The electric train (ET) is a great energy consumer in an electrical network, which regenerates a high amount of energy into the grid at the braking moment. Therefore, in this paper, the optimal operation of the electric railway system is proposed considering renewable sources, hybrid storage systems (battery and ultracapacitor units), switchable capacitor banks, and regenerative braking energy of ET at the same time to attain a favorable economic, environmental and operational condition. Renewable sources reduce operating costs and environmental pollution. The storage system is utilized as a flexible source along with renewable generations. The ultracapacitor stores the ET braking energy, and the capacitor bank regulates the voltage. The scheme minimizes the expected network operating cost, while the AC optimal power flow and other technical limits are considered. This problem is nonlinear, and a linearized approximation formulation is implemented to achieve a unique optimal solution with the best and minimum computation time. The unscented transformation method is used to model uncertainties of load, ETs’ energy consumption, and renewable energies. Finally, by evaluating the numerical results, it is illustrated that the efficiency of the proposed method simultaneously improves the economic, environmental, and operational features of the network.
In this work, nanocomposites of polyvinyl chloride poly(epichlorohydrin-co-ethylene oxide)(ECO)/organoclay were prepared via melt processing and various parameters including nanoclay and rubber content (ECO) as well as rotor speed were tuned to find the optimum formulation for the highest thermal stability. The prepared products were characterized by X-ray diffraction as well as thermogravimetric analysis (TGA), derivative thermogravimetric (DTG) and differential thermal analysis XRD results showed that rotor speeds higher than 70 r/min are crucial for obtaining highly intercalated products with good thermal stability. From DTG analyses, it was observed that at lower concentrations of rubber, the rate of mass loss is higher which results in faster dehydrochlorination of the composite. The sample prepared with 2 phr OMMT, 30 phr rubber, and 70 r/min rotor speed showed the highest thermal stability. The selected nanocomposite showed the first weight loss at 294 [Formula: see text]. Results of this research showed that even a slight change in each parameter has a great influence on thermal properties of the nanocomposites. The hydrogen bonding mode between ECO and organoclay were estimated by theoretical calculations using GAUSSIAN software. From the obtained results, the miscibility of OMMT and ECO polymer is related to the hydrogen bonds which are more preferred at chlorine atom of ECO polymer.
This article evaluates the wave velocity of a leptadenia pyrotechnica rheological elastomer (LPRE) microbeam surrounded by micro piezoelectric and porosity of functionally graded materials' (FGMs) layers. Different models of graphene nano plates (GPLs) for reinforcing the face sheet are assumed by adopting Halpin–Tsai modified micromechanics theory to obtain the Poisson's ratio and Young modulus of the smart layer. The material properties of the whole system as a viscoelastic state are hypothesized using the Kelvin–Voigt theory. The motion final equations are gained by utilizing the theory of Timoshenko and the couple stress model. An analytical solution method is adopted for computing the velocity of wave, cutoff frequency, and escape frequency from the motion final equations. The influences of different distribution and volume percent of GPLs, FGM properties as well as gradient index, the numeral parameter of any layer, damping of structure, and exerted voltage on the velocity of wave microbeam are studied. Moreover, it has been seen that a rise in the FGM index leads to reduced phase velocity, cutoff, and escape frequencies. Meanwhile, enhancing the volume percent of GPLs increases the wave velocity of the microstructure beam.
Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.
The most important subjects in the memory-based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy-genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold-start challenge.
As one of the significant issues in social networks analysis, the influence maximization problem aims to fetch a minimal set of the most influential individuals in the network to maximize the number of influenced nodes under a diffusion model. Several approaches have been proposed to tackle this NP-hard problem. The traditional approaches failed to develop an efficient and effective solution due to the exponential growth of the size of social networks (due to massive computational overhead). In this paper, a three-stage framework based on the community detection approach is devised, namely LGFIM. In the first stage, the search space was controlled by partitioning the network into communities. Simultaneously, three heuristic methods were presented for modifying the community detection algorithm to extract the optimal communities: core nodes selection, capacity constraint on communities, and communities combination. These extracted communities were highly compatible with the information propagation mechanism. The next stages apply a scalable and robust algorithm at two different levels of the network: 1. Exploring the local scope of communities to select the most influential nodes of each community and construct the potential influential nodes set 2. Exploring the global scope of the network to select the target influential nodes among potential influential nodes set. Experimental results on various real datasets proved that LGFIM could achieve remarkable results compared with the state-of-the-art algorithms, especially acceptable influence spread, much better running time, and more applicable to massive social networks.
