Study of subsynchronous resonance and its countermeasure using static VAR compensator

This project includes the study of Subsynchronous resonance (SSR) phenomenon which occurs in a power system having series capacitor compensated transmission line. Static VAR compensators can be used to damp SSR oscillations besides controlling the system voltage. The First IEEE benchmark model and eigenvalue techniques are applied in the project to study the behavior of turbo-generator connected to the series compensated transmission line

Decoding Digestive Dilemmas: ChatGPT outperforms Bard in Gastroenterology Clinical Questions from MKSAP-19

Abstract Aim: This study aims to evaluate the performance of large language models (LLMs), specifically OpenAI’s ChatGPT and Google Bard, in answering gastroenterology clinical questions from the Medical Knowledge Self-Assessment Program-19 (MKSAP-19), thereby assessing their potential utility in clinical decision-making within the field of gastroenterology. Materials and Methods: A comparative analysis was conducted using a dataset of 50 gastroenterology questions from MKSAP-19, assessing the ability of ChatGPT and Bard to provide correct answers without prior training or access to MKSAP-19 materials. The performance of each LLM was evaluated based on the percentage of correct answers, with a passing score set at 50%. Results: ChatGPT outperformed Bard, achieving a 68% success rate in answering the questions correctly, compared to Bard’s 44%. ChatGPT attempted all questions, while Bard abstained from answering two. The analysis also identified specific areas where both LLMs struggled, indicating gaps in their clinical reasoning capabilities. Conclusions: ChatGPT demonstrated a higher efficacy in clinical decision-making for gastroenterology questions than Bard, suggesting the potential of LLMs as supplementary tools in clinical settings. However, the limitations of LLMs, including their inability to interpret images and consider real-life factors such as social determinants of health, highlight the need for further development before they can independently guide medical decisions

Decoding Digestive Dilemmas: ChatGPT outperforms Bard in Gastroenterology Clinical Questions from MKSAP-19

Abstract Aim: This study aims to evaluate the performance of large language models (LLMs), specifically OpenAI’s ChatGPT and Google Bard, in answering gastroenterology clinical questions from the Medical Knowledge Self-Assessment Program-19 (MKSAP-19), thereby assessing their potential utility in clinical decision-making within the field of gastroenterology. Materials and Methods: A comparative analysis was conducted using a dataset of 50 gastroenterology questions from MKSAP-19, assessing the ability of ChatGPT and Bard to provide correct answers without prior training or access to MKSAP-19 materials. The performance of each LLM was evaluated based on the percentage of correct answers, with a passing score set at 50%. Results: ChatGPT outperformed Bard, achieving a 68% success rate in answering the questions correctly, compared to Bard’s 44%. ChatGPT attempted all questions, while Bard abstained from answering two. The analysis also identified specific areas where both LLMs struggled, indicating gaps in their clinical reasoning capabilities. Conclusions: ChatGPT demonstrated a higher efficacy in clinical decision-making for gastroenterology questions than Bard, suggesting the potential of LLMs as supplementary tools in clinical settings. However, the limitations of LLMs, including their inability to interpret images and consider real-life factors such as social determinants of health, highlight the need for further development before they can independently guide medical decisions

Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against H-infinity optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and H-infinity optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.Comment: 12 pages, 22 figure

Functionality of C-Reactive Protein for Atheroprotection

C-reactive protein (CRP) is a pentameric molecule made up of identical monomers. CRP can be seen in three different forms: native pentameric CRP (native CRP), non-native pentameric CRP (nonnative CRP), and monomeric CRP (mCRP). Both native and nonnative CRP execute ligand-recognition functions for host defense. The fate of any pentameric CRP after binding to a ligand is dissociation into ligand-bound mCRP. If ligand-bound mCRP is proinflammatory, like free mCRP has been shown to be in vitro, then mCRP along with the bound ligand must be cleared from the site of inflammation. Once pentameric CRP is bound to atherogenic low-density lipoprotein (LDL), it reduces both formation of foam cells and proinflammatory effects of atherogenic LDL. A CRP mutant, that is non-native CRP, which readily binds to atherogenic LDL, has been found to be atheroprotective in a murine model of atherosclerosis. Thus, unlike statins, a drug that can lower only cholesterol levels but not CRP levels should be developed. Since non-native CRP has been shown to bind to all kinds of malformed proteins in general, it is possible that non-native CRP would be protective against all inflammatory states in which host proteins become pathogenic. If it is proven through experimentation employing transgenic mice that non-native CRP is beneficial for the host, then using a small-molecule compound to target CRP with the goal of changing the conformation of endogenous native CRP would be preferred over using recombinant non-native CRP as a biologic to treat diseases caused by pathogenic proteins such as oxidized LDL