Showing papers by "University of Tabriz published in 2019"

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Abstract: There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

Review on photocatalytic conversion of carbon dioxide to value-added compounds and renewable fuels by graphitic carbon nitride-based photocatalysts

Abstract: Photocatalytic reduction of CO2 is known as one of the most promising methods to produce valuable fuels and value-added compounds. To overcome selectivity and efficiency downsides, various photocat...

Bioactive food derived peptides: a review on correlation between structure of bioactive peptides and their functional properties

Abstract: The presence of bioactive peptides has already been reported in many foods such as milk, fermented products, plant and marine proteins. Bioactive peptides are sequences between 2 and 20 amino acids that can inhibit chronic diseases by modulating and improving physiological functions, so these peptides contribute in holding the consumer health. Also, bioactive peptides can affect pro-health or functional properties of food products. Fractionation of the protein hydrolysate revealed a direct relationship between their structure and functional activity. So, this review focuses on different factors effecting on bioactive peptide structures, biological and functional properties such as antihypertensive, antioxidative, hypocholesterolemic, water-holding capacity, foaming capacity, emulsifying properties and solubility. Also, this review looks at the identified bioactive peptides from food protein sources as potential ingredients of health promoting functional foods.

Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)

Abstract: This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map...

Switched-Capacitor-Based Single-Source Cascaded H-Bridge Multilevel Inverter Featuring Boosting Ability

Abstract: Cascaded multilevel inverter (CMI) is one of the most popular multilevel inverter topologies. This topology is synthesized with some series-connected identical H-bridge cells. CMI requires several isolated dc sources which brings about some difficulties when dealing with this type of inverter. This paper addresses the problem by proposing a switched-capacitor (SC)-based CMI. The proposed topology, which is referred to as switched-capacitor single-source CMI (SCSS-CMI), makes use of some capacitors instead of the dc sources. Hence, it requires only one dc source to charge the employed capacitors. Usually, the capacitor charging process in a SC cell is companied by some current spikes which extremely harm the charging switch and the capacitor. The capacitors in SCSS-CMI are charged through a simple auxiliary circuit which eradicates the mentioned current spikes and provides zero-current switching condition for the charging switch. A computer-aid simulated model along with a laboratory-built prototype is adopted to assess the performances of SCSS-CMI, under different conditions.

MicroRNA replacement therapy in cancer.

Abstract: Despite the recent progress in cancer management approaches, the mortality rate of cancer is still growing and there are lots of challenges in the clinics in terms of novel therapeutics. MicroRNAs (miRNA) are regulatory small noncoding RNAs and are already confirmed to have a great role in regulating gene expression level by targeting multiple molecules that affect cell physiology and disease development. Recently, miRNAs have been introduced as promising therapeutic targets for cancer treatment. Regulatory potential of tumor suppressor miRNAs, which enables regulation of entire signaling networks within the cells, makes them an interesting option for developing cancer therapeutics. In this regard, over recent decades, scientists have aimed at developing powerful and safe targeting approaches to restore these suppressive miRNAs in cancerous cells. The present review summarizes the function of miRNAs in tumor development and presents recent findings on how miRNAs have served as therapeutic agents against cancer, with a special focus on tumor suppressor miRNAs (mimics). Moreover, the latest investigations on the therapeutic strategies of miRNA delivery have been presented.

Chitosan in Biomedical Engineering: A Critical Review

Abstract: Biomedical engineering seeks to enhance the quality of life by developing advanced materials and technologies. Chitosan-based biomaterials have attracted significant attention because of having unique chemical structures with desired biocompatibility and biodegradability, which play different roles in membranes, sponges and scaffolds, along with promising biological properties such as biocompatibility, biodegradability and non-toxicity. Therefore, chitosan derivatives have been widely used in a vast variety of uses, chiefly pharmaceuticals and biomedical engineering. It is attempted here to draw a comprehensive overview of chitosan emerging applications in medicine, tissue engineering, drug delivery, gene therapy, cancer therapy, ophthalmology, dentistry, bio-imaging, bio-sensing and diagnosis. The use of Stem Cells (SCs) has given an interesting feature to the use of chitosan so that regenerative medicine and therapeutic methods have benefited from chitosan-based platforms. Plenty of the most recent discussions with stimulating ideas in this field are covered that could hopefully serve as hints for more developed works in biomedical engineering.

