Background Checks for Firearms Sales and Loans: Law, History, and Policy

This article examines past and present systems requiring that a person receive permission before buying or borrowing a firearm. The article covers laws from the eighteenth century to the present. Such laws have traditionally been rare in the United States. The major exceptions are antebellum laws of the slaves states, and of those same states immediately after the Civil War, which forbade gun ownership by people of color, unless the individual had been granted government permission. Today “universal background checks” are based on a system created by former New York City Mayor Michael Bloomberg and his “Everytown” lobby. Such laws have been enacted in several states, and also proposed as federal legislation. Besides covering the private sale of firearms, they also cover most loans of firearms and the return of loaned firearms. By requiring that almost all loans and returns may only be processed by a gun store, these laws dangerously constrict responsible firearms activities, such as safety training and safe storage. Massachusetts, Connecticut, and California are among the jurisdictions which have enacted less restrictive, more effective legislation which create controls on private firearms sales, without inflicting so much harm on firearms safety.

Short-term Load Forecasting Using Neural Network For Future Smart Grid Application

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.