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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Reliable overhead transmission lines have a vital role in maintaining the reliability of power systems at a desirable level and ensuring the continuity of electrical energy. In this regard, a proper preventive maintenance program can ensure the reliable functioning of transmission lines. Based on the concept of reliability-centered maintenance, this paper proposes a preventive maintenance strategy that can be implemented in real power systems due to the limited data requirements. The priority of each line for preventive maintenance was obtained by two indices revealing the technical condition and importance of each line in the network. The proposed method was applied to a real transmission network in Khorasan province of Iran. In order to extract the condition index of lines, failure statistics during six recent years as well as technical data of lines were investigated. Moreover, this paper presents a two-stage method to determine the importance index of each line by evaluating the consequences of line failures on the network. The presented results show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

This qualitative study aimed to explore Iranian women's life stories living with compulsive sexual behaviour. Data were collected between 2014 and 2016 in two cities. Forty‐four semi‐structured interviews were conducted by using theoretical sampling and constant comparative analysis. Four thematic categories were constructed from the data, including "scars left on the spirit and mind,"; "bizarre beliefs regarding sexuality and gender,"; "compensation for emptiness and the loss,"; and "difficulty in emotional regulation." This paper shows how abusive patterns of relationships in childhood could lead to sexual compulsivity. The paper results can direct our attention to the importance of health and social care and training to recognise abusive situations and support these children. [ABSTRACT FROM AUTHOR]

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software's capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model's characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models' characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of modeling financial time series as a chaotic dynamic rather than a stochastic system. The dynamical properties of a financial time series of an economic institution in Iran were analyzed to identify the potential occurrence of the low-dimensional deterministic chaos. This paper applies several classic nonlinear techniques for detecting the chaotic nature of the time series of loan payment portion and proposes a modified nonlinear predictor scheme for forecasting the future levels of the nonperforming loan. The auto mutual information was implemented to estimate the delay time dimension, and Cao's approach, along with correlation dimension methodology, quantified the embedding dimension of the time series. The results reveal a low embedding dimension implying the chaotic nature exists in the financial data. The maximum Lyapunov exponent measure is also adopted to investigate the divergence or convergence of the trajectories. Since positive Lyapunov exponents are revealed, the long-term unpredictability of the time series is proved. Lastly, a modified nonlinear local approximator is developed to forecast the short-term history of the time series. Numerical simulations are provided to illustrate the adopted nonlinear techniques. The results reported in this paper could have implications for commercial bank managers who could use the nonlinear models for early detection of the possible nonperforming loans before they become uncontrollable. [ABSTRACT FROM AUTHOR]

Summary: In the present paper, the static approximate Newton‐Raphson‐Sidel (ANRS) and the developed down‐hill (DDH) algorithm methods were used. To take advantage of both methods, ANRS‐DDH and the revised holomorphic embedding load flow (RHELF) were combined. An approximation of the potential energy boundary surface (APEBS) was also employed in combination with determine transient stability and correct the previous methods. A combination of static method and transient stability led to a combined approach for dynamic available transfer capability (C‐DATC) computation. It is shown that a small correction for these four methods makes the proposed method faster or more accurate. The average root mean square error of the proposed method is half the other methods and the average relative speed of calculations in the proposed methods is 10% higher than the other method. These were confirmed in the networks of Iowa State (145 bus), modified IEEE 118‐bus, and the west of Iran (848 bus) with a large number of wind farms. Finally, the present paper demonstrated that the suggested approach is ideal not only for the operation mode but also for the planning mode and the existence of wind farms. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an inflammatory disease damaging the myelin sheath in the central and peripheral nervous system in the brain and spinal cord. Optic Neuritis (ON) is one of the most prevalent ocular demonstrations of MS. The current diagnosis protocol for MS is MRI, but newer modalities like Optical Coherence Tomography (OCT) are already of interest in early detection and progression analysis. OCT reveals the symptoms of MS in the Central Nervous System (CNS) through cross-sectional images from neural retinal layers. Previous works on OCT were mostly focused on the thickness of retinal layers; however, texture features seem also to have information in this regard. In this research, we introduce a new pipeline that constructs layer-stacked (LS) images containing data from each specific layer. A variety of texture features are then extracted from LS images to differentiate between healthy controls and ON/None-ON MS cases. Furthermore, the definition of texture extraction methods is tailored for this application. After performing a vast survey on available texture analysis methods, a treasury of powerful features is collected in this paper. As a primary work, this paper shows the ability of such features in the diagnosis of HC and MS (ON and None-ON) cases. Our findings show that the texture features are powerful to diagnose MS cases. Furthermore, adding information of conventional thickness values to texture features improves considerably the discrimination between most of the target groups including HC vs. MS, HC vs. MS-None-ON, and HC vs. MS-ON. [ABSTRACT FROM AUTHOR]

