Model Validation for Bridge-Road-Vehicle Dynamic Interaction System

The dynamic response of highway bridges subjected to moving truckloads has been observed to be dependent on (1) dynamic characteristics of the bridge; (2) truck configuration, speed, and lane position on the bridge; and (3) road surface roughness profile of the bridge and its approach. Historically, truckloads were measured to determine the load spectra for girder bridges. However, truckload measurements are either made for a short period of time [for example, weigh-in-motion (WIM) data] or are statistically biased (for example, weigh stations) and cost prohibitive. The objective of this paper is to present results of a 3D computer-based model for the simulation of multiple trucks on girder bridges. The model is based on the grillage approach and is applied to four steel girder bridges tested under normal truck traffic. Actual truckload data collected using a discrete bridge WIM system are used in the model. The data include axle loads, truck gross weight, axle configuration, and statistical data on multiple presence (side by side or following). The results are presented as a function of the static and dynamic stresses in each girder and compared with code provisions for dynamic load factor. The study provides an alternate method for the development of live-load models for bridge design and evaluation.     

Assessing the potential of renewable energy sources in Turkey

To meet Turkey’s growing energy demand, the installed electric power capacity of 27.8 GW in 2001 has to be doubled by 2010 and increased fourfold by 2020. The difference between Turkey’s total primary energy supply (TPES) of from its own sources and total final consumption (TFC) is projected grow from 1 quad (1.06–2.06) in 1999 to 5.71 quads (2.79–8.5) in 2020 (1 QUAD=293.071 TWh). Turkey’s limited amount of fossil fuels has a present average ratio of proved reserves of 97.38 quads to production rate of 3.2 quads yr⁻¹ of about 30 years. Turkey’s reliance on fossil fuel-based energy systems to meet the growing demand is most likely to exacerbate the issues of energy insecurity, national environmental degradation, and global climate change in increasing proportions. Economically-feasible renewable energy potential in Turkey is estimated at a total of ca. 1.69 quads yr⁻¹ (495.4 TWh yr⁻¹) with the potential for 0.67 quads yr⁻¹ (196.7 TWh yr⁻¹) of biomass energy, 0.42 quads yr⁻¹ (124 TWh yr⁻¹) of hydropower, 0.35 quads yr⁻¹ (102.3 TWh yr⁻¹) of solar energy, 0.17 quads yr⁻¹ (50 TWh yr⁻¹) of wind energy, and 0.08 quads yr⁻¹ (22.4 TWh yr⁻¹) of geothermal energy. Pursuit and implementation of sustainability-based energy policy could provide about 90 and 35% of Turkey’s total energy supply and consumption projected in 2010, respectively. Utilization of renewable energy technologies for electricity generation would necessitate about 23.2 Mha (29.8%) of Turkey’s land resources.     

The role of diagnostic laparoscopy in treatment of penetrating injuries of the lower thoracic wall and anterior abdominal wall

Penetrating injuries of the lower thoracic wall and anterior abdominal wall cause difficulties in the decision for laparotomy. For gunshot wounds laparotomy without further investigations is in most cases justified, but in other penetrating traumata one should use every diagnostic modality to prevent unacceptably high negative laparotomy rates. We performed diagnostic laparoscopy (DL) on 39 patients with penetrating injuries of the anterior abdominal wall and/or lower thoracic wall. Of these 39 patients, 25 had negative and 14 positive results. We had only one false-negative finding. No false-positive result occurred. We think that DL is a very reliable diagnostic tool which requires a relatively high technology.     

Connecting the dots: Understanding the interrelated impacts of type,quality and children's awareness of design features and the mathematics content learning goals in digital math games and related learning outcomes

This study focused on an examination of how type, quality and children's awareness of design features in digital math games, along with an awareness of the mathematics goals of the game, were related to learning outcomes. We conducted a parallel conversion mixed methods study with 45 students in Grades 3 and 4 (ages 9–10). Students participated in clinical interviews using three digital math games. The results suggest a moderated mediation relationship in which the quality of the design features moderated the mediating impact of children's awareness of the game's design features, specifically when the child was aware of the mathematics content learning goal (MCLG) of the game. These findings show how important it is that design features are of high quality in a digital math game, and how this is intertwined with children's awareness of the features and the MCLG. When these variables intertwined in just the right way, the interactions between the children and the digital math game afforded mathematical learning growth.     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

A new mixed integer linear programming model for product development using quality function deployment

Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linear programming in the QFD optimization is overcome. Finally, the model is illustrated through an example.     

Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.