Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

A pipeline structure for the block QR update in digital signal processing

The Journal of Supercomputing - There exist problems in the field of digital signal processing, such as filtering of acoustic signals that require processing a large amount of data in real time....     

Runtime verification of autopilot systems using a fragment of MTL- $${int }$$

International Journal on Software Tools for Technology Transfer - Current real-time embedded systems development frameworks lack support for the verification of properties using explicit time where...     

Capture of UAVs Through GPS Spoofing Using Low-Cost SDR Platforms

Wireless Personal Communications - The increased use of unmanned aerial vehicles (UAVs), better known as drones, by civilians has grown exponentially and their autonomous flight control systems...     

Eco-efficiency and techno-economic analysis for maleic anhydride manufacturing processes

Clean Technologies and Environmental Policy - Maleic anhydride may be obtained from different technological routes, being the selective oxidation of benzene and oxidation of butane the only ones...     

Fracture toughness of high strength seamless pipe steel from SE(T) and SE(B) specimens evaluated by different standards

Recently released, BS 8571:2014 standard offers an alternative for the experimental determination of fracture toughness and resistance curves of metallic materials from SE(T) specimens. Similar in stress conditions and constraint to cracked tubes, specimens in SE(T) geometry should yield less conservative toughness values than conventional high‐constraint specimens, such as SE(B) geometry. However, the convention to determine the J‐integral fracture toughness proposed in the new BS standard is different from ASTM standards. In this work, SE(T) and SE(B) specimens of similar dimensions of a tough high‐strength seamless pipe steel were tested following the BS 8571:2014 and ASTM E1820‐16 standards, respectively. Because of the different standardized definitions, SE(T) specimens yield lower fracture toughness than SE(B) specimens, which could lead to more conservative results in structural integrity analysis. This investigation also suggests the introduction of the blunting line concept in the BS 8571:2014 standard in order to minimize this problem.     

Grape,apple and pineapple juice characterisation and detection of mixtures

Several multivariate statistical methods (principal component, cluster, stepwise discriminant and multiple linear regression analysis) were applied to analytical data obtained by conventional methods and to analytical data for free amino acids, in an attempt to distinguish between grape, apple and pineapple juice. Analysis for methionine, proline, asparagine, arginine and glutamic acid made it possible to differentiate among the 34 samples of grape, apple and pineapple juices studied. Multiple linear regression analysis of computer-simulated mixtures of grape and apple juices made it possible to detect the proportion of each fruit within the blend.