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Epileptic seizures can be dangerous as they cause sudden and uncontrolled electrical activity in the brain which can lead to injuries if one falls or loss of control over physical functions. To mitigate these risks, machine learning and deep learning algorithms are being developed to anticipate seizure occurrences. Accurate prediction of seizures could enable patients to adopt preventive strategies or initiate medical interventions to halt seizures, thereby minimizing injuries and enhancing safety for individuals afflicted with epilepsy. This paper aims to combine neural networks and Ensemble learning to enhance the accuracy of diagnosing epileptic seizures. By leveraging the strengths of both techniques, the precision in seizure diagnosis can be significantly improved. It also improves the evaluation metrics used in machine learning methodologies for a more comprehensive assessment of diagnostic outcomes. This approach ensures a thorough understanding of the effectiveness of the proposed approach. In this paper, a model with a supreme precision rate is developed to detect epileptic seizures with the help of EEG signals. This study uses an ensemble method, which employs several algorithms, for instance XGB, SVM, RF, and BiLSTM. The used dataset is open access from Bonn University. The proposed methodology reached 98.52% accuracy, 97.37% precision, 95.29% recall, and 96.32% F1-score, respectively.

This paper investigates nested lattice codes generated through Construction A from the ring of integers of an imaginary quadratic field. Our primary goal is to offer a streamlined proof of the existence of nested lattice codes that can attain the capacity of an Additive White Gaussian Noise (AWGN) channel. We alter the random ensemble of nested lattice codes by introducing discrete random dithers instead of continuous random dithers. This adjustment enables us to draw a parallel between nested lattice codes and nested linear codes, facilitating a proof that remains as straightforward as that used for nested linear codes. Furthermore, we demonstrate that this collection of lattices exhibits favorable properties for Mean Square Error (MSE) quantization under specific constraints.

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as “a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector.

In this paper, we present a real-life, cost-effective implementation of physical layer security (PLS) using friendly jamming (FJ) and the IEEE 802.11 technology. Our approach is based on a recent development in software-defined networking (SDN) called spectrum programming, where a network controller can execute an intelligent access point (AP) selection algorithm to connect the user station to the AP that provides the most secrecy while exploiting idle APs as jammers. Considering a system with two APs, our first contribution is a theoretical optimization of the power of FJ based on system power parameters, as well as distances between the two APs and the user station and the eavesdropper station. Our second contribution is demonstrating not only that PLS can be implemented with commercial-off-the-shelf Wi-Fi devices, but also that our theoretical network-centric approach allows for a significant increase of secrecy capacity and secrecy coverage by applying FJ. Our experiments show that the theoretical optimization of the transmit power of the jamming AP is valid in practice, effectively maximizing the throughput gap between the user and the eavesdropper. To our best knowledge, this is the first work linking information-theoretic optimization of FJ in PLS to a real-world implementation, which is compatible with Wi-Fi standards and devices.

The importance of knowledge organization and information retrieval techniques has been evident throughout human history, becoming even more crucial in the digital age. While computer systems and the web have facilitated information retrieval, challenges arose with the increasing volume of data. The introduction of Semantic Web technologies aimed to enhance precision and accuracy by converting the web into a structured data format. The Cultural Heritage (CH) community has been at the forefront of adopting Semantic Web practices to promote interoperability and shared understanding. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it’s time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