Automation has attracted interest from the industry sector for its potential to improve energy efficiency, cost efficiency, and environmental performance. By elevating the LoA to the highest degree, associated costs will grow accordingly and its implementation will be far more complicated. This will also result in losing workers and decreasing environmental pollutants. On the other hand, increasing power consumption at high levels of automation leads to the production of greenhouse gases. This paper aims to increase the level of automation (LoA) considering the concept of sustainability. This study presents fuzzy multi-objective programming to determine the optimal LoA considering sustainability factors to achieve competitive advantages. To solve the model, the Zimmermann max-min approach was adopted and a cosmetics factory in Iran was chosen to optimize LoA according to this model. The results showed that it is possible to improve the LoA and also consider sustainability factors with the available resources without using the highest LoA. This study can help managers optimize the LoA in their organizations considering the current resources and sustainability issues, and control the company's return on investment and cost of overhead. They can run the model with every definition of LoA proposed till now. This research can benefit the environment and the workers' health in the production line by reducing environmental pollutants and prevent the dismissal of all personnel due to its negative social effects. It also reduces the risk of COVID-19 by minimizing the number of workers. So far, a mathematical model for selecting optimal LoA in the chemical industry considering sustainability has not been presented. [ABSTRACT FROM AUTHOR]

An efficient banking system with the right monetary policy by controlling liquidity and inflation and directing resources to productive economic activities plays an essential role in economic development. However, banks' performance is influenced by various political, economic, managerial, and social factors, and the study of these factors has been considered the topic of interest to researchers. This paper uses the structural equation modeling method to investigate the effect of ownership structure and corporate governance on listed banks' performance in the Tehran Stock Exchange from 2011 to 2017. Based on the results, ownership structure dimensions have a relatively insignificant impact on corporate governance. However, the financial performance dimension has a statistically significant negative effect. The results also indicate that corporate governance is significantly associated with a positive effect on financial performance. Consequently, the results indicate that corporate governance may mitigate the negative impact that ownership structure dimensions may have on bank financial performance. [ABSTRACT FROM AUTHOR]

One of the rare manifestations of fascioliasis is liver abscess. In this paper, we report the case of a 38-year-old woman with a liver abscess caused by Fasciola hepatica (F. hepatica). The patient was referred to the clinic with recurrent fever, right upper quadrant (RUQ) pain, and a large abscess in her liver. Despite the consumption of an antibiotic drug, she still had symptoms. The symptoms began to disappear upon starting the consumption of triclabendazole (TCBZ). Fascioliasis can manifest itself with unusual symptoms that provide no specific clue for its diagnosis. Therefore, it is important to consider F. hepatica in the differential diagnosis of liver abscess, especially in endemic regions. [ABSTRACT FROM AUTHOR]

The functional classification system (FCS) of roads means categorizing roads based on their service characteristics. The two primary considerations in classifying highway and street networks are accessibility and mobility, where by increasing the role of one, the other's role is reduced. In this paper, besides the conventional variables such as geometric design characteristics, parking lots, land use features, and accessibility; the Sydney Coordinated Adaptive Traffic System (SCATS) data following the real-time traffic flow and average speed of vehicles collected by Location-Based Services (LBS) are considered as new variables for estimating the FCS. Linear regression is used to model the importance of the variables. The chi-square test compared the observational and predicted speeds in the five categories of roads in Tehran, the capital of Iran. Results show that on-street parking has the highest impact and the land use variable has the lowest impact on speed that changes the FCS. Moreover, the presented classification was one to two categories compared with the conventional FCS presented in manuals in the case of Tehran's transportation network as a developing city. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year. [ABSTRACT FROM AUTHOR]

