Synthesis and characterization of cellulose acetate from cellophane industry residues. Application as acetaminophen controlled-release membranes

Journal of Thermal Analysis and Calorimetry - Cellophane film production generates cellulosic residues from scraps, edges, and low-quality films. In this work, cellophane was used as a raw material...     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

Inverse central ordering for the Newton interpolation formula

Numerical Algorithms - An inverse central ordering of the nodes is proposed for the Newton interpolation formula. This ordering may improve the stability for certain distributions of nodes. For...     

Topology optimization of composite hyperelastic material using SPIMFO-method

Meccanica - Fiber reinforced materials are used in assorted engineering application and for this reason, new additive manufacturing technologies have been developed for this type of materials. With...     

One Spot Microwave Synthesis and Characterization of Nitrogen-Doped Carbon Dots with High Oxygen Content for Fluorometric Determination of Banned Sudan II Dye in Spice Samples

Journal of Fluorescence - A simple microwave-assisted synthesis of nitrogen-doped carbon dots with high oxygen content (O-N-CDs) was carried out with citric acid as a carbon source and...     

Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.     

Two algorithms for the exchange lemma

Numerical Algorithms - In this note, we present two new algorithms for the Steinitz Exchange Lemma. They are grounded on a single application of a procedure (finding either a row echelon form or a...