While satellite persistent scatterer synthetic aperture radar (SAR) interferometry is an effective technique to monitor the health of structures via selection of long-term coherent pixels, detailed interpretation of displacement measurements requires knowledge of which surfaces, the reflection is coming from. Ray tracing algorithms can be used to simulate SAR backscatter for structures, and link observed PS pixels to specific parts of structures. We investigate the reflectivity of three bridges in London for a high-resolution TerraSAR-X dataset, using a ray tracing technique. Artificial reflectors are mounted on one of the bridges. We compare the simulated backscatter with the location of points selected as PS pixels using a stack of 38 TerraSAR-X images. The results confirm that we can predict overall scattering behavior of a bridge using SAR simulation techniques when we have access to a 3-D model of the structure. However, the results of simulation depend on the level of details in the 3-D model and a high-detailed 3-D model including corner reflectors allows the ray tracing technique to perfectly simulate position of the strong scatterers. This approach can help designers increase the SAR reflectivity of a bridge in the design phase of structural bridge assets, or in a retrofit phase, by installing artificial reflectors. We also link the strong scatterers in the reflectivity map to the corresponding scattering surfaces in the structural model that contributed to the signal. This allows the end-users of the InSAR products to better understand which sections of a bridge are moving when a PS pixel indicates displacement.

The advent of human emotion data availability signifies a pivotal advancement, propelling intelligent systems to elevate human-technology interaction. The fusion of emotions with pioneering technologies such as artificial intelligence (AI), geospatial information systems (GIS), and extended reality (XR) heralds a groundbreaking era of emotion-driven intelligent systems, poised to revolutionize urban applications. In this context, our research carves a distinctive niche by engaging in a comprehensive review of pertinent literature, culminating in the formulation of an incisive 3-layer framework. Encompassing the bedrock of emotion analysis, seamless technology integration, intricate privacy considerations, and pragmatic implementation, this framework serves as a beacon for future endeavors. Most notably, we introduce the trailblazing concept of emotion-intelligent systems, an outcome of the harmonious blend of emotion data and pioneering technologies. Our research navigates the intricate terrain of cultivating emotionally sentient intelligent systems, deftly addressing complexities and surmounting challenges to forge a path towards a more emotionally attuned generation. With transformative implications, these avant-garde systems stand poised to reshape human-technology interfaces, thereby beckoning novel avenues for future exploration.

In this paper, we present two distinct hardware dynamic memory allocation schemes that are based on the binary buddy system algorithm. Our aim is to mitigate internal fragmentation without impacting the area and performance of the system. The first scheme introduces a parallel design for calculating the addresses of free blocks, which results in a decrease in allocation latency while maintaining acceptable resource utilization. This scheme is particularly well-suited for managing a limited number of minimum allocable units (MAU). On the other hand, the second allocator can handle a large number of MAUs due to its innovative searching mechanism. This allocator exhibits lower resource consumption and operates with an acceptable allocation latency. Furthermore, to decrease internal fragmentation, we develop a novel update mechanism for allocating information data structures in both methods. By employing these two allocator schemes, we can improve the efficiency and resource management of dynamic memory allocation for hardware systems. Experimental results demonstrate that the first and second proposed schemes achieve a minimum allocation speed-up of $\times 2$ and $\times 1.8$ compared to their counterparts. At the same time, they achieve a reduction of at least 78% and %88 in resource utilization, respectively. The results show that the total fragmentation is reduced by at least 14% due to the lower internal fragmentation.

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints’ uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

The ability to perform automated conversions between different data formats is key to achieving interoperability between heterogeneous systems. Conversions require the definition of mappings between concepts of separate data specifications, which is typically a difficult and time-consuming task. In this article, we present a technique that exploits, in part, semantic web technologies to automatically suggest mappings to users based on both linguistic and structural similarities between terms of different data specifications. In addition, we show how a machine-learned linguistic model created by gathering data from domain-specific sources can help increase the accuracy of the suggested mappings. The approach has been implemented in our prototype tool, SMART (SPRINT Mapping & Annotation Recommendation Tool), and it has been validated through tests using specifications from the transportation domain.

