Semnan University

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators. This approach can accurately forecast the hourly loads of weekdays, as well as, of weekends and public holidays. It is shown that the proposed method can provide more accurate results than the conventional techniques, such as artificial neural networks or Box-Jenkins models. In addition to hourly loads, daily peak load is an important problem for dispatching centers of a power network. Most of the common load forecasting approaches do not consider this problem. It is shown that the proposed method can exactly forecast the daily peak load of a power system. Obtained results from extensive testing on Iran's power system network confirm the validity of the developed approach.

Plants are one of the best sources to obtain a variety of natural surfactants in the field of green synthesizing material. Sambucus ebulus, which has unique natural properties, has been considered a promising material in traditional Asian medicine. In this context, zinc oxide nanoparticles (ZnO NPs) were prepared using S. ebulus leaf extract, and their physicochemical properties were investigated. X-ray diffraction (XRD) results revealed that the prepared ZnO NPs are highly crystalline, having a wurtzite crystal structure. The average crystallite size of prepared NPs was around 17 nm. Green synthesized NPs showed excellent absorption in the UV region as well as strong yellow-orange emission at room temperature. Prepared nanoparticles exhibited good antibacterial activity against various organisms and a passable photocatalytic degradation of methylene blue dye pollutants. The obtained results demonstrated that the biosynthesized ZnO NPs reveal interesting characteristics for various potential applications in the future.

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

In this paper, an efficient method based on a new fuzzy neural network is proposed for short-term price forecasting of electricity markets. This fuzzy neural network has inter-layer and feed-forward architecture with a new hypercubic training mechanism. The proposed method predicts hourly market-clearing prices for the day-ahead electricity markets. By combination of fuzzy logic and an efficient learning algorithm, an appropriate model for the nonstationary behavior and outliers of the price series is presented. The proposed method is examined on the Spanish electricity market. It is shown that the method can provide more accurate results than the other price forecasting techniques, such as ARIMA time series, wavelet-ARIMA, MLP, and RBF neural networks.

This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators. This approach can accurately forecast the hourly loads of weekdays, as well as, of weekends and public holidays. It is shown that the proposed method can provide more accurate results than the conventional techniques, such as artificial neural networks or Box-Jenkins models. In addition to hourly loads, daily peak load is an important problem for dispatching centers of a power network. Most of the common load forecasting approaches do not consider this problem. It is shown that the proposed method can exactly forecast the daily peak load of a power system. Obtained results from extensive testing on the Iran's power system network confirm the validity of the developed approach.

Natural bone constitutes a complex and organized structure of organic and inorganic components with limited ability to regenerate and restore injured tissues, especially in large bone defects. To improve the reconstruction of the damaged bones, tissue engineering has been introduced as a promising alternative approach to the conventional therapeutic methods including surgical interventions using allograft and autograft implants. Bioengineered composite scaffolds consisting of multifunctional biomaterials in combination with the cells and bioactive therapeutic agents have great promise for bone repair and regeneration. Cellulose and its derivatives are renewable and biodegradable natural polymers that have shown promising potential in bone tissue engineering applications. Cellulose-based scaffolds possess numerous advantages attributed to their excellent properties of non-toxicity, biocompatibility, biodegradability, availability through renewable resources, and the low cost of preparation and processing. Furthermore, cellulose and its derivatives have been extensively used for delivering growth factors and antibiotics directly to the site of the impaired bone tissue to promote tissue repair. This review focuses on the various classifications of cellulose-based composite scaffolds utilized in localized bone drug delivery systems and bone regeneration, including cellulose-organic composites, cellulose-inorganic composites, cellulose-organic/inorganic composites. We will also highlight the physicochemical, mechanical, and biological properties of the different cellulose-based scaffolds for bone tissue engineering applications.

Water pollution by organic pollutants is an ever-increasing problem for the global concerns. Some recently published papers are reviewed and summarized with the focus being on the photocatalytic oxidation of tetracycline in wastewater effluent. In this review, the effects of various operating parameters on the photocatalytic degradation of tetracycline are presented. Recent findings suggested that type of photocatalyst and composition, initial substrate concentration, amount of catalyst, pH of the reaction medium, ionic components in water, solvent types, and oxidizing agents/electron acceptors can play an important role on the photocatalytic degradation of tetracycline.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are elucidated as cells that can perpetuate themselves via autorestoration. These cells are highly resistant to current therapeutic approaches and are the main reason for cancer recurrence. Radiotherapy has made a lot of contributions to cancer treatment. However, despite continuous achievements, therapy resistance and tumor recurrence are still prevalent in most patients. This resistance might be partly related to the existence of CSCs. In the present study, recent advances in the investigation of different biological properties of CSCs, such as their origin, markers, characteristics, and targeting have been reviewed. We have also focused our discussion on radioresistance and adaptive responses of CSCs and their related extrinsic and intrinsic influential factors. In summary, we suggest CSCs as the prime therapeutic target for cancer treatment.

