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This study aimed to compare the amount of debris extrusion of four endodontic systems made of Nickle‐Titanium alloy. This in vitro study was done on 80 extracted primary molars. They were selected by cone‐beam computed tomography and randomly divided into four groups (n = 20) to be prepared to the apical size of 25 by one of the systems: Reciproc, Protaper Universal, Neolix, or Hyflex CM. Debris was collected into Eppendorf microtubes and placed in an incubator to evaporate the washing solution. Debris was weighed by a digital scale of 0.01 g precision. Data were statistically analysed using SPSS software. Tukey’s comparison was used to determine the difference between the four file systems (α = 0.05). Debris extrusion after Reciproc preparation (0.00320) was significantly higher than the other (P < 0.05), with no significant difference having been observed among the other systems (P > 0.05). It can be concluded that all systems under investigation exhibited debris extrusion.

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Ground vibration is one of the most important undesired phenomena resulting from blasting operations imposing damages to facilities and buildings on the one hand, and creating environmental problems in open pit mining on the other. Therefore, the present study aims to provide an optimized classification binary model to identify the blasting patterns with an acceptable ground vibration intensity to reduce the damages resulting from this artificial phenomenon. This study uses a binary method to provide an optimized classification model for predicting and evaluating the blasting patterns with the minimum ground vibration. Group Method of Data Handling-Type Neural Network is used as one of the most practical optimization algorithms to solve complicated and uncertain problems in this modelling. In this study, by collecting the data of 52 different blasting patterns from Soungun copper mine, some of the most important geometric properties and the amount of ammonium nitrate fuel oil consumed in each blasting pattern are recorded. In addition, based on expertise and experience of experts, the degree of ground vibration produced by each blasting is qualitatively classified into four different ranges of very high, high, normal and low in the form of unacceptable (very high and High) and acceptable (normal and low) clusters. Based on the results obtained from the analyses, the developed model has a high flexibility and ability in the binary prediction of blasting patterns with an acceptable vibration magnitude.

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms.

This paper aims to address the challenges faced in selecting the most suitable oil and gas (OG) well alternative for stimulation operations to improve production and recovery possibilities in hydrocarbon reservoirs. To achieve this, a novel combination of multiple criteria decision-making (MCDM) models has been proposed, and an illustrative study has been carried out in Iranian hydrocarbon reservoirs. Fourteen criteria based on engineering and managerial perspectives have been identified, and the appropriate weights of these criteria have been determined using a novel interval-valued spherical fuzzy (IVSF) entropy method. Four fuzzy ranking algorithms have been established to select the proper well, and the achieved results have been combined using the Borda method. To evaluate the robustness of experimental results, a sensitivity analysis has been implemented. The proposed method not only enhances the accuracy of OG reservoir selection but also reduces the risk associated with conventional economic predictions for carbonate reservoirs by considering the influencing factors of the development process. Overall, this paper offers an efficient and effective approach for selecting the best OG well alternative in the National Iranian Oil Company (NIOC), which can be valuable for both managerial and technical perspectives in the oil and gas sector.

Job scheduling has always been a challenging task for production managers. It is of special importance if it involves multi-objectives and parallel machines. In this research, our direction is largely motivated by the uncertainty emerged in production systems, where processing times of jobs and setup times of machines are not fixed and they are subject to some uncertainties. Our paper presents a mixed-integer goal programming model considering fuzzy approach for parallel machines environments with separable jobs, sequence-dependent