In today's world, everything is connected via the Internet. Smart cities are one application of the Internet of Things (IoT) that is aimed at making city management more efficient and effective. However, IoT devices within a smart city may collect sensitive information. Protecting sensitive information requires maintaining privacy. Existing smart city solutions have been shown not to offer effective privacy protection. We propose a novel continuous method called Differential Privacy-Preserving Smart City (DPSmartCity). When the IoT device produces sensitive data, it applies differential privacy techniques as a privacy-preserving method that uses Laplace distributions or exponential distributions. The controller receives the perturbed data and forwards it to the SDN. SDN controllers eventually send the data to the cloud for further analysis. Accordingly, if the data is not sensitive, it is directly uploaded to the cloud. In this way, DPSmartCity provides a dynamic environment from the point of view of privacy preservation. As a result, adversaries are unable to easily compromise the privacy of the devices. The solution incurs at most 10-18% overhead on IoT devices. Our solution can therefore be used for IoT devices that are capable of handling this overhead.

Controlled islanding is the last remedial action to prevent cascading outages or blackouts in power systems. Conventional methods presented for controlled islanding strategy determination, particularly those calculating load shedding values using optimization methods, are not fast enough in online applications for modern power systems. In this paper, a novel learning-based approach is introduced for online coherency-based controlled islanding in transmission systems. The proposed approach presents a prediction and optimization model, which is faster than conventional optimization-based models in two ways. Firstly, the proposed approach uses a classification model to predict the splitting scheme in a short time following the occurrence of a disturbance, and secondly in the proposed approach, a simpler optimization problem with fewer variables is solved to find the load shedding amount required in each area. In the proposed load shedding approach, some candidate system partitioning schemes are calculated beforehand and therefore, the load shedding optimization problem is simplified significantly compared to similar optimization-based approaches. Note that appropriate features, which are used in this paper as the input of the classifier, are acquired by processing post-disturbance phase angle variations, which are measured across the network. The proposed approach is simulated on the 16-machine, 68-bus system, and its accuracy and efficacy have been demonstrated.

A noticeable proportion of larger brain metastases (BMs) are not locally controlled after stereotactic radiotherapy, and it may take months before local progression is apparent on standard follow-up imaging. This work proposes and investigates new explainable deep-learning models to predict the radiotherapy outcome for BM. A novel self-attention-guided 3D residual network is introduced for predicting the outcome of local failure (LF) after radiotherapy using the baseline treatment-planning MRI. The 3D self-attention modules facilitate capturing long-range intra/inter slice dependencies which are often overlooked by convolution layers. The proposed model was compared to a vanilla 3D residual network and 3D residual network with CBAM attention in terms of performance in outcome prediction. A training recipe was adapted for the outcome prediction models during pretraining and training the down-stream task based on the recently proposed big transfer principles. A novel 3D visualization module was coupled with the model to demonstrate the impact of various intra/peri-lesion regions on volumetric multi-channel MRI upon the network’s prediction. The proposed self-attention-guided 3D residual network outperforms the vanilla residual network and the residual network with CBAM attention in accuracy, F1-score, and AUC. The visualization results show the importance of peri-lesional characteristics on treatment-planning MRI in predicting local outcome after radiotherapy. This study demonstrates the potential of self-attention-guided deep-learning features derived from volumetric MRI in radiotherapy outcome prediction for BM. The insights obtained via the developed visualization module for individual lesions can possibly be applied during radiotherapy planning to decrease the chance of LF.

A new approximate full adder (FA) and a new approximate subtractor are presented, both of them have 8 transistors, and their areas are 0.1944 and $0.1689~\mu $ m2, respectively. The FA experiences three errors, while the subtractor shows two errors. In both circuits, to improve the speed, output swing, and drivability, the gate diffusion input (GDI) and dynamic threshold (DT) techniques are implemented by carbon nanotube field effect transistor (CNTFET) technology. The FA and subtractor in order are embedded in an 8-bit ripple carry adder (RCA) and an 8-bit subtractor, then they make a new approximate multiplier-free discrete cosine transform (DCT). The 8-point approximate DCT manipulation requires only addition and no multiplication. So, computational complexity is brought down. The DCT shows power delay product (PDP), peak signal-to-noise ratio (PSNR), and a figure of merit (FoM) of 63.61 fJ, 34.96 dB, and 2.39, respectively. The features of the presented approximate DCT confirm its application for image compression and noise removal in medical images. [ABSTRACT FROM AUTHOR]