Water production from unconventional reservoirs especially shale reservoirs has always been considered as the major challenges for petroleum industries that needs special requirements and facilities to treat the flow-back water and reinject to the reservoirs. The objective of this paper is to statistically measure the provided freshwater from treated water that is produced from reused water. The treated water is provided from different processes such as hydraulic fracturing, chemical enhanced oil recovery methods (such as polymer, surfactant, and foam flooding that are the main important techniques in recovery processes) from eight wells in Bangestan field in South of Iran. Moreover, photofenton-flotation separation methods are used in this field to treat the produced water from different processes. As a result, saving water percentage in the studied field for hydraulic fracturing processes is about 82% and therefore, the required fresh water is about 18%. Moreover, saving water percentage in the studied field for chemical recovery techniques is about 70% and therefore, the required fresh water is about 29%. The findings of this study can help for better understanding of the required fresh water for 1000 inhabitants can be saved through this system. [ABSTRACT FROM AUTHOR]

From the beginning of creation, human beings have realized the importance of energy for survival. They have always devoted a significant part of their energy to provide the required energy. Moreover, it can be said that energy resources have an essential role in the life and evolvement of societies. On the contrary, with the depletion of energy resources, severe environmental pollution caused by the consumption of petroleum products, and high cost of energy in the production cycle, allocating these resources must be carried out carefully. This paper aims to look at the connection between energy consumption and sustainable economic welfare in OPEC countries (Iran, Kuwait, Saudi Arabia, UAE, Ecuador, and Venezuela) during 2019–2009, utilizing the panel data method. Finally, we presented a CNN architecture for forecasting welfare levels in the case study countries. As a result, the relationship between energy consumption and economic growth among selected countries from energy-producing countries was compared in this analysis, which used the standard Granger causality test and the Granger causality test in several domains. The findings suggest that economic growth and inflation positively impact energy consumption in the countries studied. In addition, energy consumption positively impacts these countries' sustainable economic welfare, while inflation has a negative effect. In addition, the findings of the standard Granger causality test indicate a one-way causal association between energy consumption and economic development in Iran and Venezuela, as well as one-way causality from GDP to energy consumption in Ecuador, the United Arab Emirates, Saudi Arabia, and Kuwait. Also, based on the results of the CNN method, the RMSE are 1.75, 3.81, 1.39, 0.52, 0.69, and 1.72 for Ecuador, Emirates, Iran, Saudi Arabia, Kuwait, and Venezuela, respectively. [ABSTRACT FROM AUTHOR]

SARS‐COV2 vaccines were approved without long‐term monitoring due to emergent situations. This has raised some issues about the timing and protocol of receiving vaccines in specific situations such as patients receiving immunomodulatory agents including rituximab, which is widely used for various disorders such as multiple sclerosis, pemphigus, and many rheumatologic disorders. We described two cases of pemphigus vulgaris (a new case and one with flare‐up) following vaccination with Astrazeneca in Iran and reviewed the existing data in this regard through searching on PubMed, Google Scholar, and Scopus. All of the relevant papers published until June 28, 2021, which we could access their full‐texts were included. We found some recommendations made by rheumatologists, neurologists, and dermatologists in regard to vaccination timing in this group of patients and tried to summarize them to provide a practical guide for clinicians. Clinicians should perform a careful, individualized risk–benefit assessment for their patients and consider a delay in rituximab administration after completion of COVID vaccination if there is not any considerable risk of disease relapse or organ failure. Moreover, choosing vaccines with potential of providing protection after single dose, especially in countries with limited access to vaccines may be a reasonable approach. [ABSTRACT FROM AUTHOR]

One of the most important environmental issues today is the water crisis and the ensuing security challenges. Of these, transboundary waters pose the most significant security challenges. Resolving these conflicts and agreements over transboundary waters has always faced a variety of challenges, making it difficult to reach a mutually agreed solution. One of the transboundary water conflicts that have been exacerbating in recent years is the conflict between Afghanistan, Iran, and Turkmenistan over the use of the Harirud river water resources. (e present paper aims to analyze, using the Graph Model for Conflict Resolution (GMCR), a game theory model, the conflict between the three countries regarding the utilization of the water resources of the border river, Harirud. To this purpose, first, the current state of the conflict was investigated. (en, each of the three countries’ possible options and preferences was defined according to the past and present state of the conflict as well as the possible states. By defining the permissible movements and priorities of each decision maker, the equilibrium of the conflict was obtained. Next, four scenarios were defined, the equilibria were extracted in each scenario, and the results were interpreted. (e implementation of the GMCR model algorithm regarding the Harirud water conflict between the three countries indicated that the current state is the most likely outcome of the conflict as none of the parties involved, given their preferences, and is motivated or able to change their strategy to help the conflict to a more favorable state. [ABSTRACT FROM AUTHOR]

