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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]

In this paper, an integrated workflow based on recent geoscience data is presented for assessing the reservoir characterization and structural interpretation of the Burgan formation, a highly productive formation in the Abadan plain, SW Iran. Utilizing newly acquired high-resolution SEM images, we evaluated the pore size, pore distribution, and pore aspect ratio of Burgan formation. The watershed segmentation algorithm is also capable of detecting throats and closed pores. The porosity fractions from SEM images are used for calibration of the porosity log at several well locations in order to perform petrophysical modeling. Due to the complexity of facies behavior in the study area, we utilized supervised Bayesian classifiers using P-wave velocity, density, and facies log data in order to incorporate spatial dependence into the model. The confusing matrix and machine learning metrics including Accuracy (97.01%), Precision (93.88%), F1 Score (94.16%), and False Positive Rate (2.52%), indicate that the classifier has been properly trained at well locations. A reasonable match is evident between the modeled petrophysical parameters and the true (core) porosity and water saturation at the location of the test well. Furthermore, we have demonstrated the validity of assumptions concerning the dominance of extensional structure in the Abadan plain by using interpreted seismic data. The presented workflow can be used to optimize drilling operations and reduce risks in similar geological settings in the studied formation. Highlights: • The vital pore properties have been evaluated using newly obtained SEM images. • Bayesian classification is a reliable classifier for complex facies modeling. • The dominant extensional structure can be observed in the Abadan plain. • The watershed segmentation algorithm can detect closed pores and throats. [ABSTRACT FROM AUTHOR]

Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross‐gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross‐gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two‐dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross‐gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross‐gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north‐western Iran. In this application case, the lower density of salt minerals such as potash, compared to its surrounding sedimentary sequences, motivates a gravity study. Furthermore, the relative lower susceptibility of these salt minerals, alongside their diamagnetic effect, makes them a suitable target for magnetic surveys. Separate gravity and magnetic studies had been performed over the deposit; however, a constitutive relationship between density and magnetization within the area of interest supporting a joint inversion had not been established. In this paper, we apply the sequential cross‐gradient approach to perform the first full joint inversion for the AjiChai salt deposit. The magnetic inversion here is performed to recover the magnetization amplitudes rather than the magnetization vector. In fact, we assume there is no remanent magnetization and, therefore, that the magnetization vector is constant and parallel to the geomagnetic field direction. The constructed density and magnetization models are of high concordance with available geological information and previous studies including drilling results. In addition, unlike previous separate inversion models, the models are structurally and geometrically similar. [ABSTRACT FROM AUTHOR]

The Cauchy problem for Helmholtz equation, for moderate wave number |$k^{2}$|⁠ , is considered. In the previous paper of Achieng et al. (2020, Analysis of Dirichlet–Robin iterations for solving the Cauchy problem for elliptic equations. Bull. Iran. Math. Soc.), a proof of convergence for the Dirichlet–Robin alternating algorithm was given for general elliptic operators of second order, provided that appropriate Robin parameters were used. Also, it has been noted that the rate of convergence for the alternating iterative algorithm is quite slow. Thus, we reformulate the Cauchy problem as an operator equation and implement iterative methods based on Krylov subspaces. The aim is to achieve faster convergence. In particular, we consider the Landweber method, the conjugate gradient method and the generalized minimal residual method. The numerical results show that all the methods work well. In this work, we discuss also how one can approach non-symmetric differential operators by using similar operator equations and model problems which are used for symmetric differential operators. [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]

In the real word, the decision maker may want to use revenue and cost eficiency analysis for existing units compared to virtual units, traditional DEA models can no longer be used and FDH models must be used to evaluate the efficiency of decision-making units. In this paper, we develop the revenue and cost efficiency models based on the FDH model and obtain the efficiency scores based on pair wise comparisons. In the following, we will develop the proposed models for the two-stage network structure and obtain the desired scores of inputs and outputs by considering the input and output prices. An algorithm for measuring the revenue and cost efficiency is presented based on the ratio of inputs and outputs. Finally, we use the proposed algorithm to evaluate the efficiency of 13 airports in Iran with a two-stage network structure without considering the constraint of convexity and present the results of the research. [ABSTRACT FROM AUTHOR]

Improving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14‐bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs. [ABSTRACT FROM AUTHOR]

• A new method based on the discrete energy separation algorithm presents to HIF and LIF detection in distribution system. • Simple implementation and low computational burden are main advantages of the method. • The method detects HIF and LIF from other disturbances with high detection accuracy and low detection time even in noisy condition. • The method has proper performance in detection of recorded real HIFs in distribution system. • CT saturation has no impact on the detection accuracy and time of the method in LIF detection. Due to the low fault current of high impedance fault (HIF), it is not recognized by overcurrent relays. Moreover, due to the similar characteristics of HIF and other disturbances like capacitor bank (CB) and non-linear load (NL) switching, detection of HIF is one of the main challenges in protection of distribution system. Furthermore, detection of low impedance fault (LIF) is important for protection system. In this paper, a new statistical method is proposed based on a discrete time separation algorithm of energy for detection of HIF and LIF from other disturbances to improve detection accuracy and time. The method consists of two main parts, the first one detects switching on/off the linear load, switching off the CB and switching off the NL from abnormal conditions, while the second part identifies the HIF and LIF from switching on the CB and switching on the NL. High detection speed, simple implementation, and low computational burden are advantages of the method. The method is tested on IEEE 13-node and IEEE 34-node distribution test systems, and a real feeder in Iran. Obtained results show that the high efficiency of the proposed method in detection of disturbances even in noisy conditions, with low detection time. [ABSTRACT FROM AUTHOR]