Rheological properties of biopolymers drilling fluids

Drilling muds are complex fluids, generally used to clean the well, maintain hole integrity, transport the rock cuttings, lubricate the drill bit and control formation pressures. Two basic types of drilling fluids are used, water based muds (WBM) and oil based muds (OBM). OBM are very effective but polluting, and environmental regulations continue to restrict the use of oil based muds in many areas of the world. In order to reduce the mud toxicity, we developed water based mud systems using two biopolymers, which are xanthan gum and scleroglucan, generally proposed for high permeability reservoirs or for complex geometries such as horizontal wells. In this study, we evaluated the rheological behaviour of different samples and we determined the effect of components such as clay, calcium carbonate and potassium chloride. This formulations exhibit non-Newtonian rheological behaviour which can be described well by the tree parameter in Herschel–Bulkley rheological model.     

Spontaneous Formation of Liquid Crystals in Ultralarge Graphene Oxide Dispersions

A novel process is developed to synthesize graphene oxide sheets with an ultralarge size based on a solution‐phase method involving pre‐exfoliation of graphite flakes. Spontaneous formation of lyotropic nematic liquid crystals is identified upon the addition of the ultralarge graphene oxide sheets in water above a critical concentration of about 0.1 wt%. It is the lowest filler content ever reported for the formation of liquid crystals from any colloid, arising mainly from the ultrahigh aspect ratio of the graphene oxide sheets of over 30 000. It is proposed that the self‐assembled brick‐like graphene oxide nanostructure can be applied in many areas, such as energy‐storage devices and nanocomposites with a high degree of orientation.     

Modeling of nucleate boiling heat transfer under an impinging free jet

A model using an analytical/empirical approach has been developed to predict the rate of heat transfer in the stagnation region of a planar jet impinging on a horizontal flat surface. The model has been developed based on the hypothesis that bubble-induced mixing would result in enhanced or additional diffusivity. The additional diffusivity has been included in the diffusion term of the conservation equations. The value of the effective diffusivity has been correlated with jet parameters (velocity and temperature) and surface temperature using experimental data. The important aspects of the bubble dynamics (generation frequency and average bubble diameter) have been acquired using high-speed imaging and an intrusive optical probe. The applicability of the proposed model has been investigated under conditions of partial and fully-developed nucleate boiling. Experiments have been carried out using water at atmospheric pressure, mass flux in the range of 388–1649 kg/m² s, degree of sub-cooling in the range of 10–28 °C, and surface temperature in the range of 75–120 °C. Results showed that the proposed model is able to predict the surface heat flux with reasonable accuracy (+30% and ?15%).     

Differential Scanning Calorimetry of Aspartame-Caffeine Mixture

The possible interaction between aspartame and caffeine was investigated by comparing the thermal behavior, using differential scanning calorimetry, of physical mixtures of aspartame and caffeine along with mixtures, in the same molar ratios, obtained as the co-precipitate. Caffeine was found to form several complexes with aspartame. These complexes were found to be dependent on the molar ratios of aspartame to caffeine. The stoichiometry of the aspartame-caffeine complexes were determined from the enthalpy change of the DSC transitions resulting from the complex formation.     

Low-Power Tunable Analog Circuit Blocks Based on Nanoscale Double-Gate MOSFETs

We illustrate unique examples of low-power tunable analog circuits built using independently driven nanoscale DG-MOSFETs, where the top gate response is altered by application of a control voltage on the bottom gate. In particular, we provide examples for a single-ended CMOS amplifier pair, a Schmitt trigger circuit and a operational transconductance amplifier C filter, circuit blocks essential for low-noise high-performance integrated circuits for analog and mixed-signal applications. The topologies and biasing schemes explored here show how the nanoscale DG-MOSFETs may be used for efficient, tolerant and smaller circuits with tunable characteristics.     

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.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Design of sliding mode and model reference adaptive control strategies for multivariable tape transport mechanism: a performance comparison

This paper presents sliding mode control and model reference adaptive control strategies for the tape transport mechanism. A nonlinear multivariable MIMO model of the process, consisting of take-up and supply reel servos for tape tension control and capstan servo for speed control is considered. The sliding mode control is applied for the nonlinear dynamic model of the process, while the model reference adaptive control deals with the linearized one. Moreover, in order to associate with the realistic model of system, design of controllers is accomplished with respect to parametric uncertainties. It is shown that both control strategies can guarantee asymptotic stability of the closed-loop system and tracking of desired outputs with the appropriate pace in the presence of uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed controllers.