University of Shahrood

Abstract Heavy metals like arsenic, copper, cadmium, chromium, nickel, zinc, lead, and mercury are major pollutants of fresh water reservoirs because of their toxic, non‐biodegradable, and persistent nature. The industrial growth is the major source of heavy metals introducing such pollutants into different segments of the environment including air, water, soil, and biosphere. Heavy metals are easily absorbed by fishes and vegetables due to their high solubility in the aquatic environments. Hence, they may accumulate in the human body by means of the food chain. Various methods have been developed and used for water and wastewater treatment to decrease heavy metal concentrations. These technologies include membrane filtration, ion‐exchange, adsorption, chemical precipitation, nanotechnology treatments, electrochemical and advanced oxidation processes. In this review, the methods as well as their mechanisms and efficiency are discussed.

Supply chain network design (SCND) is one of the most crucial planning problems in supply chain management (SCM). Nowadays, design decisions should be viable enough to function well under complex and uncertain business environments for many years or decades. Therefore, it is essential to make these decisions in the presence of uncertainty, as over the last two decades, a large number of relevant publications have emphasized its importance. The aim of this paper is to provide a comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty. The paper is organized in two main parts to investigate the basic features of these studies. In the first part, planning decisions, network structure, paradigms and aspects related to SCM are discussed. In the second part, existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastic programming, robust optimization, and fuzzy mathematical programming are explored in terms of mathematical modeling and solution approaches. Finally, the drawbacks and missing aspects of the related literature are highlighted and a list of potential issues for future research directions is recommended.

The mechanism of the CQDs-based sensors.

), drug delivery, cancer therapy, their potential to pass blood-brain barrier (BBB), and gene delivery are discussed.

Soil salinization is one of the major environmental stressors hampering the growth and yield of crops all over the world. A wide spectrum of physiological and biochemical alterations of plants are induced by salinity, which causes lowered water potential in the soil solution, ionic disequilibrium, specific ion effects, and a higher accumulation of reactive oxygen species (ROS). For many years, numerous investigations have been made into salinity stresses and attempts to minimize the losses of plant productivity, including the effects of phytohormones, osmoprotectants, antioxidants, polyamines, and trace elements. One of the protectants, selenium (Se), has been found to be effective in improving growth and inducing tolerance against excessive soil salinity. However, the in-depth mechanisms of Se-induced salinity tolerance are still unclear. This review refines the knowledge involved in Se-mediated improvements of plant growth when subjected to salinity and suggests future perspectives as well as several research limitations in this field.

Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.

Abstract Negative environmental impact of fossil fuel consumption highlight the role of renewable energy sources and give them a unique opportunity to grow and improve. Among renewable energy sources solar energy attract more attention and many studies have focused on using solar energy for electricity generation. Here, in this study, solar energy technologies are reviewed to find out the best option for electricity generation. Using solar energy to generate electricity can be done either directly and indirectly. In the direct method, PV modules are utilized to convert solar irradiation into electricity. In the indirect method, thermal energy is harnessed employing concentrated solar power (CSP) plants such as Linear Fresnel collectors and parabolic trough collectors. In this paper, solar thermal technologies including soar trough collectors, linear Fresnel collectors, central tower systems, and solar parabolic dishes are comprehensively reviewed and barriers and opportunities are discussed. In addition, a comparison is made between solar thermal power plants and PV power generation plants. Based on published studies, PV‐based systems are more suitable for small‐scale power generation. They are also capable of generating more electricity in a specific area in comparison with CSP‐based systems. However, based on economic considerations, CSP plants are better in economic return.

We consider an energy management system that controls a cluster of price-responsive demands. Besides these demands, it also manages a wind-power plant and an energy storage facility. Demands, wind-power plant, and energy storage facility are interconnected within a small size electric energy system equipped with smart grid technology and constitute a virtual power plant that can strategically buy and sell energy in both the day-ahead and the real-time markets. To this end, we propose a two-stage procedure based on robust optimization. In the first stage, the bidding strategy in the day-ahead market is decided. In the second stage, and once the actual scheduling in the day-ahead market is known, we decide the bidding strategy in the real-time market for each hour of the day. We consider that the virtual power plant behaves as a price taker in these markets. Robust optimization is used to deal with uncertainties in wind-power production and market prices, which are represented through confidence bounds. Results of a realistic case study are provided to show the applicability of the proposed approach.

