Indoor Multipurpose Use and Training Facility Revenue Bond Funds, Iowa State University of Science and Technology, Independent Auditor's Report, Basic Financial Statements and Supplementary Information, June 30, 2006

State University Audit Report

La prévention du tabagisme passif [Prevention of air pollution by indoor tobacco smoke in France]

A majority of smokers and non-smokers mind tobacco smoke. Passive smoking causes death by sudden infant death, lung cancer and coronary heart disease. 3000 to 6000 persons are killed every year in France. The lack of implementation of the Evin's law published in 1991 explains why non-smokers are not given the protection they can expect. The trend of scientific knowledge and of French and international public opinions support a growing demand for a complete protection of non-smokers with a total ban of smoking in all public or working places.

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Audit of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology (Iowa State University) as of and for the year ended June 30, 2007

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Report of the Indoor Multipurpose Use and Training Facility Revenue Bond Funds of Iowa State University of Science and Technology as of and for the year ended June 30, 2008

Preliminary study of insects associated to indoor body decay in Colombia

Preliminary study of insects associated to indoor body decay in Colombia. This is the first report studying insects associated to indoor body decay process of a white pig (Sus scrofa) (Artiodactyla, Suidae) in a controlled indoor environment in an urban area of Florencia city, Amazonia Piedmont, Colombia. For a period of 54 days, 9,220 individuals (immature and adults), distributed in 3 orders, 5 families, 10 genera, and 10 species were collected using entomological nets and tweezers. Five decaying stages are described (fresh, bloated, active decay, advanced decay and remains). During the fresh stage we recorded Cochliomyia macellaria (Fabricius, 1775), Chrysomya albiceps (Wiedemann, 1819), Ophyra aenescens (Wiedemann, 1830), Oxysarcodexia sp., Lepidodexia sp. and Lasiophanes sp.; during the bloating stage C. macellaria, C. albiceps, Lucilia eximia (Wiedemann, 1819), Hemilucillia semidiaphana (Rondani, 1850), Musca domestica Linnaeus, 1758, O. aenescens, Oxysarcodexia sp., Lepidodexia sp., Dermestes maculatus De Geer, 1774 and Lasiphanes sp.; during the active decay C. macellaria, C. albiceps, L. eximia, M. domestica, O. aenescens, Lepidodexia sp. D. maculatus and Lasiophanes sp.; during the advanced decay C. macellaria, C. albiceps, M. domestica, Lepidodexia sp. and Lasiophanes sp.; and during the remains stage C. albiceps, D. maculatus and Lasiophanes sp. The insects were sorted out in 3 ecological categories; necrophagous, predators and parasites and sarco-saprophagous. According to Chao and Jack estimators, total richness was observed on day 20, with 100% of the expected species.

On-shoe wearable sensors for gait and turning assessment of patients with Parkinson's disease.

Assessment of locomotion through simple tests such as timed up and go (TUG) or walking trials can provide valuable information for the evaluation of treatment and the early diagnosis of people with Parkinson's disease (PD). Common methods used in clinics are either based on complex motion laboratory settings or simple timing outcomes using stop watches. The goal of this paper is to present an innovative technology based on wearable sensors on-shoe and processing algorithm, which provides outcome measures characterizing PD motor symptoms during TUG and gait tests. Our results on ten PD patients and ten age-matched elderly subjects indicate an accuracy ± precision of 2.8 ± 2.4 cm/s and 1.3 ± 3.0 cm for stride velocity and stride length estimation compared to optical motion capture, with the advantage of being practical to use in home or clinics without any discomfort for the subject. In addition, the use of novel spatio-temporal parameters, including turning, swing width, path length, and their intercycle variability, was also validated and showed interesting tendencies for discriminating patients in ON and OFF states and control subjects.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults

In order to distinguish dysfunctional gait; clinicians require a measure of reference gait parameters for each population. This study provided normative values for widely used parameters in more than 1400 able-bodied adults over the age of 65. We also measured the foot clearance parameters (i.e., height of the foot above ground during swing phase) that are crucial to understand the complex relationship between gait and falls as well as obstacle negotiation strategies. We used a shoe-worn inertial sensor on each foot and previously validated algorithms to extract the gait parameters during 20 m walking trials in a corridor at a self-selected pace. We investigated the difference of the gait parameters between male and female participants by considering the effect of age and height factors. Besides; we examined the inter-relation of the clearance parameters with the gait speed. The sample size and breadth of gait parameters provided in this study offer a unique reference resource for the researchers.

Contribution of fine particulate matter sources to indoor exposure in bars, restaurants, and cafes

This study investigated the contribution of sources and establishment characteristics, on the exposure to fine particulate matter (PM(2.5)) in the non-smoking sections of bars, cafes, and restaurants in central Zurich. PM(2.5)-exposure was determined with a nephelometer. A random sample of hospitality establishments was investigated on all weekdays, from morning until midnight. Each visit lasted 30 min. Numbers of smokers and other sources, such as candles and cooking processes, were recorded, as were seats, open windows, and open doors. Ambient air pollution data were obtained from public authorities. Data were analysed using robust MM regression. Over 14 warm, sunny days, 102 establishments were measured. Average establishment PM(2.5) concentrations were 64.7 microg/m(3) (s.d. = 73.2 microg/m(3), 30-min maximum 452.2 microg/m(3)). PM(2.5) was significantly associated with the number of smokers, percentage of seats occupied by smokers, and outdoor PM. Each smoker increased PM(2.5) on average by 15 microg/m(3). No associations were found with other sources, open doors or open windows. Bars had more smoking guests and showed significantly higher concentrations than restaurants and cafes. Smokers were the most important PM(2.5)-source in hospitality establishments, while outdoor PM defined the baseline. Concentrations are expected to be even higher during colder, unpleasant times of the year. PRACTICAL IMPLICATIONS: Smokers and ambient air pollution are the most important sources of fine airborne particulate matter (PM(2.5)) in the non-smoking sections of bars, restaurants, and cafes. Other sources do not significantly contribute to PM(2.5)-levels, while opening doors and windows is not an efficient means of removing pollutants. First, this demonstrates the impact that even a few smokers can have in affecting particle levels. Second, it implies that creating non-smoking sections, and using natural ventilation, is not sufficient to bring PM(2.5) to levels that imply no harm for employees and non-smoking clients. [Authors]

Behaviour of pre-parturient sows housed in intensive outdoor or indoor systems

This work compared the behaviour of pre-parturient sows housed in intensive confined and outdoor systems, during the morning and afternoon prior to delivery. Eight sows were kept individually in farrowing crates or in paddocks with access to fresh pasture from 8 to 10 days before expected parturition. All outdoor sows built a nest within 24 hours before farrowing. On the morning and afternoon before farrowing they spent 6.5% of the time collecting grass and 7.5% of the time organizing the nest. Outdoor sows were observed more often than confined sows rooting (4.60% vs. 0.25%), feeding (10.2% vs. 3.0%), standing (51% vs. 13%) and walking (8% vs. 0%). Indoor sows redirected the nesting behaviour to other behaviours like attempts to dig the ground, nosing, biting and rooting parts of the crate, feeder or drinker, during 4.7% of the time. They also spent more time than outdoor sows inactive (85% vs. 60%), lying (72% vs. 41%), drinking (2.1% vs. 0.5%) and vacuum chewing (3.7% vs. 0.1%). The pre-parturient behaviour of sows was considerably affected by the husbandry system. The outdoor system appears to be more appropriate for the sows' welfare than the conventional confinement.