We have investigated the preparation and nonlinear optical characterization of Ge 20 Te 75 Bi 5 (GTB) nano colloidal solutions using the z-scan technique for three different concentrations. The transmission spectra of GTB nano colloid films have been investigated. FTIR spectra have been measured to confirm the presence of amines in nano colloidal solutions. Z-scan traces at 78 × 10 12 W/cm 2 laser intensity were used to study the nonlinearity of the samples. The nonlinear absorption coefficient increases with an increase in the concentration of nano colloids. The reported high nonlinear response is useful for optical limiting applications. The optical limiting behavior is also studied against different concentrations. ARTICLE HISTORY
Helicoidal schemes possessed by biological creatures possess high strength and stiffness. The adoption of the layup configurations possessed by these biological creatures is not fully explored. The present article aims to carry out the free vibration analysis of biological-inspired laminated composite (B-ILC) plates having helicoidal layup. The analysis is carried out using higher-order zigzag theory (HOZT) as due to the presence of several layers, the HOZT can predict the behavior accurately compared to the shear deformation theory. Based on Hamilton's principle, the governing equations are worked out and analyzed using the finite element method. The influence of boundary conditions, geometric properties, number of layers, the skew angle of the plate, and material properties on the free vibration behavior are studied in detail.
Fog integrated Cloud Computing is a distributed computing paradigm where near-user end devices known as fog nodes cooperate with cloud resources hosted at distant datacentres for providing computational and storage services to end user applications. One of the most challenging issues in fog integrated cloud based system is task scheduling. Most of the existing scheduling approaches involve centralized decision making which fail to exploit the advantages that may be achieved by a decentralized approach, that directly maps with the distributed architecture of fog based systems. This work proposes a decentralized heuristic algorithm for scheduling real-time IoT applications bounded by tolerable latency as the Quality of Service (QoS) constraint. The proposed technique aims to take into consideration the resource constraints of the fog resources to yield a schedule that not only meets the QoS requirements defined in terms of tolerable latency but also improves the response time of applications hosted on a fog-cloud infrastructure. Performance evaluation on different IoT applications indicate that the presented algorithm delivers better performance by reducing response time by 11% on an average in comparison to the other state-of-the-art policies.
Breast cancer is the second utmost common cancer among women. In this research study, two feature selection methods, namely Consistency first Search-Best First search (CFS-BFS) and Consistency-Best First Search (Consistency-BFS) are used to find out the significant biomarkers for breast cancer. The feature selection methods pick a small count of important and relevant genes providing a higher degree of accuracy. The count of biomarkers selected in Consistency-BFS is less in comparison to CFS-BFS method. Three common identified biomarkers’ genes are recognised to help in diagnosis and prognosis of breast cancer, namely SLC39A6, ESR1, and CDC20 are selected on the basis of histologic-grade and molecular subtype of breast cancer. These three genes identified are even found in PAM50 and judged for survival variances using Kaplan-Meier Survival Model. The result suggested that overexpression of SLC39A6, ESR1, and CDC20 proves to be an influencing factor for the poor prognosis of patients suffering from breast cancer. The proposed method is successful in creating a prognostic gene signature in forecasting the survival likelihood of patients.
Development of high-rise project is a complex phenomenon. Management of such projects involves integration of various processes and activities which contains several risks. Few such risks are land disputes, shortage of manpower, materials, tools and machineries, delay in approval from government, poor scheduling, poor estimate, cash flow shortage, etc. These risks may be interrelated and its impact may be progressive on other activities and processes. The above risks may have serious impact on overall quality, budget and schedule of project and ultimately non meeting of “Business Objectives” like financial loss, damage to brand reputation, court cases, stalling of project etc. To overcome these issues, identification of major risks, analysis of the causes and mitigation steps should be implemented throughout the project duration for the successful development of high-rise projects. In the present work effort has been made to study these risks and causes through literature review, field experience and interviewing the concerned experts. The authors attempted to aware project managers, contractors and consultants about major risks and causes in high-rise project across India, so that it’s serious impacts on project or business objectives could be avoided or mitigated.
In recent years, perovskite solar cells (PSCs) have been in huge demand because of their ease of production, low cost, flexibility, long diffusion length, lightweight, and higher performance compared to their counterparts. The PSCs have demonstrated remarkable progress with PCE up to 25.7% using FAPbI3 as an active layer component. However, the commercial viability of perovskite solar cells has been restricted by a wide range of factors, such as lower PCE and device stability. Further, the photocurrents in PSCs are close to the maximum Shockley‐ Queisser (SQ) limit. The focus now is on enhancing the open‐circuit voltage and fill factor through modifying charge‐selective contacts, the morphology of perovskite material, and interface modification. The large grain size, uniformity, and coverage area distinguish the crucial factors affecting the PCE of PSCs. Long‐term device stability and degradation mechanisms have also shown significant dependence on the device structure. Therefore, the tailoring of the device structure continues to play a crucial role in the device’s performance and stability. In this review, the illustration of the structural development of perovskite solar cells, including advanced interfacial layers and their associated parameters, has been discussed in detail. In addition, the challenges which hinder the performance of the PSCs have also been discussed. This article is protected by copyright. All rights reserved.