Key players in photosynthesis, iron (Fe) and magnesium (Mg), in nano and common forms were considered for foliar application of black-eyed pea. Factorial experiments in three replicates were designed based on completely randomized blocks containing Fe (0, 0.25, and 0.5 g L−1; in two forms: nano and common) and Mg (0, 0.5 g L−1 nano, and 0.5 g L−1 common). The elements were applied 56 and 72 days after sowing over the leaves, and data were collected after day 85. Iron had significant effect on yield, leaf Fe content, stem Mg content, plasma membrane stability, and chlorophyll content. The greatest effect was obtained by two treatment combinations of 0.5 g L−1 common Fe + 0.5% nano-Mg and 0.5 g L−1 common Fe + 0.5 g L−1 common Mg. In general, almost all analyzed traits were improved by foliar application of these two elements, probably as a result of more efficient photosynthesis.

Evaporation accounts for varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resources management in watersheds. Given the complex and nonlinear behavior of the evaporation component, and according to the fact that this parameter is not measured at many meteorological stations, at least during some timeframes, and that the meteorological stations measuring this component are not properly distributed in many developing countries, including Iran, the main objective of this work was to predict the evaporation component at two meteorological stations (Rasht and Lahijan) located in Gilan province in northern Iran over the 2006-2016 time period. To that end, those meteorological parameters recorded at the two stations which had the highest impact on evaporation prediction were identified using Pearson correlation coefficient. Selected parameters were then used, under separate scenarios, as inputs to support vector regression (SVR) and SVR model coupled with firefly algorithm (SVR-FA) in order to simulate evaporation values on a daily scale. Evaporation amounts showed the highest correlation with net solar radiation and saturation vapor pressure deficit at Lahijan and Rasht stations, respectively. Root mean square error values of evaporation prediction at testing phase of SVR and SVR-FA ranged from 1.05 to 1.43 and 1.02 to 1.31 mm, respectively, at Lahijan station and from 1.02 to 1.28 and 0.88 to 1.17 mm, respectively, at Rasht station for various scenarios. For underpredicted evaporation data set, the magnitude of RMSE reduction from SVR1 to SVR7 was 27% at Lahijan and 18% at Rasht station; whereas RMSE decrement from SVR-FA1 to SVR-FA7 was 18 and 26 percent at Lahijan and Rasht stations, respectively. This means that for the underpredicted data set, the role of increasing the number of SVR and SVR-FA input parameters in decreasing evaporation prediction error has been more conspicuous at Lahijan and Rasht stations, respectively. Analysis of SVR and SVR-FA performance at various 2-mm intervals of measured evaporation showed that prediction error has generally been increasing with increment of evaporation values, with the highest errors observed at the 8-10 mm interval for both Lahijan and Rasht stations (error rates of 3.42 and 2.42 mm/day at Lahijan and 6.13 and 5.84 mm/day at Rasht station, with SVR1 and SVR-FA1 models, respectively).

The present review paper aims to document the latest developments on the applications of nanofluids as working fluid in parabolic trough collectors (PTCs). The influence of many factors such as nanoparticles and base fluid type as well as volume fraction and size of nanoparticles on the performance of PTCs has been investigated. The reviewed studies were mainly categorized into three different types of experimental, modeling (semi-analytical), and computational fluid dynamics (CFD). The main focus was to evaluate the effect of nanofluids on thermal efficiency, entropy generation, heat transfer coefficient enhancement, as well as pressure drop in PTCs. It was revealed that nanofluids not only enhance (in most of the cases) the thermal efficiency, convection heat transfer coefficient, and exergy efficiency of the system but also can decrease the entropy generation of the system. The only drawback in application of nanofluids in PTCs was found to be pressure drop increase that can be controlled by optimization in nanoparticles volume fraction and mass flow rate.

