Creating a New Model to Predict Cooling Tower Performance and Determining Energy Saving Opportunities through Economizer Operation

Cooling towers form an important part of chilled water systems and perform the function of rejecting the heat to the atmosphere. These systems are often not operated optimally, and cooling towers being an integral part of the system present a significant area to study and determine possible energy saving measures. Operation of cooling towers in economizer mode in winter and variable frequency drives (VFDs) on cooling tower fans are measures that can provide considerable energy savings. The chilled water system analysis tool (CWSAT) software is developed as a primary screening tool for energy evaluation for chilled water systems and quantifies the energy usage of the various components and typical measures that can be applied to these systems to conserve energy, all while requiring minimum number of inputs to analyze component-wise energy consumption and incurred overall cost. A careful investigation of the current model in CWSAT indicates that the prediction capability of the model at lower wet bulb temperatures and at low fan power is not very accurate. A new model for accurate tower performance prediction is imperative, since economizer operation occurs at low temperatures and most cooling towers come equipped with VFDs. In this thesis, a new model to predict cooling tower performance is created to give a more accurate prediction of energy savings for a tower. Further the economic feasibility of having additional cooling tower capacity to allow for economizer cooling, in light of reduced tower capacity at lower temperatures is investigated

Moderating Social Media Discourse for a Healthy Democracy

The prevalence of hate speech and misinformation on the internet, heightened by the COVID-19 pandemic, directly harms minority groups that are the target of vitriol, as well as our society at large (Müller & Scwarz, 2020). In addition, the intersection between the two only exacerbates their harmful effects leading to an increase in intolerance and polarization (Kim & Kesari 2021). Current platform moderation techniques, as well as Section 230 under the Communications Decency Act, have been insufficient in addressing this problem, resulting in a lack of transparency from internet service providers, clear boundaries on user-platforms relations, and sufficient tools to handle a rapidly expanding internet. To address this problem space, we advocate for the following solutions: 1. Algorithmic governance & transparency: Internet Service Providers should be more transparent with users about content moderation policies and algorithms, and clarify users’ basic rights on the platform. 2. Flagging recommendations: We advocate a more effective, efficient and comprehensive flagging system through a combined strategy of content- and user-based approaches. 3. Multiplatform collaboration: Fighting harmful online content requires a collaborative effort among policy makers, civil society groups, researchers, and different platforms. 4. Long-term considerations: Building a regular and prolonged tracking system is essential to make anti-misinformation efforts more efficient and effective, especially in complex scenarios.Journalism and Medi

Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition

Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a single unsupervised task is used for pre-training followed by fine-tuning on the downstream task. In this work, we introduce a novel method combining multi-modal and multi-task unsupervised pre-training with a translation-based supervised mid-training approach. We empirically demonstrate that such a multi-stage approach leads to relative word error rate (WER) improvements of up to 38.45% over baselines on both Librispeech and SUPERB. Additionally, we share several important findings for choosing pre-training methods and datasets.Comment: Accepted in LREC-COLING 2024 - The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluatio

Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion

We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM). Experiments on the Switchboard human-human conversation dataset demonstrate that our approach consistently outperforms the baseline models with single modality. We also develop a novel multi-task instruction fine-tuning strategy to further benefit from LLM-encoded knowledge for understanding the tasks and conversational contexts, leading to additional improvements. Our approach demonstrates the potential of combined LLMs and acoustic models for a more natural and conversational interaction between humans and speech-enabled AI agents.Comment: To appear in IEEE ICASSP 202

Assessment of the quality, content, and reliability of YouTube® videos on diabetes mellitus and polycystic ovary syndrome:a systematic review with cross-sectional analysis comparing peer-reviewed videos

YouTube® is one of the leading platforms for health information. However, the lack of regulation of content and quality raises concerns about accuracy and reliability. CoMICs (Concise Medical Information Cines) are evidence-based short videos created by medical students and junior doctors and reviewed by experts to ensure clinical accuracy. We performed a systematic review to understand the impact of videos on knowledge and awareness about diabetes and PCOS. We then evaluated the quality of YouTube® videos about diabetes and PCOS using various validated quality assessment tools and compared these with CoMICs videos on the same topics. Quality assessment tools like DISCERN, JAMA benchmark criteria, and global quality scale (GQS) score were employed. Some of the authors of this study also co-authored the creation of some of the CoMICs evaluated. Our study revealed that while videos effectively improve understanding of diabetes and PCOS, there are notable differences in quality and reliability of the videos on YouTube®. For diabetes, CoMICs videos had higher DISCERN scores (CoMICs vs YouTube®: 2.4 vs 1.6), superior reliability (P &lt; 0.01), and treatment quality (P &lt; 0.01) and met JAMA criteria for authorship (100% vs 30.6%) and currency (100% vs 53.1%). For PCOS, CoMICs had higher DISCERN scores (2.9 vs 1.9), reliability (P &lt; 0.01), and treatment quality (P &lt; 0.01); met JAMA criteria for authorship (100% vs 34.0%) and currency (100% vs 54.0%); and had higher GQS scores (4.0 vs 3.0). In conclusion, CoMICs outperformed other similar sources on YouTube® in providing reliable evidence-based medical information which may be used for patient education.</p

A Private cloud with live Performance MonitoringAnalysis

The open-source monitoring program Prometheus is available. Open-Stack is becoming the most popular open source cloud platform because to ongoing developments in cloud computing. In order to guarantee the reliability and stability of cloud platforms when utilised as production systems, thorough system monitoring is an essential link and a critical method. Along with the capabilities of the Open-Stack cloud platform, this article uses Prometheus to collect monitoring data, takes use of the real-time monitoring data visualization application grafana, and develops a comprehensive, clever, and efficient monitoring system. Testing is a viable method for improving the dependability and stability of the Open-Stack cloud platform