271 resultados para artificial


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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To evaluate the bond strength between two types of acrylic resin teeth and a microwave denture base resin after immersion in disinfectant solutions for 180 days. Eighty specimens made of acrylic resin teeth (Biotone and Biotone IPN) attached to a microwave polymerized denture base resin (Nature-Cryl MC) were divided into eight groups (n = 10) according to the treatment (distilled water-control, 2% chlorhexidine digluconate, 1% sodium hypochlorite and sodium perborate solution-Corega Tabs). The shear strength tests (MPa) were carried out using a universal testing machine with a 0.5 mm/min speed. Data analysis was performed using ANOVA and multiple comparison Student-Newman-Keuls post hoc test (α = 0.05). Biotone IPN showed similar results among the groups (distilled water, 8.25 ± 1.81 MPa; chlorhexidine, 7.81 ± 3.34 MPa; hypochlorite, 7.75 ± 3.72 MPa; and Corega Tabs, 7.58 ± 2.27 MPa, whereas Biotone showed significantly lower shear bond strength values for the groups immersed in Corega Tabs (5.25 ± 3.27 MPa) and chlorhexidine (6.08 ± 2.35 MPa). Soaking the dentures in 1% sodium hypochlorite could be recommended as a disinfectant solution for dentures fabricated with conventional acrylic resin denture teeth and microwave denture base resin. For dentures fabricated with IPN teeth and microwave denture base resin, all the soaking solutions evaluated in this study could be suggested to denture wearers.

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Esthetics and durability of materials used to fabricate artificial eyes has been an important issue since artificial eyes are essential to restore esthetics and function, protect the remaining tissues and help with patients' psychological therapy. However, these materials are submitted to degrading effects of environmental agents on the physical properties of the acrylic resin. OBJECTIVE: This study assessed the color stability of acrylic resins used to fabricate sclera in three basic shades (N1, N2 and N3) when subjected to accelerated aging, mechanical and chemical polishing. MATERIAL AND METHODS: Specimens of each resin were fabricated and submitted to mechanical and chemical polishing. Chromatic analysis was performed before and after accelerated aging through ultraviolet reflection spectrophotometry. RESULTS: All specimens revealed color alteration following polishing and accelerated aging. The resins presented statistically significant chromatic alteration (p<0.01) between the periods of 252 and 1008 h. CONCLUSIONS: Both polishing methods presented no significant difference between the values of color derivatives of resins.

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The osseointegrated implants created a progress in oral rehabilitation. But this rehabilitation may be complicated by anatomical conditions, mainly in maxillary anterior region, where a higher aesthetic is required. The aim of study was to report one case which the use of angled abutments were necessary to allow the correct positioning of the prosthesis and also the prosthetic reconstruction with gingival-colored ceramic to provide best aesthetic result for patient. The aesthetic and functional results provided greater patient satisfaction with her smile. The correct planning is essential to provide to patient the best aesthetic and functional results, being necessary the use of abutments and gingival-colored ceramic to reach better results. Uniterms: dental implantacion, esthetics, mouth rehabilitation.

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The aim of this study was to evaluate the opacity of the polymethylmethacrylate for ocular prosthesis using an ocular button, a colorless resin for the ocular prostheses, and 4 brands of acrylic colorless resin in the function of weathering (0, 504, or 1008 h) and thickness (1 and 3.5 mm). One hundred twenty specimens were confectioned (made) and allocated into 12 groups (n = 10). Opacity analysis was carried out with a spectrophotometer of visible ultraviolet reflection before and after weathering by 504 and 1008 hours. Data for the opacity were expressed in ΔE. Data were then analyzed statistically by analysis of variance and the Tukey test (P < 0.01). The data demonstrated statistically significant differences; manufactured ocular button (ΔE = 47.4) and the resin Vipi Cril (ΔE = 38.11) presented greater and minor values of opacity, respectively. The weathering showed statistical difference among times (0 h, ΔE = 36.32; 504 h, ΔE = 39.98; and 1008 h, ΔE = 43.9). Ocular button and evaluated resins presented greater values of opacity when presented in 3.5 mm in thickness. The values of opacity increased with the progression of the time of weathering, independent of the evaluated material and the thickness.

