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AbstractThis article presents a wideband metamaterial absorber (WMMA) for the visible and near-infrared (NIR) region application. The proposed metamaterial absorber comprises three layers of sandwiched (metal-dielectric-metal) model based on Nickel-Polyamide-Nickel. The multiple resonators on top layers achieve an average absorption of 98.16% over a range from 380 to 2300 nm with a compact unit cell size of 100 × 100 × 42 nm3. The designed WMMA shows 99% perfect absorption for a large bandwidth of 685 nm. Considering its wideband absorption, the physical absorption phenomenon was also explained in terms of the proposed WMMA's surface electric field, magnetic field, and current distribution. Also, the effects of materials and structural parameters on absorption performance with a FOM have been studied. The absorption performance is analysed with various polarization angles to demonstrate polarization insensitivity. The designed WMMA shows the absorption of >70% at large incident angles of 70°. The perceptible contribution of this WMMA is large unity absorption bandwidth at visible to NIR region with high incident angle stability, polarisation insensitivity, thermal robustness of the constituting metal and simple nanostructure geometry. Therefore, the proposed WMMA has good potential in sensors, thermal emitters, photodetectors, and absorber applications.

Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel HZNN model, called HZ-QNARE, is presented for solving the TQNARE. The model functions fairly well, as demonstrated by two simulation tests. Additionally, the results demonstrated that, while both approaches function remarkably well, the HZNN architecture works better than the ZNN architecture.

Many techniques have been recently employed by researchers to address the challenges posed by fractional differential equations. In this paper, we investigate the concept of Ulam-Hyers stability for a class of neutral fractional stochastic differential equations by using the Banach fixed point theorem and the stochastic analysis techniques. An example is presented at the end of the paper to show the interest and the applicability of the results.

Driver drowsiness is one of the leading causes of road accidents resulting in serious physical injuries, fatalities, and substantial economic losses. A sophisticated Driver Drowsiness Detection (DDD) system can alert the driver in case of abnormal behavior and avoid catastrophes. Several studies have already addressed driver drowsiness through behavioral measures and facial features. In this paper, we propose a hybrid real-time DDD system based on the Eyes Closure Ratio and Mouth Opening Ratio using simple camera and deep learning techniques. This system seeks to model the driver's behavior in order to alert him/her in case of drowsiness states to avoid potential accidents. The main contribution of the proposed approach is to build a reliable system able to avoid false detected drowsiness situations and to alert only the real ones. To this end, our research procedure is divided into two processes. The offline process performs a classification module using pretrained Convolutional Neural Networks (CNNs) to detect the drowsiness of the driver. In the online process, we calculate the percentage of the eyes' closure and yawning frequency of the driver online from real-time video using the Chebyshev distance instead of the classic Euclidean distance. The accurate drowsiness state of the driver is evaluated with the aid of the pretrained CNNs based on an ensemble learning paradigm. In order to improve models' performances, we applied data augmentation techniques for the generated dataset. The accuracies achieved are 97 % for the VGG16 model, 96% for VGG19 model and 98% for ResNet50 model. This system can assess the driver's dynamics with a precision rate of 98%.

This paper presents a conductive component tailored to a flexible substrate using Al-doped CoxCa(0.90−x)Ni0.10Fe2O4 (x = 0.25, 0.50, and 0.75) for visible to near-infrared (NIR) spectra in magneto-optical applications. The developed nanoparticles show uniformity, nanosized grains, and capillary nanopore fusion characteristics, which are confirmed by x-ray diffraction (XRD), field emission scanning electron microscopy, and energy-dispersive x-ray spectroscopy analyses, respectively. The XRD analysis revealed crystallite sizes of 33.36, 37.08, and 44.25 nm and particle sizes of 45.6, 34.6, and 31.5 nm for the compositions x = 0.25, 0.50, and 0.75, respectively. The Al-doped nanoparticles are converted to a flexible solid substrate utilizing a polyvinyl alcohol matrix, facilitating conformality to build complex shapes and broadening their application scope. The structure shows higher absorption across 450–720 nm, 480–720 nm, and 200–850 nm spectra for x = 0.25, 0.50, and 0.75, respectively. The distinctive magnetic and electrical properties are also evaluated through magnetic force microscopy and conductive atomic force microscopy, culminating in a substrate with exceptional control over light–matter interactions with smooth surfaces with lower surface roughness. The vibrating sample magnetometer analysis of the substrate shows how varying cobalt content affects magnetic properties relevant for visible to near-infrared (NIR) applications, offering insights into coercivity, magnetization, and retentivity changes at different x values. The perceptible novelties of this work are advancements in material sciences aimed at enhancing light manipulation and flexibility for electronic devices.