Using Computational Fluid Dynamics (CFD), the blood flow through carotid artery is studied to examine the effect of flow on hemodynamics parameters. Geometrical carotid artery model is designed using ANSYS Fluent, and results of non-Newtonian model are studied. The numerical results are presented in terms of velocity, wall shear stress and velocity vector on carotid artery due to the deposition of different shape and size of arterial stenosis such as plaque shape 1, plaque shape 2, cosine plaque and irregular plaque shapes of stenosis. It is found that area of blockage plays an important role as the stenosis in the artery increases the flow velocity, and wall shear stress also increases in the stenotic region. At the stenosis, velocity is observed maximum compared to pre- and post-stenosis. The highest level of WSS was observed for the plaque shape 2 of stenosis followed by plaque shape 1, irregular plaque shape and then by cosine-shaped stenosis. The flow velocity across stenosis is more for plaque shape 2 followed by plaque shape 1, irregular and cosine plaque shape.
The emergence and immersion of technology are essential for creating smart cities. Therefore, technology is vital in developing sustainable, smart, and resilient cities. Using cutting-edge technologies like cloud computing, blockchain, and artificial intelligence, the concept of a “smart city” envisions a future in which smart gadgets execute various applications with minimal resource consumption. The study uses bibliometric analysis using software R studio and package biblioshiny. The study incorporates publications from the Scopus database from the last few years using keywords like smart cities, technology, Information Communication Technology, Artificial Intelligence, and digitalization. The study is an attempt to answer questions. Firstly, what are the literature characteristics produced during the last two years? Secondly, to identify the most relevant keywords related to technology were highlighted by different researchers in their study about smart cities, and lastly, to create a path that can illustrate the emerging and immersing technologies in developing smart cities. The study’s findings would be purposeful for the agencies developing technology to develop sustainable smart cities.
The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this paper, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this paper, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection, and effective modeling to create a robust news popularity Prediction Model. Three kinds of features – meta features, text features, and image features are used to design an influential and robust model. The performance measure Root Mean Squared logarithmic error (RMSLE) is used to validate the outcome of the proposed model. Further, the most important features are sought out for the proposed model to verify the dependence of the model on text and image features.
Cell-free massive multiple-input multiple-output (CFMM) networks with its ubiquitous coverage at high spectral efficiency (SE), is one of the promising technology for 5G and beyond system. In this study, We propose a new framework for downlink (DL) CFMM system operating under Rayleigh fading channel model. We introduce new deep learning-based precoding scheme that improve the performance of the proposed system by reducing run time and computational complexity as compared to conventional linear precoding schemes. We also introduce an improved version of basic scalable pilot assignment algorithm which further enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme considering channel estimation error and pilot contamination(PC), which is then compared with Minimum Mean Square Error(MMSE), Regularised Zero Forcing (RZF) and Maximum Ratio (MR) combining techniques. We analyse the proposed scheme with perfect channel state information(CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration, non-linear and linear precoding techniques. Numerical results shows that the proposed deep learning based precoding scheme outperforms other conventional techniques. endabstract
Cell-free massive multiple-input multiple-output (CFMM) network is projected as the latest technology for the fifth-generation and beyond wireless networks. The recent research trend is to extensively study and analyse CFMM network for its advantages and bottlenecks. The CFMM network is strongly affected by pilot contamination (PC) which is one of the bottlenecks due to which quality of service (QoS) and accuracy of channel estimation gets impacted. Therefore, we address this problem by presenting a novel pilot assignment algorithm to mitigate PC and deep learning aided channel estimation for reducing channel estimation error for the CFMM systems to maximize spectral efficiency. We derive achievable UL and DL spectral efficiency (SE) expressions for the proposed system, and compared with Minimum Mean Square Error(MMSE) and Maximum Ratio (MR) combining techniques. The performance of cellular massive MIMO is derived for comparison. For the same cellular set up,the proposed CFMM system achieves higher SE than the cellular massive MIMO. Numerical results prove the efficacy of the proposed CFMM system to some of the existing schemes in this domain.
