• Restorative Dentistry and Endodontics
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• v.42 no.2
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• pp.105-110
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• 2017

Objectives: This study investigated the cleaning ability of ultrasonically activated irrigation (UAI) and a novel activation system with reciprocating motion (EC, EasyClean, Easy Equipamentos $Odontol\acute{o}gicos$) when used with a relatively new chelating agent (QMix, Dentsply). In addition, the effect of QMix solution when used for a shorter (1 minute) and a longer application time (3 minutes) was investigated. Materials and Methods: Fifty permanent human teeth were prepared with K3 rotary system and 6% sodium hypochlorite. Samples were randomly assigned to five groups (n = 10) according to the final irrigation protocol: G1, negative control (distilled water); G2, positive control (QMix 1 minute); G3, QMix 1 minute/UAI; G4, QMix 1 minute/EC; G5, QMix 3 minutes. Subsequently the teeth were prepared and three photomicrographs were obtained in each root third of root walls, by scanning electron microscopy. Two blinded and pre-calibrated examiners evaluated the images using a four-category scoring system. Data were statistically analyzed using Kruskal-Wallis and Dunn tests (p < 0.05). Results: There were differences among groups (p < 0.05). UAI showed better cleaning ability than EC (p < 0.05). There were improvements when QMix was used with auxiliary devices in comparison with conventional irrigation (p < 0.05). Conventional irrigation for 3 minutes presented significantly better results than its use for 1 minute (p < 0.05). Conclusions: QMix should be used for 1 minute when it is used with UAI, since this final irrigation protocol showed the best performance and also allowed clinical optimization of this procedure.

• Restorative Dentistry and Endodontics
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• v.45 no.3
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• pp.28.1-28.13
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• 2020

Objectives: To evaluate the outcome of in vitro studies comparing the effectiveness of QMix irrigant in removing the smear layer in the root canal system compared with other irrigants. Materials and Methods: The research question was developed by using Population, Intervention, Comparison, Outcome and Study design framework. Literature search was performed using 3 electronic databases PubMed, Scopus, and EBSCOhost until October 2019. Two reviewers were independently involved in the selection of the articles and data extraction process. Risk of bias of the studies was independently appraised using revised Cochrane Risk of Bias tool (RoB 2.0) based on 5 domains. Results: Thirteen studies fulfilled the selection criteria. The overall risk of bias was moderate. QMix was found to have better smear layer removal ability than mixture of tetracycline isonomer, an acid and a detergent (MTAD), sodium hypochlorite (NaOCl), and phytic acid. The efficacy was less effective than 7% maleic acid and 10% citric acid. No conclusive results could be drawn between QMix and 17% ethylenediaminetetraacetic acid due to conflicting results. QMix was more effective when used for 3 minutes than 1 minute. Conclusions: QMix has better smear layer removal ability compared to MTAD, NaOCl, Tubulicid Plus, and Phytic acid. In order to remove the smear layer more effectively with QMix, it is recommended to use it for a longer duration.

• Restorative Dentistry and Endodontics
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• v.45 no.2
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• pp.23.1-23.12
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• 2020

Objectives: This study aimed to summarize the outcome of in vitro studies comparing the antibacterial effectiveness of QMix with other irrigants against Enterococcus faecalis. Materials and Methods: The research question was developed by using population, intervention, comparison, outcome, and study design framework. The literature search was performed using 3 electronic databases: PubMed, Scopus, and EBSCOhost until October 2019. The additional hand search was performed from the reference list of the eligible studies. The risk of bias of the studies was independently appraised using the revised Cochrane Risk of Bias tool (RoB 2.0). Results: Fourteen studies were included in this systematic review. The overall risk of bias for the selected studies was moderate. QMix was found to have a higher antimicrobial activity compared to 2% sodium hypochlorite (NaOCl), 17% ethylenediaminetetraacetic acid (EDTA), 2% chlorhexidine (CHX), mixture of tetracycline isonomer, an acid and a detergent (MTAD), 0.2% Cetrimide, SilverSol/H2O2, HYBENX, and grape seed extract (GSE). QMix had higher antibacterial efficacy compared to NaOCl, only when used for a longer time (10 minutes) and with higher volume (above 3 mL). Conclusions: QMix has higher antibacterial activity than 17% EDTA, 2% CHX, MTAD, 0.2% Cetrimide, SilverSol/H2O2, HYBENX, GSE and NaOCl with lower concentration. To improve the effectiveness, QMix is to use for a longer time and at a higher volume.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.281-288
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• 2022

AGVs are often used in industrial applications to transport heavy materials around a large industrial building, such as factories or warehouses. In particular, in fulfillment centers their usefulness is maximized for automation. To increase productivity in warehouses such as fulfillment centers, sophisticated path planning of AGVs is required. We propose a scheme that can be applied to QMIX, a popular cooperative MARL algorithm. The performance was measured with three metrics in several fulfillment center layouts, and the results are presented through comparison with the performance of the existing QMIX. Additionally, we visualize the transport paths of trained AGVs for a visible analysis of the behavior patterns of the AGVs as heat maps.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.