The penetration of renewable energy sources has been intensified during the last decade to tackle the climate crisis by providing clean energy. Among various renewable energy technologies, wind turbines and photovoltaic systems have received increasing attention from investors. Generally, electronic power converters are used to control renewable generations. The present study discusses the power management of smart distribution networks enriched with wind and photovoltaic units. The model aims to minimize the expected network operating cost of the system formulated as an objective function regarding AC optimal power flow constraints. In addition, stochastic programming based on unscented transformation is adopted to model the probable behavior of loads, renewable generations, and energy market prices. The model employs a linear approximation model to burden the complexity of the problem and achieve the optimum solution. The problem is tested to a 33-bus system using the General Algebraic Modeling System (GAMS). The obtained results confirm the proposed model’s potential in reducing energy costs, power losses, and voltage deviations compared to conventional power flow studies. In the proposed scheme compared to network load distribution studies, the active and reactive power losses, network energy costs, and voltage deviations are improved by about 40.7%, 33%, 36%, and 74.7%, respectively.
A biometric method for identifying people is face recognition. In the face recognition process, the key step is to extract the distinctive features of each person’s image. One of the most widely used tools for this purpose is the Gabor filter bank. A Gabor filter bank can extract powerful distinguishing features from a face image, but the disadvantage is that it imposes a high computational complexity on the face recognition system. The present paper introduces two new Gabor filter banks, i.e., the Optimal Gabor Filter Bank (OGFB) and the Personal Gabor Filter Bank (PGFB), which can reduce the computational complexity of a face recognition system by more than 7.5 and 30 times, respectively. It also introduces a new feature called Square Region of Face (SRoF) which is as easy to implement as global features, while taking into account the geometric position of facial features, including eyes, nose, and lips. This new feature is resistant to changes of hairstyle, eyebrows shape, and their color, as well as to the covered part of faces especially by different types of Islamic veils. Experiments on benchmark datasets of Caltech, Yale, Feret, and CsetM show that the proposed methods achieve better or competitive classification accuracy compared to several recent face recognition systems.
In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants’ behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters.
Thickness-loss is the most common problem in carpet after static and dynamic loading. Pile yarn properties as well as carpet structural parameters are mainly responsible for carpet thickness-loss. In the present research, an advanced version of recently developed fuzzy logic model is introduced. The model is able to predict thickness-loss of polyester carpets based on carpet pile density, pile height, and pile yarn count. Experimental work was performed to provide data for model knowledge base. Genetic algorithm was employed to optimize the fuzzy logic model parameters. Finally, lowest possible thickness-loss value together with corresponding values of carpet and pile yarn parameters bring this result was defined, using developed model. Modeling results showed that the model attained correlation coefficients as 0.9932, 0.9911, 0.9950, and 0.9957 between predicted and experimental values of carpet thickness-loss after low and high dynamic loading and static loading with short and long relaxation times, respectively. On the other hand, model predictions in four new unsighted conditions have brought correlation coefficients as 0.82, 0.89, 0.88, and 0.90 for low and high dynamic loading and static loading after short and long relaxation times, respectively. These results denote acceptable reliability of new developed model. Eventually, it is defined that levels of 850, 7.5, and 957.5 for carpet pile density, pile height, and pile yarn linear density, respectively, bring minimum carpet thickness-loss.
This paper inspects customer multi-carrier microgrid deployments' techno-economic viability and assists investors in deciding whether or not to invest in multi-carrier microgrid installations equipped with smart demand-side technologies. The solution of the proposed model determines the optimal mix and size of distributed energy resources, and identifies the ideal participation rate of potential responsive customers within the multi-carrier microgrid. The objective of the proposed model is to minimize the overall deployment cost comprising the investment and replacement of distributed energy resources, demand-side smart measurement and informing appliances, loan payoff, operation, maintenance, peak demand charge, energy demand shifting reward or penalty, emission, and unserved energy while ensuring the desired levels of reliability and online reserve. The model also considers incentive policies to encourage customers to install demand-side smart technologies to participate in demand response programs actively. The planning problem is formulated by mixed-integer programming. The proposed model is applied to an industrial zone as an aggregate load. Numerical simulations exhibit the model's efficacy and scrutinize in-depth, the effect of a variety of factors on multi-carrier microgrid planning results, including the extents of the capital investment fund and loan in addition to demand response enabling technology cost.
