Selection and ranking of dental restorative composite materials using hybrid Entropy-VIKOR method: An application of MCDM technique

Effects of Hexagonal Boron Nitride and Mesoporous Silica Nanoparticles on the Morphology, Mechanical Properties and Antimicrobial Activity of Dental Composites

Hexagonal boron nitride (HBN), an artificial material with unique properties, is used in many industries. This article focuses on the extent to which hexagonal boron nitride and silica nanoparticles (MSN) affect the physicochemical and mechanical properties and antimicrobial activity of prepared dental composites. In this study, HBN, and MSN were used as additives in dental composites. 5% and 10% by weight of HBN are added to the structure of the composite materials. FTIR analysis were performed to determine the components of the produced boron nitride powders, hexagonal boron nitride-containing composites, and filling material applications. The structural and microstructural properties of dental composites have been extensively characterized using X-ray diffractometry (XRD). Surface morphology and distributions of nano boron nitride were determined by scanning electron microscopy (SEM)-EDS. In addition, the solubility of dental composites in water and their stability in water and chemical solution (Fenton) were determined by three repetitive experiments. Finally, the antimicrobial activity of dental composites was detected by using Minimum Inhibitory Concentration (MIC) measurement, as well as Minimum Fungicidal Concentration (MFC) method against yeast strain Saccharomyces cerevisiae, and Minimum Bactericidal Concentration (MBC) method against bacteria strains, Staphylococcus aureus and Escherichia coli. Since the HMP series have better antimicrobial activity than the HP series, they are more suitable for preventing dental caries and for long-term use of dental composites. In addition, when HMP and HP series added to the composite are compared, HMP-containing dental composites have better physicochemical and mechanical properties and therefore have a high potential for commercialization.

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

Effective multi-attribute group decision-making approach to study astronomy in the probabilistic linguistic q-rung orthopair fuzzy VIKOR framework

This study employs a novel fuzzy logic-based framework to address multi-attribute group decision-making problems commonly encountered in modern astronomy. Our approach utilizes the probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) to handle the inherent uncertainties associated with astronomical data. The PLq-ROFS offers significant advantages over existing fuzzy sets like probabilistic hesitant, linguistic intuitionistic, and linguistic Pythagorean fuzzy sets, which comprise both stochastic and non-stochastic uncertainties simultaneously. To aggregate the probabilistic linguistic decision information effectively, we propose two novel operators: the PLq-ROF weighted power average (PLq-ROFWPA) and the PLq-ROF weighted power geometric (PLq-ROFWPG). These operators form the foundation of a novel method within the PLq-ROF environment. Furthermore, this study integrates the PLq-ROF framework with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, a widely used decision-making (DM) tool known for its ability to balance group utility maximization with individual regret minimization. This integration leads to the PLq-ROF-VIKOR model, a novel approach for ranking alternative solutions based on the subjective preferences of decision-makers. The effectiveness of the proposed method is demonstrated through a real-world case study in astronomy, accompanied by both parameter and comparative analyses. These analyses highlight the efficiency and accuracy of the PLq-ROF-VIKOR model, ultimately leading to the conclusion that cosmology is the most optimal key finding in this case study.

Evaluation of compressive strength, microhardness and solubility of zinc-oxide eugenol cement reinforced with E-glass fibers

BACKGROUND

Zinc-oxide eugenol (ZOE) cements are among the most used temporary materials in dentistry. Although ZOE has advantages over other temporary fillers, its mechanical strength is weaker, so researchers are working to improve it. E-glass fibers have emerged as promising reinforcing fibers in recent years due to their strong mechanical behavior, adequate bonding, and acceptable aesthetics.

OBJECTIVES

To evaluate and compare the compressive strength, surface microhardness, and solubility of the ZOE and those reinforced with 10 wt.% E-glass fibers.

METHODS

A total of 60 ZEO specimens were prepared; 30 specimens were reinforced with 10 wt.% E-glass fibers, considered modified ZOE. The characterization of the E-glass fibers was performed by XRF, SEM, and PSD. The compressive strength, surface microhardness, and solubility were evaluated. Independent sample t-tests were used to statistically assess the data and compare mean values (P ≤ 0.05).

