Facile one‐pot hydrothermal synthesis of stable and biocompatible fluorescent carbon dots from lemon grass herb

Luminescent carbon‐based nanomaterials hold great promise due to their stable photo‐physical behaviour, biocompatibility and lower toxicity. This work involves economic and facile one‐pot green synthesis of water‐soluble nanostructures from lemon grass (LGNS) [Cymbopogon citratus (DC) Stapf] as carbon source. High‐resolution transmission electron microscopy confirmed the formation of LGNS with lattice spacing of 0.23 nm matching low‐dimensional graphitic structures. The strong absorption exhibited at 278 nm could be attributed to л‐states of sp² /sp³ hybridisation in carbon nanostructures. Fluorescence spectroscopy of LGNS exhibited strong excitation‐dependent emission properties over a broad range of wavelengths from 300 to 600 nm. Quantitatively, these LGNS were estimated to have quantum yield of 23.3%. Biomass derived LGNS could be potentially exploited for wide variety of applications like bioimaging, up‐conversion, drug delivery and optoelectronic devices. To this extent, synthesised LGNS were used to image yeast cells via multicolour/multi‐excitation fluorescence imaging.Inspec keywords: fluorescence, carbon, nanofabrication, photoluminescence, toxicology, transmission electron microscopy, cellular biophysics, biomedical optical imaging, nanomedicine, biomedical materials, microorganisms, liquid phase depositionOther keywords: one‐pot hydrothermal synthesis, biocompatible fluorescent carbon dots, lemon grass herb, luminescent carbon‐based nanomaterials, stable photophysical behaviour, toxicity, water‐soluble nanostructures, carbon source, high‐resolution transmission electron microscopy, low‐dimensional graphitic structures, hybridisation, carbon nanostructures, fluorescence spectroscopy, excitation‐dependent emission properties, biomass derived LGNS, bioimaging, drug delivery, optoelectronic devices, yeast cell image, multicolour‐multiexcitation fluorescence imaging, C     

使用基于区域的触发调试DDR内存接口

正DDR总线协议允许信号在不活动时进入空闲状态或三态。在DDR接口上调试或执行JEDEC合规性测量时,通常只需在使用示波器采集的信号的合格部分执行某些测量,如在READ或WRITE突发期间或在总线与特定rank的事务中。在DDR上捕获和找到正确的波形部分并进行分析极具挑战性,因为它可能要求采集和分类数千     

On downstream transmissions in EPON Protocol over Coax (EPoC): An analysis of Coax framing approaches and other relevant considerations

We present different mechanisms for downstream transmissions in the coax segment of Ethernet Passive Optical Network (EPON) Protocol over Coax (EPoC). EPoC is the transparent extension of EPON over a cable operator’s Hybrid Fiber-Coax network. For managing and controlling such a hybrid network, a network operator prefers to have a unified scheduling, management, and quality of service environment that includes both the optical and coax portions of the network. In EPoC, this is achieved by extending the EPON Medium Access Control to run over the coax physical layer, to have a centralized end-to-end network control from the cable head-end to the end users premises. In this paper, we focus on the downstream transmissions in EPoC. We study three different framing approaches for downstream coax frames based on how sub-carriers in an orthogonal frequency division multiplexed symbol are modulated. We discuss the merits and demerits of each approach and then compare them based on their control overheads and the maximum average data transmission rates each of them can achieve. We analyze how different parameters such as modulation profile, symbol duration, number of sub-carriers and length of resource blocks affect the data rates and the performance of downstream transmissions. We present simulation results to examine the implications of these factors on packet-level performance, such as delay. The results indicate that dynamic and hybrid framing approaches tend to perform better than static approaches, when traffic and usage pattern are identical to those in real-world scenarios. Finally, we outline the important engineering and research problems in this area which can be topics of future research.     

Polyaniline‐grafted polyurethane coatings for corrosion protection of mild steel surfaces

We report the superior corrosion‐resistant properties of conducting polyurethane networks of polyaniline (PANI), poly‐m‐aminophenol (PmAP), and poly‐o‐anisidine (PoA) coated on mild steel panels. These networks were prepared by blending conducting polyanilines with isocyanate‐containing prepolyurethanes. Free‐standing polyurethane films were obtained after a moisture cure for several days to ensure complete reaction of the excess isocyanate. The films were electrochemically active with conductivity in the range of 10^?2 to 10^?3 S/cm. The solution blends and formed films were characterized by infrared, ultraviolet, thermogravimetric analysis, and differential scanning calorimetry. Electrochemical corrosion studies of the coated films on mild steel panels showed excellent corrosion protection in the following order: PU‐PANI > PU‐PmAP > PU‐PoA. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 45806.     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

