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Objective: To evaluate the effect of different adhesives and veneering resins on the shear bond strength (SBS) of polyetheretherketone (PEEK). Methods: A total of 138 PEEK specimens were randomly divided into 6 groups according to adhesive material application: Control (C, no application), Adhese Universal (A) (Ivoclar Vivadent, Schaan, Liechtenstein), Gluma Bond Universal (G) (Heraeus Kulzer, South Bend, IN, USA), G-PremioBOND (P) (GC Corporation, Tokyo, Japan), Single Bond Universal (S) (3M, Saint Paul, MN, USA) and visio.link (V) (Bredent, Senden, Germany). Each adhesive group was divided into two subgroups according to the type of veneering material: Estenia direct composite (D) and Gradia Plus indirect composite (IN) (both GC Corporation). After the veneering process, the specimens were aged by thermal cycling. Kruskal-Wallis and Mann-Whitney U tests were used for SBS analysis (P < 0.05). Results: The highest SBS results were obtained in the VIN group, followed by the VD, PD, GIN, AIN, AD, SIN, SD, PIN, GD, CIN and CD groups, respectively (P = 0.001). There were no significant differences in terms of the type of veneering composite when the same adhesive was applied (P > 0.05), except for Gluma Bond Universal (P = 0.009). All the adhesives tested showed clinically acceptable SBS results. Conclusion: Visio.link offered the highest adhesion to PEEK, whereas the tested universal adhesives may be used as an alternative to visio.link in clinical settings. It was determined that changing the veneer type has no statistical difference when the same adhesive material is used. [ABSTRACT FROM AUTHOR]

The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana. [ABSTRACT FROM AUTHOR]