Neural plasticity in adults with amblyopia

Amblyopia is a neuronal abnormality of vision that is often considered irreversible in adults. We found strong and significant improvement of Vernier acuity in human adults with naturally occurring amblyopia following practice. Learning was strongest at the trained orientation and did not transfer to an untrained task (detection), but it did transfer partially to the untrained eye (primarily at the trained orientation). We conclude that this perceptual learning reflects alterations in early neural processes that are localized beyond the site of convergence of the two eyes. Our results suggest a significant degree of plasticity in the visual system of adults with amblyopia.     

Growth and Characterization of YbBCO Films on Textured Gd2O3 Buffer-Layered Ni Tapes: All Sol–Gel Process

YbBa₂Cu₃O_7?x (YbBCO) thick films were grown on buffered, cube-textured Nickel tapes by sol–gel dip-coating method. Yb-123 films were prepared using solutions of Yb, Ba, and Cu organometallic compounds. A solution-based Gd₂O₃ buffer layer was deposited by dip-coating process with excellent texture and uniformity. The texture development and surface morphology of the buffer-layer films were examined by X-ray diffraction, pole figures, and ESEM analysis. Microstructure and characterization of Yb-123 films were done by ESEM, EDS, and XRD analysis. T _c and J _c were conducted by four-wire measurement method     

Real-Time Edge Follow: A Real-Time Path Search Approach

Real-time path search is the problem of searching a path from a starting point to a goal point in real-time. In dynamic and partially observable environments, agents need to observe the environment to track changes, explore to learn unknowns, and search suitable routes to reach the goal rapidly. These tasks frequently require real-time search. In this paper, we address the problem of real-time path search for grid-type environments; we propose an effective heuristic method, namely a real-time edge follow alternative reduction method (RTEF-ARM), which makes use of perceptual information in a real-time search. We developed several heuristics powered by the proposed method. Finally, we generated various grids (random-, maze-, and U-type), and compared our proposal with real-time A*, and its extended version real-time A* with n-look-ahead depth; we obtained very significant improvements in the solution quality.     

On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Engineering with Computers - A novel Harris hawks optimization algorithm is applied to microchannel heat sinks for the minimization of entropy generation. In the formulation of the heat transfer...     

Maximization of total surface area of Pt-SnO<Subscript>2</Subscript>/Al<Subscript>2</Subscript>O<Subscript>3</Subscript> catalyst by the Taguchi method

The maximization of the total surface area of Pt-SnO₂/Al₂O₃ catalyst was studied by using the Taguchi method of experimental design. The catalysts were prepared by sol-gel method. The effects of HNO₃, H₂O and aluminum nitrate concentrations and the stirring rate on the total surface area were studied at three levels of each. L₉ orthogonal array leading nine experiments was used in the experimental design. The parameter levels that give maximum total surface area were determined and experimentally verified. In the range of conditions studied it was found that, medium levels of HNO₃ and H₂O concentration and lower levels of aluminum nitrate concentration and stirring rate maximize the total surface area.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.     

Development of a linear predictive model for carbon dioxide sequestration in deep saline carbonate aquifers

CO₂ injection into deep saline aquifers is a preferred method for mitigating CO₂ emission. Although deep saline aquifers are found in many sedimentary basins and provide very large storage capacities, several numerical simulations are needed before injection to determine the storage capacity of an aquifer. Since numerical simulations are expensive and time-consuming, using a predictive model enables quick estimation of CO₂ storage capacity of a deep saline aquifer. In order to create a predictive model, the ranges of variables that affect the CO₂ storage capacity were determined from published literature data. Correlations found in literature were used for other important parameters such as pore volume compressibility and density of brine. Latin hypercube space filling design was used to construct 100 simulation cases prepared using CMG STARS. The simulation period covered a total of 300 years of CO₂ storage. By using a least-squares method, linear and nonlinear predictive models were developed to estimate CO₂ storage capacity of deep saline carbonate aquifers. Numerical dispersion effects were considered by decreasing the grid dimensions. It was observed that a dimensionless linear predictive model is better than the nonlinear. The sensitivity analyses showed that the most important parameter that affects CO₂ storage capacity is depth. Most of the (up to 90%) injected gas dissolved into the formation water and a negligible amount of CO₂ reacted with carbonate.     

A Low-Cost 128 × 128 Uncooled Infrared Detector Array in CMOS Process

This paper discusses the implementation of a low-cost 128 times 128 uncooled infrared microbolometer detector array together with its integrated readout circuit (ROC) using a standard 0.35 mum n-well CMOS and post-CMOS MEMS processes. The detector array can be created with simple bulk-micromachining processes after the CMOS fabrication, without the need for any complicated lithography or deposition steps. The array detectors are based on suspended p⁺-active/n-well diode microbolometers with a pixel size of 40 mum times 40 mum and a fill factor of 44%. The p⁺-active/n-well diode detector has a measured dc responsivity (R) of 4970 V/W and a thermal time constant of 36 ms at 50 mtorr vacuum level. The total measured rms noise of the diode type detector is 0.69 muV for an 8 kHz bandwidth, resulting in a detectivity (D*) of 9.7 times 10⁸ cm ldr Hz^1/2/W. The array is scanned by an integrated 32-channel parallel ROC including low-noise differential preamplifiers with an electrical bandwidth of 8 kHz. The 128 times 128 focal plane array (FPA) has one row of infrared-blind reference detectors that reduces the effect of FPA fixed pattern noise and variations in the operating temperature relaxing the requirements for the temperature stabilization. Including the noise of the reference and array detectors together with the ROC noise, the fabricated 128 times 128 FPA has an expected noise equivalent temperature difference (NETD) value of 1 K for f/1 optics at 30 frames/s (fps) scanning rate. This NETD value can be decreased to 350 mK by improving the post-CMOS fabrication steps and increasing the number of readout channels.