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In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system's unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively. [ABSTRACT FROM AUTHOR]

In this paper, the 3D Darcy–Forchheimer flow model (TDDF-FM) is discussed by utilizing the artificial intelligence-based backpropagated neural networks with the algorithm of Levenberg–Marquardt (BNN-ALM) along with the convective conditions. The effects of Brownian diffusion and thermophoresis are also examined. The governing partial differential equations are transformed into a system of ODEs. Lobatto IIIA method is used to interpret the reference dataset of BNN-ALM for different scenarios of TDDF-FM by variation of porosity parameter, Forchheimer parameter, ratio parameter, Prandtl number, Biot number, Brownian motion parameter, Schmidt number, strength parameter of homogeneous reaction and thermophoresis parameter. The solutions are computed for the designed TDDF-FM by testing, training and validation processes of BNN-ALM. The performance analysis of the designed BNN-ALM is validated by histogram analysis, regression studies and the results of mean square error (MSE). Graphs are presented for temperature profile, concentration profile and concentration rate profile. The increasing values of porosity parameter, Forchheimer parameter and Biot number enhance the temperature distribution, whereas the temperature profile shows a decreasing behavior with the increase in Prandtl number and ratio parameter. The concentration profile increases with the increase in porosity parameter, Forchheimer parameter and thermophoresis parameter and it decreases with the increasing behavior of Brownian motion parameter, ratio parameter and Schmidt number. The concentration rate profile decreases with the increase in porosity parameter, Forchheimer parameter and strength parameter of homogeneous reaction and it increases with the ratio parameter. The best performance values for different cases of scenarios are 1.42E−08, 1.70E−08, 1.78E−08, 1.61E−08, 2.43E−08, 1.05E−08, 1.30E−08, 1.59E−08 and 2.31E−08, attained at 215, 186, 178, 99, 125, 213, 187, 190 and 191 epochs. The values for the Gradient factor are 9.93E−08, 9.86E−08, 9.99E−08, 2.97E−07, 1.23E−07, 9.94E−08, 9.94E−08, 9.87E−08 and 9.94E−08, where the value of regression is R = 1 for the training, testing and validation data. [ABSTRACT FROM AUTHOR]

This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs). The governing partial differential equations (PDEs) of MHD-VFFM are transformed into ODEs by applying suitable similarity transformations. The reference dataset is obtained from Adam numerical solver by the variation of Hartmann number (Ha), thickness parameter (α) , power index (n) , thermophoresis parameter (Nt), Brinkman number (Br), Lewis number (Le) and Brownian diffusion parameter (Nb) for all scenarios of proposed ALM-BPNN. The reference data samples arbitrary selected for training/testing/validation are used to find and analyze the approximated solutions of proposed ALM-BPNNs as well as comparison with reference results. The excellent performance of ALM-BPNN is consistently endorsed by Mean Squared Error (MSE) convergence curves, regression index and error histogram analysis. Intelligent computing based investigation suggests that the rise in values of Ha declines the velocity of the fluid motion but converse trend is seen for growing values of n. The rising values of Ha, Nt and Br improve the heat transfer but converse trend is seen for growing values of α. The inclining values of Nt incline the mass transfer but it shows reverse behavior for escalating values of Le. The inclining values of Br incline the EP. [ABSTRACT FROM AUTHOR]

We have developed an analytical model for polycrystalline-based organic thin-film transistors (OTFTs) that employs, as far as possible, new concepts on carrier injection to the conventional polysilicon model. The drain current equations, both in diffusion and drift regimes, predict the voltage and temperature dependencies on the various device and circuit parameters. Interestingly, upon direct comparison with previously developed disordered model, similarities between the two are not thought to be coincidental. The effect of gate voltage on surface potential is affected by the Fermi level pinning in the grain boundary, which is assumed to consist of mainly disordered material. This work also highlights the problem of using drift mobility, as an organic circuit design parameter, and consequently alternative quantities are proposed for simpler circuits such as an inverter. Upon validation of the model, relatively good fits are obtained with the experimental data on TIPS-based TFTs. The divergence at low drain voltages are thought to be associated with short channel and/or high contact resistance effects. [ABSTRACT FROM AUTHOR]

Features a novel variable-gain low-noise amplifier (LNA) designed by circuit design engineers, Nuntha Kumar K and Kumarasamy Raja M at the Singapore Institute of Microelectronics. Handheld devices that use wireless transceivers; Advantages from a smaller form of transistors; Function and features of LNA; Discussion on wireless communication standards. INSET: How a Variable-Gain Low-Noise Amplifier Works.

