Microstructure,Mechanical, and Scratch Resistance Properties of TiAlCrNbN-Graded Composite Coating Deposited on AISI H13 Steel Substrate with Pulsed DC Closed Field Unbalanced Magnetron Sputtering Method

Structure and adhesion properties of TiAlCrNbN coatings were investigated. These coatings were deposited onto AISI H13 steel substrate using pulsed dc closed field unbalanced magnetron sputtering at different deposition parameters including duty cycle, bias voltage, and working pressure. The coatings have been characterized by X-ray diffraction, scanning electron microscopy and energy dispersive spectroscopy. The TiAlCrNbN-graded composite coatings have a dense and columnar structure. The X-ray diffraction patterns of coatings exhibited predominantly c-TiAlCrN, h-NbN, and h-TiAlN reflections. Scratch resistance test showed that the highest adhesion strength was attained as 68 N at 2.5 μs duty time, 100 V bias voltages, and 3 × 10^?3 Torr deposition parameters. The lowest adhesion strength was obtained as 55 N at 0.5 μs duty time, 50V bias voltage, and 2 × 10^?3 Torr deposition parameters.     

Nanoflower hydroxyapatite's effect on the properties of resin-based dental composite

To investigate the reinforcing effect of nanoflower-like hydroxyapatite (NFHA) in resin-based dental composites, we synthesized a novel NFHA using microwave irradiation (MW), hydrothermal treatment (HT), and sonochemical synthesis (SS). Silanized NFHA was then used as the reinforcing filler in dental resin composites. We characterized the structure and morphology of various HA nanostructures using x-ray diffraction, scanning electron microscope, and TEM. The mechanical performance of dental resin composites reinforced with silanized NFHA was measured using a universal testing machine. Spherical HA, synthesized through chemical precipitation (CP), served as the control group. One-way analysis of variance was employed for the statistical analysis of the acquired data. The results demonstrate that the nanoflower morphology significantly was improved mechanical and physical properties. After conducting trials, the NFHA synthesized using MW and HT showed a substantial enhancement in mechanical and physical properties compared to the other structures. Therefore, it can be concluded that NFHA can serve as a novel reinforcing HA filler, providing regenerative properties to resin composites with sufficient mechanical strength.     

Effect of different denture adhesives on retention of complete dentures: an in vivo study

The increase in prevalence of tooth loss with the effect of population aging produces the growing need for complete dentures. The success and acceptance of complete dentures by the patient depends on sufficient retention and stability. Therefore, denture adhesives are regularly used by denture wearers to improve the function of complete denture. We evaluated the effect of three different denture adhesives (Corega, Protefix, Fittydent) on the retention of maxillary complete denture (MCD) using with digital dynamometer (DD). For this purpose, denture adhesives were applied on MCDs of 30 participants. After chewing procedure, the force was applied at 45° to the palatal surface of denture by DD. Dislodgement force was recorded by means of Newton. There were four measurements on each patient including; group of control: Group C; Group CR: Corega; Group F: Fittydent; Group P: Protefix. The result of the study was statistically evaluated by using analysis of variance (ANOVA) and Tukey HSD test. Statistics of ANOVA showed a significant difference among all the four groups (p = 0.00, <0.05). Tukey HSD test indicated that there was a statistical difference between Group F and the other groups, but there was not a significant difference between the other groups. The highest adhesive strength value was observed in group F, the lowest in group C. Use of denture adhesives improved the retentive strength of complete denture.     

Effective methods for increasing aggregate diversity in recommender systems

In order to make a recommendation, a recommender system typically first predicts a user’s ratings for items and then recommends a list of items to the user which have high predicted ratings. Quality of predictions is measured by accuracy, that is, how close the predicted ratings are to actual ratings. On the other hand, quality of recommendation lists is evaluated from more than one perspective. Since accuracy of predicted ratings is not enough for customer satisfaction, metrics such as novelty, serendipity, and diversity are also used to measure the quality of the recommendation lists. Aggregate diversity is one of these metrics which measures the diversity of items across the recommendation lists of all users. Increasing aggregate diversity is important because it leads a more even distribution of items in the recommendation lists which prevents the long-tail problem. In this study, we propose two novel methods to increase aggregate diversity of a recommender system. The first method is a reranking approach which takes a ranked list of recommendations of a user and reranks it to increase aggregate diversity. While the reranking approach is applied after model generation as a wrapper the second method is applied in model generation phase which has the advantage of being more efficient in the generation of recommendation lists. We compare our methods with the well-known methods in the field and show the superiority of our methods using real-world datasets.     

Clustering-based diversity improvement in top-N recommendation

The major aim of recommender algorithms has been to predict accurately the rating value of items. However, it has been recognized that accurate prediction of rating values is not the only requirement for achieving user satisfaction. One other requirement, which has gained importance recently, is the diversity of recommendation lists. Being able to recommend a diverse set of items is important for user satisfaction since it gives the user a richer set of items to choose from and increases the chance of discovering new items. In this study, we propose a novel method which can be used to give each user an option to adjust the diversity levels of their own recommendation lists. Experiments show that the method effectively increases the diversity levels of recommendation lists with little decrease in accuracy. Compared to the existing methods, the proposed method, while achieving similar diversification performance, has a very low computational time complexity, which makes it highly scalable and allows it to be used in the online phase of the recommendation process.