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Amphotericin B has long been regarded as the gold standard for treating invasive fungal infections despite its toxic potential. The main objective of this research was to develop a novel IONPs@CS-AmB formulation in a cost-effective manner in order to enhance AmB delivery performance, with lowering the drug's dose and adverse effects. The chitosan-coated iron oxide nanoparticles (IONPs@CS) were synthesized afterward, AmB-loaded IONPs@CS (IONPs@CS-AmB) prepared and characterized by AFM, FT-IR, SEM, EDX, and XRD. Biological activity of the synthesized NPs determined and the cytotoxicity of IONPs@CS-AmB evaluated using the MTT and in vitro hemolysis tests. The IONPs@CS-AmB was synthesized using the coprecipitation method with core-shell structure in size range of 27.70 to ∼70 nm. The FT-IR, XRD and EDX pattern confirmed the successful synthesis of IONPs @CS-AmB. The IONPs@CS-AmB exhibited significant antifungal activity and inhibited the metabolic activity of Candida albicans biofilms. The hemolysis and MTT assays showed that IONPs@CS-AmB is biocompatible with high cell viability when compared to plain AmB and fungizone. The IONPs@CS-AmB is more effective, less toxic and may be a suitable alternative to conventional drug delivery. IONPs@CS-AmB may be a viable candidate for use as a microbial-resistant coating on the surfaces of biomedical devices.

Synthesis of self-assembled nanogels of PEG-grafted poly HPMA with bis(α-cyclodextrin) containing disulfide linkage.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. 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 (CMR) 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 due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to 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. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR 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. The most important findings of this study are presented in the conclusion section.

Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining.

The global prevalence of cancer is increasing, necessitating new additions to traditional treatments and diagnoses to address shortcomings such as ineffectiveness, complications, and high cost. In this context, nano and microparticulate carriers stand out due to their unique properties such as controlled release, higher bioavailability, and lower toxicity. Despite their popularity, they face several challenges including rapid liver uptake, low chemical stability in blood circulation, immunogenicity concerns, and acute adverse effects. Cell-mediated delivery systems are important topics to research because of their biocompatibility, biodegradability, prolonged delivery, high loading capacity, and targeted drug delivery capabilities. To date, a variety of cells including blood, immune, cancer, and stem cells, sperm, and bacteria have been combined with nanoparticles to develop efficient targeted cancer delivery or diagnosis systems. The review paper aimed to provide an overview of the potential applications of cell-based delivery systems in cancer therapy and diagnosis.

Objective: Stakeholders such as shareholders, legal entities, investors, and the financial services industry are often praised for their vital role in society. This issue occurs while others believe that the financial industry needs to strengthen the corporate strategy system to increase organizational capabilities and economic growth. A third group believes that those are involved in the financial industry can bring far-reaching benefits to society by reducing injustice in the distribution of poverty and income. Many researchers consider ethics as an important factor in dealing with behavioral anomalies. The purpose of this study is to explain the ethical and unethical behaviors of earnings management using advanced structural models. Method: The present study is of semi-experimental and semi-applied type and is performed by the qualitative or judgmental method in different stages. The field research was conducted after a random selection of 403 financial managers of the Tehran Stock Exchange. Results: Analysis of relationships between variables based on the method of structural equations showed that ethical pressure, organizational ethical beliefs, the culture of aversion to power, and ethical justification affect profit management behavior. Conclusion: The analysis of opinions consistency showed that there is a consensus about the ethical and immoral behaviors of earnings management between types of public opinions, regardless of gender, age, experience, marital status, and level of education and the public unanimously exposes the high importance of earnings management behaviors. Some compelling factors made it clear. After diagnostic tests consisting of the evaluation of normality of the distribution of variables, homogeneity of variances, confirmatory factor analysis, and validation of the estimation of the structural equation model, we used advanced structural equation models, path analysis, and regression estimation to interpret the direct relationship between directional behavior and the effective factors of moderating variables in these relationships. Finally, the coefficient of determination was estimated to validate the relationships and the t-student statistic was used to measure the significance of these relationships. Summarizing the relationships among variables showed that ethical pressures have positive impacts on the formation of one's moral beliefs and one's level of moral justification. Ethical justification has a positive impact on earnings management behaviors. The level of one's moral beliefs has a positive effect on his or her moral justification. Organizational culture for individual empowerment has a positive effect on ethical beliefs, ethical justification, and ultimately, on profit management behaviors. A person's moral beliefs exacerbate the relationship between the moral pressure on the individual and his or her level of moral justification. Finally, one's escapist culture exacerbates the relationship between ethical pressures on the individual and the emergence of profit management behaviors.

Objective: The goal of the present study is to investigate investment opportunities and lack of in time auditor reporting. Methodology: This research is applied regarding the aim and is descriptive due to data collection method utilized and is correlation type. The statistical population of this research entails firms enlisted in Tehran Stock Exchange. Data analysis was done using SPSS18 and EVIEWS8 software. Results: Regarding the meaningfulness of the total regression and the meaningfulness of the effect of important variables under investigation to identify the relationship between investment opportunities and lack of in time auditor report in the regression equation the research hypotheses were approved. Conclusion: Finally, it is concluded that there has been a meaningful relationship between investment opportunities and lack of in time reporting by the auditor.

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.

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.

