Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Multihazard Scenarios for Analysis of Compound Extreme Events

Abstract: California Energy Commission [500-15-005]; National Science Foundation Hazards-SEES Program [DMS 1331611]; National Oceanic and Atmospheric Administration Ecological Effects of Sea Level Rise Program [NA16NOS4780206]

Normative, social and cognitive predictors of entrepreneurial interest in china, vietnam and the philippines

Abstract: This study addresses normative, social and cognitive factors related to the interest in becoming an entrepreneur in China, Vietnam, and the Philippines. The study's findings are based on surveys of 782 business students in these countries. A rather consistent pattern of country differences was found on most of the measures, which may reflect differences in the historical, cultural, economic and political contexts of these nations. The results of this study emphasize the importance of developing both self-efficacy and close social supports in enhancing potential for entrepreneurial activity in these countries.

The role of evolution in the invasion process.

Abstract: Biological invasions occur when organisms are transported and become established in a new range in which they persist, proliferate, and spread (1). The negative consequences of invasions include loss of native biological diversity and community structure (and in extreme cases, the extinction of native species) (2); modification of ecosystem processes such as nutrient cycling and productivity patterns; alteration of disturbance regimes, especially the frequency of wildfires (3); reduced agricultural productivity; human health concerns; and enormous economic costs (1, 4). Consequently, invasive species are now considered to be one of the leading contributors to global change (5) and thus have been the focus of an extensive amount of ecological and ecosystem-level research. Much of this research has focused on answering a series of questions associated with predicting invasions: Which species will become invasive? Which life-history traits contribute to invasiveness? Which communities are susceptible to invasion? What will be the ecological and ecosystem-level consequences of invasion? Unfortunately, answers to these questions remain elusive (1). It is surprising to note, however, that little research has focused on the evolutionary aspects of biological invasions and addressed how evolutionary mechanisms may contribute to the success of an invasion (2, 6, 7). In contrast, the research of Lavergne and Molofsky (8) reported in this issue of PNAS examines the factors that contribute to range expansion and the invasiveness of Phalaris arundinacea L. (reed canarygrass) in North America and provides powerful insights into the role of evolution in the invasion process. The invasion process can be viewed as a series of steps that are initiated when propagules of a species (seeds, eggs, larvae, vegetative material, mature individuals, etc.) are sampled in their native range and transported to a new area (1, 4). These immigrants probably experience high mortality rates while in transit, and … †E-mail: snovak{at}boisestate.edu

Real-Time Detection of False Data Injection in Smart Grid Networks: An Adaptive CUSUM Method and Analysis

Abstract: A smart grid is delay sensitive and requires the techniques that can identify and react on the abnormal changes (i.e., system fault, attacker, shortcut, etc.) in a timely manner. In this paper, we propose a real-time detection scheme against false data injection attack in smart grid networks. Unlike the classical detection test, the proposed algorithm is able to tackle the unknown parameters with low complexity and process multiple measurements at once, leading to a shorter decision time and a better detection accuracy. The objective is to detect the adversary as quickly as possible while satisfying certain detection error constraints. A Markov-chain-based analytical model is constructed to systematically analyze the proposed scheme. With the analytical model, we are able to configure the system parameters for guaranteed performance in terms of false alarm rate, average detection delay, and missed detection ratio under a detection delay constraint. The simulations are conducted with MATPOWER 4.0 package for different IEEE test systems.