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Traditional malware propagation control schemes do not prevent device-to-device (D2D) malware spread, have high time cost, and may result in low probability of detecting compromised devices. Moreover, the unprecedented scale and heterogeneity of Internet of Things (IoT) devices make these schemes inapplicable to IoT networks. Therefore, to rectify these issues, this article presents a secure patching framework for IoT with different network isolation levels to efficiently mitigate and control malware propagation. It uses remote attestation to detect compromised devices with a high probability and identify the origin of malicious activities. It also proposes virtual patching of devices via physical unclonable functions (PUFs) to contain the malware spread. The isolation levels are based on the susceptible, exposed, infected, and resistant (SEIR) model that act as an access control list to quantify device operation and mitigate D2D malware spread. We present a security analysis based on the access control logic model. A performance evaluation with a comparative analysis is also discussed using the SEIR model. These analyses confirm the reduction in patching time and superior performance of our framework, i.e., with 10% of initially infected devices, IoT-Proctor had a reduction rate of malware five times faster than the existing techniques.

The catalysts with higher efficiencies at mild reaction conditions are of great importance for development of ammonia synthesis with zero CO2 emissions. Here, we report, highly efficient mixed oxide supported Ru catalysts for ammonia synthesis. MgO-CeO2 supports with different Mg and Ce contents were fabricated and loaded with 1% Ru using impregnation technique. MgO-CeO2 supported Ru catalysts demonstrated efficient ammonia synthesis. Mixed oxide MgO-CeO2 supported catalysts even with higher MgO content were established with comparable ammonia synthesis yields to that obtained from pure CeO2 supported catalyst under same reaction conditions. The catalytic activity was highly dependent on the texture and physical properties of the catalysts. Presence of highly dispersed Ce and Ru has been suggested as a possible explanation for the higher yield of ammonia obtained from Ru/MgO-CeO2 catalysts having Ru selectively deposited on CeO2.

An intrabody area nanonetwork (intra-BANN) is a set of nanoscale devices, which have outstanding cellular level precision and accuracy for enabling noninvasive healthcare monitoring and disease diagnosis. In this article, we design a novel feedforward neural networks (FFNNs) based data aggregation scheme that integrates the attributes of artificial intelligence to boost the computational intelligence of intra-BANNs for prolonged network lifetime. In the proposed scheme, data division and labeling are performed to transmit detected information using two different types of packets with different sizes to save energy resources and to avoid redundant data transmission. FFNN-based periodic data transmission exploits the fitness function approximation characteristics of FFNN to increase the transmission probability of critical information with minimum energy consumption and delay, whereas our proposed event-driven data transmission also ensures the transmission of high priority data with minimal delay and storage overhead. The detailed evaluation and comparison of our proposed framework with three existing schemes conducted using the Nano-Sim tool highlight that our proposed scheme performs 50%–60% better than state-of-the-art schemes in terms of residual energy, delay, and packet loss.

Growing population and depleting resources are forcing the manufacturers to become more competent and sustainable. Customers are becoming more and more informed about the product they consume, and therefore, manufacturers need to be flexible and open to new changes taking place. Big Data Analytics (BDA) is one such technology that has transformed the manufacturing sector. Organizations have now become more flexible with regards to their processes and, at the same time, have also improved their product quality. Here in this paper, we have analyzed various advantages of Big Data that manufacturing organizations can exploit for their betterment. We have also discussed examples of certain leading manufacturers and how they can lead to big data-enabled manufacturing. Lastly, we discuss certain shortcomings that need to be addressed to make big data analytics viable for organizations of all sorts and not just for top manufacturers.

This paper aims to optimally design a PV/Wind/Diesel Hybrid Microgrid System (HMS) for a small number of houses considering load uncertainty for the city of Yanbu, Saudi Arabia. Designing such a hybrid system with all the renewable and non-renewable sources, storage devices, converters, and loads is a complicated task. A multiobjective approach has been adopted to optimize the microgrid design. Two methodologies are available for solving such multiobjective problems. In the first approach, the problem is transformed into a single objective one (using aggregation, for instance), whereas, the second technique treats objectives simultaneously and independently as adopted in this paper. The proposed approach offers the Pareto front; a set of solutions in one run opening the door of choosing the most suitable solution from the available options based on the experience, expertise and requirement of the designer. This paper presents a novel approach of using Decomposition Based Multiobjective Evolutionary Algorithm (MOEA/D) to optimally design the PV/Wind/Diesel HMS considering load uncertainty. Loss of Power Supply Probability (LPSP) and Cost of Electricity (COE) are considered as the objective functions of the optimization problem. Furthermore, two separate load cases of 5 and 10 houses are tested to verify the robustness of the approach. The obtained results are beneficial in assisting researchers and practitioners in selecting the optimal configuration of the microgrid.

This study proposes a Pade approximation based hybrid mesh free framework for numerical solutions of nonlinear partial differential equations. The proposed framework involves three novel aspects. Firstly, the conjunction of Pade approximation based residual functional and penalty function approach for handling boundary/Initial conditions is employed to design an equivalent optimization problem. Secondly, the notion of cooperative multi-simplex searches in the environment of Nelder-Mead Simplex (NMS) algorithm is introduced. Finally, a hybrid of multi-simplex search and Differential Evolution (DE) is proposed to efficiently cope with the modelled problem. The developed method is named as Pade Evolutionary Cooperative Multi-Simplex (PECMS) algorithm. The results of the developed PECMS scheme on illustrative examples are compared with three well-known methods, namely, Differential Transform Method (DTM), Adomian Decomposition Method (ADM) and the Variational Iteration Method (VIM). The comparisons demonstrate that the current approach possesses high accuracy when compared to the competing methods.

