Enhancement of magnetic and dielectric properties of Ni0.25Cu0.25Zn0.50Fe2O4 magnetic nanoparticles through non-thermal microwave plasma treatment for high-frequency and energy storage applications

Spinel ferrites are widely investigated for their widespread applications in high-frequency and energy storage devices. This work focuses on enhancing the magnetic and dielectric properties of Ni0.25Cu0.25Zn0.50 ferrite series through non-thermal microwave plasma exposure under low-pressure conditions. A series of Ni0.25Cu0.25Zn0.50 ferrites was produced using a facile sol-gel auto-ignition approach. The post-synthesis plasma treatment was given in a low-pressure chamber by sustaining oxygen plasma with a microwave source. The structural formation of control and plasma-modified ferrites was investigated through X-ray diffraction analysis, which confirmed the formation of the fcc cubical structure of all samples. The plasma treatment did not affect crystallize size but significantly altered the surface porosity. The surface porosity increased after plasma treatment and average crystallite size was measured as about similar to 49.13 nm. Morphological studies confirmed changes in surface morphology and reduction in particle size on plasma exposure. The saturation magnetization of plasma-exposed ferrites was roughly 65% higher than the control. The saturation magnetization, remnant magnetization, and coercivity of plasma-exposed ferrites were calculated as 74.46 emu/g, 26.35 emu/g, and 1040 Oe, respectively. Dielectric characteristics revealed a better response of plasma-exposed ferrites to electromagnetic waves than control. These findings suggest that the plasma-exposed ferrites are good candidates for constructing high-frequency devices.Web of Science1519art. no. 689

Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data

Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. Our findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. We tested our model against the industry standard, ResNet 50, as well as the state-of-the-art, VGG 16. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research

Nature inspired MIMO antenna system for future mmWave technologies

In this work, a new Multiple Input Multiple Output (MIMO) antenna system with a novel shape inspired by nature is proposed for Fifth-Generation (5G) communication systems. The antenna is designed on a Rogers 5880. The dielectric constant of the substrate is 2.2, and the loss tangent is assumed to be 0.0009. The gain of the system for the desired bandwidth is nearly 8 dB. The simulated and the measured efficiency of the proposed system is 95% and 80%, respectively. To demonstrate the capability of the system as a potential candidate for future 5G communication devices, MIMO key performance parameters such as the Envelope Correlation Coefficient (ECC) and Diversity Gain (DG) are computed. It is found that the proposed system has low ECC, constant DG, and high efficiency for the desired bandwidth.Ministry of Education; Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabi

Line-of-Sight-Based Coordinated Channel Resource Allocation Management in UAV-Assisted Vehicular Ad Hoc Networks

In Vehicular Ad Hoc Networks (VANETs), the Road-Side-Units (RSUs) that act as base station for serving vehicles may experience significant traffic and congestion during peak hours due to the density of vehicles. To solve this problem, we suggest a coordinated strategy including Unmanned-Aerial-Vehicles (UAVs) on-demand use to supplement and service RSUs and other vehicles. Better resource allocation strategies are required to handle this issue because RSUs and UAVs may share the same set of channels, which will lead to interference. In this research, we propose a UAV-Assisted Protocol (UAVa) to improve network performance, especially throughput. The platform for resource allocation between UAVs and RSUs established by our suggested protocol is cooperative and well-coordinated. The criterion of channel allocation is based on the probability that a Line-of-Sight (LoS) link will exist between the vehicle and the UAV/RSU. The UAV serves the car if the LoS probability is high; otherwise, the RSU offers the service. Signals are successfully decoded to reduce interference if their Signal-to-Interference-plus-Noise-Ratio (SINR) is higher than a set SINR threshold. In contrast, other signals are subject to a back-off timer. We show through simulations that including UAVs is more effective and provides a reliable communication system in VANETs. By implementing a probabilistic access mechanism, our coordinated vehicular network efficiently lessens the stress on RSUs while on the load to UAVs, especially in locations with high traffic density. The LoS connectivity and optimized channel allocation offer better quality of service through RSU and UAVs in this cooperative environment

Dual-polarized 8-port sub 6 GHz 5G MIMO diamond-ring slot antenna for smart phone and portable wireless applications.