Summary: The purpose of this paper is to present a model for optimal placement of distributed generation units and sectionalizing switches (relocation of the existing switches and installation of new ones) to increase distribution network resilience against the threat of transmission system outage. The proposed model is formulated as a multi‐objective optimization problem with the objectives of minimizing investment costs and maximizing load restoration. In this regard, distribution system operation during normal conditions as well as transmission system outages is considered. In addition, the unique characteristics of the real power grid in Khuzestan province in southwestern of Iran are addressed in the proposed model which makes the model applicable to the real networks with similar characteristics in many parts of the Middle East. This grid has two completely different load patterns (in terms of load curve shape and peak load) corresponding to two hot and temperate periods, which causes completely different concerns for the network's operator. The proposed model is solved using multi‐objective genetic algorithm (NSGA‐II), and it is implemented on the 33‐node test system and the results are analyzed. Moreover, it is applied to the 327‐node real‐world test system in Khuzestan province to demonstrate its applicability to large networks. In both cases, the results confirm the effectiveness of the proposed optimization model. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the effects of natural variables, including precipitation and flow rate, on the quality of the Zarjoub River in Guilan province, Iran. The new hydrological insight in this study is a lack of national research focused on the dual effects of rainfall and flow rate on river water quality in coastal areas along the Anzali Wetland. To investigate the effect of precipitation and flow rate on river water quality, nine water quality variables were monitored during the 10-year period. In this article, (a) the existence of trends and the best fitted models of water quality parameters and the discharge and precipitation were analysed using statistical techniques and (b) the relationships between concentration of constituents with the discharge and precipitation on the up-stream and middle station were also examined. Box plots, for explaining the distribution of a data collection, were used. The results showed the existence of trend of water quality parameters with river flow and rainfall. As presented in Section 3, with increasing precipitation and flow rate, concentration of all constituents, except pH and SO₄2, decreased. On the contrary, the maximum amount of water quality elements was observed in low precipitations; therefore, the maximum concentration occurred in less than 15 mm precipitation. Simple regression was used to evaluate the discharge concentration and precipitation concentration. According to the correlation coefficient (r), the relationship between concentration and precipitation is weaker than (0.238) discharge concentration (0.699). The results further showed climate change and river water quality to be related. [ABSTRACT FROM AUTHOR]

Ensuring international transport corridors' safety is essential for countries along the routes. For the sustainable development of international transport channels, this paper discusses how to evaluate the operational risks and how to alleviate the adverse effects caused by emergencies. First, an indicator system of international transport corridors' operational risks was constructed, which consists of 30 indicators from five dimensions of politics, economy, society, safety, and technology. Secondly, a comprehensive scoring approach combined with a network analysis method was applied to examine the effects caused by operational hazards. On this basis, the quantitative method and rigorous statistical analysis were used to evaluate the China-Pakistan-Iran-Turkey International Transport Corridor in the south line of the Silk Road Economic Belt, whose operational risks from 2010 to 2019 were analyzed. Finally, the operational risk index was discussed in detail. Results demonstrate the following. (1) During the China-Pakistan-Iran-Turkey International Transport Corridor, the Pakistan section has the highest operational risk index, and the next culprit is Turkey, followed by Iran. The Chinese section has the lowest operational risk index. (2) Pakistan has been trapped in severe political risks and security risks in recent years. Turkey suffers from severe security risks due to the Kurdish problem. Iran ranks first in terms of economic risk and social risk. Special attention should be paid to the natural risks for China. (3) Pakistan's operational risks will exhibit consistently high in the future, while China appears downwards in long-term trend. The operational risk index in Iran is on the rise. Turkey shows a flattening of and then a slight decline in the foreseeable future. [ABSTRACT FROM AUTHOR]