Disturbance detection for optimal database storage in electrical distribution systems using artificial immune systems with negative selection

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

Light emitting diode (LED) use in artificial lighting for broiler chicken production

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

Use of plastic tips in artificial feeding of Dermacentor (Anocentor) nitens females Neumann, 1897 (Acari: Ixodidae)

The establishment of laboratory colonies of ticks is often hampered by their lack of adaptation to alternative hosts. The aim of this study was to artificially feed partially engorged Dermacentor (Anocentor) nitens females through plastic tips, and to identify what are the optimal conditions of application of this technique to get as much as possible close to the natural conditions. The technique of artificial feeding through plastic tips allowed the engorgement of D. nitens ticks to a final weight within the normal range for the species. (C) 2014 Published by Elsevier GmbH.

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

Avaliação in vitro do efeito das tinturas de Camellia sinensis e de Aloe arborescens na remineralização de lesão de cárie artificial em esmalte dentário humano

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Organization model for mobile wireless sensor networks inspired in artificial bee colony

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

Impact of the temperament of Nellore cows on the quality of handling and pregnancy rates in fixed-time artificial insemination

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Deformation and tidal evolution of close-in planets and satellites using a Maxwell viscoelastic rheology

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

Color stability of the artificial iris button in an ocular prosthesis before and after acrylic resin polymerization

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

Comparative efficacy of exogenous eCG and progesterone on endogenous progesterone and pregnancy in Holstein cows submitted to timed artificial insemination

Coordenação de Apoio de Pessoal de Nível Superior (CAPES)

Real-time characterization of the uterine blood flow in mares before and after artificial insemination

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

Bond strength of artificial teeth attached to a microwave-polymerized denture base resin after immersion in disinfectant solutions

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.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

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.