This manuscript presents high performance dual polarized eight-element multiple input multiple output (MIMO) fifth generation (5G) smartphone antenna. The design consists of four dual-polarized microstrip diamond-ring slot antennas, positioned at corners of printed circuit board (PCB). Cheap Fr-4 dielectric with permittivity 4.3 and thickness of 1.6mm is used as substrate with overall dimension of 150 × 75 × 1.6 mm3. In mobile system due to limited space mutual coupling between nearby antenna elements is an issue that distort MIMO antenna performance. Defected ground structure is used to control coupling. The defected ground structure has advantages like ease of fabrication, compact size and high efficiency as compare to other techniques. Less than 30dB coupling is achieved for adjacent elements. The -10 dB impedance bandwidth of 700 MHz is achieved for all radiating elements ranging from 3.3 GHz to 4.1 GHz. The value is about 900MHz for -6dB. The proposed antenna offers good results in terms of fundamental antenna parameters like reflection coefficient, transmission coefficient, maximum gain, total efficiency. The antenna achieved average gain more than 3.8dBi and average radiation efficiency more than 80% for single dual polarized element. The antenna provides sufficient radiation coverage in all sides. The MIMO antenna characteristics like diversity gain (DG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC) and channel capacity are calculated and found according to standards. Furthermore, effect of user on antenna performance in data-mode and talk-mode are studied. Proposed design is fabricated and tested in real time. The measured results shows that proposed design can be used in future smartphones applications. The design is compared with some of the existing work and found to be the best one in many parameters and can be used for commercial use

Photocatalytic response of plasma functionalized and sonochemically TiO2/BiOBr coated fabrics for self-cleaning application

Sonochemical synthesis of nanophotocatalysts to produce functionalized fabrics is gaining significant attention worldwide. This study deals with coating sonochemically synthesized TiO2 and TiO2/BiOBr photocatalysts on pre-coating plasma functionalized cotton fabric. The photocatalytic activity of pristine, plasma-functionalized, and photocatalyst-coated fabrics was checked by degrading methyl red, Rhodamine B, and methyl orange under sunlight irradiation. The surface morphology, optical properties, structure, and purity of the coating material were elaborated using UV-visible spectroscopy, electrical resistivity measurements, x-ray diffraction measurements, inductively coupled plasma atomic emission spectroscopy analysis, scanning electron microscopy, Fourier transform infrared spectroscopy, and photoluminescence spectroscopy. The nanoparticle-coated fabrics significantly reduced the photoluminescence intensity compared to plasma-functionalized fabrics. The TiO2/BiOBr decorated fabric had significantly higher photocatalytic efficiency than all other fabric samples. This photocatalyst showed 84% efficiency against Rhodamine B, 58% against methyl orange, and 55% against methyl red. The-self-cleaning UV protection applications of these photocatalyst-decorated fabrics are suggested in this study

Study of dual Z-scheme photocatalytic response of TiO2/Ag/ZnO coating on plasma-modified cotton fabric for self-cleaning application

An innovative approach was adopted to improve the photocatalytic response of nanoparticle-coated cotton fabric for self-cleaning application. Fabrics with layers of TiO2, Ag, and ZnO nanoparticles were assessed for photodegradation of Rhodamine B, methyl orange, and methyl red. A dual-scheme charge transfer method was designed for the photocatalytic activity of TiO2/Ag/ZnO nanoparticles on cotton fabric. To produce the multilayer structure of nanoparticles, the fabric was first functionalized with atmospheric pressure nonthermal plasma and then sonochemically coated with TiO2/Ag/ZnO in a layered form. The plasma functionalization enhanced the stability of TiO2/Ag/ZnO nanoparticles on the fabric. It was revealed that a combination of Ag, TiO2, and ZnO nanoparticles produced a Schottky barrier among the silver metal and metal oxides (TiO2 and ZnO), resulting in enhanced photocatalytic properties. Methyl red underwent the highest photocatalytic degradation of 93% over the designed photocatalyst-coated fabric after 120 min of light exposure. This study provides a promising strategy for improving the photocatalytic self-cleaning efficacy of nanocoated fabrics

Indoor water splitting for hydrogen production through electrocatalysis using composite metal oxide catalysts

