University of Mohaghegh Ardabili

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

Among the most challenging problems that human beings appear to face are depleting energy sources and increasing environmental pollutions. Heterogeneous photocatalytic processes are the most rewarding technology to generate renewable energy and degrade environmental pollutants. In these processes, semiconductors are used as photocatalysts. ZnO is a widely used photocatalyst, because of its strong oxidation ability, cost effectiveness, non-toxicity, versatility in synthesis, abundance in nature, and ease of crystallization. However, pure ZnO has some drawbacks, due to its wide band gap, poor solar-light utilization, and rapid recombination of the photoinduced charge carriers. Modification of ZnO using different strategies including coupling with narrow band gap semiconductors, noble metal deposition, surface sensitization by organic dyes, and elemental doping can easily address these shortcomings. In addition, separation of photocatalysts from the treated systems limits their broad applications. Incorporation of photocatalysts in magnetic materials will help their recycling using external magnetic field. This combination leads to a new generation of photocatalysts, known as magnetically separable photocatalysts. The present review provides helpful insights into preparation of magnetically separable photocatalysts based on ZnO and their applications for degradations of different pollutants.

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 photocatalysts have been designed and developed. This review discusses the state-of-the-art in photo-conversion of CO2 over graphitic carbon nitride (g-C3N4)-based composites. The modification strategies to improve photocatalytic activity of g-C3N4 were classified into different categories and discussed as structural modifications, elemental doping, copolymerization, fabricating heterojunctions between g-C3N4 and other semiconductors, Z-scheme heterojunctions, noble metal/g-C3N4 photocatalysts, and design of ternary nanocomposites based on g-C3N4. Finally, perspectives and future research works in this field were also outlined.

In this paper, a method for placement of distributed generation (DG) units in distribution networks has been presented. This method is based on the analysis of power flow continuation and determination of most sensitive buses to voltage collapse. This method is executed on a typical 34-bus test system and yields efficiency in improvement of voltage profile and reduction of power losses; it also may permit an increase in power transfer capacity, maximum loading, and voltage stability margin.

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.

Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people's health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.

The domestic Bactrian camels were treated as one of the principal means of locomotion between the eastern and western cultures in history. However, whether they originated from East Asia or Central Asia remains elusive. To address this question, we perform whole-genome sequencing of 128 camels across Asia. The extant wild and domestic Bactrian camels show remarkable genetic divergence, as they were split from dromedaries. The wild Bactrian camels also contribute little to the ancestry of domestic ones, although they share close habitat in East Asia. Interestingly, among the domestic Bactrian camels, those from Iran exhibit the largest genetic distance and the earliest split from all others in the phylogeny, despite evident admixture between domestic Bactrian camels and dromedaries living in Central Asia. Taken together, our study support the Central Asian origin of domestic Bactrian camels, which were then immigrated eastward to Mongolia where native wild Bactrian camels inhabit.

This study was performed to determine the effect of nano-iron oxide on soybean yield and quality. Field experiment was designed based on randomized complete block design with three replications. Treatments were five levels of nano-iron oxide (0, 0.25, 0.5, 0.75 and 1 g l-1). Results showed that nano-iron oxide at the concentration of 0.75 g l-1 was increased leaf + pod dry weight and pod dry weight. The highest grain yield was observed with using 0.5 g l-1 nano-iron oxide that showed 48% increase in grain yield in comparison with control. Other measured traits were not affected by the iron nano- particles.

Carbon-based quantum particles, especially spherical carbon quantum dots (CQDs) and nanosheets like graphene quantum dots (GQDs), are an emerging class of quantum dots with unique properties owing to their quantum confinement effect. Many reviews appeared recently in the literature highlighting their optical properties, structures, and applications. These papers cover a broad spectrum of carbon-based nanoparticles, excluding a more detailed discussion about some important aspects related to the definition of carbon-based particles and the correlation of optical and electrochemical aspects in relation to sensing and biomedical applications. A large part of this review is devoted to these aspects. It aims, in particular, to act as a bridge between optical and electrochemical aspects of carbon-based quantum particles, both of which are associated with the electronic nature of carbon-based quantum particles. A special focus will be on their use in electroanalysis, notably their benefits in redox, and in electrochemical analysis with emphasis on their application as sensors. Electroanalysis is an easy and cost-effective means of providing qualitative and quantitative information of a specific analyte in solution in a time scale of some minutes. The integration of carbon-based quantum particles into these detection schemes as well as their incorporation into composite nanomaterials have largely improved detection limits with possibilities for their integration in aspects ranging from point-of-care devices to personalized medicine. This review will focus on some of these aspects while also covering the nanomedical aspects of carbon-based quantum particles, ultimately correlated for such developments.

