A physics-based electrical model of a magnetically sensitive AlGaN/GaN HEMT

Journal of Computational Electronics - The electrical model of a magnetically sensitive two-drain contact AlGaN/GaN HEMT (MagHEMT) is described for the first time and implemented in...     

On-column ion-exchange preconcentration of inorganic anions in open tubular capillary electrochromatography with elution using transient-isotachophoretic gradients. 3. Implementation and method development

A solid-phase extraction method based on an ion-exchange retention mechanism has been used for in-line preconcentration of inorganic anions prior to their separation by capillary electrophoresis (CE). A single capillary containing a preconcentration and a separation zone has been used in a commercial CE instrument without instrumental modification. Analyte anions were retained on a preconcentration zone comprising an adsorbed layer of cationic latex particles, while separation was achieved in a separation zone comprising fused silica modified by adsorption of a cationic polymer. Elution of the adsorbed analytes was achieved using an eluotropic gradient formed by a transient isotachophoretic boundary between a fluoride electrolyte and a naphthalenedisulfonate electrolyte. Optimization of the electrolyte concentrations, sample injection times, and back-flushing times allowed the successful separation of sub-ppb levels of inorganic anions using a 100-min injection at 2 bar pressure, introducing over 40 capillary volumes of sample. A method based on a 10-min injection allowed a 100-fold increase in sensitivity over conventional hydrodynamic injection for Br-, I-, NO3-, CrO4(2-), and MoO4(2-) with a total analysis time of 25 min. Detection limits were dependent on the injection time but were in the range 2.2-11.6 ppb for a 10-min injection time. This approach was used to determine NO3- in Antarctic ice cores where the analysis could be performed using a sample volume 100 times less than that used for ion chromatography.     

Investigation of Water Adsorption on Ketotifen Fumarate by Inverse Gas Chromatography

A gas chromatographic technique of elution by characteristic point has been used to study water adsorption on ketotifen fumarate in the temperature range 343-383 K. The entropy of adsorption, 50.10 kJ mol^-1, indicates a specific interaction between water and ketotifen fumarate. The isosteric heats of adsorption were viewed as a function of surface coverage, with the results indicating a strong adsorbate-adsorbate interaction.     

A comparison of algorithms for inference and learning in probabilistic graphical models

Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy” belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.     

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.     

The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements

In this paper a brute force logistic regression (LR) modeling approach is proposed and used to develop predictive credit scoring model for corporate entities. The modeling is based on 5 years of data from end-of-year financial statements of Serbian corporate entities, as well as, default event data. To the best of our knowledge, so far no relevant research about predictive power of financial ratios derived from Serbian financial statements has been published. This is also the first paper that generated 350 financial ratios to represent independent variables for 7590 corporate entities default predictions’. Many of derived financial ratios are new and were not discussed in literature before. Weight of evidence (WOE) method has been applied to transform and prepare financial ratios for brute force LR fitting simulations. Clustering method has been utilized to reduce long list of variables and to remove highly correlated financial ratios from partitioned training and validation datasets. The clustering results have revealed that number of variables can be reduced to short list of 24 financial ratios which are then analyzed in terms of default event predictive power. In this paper we propose the most predictive financial ratios from financial statements of Serbian corporate entities. The obtained short list of financial ratios has been used as a main input for brute force LR model simulations. According to literature, common practice to select variables in final model is to run stepwise, forward or backward LR. However, this research has been conducted in a way that the brute force LR simulations have to obtain all possible combinations of models that comprise of 5–14 independent variables from the short list of 24 financial ratios. The total number of simulated resulting LR models is around 14 million. Each model has been fitted through extensive and time consuming brute force LR simulations using SAS® code written by the authors. The total number of 342,016 simulated models (“well-founded” models) has satisfied the established credit scoring model validity conditions. The well-founded models have been ranked according to GINI performance on validation dataset. After all well-founded models have been ranked, the model with highest predictive power and consisting of 8 financial ratios has been selected and analyzed in terms of receiver-operating characteristic curve (ROC), GINI, AIC, SC, LR fitting statistics and correlation coefficients. The financial ratio constituents of that model have been discussed and benchmarked with several models from relevant literature.     

Comparative analysis of the DC performance of DG MOSFETs on highly-doped and near-intrinsic silicon layers

A comparison of dc characteristics of fully depleted double-gate (DG) MOSFETs with respect to low-power circuit applications and device scaling has been performed by two-dimensional device simulation. Three different DG MOSFET structures including a conventional N⁺ polysilicon gate device with highly doped Si layer, an asymmetrical P⁺/N⁺ polysilicon gate device with low doped Si layer and a mid-gap metal gate device with low doped Si layer have been analysed. It was found that DG MOSFET with mid-gap metal gates yields the best dc parameters for given off-state drain leakage current and highest immunity to the variation of technology parameters (gate length, gate oxide thickness and Si layer thickness). It is also found that an asymmetrical P⁺/N⁺ polysilicon gate DG MOSFET design offers comparable dc characteristics, but better parameter immunity to technology tolerances than a conventional DG MOSFET.     

The role of visual and nonvisual feedback in a vehicle steering task.

This article investigates vehicle steering control, focusing on the task of lane changing and the role of different sources of sensory feedback. Participants carried out 2 experiments in a fully instrumented, motion-based simulator. Despite the high level of realism afforded by the simulator, participants were unable to complete a lane change in the absence of visual feedback. When asked to produce the steering movements required to change lanes and turn a corner, participants produced remarkably similar behavior in each case, revealing a misconception of how a lane-change maneuver is normally executed. Finally, participants were asked to change lanes in a fixed-based simulator, in the presence of intermittent visual information. Normal steering behavior could be restored using brief but suitably timed exposure to visual information. The data suggest that vehicle steering control can be characterized as a series of unidirectional, open-loop steering movements, each punctuated by a brief visual update. (PsycINFO Database Record (c) 2010 APA, all rights reserved)