Search

Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based precipitation products are potential sources of data as alternatives for ground-based measurements. However, for a reliable application, they should be evaluated in different regions. In this paper, two satellite-based rainfall products, namely Modern-Era Retrospective Analysis for Research and Applications (MERRA)-Land and Global Land Data Assimilation System-2 (GLDAS-2), have been evaluated against ground-based observations in terms of precipitation and their application for drought analysis. At first, the coarse-resolution MERRA-Land is downscaled to the finer resolution of interest for better comparison. After comparison of these datasets against ground-based observations in terms of precipitation, it is concluded that MERRA-Land can better estimate precipitation. Then, the nonparametric SPIs at various timescales are derived to analyze how well MERRA-Land performs in drought monitoring. Different categorical and statistical error indices are used to assess the efficiency of MERRA-Land in capturing drought events. The results revealed that the downscaled MERRA-Land data can properly detect short-term and mid-term drought events known as agricultural and meteorological droughts throughout the study area, respectively. In addition, drought maps show that the majority of lands experience mid-term scale drought which are in agreement with ground-based observations. The methodology adopted in this study can be applied in areas lacking in rain-gauge stations which significantly extend current capabilities for drought monitoring and early warning systems.

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.

Amphotericin B (AMB), with widespread antifungal and anti-parasitic activities and low cross-resistance with other drugs, has long been identified as a potent antimicrobial drug. However, its clinical toxicities, especially nephrotoxicity, have limited its use in clinical practice. Lately, nano-based systems have been the subject of serious research and becoming an effective strategy to improve toxicity and antimicrobial potency. Commercial AMB lipid formulations have been developed in order to improve the therapeutic index and nephrotoxicity, while limited use is mainly due to their high cost. The review aimed to highlight the updated information on nanotechnology-based approaches to the development of AMB delivery and targeting systems for treatment of fungal diseases and leishmaniasis, regarding therapeutic challenges and achievements of various delivery systems.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

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.

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.

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application., MINECO/ FEDER under the RTI2018-098913-B100 CV20-45250 and A-TIC- 080-UGR18 projects

Groundwater pollution is a serious threat to water resources which attracts hydrologists attention for sustainable management. Comprehensive assessment of groundwater vulnerability, however, requires uncertainty analysis in conjunction with a proper method to determine prone areas to contaminants. This paper provides a new fuzzy optimization methodology using improved DRASTIC method to model groundwater vulnerability risk assessment which considers uncertainties embedded in the input parameters, optimizes the weights and modifies the rates of the model simultaneously. To better represent hydrogeological characteristics of an area, the rating scores of original DRASTIC method is modified using Wilcoxon test considering nitrate concentration (as the main interfering pollutant in the study area). Spearman correlation coefficient between vulnerability indices and nitrate concentration is used as a factor to measure how well improved DRASTIC performs for vulnerability assessment as compared to the original method. The results show that the correlation coefficient significantly increased from 0.573 to 0.789. To address uncertainties associated with the input and output of the model, reduced fuzzy transformation method (FTM) is empowered by genetic algorithm (GA) to consider uncertainties associated with input parameters as well as optimizing weights of improved DRASTIC model. The results show how correlation coefficient changes at different uncertainty levels. Considering uncertainties in the inputs, correlation coefficient changes from 0.746 to almost 0.758 at α-cut level equal to 0 in comparison with that of equal to 1. Comparison of the risk maps of improved DRASTIC and fuzzy model at different uncertainty levels reveals that the model performs robustly under uncertainties mainly since the vulnerability trend and more importantly the severity of vulnerability indices have not changed remarkably. Based on these maps, east and southeastern parts of the study area are highly susceptible to contamination where intense industrial and agricultural activities can be seen. This framework provides helpful information for decision-makers to consider risk assessment at different uncertainty levels as it offers a continuous range of vulnerability indices rather than fixed ones. Also, vulnerability risk assessment maps demonstrate vulnerability trend throughout the area for further controlling or remedying actions of groundwater network.

Nowadays, great attention has been paid to the design and development of novel macromolecular constructions in drug delivery. Hyperbranched polymers (HBPs) are amongst various types of polymeric structures with exceptional physicochemical properties and biomedical applications relevant to their dendritic structure. Unlike perfect dendrimers, HBPs can be produced via one-step polymerization reactions. Moreover, they can be applied as building blocks in preparing dendronized, dendrigraft or dendritic nanostructures. Hyperbranched polyglycerols (HPGs) and their derivatives are widely regarded as promising biomaterials in a variety of diagnostic and therapeutic applications through solubilization, microencapsulation or conjugation with diagnostic agents, drugs, genetic materials, proteins, peptides, etc. HPGs have good chemical stability, inertness under biological conditions, water-solubility with low viscosity and multi-functionalization, which prompt their application as ideal or interesting nanocarriers or modifying agents. Herein, it was aimed to review recent developments and targeted approaches in utilizing HPGs for delivery of drugs, proteins and nucleic acids, as well as tissue engineering, diagnostic and theranostic applications.