Fake news detection and classification using hybrid BiLSTM and self-attention model

Living in the twenty-first century, from shopping to reading news articles everything has changed, everything has become online. Anyone can access most of everything with a single touch from a cell phone. Internet is the new normal, everyone is very much attached to it. Reading news online is something very common among people of all age groups, thousands of articles are being published on various online media portals online every hour. These articles are not necessarily genuine always, sometimes false information is written knowingly and sometimes knowingly. It is very much needed to keep these articles away from the users. Many kinds of research have been conducted using traditional mathematical models and sequential neural networks to detect this fraud news online. In most of these studies, the news is being analysed in a unidirectional way. Therefore, a need of changing current mechanisms is required to increases the accuracy of false news detection. In this paper, we propose a Bi-LSTM based (Bidirectional long short term memory) deep learning approach by adding self-attention on top of it. This helps in developing a higher clarity, which is the most challenging part of the deep learning paradigm. The classification result demonstrated that the proposed hybrid deep learning model outperforms existing models with an accuracy score of 98.65%.

     

Mycobacterium leprae binds to a 25-kDa phosphorylated glycoprotein of human peripheral nerve

Mycobacterium leprae, the causative agent of leprosy, specifically invades and destroys the peripheral nerve, which results in the main clinical manifestation of the disease. Little is known about the bacteria-nerve protein interaction. We show in the present work that M leprae binds to a 25 kDa glycoprotein from human peripheral nerve. This protein is phosphorylatable and it binds to lectins which have alpha-mannose specificity. This M leprae-protein interaction could be of importance in the pathogenesis of leprosy.     

Intelligent channel estimation in millimeter wave massive MIMO communication system using hybrid deep learning with heuristic improvement

The major goal of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is to get effective channel state information (CSI). Most of the recent works use nuclear norm theory for recovering the low-rank scheme of channels. Some suboptimal solutions to the rank minimization problem can occur while addressing the nuclear norm-based convex problem, which degrades the accuracy of channel estimation. Some works recover the channel with the assumption of the mmWave channel using an over-complete dictionary. On the other hand, the accuracy of available CSI may openly influence the efficiency of mmWave communications. The main intention of this paper is to develop an enhanced channel estimation model with an optimized hybrid deep learning model. Here, the integration of deep neural network (DNN) and long short-term memory (LSTM) form the hybrid deep learning model termed optimized D-LSTM, which is modified by the opposition searched exploration-based Harris hawks optimization (OE-HHO). The input to the proposed hybrid deep learning is taken as the correlation among the received signal vectors and the measurement matrix for predicting the beam space channel amplitude. Finally, the successful channel estimation is observed by deep hybrid learning by the experimental outcomes, which also demonstrate that the proposed channel estimation model overwhelms the conventional models in terms of Normalized Mean-Squared Error (NMSE) and spectral efficiency. The experimental results show that the designed OE-HHO method obtains 9.2%, 8.9%, 8.65%, and 0.47% progressed than DA, DHOA, GWO, and HHO, respectively. Therefore, higher efficiency is observed by OE-HHO based mmWave MIMO communication system.     

Design of novel compact eight-element lotus shaped UWB-MIMO antenna with triple-notch characteristics on hollow substrate

The design of novel compact two-element and eight-element lotus shaped multiple-input-multiple-output (MIMO) antenna system employing pattern diversity with enhanced isolation characteristics is presented. The proposed two-element antenna system is arranged rotationally on a square-hollow substrate resulting in an eight-element MIMO antenna system employing pattern diversity. The developed eight-element MIMO antenna system resonates in the frequency range 3.1 to 14.6 GHz housing the complete UWB band with triple band-notch characteristics at 3.7–4.5 GHz (C-band satellite down link [3.7–4.2 GHz]), 5.1–5.9 GHz (WLAN) and 6.8–8.25 GHz (X-band satellite down link (7.25–7.75 GHz) and up link (7.9–8.4 GHz)) bands. The antenna system gives element-to-element isolation of more than 25 dB in the majority of the operating band with a peak gain of 6.8 dBi and a maximum 90% efficiency. The important MIMO metrics like ECC (envelope correlation coefficient), DG (diversity gain), total active reflection coefficient (TARC), channel capacity losses (CCL) and MEG (mean effective gain) are presented for both two-element and eight-element to estimate the performance the proposed antennas in multi-antenna environments. The both two- and eight-element designs are fabricated and the measured results of those are well agreed with simulation results.