Implementation of Attending Narcotics Anonymous Meetings in Addiction Medicine Curriculum

Following the COVID-19 pandemic in 2020, opioid-related deaths were augmented, highlighting an area of concern for the state of New Jersey. Although the state has taken steps to combat the number of opioid-related deaths, this continues to be a problem emphasizing the need for increased interventions to decrease the number of opioid-related deaths and to improve long-term healthcare outcomes. Rates of stigma surrounding addiction, and more specifically, opioid addiction, are high within the general population and amongst healthcare professionals.2,3 A national survey conducted in 2016 revealed that two-thirds of primary care physicians surveyed viewed people with opioid use disorder as dangerous.2 In those experiencing substance abuse disorder, the anticipation and fear of bias contribute to isolation, hiding high-risk activities, and can prevent individuals from seeking treatment, such as harm-reduction programs.2 A proposed intervention to help combat this problem is the implementation of attending Narcotics Anonymous meetings into the Area of Distinction (AOD) in the Addiction Medicine curriculum. This intervention allows medical students to listen to the experiences of people dealing with substance abuse disorder, helping dispel stigmas and implicit biases that may exist early in a student\u27s medical career. By providing early exposure to variations in how addiction can present itself and affect members of our community, it is possible to improve healthcare outcomes and, hopefully, patient retention

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

Electric vehicles (EVs) have become a prominent alternative to fossil fuel vehicles in the modern transportation industry due to their competitive benefits of carbon neutrality and environment friendliness. The tremendous adoption of EVs leads to a significant increase in demand for charging infrastructure. But, the scarcity of charging stations (CSs) concerns efficient and reliable EV charging. Existing studies discussed EV energy consumption prediction schemes at the CS without analyzing the affecting parameters such as energy demand, weather, day, etc. In this regard, we have proposed an energy consumption and distribution framework for EVs in a smart grid environment for efficient EV charging after analyzing the affecting parameters such as location, weekday, weekend, and user. Moreover, we have considered EV dataset to perform a detailed and deep analysis of energy consumption patterns based on the aforementioned parameters such as CS (Station ID) within the location (Location ID), weekday, weekend, and user (UserID). The main aim is to understand the smart grid-based electricity distribution to the CS by analyzing energy consumption patterns for reliable EV charging. We have done different analysis on different parameters and present their graphical representations