State-of-the art research on conventional microwave substrates reveals limitations of restricted flexibility, poor adaptability to irregular surfaces, and reduced effectiveness in complex sensing environments, leading to the development of a flexible microwave substrate using Co–Ca ferrite for feasible applications in detecting industrial chemicals. In this paper, CoxCa (0.90-x) Ni0.10Fe2O4 (x = 0.25, 0.50, and 0.75) based flexible substrate is developed through the sol-gel method, and a rectangular enclosed cross-dumbbells (RECD) metamaterial structure is designed for industrial chemical contamination sensing. The X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) are used to morphological analysis of the nanoparticles of the substrate. XRD patterns shows the presence of crystalline phases, which facilitate the determination of strain within these materials. FESEM images demonstrate a structural composition characterized by nanosized grains and capillary-nanopores, where these nanosized grains are found uniformly distributed across the film's surface and results structural homogeneity. The synthesized nanoparticles are employed as a solid substrate utilizing PVA (polyvinyl alcohol) to ensure a uniform structure. The dielectric constant of the developed substrate is measured using the DAK 3.5 dielectric measurement kit, and it shows dielectric permittivity values ranging from 1.77 to 3.32 with loss tangents ranging from 0.031170 to 0.1929 for x = 25 % to x = 75 %. Metamaterial characteristics of the fabricated RECD structure on Co–Ca ferrite is analysed and μ-negative characteristics is achieved from 4.7 GHz to 6 GHz. Additionally, the reflection coefficient of the RECD metamaterial is also investigated for different bending condition up to an angle of 30ο. The simulated and measured S21 results strongly match each other. The sensing performance is evaluated for three concentrations of methanol and ethanol, demonstrating excellent contamination sensing capabilities with sequential frequency shifting. This study undoubtedly revealed that Co–Ca ferrite-based flexible metamaterial have great potential for high performance industrial chemical contamination sensing applications.

This article proposed a new triple-band transmission block metamaterial (MTM) for 5G mm-wave applications. The peak transmission-blocking attributes are achieved at 27.09 GHz, 38.71 GHz, and 41.81 GHz frequency by interconnected square split ring resonators (ISSRR). A thin Rogers RT5880 substrate material of thickness 0.79 mm is employed to design the MTM structure, and the unit cell dimension is 5 × 5 mm2. The designed metamaterial shows a −10 dB transmission coefficient (S21) from 24.60 to 28.47 GHz, 36.50–39.65 GHz and 40.77–44 GHz frequency bands. The design evolution, metamaterial properties, surface current distribution, electric field, magnetic field, and equivalent circuit model are investigated to understand the transmission-blocking property of the MTM. The simulated results of the MTM are validated by measurement of the fabricated prototype, and both results agree well. The transmission-blocking attributes lead the proposed MTM as a potential candidate for different fields like EMI shielding, sensing, and antenna performance enhancement. The dielectric sensor performance of the MTM structure is investigated for different dielectric materials. Besides, MIMO antenna performances are also investigated, and gains of 1–3 dBi, isolations of 5–10 dB, and the radiation pattern deflection angle of 45˚ are enhanced by the proposed MTM. These significant features and applications of the proposed MTM make it a special candidate for 5G mm-wave applications.