Load and price forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and price forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.

In the present work, the usefulness of ultrasonic power as a dispersion and mixing tool to accelerate the adsorption of Safranin O (SO), methylene blue (MB), Pb(2+) ions and Cr(3+) ions onto the novel composite Fe3O4-NPs-AC adsorbent was investigated. This new material was extensively characterized and analyzed by different techniques such as XRD, FESEM, Raman spectroscopy and FT-IR. Central composite design (CCD) based on designed runs revealed that adsorbent mass, sonication time, MB concentration, SO concentration, Pb(2+) ion and Cr(3+) ion concentration and some of their interactions have significant contributions to the target compounds removal percentages. A combination of response surface methodology and Design-Expert software was used to qualify and estimate the influence and magnitude of each terms contribution to the response. An optimization study using the following investigated increments of the effective variables, adsorbent mass (0.01-0.03 g), sonication time (2-6 min), initial dye concentration (5-25 mg L(-1)), and initial metal ion concentration (20-60 mg L(-1)), revealed that fixing the experimental variables at 0.025 g of Mn-Fe3O4-NPs-AC, with a 3 min sonication time, and 20 mg L(-1) of MB, 10 mg L(-1) of SO, 38 mg L(-1) of Pb(2+) ions and 42 mg L(-1) of Cr(3+) ions at room temperature lead to the achievement of the best characteristics and performance. Conduction of 32 experiments according to the limitations of CCD and a subsequent analysis of variance (ANOVA) gave useful information about the significant and also approximate contributions of each term (main and interaction of variables) in an empirical equation for the expected response. The results indicate that the R(2) values are more than 0.988 and the adjusted R(2) values are in reasonable agreement with R(2). Under the optimal conditions, the MB, SO, Pb(2+) ion and Cr(3+) ion removal efficiencies reached 99.54%, 98.87%, 80.25% and 99.54% after 3 min, while their equilibrium data with high performance can be represented by Langmuir isotherms and a pseudo second-order kinetic model. The maximum adsorption capacities for the single component system, 229.4 mg g(-1) for MB, 159.7 mg g(-1) for SO, 139.5 mg g(-1) for Pb(2+) ions and 267.4 mg g(-1) for Cr(3+) ions, support the high efficiency of Mn-Fe3O4-NPs-AC as a new adsorbent.

It is now well established that electrochemical systems can optimally perform only within a narrow range of temperature. Exposure to temperatures outside this range adversely affects the performance and lifetime of these systems. As a result, thermal management is an essential consideration during the design and operation of electrochemical equipment and, can heavily influence the success of electrochemical energy technologies. Recently, significant attempts have been placed on the maturity of cooling technologies for electrochemical devices. Nonetheless, the existing reviews on the subject have been primarily focused on battery cooling. Conversely, heat transfer in other electrochemical systems commonly used for energy conversion and storage has not been subjected to critical reviews. To address this issue, the current study gives an overview of the progress and challenges on the thermal management of different electrochemical energy devices including fuel cells, electrolysers and supercapacitors. The physicochemical mechanisms of heat generation in these electrochemical devices are discussed in-depth. Physics of the heat transfer techniques, currently employed for temperature control, are then exposed and some directions for future studies are provided.

In a competitive electricity market, price forecasts are important for market participants. However, electricity price is a complex signal due to its nonlinearity, nonstationarity, and time variant behavior. In spite of much research in this area, more accurate and robust price forecast methods are still required. In this paper, a combination of a feature selection technique and cascaded neuro-evolutionary algorithm (CNEA) is proposed for this purpose. The feature selection method is an improved version of the mutual information (MI) technique. The CNEA is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithm (EA). An iterative search procedure is also incorporated in our solution strategy to fine-tune the adjustable parameters of both the MI technique and CNEA. The price forecast accuracy of the proposed method is evaluated by means of real data from the Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. The method is also compared with some of the most recent price forecast techniques.

The procedures used to experimentally determine the quality parameters of a biodiesel are lengthy and expensive. Occasionally it may be impossible to obtain a sufficient amount of oil for the relevant analyses. This is often the case for algal biodiesel, for example. Here we report on a new software package, the BiodieselAnalyzer© Version 1.1, for predicting the properties of a prospective biodiesel. BiodieselAnalyzer© can estimate 16 different quality parameters of a biodiesel based on the fatty acid methyl ester profile of the oil feedstock used in making it. The current version of the BiodieselAnalyzer© is intended for the Windows platform and is publically available at http://www.brteam.ir/biodieselanalyzer.

