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.     

Cooperative actuator fault accommodation in formation flight of unmanned vehicles using relative measurements

In this article, the cooperative fault accommodation in formation flight of unmanned vehicles is investigated through a hierarchical framework. Three levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery (FLFR) and a high-level (HL). In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is introduced at the HL hierarchy to identify a partially low-level (LL) recovered vehicle due to inaccuracy in the fault estimate which results in violating the error specification of the formation mission. The HL supervisor then activates the FLFR module to compensate for the performance degradations of the partially LL recovered vehicle at the expense of the other healthy vehicles. Both centralised and decentralised control approaches are developed for our proposed cooperative fault recovery technique. A robust H _∞ controller is designed in which the parameters of the controller are adjusted to accommodate for the partially LL-recovered vehicle by enforcing that the other healthy vehicles allocate more control effort to compensate for the performance degradations of the faulty vehicle. Numerical simulations for a formation flight of five satellites are provided in the deep space, which do indeed confirm the validity and effectiveness of our proposed analytical work.     

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