This paper presents a hyperbolic shear deformation theory and discusses its application to investigate the bending and buckling behavior of functionally graded carbon nanotubes-reinforced composite (FGCNTRC) beams. The proposed theory satisfies the parabolic variation of shear stress distribution throughout the thickness and fulfills the zero condition of shear stress on the upper and bottom surfaces of the FG-CNTRC beams. Therefore, there is no need to use any correction factor concerning conventional equivalent single-layer theories. Five different types of CNTs reinforcement distribution are considered for the analysis while assuming a power-law function variation of the material properties in the thickness direction. The governing equations are solved using a finite element method, where several new numerical results are presented to demonstrate the robustness and reliability of the proposed model. The compartive study shows that the proposed element model is: (a) accurate and comparable with the literature; (b) of a faster rate of convergence to the reference solution; (c) excellent in terms of numerical stability; (d) valid for both symmetric and non-symmetric FG-CNTRC beams. Results also show the validity of the proposed formulation for both thin and thick FG-CNTRC beams. In addition, the effect of various material and geometric parameters such as the CNTs volume fraction, distribution patterns of CNTs, boundary conditions, and the length-to-thickness ratio is investigated on the bending and buckling responses of FG-CNTRC beam structures. Several new referential results are also reported for the first time, which will serve as a benchmark for future studies in a similar direction
Security and privacy are two main dominant features of any communication system. In this paper, physical layer security of free space optical communication system using chaotic modulation scheme i.e., differential chaos shift keying (DCSK) is analyzed, where eavesdropper is actively present near the receiver and interfering between the transmission of secret messages from a transmitter to the receiver. In this manuscript, we have derived analytical expressions for the average secrecy capacity and secrecy outage probability which is used as a metric for secrecy performance analysis. The channel characterization is carried out using gamma–gamma model for weak-to-strong turbulence conditions. The effect of physical layer parameters like transmission link length, spreading length, etc. are considered for evaluating the security performance of the system. Numerical analysis is carried out and graphical results are presented. The results depicted that a very good average secrecy capacity can be achieved even in the presence of eavesdropper, however, it requires a tradeoff between high signal-to-noise ratio of main channel and large values of spreading factor. The proposed system is very promising for the future secured communication systems.
Available shear deformation theories (SDTs) in the literature have their own merits and demerits. Among SDTs, first-order shear deformation theory (FSDT) and higher-order shear deformation theories (HSDT) are most widely used for the analysis of laminated composite and sandwich (LCS) beams. However, these theories are not able to predict the continuation of transverse shear stresses at interfaces across the thickness of the LCS beams. Due to the assumption of the constant variation of the transverse displacement field across the thickness of the layer, the FSDT is not able to predict the values for the transverse normal stresses. The present work aims to transform the stress variations across the thickness of LCS beams obtained from FSDT to the 3D Elasticity solutions using Gaussian Process Regression (GPR) based surrogate model. Further, the surrogate model is exploited to predict the variation of transverse normal stresses σzz across the thickness. Without large computational efforts, the proposed methodology will be able to capture the through-thickness stress variations equivalent to 3D Elasticity solutions, leading to an accurate yet efficient prediction.
In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.
In biological systems, the unprompted assembly of DNA molecules by cationic ligands into condensed structures is ubiquitous. The ability of ligands to provoke DNA packaging is crucial to the molecular organization and functional control of DNA, yet their underlined physical roles have remained elusive. Here, we have examined the DNA condensation mechanism of four cationic ligands, including their primary DNA-binding modes through extensive biophysical studies. We observed contrasting changes for these ligands binding to poly[dGdC]:poly[dGdC] (GC-DNA) and poly[dAdT]:poly[dAdT] (AT-DNA). Based on a CD spectroscopic study, it was confirmed that only GC-DNA undergoes B- to Ψ-type DNA transformation in the presence of ligands. In the fluorescence displacement assay (FDA), the ability of ligands to displace GC-DNA-bound EtBr follows the order: protamine21+ > cohex3+ > Ni2+ > spermine4+, which indicates that there is no direct correlation between the ligand charge and its ability to displace the drug from the DNA, indicating that GC-DNA condensation is not just influenced by electrostatic interaction but ligand-specific interactions may also have played a crucial role. Furthermore, the detailed ITC-binding studies suggested that DNA-ligand interactions are generally driven by unfavorable enthalpy and favorable entropy. The correlations from various studies insinuate that cationic ligands show major groove binding as one of the preferred binding modes during GC-DNA condensation.
Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.
Software projects reckon on the bug tracking systems to guide software maintenance activities. The critical information about the nature of the crash is carried by the bug reports which are submitted to bug repositories. This information is in free form text format and is submitted by users or developers. A large amount of bug reports gets collected in bug repositories. Out of these submitted bugs, many reports are mere identical of the already existing bugs. Furthermore, not all non-duplicate bugs are reproducible in nature. This paper introduces DENATURE, a two step framework for detecting duplication and identifying bug type. The proposed framework will help to minimize time and developer’s effort utilized in resolution of bug reports which will further improvise overall software quality. Information retrieval techniques are used for finding duplicate bugs and machine learning classification techniques are used for identifying the type of bug report. Through experiments, we found that the proposed framework obtained prediction accuracy up to 88.81%.
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Sector-23A, 122017, Gurgaon, Haryana, India
Head of institution
Prof. Prem Vrat