Owing to its captivating properties, Graphene-based materials (GMBs) have appeared as a broad-spectrum antimicrobial material and attracting scientists and researchers’ attention. By focusing on the recent advances in the polymeric nanocomposite’s field, current study is positioned on the graphene-based polymeric composites having antibiofilm and antimicrobial activities to confront the current situations of microbial resistance against antibiotics and other drugs. Furthermore, this review summarizes the influence of different factors on the antimicrobial activities of graphene-based polymeric composites like size, shape, number of layers, concentration, dispersibility, and functionalization, and also shed the light on the possible mode of actions of these materials. Graphene-based polymeric composites exert its antimicrobial action by physical damages (e.g., direct sharp edges’ contact to cell membranes of bacteria, wrapping of the bacterial cell, phospholipid extraction, and photo-thermal ablation) and chemical damages (e.g. oxidative-stress and charge transfer). Besides, this review article thoroughly describes the antimicrobial applications of graphene-based polymeric composites, which are because of their good biocompatibility and superior antimicrobial properties such as wound bandages and dressings, antimicrobial coatings, drug delivery, food packaging, hydrogel formation, and water disinfection. Present review may stimulate major concerns, spur further developments, and provide valuable insight into the promising fields.

The highest power conversion efficiency (PCE) for organic-inorganic perovskite solar cells based on lead is reported as 25.2% in 2019. Lead-based hybrid perovskite materials are used in several photovoltaics applications, but these are not highly favored due to the toxicity of lead and volatility of organic cations. On the other hand, hybrid lead-free double perovskite has no such harm. In this research study, SCAPS numerical simulation is utilized to evaluate and compare the results of perovskite solar cell based on double perovskite FA 2 BiCuI 6 and standard perovskite CH 3 NH 3 PbI 3 as an active layer. The results show that the power conversion efficiency obtained in the case of FA 2 BiCuI 6 is 24.98%, while in the case of CH 3 NH 3 PbI 3 , it is reported as 26.42%. This indicates that the hybrid organic-inorganic double perovskite FA 2 BiCuI 6 has the ability to replace hybrid organic-inorganic perovskite CH 3 NH 3 PbI 3 to expand next-generation lead-free harmless materials for solar cell applications.

It is considered that there is a fascinating issue in theoretical chemistry to predict the physicochemical and structural properties of the chemical compounds in the molecular graphs. These properties of chemical compounds (boiling points, melting points, molar refraction, acentric factor, octanol-water partition coefficient, and motor octane number) are modeled by topological indices which are more applicable and well-used graph-theoretic tools for the studies of quantitative structure-property relationships (QSPRs) and quantitative structure-activity relationships (QSARs) in the subject of cheminformatics. The π -electron energy of a molecular graph was calculated by adding squares of degrees (valencies) of its vertices (nodes). This computational result, afterwards, was named the first Zagreb index, and in the field of molecular graph theory, it turned out to be a well-swotted topological index. In 2011, Vukicevic introduced the variable sum exdeg index which is famous for predicting the octanol-water partition coefficient of certain chemical compounds such as octane isomers, polyaromatic hydrocarbons (PAH), polychlorobiphenyls (PCB), and phenethylamines (Phenet). In this paper, we characterized the conjugated trees and conjugated unicyclic graphs for variable sum exdeg index in different intervals of real numbers. We also investigated the maximum value of SEIa for bicyclic graphs depending on a > 1 .

The bi-directional flow of energy and information in the smart grid makes it possible to record and analyze the electricity consumption profiles of consumers. Because of the increasing rate of inflation over the past few years, people started looking for means to use electricity illegally, termed as electricity theft. Many data analytics techniques are proposed in the literature for electricity theft detection (ETD). These techniques help in the detection of suspected illegal consumers. However, the existing approaches have a low ETD rate either due to improper handling of the imbalanced class problem in a dataset or the selection of inappropriate classifier. In this paper, a robust big data analytics technique is proposed to resolve the aforementioned concerns. Firstly, adaptive synthesis (ADASYN) is applied to handle the imbalanced class problem of data. Secondly convolutional neural network (CNN) and long-short term memory (LSTM) integrated deep siamese network (DSN) are proposed to discriminate the features of both honest and fraudulent consumers. Specifically, the task of feature extraction from weekly energy consumption profiles is handed over to the CNN module while the LSTM module performs the sequence learning. Finally, the DSN contemplates on the shared features provided by the CNN-LSTM and applies final judgment. The data analytics is performed on different train–test ratios of the real-time smart meters’ data. The simulation results validate the proposed model’s effectiveness in terms of high area under the curve, F 1 -Score, precision and recall.

We have successfully grown Zn3N2 thin films using a simple and cost effective approach. Growth was achieved in a vacuum tube furnace by evaporating Zinc powder at 850 OC and flowing nitrogen gas into the tube at the rate of 150 sccm. After achieving evaporation temperature of 850 OC in the tube, nitrogen gas was filled in the vacuum tube having working pressure of 5 atm and then system was kept in the nitrogen environment for 1 h at a constant flow rate of 150 sccm. In order to grow quality thin films, a bunch of samples were grown by varying the various growth parameters during the growth process. The first batch of samples was grown using different source to substrate distances (21–25 cm with a step of 2 cm) and pieces of a representative sample were post growth annealed in a nitrogen environment in the temperature range 350–600 °C. The obtained Zn3N2 thin films were characterized by X-Ray Diffraction, UV/Vis spectroscopy and Scanning Electron Microscope (SEM). XRD data showed a strong peak at 36.60° which is related to the (400) plan of pure Zn3N2 for all samples along with some additional peaks due to Zn structure. The optimum values of source to substrate distance and post growth annealing temperature are found to be 23 cm and 600 °C respectively.

