Search

Critical infrastructure is defined as the core health, safety, and security facilities that maintain key social functions at the time of an emergency. With the weather instability driven by the effects of climate change, many normally dry lands are facing an increase in rainfall-induced floods. The expected lack of preparedness in these cities to handle floods raises concerns regarding the possible impacts on regular civilian’s lives. This problem requires a thorough analysis of cities’ ability to keep critical infrastructure safe, accessible, and easy to reach during disrupting events such as floods. In this study, we propose a new methodology to analyze the impact of floods on arid cities’ critical infrastructure using open data. We showcase our methodology on the city of Dubai which was hit by a thunderstorm earlier this year causing nation-wide floods. Our quantitative analysis was capable of determining that the Al Quoz area suffered the most from floods due to issues related to the city’s connectivity and critical infrastructure distribution. The main road connecting both sides of the area was completely blocked due to the floods with distant alternative connections while critical infrastructure was mainly concentrated on one of the two sides only. This resulted in a high increase in travel time delay and lower reachability scores. On the other hand, the Airport and Trade Center areas show a high reachability and low travel time delays as per the results obtained.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

BackgroundHealthcare crowdsourcing events (e.g. hackathons) facilitate interdisciplinary collaboration and encourage innovation. Peer-reviewed research has not yet considered a healthcare crowdsourcing event focusing on generative artificial intelligence (GenAI), which generates text in response to detailed prompts and has vast potential for improving the efficiency of healthcare organizations. Our event, the New York University Langone Health (NYULH) Prompt-a-thon, primarily sought to inspire and build AI fluency within our diverse NYULH community, and foster collaboration and innovation. Secondarily, we sought to analyze how participants' experience was influenced by their prior GenAI exposure and whether they received sample prompts during the workshop.MethodsExecuting the event required the assembly of an expert planning committee, who recruited diverse participants, anticipated technological challenges, and prepared the event. The event was composed of didactics and workshop sessions, which educated and allowed participants to experiment with using GenAI on real healthcare data. Participants were given novel "project cards" associated with each dataset that illuminated the tasks GenAI could perform and, for a random set of teams, sample prompts to help them achieve each task (the public repository of project cards can be found at https://github.com/smallw03/NYULH-Generative-AI-Prompt-a-thon-Project-Cards). Afterwards, participants were asked to fill out a survey with 7-point Likert-style questions.ResultsOur event was successful in educating and inspiring hundreds of enthusiastic in-person and virtual participants across our organization on the responsible use of GenAI in a low-cost and technologically feasible manner. All participants responded positively, on average, to each of the survey questions (e.g., confidence in their ability to use and trust GenAI). Critically, participants reported a self-perceived increase in their likelihood of using and promoting colleagues' use of GenAI for their daily work. No significant differences were seen in the surveys of those who received sample prompts with their project task descriptions.ConclusionThe first healthcare Prompt-a-thon was an overwhelming success, with minimal technological failures, positive responses from diverse participants and staff, and evidence of post-event engagement. These findings will be integral to planning future events at our institution, and to others looking to engage their workforce in utilizing GenAI.

Abstract In the last few decades, there has been a definitive shift in public support for compact development – characterized by high densities, more multifamily residential use, and effective public transit. The allure of compact development is because, along with sustainability benefits, it offers multiple lifestyle benefits, such as more significant opportunities for socialization and easy access to amenities. Greater possibilities of frequent and longer interpersonal interactions attract residents to such communities. However, given the recent pandemic, it is logical to be concerned about how future public support for compact development may change. This study analyzes data from a national online survey (n = 1100) conducted in the United States toward the end of the COVID-19 pandemic (April 2022). This research aims to assess the relationship between perceived concern for COVID-19 transmission and public support for compact development. The results from SEM analysis suggest that people more concerned about COVID-19 transmission are less supportive of compact development policies. People from areas with higher density and more COVID-19 cases are likely to have greater concern for COVID-19 transmission, which may decrease support for compact development in these areas (mediated relationship). Individuals who rely on news, online media, friends or family for COVID-19 information and single-family residents are also less likely to support compact development. In contrast, while older adults are likely to have higher concern for COVID-19 transmission, they are likely to support compact development. Higher-income households are less likely to be concerned about COVID-19 transmission but are more supportive of compact development. These findings suggest that the perceived threat of disease transmission will likely result in decreased public support for compact development. To ensure continued public support, urban policymakers must allay public fear of virus transmission in compact built environments by incorporating public health measures for controlling virus transmission in compact urban environments.

Abstract This paper proposes a disturbance‐observer‐based control (DOBC) scheme for frequency and voltage regulation in modern power systems with high renewable energy sources (RES) penetration. The proposed approach acts as a feed‐forward control that improves the dynamic performance of the conventional proportional‐integral‐derivative controller. The proposed voltage and frequency control has been validated through hardware‐in‐loop implementation on OPAL‐RT, and testing on laboratory‐scale experimental test setup. The robustness of the proposed control scheme has been validated through simulations under worst‐case and stochastic uncertainties to mitigate real‐time variability in RES output and load. Real‐time simulation results depict superior performance of the proposed control strategy in comparison to several well‐established techniques under practical operating conditions, in the presence of communication delay and white noise. To validate the proposed control on laboratory‐scale experimental setup, the digital twin of the physical plant transfer function has been designed. Results reveal that the proposed DOBC control scheme drastically improves the system performance without rendering much computational burden under practical operation scenarios.