Fermentation is thought to be born in the Fertile Crescent, and since then, almost every culture has integrated fermented foods into their dietary habits. Originally used to preserve foods, fermentation is now applied to improve their physicochemical, sensory, nutritional, and safety attributes. Fermented dairy, alcoholic beverages like wine and beer, fermented vegetables, fruits, and meats are all highly valuable due to their increased storage stability, reduced risk of food poisoning, and enhanced flavor. Over the years, scientific research has associated the consumption of fermented products with improved health status. The fermentation process helps to break down compounds into more easily digestible forms. It also helps to reduce the amount of toxins and pathogens in food. Additionally, fermented foods contain probiotics, which are beneficial bacteria that help the body to digest food and absorb nutrients. In today's world, non-communicable diseases such as cardiovascular disease, type 2 diabetes, cancer, and allergies have increased. In this regard, scientific investigations have demonstrated that shifting to a diet that contains fermented foods can reduce the risk of non-communicable diseases. Moreover, in the last decade, there has been a growing interest in fermentation technology to valorize food waste into valuable by-products. Fermentation of various food wastes has resulted in the successful production of valuable by-products, including enzymes, pigments, and biofuels.

Bacterial infections remain one of the principal causes of morbidity and mortality worldwide. The number of deaths due to infections is declining every year by only 1% with a forecast of 13 million deaths in 2050. Among the 1400 recognized human pathogens, the majority of infectious diseases is caused by just a few, about 20 pathogens only. While the development of vaccinations and novel antibacterial drugs and treatments are at the forefront of research, and strongly financially supported by policy makers, another manner to limit and control infectious outbreaks is targeting the development and implementation of early warning systems, which indicate qualitatively and quantitatively the presence of a pathogen. As toxin contaminated food and drink are a potential threat to human health and consequently have a significant socioeconomic impact worldwide, the detection of pathogenic bacteria remains not only a big scientific challenge but also a practical problem of enormous significance. Numerous analytical methods, including conventional culturing and staining techniques as well as molecular methods based on polymerase chain reaction amplification and immunological assays, have emerged over the years and are used to identify and quantify pathogenic agents. While being highly sensitive in most cases, these approaches are highly time, labor, and cost consuming, requiring trained personnel to perform the frequently complex assays. A great challenge in this field is therefore to develop rapid, sensitive, specific, and if possible miniaturized devices to validate the presence of pathogens in cost and time efficient manners. Electrochemical sensors are well accepted powerful tools for the detection of disease-related biomarkers and environmental and organic hazards. They have also found widespread interest in the last years for the detection of waterborne and foodborne pathogens due to their label free character and high sensitivity. This Review is focused on the current electrochemical-based microorganism recognition approaches and putting them into context of other sensing devices for pathogens such as culturing the microorganism on agar plates and the polymer chain reaction (PCR) method, able to identify the DNA of the microorganism. Recent breakthroughs will be highlighted, including the utilization of microfluidic devices and immunomagnetic separation for multiple pathogen analysis in a single device. We will conclude with some perspectives and outlooks to better understand shortcomings. Indeed, there is currently no adequate solution that allows the selective and sensitive binding to a specific microorganism, that is fast in detection and screening, cheap to implement, and able to be conceptualized for a wide range of biologically relevant targets.

Development of biologically inspired green synthesis of silver nanoparticles has attracted considerable worldwide attention in matter of medical science and disease treatment. Herein, the green synthesis of silver nanomaterials using organic green sources has been evaluated and discussed. These kinds of materials are widely used for treatment of antibiotic-resistant bacteria, cancer and etc due to their elegant properties compared with other chemical ways and drugs. Moreover, the outcome of green-based approaches were compared with chemical procedures and obtained data were examined via various analyses including UV-visible spectroscopy, scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscope (TEM), atomic force microscopy (AFM) and Fourier transforms infrared spectroscopy (FT-IR). In this study, variety of green methods were investigated to present a summary of recent achievements toward highlighting biocompatible nanoparticles, all of which can reduce the toxicity of nanoparticles, make them eco-friendly, reduce their side effects and decrease the production cost. The nature of these biological organisms also affect the structure, shape, size and morphology of synthesized nanoparticles.

Chitosan is mainly derived from seafood by-products and the thereof chitosan nanoparticles (CNPs) are known as nontoxic, biocompatible, biodegradable and functionalized nanostructures. CNPs, as green fillers, showed an appropriate potential in reinforcement of various biodegradable composites for food packaging and biomedical applications. After evaluation of different fabrication approaches and characterization techniques of CNPs, the changes in physical, mechanical, thermal, structural, morphological, and antimicrobial attributes of nanobiocomposites as a result of CNPs addition are discussed. The influence of bioactive loaded-CNPs and hybrid CNPs with metal nanoparticles, graphene, and montmorillonite in nanocomposites is also presented. Finally, the safety aspects of CNPs-loaded structures are highlighted to evaluate their implementation in food packaging and biomedical systems. It can be concluded that regardless of a few drawbacks, CNPs are promising nanomaterials to improve various operational, structural and antimicrobial properties of biocomposites for various applications in food packaging, delivery systems and biomedical uses.