Topological index (TI) is a numerical invariant that helps to understand the natural relationship of the physicochemical properties of a compound in its primary structure. George Polya introduced the idea of counting polynomials in chemical graph theory and Winer made the use of TI in chemical compounds working on the paraffin's boiling point. The literature of the topological indices and counting polynomials of different graphs has grown extremely since that time. Metal-organic network (MON) is a group of different chemical compounds that consist of metal ions and organic ligands to represent unique morphology, excellent chemical stability, large pore volume, and very high surface area. Working on structures, characteristics, and synthesis of various MONs show the importance of these networks with useful applications, such as sensing of different gases, assessment of chemicals, environmental hazard, heterogeneous catalysis, gas and energy storage devices of excellent material, conducting solids, super-capacitors and catalysis for the purification, and separation of different gases. The above-mentioned properties and physical stability of these MONs become a most discussed topic nowadays. In this paper, we calculate the M-polynomials and various TIs based on these polynomials for two different MONs. A comparison among the aforesaid topological indices is also included to represent the better one.

With data comes uncertainty, which is a widespread and frequent phenomenon in data science and analysis. The amount of information available to us is growing exponentially, owing to never-ending technological advancements. Data visualization is one of the ways to convey that information effectively. Since the error is intrinsic to data, users cannot ignore it in visualization. Failing to observe it in visualization can lead to flawed decision-making by data analysts. Data scientists know that missing out on uncertainty in data visualization can lead to misleading conclusions about data accuracy. In most cases, visualization approaches assume that the information represented is free from any error or unreliability; however, this is rarely true. The goal of uncertainty visualization is to minimize the errors in judgment and represent the information as accurately as possible. This survey discusses state-of-the-art approaches to uncertainty visualization, along with the concept of uncertainty and its sources. From the study of uncertainty visualization literature, we identified popular techniques accompanied by their merits and shortcomings. We also briefly discuss several uncertainty visualization evaluation strategies. Finally, we present possible future research directions in uncertainty visualization, along with the conclusion.

Purpose This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed. Design/methodology/approach In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions. Findings This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader. Practical implications The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios. Originality/value This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

This study reports the fabrication of Fe2O3, Bi2O3, and BiFeO3, characterization and evaluation of the photocatalytic performances for methylene blue dye degradation. The materials were synthesized by precipitation method and characterized by scanning electron microscopy, X-ray diffraction, energy dispersive X-rays analyses, and Fourier transform infrared analyses. The photocatalytic activities of Fe2O3, Bi2O3, and BiFeO3 were compared by performing degradation experiments with 50 mL of 100 mg/L methylene blue solution. The as-prepared BiFeO3 was found as 2.4 times and 1.7 times more effective than Fe2O3 and Bi2O3, with a 79, 47, and 57% catalytic activity, respectively. The degradation of methylene blue over the BiFeO3 catalyst was optimized in terms of pH, catalyst dosage, temperature, and methylene blue concentration. The Eley–Rideal mechanism was proposed to describe the reaction kinetics in terms of the first order and second order kinetics model. Activation energy E (kJ/mol), enthalpy ΔH (kJ/mol), entropy ΔS (J/mol) and free energy ΔG (kJ/mol) were calculated as 20.8, 18.2, 197.5 and −45.3 respectively. The negative value of free energy shows that photodegradation is favored in present conditions.

This research work aims at modeling the sewing thread consumption for stitch class 301 through image analysis by using Fourier series. A mathematical model was developed by using the geometry of stitch class 301 for calculating sewing thread consumption. The modeling of stitch class 301 was conducted by changing the stitch density and thickness of the stitched fabric. The stitch density was adjusted by varying the stitch length (2, 3 and 4 mm) and the thickness of stitched fabric (2, 3 and 4 plies of fabric).The interlacement of sewing thread between the needle and bobbin was used for the measurement of arc length of one stitch. The total consumption of sewing thread was determined by multiplying the arc length by 4. The developed model was verified by using nine different stitched samples for comparing the predicted and measured values of sewing thread consumption. This model predicted the sewing thread consumption with 95% accuracy. Error percentages were also calculated for determining the major influencing factors that affected the sewing thread consumption. The proposed model can effectively be employed in garment industries for determining sewing thread consumption.

The present work covers the flow and heat transfer model for the power-law nanofluid in the presence of a porous medium over the penetrable plate. The flow is caused by the impulsive movement of the plate embedded in Darcy’s type porous medium. The flow and heat transfer model has been examined with the effect of linear thermal radiation and the internal heat source or sink in the flow regime. The Rosseland approximation is utilized for the optically thick nanofluid. To form the closed-form solutions for the governing partial differential equations of conservation of mass, momentum, and energy, the Lie symmetry analysis is used to get the reductions of governing equations and to find the group invariants. These invariants are then utilized to obtain the exact solution for all three cases, i.e., shear thinning fluid, Newtonian fluid, and shear thickening fluid. In the end, all solutions are plotted for the cu -water nanofluid and discussed briefly for the different emerging flow and heat transfer parameters.

