Gas chromatography-microchip atmospheric pressure chemical ionization-mass spectrometry

An atmospheric pressure chemical ionization (APCI) microchip is presented for combining a gas chromatograph (GC) to a mass spectrometer (MS). The chip includes capillary insertion channel, stopper, vaporizer channel, nozzle and nebulizer gas inlet fabricated on the silicon wafer, and a platinum heater sputtered on a glass wafer. These two wafers are joined by anodic bonding creating a two-dimensional version of an APCI microchip. The sample from GC is directed via heated transfer line capillary to the vaporizer channel of the APCI chip. The etched nozzle forms narrow sample plume, which is ionized by an external corona discharge needle, and the ions are analyzed by a mass spectrometer. The GC-microchip APCI-MS combination provides an efficient method for qualitative and quantitative analysis. The spectra produced by microchip APCI show intensive protonated molecule and some fragmentation products as in classical chemical ionization for structure elucidation. In quantitative analysis the GC-microchip APCI-MS showed good linearity (r(2) = 0.9989) and repeatability (relative standard deviation 4.4%). The limits of detection with signal-to-noise ratio of three were between 0.5 and 2 micromol/L with MS mode using selected ion monitoring and 0.05 micromol/L with MS/MS using multiple reaction monitoring.     

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.     

Urban landscape structure anatomy: Structure patterns and typology identification in the space-time of Setif City,Algeria

The definition of urban form and its elements raises a wide debate in urban morphology. This study presents a proposal to understand the structuring of the urban landscape of Setif City over 170 years (1848-2018) by abstracting its main elements into permanent structure (PS_UL). It corresponds to the street network, more static to the change of the town plan, and flexible structure (FS_UL), more resilient to change and explains the superimpositions of morphological components (plot, building, and land use). This work aims to identify typological patterns of structures suggested in space-time through the inspection of the growth of the street network and the filling of morphological layers framed by the concept of urbanity. The methodology is based on syntactic (diachronic) morphogenetic analysis, with statistical analyses as unsupervised classification. Results indicate that PS_UL presents an accumulation of microincrements produced with a similar degree of coherence in different urban fabrics, which explain the local patterns of street network structuring. The typology of FS_UL patterns indicates an unbalanced growth by the arrangement between morphological elements, resulting in a variation in the degree of urbanity.     

Couple stress flow of exponentially stretching sheet with Cattaneo–Christov heat flux model

This paper deals with the effect of three-dimensional magnetohydrodynamic flow for a couple of stress fluids on an exponentially stretching sheet. The magnetic field is implemented normally to the surface. To observe the transfer of heat phenomenon, the Cattaneo–Christov flux model of heat is employed. Using similarity transformation, the substantial differential equations are reformed into ordinary differential equations. Eventually, the effects of different physical parameters are studied graphically. The drawback in Fourier heat flux model is removed by adding a new paramter known as thermal relaxation time by Cattaneo. This perimeter allows heat transportation by way of propagation of waves thermally at a defined speed. After this, the Cattaneo law is further modified by Christov–Christov to replace the ordinary derivative along Oldroyd's upper-convective derivative.     

New Proposed Algorithms for Infrared Video Sequences Non-uniformity Correction

Wireless Personal Communications - Infrared (IR) video sequences Improvement is a difficult task because of many factors for example low dynamic range, noise and Non-Uniformity (NU) effect. The NU...     

Radiative unsteady hydromagnetic 3D flow model for Jeffrey nanofluid configured by an accelerated surface with chemical reaction

The current exploration reveals the unsteady three‐dimensional flow of Jeffrey nanofluid over a bidirectional oscillatory stretching surface. The Brownian motion and thermophoresis phenomenon has been scrutinized by utilizing Buongiorno's nanofluid model. The heat transfer analysis is carried out in the presence of thermal radiation and heat generation/absorption features. Furthermore, chemical reaction and magnetic effects are also deliberated. The flow has been generated by a bidirectional periodically accelerated heated surface. The formulated nonlinear problem is condensed into a dimensionless form via apposite transformations, and then analytic series solution is computed via homotopic technique. Comprehensive graphical evaluations for numerous prominent flow constants on associated profiles are performed. In addition, the tabulated numerical calculations for the local Nusselt and Sherwood numbers are also presented. The current analysis reported that both components of velocities have an increasing tendency for higher Deborah number, whereas an adverse influence is observed for the ratio of relaxation and retardation times parameter. Moreover, the concentration profile diminishes for the increasing variation of the chemical reaction parameter.     

协作多输入多输出环境反向散射通信系统遍历速率分析

针对传统物联网（IoT）能耗大、频谱资源稀缺的问题,提出了由环境反向散射、协作接收机（CRx）和环境射频（RF）源共同构建的协作多输入多输出环境反向散射通信（MIMO-AmBC）系统模型。首先,通过使用PSR方案对该系统模型进行分析,推导出信噪比（SNR）;其次,推导出主链路和反向散射链路的遍历速率近似表达式,并得到反向散射链路遍历速率最大值表达式;最后,将其与传统蜂窝网络、CSR方案作对比。实验结果验证了理论推导的正确性并给出有意义结论：1）反向散射链路速率随接收天线数对数增长,与发射天线数无关。2）在SNR为10 dB时,PSR方案的和速率比传统方案、CSR方案分别提升36.8%和29.9%,虽然PSR方案的主链路速率比CSR方案降低5.5%,但反向散射链路的遍历速率比CSR方案提升7.7倍。为实际应用选取AmBC共生方案提供了理论参考。     

Thermal Conductivity of Multiwalled Carbon Nanotubes-Kapok Seed Oil-Based Nanofluid

The synthesis of a nanofluid from multiwalled carbon nanotubes (MWCNTs) and Kapok seed oil by a one-step method is reported. The nanofluid showed excellent stability of nanoparticle dispersion in the base fluid. Furthermore, this study deals with the prediction of the thermal conductivity of the MWCNTs-kapok seed oil nanofluid. To improve the prediction of the thermal conductivity of the nanofluid, the artificial neural network (ANN) computing approach was used with different algorithms including the back-propagation, Levenberg-Marquardt, and genetic algorithm (GA). Finally, the ANN-GA model is recommended for the prediction of thermal conductivity with higher accuracy.