granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional machine learning models. This article reviews some newly developed techniques in terms of granular framework settings. 

Earthquake as the most devastating natural disaster in urban areas causes huge physical and human damages worldwide. One way to assist reducing the impact of the earthquake on people and infrastructures is to produce a reliable seismic vulnerability map. The physical seismic vulnerability of a region as a multi criteria problem is concerned with seismic intensity, land slope, the number of building floors, building age and quality. Among the most important sources of uncertainty in determining the vulnerability of each urban statistical unit, is the uncertainty related to the conflicts in expert opinions concerning the level of severity of the seismic vulnerability. The main objective of this paper is to manage uncertainty considering different vulnerability classes allocated by the experts in integration of the concerned parameters. In this model, to reduce the uncertainty in the decision making process related to the expert opinions on allocating a seismic physical vulnerability class to each urban statistical unit, interval mathematics, genetic algorithm and granular computing methods are used. The physical seismic vulnerability map has been produced for Tehran on the basis of activation of North Tehran fault. Among 3174 urban statistical units, 150 randomly selected samples have been selected by 5 experts in related geoscience fields. The experts are asked to fill a questionnaire for allocating the physical seismic vulnerability of the samples. Due to the disaggregation in the experts’ knowledge on the physical seismic vulnerability of each statistical unit, their opinions have been integrated using interval mathematics. For the conflict resolution among the experts, genetic algorithm is used. granular computing has been applied to manage the uncertainty caused by the large amount of information achieved from the parameters affecting the physical vulnerability to assess the seismic physical vulnerability. The relations among the input parameters and the vulnerability classes are presented in a decision table. The rules with a minimum conflict from the decision table are extracted. The vulnerability classes have been sorted from 1 to 5 considering 1 as the least vulnerable class and 5 as the most vulnerable class. According to the results, most of the statistical units in Tehran fall within interval class vulnerabilities of [3 4] and [5 4]. To compare the similarity between the results of the model and those of the previous research by Khamespnah in the same study area, who used an integrated model of granular computing and rough set theory, Spearman rank correlation coefficient was employed. The value of this coefficient was 0.47 that shows some similarities between the results. The accuracy of 76% was achieved in this research using Kappa index verifying the importance of managing uncertainty using interval mathematics.

Tehran, capital of Iran, is located on few known (Mosha, North Tehran Fault and South and North Ray) and unknown faults which expose this mega city to huge earthquakes’ effects. In addition to considerable seismic hazard in Tehran the existence of old and non-standard buildings make the repercussions even worse. Determining locations and intensity of seismic vulnerability of a city is considered as a complicated disaster management problem. As, this problem generally depends on various criteria and expert’s opinions, one of the most important challenges concerned is the existence of uncertainty regarding inconsistency in expert’s view. Uncertainty in seismic vulnerability map would results biases in risk management which has multilateral effects on decision makings. Some multi-criteria evaluation methods have recently been proposed to handle some aspects of uncertainties in the process of producing the seismic vulnerability map for Tehran. granular computing approach is proposed in this paper to overcome the limitation of the abovementioned existing algorithms. It can be regarded for learning classification rules by considering the two basic issues: concept formation (making granules) and concept relationships identification (relationship between granules). One of the most important features of this method with respect to previous studies is inference of more compatible rules having zero inconsistency extracted from existing training databases. Furthermore, in this approach, non-redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more non-redundant rules. In this study the result of north Tehran fault hazard analysis is applied to the vulnerability assessment process and activation of other faults have been ignored. It is assumed that the northern fault of Tehran is activated and then the classification rules of seismic physical vulnerability are inducted from granular computing tree. A pilot area of Tehran Metropolitan Area located in the north of Iran was selected for the purpose of this study.

This paper aims at introducing two types of orthopair soft sets, which might serve as novel approaches to granular computing based on parametrization. These generalized soft sets emerge naturally when linguistic parameters are employed to convey uncertainty attached to elements of certain sets. Concepts of uncertainty measures attached to the parameters and the whole orthopair soft sets are presented as well. The proposed uncertainty measures are useful for classifying elements of the set of parameters. Di erent types of granularity measures associated with parameters are presented and are extended to orthopair soft sets. Collective wisdom is helpful in decision making based on consensus. A numerical example is given to demonstrate how orthopair soft sets can be employed in this regard.

Classifying objects based on the simultaneous impact of various parameters has always been challenging due to heterogeneity, impact conflict, and sometimes parameter uncertainty. The purpose of this study is to provide a method for classifying such data. In the proposed method, fuzzy hypergraphs were used to define the granular structures in order to apply the simultaneous effect of heterogeneous and weighted parameters in the classification. This method has been implemented and validated on Fisher's intuitive research in relation to the classification of iris flowers. Evaluation and comparison of the proposed method with Fisher’s experimental results showed higher efficiency and accuracy in flower classification. The proposed method has been used to assess the seismic risk of 50,000 buildings based on 10 heterogeneous parameters. Seismic risk classification showed that more than 88% of buildings were classified, and 12% of buildings that could not be classified due to excessive scatter of parameter values were classified using a very small confidence radius. The results indicate the ability of the proposed method to classify objects with the least similarity and number of effective parameters in classification.

Objective: Toxoplasmosis, caused by an intracellular protozoan parasite, and the Toxoplasma gondii, is widespread throughout the world. In recent years, significant progress has been made in the identification of vaccine candidates which could induce a protective response. GRA7, an excretory 29 kDa Toxoplasma gondii a dense granular antigen released by infected host cells. In tachyzoite-infected cells, p29 accumulates within the parasitophorous vacuole and co-localizes with its delimiting membrane. Materials and Methods: In the present work, first genomic DNA of Toxoplasma gondii was extracted and used for amplifying of GRA7 gene as a template. Then PCR product was extracted from agarose gel and cloned into TOPO vector. The plasmid containing GRA7 gene was extracted from the transformed bacteria (TOP10 strain) and sequenced. Results: Sequence analysis of GRA7 gene cloned into TOPO vector showed only one base difference when composed with the gene bank sequence for RH strain was only one base. Conclusion: The results indicated that this clone is suitable for subcloning in Prokaryotic and Eukaryotic plasmid.

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

Conventional stone columns are widely used to improve soft soils in which high compressibility and low shear strength are observed. In very soft soils, granular columns may undergo excessive bulging due to a lack of lateral support provided by the surrounding soil. Wrapping the granular columns with appropriate geosynthetic material can reduce the total and differential settlements while improving the load-carrying capacity of the reinforced ground. A compacted sand or gravel mat (known as a working platform) placed below the embankment is commonly used to prevent excessive lateral deformation of the foundation’s soft soil. In the circumstance of very high applied load, this granular mat may be further reinforced with a geogrid layer to enhance its effectiveness and control the overall stability of the embankment. This paper presents the results of a series of three-dimensional numerical analyses to study the development of hoop forces in geosynthetic encasement under different combinations of working platform thickness and basal geogrid stiffness. The results showed that for a constant working platform thickness, the hoop forces increased with the height of the embankment. The maximum values of the encasement hoop forces were also observed to reduce significantly as the thickness of the working platform increased