The present study outlines the synthesis of transition metal oxide- (TMO-) multiwall carbon nanotubes- (MWCNTs-) based composites for photocatalytic application. MWCNTs were functionalized/purified by treating with H2SO4 and HNO3 to improve their dispersion in water. The TMOs (ZrO2, V2O5) were decorated on MWCNTs by the hydrothermal method to yield V2O5/MWCNTs and ZrO2/MWCNTs composites. Subsequently, these composites were characterized for their structural/morphological studies by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Photocatalytic activities of TMO/MWCNTs composites were investigated by degradation phenomenon of methylene blue (MB) dye in aqueous solution. It was observed that the prepared composites best performed in the presence of H2O2 under ultraviolet irradiation. The maximum observed degradation efficiencies for ZrO2/MWCNTs and V2O5/MWCNTs were 49% and 96%, respectively.

Plant disease detection is a critical issue that needs to be focused on for productive agriculture and economy. Detecting plant disease using traditional methods is a tedious job as it requires a tremendous amount of work, time, and expertise. Automatic plant disease detection is an important research area that has recently gained a lot of attention among the academicians, researchers, and practitioners. Machine Learning and Deep Learning can help identify the plant disease at the initial stage as soon as it appears on plant leaves. In this state-of-an-art review, a thorough investigation has been performed to evaluate the possibility of using Machine Learning models to identify plant diseases. In this study, diseases and infections of four types of crops, i.e., Tomato, Rice, Potato, and Apple, are considered. Initially, numerous possible infections and diseases on these four kinds of crops are studied along with their reason for the occurrence and possible symptoms for their detections. An in-depth study of the different steps involved in plant disease detection and classification using Machine Learning and Deep Learning is provided. Various datasets available online for plant disease detection have also been presented. Along with this, a detailed study on various existing Machine Learning and Deep Learning-based classification models proposed by different researchers across the world for four considered crops in terms of their performance evaluations, the dataset used, and the feature extraction method is discussed. At last, various challenges in the use of machine learning and deep learning for plant disease detection and future research directions are enumerated and presented.

Earth-abundant, non-precious metal-based bifunctional electrocatalysts with efficient water splitting activity are of valuable importance in the limitation of energy losses in an alkaline environment. Herein, we report NiCo–NiCoO2 nano-heterostructures embedded on the oxidized surface of copper foam (NiCo–NiCoO2@Cu2O@CF) as an efficient bifunctional electrocatalyst for overall water splitting in 1 M KOH electrolyte solution. In this study, metallic Ni and Co interlinkage with NiCoO2 nanoparticles (NPs) are suggested to form by thermal decomposition of nickel-cobalt hydroxide precursors embedding on copper foam under a nitrogen environment. Bimetallic thin layered nano-heterostructures of NiCo–NiCoO2@Cu2O@CF exhibits a synergic effect of doubly active metals Ni and Co to achieve remarkable small overpotentials of 133 and 327 mV to achieve a current density of 10 mA cm−2 for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The influential synergetic and structural effects have been extensively discussed to understand the overall water splitting for designing an efficient electrocatalyst. Hence, this phenomenon for surface modification of conductive substrate (CF) with a suitable combination of metal/metal oxide alloying as catalytic material helps us to design and synthesize low cost, highly efficient, non-precious metal-based electrocatalysts for overall water splitting.

The integration of Internet of Vehicles (IoV) with social networks has introduced Social IoV (SIoV) that will offer new applications in vehicular networks, e.g., personalized recommendations and route planning. This will be facilitated by heterogeneous access technologies and edge computing to offload tasks from vehicles via secure resource assignment. Thus, each vehicle in SIoV acts as a social subject that manages its own network. SIoV will lead to an explosive growth in network size, and induce issues like scalability and resource discovery. Blockchain is a potential candidate to address these, however, it is not suitable for SIoV with traditional proof-of-work (PoW) consensus. In this paper, we propose a framework that uses a dynamic PoW (dPoW) consensus with a checkpoint mechanism and a resource assignment policy. The dPoW consensus has different mining difficulty levels that change according to the communication traffic, whereas the checkpoint defines an alternative mechanism to generate the next block hash. The assignment policy manages an access control list to mandate the edge modules to securely distribute resources among vehicles. To study the feasibility of our framework, we present a formal security analysis using the Access Control Logic model. For the performance analysis, we use three metrics, i.e., scalability, latency, and security. With these analyses, we demonstrate that our framework offers enhanced security and can scale with a minimal increase in computation overhead. A case study with a comparative analysis is also discussed that evaluates the network dynamics and attests the superior performance of the framework under a real-life vehicular network scenario.

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.

In this manuscript, we have reported the crystal quality control of ZnSnO nanostructures during thermal evaporation growth in the presence and absence of oxygen gas during the growth process. To test this hypothesis, we have prepared two set of samples by evaporating Zn and tin powders (5:1 ratio) on Si substrate under similar growth conditions. But one set of samples was prepared in the presence of oxygen gas with a flow rate of 100sccm while other set of samples was grown under the absence of oxygen gas. Structural study revealed the formation of hexagonal structures of ZnSnO for both sets of samples while the Raman spectroscopy also supported the XRD data. But the observed morphology (SEM images) showed the different behavior of samples i. e porous structure of the samples grown using oxygen precursor while the nano-needle shape morphology was observed for the sample grown without oxygen. Thermo power measurements demonstrated that sample grown using oxygen precursor possess the highest value of Seebeck voltage along with other thermoelectric parameters when we compared with the samples grown without the presence of oxygen gas. The highest thermo power ability was related to the formation of porous structures which is a major cause of mobility enhancement.

