Showing papers by "Kharazmi University published in 2019"

Iterated Greedy methods for the distributed permutation flowshop scheduling problem

Abstract: Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.

An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

Abstract: This paper proposes a hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the Water Cycle Algorithm to enhance its exploitation ability. In addition, to increase randomization in the new hybrid method, the streams in the Water Cycle Algorithm are allowed to update their position using a random walk (Levy flight). The random walk significantly improves the exploration ability of the Water Cycle Algorithm. The performance of the new hybrid Water Cycle–Moth-Flame Optimization algorithm (WCMFO) is investigated in 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of the WCMFO are compared to the other state-of-the-art metaheuristic algorithms. The results show that the hybrid method is able to outperform the other state-of-the-art metaheuristic algorithms in majority of the benchmark functions. To evaluate the efficiency of the WCMFO in solving complex constrained engineering and real-life problems, three well-known structural engineering problems are solved using WCMFO and the results are compared with the ones of the other metaheuristics in the literature. The results of the simulations revealed that the WCMFO is able to provide very competitive and promising results comparing to the other hybrid and metaheuristic algorithms.

Obesity and anxiety symptoms: a systematic review and meta-analysis

Abstract: Obesity and anxiety are the two most common health problems and increased body mass index can be lead to anxiety. The aim of this meta-analysis was to investigate the frequency of anxiety symptoms in people who are obese/overweight. For this purpose the authors systematically searched keywords in the databases PubMed, Scopus, PsycINFO and Google scholar through August 2018. After a comprehensive review, 25 studies were included into the meta-analysis. Results of the meta-analysis showed that the frequency of anxiety in obesity had a pooled odds ratio (OR) of 1.30 and a 95% confidence interval (CI) of 1.20–1.41 and in overweight had an OR of 1.10 and CI of 1.00–1.21. Comparison between obesity/overweight and normal weight showed high frequency of anxiety in obesity/overweight with respect to subgroups (sex, obesity and anxiety assessment, adjusted/unadjusted, anxiety duration and age). Evaluation of 25 studies included in the meta-analysis showed publication bias. Overall, findings indicate anxiety occurs more frequently in obese/overweight people compared with normal weight people.

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

Abstract: In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory (70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party (ILWP), Forestry, Rangeland and Watershed Organisation (FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve (AUC), frequency ratio (FR) and seed cell area index (SCAI). Normalised difference vegetation index, land use/ land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models (AUC = 0.912 (91.2%) and 0.907 (90.7%), respectively) had high predictive accuracy than the LNRF model alone (AUC = 0.855 (85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application

Abstract: This research proposes a new multi-objective mathematical model to design efficient and effective blood supply chain network in earthquakes. For the first time in this field of knowledge, the devastating impact of earthquake destruction radius is considered on blood supply chain network based on its magnitude. Two different transportation means, with variant speed and capacity, are employed to carry the blood from blood collection centers to blood centers. However, the number of available conveyors is limited in each site. To solve the proposed multi-objective mixed integer linear programming model, five multi-objective decision making methods as well as the lexicographic weighted Tchebycheff method are utilized to provide the decision maker with Pareto optimal solutions. Further, the application of the proposed multi-objective mathematical model is investigated in a real-world case study using data from the latest earthquakes in one of the recent activated faults of Iran’s capital, Tehran, which is considered to be a potential place for a severe earthquake. Using different solution approaches, various Pareto optimal solutions are obtained for the case study. The results indicated that the proposed mathematical model is able to design the most cost and time efficient blood supply chain in a severe earthquake. At the end, sensitivity analyses are performed to explore the effects of any changes in main parameters of the multi-objective mathematical model on the objective functions value to demonstrate the most critical parameter.

Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design

Abstract: This research proposes a new tri-objective mathematical model for designing blood supply chain network in emergency situations. The mathematical model aims to minimize total supply chain costs and transportation time between facilities while maximizing total testing reliability of the donated blood in the laboratories. The model considers five echelons including blood donor groups, blood collection facilities, laboratories, blood centers and hospitals. Different transportation means with variant speed and capacity are considered in the model to carry the blood between facilities. Since, most of the main parameters of the mathematical model are tainted with uncertainty in real-world applications, two robust possibilistic flexible chance constraint programming (RPFCCP) and possibilistic flexible chance constraint programming models are developed to provide risk-averse and robust solutions to the decision makers. In addition, the application of the proposed multi-objective mathematical model is investigated in a real-world case study using real data on Iran’s capital, Tehran, which is considered to be a potential place for a destructive earthquake. Using different realizations, the applicability and efficiency of the models are investigated in the case study. The results indicated that the RPFCCP model is able to handle uncertainty in the parameters of the objective function and constraints more efficiently and is able to provide robust and risk-averse solutions for the problem which are resistant to different scenarios.