Abstract Microgrid can contribute to the day‐ahead market by submitting bids to minimize its costs. The bidding problem is challenging due to various uncertainties. Thus, the present study provides a comprehensive optimal bidding strategy to determine the optimal power bids of a multi‐carrier microgrid despite the interdependency of power and gas market prices on the day‐ahead and real‐time markets. In this work, the stochastic energy bidding in the proposed multi‐carrier microgrid is solved via a two‐stage procedure to benefit from day‐ahead and real‐time markets. In the first stage, the operator provides hourly energy bids to the distribution system operator regardless of uncertainty. Then, in the second stage, taking into account the confirmed day‐ahead bids, the multi‐carrier microgrid operator serves to balance the load in the real‐time market stemmed from various uncertain parameters. This problem is solved as mixed‐integer linear programming by CPLEX of the GAMS solver. A scenario reduction method is also employed to burden the complexity of the problem. Numerical results show the usefulness of the proposed model.
Microgrids are inherently subject to a variety of cyber-physical threats due to potential vulnerabilities in their cyber systems. In this context, this paper introduces a cyber-attack-resilient design of a multi-carrier microgrid to avoid the loss of critical loads. The objective of the proposed model is to minimize the total planning cost of multi-carrier microgrids, which incorporates the investment and replacement costs of distributed energy resources, operation and maintenance costs, peak demand charges, emission costs, unserved energy costs, and potential reinforcement costs to handle cyber-physical attacks. Not only is the proposed multi-carrier microgrid planning approach able to determine the optimal size of multi-carrier microgrids, but it also identifies and reinforces the system to handle cyber-physical attacks by serving critical loads. The proposed multi-carrier microgrid planning model is formulated as a mixed-integer programming problem and solved using the GAMS 24.1 software. To evaluate the effectiveness of the proposed integrated resource planning model, it is applied to a real-world industrial park test-case system. Numerical simulations demonstrate the effectiveness of the resilience-oriented multi-carrier microgrid planning model. Importantly, the simulation results indicate the economic viability of multi-carrier microgrids optimized by the proposed model. Also, the model sensitivity of various decision variables has been analyzed.
Several studies have been reported for optimal operation of electrical railway systems (ERSs). However, the stochastic energy management of ERSs, including renewable energy resources (RERs), has received less attention. The RERs’ uncertainties might affect the ERSs. On the other hand, the calculation time of the Monte Carlo simulation (MCS)-based approaches is an essential challenge, which should be solved, particularly in real-time decisions and recursive optimization problems. Thus, it is crucial to study the ERSs' stochastic behaviors and uncertainties, including RERs. This paper tries to overcome the discussed concerns and challenges by proposing a novel ERS’s optimal stochastic energy management using clustering algorithms. In this paper, the backward scenario reduction algorithm has been used. In addition, regenerative braking energy (RBE) and energy management systems (ESSs) have been studied. Studying the impacts of changes in the number of passengers on the optimized operation of ERSs and investigating the interaction between the utility grid and the ERS are other contributions of this research. The proposed method is applied to an actual test system of Tehran Urban and Suburban Railway Operation Company (TUSROC). Test results are validated by comparing with available MCS-based methods. Simulation results illustrate the accuracy and computation time advantages of the proposed method. Simulation results illustrate that <0.6% inaccuracy appears in the proposed method, while it would be 500 times faster than MCS-based ones. The comparative test results show the advantages of the introduced method.
In medicine field, dynamic investigation of aorta artery has received attention due to its notable functions and significance upon performance of heart and individual health. Because, aortic injuries cause lethal occurrences having numerous mortality rates. Therefore, more probes, in particular, dynamic response of aorta arteries conveying blood flow containing pharmaceutical nanoparticles is essential. In present research, we attempt to model biomechanically the dynamic instability assessment of aorta arteries with atherosclerosis in tissue matrix conveying blood including pharmaceutical magnetic nanoparticles. Thus, according to classical cylindrical shell theory, the aorta arteries will be considered as elastic cylindrical vessels and symmetric lipid tissue is utilized in order to model atherosclerosis in the artery. Applying magnetic field to nanoparticles results in attraction of lipid tissue in artery. Moreover, the nature of blood flow is regarded non-Newtonian based on Casson, Carreau and power law models. Using Hamilton's principle, the motion equations are derived and based on differential quadrature method (DQM), the dynamic instability region (DIR) of aorta artery is obtained. The influences of different variables such as magnetic field, magnetic nanoparticle's volume percent, combined effects of the tissue, lipid's height and length and non-Newtonian models upon dynamic behavior of aorta artery are investigated. According to the results, increase in lipid's height leads to increase in resonance frequency of aorta arteries. This article is protected by copyright. All rights reserved.
