Implementació d'un ERP open source amb programació a mida

This work presents, from the perspective of a freelancer professional, a case study of a practical and real implementation of an Open Source ERP software suite to a very small company, including the development of a custom software module to adapt the suite to the particular needs of the company.

Servei de rutes basat en Open Source Routing Machine

Aquesta memòria descriu la preparació, l'execució i els resultats obtinguts d'implementar un sistema calculador de rutes. El projecte Open Source Routing Machine és un motor calculador de rutes d'alt rendiment que utilitza les dades de OpenStreetMaps per calcular el camí més curt entre dos punts. En aquest projecte final no únicament es volen utilitzar les dades OpenStreetMap sinó que també es pretenen utilitzar dades pròpies en format shapefile i poder visualitzar-los en un visor web. Aquest visor permet a l'usuari, de forma senzilla, sol•licitar rutes al servidor OSRM creat, obtenint la ruta desitjada en molt pocs milisegons

Au bord d'une source

Soitinnus: Piano.

Comparative effects of macro-sized aluminum oxide and aluminum oxide nanoparticles on erythrocyte hemolysis: influence of cell source, temperature and size

Al2O3 is the most abundantly produced nanomaterial and has been used in diverse fields, including the medical, military and industrial sectors. As there are concerns about the health effects of nanoparticles, it is important to understand how they interact with cells, and specifically with red blood cells. The hemolysis induced by three commercial nano-sized aluminum oxide particles (nanopowder 13 nm, nanopowder <50 nm and nanowire 2-6 nm × 200-400 nm) was compared to aluminum oxide and has been studied on erythrocytes from humans, rats and rabbits, in order to elucidate the mechanism of action and the influence of size and shape on hemolytic behavior. The concentrations inducing 50% hemolysis (HC50) were calculated for each compound studied. The most hemolytic aluminum oxide particles were of nanopowder 13, followed by nanowire and nanopowder 50. The addition of albumin to PBS induced a protective effect on hemolysis in all the nano-forms of Al2O3, but not on Al2O3. The drop in HC50 correlated to a decrease in nanomaterial size, which was induced by a reduction of aggregation Aluminum oxide nanoparticles are less hemolytic than other oxide nanoparticles, and behave differently depending on the size and shape of the nanoparticles. The hemolytic behavior of aluminum oxide nanoparticles differs from that of aluminum oxide.

Alternative Techniques of Neural Signal Processing in Neuroengineering

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics.

Comparative effects of macro-sized aluminum oxide and aluminum oxide nanoparticles on erythrocyte hemolysis: influence of cell source, temperature and size

Al2O3 is the most abundantly produced nanomaterial and has been used in diverse fields, including the medical, military and industrial sectors. As there are concerns about the health effects of nanoparticles, it is important to understand how they interact with cells, and specifically with red blood cells. The hemolysis induced by three commercial nano-sized aluminum oxide particles (nanopowder 13 nm, nanopowder <50 nm and nanowire 2-6 nm × 200-400 nm) was compared to aluminum oxide and has been studied on erythrocytes from humans, rats and rabbits, in order to elucidate the mechanism of action and the influence of size and shape on hemolytic behavior. The concentrations inducing 50% hemolysis (HC50) were calculated for each compound studied. The most hemolytic aluminum oxide particles were of nanopowder 13, followed by nanowire and nanopowder 50. The addition of albumin to PBS induced a protective effect on hemolysis in all the nano-forms of Al2O3, but not on Al2O3. The drop in HC50 correlated to a decrease in nanomaterial size, which was induced by a reduction of aggregation Aluminum oxide nanoparticles are less hemolytic than other oxide nanoparticles, and behave differently depending on the size and shape of the nanoparticles. The hemolytic behavior of aluminum oxide nanoparticles differs from that of aluminum oxide.

Neural mechanisms underlying adaptive actions after slips

An increase in cognitive control has been systematically observed in responses produced immediately after the commission of an error. Such responses show a delay in reaction time (post-error slowing) and an increase in accuracy. To characterize the neurophysiological mechanism involved in the adaptation of cognitive control, we examined oscillatory electrical brain activity by electroencephalogram and its corresponding neural network by event-related functional magnetic resonance imaging in three experiments. We identified a new oscillatory thetabeta component related to the degree of post-error slowing in the correct responses following an erroneous trial. Additionally, we found that the activity of the right dorsolateral prefrontal cortex, the right inferior frontal cortex, and the right superior frontal cortex was correlated with the degree of caution shown in the trial following the commission of an error. Given the overlap between this brain network and the regions activated by the need to inhibit motor responses in a stop-signal manipulation, we conclude that the increase in cognitive control observed after the commission of an error is implemented through the participation of an inhibitory mechanism.

The Connectivity of Musical Aptitude and Foreign Language Learning Skills: Neural and Behavioural Evidence

Given the structural and acoustical similarities between speech and music, and possible overlapping cerebral structures in speech and music processing, a possible relationship between musical aptitude and linguistic abilities, especially in terms of second language pronunciation skills, was investigated. Moreover, the laterality effect of the mother tongue was examined with both adults and children by means of dichotic listening scores. Finally, two event-related potential studies sought to reveal whether children with advanced second language pronunciation skills and higher general musical aptitude differed from children with less-advanced pronunciation skills and less musical aptitude in accuracy when preattentively processing mistuned triads and music / speech sound durations. The results showed a significant relationship between musical aptitude, English language pronunciation skills, chord discrimination ability, and sound-change-evoked brain activation in response to musical stimuli (durational differences and triad contrasts). Regular music practice may also have a modulatory effect on the brain’s linguistic organization and cause altered hemispheric functioning in those who have regularly practised music for years. Based on the present results, it is proposed that language skills, both in production and discrimination, are interconnected with perceptual musical skills.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

Carotenoids production: microorganisms as source of natural dyes

Carotenoids are natural dyes synthesized by plants, algae and microorganisms. Application in many sectors can be found, as food dyeing and supplementation, pharmaceuticals, cosmetics and animal feed. Recent investigations have shown their ability to reduce the risks for many degenerative diseases like cancer, heart diseases, cataract and macular degeneration. An advantage of microbial carotenoids is the fact that the cultivation in controlled conditions is not dependent of climate, season or soil composition. In this review the advances in bio-production of carotenoids are presented, discussing the main factors that influence the microbial production of these dyes in different systems.

Chemical composition, circadian rhythm and antibacterial activity of essential oils of piper divaricatum: a new source of safrole

The essential oils from leaves, stems and fruits of Piper divaricatum were analyzed by GC-MS. The tissues showed high safrole content: leaves (98%), fruits (87%) and stems (83%), with yields of 2.0, 4.8 and 1.7%, respectively. This is a new alternative source of safrole, a compound widely used as a flavoring agent and insecticide. The leaf's oil showed antibacterial activity against gram-negative bacteria while safrole was active against Salmonella Typhimurium and Pseudomonas aeruginosa. In addition, the study of circadian rhythm of the safrole concentration in the essential oils of leaves showed a negligible variation of 92 to 98%.

Tropical biodiversity: has it been a potential source of secondary metabolites useful for medicinal chemistry?

The use of natural products has definitely been the most successful strategy in the discovery of novel medicines. Secondary metabolites from terrestrial and marine organisms have found considerable use in the treatment of numerous diseases and have been considered lead molecules both in their natural form and as templates for medicinal chemistry. This paper seeks to show the great value of secondary metabolites and emphasize the rich chemical diversity of Brazilian biodiversity. This natural chemical library remains understudied, but can be a useful source of new secondary metabolites with potential application as templates for drug discovery.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.