The present study demonstrated that chitin-based nanofibers (CNFs) trigger the chitinase genes (PGIP1 and CaChi2), while elevating salicylic acid that can protect plants against pathogens. Cross-talk between this genetic induction and salicylic-acid-mediated immune response was also observed, which may arm a plant against multiple pathovars. Crab and mushroom based CNFs were synthesized by electrospinning and ball milling techniques. Plants (mung bean, Vigna radiata) (pepper, Capsicum annuum) were pre-inoculated with CNFs and treated with the pathogens Scrolotium rolfsii for pepper and Macrophomina phaseolina for mung bean and shrimp-based CNFs were used as a control. Treated plants had elevated levels of chitinase genes in response to CNFs at inoculation concentrations10 mg/mL both in soil and media, to protect them against the pathogenic fungal disease. After 24 h of exposure to the pathogens, qRT-PCR showed genes class II chitinase gene (CaChi2) and polygalacturonase inhibitor protein 1 (PGIP1) to be up-regulated in both root and shoot at 0.1 and 1 mg/mL of inoculation, respectively. The ball milled mushroom CNFs were sufficient to trigger the membrane based enzymes with less diameter (≥15 nm) to be most efficient versus others. In vitro analysis showed IC

The impact of reaction parameters and surface characteristics on NH3 synthesis activity of the Ru/CeO2 catalyst was explored. An exceptionally higher NH3 synthesis activity was observed at 375 °C and 2.5 MPa gauge pressure. The H2/N2 ratio among the reactants strongly affected the catalytic activity. The catalytic activity enhanced at higher temperatures for a higher H2/N2 ratio, while the lower H2/N2 ratio was suitable for improved NH3 synthesis at a lower temperature while working at 2.5 MPa gauge pressure. Likewise, NH3 synthesis activity of Ru/CeO2 catalyst enhanced abruptly on increasing pressure at relatively higher temperature conditions, using a reactant flow with a higher H2/N2 ratio. The NH3 synthesis activity enhanced with time on stream. This increase in activity was associated with an increase in Ru particles with high dispersion on the surface of the catalyst during NH3 synthesis, confirmed by FIB-TEM and EDS analysis of the cross-sections of catalyst particles.

Metal-organic frameworks (MOF(n)) are organic-inorganic hybrid crystalline porous materials that consist of a regular array of positively charged metal ions surrounded by organic ‘linker’ molecules. The metal ions form nodes that bind the arms of the linkers together to form a repeating, cage-like structure. Moreover, in a chemical structure or molecular graph, edges and vertices are known as bonds and atoms, respectively. Metric dimension being a subsets of atoms with minimum cardinality is used in the substrcturing of the chemical compounds in the molecular structures. Fractional metric dimension is weighted version of metric dimension that associate a numeric value to the identified subset of atoms. In this paper, we have computed the fractional metric dimension of metal organic framework (MOF(n)) for n ≡ 0(mod)2.

A Brain-computer interface (BCI) using an electroencephalogram (EEG) signal has a great attraction in emotion recognition studies due to its resistance to humans’ deceptive actions. This is the most significant advantage of brain signals over speech or visual signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that a lot of effort is required for manually feature extractor, EEG recordings show varying distributions for different people and the same person at different time instances. The Poor generalization ability of the network model as well as low robustness of the recognition system. Improving algorithms and machine learning technology helps researchers to recognize emotion easily. In recent years, deep learning (DL) techniques, specifically convolutional neural networks (CNNs) have made excellent progress in many applications. This study aims to reduce the manual effort on features extraction and improve the EEG signal single model’s emotion recognition using convolutional neural network (CNN) architecture with residue block. The dataset is shuffle, divided into training and testing, and then fed to the model. DEAP dataset has class 1, class 2, class 3, and class 4 for both valence and arousal with an accuracy of 90.69%, 91.21%, 89.66%, 93.64% respectively, with a mean accuracy of 91.3%. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively, with a mean accuracy of 94.13% on the SEED dataset. The experimental results indicated that CNN Based on residual networks can achieve an excellent result with high recognition accuracy, which is superior to most recent approaches.

This paper presents machine learning techniques for short-term wind speed prediction by exploiting their computational intelligence capabilities such as random forest regression (RFR), support vector regression (SVR), radial basis function neural networks (RBFNN), and long short-term memory (LSTM) on various wind farm datasets located in Pakistan. Initially, predictions are obtained by employing baseline regressors, RFR and RBFNN in terms of error indices. Later, a stacked LSTM forecast engine (SLFE) has been proposed to improve the efficacy, accuracy, and prediction capability of the forecast engine by using leaky rectified linear units (Leaky ReLU) as kernel function. SLFE has been implemented and tested on the datasets acquired from four different wind farms having a temporal locality of 10 min for short-term wind speed prediction. Furthermore, an ensemble of stacked LSTM with RFR, SVR, and RBFNN has also been developed for the comparison. The strength of the SLFE has been evaluated in terms of various performance measures such as mean absolute error (MAE), root mean squared error (RMSE), R2Score, and explained variance score (EVS). The efficacy of the proposed models is evaluated in terms of performance metrics, MAE, RMSE, R2Score and EVS, 0.043, 0.065, 0.813, and 0.814, respectively, demonstrating the worth of the forecast engine. Additionally, statistical one-way ANOVA is also carried out with multiple independent executions of the proposed algorithm to demonstrate the robustness, efficiency, and reliability of the model.

