Base fluids with CNTs as nanoparticles through non-Darcy porous medium in convectively heated flow: A comparative study

Comparative analysis for flow of CNTs nanofluids is discoursed in the presence of non-Darcy porous medium. The consequences of homogeneous/heterogeneous process and heat transfer through convection are employed. The flow induced is due to non-linear stretching sheet of variable thickness. The bottom of the variable thickness sheet is heated by convective processes from a heated fluid. The velocity, temperature and concentration functions are formulated for the stretched flow problem. Convergence control variables and square residual errors for series solutions are obtained through OHAM (Optimal Homotopy Analysis Method). Biot number corresponds to larger temperature distribution in case of MWCNT than SWCNT. Comparison of nanoparicles SWCNT and MWCNT for the CNTs nanofluid fluids is highlighted. Water and engine oil CNTs fluids have higher magnitude of Nusselt number when compared with kerosene oil CNT fluid. The heat transfer rate in the presence of MWCNT is higher than SWCNT. Comparison of present study with previous published data is made. The outcomes are found in favorable agreement.     

Liquid-Metal Synthesized Ultrathin SnS Layers for High-Performance Broadband Photodetectors

Atomically thin materials face an ongoing challenge of scalability, hampering practical deployment despite their fascinating properties. Tin monosulfide (SnS), a low-cost, naturally abundant layered material with a tunable bandgap, displays properties of superior carrier mobility and large absorption coefficient at atomic thicknesses, making it attractive for electronics and optoelectronics. However, the lack of successful synthesis techniques to prepare large-area and stoichiometric atomically thin SnS layers (mainly due to the strong interlayer interactions) has prevented exploration of these properties for versatile applications. Here, SnS layers are printed with thicknesses varying from a single unit cell (0.8 nm) to multiple stacked unit cells (≈1.8 nm) synthesized from metallic liquid tin, with lateral dimensions on the millimeter scale. It is reveal that these large-area SnS layers exhibit a broadband spectral response ranging from deep-ultraviolet (UV) to near-infrared (NIR) wavelengths (i.e., 280–850 nm) with fast photodetection capabilities. For single-unit-cell-thick layered SnS, the photodetectors show upto three orders of magnitude higher responsivity (927 A W⁻¹) than commercial photodetectors at a room-temperature operating wavelength of 660 nm. This study opens a new pathway to synthesize reproduceable nanosheets of large lateral sizes for broadband, high-performance photodetectors. It also provides important technological implications for scalable applications in integrated optoelectronic circuits, sensing, and biomedical imaging.     

Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.     

QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.     

Effect of Pd sensitization on gas-sensing performance of vanadium pentoxide-reduced graphene oxide composite

Current work clearly demonstrates Pd sensitization on hydrothermally prepared vanadium pentoxide (V₂O₅) and on its composite with reduced graphene oxide (rGO) by a simple dip and dry method for NO₂ gas sensing. Orthorhombic crystal structure of prepared sample was confirmed with the help of X-Ray Diffraction (XRD) technique. The presence of palladium (Pd) was confirmed by energy-dispersive spectroscopy (EDS) and X-Ray photoelectron spectroscopy (XPS), while fourier transform infra-red (FTIR) spectroscopy for functional group detection. Porous nano-structure morphology was confirmed with the help of scanning electron microscopy (SEM). Transmission electron microscopy (TEM) technique confirms presence of Pd nanoparticles (NPs) with an average diameter of?~?20 nm. The photoluminescence (PL) peak arising at 641.75 nm gives the confirmation to the mid-gap states generated by oxygen vacancies. Finally, the gas-sensing performance of the prepared sensing material was measured with the help of a RIGOL digital multimeter. The result shows that 33.68% gas response towards 100 ppm NO₂ gas at relatively low-working temperature (150 °C) along with considerable 47 and 592 s response/recovery time, respectively. The sensing material shows good reproducibility and high stability (Gas Response?~?29%) even after 63 days. Hence Pd sensitized V₂O₅- rGO is a promising candidate for gas sensors working at relatively low working temperatures.

     

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.     

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.     

Magnetohydrodynamic stagnation point flow of third-grade liquid toward variable sheet thickness

Neural Computing and Applications - The current study examines the boundary layer stagnation point flow of third-grade fluid toward a stretching surface with variable thickness. Electrically...