Search results for " data"
showing 10 items of 7516 documents
Activation of nitric oxide signaling by the rheumatoid arthritis shared epitope
2006
Objective. Susceptibility to rheumatoid arthritis (RA) is closely associated with HLA–DRB1 alleles encoding a shared epitope (SE) in positions 70–74 of the HLA–DR chain. The mechanistic basis for this association is unknown. Given the proposed pathogenic role of nitric oxide (NO) in RA, this study was undertaken to examine whether the SE can trigger NO signaling events. Methods. The intracellular levels of NO were measured with the fluorescent NO probe 4,5diaminofluorescein diacetate and by the 2,3diaminonaphthalene method. NO synthase activity was determined by measuring the rate of conversion of radioactive arginine to citrulline. Levels of cGMP were measured with a commercial enzyme-link…
New Contributions toPseudonapomyza(Diptera: Agromyzidae) from Spain: Addition of Three New Species
2010
The genus Pseudonapomyza (Diptera: Agromyzidae) includes the main leafminer pests for monocots. Three new species are described that were captured using Malaise traps in "Tinença de Benifassà", "Font Roja" and "Lagunas de La Mata-Torrevieja" (Spain) Natural Parks: Pseudonapomyza curvata n. sp., P. longitata n. sp., and P. sicicornis n. sp. Systematics. Ecological data are discussed.
A Data-Based Approach for Modeling and Analysis of Vehicle Collision by LPV-ARMAX Models
2013
Published version of an article in the journal: Journal of Applied Mathematics. Also available from the publisher at: http://dx.doi.org/10.1155/2013/452391 Open Access Vehicle crash test is considered to be the most direct and common approach to assess the vehicle crashworthiness. However, it suffers from the drawbacks of high experiment cost and huge time consumption. Therefore, the establishment of a mathematical model of vehicle crash which can simplify the analysis process is significantly attractive. In this paper, we present the application of LPV-ARMAX model to simulate the car-to-pole collision with different initial impact velocities. The parameters of the LPV-ARMAX are assumed to …
A Fast Imaging Technique Applied to 2D Electrical Resistivity Data
2014
A new technique is proposed to process 2D apparent resistivity datasets, in order to obtain a fast and contrasted resistivity image, useful for a rapid data check in field or as a starting model to constrain the inversion procedure. In the past some modifications to the back-projection algorithm, as well as the use of filtering techniques for the sensitivity matrix were proposed. An implementation of this technique is proposed here, considering a two-step approach. Initially a damped least squares solution is obtained after a full matrix inversion of the linearized geoelectrical problem. Furthermore, on the basis of the results, a subsequent filtering algorithm is applied to the Jacobian ma…
Delay-Probability-Distribution-Dependent FIR Filtering Design with Envelope Constraints
2013
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2013/930927 Open Access This paper studies the problem of H∞ finite-impulse response (FIR) filtering design of time-delay system. The time-delay considered here is time-varying meanwhile with a certain stochastic characteristic, and the probability of delay distribution is assumed to be known. Furthermore, the requirement of pulse-shape is also considered in filter design. Employing the information about the size and probability distribution of delay, a delay-probability-distribution-dependent criterion is proposed for the filtering error syst…
Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching
2018
This article examines how students (N=198; aged 13 to 17) experienced the new methods for sensor-based learning in multidisciplinary teaching in lower and upper secondary education that combine the use of new sensor technology and learning from self-produced well-being data. The aim was to explore how students perceived new methods from the point of view of their learning and did the teaching methods provide new information that could promote their own well-being. We also aimed to find out how to collect digital well-being data from a large number of students and how the collected big data set can be utilized to predict school success from the students’ well-being data by using machine lear…
A weighted distance-based approach with boosted decision trees for label ranking
2023
Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more seriou…
Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations
2023
PurposeThe nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.Design/methodology/approachThe authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and imple…
Neural Classification of HEP Experimental Data
2009
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topol…
A vision system for symbolic interpretation of dynamic scenes using arsom
2001
We describe an artificial high-level vision system for the symbolic interpretation of data coming from a video camera that acquires the image sequences of moving scenes. The system is based on ARSOM neural networks that learn to generate the perception-grounded predicates obtained by image sequences. The ARSOM neural networks also provide a three-dimensional estimation of the movements of the relevant objects in the scene. The vision system has been employed in two scenarios: the monitoring of a robotic arm suitable for space operations, and the surveillance of an electronic data processing (EDP) center.