The success of a power system protection scheme so much depends upon the optimal coordination of its different units, which are protective relays. The task of finding the optimal set of relay parameters or settings can be achieved by solving a nonlinear and highly constrained optimization problem. This paper develops a new variable neighborhood search (VNS) algorithm as a potent tool for coordinating the controlling parameters of directional overcurrent relays (DOCRs), particularly the current settings and tripping times. VNS is an optimization algorithm which works based on a systematic change of neighborhood while searching the optimal solution of a given problem in both descent and perturbation phases. The performance of the proposed VNS-based algorithm is demonstrated on the 8-bus, 9-bus and 15-bus power system networks with diverse scenarios. The obtained outcomes are compared with other competitive customary algorithms. Thus, the superiority of the proposed technique is validated in coordinating the DOCRs for better protection of the power system.

The use of cross-tie is one of the effective countermeasures to suppress the undesired cable vibration in the cable-stayed bridges. The major benefits offered by the cross-tie solution are the increase in the in-plane stiffness and the flow of energy toward the higher order nodes. However, the formation of closely spaced local modes is one of the major disadvantages of the Cross-tied cable networks. There are only few studies available to understand the formation of local modes. In the current study, an energy-based approach is developed to differentiate between the global and the local modes. In the proposed approach, the kinetic energy equations are formulated to compute the energy stored in arbitrary cable segments. In the current study, the advantages of the proposed energy-based approach over the existing amplitude-based approach have been discussed. The suggested approach has been applied to multiple cable networks, and a comparison has been drawn between the amplitude-based approach and the proposed energy-based approach.

Massive Multiple Input Multiple Output (m-MIMO) or Large Scale Antenna System (LSAS) is the latest version of cellular network technology with the purpose to send data from the base station to different users. The huge determined expansion in the mobile equipment and data rate requirement has created the rigid necessity for future wireless communication networks. The m-MIMO envisages a significant increase in the capacity but at the verge of excessive hardware complication. In this paper, we put forward a less complex precoding scheme based on the hybrid architecture to achieve a performance higher than the conventional baseband Minimum Mean Square Estimation (MMSE) precoding. In this paper, we propose a precoding schema namely amplitude and phase MMSE (APMMSE) to attain the performance of traditional MMSE precoding. The informed greedy best-first search has offered to estimate the baseband precoding matrix. It formulates the amplitude of the baseband precoder. Radio Frequency Precoder modifies the phase and provides an optimal signal-to-interference -noise ratio SINR and thus improves spectral efficiency of hybrid architecture.

Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.

Hydrogen treatment is a facile and efficient approach for the enhancement in the functioning of TiO2 nanoparticles for dye-sensitized solar cells (DSSC). In this work, TiO2 nanoparticles have been synthesized in the hydrogen environment followed by the deposition onto FTO glass substrates with various film thickness as photoanodes for DSSC. The synthesized hydrogen treated TiO2 nanoparticles based photoanodes have showed significantly improved photocurrent in the resulting fabricated devices. SEM and TEM analyses have confirmed the particle size and morphology of TiO2 nanoparticles at various magnifications. The crystalline structure and phase identification were studied by XRD analysis and Raman spectroscopic measurements. The UV–Vis spectroscopy analysis was carried out to find the response of samples for ultraviolet and visible light. The current-voltage measurements have confirmed the improvement of photocurrent that is principally due to improved photo-activity of hydrogen treated TiO2 nanoparticles. Moreover, hydrogen treated TiO2 nanoparticles-based photoanode with the film thickness of 11.65 μm has remarkably enhanced power conversion efficiency of 6.05% in DSSCs. The ability of highly photoactive hydrogen treated TiO2 nanoparticles will provide the new openings in different fields that include photo-electrochemical water splitting and in many other applications.

Local energy generation and peer to peer (P2P) energy trading in the local market can reduce the energy consumption cost, emission of harmful gases (as renewable energy sources are used to generate energy at user's premises) and increase the smart grid resilience. However, local energy trading with peers can have trust and privacy issues. A centralized system can be used to manage this energy trading but it increases the overall cost of the system and also faces several issues. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based system is proposed. It is fully decentralized and allows the market members to interact with each other and trade energy without involving a third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with the main smart contract, which requests P2P and prosumer to grid smart contracts for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio of electricity.

The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR.

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.

The COVID-19 outbreak is a worldwide disaster that has disrupted several aspects of human life and its community living. This pandemic has also affected the existing supply chain and the way it was being managed. Therefore, this study’s primary objective is to identify and discuss the significant impact of COVID-19 on the current supply chain, with specific reference to the medical supply chain. This article then recommends the potential solution measures that can reduce the impact of COVID-19 on the existing supply chain during and after the COVID-19 pandemic. In order to do so, we have identified and discussed various significant impacts of COVID-19 on the supply chain. Identifying the impacts helps the policy planner formulate the policies to mitigate these impacts and recover the supply chain at their functional level. Further, this study also suggests possible solution measures that can be adopted to reduce the disruption of the supply chain. These solution measures help management develop the action plan for early recovery from the COVID-19 disruption.

The coronavirus (COVID-19) pandemic is one of the biggest challenges in the field of healthcare. Nanomedicine is a developing area that has the potential to treat various diseases and control infections. Now, its applications are open for the treatment of COVID-19. We have studied relevant papers through Scopus, Google Scholar, Science Direct and ResearchGate on nanomedicine in context of COVID-19. This paper provides detailed information about nanomedicine in the context of healthcare. It further identifies the primary challenges faced in the current situation. This study provides details about the advancements in the area of nanomedicine in healthcare for fighting the COVID-19 pandemic. Finally, we have identified and discussed various significant applications of nanomedicine in solving challenges thrown by the COVID-19 pandemic. Researchers can work on developing applications of nanoparticles with the size of the novel Coronavirus. Nanomedicine is helpful to repair the cells of an infected patient the help of repair proteins. It also plays a vital role in testing medicine and helps many clinical trials get approval from healthcare agencies. In the future, nanomedicine will be helpful for fighting against this pandemic and creating advancements in healthcare.