In this research paper, we observed the Prandtl–Eyring magneto hydrodynamic fluid model (PE-MHDFM) by applying the Bayesian regularization scheme as backpropagated artificial neural networks (BRS-BANNs). Effect of suction/injection at the wall is the source of convective steady flow. The nonlinear partial differential equations (PDEs) of PE-MHDFM are converted into ordinary differential equations (ODE) by applying some suitable similarity transformation. These ODEs are solved by utilizing Lobatto IIIA numerical procedure to acquire the reference dataset for different scenarios of BRS-BANN. The reference dataset is used to design the solver BRS-BANN. Further, the performance of BRS-BANN is clarified by MSE results, error analysis plots, regression and error histogram. Moreover, the solution of PE-MHDFM is observed through the validation, training and testing procedures. It is observed that the best correlation between the targeted values outcomes of the study is matched effectively, which definitely authenticates the validity and reliability of the designed solver. Furthermore, the impacts on the velocity profile and temperature profile are examined by the variation of different physical quantities along with their comparison with state-of-the-art Lobatto IIIA numerical approach. [ABSTRACT FROM AUTHOR]

During the pandemic, the most significant reason for the deep concern for COVID-19 is that it spreads from individual to individual through contact or by staying close with the diseased individual. COVID-19 has been understood as an overall pandemic, and a couple of assessments is being performed using various numerical models. Machine Learning (ML) is commonly used in every field. Forecasting systems based on ML have shown their importance in interpreting perioperative effects to accelerate decision-making in the potential course of action. ML models have been used for long to define and prioritize adverse threat variables in several technology domains. To manage forecasting challenges, many prediction approaches have been used extensively. The paper shows the ability of ML models to estimate the amount of forthcoming COVID-19 victims that is now considered a serious threat to civilization. COVID-19 describes the comparative study on ML algorithms for predicting COVID-19, depicts the data to be predicted, and analyses the attributes of COVID-19 cases in different places. It gives an underlying benchmark to exhibit the capability of ML models for future examination. [ABSTRACT FROM AUTHOR]

Influence of Ni11+ swift heavy ion (SHI) irradiation induced modifications on structural and morphological aspects of CuFe2O4 nanoparticles have been studied. The bulk sample is synthesized using co-precipitation technique. The cubic spinel structure is identified using X-ray diffraction technique and these nanoparticles have an average grain size of about 10 nm. These materials are irradiated at various fluences of 1 × 1011, 1 × 1012, 1 × 1013, 1 × 1014, and 5 × 1014ions/cm2 using Ni11+ ion beam of 150 MeV energy. The XRD pattern of the irradiated material shows the change in crystalline nature. The spinel ferrite (311) peak, which is having maximum intensity in the pristine, decreases at the fluence of 1 × 1011 and 1 × 1012ions/cm2 and then completely vanishes on further irradiation. However, a new X-ray peak arises in the (222) direction. All the other peaks in the pristine sample vanish on irradiation elucidating the creation of amorphization nature. Scanning electron microscope (SEM) analysis shows fine blocks of material for pristine and partial agglomeration on irradiation. Notably, a large number of holes are present at the fluence of 5 × 1014ions/cm2 due to heavy ion irradiation. The phase change from crystalline to amorphous nature on XRD and the creation of holes from SEM is discussed on the basis of electrophonon coupling and it invokes the thermal spike theory. [ABSTRACT FROM AUTHOR]