202012 bcrc

As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

Heterogeneous photocatalytic reactions have experienced many efforts in developing new materials to tackle environmental and energy crises through utilizing appropriate photocatalysts in wastewater treatment, H2 generation, organic transformations, CO2 reduction, N2 photofixation, and biomass conversion. While these processes are addressed in the literature separately, a recent innovative viewpoint is to employ a photocatalytic system to achieve simultaneously two or more functions. The challenging point is that the combination of two functions in one photocatalytic system requires a novel design and engineering of an appropriate semiconductor photocatalyst with special characteristics for each application in a particular environment. Recently, graphitic carbon nitride (g-C3N4) with its unique physicochemical properties has gained tremendous attention among researchers due to its great potential for utilization as a dual-functional photocatalyst. In this study, the role of morphological engineering and band gap manipulation in heterojunction formation of g-C3N4 will be considered. These newly applied strategies are useful to improve the photocatalytic activity of g-C3N4 in different simultaneous reactions. Furthermore, detailed information on the application of g-C3N4-based materials in dual-functional simultaneous processes will be discussed in different reactions: namely, (i) photocatalytic H2 generation combined with oxidation of organic pollutants, (ii) photocatalytic mineralization of organic pollutants and reduction of the obtained CO2, (iii) photocatalytic removal of a mixture of organic pollutants and heavy metals, (iv) H+ and CO2 reduction, (v) photocatalytic H2 generation in conjunction with oxidation of organic substrates/biomass to value-added products, and (vi) simultaneous H2 and H2O2 production. These combined approaches could provide efficient and sustainable strategies for simultaneous reactions involved in both energy and environmental issues.

The aim of the present research was to determine the relationship of resilience and perceived stress with life satisfaction in the students of success and failure. The research sample consisted of 120 who were selected from among the students of success and failure through the random sampling method. To collect the data, Resilience Scale, Perceived Stress Scale and Life Satisfaction Scale were used. The results showed that resilience and perceived positive stress are positively related to life satisfaction in the students of success and failure (P < 0.01). Also, perceived negative stress is negatively related to life satisfaction in the students of success and failure (P < 0.01). The result of multiple regression showed that psychological resilience and perceived stress explained 31 and 49 percent of variance of life satisfaction in the students of success and failure, respectively. The results that increase of resilience, and decrease of stress become more satisfied leads to more satisfaction causes they feel better and developed resources for living well.

On the basis of the side effects of detrimental synthetic chemicals, introducing healthy, available, and effective bioagents for pest management is critical. Due to this circumstance, several studies have been conducted that evaluate the pesticidal potency of plant-derived essential oils. This review presents the pesticidal efficiency of essential oils isolated from different genera of the Lamiaceae family including Agastache Gronovius, Hyptis Jacquin, Lavandula L., Lepechinia Willdenow, Mentha L., Melissa L., Ocimum L., Origanum L., Perilla L., Perovskia Kar., Phlomis L., Rosmarinus L., Salvia L., Satureja L., Teucrium L., Thymus L., Zataria Boissier, and Zhumeria Rech. Along with acute toxicity, the sublethal effects were illustrated such as repellency, antifeedant activity, and adverse effects on the protein, lipid, and carbohydrate contents, and on the esterase and glutathione S-transferase enzymes. Chemical profiles of the introduced essential oils and the pesticidal effects of their main components have also been documented including terpenes (hydrocarbon monoterpene, monoterpenoid, hydrocarbon sesquiterpene, and sesquiterpenoid) and aliphatic phenylpropanoid. Consequently, the essential oils of the Lamiaceae plant family and their main components, especially monoterpenoid ones with several bioeffects and multiple modes of action against different groups of damaging insects and mites, are considered to be safe, available, and efficient alternatives to the harmful synthetic pesticides.

Salt stress severely reduces growth and yield of plants. Considering the positive effects of selenium (Se) and chitosan (Cs) separately against abiotic stress, in these experiments, we synthesized chitosan–selenium nanoparticles (Cs–Se NPs) and investigated their ability to reduce the negative effects of salt stress on growth and some biochemical parameters of bitter melon (Momordica charantia). Bitter melon plants were grown at three NaCl salinity levels (0, 50, and 100 mM) and a foliar spray of Cs–Se NPs (0, 10, and 20 mg L−1) was applied. Some key morphological, biochemical, and physiological parameters in leaf samples and essential oil from fruit were measured at harvest. Salinity decreased growth and yield while foliar application of Cs–Se NPs increased these critical parameters. Furthermore, Cs–Se NPs enhanced bitter melon tolerance to salinity by increasing antioxidant enzyme activity, proline concentration, relative water content, and K+, and decreasing MDA and H2O2 oxidants and Na aggregation in plant tissues. Yield was also improved, as the highest amount of essential oils was produced by plants treated with Cs–Se NPs. Generally, the greatest improvement in measured parameters under saline conditions was obtained by treating plants with 20 mg L−1 Cs–Se NPs, which significantly increased salinity tolerance in bitter melon plants.

Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area.