Although Amphotericin B (AMB) is considered the most effective anti-mycotic agent for treating Candida infections, its clinical use is limited due to its high toxicity. To address this issue, we developed cholesterol-based dendritic micelles of hyperbranched polyglycerol (HPG), including cholesterol-cored HPG (Chol-HPG) and cholesterol end-capped HPG (HPG@Chol), for AMB delivery. The findings suggested that the presence of cholesterol moieties could control AMB loading and release properties. Dendritic micelles inhibited AMB hemolysis and cytotoxicity in HEK 293 and RAW 264.7 cell lines while increasing antifungal activity against C. albicans biofilm formation. Furthermore, significantly lower levels of renal and liver toxicity biomarkers compared to Fungizone® ensured AMB-incorporated dendritic micelle biosafety, which was confirmed by histopathological evaluations. Overall, the Chol-HPG and HPG@Chol dendritic micelles may be a viable alternative to commercially available AMB formulations as well as an effective delivery system for other poorly soluble antifungal agents.

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 development of natural biomaterials applied for hard tissue repair and regeneration is of great importance, especially in societies with a large elderly population. Self-assembled peptide hydrogels are a new generation of biomaterials that provide excellent biocompatibility, tunable mechanical stability, injectability, trigger capability, lack of immunogenic reactions, and the ability to load cells and active pharmaceutical agents for tissue regeneration. Peptide-based hydrogels are ideal templates for the deposition of hydroxyapatite crystals, which can mimic the extracellular matrix. Thus, peptide-based hydrogels enhance hard tissue repair and regeneration compared to conventional methods. This review presents three major self-assembled peptide hydrogels with potential application for bone and dental tissue regeneration, including ionic self-complementary peptides, amphiphilic (surfactant-like) peptides, and triple-helix (collagen-like) peptides. Special attention is given to the main bioactive peptides, the role and importance of self-assembled peptide hydrogels, and a brief overview on molecular simulation of self-assembled peptide hydrogels applied for bone and dental tissue engineering and regeneration. Graphic abstract

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.

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.

Renal ischemia/reperfusion (I/R) injury is responsible for significant mortality and morbidity during renal procedures. Nitric oxide (NO) deficiency is known to play a crucial role in renal I/R injury; however, low stability and severe toxicity of high concentrations of NO have limited its applications. Herein, we developed an in-situ forming Fmoc-dipheylalanine hydrogel releasing s-nitroso-n-acetylpenicillamine (FmocFF-SNAP) for renal I/R injury. Fmoc-FF hydrogel comprising of β-sheet nanofibers was prepared through the pH-titration method. It was then characterized by electron microscopy, pyrene assay, and circular dichroism techniques. Mechanical properties of Fmoc-FF hydrogel (thixotropy and syringeability) were investigated by oscillatory rheology and texture analysis. To assess the therapeutic efficiency in the renal I/R injury model, expression of inducible nitric oxide synthase (iNOS) and endothelial nitric oxide synthase (eNOS) was measured in various samples (different concentrations of free SNAP and FmocFF-SNAP, unloaded Fmoc-FF, and sham control) by real-time RT-PCR, ROS production, serum biomarkers, and histopathological evaluations. According to the results, Fmoc-FF self-assembly in physiologic conditions led to the formation of an entangled nanofibrous and shear-thinning hydrogel. FmocFF-SNAP exhibited a sustained NO release over 7 days in a concentration-dependent manner. Importantly, intralesional injection of FmocFF-SNAP caused superior recovery of renal I/R injury when compared to free SNAP in terms of histopathological scores and renal function indices (e.g. serum creatinine, and blood urea nitrogen). Compared to the I/R control group, biomarkers of oxidative stress and iNOS expression were significantly reduced possibly due to the sustained release of NO. Interestingly, the eNOS expression showed a significant enhancement reflecting the regeneration of the injured endothelial tissue. Thus, the novel FmocFF-SNAP can be recommended for the alleviation of renal I/R injury.

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.

Due to anthropogenic influences and large amounts of pollutant released into the groundwater, it is vital to investigate groundwater quality and to characterize susceptible areas to contamination. In this paper, a new optimization-based methodology is proposed for determining groundwater risk using DRASTIC model based on genetic algorithm optimization model and Wilcoxon test. The correlation coefficient between DRASTIC/modified DRASTIC indices and nitrate concentrations in monitoring wells is used as a criteria for evaluating the efficiency of the proposed models. In this regard, because of the unsatisfactory original DRASTIC’s result, sensitivity analysis, genetic algorithm (GA), and Wilcoxon test (1945) are carried out to tackle the subjectivity associated with the original DRASTIC model and obtain better and reliable results. The results indicate that application of Wilcoxon test and GA optimization outperforms the others. Consequently, the correlation coefficient increased remarkably as compared to the original DRASTIC model (from 0.57 to 0.82). The proposed optimization process is adaptable to be applied in different case studies; mainly since it has the ability to optimize the weights of the model based on hydrogeological characteristics of the aquifer. Finally, the risk maps of the models are prepared using ArcGIS® to determine the most vulnerable areas.

The present work for the first time investigates the effect of Bacillus amyloliquefaciens, M3, on a new poly(ether-urethane-urea) (PEUU). PEUU was synthesized via reaction of 4,4'-methylenebis(4-phenylisocyanate) (MDI), L-leucine anhydride cyclopeptide (LACP) as a degradable monomer and polyethylene glycol with molecular weight of 1000 (PEG-1000). Biodegradation of the synthesized PEUU as the only source for carbon and nitrogen for M3 was studied. The co-metabolism biodegradation of the polymer by this organism was also investigated by adding mannitol or nutrient broth to the basic media. Biodegradation of the synthesized polymer was followed by SEM, FT-IR, TGA, and XRD techniques. It was shown that incubation of PEUU with M3 resulted in a 30-44 % reduction in polymer's weight after 1 month. This study indicates that the chemical structure of PEUU significantly changes after exposure to M3 due to hydrolytic and enzymatic degradation of polymer chains. The results of this work supports the idea that this poly(ether-urethane) is used as a sole carbon source by M3 and this bacterium has a good capability for degradation of poly(ether-urethane)s.