Synthesis and Ultrasonic Investigations of CuO-PVA Nanofluid

Study of nanofluids is important for different types of heat transfer management systems. Cupric oxide nanoparticles (CuO NPs) were prepared by the chemical route and different nanofluid samples of CuO NPs dispersed in PVA in dif- ferent concentrations were prepared using ultrasonication. The apparatus acoustic particle sizer (APS-100) was used to make high precision measurements of the ultrasonic attenuation depending upon different frequencies in the frequency range 48 to 99 MHz. The ultrasonic attenuation data are inverted to particle size distribution (PSD) and are used for particle size determination of CuO NPs. Temperature dependent ultrasonic velocity in the samples is also measured. The results of ultrasonic spectroscopy are compared with the microscopic measurements such as transmission electron microscopy (TEM) and X-ray diffraction (XRD). There is good agreement between data produced by ultrasonic spec- troscopy and the microscopic measurements.

Structural characterization of Nd-doped in silica host matrix prepared by wet chemical process

Silica host matrix containing neodymium which is potentially important for the formation of nanocrystalline metal oxides was prepared by solgel method, using tetra-ethoxysilane and Nd（NO3）3 as precursor materials.The prepared samples were changed from amorphous to nanocrystallites phase at sintered temperature 550 °C（4 h）, 750 °C（8 h） and 950 °C（12 h）.The thermally treated sample microstructures were investigated using X-ray diffraction（XRD）, Fourier transformation infrared spectroscopy（FTIR）, and scanning electron mi-croscopy（SEM）.While a further increase of the temperature at 750 °C and annealing time（8 h） resulted in the formation of cubic and hexagonal Nd2O3 nanocrystallites.At constant sintering temperature 950 °C for 12 h, the samples showed sharper and intense peaks.The sizes of Nd2O3 nanocrystallites were characterized by XRD with average size ～46 nm.

Development of Impedimetric Biosensor for Total Cholesterol Estimation Based on Polypyrrole and Platinum Nanoparticle Multi Layer Nanocomposite

A novel impedimetric biosensor was fabricated for total cholesterol sensing based on platinum nanoparticle and polypyrrole multilayer nanocomposite electrode. The Pt nanoparticles (PtNP) electrochemically deposited between two polypyrrole layers on indium tin oxide (ITO) glass plates (PtNP/PPY/ITO) have offered high-electroactive surface area and favourable microenvironment for immobilization of cholesterol esterase (ChEt) and cholesterol oxidase (ChOx) resulting in enhanced electron transfer between the enzyme system and the electrode. Impedimetric response studies of the ChEt-ChOx/PtNP/ITO nanobioelectrode exhibit improved linearity (2.5 × 10-4 to 6.5 × 10-3 M/l), low detection limit (2.5 × 10-4 M/l), fast response time (25 s), high sensitivity (196 Ω/mM/cm-2) and a low value of the Michaelis-Menten constant (Km, 0.2 M/l) with a regression coefficient of 0.997.

A Comparative Study on Biosorption of Cr (VI) by <i>Fusarium solani</i>under Different Growth Conditions

Biosorption of Cr(VI) from aqueous solution was studied in a batch bioreactor using the resting cells of Fusarium solani isolated from soil. The specific Cr(VI) removal decreased with increase in pH from 2.0 to 6.0 and increased with increase in initial Cr(VI) concentration, upto 500 mg?l–1. By increasing biomass concentration from 2.0 - 5.0 g?l–1, the specific metal removal remained almost constant. The maximum specific Cr(VI) removal was 60 mg?g–1 achieved at 500 mg?l–1 initial Cr(VI) concentration and by using resting cells (36 h old). The Langmuir adsorption isotherm constants, Q0 and b were observed to be 57.1 mg?g–1 and 0.06 l?mg–1 respectively. These results were compared with the Cr(VI) removal obtained in earlier studies conducted by the present authors using non living and growing cells of F. solani.

Intrusion detection and prevention system for an IoT environment

Internet of Things(IoT)security is the act of securing IoT devices and networks.IoT devices,including industrial machines,smart energy grids,and building automation,are extremely vulnerable.With the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection Systems(IDSs)and Network-based Intrusion Prevention Systems(NBIPSs)are proposed in this study.An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering.The proposed NBIPS inspects network activity streams to identify and counteract misuse instances.The NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe substances.Networkbased IPS sensors can be installed either in an inline or a passive model.An inline sensor is installed to monitor the traffic passing through it.The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

Universal Current-Mode Biquad Filter Using a VDTA

This paper presents a new current-mode single input multi output (SIMO) type biquad employing one voltage differencing transconductance amplifier (VDTA), two grounded capacitors and a single grounded resistor. The configuration realizes all basic filter functions (i.e. Low Pass (LP), High Pass (HP), Band Pass (BP), Notch (BR) and All Pass (AP)). The natural frequency (ω0) and bandwidth (BW) are independently tunable. The workability of proposed configuration has been verified using SPICE simulation with TSMC CMOS 0.18 μm process parameters.

Electronically Controllable Fully-Uncoupled Explicit Current-Mode Quadrature Oscillator Using VDTAs and Grounded Capacitors

An electronically controllable fully uncoupled explicit current-mode quadrature oscillator employing Voltage Differencing Transconductance Amplifiers (VDTAs) as active elements has been presented. The proposed configuration employs two VDTAs along with grounded capacitors and offers the following advantageous features 1) fully and electronically independent control of condition of oscillation (CO) and frequency of oscillation (FO);2) explicit current-mode quadrature oscillations;and 3) low active and passive sensitivities. The workability of proposed configuration has been demonstrated by PSPICE simulations with TSMC CMOS 0.18 μm process parameters.