This article proposes an efficient phenomenological inversion method to determine the depth profile of a surface-breaking crack in a metal from the output signal of an alternating current field measurement (ACFM) probe. The proposed method utilizes a conjugate gradient algorithm to minimize an objective function, representing the difference between the probe predicted and actual signals in an iterative manner. The objective function is derived explicitly in terms of crack depth variables by considering a polynomial function for the field distribution in the depth direction and applying appropriate Green’s functions. This approach enhances the accuracy and computational efficiency of the inversion process, regardless of the choice of the initial crack depth profile or the presence of noise in the measurement system. The validity and efficiency of the proposed method are demonstrated by comparing the reconstructed depth profiles of several simulated and machine-made cracks with their actual data, and those obtained using the conventional phenomenological approach based on an efficient stochastic optimization scheme along with a fast pseudo-analytic ACFM probe output simulator.

In today’s world, technology has assumed an indispensable role in everyday life. However, though developments in the Internet of Things (IoT) have laid a foundation for achieving different educational purposes, IoT is not being employed to its full potential. A separate issue is that the topic of geometry has been neglected or received only limited attention in schools, particularly in the early years. Moreover, as geometry is of paramount importance, the idea of teaching geometry in early childhood should be investigated. This paper proposes an IoT-based approach for teaching basic geometric shapes to children aged 5 to 6. Specifically, five nodes were designed to act as the vertices of shapes. In addition, educational videos comprising three sections —identification, recognition, and recollection — were provided. Using a sample of 35 boys the proposed solution was assessed in terms of its effect on learning outcomes and the learning process using pre-test, simultaneous test, and post-tests. The findings revealed a noticeable improvement in learning outcomes. Furthermore, the proposed approach made learning enjoyable, which, compared to the traditional approach toward learning geometric shapes, is considered an improvement.

It has been known that the insufficiency of linear coding in achieving the optimal rate of the general index coding problem is rooted in its rate’s dependency on the field size. However, this dependency has been described only through the two well-known matroid instances, namely the Fano and non-Fano matroids, which, in turn, limits its scope only to the fields with characteristic two. In this paper, we extend this scope to demonstrate the reliance of linear index coding rate on fields with characteristic three. By constructing two index coding instances of size 29, we prove that for the first instance, linear coding is optimal only over the fields with characteristic three, and for the second instance, linear coding over any field with characteristic three can never be optimal. Then, a variation of the second instance is designed as the third index coding instance of size 58. For this instance, it is proved that while linear coding over any field with characteristic three cannot be optimal, there exists a nonlinear code over the fields with characteristic three, which achieves its optimal rate. Connecting the first and third index coding instances in two specific ways, called no-way and two-way connections, will lead to two new index coding instances of size 87 and 91, for which linear coding is outperformed by nonlinear codes. Another main contribution of this paper is the reduction of the key constraints on the space of the linear coding for the first and second index coding instances, each of size 29, into a matroid instance with the ground set of size 9, whose linear representability is dependent on the fields with characteristic three. The proofs and discussions provided in this paper through using these two relatively small matroid instances will shed light on the underlying reason causing the linear coding to become insufficient for the general index coding problem.