Accessibility, robustness, and connectivity are the salient structural properties of networks. The labelling of networks with numeric numbers using the parameters of edge or vertex weights plays an eminent role in the study of the aforesaid properties. The systems interlinked in a network are transformed into a graphical network, and specific numeric labels assigned to the converted network under certain rules assist us in the regulation of data traffic, bandwidth, and coding/decoding of signals. Two major classes of such network labellings are magic and antimagic. The notion of supera,0edge-antimagic labelling on networks was identified in the late nineties. The present article addresses supera,0edge antimagicness of union of the networks’ starSn, the pathPn, and copies of paths and the rooted product of cycleCnwithK2,m. We also provide supera,0edge-antimagic labelling of the rooted product of cycleCnand planar pancyclic networks. Further, we design a supera,0edge-antimagic labelling on a pancyclic network containing chains ofC6and three different symmetrically designed lattices. Moreover, our findings have also been recapitulated in the shape of 3-Dplots and tables.

World health organization (WHO) has declared the COVID-19 outbreak as a public health emergency of international concern and then as a pandemic on 30th of January and 11th of March 2020, respectively. After such concern, the world scientific communities have rushed to search for solutions to bring down the disease’s spread, fast-paced vaccine development, and associated medical research using modern technologies. Biosensor technologies play a crucial role in diagnosing various medical diseases, including COVID-19. The present paper describes the major advancement of biosensor-based technological solutions for medical diagnosis, including COVID-19. This review-based work covers the biosensors and their working principles in the context of medical applications. The paper also discusses different biosensors and their applications to tackle medical issues, including this ongoing pandemic.

In predicting the track-ground vibrations induced by moving trains, it is generally assumed that the irregularities of two rails of a track are identical and fully correlated. This assumption leads to identical contact forces at the contact point of wheels and rails. In the present paper, the uncorrelated profile of the two rails is considered to investigate their influence on the train-track-ground vibrations. For this purpose, a three-dimensional (3D) finite element model capable of simulating interactions between a 3D train and track by using a nonlinear Hertz contact method is developed. Nonlinear Hertz contact is represented by the tensionless stiffness between the wheels and rails. The rails are modeled as 3D Euler–Bernoulli beam elements. Solid brick elements represent the model components, such as the track (other than rail) and ground. A detailed description of an iterative numerical algorithm is presented to establish the integrations of the train and track system. This iterative numerical algorithm can calculate the responses and forces at the interaction point between each wheel and rail to incorporate the uncorrelated profile of two rails, instead of only considering the interaction at the centroid of the wheelset axle and the center of the track. A comparative study is conducted by considering various train speeds and three different track cases. The results show that an increase in train speed causes an increase in ground vibration in the case of uncorrelated profiles of two rails of a track. Moreover, the effects of correlated and uncorrelated profiles of two rails of a track are investigated in terms of the train-track-ground responses.

Metal organic graphs are hollow structures of metal atoms that are connected by ligands, where metal atoms are represented by the vertices and ligands are referred as edges. A vertex x resolves the vertices u and v of a graph G if d u , x ≠ d v , x . For a pair u , v of vertices of G , R u , v = x ∈ V G : d x , u ≠ d x , v is called its resolving neighbourhood set. For each pair of vertices u and v in V G , if f R u , v ≥ 1 , then f from V G to the interval 0,1 is called resolving function. Moreover, for two functions f and g , f is called minimal if f ≤ g and f v ≠ g v for at least one v ∈ V G . The fractional metric dimension (FMD) of G is denoted by dim f G and defined as dim f G = min g : g is a minimal resolving function of G , where g = ∑ v ∈ V G g v . If we take a pair of vertices u , v of G as an edge e = u v of G , then it becomes local fractional metric dimension (LFMD) dim l f G . In this paper, local fractional and fractional metric dimensions of MOG n are computed for n ≅ 1 mod 2 in the terms of upper bounds. Moreover, it is obtained that metal organic is one of the graphs that has the same local and fractional metric dimension.

Twin roll casting was commercialized for a strip production from ferrous and non-ferrous alloys in the 1950s; however, its application to magnesium has proven difficult and still creates major challenges. This report describes global efforts in expanding manufacturing capabilities of magnesium sheet through twin roll casting path, offering many benefits, including a reduction in number of processing steps and energy savings. In addition to hardware design, alloy transformation during processing, product microstructure and properties, examples of successful solutions along with present technology and knowledge limitations are discussed. A particular attention is paid to developments at CanmetMATERIALS, having the only in North America pilot scale twin roll casting facility, devoted to magnesium. Efforts are described that aim at design of new magnesium alloys, which could take advantage of unique processing conditions during twin roll casting and contribute to the overall progress in magnesium sheet manufacturing.

Economic load dispatch problems are most important in operation and management of the electric power systems which are formulated as optimization problems. Modern deterministic and stochastic optimization techniques are efficient in solving economic load dispatch problems without any limitation because of their capability to seek the global optimal solution. Economic load dispatch problems involve linear equality constraints which are difficult to handle along with discontinuous, non-differentiable and highly non-linear objective functions. The problem becomes challenging for optimization techniques, especially for deterministic methods. This study presents a new approach based on a hybrid algorithm consisting of Genetic algorithm and modified Hooke and Jeeves method to solve the economic load dispatch problems with equality constraints. The performance of proposed Algorithm is tested on five generating systems with valve-point effects. Test results of proposed technique are quite promising and effective, with good convergence property and generation cost to produce better quality solution compared with several state-of-the-art methods.

