Application of computational fluid dynamics for high energy efficiency design with human comfort of Cad-Vav and Ufad systems

This thesis deals with the numerical simulation of the ceiling air distribution (CAD) system and the Under Floor Air Distribution (UFAD) system based on the dimensions of BTLab at UNLV. Ceiling Air Distribution (CAD) with variable air volume (VAV) and Under Floor Air Distribution (UFAD) systems have been widely used in different countries. CAD-VAV and UFAD systems designs have been influenced by increasing emphasis on indoor air quality (IAQ), energy conservation, environmental effects, safety, and economics. So, 3-D Computational Fluid Dynamics (CFD) analysis technique was applied to design high energy efficiency and human comfort CAD-VAV and UFAD systems. The goal of this research project is to analyze energy efficiency with thermal comfort for CAD - VAV and UFAD systems and reduce the design cycle through the development of mathematical and computational models. The University of Nevada Las Vegas (UNLV) has conducted the laboratory phase of this task which was conducted by a different research team by which a test protocol has been developed and implemented in the UNLV Center, the BTLab. This experimental task is to test the performance of UFAD systems compared to CAD systems, including comfort, energy use, indoor air quality IAQ. The experiment has been conducted based on ASHRAE Standard 113-1990 - Method of Testing for Room Air Diffusion. FLUENTRTM 6.2 is a computational fluid dynamics (CFD) software package to simulate fluid flow problems. The general purpose CFD code FLUENTRTM is used as a numerical solver for the present 3D simulation. A non-staggered grid storage scheme is adopted to define the discrete control volumes. The solver used is a segregated solver which is a solution algorithm with which the governing equations are solved sequentially. The SIMPLE algorithm is used to resolve the coupling between pressure and velocity. An implicit technique is used to linearize the discrete and non-linear governing equations. The discretization method used by the FLUENTRTM is FVM in which the space is divided into a finite number of control volumes and solves the partial differential equations. Integration of the governing equations on the individual control volumes constructs algebraic equations for the discrete dependent variables such as velocities, pressure and temperature. In this research work, thermal comfort environment of the CAD & UFAD system is investigated and compared with the experimental values; From the numerical study of the BTLab for CAD system, results show that the temperature and velocity profiles inside the test space are well mixed. Three test planes have been studied to compare these numerical results with the experimental study which show good agreement. The spray angle of the swirl diffuser is considered to be the crucial part and the airflow distribution strongly depends on the spray angle. A Parametric study has been made on the spray angle of the swirl diffuser considering the spray angle from 3° to 7° in which the spray flow for single swirl diffuser is studied. From the velocity and temperature profiles and path lines, the approximated spray angle is around 5.3° which is considered to be a good choice. The UFAD system of BTLab is numerically studied and thermal load is not considered in this study. The results show that the flow from the diffuser is highly helical and twisted and a clean zone is formed as per the previous publication [50]. This shows that the obtained results from the numerical study are reasonable. These numerical results for UFAD system are benchmarked with the experimental results

Domain-specific lexicon generation for emotion detection from text.

Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to understand emotions from text are limited in effectiveness due to reliance on general purpose emotion lexicons that have static vocabulary and sentiment lexicons that can only interpret emotions coarsely. Thus emotion detection from text calls for methods and knowledge resources that can deal with challenges such as dynamic and informal vocabulary, domain-level variations in emotional expressions and other linguistic nuances. In this thesis we demonstrate how labelled (e.g. blogs, news headlines) and weakly-labelled (e.g. tweets) emotional documents can be harnessed to learn word-emotion lexicons that can account for dynamic and domain-specific emotional vocabulary. We model the characteristics of realworld emotional documents to propose a generative mixture model, which iteratively estimates the language models that best describe the emotional documents using expectation maximization (EM). The proposed mixture model has the ability to model both emotionally charged words and emotion-neutral words. We then generate a word-emotion lexicon using the mixture model to quantify word-emotion associations in the form of a probability vectors. Secondly we introduce novel feature extraction methods to utilize the emotion rich knowledge being captured by our word-emotion lexicon. The extracted features are used to classify text into emotion classes using machine learning. Further we also propose hybrid text representations for emotion classification that use the knowledge of lexicon based features in conjunction with other representations such as n-grams, part-of-speech and sentiment information. Thirdly we propose two different methods which jointly use an emotion-labelled corpus of tweets and emotion-sentiment mapping proposed in psychology to learn word-level numerical quantification of sentiment strengths over a positive to negative spectrum. Finally we evaluate all the proposed methods in this thesis through a variety of emotion detection and sentiment analysis tasks on benchmark data sets covering domains from blogs to news articles to tweets and incident reports

Opinion context extraction for aspect sentiment analysis.

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentiment co-occurrence knowledge. Further, we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context is effective in improving classification performance.Specifically combining syntactical features with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance

A unique case of atrial fibrillation secondary to squamous cell lung carcinoma

Atrial fibrillation (AF) is widely considered to be the most prevalent cardiac arrhythmia with an incidence of roughly 1-2% in the United States alone. The incidence of AF has been known to increase with advancing age and thus presents a significant burden on healthcare systems across the globe. AF arises as a result of several mechanisms including structural changes that occur to the heart overtime. Here we present a case in which a 63-year-old male with no past medical history except heavy tobacco use presented to the emergency department complaining of shortness of breath. He also endorsed having palpitations and a productive cough for several weeks prior to presenting to the emergency department. An EKG was obtained which revealed AF with rapid ventricular response. His chest x-ray revealed an irregular opacification of the left lung; however, a chest computed tomography was obtained which revealed a left hilar mass extending to the left upper lobe. The mass was causing obstruction of the left upper lobe and was encasing the left main pulmonary artery and left. This case highlights a rare etiology of AF. While many causes of AF have been elucidated, including hypertension and valvular heart disease, a much lesser-known cause includes lung carcinoma resulting in mass effect on the heart. Representing almost 19% of all cancer deaths, lung cancer is the leading cause of cancer death. Although lung cancer screenings are recommended for certain populations, the majority of lung cancer cases present at an advanced stage and thus treatment options are limited. Our patient presents a unique case involving a lung mass causing AF due to mass effect on the left heart. Although the patient in this case had other risk factors for AF including advanced age and cigarette smoking, it can be presumed that due to the anatomical location of his lung mass, his AF was a result of his SCC. Though the mortality for lung cancer remains high, new treatments, including pembrolizumab, have the potential to drastically alter the way these cancers are treated

Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

Counterspeech has been demonstrated to be an efficacious approach for combating hate speech. While various conventional and controlled approaches have been studied in recent years to generate counterspeech, a counterspeech with a certain intent may not be sufficient in every scenario. Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances. In this paper, we explore intent-conditioned counterspeech generation. At first, we develop IntentCONAN, a diversified intent-specific counterspeech dataset with 6831 counterspeeches conditioned on five intents, i.e., informative, denouncing, question, positive, and humour. Subsequently, we propose QUARC, a two-stage framework for intent-conditioned counterspeech generation. QUARC leverages vector-quantized representations learned for each intent category along with PerFuMe, a novel fusion module to incorporate intent-specific information into the model. Our evaluation demonstrates that QUARC outperforms several baselines by an average of 10% across evaluation metrics. An extensive human evaluation supplements our hypothesis of better and more appropriate responses than comparative systems.Comment: ACL 202

XSS Vulnerabilities in Cloud-Application Add-Ons

Cloud-application add-ons are microservices that extend the functionality of the core applications. Many application vendors have opened their APIs for third-party developers and created marketplaces for add-ons (also add-ins or apps). This is a relatively new phenomenon, and its effects on the application security have not been widely studied. It seems likely that some of the add-ons have lower code quality than the core applications themselves and, thus, may bring in security vulnerabilities. We found that many such add-ons are vulnerable to cross-site scripting (XSS). The attacker can take advantage of the document-sharing and messaging features of the cloud applications to send malicious input to them. The vulnerable add-ons then execute client-side JavaScript from the carefully crafted malicious input. In a major analysis effort, we systematically studied 300 add-ons for three popular application suites, namely Microsoft Office Online, G Suite and Shopify, and discovered a significant percentage of vulnerable add-ons in each marketplace. We present the results of this study, as well as analyze the add-on architectures to understand how the XSS vulnerabilities can be exploited and how the threat can be mitigated

Sonochemical Synthesis of Nano-Structured Hydroxyapatite with unique morphologies and Evaluation of Sintering Kinetics

Phase pure hydroxyapatite (HAp) (Ca10(PO4)6(OH)2) ceramic powder was synthesized from the stoichiometric solution of calcium hydroxide and orthophosphoric acid employing  sonochemical technique. Crystallinity of the HAp powder is found to be a strong function of amplitude of the ultrasound generator as revealed by XRD patterns and FTIR recorded on the samples prepared using varying amplitudes. Calcination of HAp powder beyond 700°C has resulted in the initiation of sintering as is evident from dilatometric studies and are complimented by the SEM micrographs. Activation energy of sintering of hydroxyapatite pellets using dilatometric sintering kinetic analysis has estimated to be 668±45kJ/mole corresponding to grain boundary diffusion as the prominent mass transport mechanism. Samples exhibited a density of 3.12g/cm3, close to theoretical density (~ 99 %) at the peak temperature of 1200°C. Studies on AC conductivity of the sintered samples exhibited relatively high room temperature conductivity of 5.07x10-8 S/m and a rising trend with temperature probably due to mobility of H+ and OH- ions. Attempts were also made to produce HAp nanorods sonochemically on the ordinary glass substrates immersed in the stoichiometric HAp precursor solution. Surface topographic images of the HAp deposited on glass substrate exhibited nanorods almost individually separated with an average diameter of 50 nm and 200 nm in length providing a process for synthesizing nano-structured HAp with simultaneous deposition exhibiting unique morphologies

Lexicon based feature extraction for emotion text classification.

General Purpose Emotion Lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion analysis of text. However the static and formal nature of their vocabularies make them inadequate for extracting effective features for document representation, in domains that are inherently dynamic in nature (e.g. Social Media). This calls for lexicons that are not only adaptive to the lexical variations in a domain but also provide finer-grained quantitative estimates to accurately capture word-emotion associations. In this paper we extend prior work on domain specific emotion lexicon (DSEL) generation and apply it for emotion feature extraction. We demonstrate how our generative unigram mixture model (UMM) based DSEL learnt by harnessing labelled (blogs, news headlines and incident reports) and weakly-labelled (tweets) emotion text can be used to extract effective features for emotion classification. Our results confirm that the features derived using the proposed lexicon outperform those from state-of-the-art lexicons learnt using supervised Latent Dirichlet Allocation (sLDA) and Point-Wise Mutual Information (PMI). Further the proposed lexicon features also outperform state-of-the-art features derived using a combination of n-grams, part-of-speech information and sentiment lexicons

Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge.

Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services and also formulate critical action steps to improve their performance. In this work we focus on aspect-level sentiment classification studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We introduce a novel method that combines lexical, syntactical and sentiment knowledge effectively to extract opinion context for aspects. Thereafter we validate the quality of the opinion contexts extracted with human judgments using the BLEU score. Further we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context combining syntactical with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance. From a commercial point of view, accurate aspect extraction, provides an elegant means to identify 'pain-points' in a business. Integrating our work into a commercial CX platform (https://www.sentisum.com/) is enabling the company’s clients to better understand their customer opinions