Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

SIMULTANEOUS STATE ESTIMATION AND LEARNING IN REPEATED COURNOT GAMES

The aim of this article is to propose that an intelligent agent can be able to decide properly in an incomplete information repeated Cournot game. The market model and the competitors’ decision models are not known to the players. The proposed agent employs a combination of the k-nearest neighbor (KNN) method and the Bayes classifier to predict the next action of its rivals, using the market decision history. The agent takes the predicted actions as an estimate of its next state and learns the expected payoff of its state-action pairs interactively using the reinforcement learning (RL) algorithm. The results of the proposed agent's competition with two benchmark competitors in different simulated Cournot games are presented. The simulation results show that the proposed agent can significantly earn more payoffs in comparison with the two benchmark agents.     

Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: a case study in the Sundarbans mangrove forest

Optical vegetation indices (VIs) have been used to retrieve and assess biophysical variables from satellite reflectance data. These indices, however, also are sensitive to a number of confounding factors, such as canopy geometry, soil optical properties, and solar position. This suggests that VIs should be used cautiously for biophysical parameter estimation. Among biophysical variables, chlorophyll content is of particular importance as an indicator of photosynthetic activity. The goal of this study is to investigate the performance of multispectral optical VIs for chlorophyll content estimation in the world’s largest mangrove forest, the Sundarbans, and to compare these with machine-learning algorithms (MLAs). To this end, we have investigated the performance of 15 multispectral VIs and six state-of-the-art MLAs that are widely used for adaptive data fitting. The MLAs are Artificial Neural Networks (ANNs), Genetic Algorithm (GA), Gaussian Processes for Machine Learning (GPML), Kernel Ridge Regression (KRR), Locally Weighted Polynomials (LWP), and Multivariate Adaptive Regression Splines (MARS). We use an in situ data set of reflectance and chlorophyll measurements to develop and validate our models. Each MLA was evaluated 500 times with random partitions of training and validation data. Results showed that the weight optimization and term selection used within GA produce the most reliable chlorophyll content estimation. However, green normalized difference VI (GNDVI) is a simple and computationally efficient VI that produces results that are nearly as accurate as GA in terms of model fit and performance. Results also show that all methods except ANNs and MARS produce a quasi-linear relationship between spectral reflectance and chlorophyll content. Statistical transformations of GNDVI and chlorophyll content have the capability of further reducing model error.     

Continuum and atomistic models of strongly coupled diffusion, stress, and solute concentration

Poor cyclic performance of electrodes in lithium-ion rechargeable cell batteries is calling for efforts to develop continuum models of diffusion under very large stresses and high solute concentrations. The present work is aimed to develop such a model based on input from atomistic simulations. We consider four fundamental features of highly nonlinear behavior associated with diffusion at high solute concentrations. First, the effect of solute-induced stresses on the activation energy of solute diffusion could be important. Second, the solute concentration may be subject to an upper limit if there exists a stoichiometric maximum concentration. Third, the strong influence of the change in local chemical environment on the interaction energy between solute and host atoms could play a significant role. Fourth, we include the effect of the solute concentration on the Young's modulus of the host material. A continuum model is developed and validated based on atomistic simulations of hydrogen diffusion in nickel. The influences of each feature above are clearly discussed through parametric studies.     

Fourier transform infrared spectroscopic and AC electrical conductivity studies of chitin and its derivatives

The vibrational modes and the purity index of chitin and its derivatives as biopolymers are studied by Fourier transform infrared spectroscopy in the wavenumber range from 4000 to 400 cm^?1. The absorption bands of chitin and its derivatives are assigned. New bands appeared at 1419, 1209, 915, and 800 cm^?1 for chitin derivatives due to the change of vibrational modes. The purity indexes are calculated from the ratio of the absorption band of C = O at 1662 cm^?1 and OH at 3443 cm^?1. Generally, the purity indexes of chitin derivatives are higher than that of chitin, which is due to the decrease of OH. The dielectric properties such as the real part of the dielectric constant (ε′) and loss tangent tan (δ) are measured as a function of temperature at different frequencies ranging from 200 to 2000 kHz. The increase of (ε′) and tan (δ) with increasing temperature is discussed on the basis of increasing polarizability and the decrease with increasing frequency can be attributed to the dielectric dispersion. AC electrical conductivities as a function of the reciprocal of the absolute temperature are also discussed. The mechanism of the charge carrier is discussed as a tunneling model in the temperature range from 300 to 360 K. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 936–943, 2005     

Crosslinking of ethylene propylene diene monomer (EPDM) by buckminsterfullerene

An EPDM (ethylene-propylene-ethylidene norbornene terpolymer) rubber has been dissolved in o-dichlorobenzene with 1 phr of C₆₀. When cast onto a glass slide and dried, an insoluble film exhibiting reversible elasticity forms. Received: 10 December 1996/Revised: 13 January 1997/Accepted: 17 January 1997     

Coordinated tuning of power system stabilizers in multimachine power systems

In this paper a new approach to simultaneously tuning the power system stabilizer (PSS) settings in a multimachine electric utility system is presented. The approach is based on eigenvalue assignment, which is treated as a problem of solving a set of non-linear equations. The possibility of meeting additional specified performance criteria is discussed. The flexibility of the proposed method is demonstrated using an operating utility system.     

Diagnosis of attention deficit hyperactivity disorder using non‐linear analysis of the EEG signal

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non‐linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non‐linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non‐linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.Inspec keywords: feature extraction, time series, fractals, electroencephalography, medical disorders, neurophysiology, medical signal processing, entropy, signal classification, Lyapunov methodsOther keywords: nonlinear extracted features, chaotic time series, identifying classifying children, attention deficit hyperactivity disorder, nonlinear analysis methods, electroencephalogram signals, brain neuronal activity, chaotic behaviour, chaotic indices, EEG signals, nonlinear features, approximate entropies, common behavioural disorder, early diagnosis, life quality, ADHD