Malicious User Experience Design Research for Cybersecurity

This paper explores the factors and theory behind the user-centered research that is necessary to create a successful game-like prototype, and user experience, for malicious users in a cybersecurity context. We explore what is known about successful addictive design in the fields of video games and gambling to understand the allure of breaking into a system, and the joy of thwarting the security to reach a goal or a reward of data. Based on the malicious user research, game user research, and using the GameFlow framework, we propose a novel malicious user experience design approac

Methamphetamine Effects on Adolescent Brain Development in Both Sexes

Vulnerability to drug addiction in adolescents (ADOLs) may lead to faster rates of addiction and higher rates of relapse. The brain is developing in adolescence, including the prefrontal cortex (PFC) and the nucleus accumbens (NAc), which are regions involved in self-control and reward systems, respectively. Further, females, both human and rat models, often exhibit faster rates of addiction than males. Research from this lab has found previously that adult rats of both sexes exposed to amphetamine during ADOL had reduced expression of dopamine D1 receptors (D1R) in the medial PFC but no change in the NAc. The purpose of this current study is to determine how age and sex influence methamphetamine’s (METH) effects on the brain and behavio

Transportation Energy Pathways LDRD.

This report presents a system dynamics based model of the supply-demand interactions between the USlight-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year2050. An important capability of our model is the ability to conduct parametric analyses. Others have reliedupon scenario-based analysis, where one discrete set of values is assigned to the input variables and used togenerate one possible realization of the future. While these scenarios can be illustrative of dominant trendsand tradeoffs under certain circumstances, changes in input values or assumptions can have a significantimpact on results, especially when output metrics are associated with projections far into the future. Thistype of uncertainty can be addressed by using a parametric study to examine a range of values for the inputvariables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors thatinfluence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction ofpetroleum consumption within the US LDV fleet. The underlying model emphasizes competition between13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technologicaldevelopment for the electric powertrain, battery performance, as well as the efficiency improvements inconventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. Theconsumer effective payback period, in particular, can significantly increase the market penetration rates ifextended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressiveGHG emission reduction targets, even as the current electricity source mix shifts away from coal and towardsnatural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicleefficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towardsmeeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 AcknowledgmentThe authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. ChristopherYang for their suggestions over the course of this project. This work was funded by the Laboratory DirectedResearch and Development program at Sandia National Laboratories.

Mitochondrial Genetic Background Modulates Bioenergetics and Susceptibility to Acute Cardiac Volume Overload

Dysfunctional bioenergetics has emerged as a key feature in many chronic pathologies such as diabetes and cardiovascular disease. This has led to the mitochondrial paradigm in which it has been proposed that mtDNA sequence variation contributes to disease susceptibility. In the present study we show a novel animal model of mtDNA polymorphisms, the MNX (mitochondrial–nuclear exchange) mouse, in which the mtDNA from the C3H/HeN mouse has been inserted on to the C57/BL6 nuclear background and vice versa to test this concept. Our data show a major contribution of the C57/BL6 mtDNA to the susceptibility to the pathological stress of cardiac volume overload which is independent of the nuclear background. Mitochondria harbouring the C57/BL6J mtDNA generate more ROS (reactive oxygen species) and have a higher mitochondrial membrane potential relative to those with C3H/HeN mtDNA, independent of nuclear background. We propose this is the primary mechanism associated with increased bioenergetic dysfunction in response to volume overload. In summary, these studies support the ‘mitochondrial paradigm’ for the development of disease susceptibility, and show that the mtDNA modulates cellular bioenergetics, mitochondrial ROS generation and susceptibility to cardiac stress