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This paper proposes an improvement on the recently introduced Henry Gas Solubility Optimization (HGSO) metaheuristic algorithm that simulates the Henry's gas law (i.e., the concentration of a gas sample in liquid solvent is proportional to the concentration of the sample in gas phase). As an improvement we apply quantum theory instead of standard procedure used in HSGO algorithm for updating solutions. The proposed algorithm is named as Quantum HGSO (QHGSO) algorithm in this paper. The suggested changes enhance the ability of HGSO to create a counterbalance between the exploitation and exploration for a better investigation of the solution space. For evaluating the capability of finding optimal solution of our proposed algorithm, a collection of forty-seven global optimization functions is solved. Moreover, three well-known engineering problems are studied to show the performance of the QHGSO algorithm in constrained optimization problems. Comparative results with other well-known metaheuristic algorithms have shown that the QHGSO algorithm outperforms others with higher computational performance.

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.