We study the dynamics of an electro-optical transducer in the presence of a driving field and a probe field. The nano-electro-optomechanical system (NEOMS) consists of a cavity with a fixed mirror, which is coupled to a vibrating end mirror through an optomechanical coupling, whereas the Coulomb interaction couples the moving mirror with the other moving mirror. In the probe transmission field, four-wave mixing (FWM) is reported in the presence of two external driving fields on moving mirrors. We show that the four-wave mixing exhibits controlled dynamics by varying the amplitudes and phases of the external driving fields. We present a set of experimental parameters to realize the phenomenon in present-day laboratory experiments.

Cloud computing facilitates collaboration, communication, and essential online services during the COVID-19 crisis. The current situation of the COVID-19 pandemic has compelled people to work from their homes, but they have to communicate, collaborate online. Thus, we see an essential role of cloud computing in taking up this challenge of working from home and delivering efficiently. A brief review of Cloud Computing service in the context of COVID-19 pandemic is done using recent papers’ by searching keywords such as “Cloud Computing” and “COVID-19” from PubMed’s database SCOPUS and Google Scholar. During the lockdown situation, cloud computing technology helps provide commendable service in the healthcare domain. It provides an advanced infrastructure for facilitating digital transformation. A brief discussion has been made on how cloud computing components are vital for overcoming the ongoing situation. This paper also studies the remote working of cloud computing for the COVID-19 pandemic and finally identified significant cloud computing applications for the COVID-19 pandemic. All countries focus on reducing this virus’s spread, so this technology helps minimize the spread of this virus by providing online services. It provides an innovative environment that enhances the creativity and productivity of healthcare workers. This technology is efficient in detecting, tracking, and monitoring newly infected patients. In the future, this technology will insight and provide control over this infection to save millions of lives worldwide. This technology is also quite helpful to forecast the future impact of the SARS-Co-2 virus.

This paper presents a compact and the low-cost coupled line band-pass filter with application to future generation millimetre-waves and 5G communications. The proposed approach of the filter design is based on the coupled-line and centre tapped upper and lower stepped impedance resonators. These resonators generate the sharp rejection, wide bandwidth, and abet to realize the compact filter. A detailed theoretical as well as the numerical analysis of the filter has also been investigated. As a demonstration, the proposed band-pass filter configuration has been designed and fabricated at the 33.5 GHz frequency using a low-cost PCB technique. It has observed that the proposed filter, results in a better return loss and the low insertion loss. The experimental results has been presented and compared with the simulated results and has found quite satisfactory. Moreover the results obtained validate a good agreement with each other.

Software cost estimation is the prediction of development effort and calendar time required to develop any software project. It is considered to be the very fundamental task for successful execution of an on-going project as well as budgetary requirements of futuristic projects. As accuracy in software cost estimation is very hard because of the availability of vague information at the time of inception of the software project, it prompted many researchers to explore in this domain from past decades. Their pioneer works suggest a bulk of techniques for this purpose. However, because of the availability of large number of estimation techniques, it becomes hard for any software practitioners to select an appropriate one. To help the industry practitioners in these situations, a novel analogy-centered model based on differential evolution exploration process is proposed in this research study. The proposed model has been assessed on 676 projects from 5 different data sets and the results achieved are significantly better when compared with other benchmark analogy-based estimation studies. Furthermore, being the very less computational cost of the proposed model, it is suggested that the proposed model be considered as the preliminary stage of any analogy-based software estimation technique.

Left ventricular assist devices (LVADs) are associated with major vascular complications including stroke and gastrointestinal bleeding (GIB). These adverse vascular events may be the result of widespread vascular dysfunction resulting from pre-LVAD abnormalities or continuous flow during LVAD therapy. We hypothesized that pre-existing large artery atherosclerosis and/or abnormal blood flow as measured in carotid arteries using ultrasonography are associated with a post-implantation composite adverse outcome including stroke, GIB, or death. We retrospectively studied 141 adult HeartMate II patients who had carotid ultrasound duplex exams performed before and/or after LVAD surgery. Structural parameters examined included plaque burden and stenosis. Hemodynamic parameters included peak-systolic, end-diastolic, and mean velocity as well as pulsatility index. We examined the association of these measures with the composite outcome as well as individual subcomponents such as stroke. After adjusting for established risk factors, the composite adverse outcome was associated with pre-operative moderate-to-severe carotid plaque (OR 5.08, 95% CI 1.67-15.52) as well as pre-operative internal carotid artery stenosis (OR 9.02, 95% CI 1.06-76.56). In contrast, altered hemodynamics during LVAD support were not associated with the composite outcome. Our findings suggest that pre-existing atherosclerosis possibly in combination with LVAD hemodynamics may be an important contributor to adverse vascular events during mechanical support. This encourages greater awareness of carotid morphology pre-operatively and further study of the interaction between hemodynamics, pulsatility, and structural arterial disease during LVAD support.

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side channel, and cloning attacks. To address these issues, this article presents an efficient protocol for authentication in the IoV. The proposed protocol uses physical unclonable functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three-layered infrastructure architecture for IoVs, i.e., roadside units (RSUs), RSU gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of an RSU gateway which may engulf multiple RSUs. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.