Education is a vital part of the development of society and it is changing over time in terms of methods, content, concepts, and models. Recently, it has been increasingly prevalent to benefit from the potentialities of Artificial Intelligence (AI) in addressing educational issues. In this research, the current state of the art of the integration of AI in K-12 education was provided. Specifically, different parts of education in which AI was employed along with the related AI categories were discussed according to different K-12 grades and courses. Additionally, technologies and environments that contributed to employing AI in education were discussed. To this end, a systematic literature review was conducted on articles and conference papers published between 2011 and 2021 in the Web of Science and Scopus databases. As the result of the initial search, 2075 documents were extracted and based on inclusive criteria and 210 documents were identified for further investigation. AI applications were categorized into Student performance, Teaching, Selection, and Behavior tasks, and Other. Machine Learning (ML) and Intelligent Tutoring System (ITS) were the most common approaches among AI categories. Furthermore, high school-related applications were more frequent and STEM courses were substantially targeted by AI. In conclusion, the remarkable impact of AI on education was concluded. The current study reveals information about the potentialities offered by AI in K-12 education which aids researchers in implementing AI-based education systems. As for future works, other databases such as ACM library and Google Scholar can be investigated as well. Furthermore, exploring the 95 papers that were excluded due to inaccessibility to their full texts can be taken into account. Finally, the papers can be also investigated in terms of pedagogical approaches or development tools.

A high step-up PWM non-isolated dc–dc converter with soft switching is proposed in this paper. The converter has minimum auxiliary elements to achieve high power density and high voltage gain. The ZVS operation of the main power switch results in negligible capacitive turn-on losses. Since the duty cycle of the auxiliary switch is narrow and its operation is under ZCS condition, the losses that the auxiliary circuit imposes are not significant. Also, all the diodes are operated under soft-switching conditions solving the reverse recovery problem. All the inductors are coupled on only one magnetic core, reducing size and conduction losses. Compared to the hard switched counterpart, the electromagnetic interference of the proposed converter is reduced. Furthermore, the output voltage of the proposed converter is controlled by an integral sliding mode control strategy during the load variations. Also, the proposed integral sliding mode control strategy has been compared with the PI control strategy, improving the transient response and the robustness under load variations while the switching frequency is constant. The effectiveness and accuracy of the proposed converter are verified by practical laboratory results which are obtained from a 250W prototype.

Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis (GSA) methods play an important role in XAI, as they can provide an understanding of which (groups of) parameters have high influence in the predictions of machine learning models and the output of simulators and real-world processes. In this paper, we conduct a survey into global sensitivity methods in an XAI context and present both a qualitative and a quantitative analysis of these methods under different conditions. In addition to the overview and comparison, we propose an open source application, GSAreport, that allows you to easily generate extensive reports using a carefully selected set of global sensitivity analysis methods depending on the number of dimensions and samples, to gain a deep understanding of the role of each feature for a given model or data set. We finally present the methods discussed in a complex real-world application of genomic prediction and draw conclusions about when to use which GSA methods.

In this paper, a Switched Reluctance Motor (SRM) drive system is proposed to make a trade-off between torque profile and vibration amplitude. In the proposed drive system, the power factor correction and fast demagnetization are achieved using a single-phase Vienna rectifier as a front-end converter for SRMs with a nominal voltage equal to half DC-link voltage. The three-level Vienna rectifier provides doubled negative voltage in demagnetization, resulting in a shorter current tail and faster demagnetization. But, applying higher voltage in demagnetization has a negative impact on the vibration amplitude at the point of demagnetization. Therefore, a control algorithm is designed to apply a negative modulated voltage to the motor phase, with a minimum change rate at the beginning of demagnetization and a linear average during the demagnetization interval. Accordingly, the mitigation of the vibration occurring at the commutation point and prevention of negative torque are simultaneously achieved over a wide speed range. The simulation and experimental results demonstrate the proposed drive system’s positive effect on the vibration amplitude, grid side current quality, and SRM performance in a wide range of speed variations.