Land-atmosphere interactions play an important role for hot temperature extremes in Europe. Dry soils may amplify such extremes through feedbacks with evapotranspiration. While previous observational studies generally focused on the relationship between precipitation deficits and the number of hot days, we investigate here the influence of soil moisture (SM) on summer monthly maximum temperatures (TXx) using water balance model-based SM estimates (driven with observations) and temperature observations. Generalized extreme value distributions are fitted to TXx using SM as a covariate. We identify a negative relationship between SM and TXx, whereby a 100 mm decrease in model-based SM is associated with a 1.6 °C increase in TXx in Southern-Central and Southeastern Europe. Dry SM conditions result in a 2–4 °C increase in the 20-year return value of TXx compared to wet conditions in these two regions. In contrast with SM impacts on the number of hot days (NHD), where low and high surface-moisture conditions lead to different variability, we find a mostly linear dependency of the 20-year return value on surface-moisture conditions. We attribute this difference to the non-linear relationship between TXx and NHD that stems from the threshold-based calculation of NHD. Furthermore the employed SM data and the Standardized Precipitation Index (SPI) are only weakly correlated in the investigated regions, highlighting the importance of evapotranspiration and runoff for resulting SM. Finally, in a case study for the hot 2003 summer we illustrate that if 2003 spring conditions in Southern-Central Europe had been as dry as in the more recent 2011 event, temperature extremes in summer would have been higher by about 1 °C, further enhancing the already extreme conditions which prevailed in that year.

Microgrids are a rapidly growing sector of smart grids, which will be an essential component in the trend toward distributed electricity generation. In the operation of a microgrid, forecasting the short-term load is an important task. With a more accurate short-term loaf forecast (STLF), the microgrid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. However, STLF for microgrids is a complex forecast process, mainly because of the highly nonsmooth and nonlinear behavior of the load time series. In this paper, characteristics of the load time series of a typical microgrid are discussed and the differences with the load time series of traditional power systems are described. In addition, a new bilevel prediction strategy is proposed for STLF of microgrids. The proposed strategy is composed of a feature selection technique and a forecast engine (including neural network and evolutionary algorithm) in the lower level as the forecaster and an enhanced differential evolution algorithm in the upper level for optimizing the performance of the forecaster. The effectiveness of the proposed prediction strategy is evaluated by the real-life data of a university campus in Canada.

This paper presents a risk-based approach for evaluating the participation strategy of a battery storage system in multiple markets. Simultaneous offering in day-ahead energy, spinning reserve, and regulation markets is considered in this paper. The uncertainties considered include predicted market prices as well as energy deployment in spinning reserve and regulation markets. A new nonprobabilistic model is introduced in this paper to handle the uncertain nature of spinning reserve and regulation markets. Robust optimization is implemented to model these uncertain parameters and manage their related risk. The proposed risk-based model is a max-min problem, which is converted to its equivalent ordinary maximization problem using duality theory. The presented model is linearized by implementing strong duality theory. Finally, the proposed method is tested and verified using an illustrative case study.

Abstract Prediction of solar power involves the knowledge of the sun , atmosphere and other parameters, and the scattering processes and the specifications of a solar energy plant that employs the sun's energy to generate solar power . This prediction result is essential for an efficient use of the solar power plant, the management of the electricity grid, and solar energy trading. However, because of nonlinear and nonstationary behavior of solar power time series, an efficient forecasting model is needed to predict it. Accordingly, in this paper, we propose a new forecast approach based on combination of a neural network with a metaheuristic algorithm as the hybrid forecasting engine. The metaheuristic algorithm optimizes the free parameters of the neural network. This approach also includes a 2‐stage feature selection filter based on the information‐theoretic criteria of mutual information and interaction gain, which filters out the ineffective input features. To demonstrate the effectiveness of the proposed forecast approach, it is implemented on a real‐world engineering test case. Obtained results illustrate the superiority of the proposed approach in comparison with other prediction methods.

Soil contamination by lead, zinc, iron, manganese, and copper is a widespread environmental issue associated with the mining industry. Primary sources include mining activities, production and processing operations, waste disposal and management practices, and atmospheric sediments. Soil contamination and degradation, water pollution impacting aquatic ecosystems, plant absorption leading to agricultural product contamination, health risks associated with exposure to lead, zinc, iron, manganese, and copper, along with effects on fauna and biodiversity, constitute the primary environmental and health impacts of contamination. In this study, diverse sampling and analysis methods, geographic information systems, and remote sensing techniques are investigated for monitoring and assessing soil contamination with these metals. Soil modification techniques, phytoremediation, and other strategies for reduction and modification are considered among the most crucial, alongside health protection and risk management strategies. Finally, the article explores innovative methods and solutions for mineral waste management and remediation, the application of green chemistry and sustainable practices in the mining industry, and the utilization of artificial intelligence for controlling heavy metal ion pollution.

This note studies the problem of optimal H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> filtering in networked control systems (NCSs) with multiple packet dropout. A new formulation is employed to model the multiple packet dropout case, where the random dropout rate is transformed into a stochastic parameter in the system's representation. By generalization of the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm definition, new relations for the stochastic -norm of a linear discrete-time stochastic parameter system represented in the state-space form are derived. The stochastic H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the estimation error is used as a criterion for filter design in the NCS framework. A set of linear matrix inequalities (LMIs) is given to solve the corresponding filter design problem. A simulation example supports the theory.