The study of association of fetal and maternal factors in the occurrence of hyperbilirubinemia in early neonatal period

Background: Hyperbilirubinemia in neonates is considered to be one of the common phenomena which generally occurs during the first week of life and usually leads to NICU admission in both term and preterm new-born babies. It is also regarded as one of the most common causes which leads to neonatal morbidity and mortality.Methods: A total of 100 neonates along with their mothers were enrolled in the study from time period between 2018 to March 2019. Newborns were assessed daily for the jaundice and serum bilirubin levels were done. Various fetal-maternal factors included in proforma were. analysed to find out the association of feto-maternal factors in the occurrence of significant neonatal hyperbilirubinemia. Two groups, group A =15.7 mg/dl were taken. For data analysis chi square test is applied and p-value is calculated.Results: Statistically significant association between total serum bilirubin with neonatal factors like birth weight (p<0.014), maturity (p<0.011), period of gestation (p<0.003), and heart rate abnormality (p<0.005) and maternal factors like age in years (p<0.05), oral contraceptive pills use (p<0.005),  and anti-epileptics use (p<0.034) were found to be linked to neonatal hyperbilirubinemia.Conclusions: Neonatal jaundice should be considered as the main policy in all health care settings of the country. Therefore, identification of factors affecting the incidence of jaundice can be effective in preventing susceptible predisposing factors in new-borns and high-risk mothers

Do Users Write More Insecure Code with AI Assistants?

We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.Comment: 18 pages, 16 figure

Assistive Teaching of Motor Control Tasks to Humans

Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teachingComment: 22 pages, 14 figures, NeurIPS 202

A Novel Approach for Triggering the Serverless Function in Serverless Environment

Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend

SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open source Python package specifically developed for fast data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude estimation, seismic phase picking, and polarity identification. We introduce upgraded versions of previously published models such as CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state of the art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIMERT, DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the potential to be used for real time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem

Chandrayaan-3 Alternate Landing Site: Pre-Landing Characterisation

India's third Moon mission Chandrayaan 3 will deploy a lander and a rover at a high latitude location of the Moon enabling us to carry out first ever in-situ science investigations of such a pristine location that will potentially improve our understanding on primary crust formation and subsequent modification processes. The primary landing site (PLS), is situated at 69.367621 degS, 32.348126 degE. As a contingency, an alternate landing site (ALS) was also selected at nearly the same latitude but nearly 450 km west to PLS. In this work, a detailed study of the geomorphology, composition, and temperature characteristics of ALS has been carried out using the best-ever high resolution Chandrayaan 2 OHRC DEMs and Ortho images, datasets obtained from Chandrayaan 1 and on-going Lunar Reconnaissance Orbiter. For understanding the thermophysical behaviour, we used a well-established thermophysical model. We found that the Chandrayaan 3 ALS is characterised by a smooth topography with an elevated central part. The ALS is a scientifically interesting site with a high possibility of sampling ejecta materials from Tycho and Moretus. Based on the spectral and elemental analysis of the site, Fe is found to be near approx. 4.8 wt.%, with Mg approx. 5 wt.%, and Ca approx. 11 wt.%. Compositionally, ALS is similar to PLS with a highland soil composition. Spatial and diurnal variability of around 40 K and 175 K has been observed in the surface temperatures at ALS. Although belonging to similar location like PLS, ALS showed reduced daytime temperatures and enhanced night-time temperatures compared to PLS, indicating a terrain of distinctive thermophysical characteristics. Like PLS, ALS is also seems to be an interesting site for science investigations and Chandrayaan 3 is expected to provide new insights into the understanding of lunar science even if it happens to land in the alternate landing site.Comment: 13 pages, 7 figure