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The development of the National Curriculum Parameters for High School (PCN) is established from a logic in which the disciplines are integrated and coordinated in order to promote interdisciplinarity. However, there are few didactic-pedagogic activities that enable teachers in practice to reach this goal. In this paper, we propose an experimental activity to be used with High School students, addressing the concept of viscosity from an interdisciplinary perspective between the disciplines of Physics and Chemistry. For that purpose, flow times of sucrose solutions of different concentrations will be measured in a glass Ford and these data will be used to construct a calibration curve, which will be used to determine the concentration of sucrose in artificial flavor syrup of gooseberry.

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In the search for productivity increase, industry has invested on the development of intelligent, flexible and self-adjusting method, capable of controlling processes through the assistance of autonomous systems, independently whether they are hardware or software. Notwithstanding, simulating conventional computational techniques is rather challenging, regarding the complexity and non-linearity of the production systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) performs well as noise suppression and treatment of non-linear data. Therefore, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, consequently, add value to the final product. Furthermore, Artificial Intelligence techniques such as Neuro-Fuzzy Networks (NFNs) have proven effective, since NFNs combine the ability to learn from previous examples and generalize the acquired information from the ANNs with the capacity of Fuzzy Logic to transform linguistic variables in rules.

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The aim of this work was to examine both the influence of anatomical and technical aspects on fertility rate of sheep based on the performance of transcervical artificial insemination (TCAI). Transcervical artificial insemination was performed with traction of the cervix in 122 ewes using frozen semen from 11 rams, both Santa Ines breed. The data collected were: type of external cervical opening (CO) (P - papilla; FL - flap; DB - duckbill, S - spiral; RO - rosette), duration of cervical manipulation (2-3, 4-5 and 6-7 minutes), degree of difficulty in cervical transposition (low, moderate, high) and presumed semen deposition site (SC - superficial cervical; DC - deep cervical; IU - intrauterine). The influence of these variables on pregnancy rate was evaluated. Cervical opening type and duration of cervical manipulation had no influence (p>0.05) on fertility. The degree of difficulty in cervical manipulation influenced (p<0.05) pregnancy rate, since insemination classified as low grade had 52% of pregnancy, while those classified as high recorded only 20%. The presumed site of semen deposition influenced significantly (p<0.05) fertility. Pregnancy rates of deposition at each site were: UI – 45.8%, DC – 25.7%; SC – 15.4%. As expected, deeper depositions resulted in higher fertility. In conclusion, the performance of TCAI did not depend on the anatomical classification of external cervical opening of ewe and the duration of cervical manipulation within the range tested (2-7 minutes). The TCAI may have higher fertility rates if difficulties in the application were reduced and the semen deposition was deeper.

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This work aimed to evaluate the influence of postpartum period (precocious - of 28 to 44 days and late - of 45 to 90 days) on the bovine pregnancy rate using fixed-time AI. For that, 678 cows were divided in two groups: precocious group (G-P, n=151) and late group (G-T, n=527). The animals received CIDR® + 2 mL of estradiol benzoate (BE) in the day zero (D0). After eight days (D8) the dispositive was removed and both groups received 2.5mL PGF2α, concurrent with PGF2α injection, they received either 1.5mL of eCG or temporary calf removal (RTB). In the next day (D9), the cows received 1 mL de BE and 24 hours later, the fixed-time AI was performed with Nelore bovine semen. The calves were returned to their mothers. The pregnancy rate was not different between the groups (p>0.05), G-P=40% and G-T=48%. The results indicate that females with less than 45 days of postpartum are able to hormone protocol of fixed-time IA.

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Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.