Biogas is obtained from the breakdown of biomass by microorganisms and bacteria in the absence of oxygen. Biogas is considered a renewable source of energy, similar to solar energy and wind energy. Biogas can be produced from biomass or bio-waste; thus, it is environmentally friendly. Biogas is obtained in a suspended monoxide decomposition process by anaerobic bacteria or in a fermentation process of decomposable materials such as agricultural manure, sewage, municipal waste, green waste (gardens and parks), plant material and agricultural products. Biogas is a renewable natural energy source that leaves effective effects on nature and industries. This gas is produced from the decomposition of organic materials, including animal manure, food waste and sewage. Fertilizers and waste produce biogas through anaerobic digestion (ie without the presence of oxygen). Biogas is a mixture of gases generated by decaying biodegradable material without the presence of oxygen. Its main contents are 50–70 % of methane (CH4) by volume, 30–50 % of carbon dioxide (CO2), and traces of other gases, like hydrogen sulfide (H2S) and water vapor (H2O). CO2, H2S, and water vapor content in biogas may affect the performance and life of the energy conversion devices; consequently, their removal before end-use is essential for improving the quality of biogas. This combination is an ideal option for making renewable energy. The most important advantages of biogas (production of energy, reduction of the amount of discarded waste, reduction of pathogens, conversion of waste containing organic matter into high quality fertilizer, protection of vegetation, soil, water, increasing productivity in the field of livestock and agriculture) and It is also one of the disadvantages of biogas (incomplete and small technologies, containing impurities, the effect of temperature on biogas production, unsuitable for urban and dense areas, not affordable). For economical use of biogas, the fermentation process can be carried out under controlled conditions in a relatively simple device called a digestion reservoir. This review summarizes the current state-of-the-art and presents future perspectives related to the anaerobic digestion process for biogas production. Moreover, a historical retrospective of biogas sector from the early years of its development till its recent advancements give an outlook of the opportunities that are opening up for process optimization.

Deregulation of power system has introduced new objectives and requirements for transmission expansion problem. In this paper, a static transmission expansion methodology is presented using a multi-objective optimization framework. Investment cost, reliability (both adequacy and security), and congestion cost are considered in the optimization as three objectives. To overcome the difficulties in solving the nonconvex and mixed integer nature of the optimization problems, the genetic based NSGA II algorithm is used followed by a fuzzy decision making analysis to obtain the final optimal solution. The planning methodology has been demonstrated on the IEEE 24-bus test system to show the feasibility and capabilities of the proposed algorithm. Also, in order to compare the historical expansion plan and the expansion plan developed by the proposed methodology, it was applied to the real life system of northeastern part of Iranian national 400-kV transmission grid.

Commercial wearable piezoelectric sensors possess excellent anti-interference stability due to their electronic packaging. However, this packaging renders them barely breathable and compromises human comfort. To address this issue, we develop a PVDF piezoelectric nanoyarns with an ultrahigh strength of 313.3 MPa, weaving them with different yarns to form three-dimensional piezoelectric fabric (3DPF) sensor using the advanced 3D textile technology. The tensile strength (46.0 MPa) of 3DPF exhibits the highest among the reported flexible piezoelectric sensors. The 3DPF features anti-gravity unidirectional liquid transport that allows sweat to move from the inner layer near to the skin to the outer layer in 4 s, resulting in a comfortable and dry environment for the user. It should be noted that sweating does not weaken the piezoelectric properties of 3DPF, but rather enhances. Additionally, the durability and comfortability of 3DPF are similar to those of the commercial cotton T-shirts. This work provides a strategy for developing comfortable flexible wearable electronic devices.

It is believed that fossil fuel sources are exhaustible and also the major cause of greenhouse gas emission. Therefore, it is required to increase the portion of renewable energy sources in supplying the primary energy of the world. In this study, it is focused on application of nanotechnology in exploitation of renewable energy sources and the related technologies such as hydrogen production, solar cell, geothermal, and biofuel. Here, nanotechnologies influence on providing an alternative energy sources, which are environmentally benign, are comprehensively discussed and reviewed. Based on the literature, employing nanotechnology enhances the heat transfer rate in photovoltaic/thermal (PV/T) systems and modifies PV structures, which can improve its performance, making fuel cells much cost-effective and improving the performance of biofuel industry through utilization of nanocatalysts, manufacturing materials with high durability and lower weight for wind energy industry.

Random noise elimination acts as an important role in the seismic signal processing. Generally, noise in seismic data can be divided into two categories of coherent and incoherent or random noise. Suppression of wide-band noise which is characterized by random oscillation in seismic data over time is one of the challenging issues in the seismic data processing. This paper describes a new noise suppression algorithm for seismic data denoising. The seismic data, trace-by-trace are transformed into sparse subspace using the synchrosqueezed wavelet transform, then the obtained sparse time-frequency representation is decomposed into semilow-rank and sparse components using the Optshrink algorithm. Finally, the denoised seismic trace can be recovered by back-transforming the semilow-rank component to the time domain using inverse synchrosqueezed wavelet transform. The proposed method is assessed using a single synthetic seismic trace and a synthetic seismic section with two crossover linear and curve events with two discontinuities that are buried in the random noise. We have also evaluated the method using a prestack real seismic data set from an oil field in the southwest of Iran. A comparison is performed between the proposed method and the semisoft GoDec algorithm, classical f-x singular spectrum analysis, and prediction Wiener filter. The results visually and quantitatively confirmed the superiority of the proposed method in contrast to the other well-established noise reduction methods.