An Overview of Energy Planning in Iran and Transition Pathways towards Sustainable Electricity Supply Sector

Abstract: Despite a substantial potential of renewable energy sources, the current energy supply system in Iran relies almost entirely on fossil fuel resources. It has imposed significant financial burden on the country and has led to considerable GHG emissions. Moreover, the country is confronting several challenges for harnessing alternative clean energy sources and promoting rational energy policies over the recent decades. To probe the root cause of these problems, this paper first provides an overview on the previous energy planning attempts in Iran. It shows that adequate commitment to a long-term energy planning could have meaningfully prevented these serious challenges. However, the previous studies have had some limitations in terms of employing appropriate planning tools, comprehensive evaluations, and scenarios definition and ranking. This paper thus proposes a power planning framework to assess the sustainability of future electricity scenarios for the period 2015–2050. MESSAGE, a systems engineering optimization model, is employed to evaluate the potential impacts of transitioning to a low-carbon electricity supply system. Using a combined AHP-TOPSIS method, the scenarios are then ranked based on 18 different techno-economic, environmental, and social dimensions of sustainability. The results indicate that scenario Cl_32, in which the share of non-hydro clean energy for electricity generation reaches 32%, is ranked best.

Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques

Abstract: This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs) were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points) was obtained. Kriging and inverse distance-weighted (IDW) methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC) was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13) and distance to road (0.12) played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion.

Fabrication and characterization of a high-flux and antifouling polyethersulfone membrane for dye removal by embedding Fe3O4-MDA nanoparticles

Abstract: Iron oxide (Fe3O4) magnetic nanoparticles were successfully synthesized and functionalized with (3-aminopropyl) triethoxysilane (APTES) and melamine-based dendrimer amine (MDA) groups. The resulted nanocomposites and unmodified Fe3O4 were characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and vibrating sample magnetometer (VSM) and then, added to the polyethersulfone (PES) membrane casting solution, during the phase inversion technique in order to improve its hydrophilicity, permeability, and antifouling properties. Surface and cross-sectional morphology of the resulted bare and nanocomposite membranes were characterized by SEM images. The effect of blended nanoparticles on membrane hydrophilicity and performance were determined using water contact angle, pure water flux, BSA solution filtration, and Reactive Green 19 dye solution rejection. The water contact angle for the bare PES, PES-Fe3O4 (0.5 wt%), PES-Fe3O4-APTES (0.5 wt%), and PES-Fe3O4-MDA (0.5 wt%) were measured to be 60.31°, 51.08°, 44.86°, and 37.18°, respectively. The pure water flux of the blended PES membranes was enhanced significantly compared to the bare PES due to the higher hydrophilicity. The results of fouling resistance factors including reversible, irreversible, and total fouling showed PES-Fe3O4-MDA (0.5 wt%) as the best antifouling membrane. Compared to the all fabricated membranes, PES-Fe3O4-MDA (0.5 wt%) showed the highest hydrophilicity, permeability, rejection, and antifouling properties.

Cardioprotective role of extracellular vesicles: A highlight on exosome beneficial effects in cardiovascular diseases.

Abstract: Extracellular vesicles (EVs) are nano-sized vesicles, released from many cell types including cardiac cells, have recently emerged as intercellular communication tools in cell dynamics. EVs are an important mediator of signaling within cells that influencing the functional behavior of the target cells. In heart complex, cardiac cells can easily use EVs to transport bioactive molecules such as proteins, lipids, and RNAs to the regulation of neighboring cell function. Cross-talk between intracardiac cells plays pivotal roles in the heart homeostasis and in adaptive responses of the heart to stress. EVs were released by cardiomyocytes under baseline conditions, but stress condition such as hypoxia intensifies secretome capacity. EVs secreted by cardiac progenitor cells and cardiosphere-derived cells could be pinpointed as important mediators of cardioprotection and cardiogenesis. Furthermore, EVs from many different types of stem cells could potentially exert a therapeutic effect on the damaged heart. Recent evidence shows that cardiac-derived EVs are rich in microRNAs, suggesting a key role in the controlling of cellular processes. EVs harboring exosomes may be clinically useful in cell-free therapy approaches and potentially act as prognosis and diagnosis biomarkers of cardiovascular diseases.

A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks

Abstract: With explosive development of the World Wide Web, an enormous amount of text information containing users’ feeling, emotions and opinions has been generated and is increasingly employed by individuals and companies for making decisions. Whereas unstructured form of data must be analyzed to extract and summarize the opinions in them, sentiment analysis has changed to a significant research area in the field of Natural Language Processing. In this regard, deep learning methods have attracted a lot of attentions in recent years and various deep learning models have been proven as effective network architectures for the task of sentiment analysis. However, each of them has its potentials and weak points. To eliminate their drawbacks and make optimal use of their benefits, convolutional and recursive neural network are merged into a new robust model in this paper. The proposed model employs recursive neural network due to its tree structure as a substitute of pooling layer in the convolutional network with the aim of capturing long-term dependencies and reducing the loss of local information. The proposed model is validated on Stanford Sentiment Treebank by conducting a series of experiments and empirical results revealed that our model outperforms basic convolutional and recursive neural networks while requires fewer parameters.