This study explores an optimistic approach for large-scale hydrogen production by employing electrocatalysts based on nickel, cobalt, iron, and aluminum oxides as alternatives to costlier metals. This approach offers a cost-effective solution to electrolysis in water media for hydrogen production. This investigation is focused on the electrolysis process, engaging NiO–Al2O3–CoO–Fe2O3 in 1M solution of NaOH and KOH. The environmental and economic analyses are conducted to evaluate the overall effect and cost-effectiveness of the electrolysis process. These findings provide valuable insights into the performance, feasibility, and challenges of using oxides of aluminum, nickel, iron, and cobalt in electrolysis for hydrogen production. The structural and morphological analyses of metal oxides are conducted using XRD and SEM tools, which showed reduced crystallinity and open pore structure of the samples. Cyclic Voltammetry (CV), Electrochemical Impedance Spectroscopy (EIS), and Linear Sweep Voltammetry (LSV) revealed a higher electrocatalytic activity, a larger electrochemical active surface area, a higher current density, and a high density of active sites of NiO–Al2O3–CoO–Fe2O3 composite. Electrode 1 of the composite catalyst produced 500 ml of hydrogen after 30 min of the process, while electrodes 2 and 3 produced 263 and 249 ml of hydrogen, respectively. This study also elucidated the electrocatalytic mechanism involved in water splitting using these composite materials

Enhanced Classification of Coffee Leaf Biotic Stress by Synergizing Feature Concatenation and Dimensionality Reduction

Significant yield challenges are posed by biotic stress on coffee leaves, which has a negative effect on the revenue generation of this highly utilized commodity. Numerous studies have proposed techniques for the early detection and classification of biotic stress in coffee leaves. In this study, we propose a technique called extracted feature ensemble (EFE) for classifying healthy and infected classes. Transfer learning-based convolutional neural networks (CNNs) and custom-designed features are used to improve classification performance. Under the concept of EFE, three methodologies are proposed for evaluating various extracted feature combinations and determining the effect of dimensionality on the performance of the model. In addition, a semi-segmentation approach is used to guide the extraction of informative foreground details, while non-segmented inputs are used to improve the model’s robustness against complex background noise. By improving three open-source datasets for biotic stress categorization in coffee leaves, a new dataset was created and employed. The first proposed method, ECNN, focused on the effective concatenation of five CNNs and obtained a classification accuracy of 93.45% using a decision tree classifier, exceeding the maximum individual accuracy of 86.07% from Mobile-Net v3 features. In addition, the HLGGM method was investigated, which demonstrated an enhanced accuracy of 99.16% by combining dimension-reduced Mobile-Net v3 features with handcrafted features. HLGCM, the final approach represented, aimed at extracting features from dimensionality-reduced handmade and CNN-based data, and ultimately succeeded in accomplishing an accuracy of 99.49 percent by using decision tree model. The obtained results demonstrate the efficacy of feature concatenation in enhancing the classification model’s discriminative capabilities and classification accuracy. The appropriate combination of hand-made and CNN-based features gives better accuracy and interesting insights into the effect of feature reduction on model classification efficiency. The article offers dimensionality reduction, directed learning, and feature concatenation techniques for identifying coffee leaf diseases. This work can aid in the development of computationally efficient and accurate disease control and coffee plant sustainability strategies

Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network

Next-generation cloud radio access networks (C-RANs) are anticipated to provide multi-Gbps data rate transmission and ultra-high bandwidth capacity, which is one of the key performance indicators for future mobile networks. The integral layout of fiber optics and radio network manages the capabilities of the C-RAN, but needs to be optimized in terms of cost, reliability and further scalibility. For C-RAN architectures, Radio over Fiber (RoF) transport-based fronthaul is a promising candidate but the associated issues of distortions due to nonlinear impairments (NLIs) from power amplifier, linear distortions (LDs) due to modulating lasers and high peak to average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals need to be addressed. This work investigates these performance limiting factors and presents a DSP receiver-based solution to mitigate the effects of NLIs, LDs and high PAPR. Simulations are performed by applying a various range of transmission input powers, different quadrature amplitude modulation (QAM) formats for the OFDM signal, optimized filtering at the receiver end and varying channel spacing among the optical WDM channels to analyze the performance of the proposed receiver under different conditions. The simulations and theoretical model of the proposed case studies verify that the presented solution for the RoF transport utilize less power, performs better for longer transmission distances, supports higher modulation formats and transports large number of WDM channels in the presence of NLIs and DLs as compared to the conventional RoF approach. With compensation of NLIs and LDs, transmission distance up to 10 km is investigated using 16 WDM channels with aggregate data rate of 100 Gb/s which shows that the proposed receiver can be used for future C-RAN fronthaul networks