Effect of Density on Marshall Stability of Hot-Mix Asphaltic Concrete, MLR-82-01, 1982

The Iowa D.O.T. specifications do not require 100 percent of 50 blow Marshall density (generally 94%) for field compaction. However, stabilities are determined in the Laboratory on specimens compacted to 100 percent of Marshall density. The purpose of this study is to determine the stabilities of specimens compacted to various densities which are below 100 percent of the 50 blow Marshall density.

Evaluation of Hot Mix Asphalt Moisture Sensitivity Using the Nottingham Asphalt Test Equipment, TR-555, 2010

Pavements are subjected to different stresses during their design lives. A properly designed pavement will perform adequately during its design life, and the distresses will not exceed the allowable limits; however, there are several factors that can lead to premature pavement failure. One such factor is moisture sensitivity. AASHTO T 283 is the standard test used in the moisture susceptibility evaluation of asphalt mixtures, but the results of the test are not very representative of the expected behavior of asphalt mixtures. The dynamic modulus test measures a fundamental property of the mixture. The results of the dynamic modulus test can be used directly in the Mechanistic-Empirical Pavement Design Guide (MEPDG) and are considered a very good representation of the expected field performance of the mixture. Further research is still needed to study how the dynamic modulus results are affected by moisture. The flow number test was studied in previous research as a candidate test for moisture-susceptibility evaluation, but the results of that research were not favorable. This research has four main objectives. The first objective of this research is to evaluate the usefulness of the dynamic modulus and flow number tests in moisture-susceptibility evaluation. The second objective is to compare the results to those achieved using the AASHTO T 283 test. The third objective is to study the effect of different methods of sample conditioning and testing conditions. The fourth objective of the research is to study the variability in the test results.

Manual of Laboratory Mix Design Procedure for Cold In-Place Recycling using Foamed Asphalt (CIR-foam), TR-474, 2007

The objective is to determine the optimum percentage of water needed to produce the best foam properties for a given asphalt binder. The optimum water content is determined by achieving the maximum expansion ratio and half-life of the foamed asphalt. Expansion ratio is defined as the maximum volume over its original volume and half-life is defined as the time in seconds for foam to become a half of its maximum volume.

Uses of cancer registries for public health and clinical research in Europe : results of the European Network of Cancer Registries survey among 161 population-based cancer registries during 2010-2012

AIM: To provide insight into cancer registration coverage, data access and use in Europe. This contributes to data and infrastructure harmonisation and will foster a more prominent role of cancer registries (CRs) within public health, clinical policy and cancer research, whether within or outside the European Research Area. METHODS: During 2010-12 an extensive survey of cancer registration practices and data use was conducted among 161 population-based CRs across Europe. Responding registries (66%) operated in 33 countries, including 23 with national coverage. RESULTS: Population-based oncological surveillance started during the 1940-50s in the northwest of Europe and from the 1970s to 1990s in other regions. The European Union (EU) protection regulations affected data access, especially in Germany and France, but less in the Netherlands or Belgium. Regular reports were produced by CRs on incidence rates (95%), survival (60%) and stage for selected tumours (80%). Evaluation of cancer control and quality of care remained modest except in a few dedicated CRs. Variables evaluated were support of clinical audits, monitoring adherence to clinical guidelines, improvement of cancer care and evaluation of mass cancer screening. Evaluation of diagnostic imaging tools was only occasional. CONCLUSION: Most population-based CRs are well equipped for strengthening cancer surveillance across Europe. Data quality and intensity of use depend on the role the cancer registry plays in the politico, oncomedical and public health setting within the country. Standard registration methodology could therefore not be translated to equivalent advances in cancer prevention and mass screening, quality of care, translational research of prognosis and survivorship across Europe. Further European collaboration remains essential to ensure access to data and comparability of the results.

Analysis of electricity distribution network operation business models and capitalization of control room functions with DMS

Electricity distribution network operation (NO) models are challenged as they are expected to continue to undergo changes during the coming decades in the fairly developed and regulated Nordic electricity market. Network asset managers are to adapt to competitive technoeconomical business models regarding the operation of increasingly intelligent distribution networks. Factors driving the changes for new business models within network operation include: increased investments in distributed automation (DA), regulative frameworks for annual profit limits and quality through outage cost, increasing end-customer demands, climatic changes and increasing use of data system tools, such as Distribution Management System (DMS). The doctoral thesis addresses the questions a) whether there exist conditions and qualifications for competitive markets within electricity distribution network operation and b) if so, identification of limitations and required business mechanisms. This doctoral thesis aims to provide an analytical business framework, primarily for electric utilities, for evaluation and development purposes of dedicated network operation models to meet future market dynamics within network operation. In the thesis, the generic build-up of a business model has been addressed through the use of the strategicbusiness hierarchy levels of mission, vision and strategy for definition of the strategic direction of the business followed by the planning, management and process execution levels of enterprisestrategy execution. Research questions within electricity distribution network operation are addressed at the specified hierarchy levels. The results of the research represent interdisciplinary findings in the areas of electrical engineering and production economics. The main scientific contributions include further development of the extended transaction cost economics (TCE) for government decisions within electricity networks and validation of the usability of the methodology for the electricity distribution industry. Moreover, DMS benefit evaluations in the thesis based on the outage cost calculations propose theoretical maximum benefits of DMS applications equalling roughly 25% of the annual outage costs and 10% of the respective operative costs in the case electric utility. Hence, the annual measurable theoretical benefits from the use of DMS applications are considerable. The theoretical results in the thesis are generally validated by surveys and questionnaires.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Luonnonhaltijat ‒ haltijoita luonnonympäristössä? : Nature spirits : continuity and change : Conference of Belief Narrative Network of ISFNR, Zugdidi, Georgia 1.‒4.10.2014

Konferenssiraportti

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

The Rebirth of a Communications Network: Europe at the Time of the Carolingians.

Rapport de recherche

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Development and Deployment of Wireless Sensor Network in Paddy Fields of Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the development and deployment of wireless sensor network for crop monitoring in the paddy fields of Kuttanad, a region of Kerala, the southern state of India.

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features

Influence of the friends' network in drug use and violent behavior among young people in the nightlife recreational context

Resumen tomado de la publicaci??n