In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation between the random variables, and iii) find the optimal solutions with low computational burden. To address these issues, this paper proposes a nonparametric-correlative stochastic framework for microgrids (MGs) energy management. The proposed method imposes no assumption on the probability density function of renewable generations and electrical load consumption. To this end, an improved kernel density estimator (IKDE) is presented to estimate the probability density function (PDF) of uncertain parameters, e.g., renewable generations and load. To account for the correlation between the uncertain parameters, Cholesky decomposition is utilized. Furthermore, a multi-objective MG energy management problem considering reliability has been reformulated, and to solve the problem, a sine-cosine optimization algorithm (SCOA) is developed. Numerical results demonstrate the effectiveness and superiority of the proposed stochastic framework through comparison with several optimization algorithms by reducing the total cost of MG more than 11% in comparison with several metaheuristic algorithms and stochastic frameworks with less than 1% error.

Pipelines are backbone of the transportation of gases and liquids such as oil, gasoline, water, and sewage. However, pipelines are constantly aging and sustaining damage, which may result in significant resource loss and environmental contamination. Pipelines must be inspected and maintained on a regular basis for effective functioning and to avoid cost overrun. Due to the fact that pipes are often located underground and they have different sizes and configurations, inspection including condition assessment, leak detection, and fluid quality monitoring of pipelines are challenging. For this purpose, in-pipe robots have shown promising solutions to reach the inaccessible parts of pipeline networks. In this paper, we first categorize the mechanical systems of in-pipe robots. Then, we review four missions performed by these robots, including localization, mapping, navigation, and inspection, along with the core methods used in each mission. Further, since image processing is a common and important approach to accomplish all the mentioned missions, we decided to dedicate a separate section for reviewing comprehensive categorization of image processing techniques. We also provide the list sensors used in in-pipe robots classified by the mission and the method of use.

The Internet of Things (IoT) is an emerging technology that can benefit from cloud infrastructure. In a cloud-based IoT network, a variety of data is collected by smart devices and transmitted to a cloud server. However, since the data may contain sensitive information about individuals, providing confidentiality and access control is essential to protect the users' privacy. Attribute-based encryption (ABE) is a promising tool to provide these requirements. However, most of ABE schemes neither provide efficient encryption and decryption mechanisms nor offer flexible and efficient key delegation and user revocation approaches. In this paper, to address these issues, we propose a lightweight revocable hierarchical ABE (LW-RHABE) scheme. In our scheme, computation overhead on the user side is very efficient, and most of the computational operations are performed by the cloud server. Also, using the hierarchical model, our scheme offers flexible and scalable key delegation and user revocation mechanisms. Indeed, in our scheme, key delegation and user revocation associated with each attribute can be handled by several key authorities. We provide the security definition for LW-RHABE, and we prove its security in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem.

Industry 4.0 (I4.0) includes an important regeneration of production and management systems within manufacturing, where the majority of the procedures will be entirely or partially automated. However, there are insufficient research studies related to I4.0 development assessment based on the various requirement factors to enable countries to measure their own conditions and to make future national strategies and roadmaps for their activities in this field. Thus, this paper develops a comprehensive framework to assess the countries' readiness level in I4.0 development. The research is conducted in several steps. First, the main required clusters and their criteria of I4.0 development assessment such as technological, social, economic, political and environmental clusters are determined. Second, the importance of the clusters and their criteria are specified using the Fuzzy DEMATLE and Fuzzy ANP techniques. Third, the countries are ranked using the VIKOR technique. In this study, the conditions of I4.0 development for South Korea, the United States, China, Iran, Japan, and Germany are assessed based on the proposed framework. The ranking results show the United States, Germany, and Japan got ranked first to third, based on the obtained VIKOR index, 0.000, 0.528, and 0.559, respectively. The framework is verified using sensitivity analysis and validated based on the countries' existing plans. The evaluation results are rescannable. The result shows that the United States, Germany, Japan, and South Korea got high scores especially in the technological cluster and also some challenges in the criteria related to the social and cultural clusters. On the other hand, China and Iran got the highest score in the labor force criterion of the social cluster (0.023) and the intercultural understanding criterion of the cultural cluster (0.006), respectively, however, they need to strengthen the technological cluster “to be hopeful of having” an active role in future global marketing.

Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This article presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day's classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K- NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.