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...     

Consolidation of composite cathodes with NCM and sulfide solid-state electrolytes by hot pressing for all-solid-state Li metal batteries

Journal of Solid State Electrochemistry - In this study, hot pressing was evaluated as a method of cell fabrication to increase the energy density of next-generation all-solid-state batteries with...     

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...     

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.     

Multivariate Reversed Hazard Rates and Inactivity Times of Systems

Methodology and Computing in Applied Probability - The family of the multivariate conditional hazard rate functions often reveals to be a convenient tool to describe the joint probability...     

Evaluating cost impacts on reverse logistics using an Economic Order Quantity (EOQ) model with environmental and social considerations

Central European Journal of Operations Research - Societal awareness and legislation changes concerning sustainability have affected how organizations generate value for stakeholders, as well as...     

Local linear convergence analysis of Primal–Dual splitting methods

In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice.     

Comparative bioavailability of two zolpidem hemitartrate formulations in healthy human Brazilian volunteers using high-performance liquid chromatography coupled to tandem mass spectrometry

To assess the bioequivalence of two zolpidem hemitartrate formulations in 30 healthy volunteers. Plasma samples were obtained over a 24 h period. Plasma concentrations of zolpidem were analyzed by liquid chromatography coupled to tandem mass spectrometry with positive ion electrospray ionization using multiple reaction monitoring. Values of peak concentration (C_max), area under curve (AUC), half-life, elimination constant, volume of distribution and clearance showed statistically significant differences when comparing women (604.34 ng h/ml, 127.36 ng/ml, 4.4 h, 0.18 1/h, 50.56 L and 8.55 L/h, respectively) and men (276.1 ng h/ml, 70.9 ng/ml, 3.3 h, 0.26 1/h, 91.42 L and 24.34 L/h, respectively), receiving the same dose (5 mg), respectively. The geometric means with corresponding 90% confidence interval for Test/Reference percentage ratios were 99.73% (CI 93.69–106.16) for C_max, 97.44% (90% CI = 91.85–103.37%) for area under curve of plasma concentration until the last concentration observed (AUC_last) and 98.30% (90% CI = 92.48–104.49) for the area under curve between the first sample (pre-dosage) and infinity (AUC_0–inf). Since the 90% CI for AUC_last, AUC_0–inf and C_max ratios were within the 80–125% interval proposed by the US Food and Drug Administration, it was concluded that zolpidem hemitartrate formulation (5 mg orodispersible tablet) is bioequivalent to the zolpidem hemitartrate formulation (Patz SL 5 mg sublingual tablet) with regard to both the rate and the extent of absorption. A new formulation of zolpidem 2.5 mg may be useful in women for the same clinical benefits as the 5 mg formulation in men.