Stoking the Flames of Wellness: An Exploration of Factors that Influence West Virginia Firefighters\u27 Health Behaviors

The concern over health and wellness among firefighters in the US has escalated recently due to increasing obesity rates, on-duty cardiovascular event risk, and job stress. Besides unique aspects of the work, a number of other barriers to health and wellness have been identified within the firefighter culture. Using a qualitative approach with multiple sources of data, the current study sought to answer the question, what impacts the health of firefighters in West Virginia? Eight focus groups, and Photovoice data from nine participants were inductively analyzed using guidelines from consensual qualitative research. House tours were also done to provide ethnographic data. Seven main factors were identified as impacting firefighters\u27 health in West Virginia: stress, nutrition, general factors, physical activity, sleep, motivation, and job related concerns. Participants also discussed potential solutions to health concerns among firefighters, such as incentive and education programs. Recommendations for future studies, including possible interventions, are discussed

A Proposal for an Environmental Decision Support System at the Regional Level: Concepts, Support Methodology, Tools and their Terminology

One of the goals of IIASA's research activities in the area of environmental quality modeling is the integration of data and models in a unified framework to assist decision makers with the management of complex environmental systems. Building on IIASA's work undertaken within the WELMM (Water, Energy, Land, Materials and Manpower) project of the former Resources and Environment Area and the work on Decision Support Systems of the former Management and Technology Area, a conceptual framework for an environmental decision support system (EDSS) has been developed and is presented in this paper. The proposed EDSS has been developed with the interest and the financial support of the CSI, the Center for Information Systems of the Regional Government of Piemonte, Italy. The main issue addressed by this paper is to devise a system assisting decision makers in tackling environmental problems at the regional level. These decisions are typically characterized by a combination of both structured (formalizable, described in a quantitative model) and unstructured elements (incomplete information, undefined cause-effect relationships, influence of political objectives, public perception, consideration of aesthetics, etc.). The proposed EDSS enables the user to use models and data, of relevance to a particular task, which are embedded in the EDSS in the form of a process information system. The specific feature of this process information system is that it contains processes of anthropogenic nature (the socio-economic activities being the cause of environmental impacts like power plants, industrial production units, etc.) as well as natural processes determining the spatial/temporal distribution and the extent of environmental quality changes (like the dispersion and deposition of air pollutants and their effect on human population, vegetation and wildlife). The system ensures that the data and models, which have been developed in the context of specific EDSS applications are documented right from the outset and become thus equally available for further use. This becomes especially important in view of the long-term effort to be put into the development of data and models dealing with the large number of environmental problems that governments, industry and academic institutions are confronted with at the regional level

Data-Driven Abstraction-Based Control Synthesis

This paper studies formal synthesis of controllers for continuous-spacesystems with unknown dynamics to satisfy requirements expressed as lineartemporal logic formulas. Formal abstraction-based synthesis schemes rely on aprecise mathematical model of the system to build a finite abstract model,which is then used to design a controller. The abstraction-based schemes arenot applicable when the dynamics of the system are unknown. We propose adata-driven approach that computes the growth bound of the system using afinite number of trajectories. The growth bound together with the sampledtrajectories are then used to construct the abstraction and synthesise acontroller. Our approach casts the computation of the growth bound as a robust convexoptimisation program (RCP). Since the unknown dynamics appear in theoptimisation, we formulate a scenario convex program (SCP) corresponding to theRCP using a finite number of sampled trajectories. We establish a samplecomplexity result that gives a lower bound for the number of sampledtrajectories to guarantee the correctness of the growth bound computed from theSCP with a given confidence. We also provide a sample complexity result for thesatisfaction of the specification on the system in closed loop with thedesigned controller for a given confidence. Our results are founded onestimating a bound on the Lipschitz constant of the system and provideguarantees on satisfaction of both finite and infinite-horizon specifications.We show that our data-driven approach can be readily used as a model-freeabstraction refinement scheme by modifying the formulation of the growth boundand providing similar sample complexity results. The performance of ourapproach is shown on three case studies.<br

A simple ocean bottom hydrophone with 200 megabyte data capacity

The Woods Hole Ocean Bottom Hydrophone instrument records the digitized output of a single hydrophone sensor at rates between 250 and 1200 samples per second with a dynamic range of 98 dB and can be deployed at depths to 600 meters. The unit's 200 megabyte disk recorder allows operation for periods up to 5 days. Designed for typical marine seismic refraction operations the unit is reliable and simple to deploy and recover. A detailed description is provided of the instrument design and application including transfer function, clock accuracy, data format, sample data and power requirements.Funding provided by the National Science Foundation under Grant Nos. OCE-9019918 and OCE-8917628

Ancestral alleles and population origins: Inferences depend on mutation rate

Previous studies have found that at most human loci, ancestral alleles are African, in the sense that they reach their highest frequency there. Conventional wisdom holds that this reflects a recent African origin of modern humans. This paper challenges that view by showing that the empirical pattern (of elevated allele frequencies within Africa) is not as pervasive as has been thought. We confirm this African bias in a set of mainly protein-coding loci, but find a smaller bias in Alu insertion polymorphisms, and an even smaller bias in noncoding loci. Thus, the strong bias that was originally observed must reflect some factor that varies among data sets - something other than population history. This factor may be the per-locus mutation rate: the African bias is most pronounced in loci where this rate is high. The distribution of ancestral alleles among populations has been studied using 2 methods. One of these involves comparing the fractions of loci that reach maximal frequency in each population. The other compares the average frequencies of ancestral alleles. The first of these methods reflects history in a manner that depends on the mutation rate. When that rate is high, ancestral alleles at most loci reach their highest frequency in the ancestral population. When that rate is low, the reverse is true. The other method - comparing averages - is unresponsive. Average ancestral allele frequencies are affected neither by mutation rate nor by the history of population size and migration. In the absence of selection and ascertainment bias, they should be the same everywhere. This is true of one data set, but not of 2 others. This also suggests the action of some factor, such as selection or ascertainment bias, that varies among data sets. © The Author 2007. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved

Dynamics of precipitation pattern formation at geothermal hot springs

We formulate and model the dynamics of spatial patterns arising during the precipitation of calcium carbonate from a supersaturated shallow water flow. The model describes the formation of travertine deposits at geothermal hot springs and rimstone dams of calcite in caves. We find explicit solutions for travertine domes at low flow rates, identify the linear instabilities which generate dam and pond formation on sloped substrates, and present simulations of statistical landscape evolution

Robust simulation functions with disturbance refinement

Simulation functions are Lyapunov-like functions defined over the Cartesian product of state spaces of two (un)perturbed systems, a.k.a., concrete and abstract systems, to relate output trajectories of abstract systems to those of concrete ones while the mismatch between two systems remains within some guaranteed error bounds. In this work, we approximate concrete systems with abstractions with lower dimensions (reduced-order models) and develop robust simulation functions further to consider the perturbation in the abstract system by designing an interface function for the disturbance. The proposed approach allows concrete systems to have large disturbances, which is the case in many real-life applications, while noticeably reducing the closeness error between the two systems. Accordingly, this enables controller design using a reduced-order form of the concrete system and reducing the computational load required for formal synthesis. We demonstrate the efficacy of our approaches on linear control systems by synthesising a formal controller for a 9-state area of the known New England 39-Bus Test System, using only a 3-state abstract system