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing,to extract named entities and events from unstructured data.Due to the exponential data growth in the agricultural sector,extracting significant information has become a challenging task.Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation,crop disease detection,weed removal,and yield production,still it is difficult to find the semantics between extracted information due to unswerving effects of weather,soil,pest,and fertilizer data.This paper consists of two parts.An initial phase,which proposes a data preprocessing technique for removal of ambiguity in input corpora,and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer andmultilayer perceptron to find agricultural-based named entity recognition,events,and relations between them.The proposed algorithm has been trained and tested on four input corpora i.e.,agriculture,weather,soil,and pest&fertilizers.The experimental results have been compared with existing techniques and itwas observed that the proposed algorithm outperformsWeighted-SOM,LSTM+RAO,PLR-DBN,KNN,and Na飗e Bayes on standard parameters like accuracy,sensitivity,and specificity.

A novel strategy for automatic test data generation using soft computing technique

Software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. Software development process in- vests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear struc- ture of software. Moreover, test case type and scope deter- mines the quality of test data. To address this issue, software testing tools should employ intelligence based soft comput- ing techniques like particle swarm optimization （PSO） and genetic algorithm （GA） to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing ex- periments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test ad- equacy criterion as branch coverage. The performance ade- quacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work.

Cloud‐based video streaming services:Trends,challenges,and opportunities

Cloud computing has drastically changed the delivery and consumption of live streaming content.The designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time services.The different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is underlined.Issues such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are discussed.In addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are addressed.The future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and technology.For technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant implications.Suggestions on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

Viscosities and ultrasonic speeds of binary mixtures of benzene with triethylamine and tributylamine at different temperatures:An experimental and theoretical study

The viscosities, η, and ultrasonic speeds, u, of pure benzene, triethylamine, （TEA） tributylamine, （TBA）, and those of their binary mixtures, with benzene as common component, covering the whole composition range have been measured at 278.15 K, 283.15 K, 288.15 K, 293.15 K, 298.15 K, 303.15 K, 308.15 K, 313.15 K, and 318.15 K. From the experimental data the deviations in viscosity, △η, deviations in Gibbs free energy, AG, deviations in ultrasonic speed, △u, deviations in entropies, △S^＊, and deviations in enthalpies, △H^＊, of activation of viscous flow have been determined. The sign and magnitude of these parameters were found to be sensitive towards interactions prevailing in the studied systems. Further, the excess molar volumes, VE, were calculated using data for the binary mixtures. Moreover, theoretical values of viscosities and ultrasonic speeds of the binary mixtures were calculated using different empirical relations and theories. The results were in experimental and theoretical values. discussed in terms of average deviations （AD）

Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis.Therefore,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones.In this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud images.DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy.Delivery time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances.The satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its subcontinent.The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

Double co-host emitter based top emitting white organic light emitting diodes with enhanced brightness and efficiency

Novel and simplified bi-layer top-emitting white organic light emitting diodes(OLEDs)with dual co-host emitters have been simulated and analyzed.They consist of yellow-green emitting layer(EML)as electron transport layer(ETL)and blue EML as hole transport layer(HTL).Novelty of the device lies in simplification of tri-layer white OLED to a bi-layered device which is done by merging yellow-green EML with ETL and blue EML with HTL.The simulated devices show Commission Internationale de L’Eclairage(CIE)colour coordinates well within the emission range of white light.The results show that device A with 5,6,11,12-tetraphenylnaphthacene(rubrene)doped ETL has achieved the lowest luminance but longest excited state lifetime.Device D with tris-(8-hydroxyquinoline)aluminum:4-(dicyanomethylene)-2-t-butyle-6-(1,1,7,7-tetra-methyljulolidyl-9-enyl)4 H-pyran(Alq3:DCJTB)as ETL which emits yellow light and 2,7-bis[N,N-bis(4-methoxy-phenyl)amino]-9,9-spirobifluorene(MS-TPD):bis(2-methyl-8-quninolinato)-4-phenylphenolate alu-minium(BAlq)as HTL which is responsible for blue light emission is found to have best characteristics when compared to other simulated devices.It has a maximum luminance of 10000 cd/m2 and current efficiency of 15.25 cd/A,respectively,and CIE coordinates are at(0.329,0.319).The device is found to be compatible to be used in solid state lighting applications because of the low driving voltage of the device.

High performance 14-bit pipelined redundant signed digit ADC

A novel architecture of a pipelined redundant-signed-digit analog to digital converter（RSD-ADC） is presented featuring a high signal to noise ratio（SNR）, spurious free dynamic range（SFDR） and signal to noise plus distortion（SNDR） with efficient background correction logic. The proposed ADC architecture shows high accuracy with a high speed circuit and efficient utilization of the hardware. This paper demonstrates the functionality of the digital correction logic of 14-bit pipelined ADC at each 1.5 bit/stage. This prototype of ADC architecture accounts for capacitor mismatch, comparator offset and finite Op-Amp gain error in the MDAC（residue amplification circuit）stages. With the proposed architecture of ADC, SNDR obtained is 85.89 d B, SNR is 85.9 d B and SFDR obtained is 102.8 d B at the sample rate of 100 MHz. This novel architecture of digital correction logic is transparent to the overall system, which is demonstrated by using 14-bit pipelined ADC. After a latency of 14 clocks, digital output will be available at every clock pulse. To describe the circuit behavior of the ADC, VHDL and MATLAB programs are used. The proposed architecture is also capable of reducing the digital hardware. Silicon area is also the complexity of the design.