RESULTS

The results revealed that the modified ZOE showed a significantly higher mean value of compressive strength and surface microhardness while having a significantly lower mean value of solubility compared to unmodified ZOE (P ≤ 0.05).

CONCLUSION

The modified ZOE with 10 wt.% E-glass fibers had the opportunity to be used as permanent filling materials.

A Temporary Acrylic Soft Denture Lining Material Enriched with Silver-Releasing Filler-Cytotoxicity, Mechanical and Antifungal Properties

Colonization of temporary denture soft linings and underlying tissues by yeast-like fungi is an important clinical problem due to the negative influence on the process of prosthetic treatment. Typical hygienic procedures are often insufficient to prevent fungal infections, so in this study, an antimicrobial filler (silver sodium hydrogen zirconium phosphate) was introduced into acrylic soft liner at concentrations of 1, 2, 4, 6, 8 and 10% (w/w). The effect of this modification on antifungal properties against Candida albicans, cytotoxicity, Shore A hardness, tensile strength and tensile bond strength, sorption and solubility was investigated, considering the recommended 30-day period of temporary soft lining use. The most favorable compilation of properties was obtained at a 1 to 6% filler content, for which nearly a total reduction in Candida albicans was registered even after 30 days of sample storing. The tensile and bond strength of these composites was at the desired and stable level and did not differ from the results for the control material. Hardness increased with the increasing concentration in filler but were within the range typical for soft lining materials and their changes during the experiment were similar to the control material. The materials were not cytotoxic and sorption and solubility levels were stable.

Determining the appropriate natural fibers for intelligent green wearable devices made from biomaterials via multi-attribute decision making model

Intelligent and green wearable technology becomes essential for new modern societies. This work introduces a multi criteria decision making model to properly assess and compare relative desired criteria for selecting the most suitable constituents for green body wearable bio-products made from bio-based materials. It aims to enhance the sustainability of intelligent green wearable devices by providing support in the selection process of lightweight, eco-friendly materials suitable for personal body wearable bio-products made of natural fiber composites to improve qualities that may help in better monitoring human vital signs and thereby address the health care concern. The relative intrinsic characteristic and merits of various natural fibers were utilized to compare and evaluate their relative performance in bio-composites. The model considered several evaluation factors like mechanical performance including tensile strength and modulus of elasticity, comfortability including size and weight, availability, fiber orientation, cellulose content, and cost. Results have demonstrated different priorities of the considered natural fibers relative to each evaluation factor. However, the model was capable of properly evaluating and ranking the best fibers relative to the whole conflicting evaluation criteria simultaneously. The closeness of priorities in several cases emphasizes upon using such decision making models to be able to judge the relative merits of natural fibers for such applications. It can also help designers to avoid bias during determining the best alternatives considering several conflicting evaluation criteria.

Prioritizing biomaterials for spinal disc implants by a fuzzy AHP and TOPSIS decision making method

Considerable research has been focused on identifying the optimum biomaterial for spine implants. New technologies and materials have allowed surgeons to better grasp the biomechanical principles underpinning implant stability and function. An optimal biomaterial for total disc replacement (TDR) should include essential characteristics such as biocompatibility, long-term durability, the capacity to withstand mechanical stresses, and economic viability. Our research has focused on six biomaterials for TDR, including Ti-6Al-4V, CoCr alloy, stainless steel 316L, zirconia toughened alumina (ZTA), polyether ether ketone (PEEK) and ultra-high-molecular weight polyethylene (UHMWPE). Ten common properties, i.e., the Young's modulus, density, tensile strength, the expense of the manufacturing process, the cost of raw material, wear rate, corrosion resistance, thermal conductivity, fracture toughness and compressive strength were utilized to assess these six different materials. The purpose of this study was to evaluate and rank the six alternative biomaterials proposed for use in the endplates and articulating surface of a spinal TDR. To accomplish this, a multi-criteria decision-making approach, namely the fuzzy analytic hierarchy process (fuzzy AHP) and the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to solve the model. For validation and robustness of the proposed method, sensitivity analysis was performed, and comparison was performed with fuzzy-VIKOR and fuzzy-MOORA methods. In light of the study's results, ZTA and Ti-6Al-4V were identified as the best suited materials for the articulating surface and endplates, respectively, in a spinal disc implant.