In this paper, an interleaved zero-voltage transition (ZVT) high step-up converter is presented. The proposed converter has the coupled inductors and the switched capacitor to increase the voltage gain, but the coupled inductors are not at the input, so the input current is continuous, which makes this converter suitable for photovoltaic systems. Since all switches, including main and auxiliary switches, are switched at zero-voltage condition, the efficiency of the converter is high. Therefore, the voltage stress on the switches has also decreased. The proposed converter has been thoroughly analysed and a prototype has been developed to prove theoretical analyses. The experimental results show an improvement of 4% in efficiency and 9 dB.µV in noise compared to hard switching counterpart.
Background: Chemical preservatives are now used in various foods to increase shelf life and maintain quality instead of its natural extracts with anti-bacterial properties from plants can be used. Hence this research was planned to evaluate and study the synergistic antibacterial effect of the methanolic extracts of Dracocephalum kotschyi (D. kotschyi) and Trachyspermum ammi (T. ammi) against standard pathogenic bacteria like: Pseudomonas aeruginosa (P. aeruginosa), Shigella dysenteriae (S. dysenteriae), Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Methods: The methanolic extract of D. kotschyi and T. ammi was prepared by the Soxhlet method. The minimum inhibitory concentration (MIC) of this methanolic extracts were determined by the microdilution method. Thus, by determining the amount of fractional inhibitory concentration index (FICI), the interaction between the methanolic extracts of D. kotschyi and T. ammi on the pathogenic bacteria was determined. Results: In this study, the MIC of the extracts of D. kotschyi and T. ammi on the pathogen; S. aureus was equal to 6.25 mg/mL and 12.5 mg/mL for S. dysenteriae, E. coli and P. aeruginosa. Hence, the combination of methanolic extracts of these plants shows a synergistic antibacterial effect (FICI < 0.5), on all tested pathogenic microorganisms was proved. Conclusion: Due to the antimicrobial synergistic effect and cost-effective production process of methanolic extracts of D. kotschyi and T. ammi, they are used as natural preservatives and flavouring agents to preserve foods.
The solar cell with wider bandwidth is interesting more than the narrowband structure. Thus, the non-homogenous structure can be used to increase the bandwidth and absorption ratio. In addition, trapping more energy is an important factor for solar cells. Therefore, the hyperbolic metamaterial is developed to provide a non-homogeneous structure. The elements shape impact on the bandwidth and electric field enhancement. The hyperbolic absorber shows 107% bandwidth which is covering 300–1100 nm and this technique can increase the bandwidth by more than 80%. In addition, as shown in this study, the hyperbolic structure can be underscored for increasing the electric and magnetic field by more than 1 dB, and thus this absorber can be used to intensity the power density to achieve higher efficiency. Finally, the nanosphere is implemented in the dielectric layer of the hyperbolic metamaterial structure to enhance the absorption of the solar cell. The P3HT is investigated as the proposed substrate for the active layer. Moreover, the spacer material can impact the reflection and the SiN layer shows the best bandwidth and reflection. This study reveals that the heterojunction structure can be supposed as a solution with hyperbolic metamaterial for plasmonic solar cells.
Functionalized SBA-15 (immobilization of Pd on the modified SBA-15) has been used as an efficient catalyst for the preparation of spiroindolines by multi-component reactions of isatins, cyclic-1,3-diketones, and 6-amino-1,3-dimethyluracil under ultrasonic irradiation in water. The catalyst has been characterized by X-ray diffraction spectroscopy (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FT-IR), N2 adsorption analysis, temperature-programmed desorption (TPD), and thermogravimetric analysis (TGA). The advantages of this method include the reusability of the catalyst, low catalyst loading, excellent yields in short reaction times and easy separation of products, and use of ultrasonic irradiation as a valuable and powerful technology.
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