A Comparison of Mobile Scanning to a Total Station Survey at the I-35 and IA 92 Interchange in Warren County, Iowa, August 15, 2012

The purpose of this project was to investigate the potential for collecting and using data from mobile terrestrial laser scanning (MTLS) technology that would reduce the need for traditional survey methods for the development of highway improvement projects at the Iowa Department of Transportation (Iowa DOT). The primary interest in investigating mobile scanning technology is to minimize the exposure of field surveyors to dangerous high volume traffic situations. Issues investigated were cost, timeframe, accuracy, contracting specifications, data capture extents, data extraction capabilities and data storage issues associated with mobile scanning. The project area selected for evaluation was the I-35/IA 92 interchange in Warren County, Iowa. This project covers approximately one mile of I-35, one mile of IA 92, 4 interchange ramps, and bridges within these limits. Delivered LAS and image files for this project totaled almost 31GB. There is nearly a 6-fold increase in the size of the scan data after post-processing. Camera data, when enabled, produced approximately 900MB of imagery data per mile using a 2- camera, 5 megapixel system. A comparison was done between 1823 points on the pavement that were surveyed by Iowa DOT staff using a total station and the same points generated through the MTLS process. The data acquired through the MTLS and data processing met the Iowa DOT specifications for engineering survey. A list of benefits and challenges is included in the detailed report. With the success of this project, it is anticipate[d] that additional projects will be scanned for the Iowa DOT for use in the development of highway improvement projects.

The PEDRO (Pocketable Electronic Devices in Radiation Oncology) project: how clinical practice is changing among young radiation oncologists.

Purpose. To evaluate the impact of mobile devices and apps on the daily clinical activity of young radiation oncologists. Methods. A web-based questionnaire was sent to 382 young (≤40 years) members of the Italian Association of Radiation Oncology (AIRO). The 14 items investigated the diffusion of mobile devices (smartphones and/or tablets), their impact on daily clinical activity, and possible differences perceived by the participants over time. Results. A total of 158 questionnaires were available for statistical evaluation (response rate 41%). Up to 75% of respondents declared they used an electronic device during their clinical activity. Conversely, 82% considered the impact of smartphones/tables on daily practice low to moderate. Daily device use increased significantly from 2009 to 2012, with high daily use rates rising from 5% to 39.9%. Fulfillment of professional needs was declared by less than 42% of respondents and compliance with app indications by 32%. Almost all physicians desired in 2012 a comprehensive website concerning a variety of apps covering radiation oncologists' needs. Conclusions. Mobile devices are widely used by young Italian radiation oncologists in their daily clinical practice, while the indications so obtained are not always followed. Nevertheless, it would be important to verify the consistency of information found within apps, in order to avoid potential errors that might be detrimental to patients.

Cambios en el uso y la concepción de las TIC, implementando el Mobile Learning.

This research analyses the actual use and conception of the ICT mobility that a life long learning group of students have. The students have participated in a Mobile Learning experience along an online postgraduate course, which was designed under a traditional e-learning perspective. The students received a tablet PC (iPad) in order to work at the course and also to use it in their personal and professional life. A complete and original pre-test / post-test questionnaire was applied before and after the course. This instrument was scientifically validated. Thru the questionnaire, uses tendency and students perceptions were studied. Frequencies, purposes, habits of use and valuation, as well as the device"s integration into their personal, social and professional life were studied. The analysis intents to apply the 'Social Technographics Profile" by Bernoff (2010) to classify, by profile groups, the users of the actual Internet. Finally a reflexion of the reasons and limits of the theory, in this study, and also the relation to reality is presented. The Inter-coding reliability and validity shows the possibility of applying the instrument on wider samples in order to get a closer look to the uses and actual conceptions of the ubiquitous ICTs.

Working in a digital world: new leadership practices for the development of a digital library workforce

Digital library developments are part of a global move in many sectors of society toward virtual work and electronic services made possible by the advances in information technology. This environment requires new attitudes and skills in the workforce and therefore leaders who understand the global changes underlying the new information economy and how to lead and develop such a workforce. This article explores ways to develop human resources and stimulate creativity to capitalize on the immense potential of digital libraries to educate and empower social change. There is a shortage of technically skilled workers and even more so of innovators. Retention and recruitment is one of the greatest obstacles to developing digital library services and information products.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Iowa Civil Rights Commission 48 Years of Working For a State Free of Discrimination Through Enforcement of Civil Rights Laws, FY2013

Annual Report for the Iowa Civil Rights Commission

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Association of working conditions with self-reported work-related symptoms: results from the Swiss dataset of the European Working Conditions Survey

Working conditions are important determinants of health. The aims of this article are to 1) identify working conditions and work characteristics that are associated with workers' perceptions that their work is harmful to their health and 2) identify with what symptoms these working conditions are associated.We used the Swiss dataset from the 2005 edition of the European Working Conditions Survey. The dependent variable was based on the question "Does your work affect your health?". Logistic regression was used to identify a set of variables collectively associated with self-reported work-related adverse health effects.A total of 330 (32%) participants reported having their health affected by work. The most frequent symptoms included backache (17.1%), muscular pains (13.1%), stress (18.3%) and overall fatigue (11.7%). Scores for self-reported exposure to physicochemical risks, postural and physical risks, high work demand, and low social support were all significantly associated with workers' perceptions that their work is harmful to their health, regardless of gender or age. A high level of education was associated with stress symptoms, and reports that health was affected by work was associated with low job satisfaction.Many workers believe that their work affects their health. Health specialists should pay attention to the potential association between work and their patients' health complaints. This is particularly relevant when patients mention symptoms such as muscular pains, backache, overall fatigue, and stress. Specific attention should be given to complaints of stress in highly educated workers.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.