisms for different behaviours of GNP fillers of different size. Multifunctional engineering biopolymers with improved performances (mechanical and electrical) and added functionalities (barrier properties) were successfully developed through controlled filler distribution and orientation using multilayer co-extrusion technology. Changes in mechanical properties of the PLA/GNP multilayer nanocomposites were successfully correlated with GNP orientation in the filled layers. Multilayer PLA/GNP nanocomposites demonstrated excellent mechanical and barrier properties with low filler loadings compared to traditional mono-extruded films

Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic

Language modeling for an inflected language such as Arabic poses new challenges for speech recognition and machine translation due to its rich morphology. Rich morphology results in large increases in out-of-vocabulary (OOV) rate and poor language model parameter estimation in the absence of large quantities of data. In this study, we present a joint morphological-lexical language model (JMLLM) that takes advantage of Arabic morphology. JMLLM combines morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items in a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates while keeping the predictive power of whole words. Speech recognition and machine translation experiments in dialectal-Arabic show improvements over word and morpheme based trigram language models. We also show that as the tightness of integration between different information sources increases, both speech recognition and machine translation performances improve

User-oriented markerless augmented reality framework based on 3D reconstruction and loop closure detection

An augmented reality (AR) system needs to track the user-view to perform an accurate augmentation registration. The present research proposes a conceptual marker-less, natural feature-based AR framework system, the process for which is divided into two stages - an offline database training session for the application developers, and an online AR tracking and display session for the final users. In the offline session, two types of 3D reconstruction application, RGBD-SLAM and SfM are integrated into the development framework for building the reference template of a target environment. The performance and applicable conditions of these two methods are presented in the present thesis, and the application developers can choose which method to apply for their developmental demands. A general developmental user interface is provided to the developer for interaction, including a simple GUI tool for augmentation configuration. The present proposal also applies a Bag of Words strategy to enable a rapid "loop-closure detection" in the online session, for efficiently querying the application user-view from the trained database to locate the user pose. The rendering and display process of augmentation is currently implemented within an OpenGL window, which is one result of the research that is worthy of future detailed investigation and development

A Phrase-Level Machine Translation Approach For Disfluency Detection Using Weighted Finite State Transducers

We propose a novel algorithm to detect disfluency in speech by reformulating the problem as phrase-level statistical machine translation using weighted finite state transducers. We approach the task as translation of noisy speech to clean speech. We simplify our translation framework such that it does not require fertility and alignment models. We tested our model on the Switchboard disfluency-annotated corpus. Using an optimized decoder that is developed for phrase-based translation at IBM, we are able to detect repeats, repairs and filled pauses for more than a thousand sentences in less than a second with encouraging results. Index Terms: disfluency detection, machine translation, speech-to-speech translation

CEO Compensation and Firm Performance: The Moderator Effects of Gender

Abstract From the last century to the present, many scholars have been interested in the relationship between CEO compensation and firm performance. They study it from different levels and perspectives, such as company type, special market, industry or country, and have obtained many theoretical and empirical discoveries. While many findings have proven that CEO compensation would influence firm performance, the intervening system controlling this influence haven’t got enough largely unexplored. The purpose of this paper is attempting to unveil the secret of ”black box” between the relationship between CEO compensation and firm performance using the agency theory and empirically test the moderating effect of gender on this association. A panel data consist of 5258 observations during 2010 to 2019 based on samples of North America listed non-financial firms has been analyzed using Ordinary Least Squares regression (OLS) for interaction model and running baseline regression for two sub-samples (i.e. firms only with female CEO vs. firms only with male CEO) and full sample, with fundamental and market information from COMPUSTAT North America and EXECUCOMP database as a proxy for gender and CEO compensation. Moreover, I hypothesize that male CEO positively enhance the link for pay-performance. My findings based on samples collected from 5258 observations of North America listed non-financial firms revealed a positive relationship between CEO option awards and firm performance. At the same time, Results indicate that gender positively effect on CEO pay-performance depending on which male CEO’s compensation is examined because they are more overconfident and prefer taking risk