An uncertainty approach for Electric Submersible Pump modeling through Deep Neural Network.

This work proposes a new methodology to identify and validate deep learning models for artificial oil lift systems that use submersible electric pumps. The proposed methodology allows for obtaining the models and evaluating the prediction's uncertainty jointly and systematically. The methodology employs a nonlinear model to generate training and validation data and the Markov Chain Monte Carlo algorithm to assess the neural network's epistemic uncertainty. The nonlinear model was used to overcome the limitations of the need for big datasets for training deep learning models. However, the developed models are validated against experimental data after training and validation with synthetic data. The validation is also performed through the models' uncertainty assessment and experimental data. From the implementation point of view, the method was coded in Python with Tensorflow and Keras libraries used to build the neural Networks and find the hyperparameters. The results show that the proposed methodology obtained models representing both the nonlinear model's dynamic behavior and the experimental data. It provides a most probable value close to the experimental data, and the uncertainty of the generated deep learning models has the same order of magnitude as that of the nonlinear model. This uncertainty assessment shows that the built models were adequately validated. The proposed deep learning models can be applied in several applications requiring a reliable and computationally lighter model. Hence, the obtained AI dynamic models can be employed for digital twin construction, control, and optimization.

Hysteresis modeling in ballistic carbon nanotube field-effect transistors.

Theoretical models are adapted to describe the hysteresis effects seen in the electrical characteristics of carbon nanotube field-effect transistors. The ballistic transport model describes the contributions of conduction energy sub-bands over carbon nanotube field-effect transistor drain current as a function of drain-source and gate-source voltages as well as other physical parameters of the device. The limiting-loop proximity model, originally developed to understand magnetic hysteresis, is also utilized in this work. The curves obtained from our developed model corroborate well with the experimentally derived hysteretic behavior of the transistors. Modeling the hysteresis behavior will enable designers to reliably use these effects in both analog and memory applications.

Electron-hole transport and photovoltaic effect in gated MoS2 Schottky junctions.

Semiconducting molybdenum disulfphide has emerged as an attractive material for novel nanoscale optoelectronic devices due to its reduced dimensionality and large direct bandgap. Since optoelectronic devices require electron-hole generation/recombination, it is important to be able to fabricate ambipolar transistors to investigate charge transport both in the conduction band and in the valence band. Although n-type transistor operation for single-layer and few-layer MoS2 with gold source and drain contacts was recently demonstrated, transport in the valence band has been elusive for solid-state devices. Here we show that a multi-layer MoS2 channel can be hole-doped by palladium contacts, yielding MoS2 p-type transistors. When two different materials are used for the source and drain contacts, for example hole-doping Pd and electron-doping Au, the Schottky junctions formed at the MoS2 contacts produce a clear photovoltaic effect.

Parasitism by argulids (Crustacea: Branchiura) in piranhas (Osteichthyes: Serrasalmidae) captured in the Caiçara bays, upper Paraguay River, Pantanal, Mato Grosso state, Brazil

In this study, 446 fishes were analyzed: 190 Pygocentrus nattereri, 193 Serrasalmus maculatus, and 63 S. marginatus.They were captured in two bays, upper and lower Caiçara, in the upper Paraguay River basin, during one hydrological cycle from May 2008 to April 2009. Six species of Branchiura were found: Dolops bidentata, D. longicauda, Dolops sp., Argulus multicolor, A. chicomendesi, and Dipteropeltis hirundo. All fish species were infested by more than one species of Branchiura and the overall prevalence was 33.4%. The following prevalences were observed: 52.6% in P. nattereri; 20.3% in S. maculatus, and 15.8% in S. marginatus. The relative condition factor (Kn) differed significantly between parasitized and non parasitized individuals only in P. nattereri and S. maculatus. There was no correlation between Kn and abundance of parasites nor between body length (Ls) and intensity of infestation, in all three host species.

Foram analisados 446 peixes: 190 Pygocentrus nattereri, 193 Serrasalmus maculatus e 63 S. marginatus, capturados nas baías Caiçara superior e inferior na bacia do alto rio Paraguai, durante um ciclo hidrológico nos meses de maio de 2008 a abril de 2009. Foram encontradas seis espécies de Branchiura: Dolops bidentata, D. longicauda, Dolops sp., Argulus multicolor, A. chicomendesi e Dipteropeltis hirundo. Todos os peixes estavam parasitados por mais de uma espécie de Branchiura e a prevalência geral foi 33,4%. As prevalências foram: P. nattereri 52,6%, S. maculatus 20,3% e S. marginatus 15,8%. O fator de condição relativo (Kn) diferiu significativamente entre indivíduos parasitados e não parasitados apenas em P. nattereri e S. maculatus. Não houve correlação significativa entre Kn e abundância de parasitos, nem entre comprimento padrão (Ls) e intensidade de infestação, nas três espécies de piranhas analisadas.