This paper provides a metamaterial for microwave absorbers relying on asymmetric square split rings with a partial E shape in the centre-based resonator. The wideband frequency range shows its sensing capabilities in identifying various oil samples. The optimized MTM unit cell has electrical dimensions of 0.122λο × 0.122λο, where is λο the predicted wavelength at 3.64 GHz resonance frequency, for a total size of 10 × 10 mm2. The FR-4 dielectric substrate, 1.6 mm in thickness, produces the unit cell. The MTM has a maximum absorption peak of 91.46 % at 3.64 GHz, 99.72 % at 5.99 GHz, 96.89 % at 7.47 GHz, and 98.45 % at 10.01 GHz. This MMA has four absorption peaks, making it suitable for use in the triple band (S, C, and X). The centre split gaps and partial E shapes increase unit cell absorption, and the defective CSRR back layer structure improves the transmission coefficient. High absorption is influenced by the central split gaps and an appropriate electric field and magnetic field optimized in the centre ring when the structure is in an Orthogonal orientation. The research used a frequency domain solver operating between 2 and 12 GHz. The scattering characteristics (coefficient of transmission) of the relevant unit cell's equivalent circuit were confirmed using ADS software and the CST result. At frequencies between 3 and 5 GHz, the proposed asymmetric unit cell functions as a double negative (DNG) metamaterial absorber due to its negative permeability and permittivity. Since oil samples have different dielectric constants, the resonance frequency and Q factor are likewise different. Here, we receive a spectrum of frequencies that can be adjusted for use in sensing. The data showed that the suggested sensor is sensitive enough to pick up a variety of oils. The proposed sensor is useful for various applications, from liquid detection to microfluidic sensing and beyond, due to its cheap cost, high sensitivity, high-quality factor, and high electromagnetic radiation (EMR).

This study introduces a Metamaterial 'M.M.' sensor designed and evaluated to detect pure and adulterated fuels and oils. The reflection coefficient was measured using advanced design systems and computer simulations, followed by computational and experimental analyses of the performance evolution of the proposed sensor. The proposed sensor was evaluated using samples of varying compositions of Coconut Oil 'C.O.', petrol and kerosene. The results demonstrated a noticeable change in the Resonance Frequency 'R.F.' of the samples upon modification of their concentration. The reflection coefficient values for petrol and a 20 % combination of kerosene were determined to be −44.81 dB at a frequency of 10.29 GHz and −41.07 dB at a frequency of 10.15 GHz. Similarly, for coconut oil and refined coconut oil, the reflection coefficient values were found to be −47.34 dB at a frequency of 11.239 GHz and −41.981 dB at a frequency of 11.15 GHz. The sensor exhibits a high-quality factor of 451.58, a good sensitivity value of 5.65 and a figure of merit of 2551.427, indicating its excellent performance and efficiency. The results demonstrate the versatility of the proposed sensor, making it suitable for a range of applications such as microfluidics and industrial settings.

The authors wish to make the following corrections to this paper [...]

A novel Compact Complementary Split Ring Resonator (CSRR) metamaterial unit cell with perfect absorption, beneficial for ambient power harvesting, is proposed to operate in the S- and C-band. The size of the structure is 15 × 15 × 1.575 mm3 with dimensions of 0.18λ0 × 0.18λ0 × 0.019λ0 at the lowest absorption frequency. The proposed compact-sized CSRR structure exhibits notable characteristics such as high absorption coefficients and polarization-insensitive behaviours across a wide range of angles. The results demonstrate a notably high absorption efficiency of 97%, 99%, and 98% at 3.7275 GHz, 4.7075 GHz, and 5.9325 GHz. A 3 × 3 array is designed and verified by experimental results. The proposed design exhibits superior performance compared to previously published designs, which suffer from significant limitations such as a large metamaterial structure, reduced absorption frequency bands, and a lower fraction of absorption bands. The proposed design exhibits potential for utilization in developing wireless sensor network applications that require compact and highly efficient harvesting systems.

In this article, we investigate the existence and uniqueness Theorem of Pantograph Hadamard fractional stochastic differential equations (PHFSDE) using the fixed-point Theorem of Banach (BFPT). According to the generalized Gronwall inequalities, we prove the stability in the sense of Ulam–Hyers (UHS) of PHFSDE. We give some examples to show the effectiveness of our results.

The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD).

Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers who intend to inflict significant infections on a power system. Simply put, an effort to phony up the consumption data of mining devices results in the furtherance of messing up the optimal energy management within the power system. Hence, this paper introduces a new cyber-attack named miner-misuse for power systems equipped by transaction tech. To overwhelm this dispute, this article also addresses an online coefficient anomaly detection approach with reliance on the reinforcement learning (RL) concept for the power system. On account of not being sufficiently aware of the system, we fulfilled the Observable Markov Decision Process (OMDP) idea in the RL mechanism in order to barricade the miner attack. The proposed method would be enhanced in an optimal and punctual way if the setting parameters were properly established in the learning procedure. So to speak, a hybrid mechanism of the optimization approach and learning structure will not only guarantee catching in the best and most far-sighted solution but also become the high converging time. To this end, this paper proposes an Intelligent Priority Selection (IPS) algorithm merging with the suggested RL method to become more punctual and optimum in the way of detecting miner attacks. Additionally, to conjure up the proposed detection approach’s effectiveness, mathematical modeling of the energy consumption of the mining devices based on the hashing rate within BFTS is provided. The uncertain fluctuation related to the needed energy of miners makes energy management unpredictable and needs to be dealt with. Hence, the unscented transformation (UT) method can obtain a high chance of precisely modeling the uncertain parameters within the system. All in all, the F-score value and successful probability of attack inferred from results revealed that the proposed anomaly detection method has the ability to identify the miner attacks as real-time-short as possible compared to other approaches.

In recent years, the occurrence of cascading failures and blackouts arising from cyber intrusions in the underlying configuration of power systems has increasingly highlighted the need for effective power management that is able to handle this issue properly. Moreover, the growing use of renewable energy resources demonstrates their irrefutable comparative usefulness in various areas of the grid, especially during cascading failures. This paper aims to first identify and eventually protect the vulnerable areas of these systems by developing a hybrid structure-based microgrid against malicious cyber-attacks. First, a well-set model of system vulnerability indices is presented to indicate the generation unit to which the lines or buses are directly related. Indeed, we want to understand what percentage of the grid equipment, such as the lines, buses, and generators, are vulnerable to the outage of lines or generators arising from cyber-attacks. This can help us make timely decisions to deal with the reduction of the vulnerability indices in the best way possible. The fact is that employing sundry renewable resources in efficient areas of the grid can remarkably improve system vulnerability mitigation effectiveness. In this regard, this paper proposes an outstanding hybrid-energy framework of AC/DC microgrids made up of photovoltaic units, wind turbine units, tidal turbine units, and hydrogen-based fuel cell resources, all of which are in grid-connect mode via the main grid, with the aim to reduce the percentage of the system that is vulnerable. To clearly demonstrate the proposed solution’s effectiveness and ease of use in the framework, a cyber-attack of the false data injection (FDI) type is modeled and developed on the studied system to corrupt information (for instance, via settings on protective devices), leading to cascading failures or large-scale blackouts. Another key factor that can have a profound impact on the unerring vulnerability analysis concerns the uncertainty parameters that are modeled by the unscented transform (UT) in this study. From the results, it can be inferred that vulnerability percentage mitigation can be achieved by the proposed hybrid energy framework based on its effectiveness in the system against the modeled cyber-attacks.

Modern intelligent energy grids enable energy supply and consumption to be efficiently managed while simultaneously avoiding a variety of security risks. System disturbances can be caused by both naturally occurring and human-made events. Operators should be aware of the different kinds and causes of disturbances in the energy systems to make informed decisions and respond accordingly. This study addresses this problem by proposing an attack detection model on the basis of deep learning for energy systems, which could be trained utilizing data and logs gathered through phasor measurement units (PMUs). Property or specification making is used to create features, and data are sent to various machine learning methods, of which random forest has been selected as the basic classifier of AdaBoost. Open-source simulated energy system data are used to test the model containing 37 energy system event case studies. In the end, the suggested model has been compared with other layouts according to various assessment metrics. The simulation outcomes showed that this model achieves a detection rate of 93.6% and an accuracy rate of 93.91%, which is greater compared to the existing methods.

The evolution of the global wireless market is accompanied by an increased need in terms of speed and number of users, lower latency, better coverage, better spectral efficiency and quality of service, etc. To meet these needs, 5G has recently been introduced as an effective solution which targets, via the large scale deployment of symmetric antennas, a wide variety of sectors such as energy, health, media, industry, transport and especially wireless cellular networks which are among the most important pillars of modern societies. Multiple Input, Multiple Output (MIMO) systems, which have been extended to “Massive MIMO” mode and which consist of increasing the number of radiating elements involved in the transmission and reception of the radio link, are a very promising solution for improving the spectral efficiency of wireless communication systems (WCSs). Motivated by the aforementioned developments, the present paper investigates the increased capacity of MIMO systems to improve transmission in WCSs using 5G. It carefully focuses on the evaluation of the development level and technical contribution of MIMO systems and millimeter wave (mmWave) bands in 5G wireless cellular networks (WCNs) and gives important recommendations.