Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

The influence of applying European default traffic values to the making of a noise map was evaluated in a typical environment like Palma de Mallorca. To assess these default traffic values, a first model has been created and compared with measured noise levels. Subsequently a second traffic model, improving the input data used for the first one, has been created and validated according to the deviations. Different methodologies were also examined for collecting model input data that would be of higher quality, by analysing the improvement generated in the reduction in the uncertainty of the noise map introduced by the road traffic noise emission

A Semantic Scraping Model for Web Resources - Applying Linked Data to Web Page Screen Scraping

In spite of the increasing presence of Semantic Web Facilities, only a limited amount of the available resources in the Internet provide a semantic access. Recent initiatives such as the emerging Linked Data Web are providing semantic access to available data by porting existing resources to the semantic web using different technologies, such as database-semantic mapping and scraping. Nevertheless, existing scraping solutions are based on ad-hoc solutions complemented with graphical interfaces for speeding up the scraper development. This article proposes a generic framework for web scraping based on semantic technologies. This framework is structured in three levels: scraping services, semantic scraping model and syntactic scraping. The first level provides an interface to generic applications or intelligent agents for gathering information from the web at a high level. The second level defines a semantic RDF model of the scraping process, in order to provide a declarative approach to the scraping task. Finally, the third level provides an implementation of the RDF scraping model for specific technologies. The work has been validated in a scenario that illustrates its application to mashup technologies

Generating descriptions that summarize geospatial and temporal data

Effective data summarization methods that use AI techniques can help humans understand large sets of data. In this paper, we describe a knowledge-based method for automatically generating summaries of geospatial and temporal data, i.e. data with geographical and temporal references. The method is useful for summarizing data streams, such as GPS traces and traffic information, that are becoming more prevalent with the increasing use of sensors in computing devices. The method presented here is an initial architecture for our ongoing research in this domain. In this paper we describe the data representations we have designed for our method, our implementations of components to perform data abstraction and natural language generation. We also discuss evaluation results that show the ability of our method to generate certain types of geospatial and temporal descriptions.

Bayesian model selection of structural explanatory models: Application to road accident data

Using the Bayesian approach as the model selection criteria, the main purpose in this study is to establish a practical road accident model that can provide a better interpretation and prediction performance. For this purpose we are using a structural explanatory model with autoregressive error term. The model estimation is carried out through Bayesian inference and the best model is selected based on the goodness of fit measures. To cross validate the model estimation further prediction analysis were done. As the road safety measures the number of fatal accidents in Spain, during 2000-2011 were employed. The results of the variable selection process show that the factors explaining fatal road accidents are mainly exposure, economic factors, and surveillance and legislative measures. The model selection shows that the impact of economic factors on fatal accidents during the period under study has been higher compared to surveillance and legislative measures.

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Comparing in vitro, in situ, and in vivo experimental data in a three-dimensional model of mammalian cochlear mechanics

Normal mammalian hearing is refined by amplification of the motion of the cochlear partition. This partition, comprising the organ of Corti sandwiched between the basilar and tectorial membranes, contains the outer hair cells that are thought to drive this amplification process. Force generation by outer hair cells has been studied extensively in vitro and in situ, but, to understand cochlear amplification fully, it is necessary to characterize the role played by each of the components of the cochlear partition in vivo. Observations of cochlear partition motion in vivo are severely restricted by its inaccessibility and sensitivity to surgical trauma, so, for the present study, a computer model has been used to simulate the operation of the cochlea under different experimental conditions. In this model, which uniquely retains much of the three-dimensional complexity of the real cochlea, the motions of the basilar and tectorial membranes are fundamentally different during in situ- and in vivo-like conditions. Furthermore, enhanced outer hair cell force generation in vitro leads paradoxically to a decrease in the gain of the cochlear amplifier during sound stimulation to the model in vivo. These results suggest that it is not possible to extrapolate directly from experimental observations made in vitro and in situ to the normal operation of the intact organ in vivo.

Stability and sensitivity analysis of Be-CoDiS, an epidemiological model to predict the spread of human diseases between countries. Validation with data from the 2014-16 West African Ebola Virus Disease epidemic.

