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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]

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]

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]

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]

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.