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 & Computer science. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Garnet: Witness to the Evolution of Destructive Plate Boundaries

Abstract: Thanks to its unique chemical and mechanical properties, garnet records evidence of rocks' paths through the crust at tectonic plate boundaries. The compositions of garnet and coexisting mineral phases permit metamorphic pressure and temperature to be determined, while garnet's compositional zoning allows the evolution of these parameters to be constrained. But careful study of garnet reveals far more, including the dehydration history of subducted oceanic crust, the depths reached during the earliest stages of continental collision, and the mechanisms driving heat and mass flow as orogens develop. Overall, chemical and textural characterization of garnet can be coupled with thermodynamic, thermoelastic, geochronologic, diffusion, and geodynamic models to constrain the evolution of rocks in a wide variety of settings.

Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Abstract: Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide.

Influencers of ethical beliefs and the impact on moral distress and conscientious objection

Abstract: Considering a growing nurse shortage and the need for qualified nurses to handle increasingly complex patient care situations, how ethical beliefs are influenced and the consequences that can occur when moral conflicts of right and wrong arise need to be explored. The aim of this study was to explore influencers identified by nurses as having the most impact on the development of their ethical beliefs and whether these influencers might impact levels of moral distress and the potential for conscientious objection. Nurses whose ethical beliefs were most influenced by their religious beliefs scored higher in levels of moral distress and demonstrated greater differences in areas of conscientious objection than did nurses who developed their ethical beliefs from influencers such as family values, life and work experience, political views or the professional code of ethics.

Participation, Representation, and Social Justice: Using Participatory Governance to Transform Representative Democracy

Abstract: The direct incorporation of citizens into complex policy-making processes is the most significant innovation of the “third wave” of democratization in the developing world. Participatory governance (PG) institutions are part of a new institutional architecture that increases the connections among citizens and government officials. This article draws from a single case of PG to explore how its particular mechanisms work to transform representative democracy. In the cases examined here, PG institutions are grafted onto representative democracy and existing state institutions. These are state-sanctioned venues that require the intense involvement of citizens and government officials, without which the programs would grind to a halt. These features can expand citizen participation, enrich political representation, and enhance social justice.

Characterization of groundwater contaminant source using Bayesian method

Abstract: Contaminant source identification in groundwater system is critical for remediation strategy implementation, including gathering further samples and analysis, as well as implementing and evaluating different remediation plans. Such problem is usually solved with the aid of groundwater modeling with lots of uncertainty, e.g. existing uncertainty in hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This paper presents an application of a dynamic framework coupling with a three dimensional groundwater modeling scheme in contamination source identification of groundwater. Markov Chain Monte Carlo (MCMC) is being applied to infer the possible location and magnitude of contamination source. Uncertainty existing in heterogonous hydraulic conductivity field is explicitly considered in evaluating the likelihood function. Unlike other inverse-problem approaches to provide single but maybe untrue solution, the MCMC algorithm provides probability distributions over estimated parameters. Results from this algorithm offer a probabilistic inference of the location and concentration of released contamination. The convergence analysis of MCMC reveals the effectiveness of the proposed algorithm. Further investigation to extend this study is also discussed.