Short-Term Scheduling Strategy for Wind-Based Energy Hub: A Hybrid Stochastic/IGDT Approach

Abstract: This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach. Considering that energy hub plays an undeniable role as the coupling among various energy infrastructures, still it is essential to be investigated in both modeling and scheduling aspects. On the other hand, penetration of wind power generation is significantly increased in energy infrastructures in recent years. In response, this paper aims to focus on the hybrid stochastic/IGDT optimization method for the optimal scheduling of wind integrated energy hub considering the uncertainties of wind power generation, energy prices and energy demands explicitly in a way that not only global optimal solution can be reached, but also volume of computations can be lighten. In addition, by the proposed hybrid model, the energy hub operator can pursue two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. This method optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming. This formulation is proposed to minimize the expected operation cost of energy hub where different energy demands of energy hub would be efficiently met. The forecast errors of uncertainties related to wind power generation and energy demands are modeled as a scenario, while an IGDT optimization approach is proposed to model electricity price uncertainty.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Abstract: Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach

Abstract: Identifying areas with high groundwater potential is important for groundwater resources management. The main objective of this study is to propose a novel classifier ensemble method, namely Random Forest Classifier based on Random Subspace Ensemble (RS-RF), for groundwater potential mapping (GWPM) in Qorveh-Dehgolan plain, Kurdistan province, Iran. A total of 12 conditioning factors (slope, aspect, elevation, curvature, stream power index (SPI), topographic wetness index (TWI), rainfall, lithology, land use, normalized difference vegetation index (NDVI), fault density, and river density) were selected for groundwater modeling. The least square support vector machine (LSSVM) feature selection method with a 10-fold cross-validation technique was used to validate the predictive capability of these conditioning factors for training the models. The performance of the RS-RF model was validated using the area under receiver operating characteristic curve (AUROC), success and prediction rate curves, kappa index, and several statistical index-based measures. In addition, Friedman and Wilcoxon signed-rank tests were used to assess statistically significant level among the new model with the state-of-the-art soft computing benchmark models, such as random forest (RF), logistic regression (LR) and naive Bayes (NB). Results showed that the new hybrid model of RS-RF had a very high predictive capability for groundwater potential mapping and exhibited the best performance among other benchmark models (LR, RF, and NB). Results of the present study might be useful to water managers to make proper decisions on the optimal use of groundwater resources for future planning in the critical study area.

Betalains, the nature-inspired pigments, in health and diseases

Abstract: Betalains are unique nitrogen-containing pigments found exclusively in families of the Caryophyllales order and some higher order fungi, where they replace anthocyanin pigments. Betalains, consisting of betacyanins and betaxanthins are generally used as color additives in food. This review discusses on the favorable effects of acute and chronic consumption of betalains, whose edible sources consist primarily of red beetroots (Beta vulgaris) and prickly pears (fruit of the Opuntia genus of cacti). Moreover, it encompasses in vivo and in vitro studies about the bioavailability and bioaccessibility of betanin and indicaxanthin. It seems that treatment with betalains and betalain-rich diets is not only nontoxic but could also prove to be a promising alternative to supplement therapies in oxidative stress-, inflammation-, and dyslipidemia-related diseases such as stenosis of the arteries, atherosclerosis, hypertension, and cancer, among others. Due to its toxicological safety, accessibility, low price, biodegradability, and potentially advantageous biological effects on health, the incorporation of betalains in food manufacturing and related industries could pave the way to overcome current concerns over the health risks of artificial colors. Nevertheless, further studies using pure betalains are required to gain a deeper understanding of their precise biological functions.

Phytoextraction of heavy metals from contaminated soil, water and atmosphere using ornamental plants: mechanisms and efficiency improvement strategies.

Abstract: Accumulation of heavy metals (HMs) in soil, water and air is one of the major environmental concerns worldwide, which mainly occurs due to anthropogenic activities such as industrialization, urbanization, and mining. Conventional remediation strategies involving physical or chemical techniques are not cost-effective and/or eco-friendly, reinforcing the necessity for development of novel approaches. Phytoextraction has attracted considerable attention over the past decades and generally refers to use of plants for cleaning up environmental pollutants such as HMs. Compared to other plant types such as edible crops and medicinal plants, ornamental plants (OPs) seem to be a more viable option as they offer several advantages including cleaning up the HMs pollution, beautification of the environment, by-product generation and related economic benefits, and not generally being involved in the food/feed chain or other direct human applications. Phytoextraction ability of OPs involve diverse detoxification pathways such as enzymatic and non-enzymatic (secondary metabolites) antioxidative responses, distribution and deposition of HMs in the cell walls, vacuoles and metabolically inactive tissues, and chelation of HMs by a ligand such as phytochelatins followed by the sequestration of the metal-ligand complex into the vacuoles. The phytoextraction efficiency of OPs can be improved through chemical, microbial, soil amending, and genetic approaches, which primarily target bioavailability, uptake, and sequestration of HMs. In this review, we explore the phytoextraction potential of OPs for remediation of HMs-polluted environments, underpinning mechanisms, efficiency improvement strategies, and highlight the potential future research directions.

A New High Step-Up Multi-Input Multi-Output DC–DC Converter

Abstract: In this paper, a new multi-input multi-output (MIMO) dc–dc converter with high step-up capability is proposed for wide power ranges. Also, in order to increase each output voltage, diode–capacitor voltage multiplier (VM) stages are utilized in the proposed converter. The number of input stages, output stages, and VM stages are arbitrary and dependent on design conditions. At first, the general structure of proposed MIMO converter is presented. Then, in order to explain the converter operation, we walk through the design of a 1-kW four-input two-output example, including loss and efficiency calculations. We validate the design with a prototype that matches efficiency calculations.