Federal University of São Carlos

About: Federal University of São Carlos is a education organization based out in São Carlos, Brazil. It is known for research contribution in the topics: Population & Microstructure. The organization has 16471 authors who have published 34057 publications receiving 456654 citations. The organization is also known as: UFSCar & Federal University of São Carlos.

Electrospinning-based (bio)sensors for food and agricultural applications: A review

Abstract: Sensors and biosensors for monitoring food traceability, quality, safety, and nutritional value are of outmost importance nowadays. Electrospinning, a simple, straightforward and versatile technique to fabricate 1D micro- and nanomaterials, is among the most potential strategies to further advance the development of chemical (bio)sensors. Electrospun nanofibers are capable of improving several attributes of chemical (bio)sensors due to the high specific surface area, high porosity and 1-D confinement characteristics. Furthermore, the possibility to buildup multifunctional nanostructures by functionalizing the nanofiber surface with a wide range of distinct nanomaterials (such as carbon nanotubes, graphene, nanoparticles and conjugated polymers), enhances the (bio)sensing capabilities through additional properties and synergistic effects. In this review, we outline the representative progress in the last decade on the development of multifunctional hybrid electrospun nanofibers of varied morphology and composition, and their applications in chemical (bio)sensor platforms for analysis of food and agricultural products.

Volume I. Introduction to DUNE

Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology.

Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

Abstract: This paper presents a methodology aimed at extracting features to obtain information that will highlight disturbances related to the field of power quality. Due to the concept of smart grids, it is clear that the classification of the disturbances should be undertaken using smart meters, so that a large amount of data corresponding to the voltage and current waveforms are not exchanged using the communication channels, i.e., between smart meter and Utility’s database server. Thus, it is necessary to ensure a balance between computational effort (arising from the implementation of algorithms on hardware) and the satisfactory performance of the algorithm for the classification of disturbances. Based on the assumption that the classification task is onerous, this paper proposes a step of feature extraction that may be calculated and analyzed offline using synthetic waveforms/signals, which are subsequently validated using field measurements. It should be noted that this offline analysis is required to determine the most relevant features for the process of classifying each disturbance. However, in order to establish the effectiveness of the feature extraction step, the response of decision trees of the C4.5 type and of artificial neural networks of the multilayer perceptron type were verified during the phase of disturbance classification. In short, good results were obtained that corroborate the hypothesis that the feature extraction step is necessary to classify disturbances effectively and with low computational effort.