AbstractThe objective of this research is to evaluate daily rain rates derived from three widely used high-resolution satellite precipitation products (PERSIANN, TMPA-3B42V7, and TMPA-3B42RT) using rain gauge observations over the entire country of Iran. Evaluations are implemented for 47 comprehensive daily rainfall events during the winter and spring seasons from 2003 to 2006. These events are selected because each encompasses more than 50% of the country's area. In this study, daily rainfall observations derived from 1180 rain gauges distributed throughout the country are employed as reference surface data. Six statistical indices: bias, multiplicative bias (MBias), relative bias (RBias), mean absolute error (MAE), root mean square error (RMSE), and linear correlation coefficient (CC), as well as a contingency table are applied to evaluate the satellite rainfall estimates qualitatively. The spatially averaged results over the entire country indicate that 3B42V7, with an average bias value of –1.47 mmd−1, RBias of –13.6%, MAE of 4.5 mmd−1, RMSE of 6.5 mmd−1, and CC of 0.61, leads to better estimates of daily precipitation than those of PERSIANN and 3B42RT. Furthermore, PERSIANN with an average MBias value of 0.56 tends to underestimate precipitation, while 3B42V7 and 3B42RT with average MBias values of 0.86 and 1.02, respectively, demonstrate a reasonable agreement in regard to rainfall estimations with the rain gauge data. With respect to the categorical verification technique implemented in this study, PERSIANN exhibits better results associated with the probability of detection of rainfall events; however, its false alarm ratio is worse than that of 3B42V7 and 3B42RT. AcknowledgementsThe authors would like to thank the two anonymous reviewers whose comments helped to improve the presentation of this article significantly.

Considering the application of human genome variation databases in precision medicine, population-specific genome projects are continuously being developed. However, the Middle Eastern population is underrepresented in current databases. Accordingly, we established Iranome database (www.iranome.com) by performing whole exome sequencing on 800 individuals from eight major Iranian ethnic groups representing the second largest population of Middle East. We identified 1,575,702 variants of which 308,311 were novel (19.6%). Also, by presenting higher frequency for 37,384 novel or known rare variants, Iranome database can improve the power of molecular diagnosis. Moreover, attainable clinical information makes this database a good resource for classifying pathogenicity of rare variants. Principal components analysis indicated that, apart from Iranian-Baluchs, Iranian-Turkmen, and Iranian-Persian Gulf Islanders, who form their own clusters, rest of the population were genetically linked, forming a super-population. Furthermore, only 0.6% of novel variants showed counterparts in "Greater Middle East Variome Project", emphasizing the value of Iranome at national level by releasing a comprehensive catalog of Iranian genomic variations and also filling another gap in the catalog of human genome variations at international level. We introduce Iranome as a resource which may also be applicable in other countries located in neighboring regions historically called Greater Iran (Persia).

Salicylic acid (SA) is a promising compound to increase plant tolerance to drought stress, and it can affect many aspects of physiological and biochemical processes. This study was focused on the changes in proteins, photosynthesis, and antioxidant system of Sardari wheat ecotypes leave in response to the application of SA under drought stress conditions. Treatments included Sardari wheat ecotypes (Baharband, Kalati, Fetrezamin, Gavdareh, Telvar, and Tazehabad), salicylic acid at 0.5 mM (controls were untreated), and drought stress (30% of the field capacity). The results showed that membrane electrolyte leakage, and lipid peroxidation of all six ecotypes, were obviously increased under drought stress conditions. On the other hand, drought stress decreased leaf chlorophyll content, photosynthetic rate, stomatal conductance, carboxylation efficiency, and transpiration rate. The results of SDS-PAGE indicated that the abundance of some protein spots was downregulated when the plants were exposed to drought stress, while other protein spots' abundance was upregulated in such a situation. Under stress conditions, the highest antioxidant enzymatic activity, photosynthetic performance, cell membrane stability, and numbers of protein bands were observed in Baharband and Telvar, while the lowest was related to Fetrezamin. Salicylic acid treatments effectively ameliorated the negative effects of drought stress on Sardari ecotypes through improving the photosynthetic performance, keeping membrane permeability, induction of stress proteins, and enhancing the activity of antioxidant enzymes. The above findings suggest that ecotype ability to maintain photosynthetic performance was important to cope with drought stress.

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