Ebola virus disease is a lethal human and primate disease that requires a particular attention from the international health authorities due to important recent outbreaks in some Western African countries and isolated cases in European and North-America continents. Regarding the emergency of this situation, various decision tools, such as mathematical models, were developed to assist the authorities to focus their efforts in important factors to eradicate Ebola. In a previous work, we have proposed an original deterministic spatial-temporal model, called Be-CoDiS (Between-Countries Disease Spread), to study the evolution of human diseases within and between countries by taking into consideration the movement of people between geographical areas. This model was validated by considering numerical experiments regarding the 2014-16 West African Ebola Virus Disease epidemic. In this article, we propose to perform a stability analysis of Be-CoDiS. Our first objective is to study the equilibrium states of simplified versions of this model, limited to the cases of one an two countries, and to determine their basic reproduction ratios. Then, in order to give some recommendations for the allocation of resources used to control the disease, we perform a sensitivity analysis of those basic reproduction ratios regarding the model parameters. Finally, we validate the obtained results by considering numerical experiments based on data from the 2014-16 West African Ebola Virus Disease epidemic.

An Investigation of Ozone Related Data and Development of a Model to Evaluate Ozone Episodes for Exceptional Event Criteria

The EPA promulgated the Exceptional Events Rule codifying guidance regarding exclusion of monitoring data from compliance decisions due to uncontrollable natural or exceptional events. This capstone examines documentation systems utilized by agencies requesting data be excluded from compliance decisions due to exceptional events. A screening tool is developed to determine whether an event would meet exceptional event criteria. New data sources are available to enhance analysis but evaluation shows many are unusable in their current form. The EPA and States must collaborate to develop consistent evaluation methodologies documenting exceptional events to improve the efficiency and effectiveness of the new rule. To utilize newer sophisticated data, consistent, user-friendly translation systems must be developed.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

A hydrological–economic model for sustainable groundwater use in sparse-data drylands: Application to the Amtoudi Oasis in southern Morocco, northern Sahara

A hydrological–economic model is introduced to describe the dynamics of groundwater-dependent economics (agriculture and tourism) for sustainable use in sparse-data drylands. The Amtoudi Oasis, a remote area in southern Morocco, in the northern Sahara attractive for tourism and with evidence of groundwater degradation, was chosen to show the model operation. Governing system variables were identified and put into action through System Dynamics (SD) modeling causal diagrams to program basic formulations into a model having two modules coupled by the nexus ‘pumping’: (1) the hydrological module represents the net groundwater balance (G) dynamics; and (2) the economic module reproduces the variation in the consumers of water, both the population and tourists. The model was operated under similar influx of tourists and different scenarios of water availability, such as the wet 2009–2010 and the average 2010–2011 hydrological years. The rise in international tourism is identified as the main driving force reducing emigration and introducing new social habits in the population, in particular concerning water consumption. Urban water allotment (PU) was doubled for less than a 100-inhabitant net increase in recent decades. The water allocation for agriculture (PI), the largest consumer of water, had remained constant for decades. Despite that the 2-year monitoring period is not long enough to draw long-term conclusions, groundwater imbalance was reflected by net aquifer recharge (R) less than PI + PU (G < 0) in the average year 2010–2011, with net lateral inflow from adjacent Cambrian formations being the largest recharge component. R is expected to be much less than PI + PU in recurrent dry spells. Some low-technology actions are tentatively proposed to mitigate groundwater degradation, such as: wastewater capture, treatment, and reuse for irrigation; storm-water harvesting for irrigation; and active maintenance of the irrigation system to improve its efficiency.

RRREG: Stata module to estimate linear probability model for randomized response data

rrreg fits a linear probability model for randomized response data

Testing the validity of the lognormal probability model : computer analysis of carbon monoxide data from U.S. cities /

Mode of access: Internet.

Instrumentation for measuring pavement-vehicle interaction. Volume II: Digalog Systems operation and maintenance manual, data acquisition system, model DLI 203. Final report.

Federal Highway Administration, Structures and Applied Mechanics Division, Washington, D.C.