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Free movement of production factors in the enlarged EU has led to immigration flows from East to West and from South to North with a significant impact on EU labor markets. Fiscal federalism also determined large immigration flows into EU area and affected unemployment rate in the EU countries. The aim of this paper is to investigate the impact of factors such as number of immigrants, tax on profits, social contributions, economic growth and population growth on unemployment rates in EU area using a panel quantile regression and an PMG-ARDL approach as robustness test during 1991–2020. The results show a positive association between population growth and unemployment rate, whereas the remaining exogenous factors are negatively associated with unemployment rate. Still, social contributions are statistically significant only for upper quantiles. The overall impact of social contribution on unemployment rate is positive as per PMG-ARDL estimations. We have also demonstrated that the immigrant flows impact on unemployment rate is very weak. The factors that are exerting the most significance influence on unemployment rate, are economic and population growth, followed by tax on profits. Findings support policy recommendation in EU area in terms of fiscal policy.

Income inequality is a major problem in most countries, particularly the underdeveloped world. Unequal distribution of income weakens the achievement of sustainable global economic growth and acts as an impasse to achieve the goals of sustainable development, including the reduction of poverty and hunger in the world. One of the most important factors affecting income inequality is the rent of natural resources, which can have different effects depending on the way natural resources are used. This study was conducted to investigate the effect of natural resource rent on income inequality, taking into account the role of the shadow economy and the human capital index. For this purpose, the annual data of 1971-2020 and the Markov-switching method were used. The results show that natural resources have a positive and significant effect on income inequality; furthermore, the relationship of shadow economy with income inequality in both regimes (low and high levels of inequality is significantly positive, and the effect of human capital on income inequality in both regimes is negative and significant. This study is presented with recommendations to politicians to promote the legalization of undeclared economic activities and reduce dependence on natural resources as mechanisms to reduce the structural problem of income inequality.

The impact of technological advances on energy consumption is one of the topics that has been considered by many researchers and policymakers. Numerous researchers have tried to evaluate this relationship based on various technology indicators. The index of economic complexity is one of the new indicators that has been used in recent years to measure the level of knowledge and technology in the production structure. In this paper, the index of economic complexity along with energy prices and GDP have been used as determining factors of energy consumption in Iran during the period 1976 to 2018. Quantile regression results show that the coefficients of the variables are different in the deciles. The impact of economic complexity on energy consumption in all deciles has been positive, indicating the dominance of the rebound effect on energy consumption. Price elasticity is less than one in all deciles and less in the higher consumption deciles. In contrast, the income elasticity of energy demand was higher in the upper deciles

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

The aim of this study is to test the validity of the Kuznets Environmental Curve (EKC) hypothesis for Iran. For this purpose, this study used the FourierARDL method and causality test with time change to analyze the short-term and long-term relationships between economic growth, economic complexity index, energy consumption, and ecological footprint. Findings from the Fourier ARDL approach confirm the existence of coherence between variables. In addition, the results of this study show that energy consumption and economic growth increase the ecological footprint in the short and long term. However, the short-term elasticity of economic growth is smaller than the long-term elasticity, meaning that the EKC hypothesis is not valid for Iran. This finding is confirmed by the causality test with time change. The overall results show that economic complexity has a diminishing effect on the ecological footprint and economic growth is not effective in solving environmental problems in Iran. Therefore, the Iranian government should encourage an environmentally friendly production process and cleaner technologies in exports to reduce environmental pollution.

Globalization is a driving force in emitting carbon dioxide (CO2), which causes countries to interact with each other economically, socially and politically. The interaction between countries will inevitably affect CO2 emissions. The aim of this study is to investigate the dynamic effects of globalization and energy consumption on environmental degradation in Iran. Therefore, it examines which variable (globalization, energy consumption or economic growth) is more effective in the process of environmental degradation. The Fully Modified Least Squares (FMOLS) and Charnes, Cooper and Rhodes (CCR) models are applied to investigate the subject over the period 1978-2016. In addition, Maki co-integration test is used to check the long-term dynamic relationships. The results show that globalization increases environmental degradation regardless of the decrease or increase in economic growth. This indicates that the scale effect of is dominant in Iran.

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation ” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model.

Existing congestion in the urban transport network involves many costs such as air pollution, delays for passengers, and increased fuel consumption. Internalizing congestion externality cost is a basis for transportation congestion pricing. This policy is implemented in many metropolitans worldwide. One of the methods used in network pricing is the use of a belt pricing. This pricing is made up of a number of arcs that surround the crowded city and passengers who are going to cross these arcs and enter this area are required to pay a fee. The most important issue in this pricing method is to determine the amount of toll imposed on these arcs. The purpose of this research is to determine optimal congestion toll in a particular cordon-based network in which user benefits are maximized. In this study, in order to find the optimal amount of toll, a combinatorial bi-level optimization problem is introduced, in such a way that net social surplus is maximized at the upper level, and user equilibrium problem with elastic demand is solved at the lower level, then genetic algorithm in the MATLAB software is used to solve this model. The results show a toll of 13 thousand Rials for entrance to cordon per hour in 2016, would increase the network user benefits by 41 percent.

The development of guides and information resources in this area is one of the effective measures for capacity building and the elimination of possible ambiguities and misunderstandings about public-private partnerships. In this study, we rank the participatory financing methods and identify the best ones based on four indicators including risk, duration, efficiency and working capital for investment in urban projects, as well as the importance of each method of participation in various urban areas. The sample consisted of municipality experts and private sector investors. The data gathering tool was a questionnaire and the AHP method, descriptive statistics; confirmatory factor analysis and Friedman test were used. The results of the analysis of AHP hierarchy showed that the best financing methods from the experts’ view are construction, operation and transfer, concession, lease, purchase of services, operations and management.

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.

• Automatic detection of epileptic seizures in EEG signals using new CADS based on artificial intelligence techniques. • The combining of fuzzy entropies for features extraction. • Autoencoder with proposed layers is used for dimension reduction of feature matrix. • The ANFIS classifier with BS optimizer is used for classification. Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them. [ABSTRACT FROM AUTHOR]

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications., Competing Interests: The authors declare no conflict of interest.