Development of a sustainable and disposable modified Bi-CdFeO electrode for electrochemical sensing of lead (II) and Acetaminophen drug molecule.

The study of research proposes a systematic pattern for optimization and fabrication of a sustainable-cost effective electrochemical sensor made by Bi-CdFeO (BCDF) nanoparticle and graphite powder. The structural examinations of synthesized BCDF materials were analyzed by specific spectral techniques viz.; P-XRD, SEM-EDX, TEM, XPS, FT-IR and DRS techniques.

Experimental investigations of dual functional substrate integrated waveguide antenna with enhanced directivity for 5G mobile communications.

Antennas with higher gain and efficiency deliver superior performance across a wide frequency range. Achieving these characteristics at high frequencies while keeping a compact size necessitates sophisticated design approaches. This research presents a substrate-integrated waveguide (SIW) cavity-backed slotted patch antenna (SPA) tailored for the 28 GHz and 34 GHz frequency bands.

Design of dual mode antenna using CMA and broadband dual-polarized antenna for 5G networks.

This article proposes a dual mode dual-polarized antenna configuration for IRNSS and fifth generation (5G) applications, operating at a frequency of 3.5 GHz based on characteristic mode analysis (CMA), and aims to provide broadband dual-polarized functionality. The original design of the antenna is a traditional patch antenna, and its dual-polarized features are determined using characteristic mode analysis.

Experimental investigation of a dual mode antenna using characteristic mode analysis with enhanced directivity for GSM/5G applications.

In this article, a dual-mode, dual-polarized antenna designed using characteristic mode analysis (CMA) is described. An elliptical-shaped patch radiator is chosen with double slits on its minor axis. This design is based on mode separation from the circular patch into the elliptical patch.

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques.

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software.

Effect of TiO Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites.

Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.

Multi-objective hybrid split-ring resonator and electromagnetic bandgap structure-based fractal antennas using hybrid metaheuristic framework for wireless applications.

Design closure and parameter optimisation are crucial in creating cutting-edge antennas. Antenna performance can be improved by fine-tuning preliminary designs created using theoretical considerations and rough dimension adjustment via supervised parameter sweeps. This paper introduces a frequency reconfigurable antenna design that can operate at 28/38 GHz frequencies to meet FCC and Ofcom standards for 5G applications and in the 18 GHz frequency band for K-band radar applications.

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions.

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity.

Assessment of the Biosensing Capabilities of SiGe Heterojunction Negative Capacitance-Vertical Tunnel Field-Effect Transistor.

In this study, a comparison of the negative capacitance vertical tunnel field-effect transistor (NC-VTFET) and VTFET for biosensing applications was conducted. Dielectrically modulated TFET demonstrates better sensitivity than the traditional metal oxide field effect transistor as a biosensor in label-free biosensing applications. The TFET biosensor, however, has much room for advancement by enhancing its DC characteristics.

Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm.

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage.

Has Machine Learning Enhanced the Diagnosis of Autism Spectrum Disorder?

Autism spectrum disorder (ASD) is a complex neurological condition that limits an individual's capacity for communication and learning throughout their life. Although symptoms of Autism can be diagnosed in individuals of different ages, it is labeled as a developmental disorder because symptoms typically start to show up in the initial 2 years of childhood. Autism has no single known cause but multiple factors contribute to its etiology in children.

Contextual Cluster-Based Glow-Worm Swarm Optimization (GSO) Coupled Wireless Sensor Networks for Smart Cities.

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load.

Unclonable fluorescence of MgO-ZrO :Tb nanocomposite for versatile applications in data security, dermatoglyphics.

Latent fingerprints (LFPs) are one among the most important types of evidences at crime scenes because of the distinctiveness and tenacity of the friction ridges in fingerprints (FPs). Therefore, it is essential in forensic science to develop a reliable method to detect LFPs. Traditional detection methods still face a number of difficulties, such as limited sensitivity, low contrast, strong background, and complex processing stages.

Detection of Breast Cancer with Lightweight Deep Neural Networks for Histology Image Classification.

Many researchers have developed computer-assisted diagnostic (CAD) methods to diagnose breast cancer using histopathology microscopic images. These techniques help to improve the accuracy of biopsy diagnosis with hematoxylin and eosin-stained images. On the other hand, most CAD systems usually rely on inefficient and time-consuming manual feature extraction methods.

-graphyne as a promising anode material for Na-ion batteries: a first-principles study.

Lithium-ion batteries (LIBs) have emerged as a technological game-changer. Due to the rising price of lithium and the environmental concerns LIBs pose, their use is no longer viable. Sodium (Na) may be the best contender among the alternatives for replacing lithium.

Hydrothermal synthesis of novel heterostructured Ag/TiO /CuFe O nanocomposite: Characterization, enhanced photocatalytic degradation of methylene blue dye, and efficient antibacterial studies.

In this work, we reported the successful synthesis of novel Ag/TiO /CuFe O ternary nanocomposite by hydrothermal technique by using TiO /CuFe O binary nanocomposite precursor that was also prepared by hydrothermal treatment by using TiO nanoparticles and CuFe O nanoparticles synthesized via sol-gel method. The synthesized nanomaterials were accessed for their morphological, structural, and optical properties. X-ray diffraction (XRD) study reveals the formation of pure Ag/TiO /CuFe O ternary nanocomposite in which the Ag, TiO , and CuFe O are in anatase, spinal, and cubic crystal phases, respectively.

Recent Advances in Green Synthesis, Characterization, and Applications of Bioactive Metallic Nanoparticles.

Nanoparticles (NPs) are elements derived from a cluster of atoms with one or more dimensions in the nanometer scale in the range of 1-100 nm. The bio nanofabrication of metallic NPs is now an important dynamic area of research, with major significance in applied research. Biogenic synthesis of NPs is more desirable than physical and chemical synthesis due to its eco-friendliness, non-toxicity, lower energy consumption, and multifunctional nature.

SARS-CoV 2 spike protein S1 subunit as an ideal target for stable vaccines: A bioinformatic study.

The Covid-19 a pandemic infectious disease and affected life across the world resulting in over 188.65 million confirmed cases across 223 countries, territories and areas with 4.06 million deaths.

ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans.

Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET).

Sensing and sensitive visualization of latent fingerprints on various surfaces using a versatile fluorescent aggregation-induced emission-based coumarin derivative.

The marked rise in criminal activity in society has made a difficult task for forensic scientists who aim to track any crime scene effectively, therefore visualization of latent fingerprints (LFPs) plays an increasingly vital role in forensics. In the present report, a highly sensitive solvatochromism, aggregation-induced emission-based 2-(4-nitrophenyl)-3H-benzocoumarin fluorescent dye (CFD) was fabricated using an ultrasonication protocol. The fluorescence properties of the CFD were analyzed using fluorescence spectrophotometer.

Deep Learning-Based Denoising for High b-Value at 2000 s/mm2 Diffusion-Weighted Imaging.

Diffusion-weighted imaging (DWI) allows white matter quantification of the white matter tracts of the brain. However, at a high b-value (≥ 2000 s/mm2), DWI acquisition suffers from noise due to longer acquisition times obscuring white matter interpretation. DWI denoising techniques can be used to acquire high b-value DWI without increasing the number of signal averages.

An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images.

The novel coronavirus disease (SARS-CoV-2 or COVID-19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID-19 detection. However, lung infection by COVID-19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets.