Analysis of a hyper-diverse seed dispersal network: modularity and underlying mechanisms

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fight versus flight: the interaction of temperature and body size determines antipredator behaviour in tegu lizards

Ectotherm antipredator behaviour might be strongly affected both by body temperature and size: when environmental temperatures do not favour maximal locomotor performance, large individuals may confront predators, whereas small animals may flee, simply because they have no other option. However, integration of body size and temperature effects is rarely approached in the study of antipredator behaviour in vertebrate ectotherms. In the present study we investigated whether temperature affects antipredator responses of tegu lizards, Tupinambis merianae, with distinct body sizes, testing the hypothesis that small tegus (juveniles) run away from predators regardless of the environmental temperature, because defensive aggression may not be an effective predator deterrent, whereas adults, which are larger, use aggressive defence at low temperatures, when running performance might be suboptimal. We recorded responses of juvenile (small) and adult (large) tegu lizards to a simulated predatory attack at five environmental temperatures in the laboratory. Most differences between the two size classes were observed at low temperatures: large tegus were more aggressive overall than were small tegus at all temperatures tested, but at lower temperatures, the small lizards often used escape responses whereas the large ones either adopted a defensive posture or remained inactive. These results provide strong evidence that body size and temperature affect the antipredator responses of vertebrate ectotherms. We discuss the complex and intricate network of evolutionary and ecological parameters that are likely to be involved in the evolution of such interactions. (C) 2009 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

An application of neural network technique to correct the dome temperature effects on pyrgeometer measurements

This work describes an application of a multilayer perceptron neural network technique to correct dome emission effects on longwave atmospheric radiation measurements carried out using an Eppley Precision Infrared Radiometer (PIR) pyrgeometer. It is shown that approximately 7-month-long measurements of dome and case temperatures and meteorological variables available in regular surface stations (global solar radiation, air temperature, and air relative humidity) are enough to train the neural network algorithm and correct the observed longwave radiation for dome temperature effects in surface stations with climates similar to that of the city of São Paulo, Brazil. The network was trained using data from 15 October 2003 to 7 January 2004 and verified using data, not present during the network-training period, from 8 January to 30 April 2004. The longwave radiation values generated by the neural network technique were very similar to the values obtained by Fairall et al., assumed here as the reference approach to correct dome emission effects in PIR pyrgeometers. Compared to the empirical approach the neural network technique is less limited to sensor type and time of day (allows nighttime corrections).

Chondroitin sulfate proteoglycans are structural renewable constituents of the rabbit vitreous body

Purpose: To characterize the vitreous intrinsic proteoglycans, investigate their dynamics, and examine their role in the supramolecular organization of the vitreous. Methods: Vitreous from normal rabbits was collected and processed for observation with the transmission electron microscope after treatment with glycosidases. Also, rabbits were injected intravitreally with [S-35]-sodium sulfate and sacrificed at several time intervals after the injection. Proteoglycans (PGs) were assayed in the vitreous supernatant or in whole samples extracted with guanidine hydrochloride by polyacrylamide or agarose gel electrophoresis, followed respectively by fluorography or autoradiography, and ion-exchange chromatography and gel-filtration chromatography, combined with glycolytic treatment of the samples. The sulfated glycosaminoglycans (GAGs) were characterized by agarose gel electrophoresis after treating vitreous samples with protease and specific glycosidases. Results: the electron microscopic study revealed a network with hyaluronic acid ( HA) as thin threads coating and connecting collagen fibrils. The elimination of the HA coat showed chondroitin sulfate granules (8-25 nm) arranged at regular intervals on the fibril surface. The chondroitinase ABC digestion, besides removing the granules, also caused the formation of thicker bundles of the collagen fibrils. The PG and GAG analysis indicated that there are three renewable PGs in the vitreous ( e. g., one heparan-and two chondroitin-sulfate ones). Conclusions: At least one of the chondroitin sulfate PGs is involved in the interactions that occur in the vitreous structure, mainly by providing adequate spacing between the collagen fibrils, a condition that is probably required for the transparency of the vitreous.

Smart structures health monitoring using artificial neural network

This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Optimization of a novel nitric oxide sensor using a latex rubber matrix

This study present a novel NO sensor made of a spin trap (iron(II)-diethyldithiocarbamate complex, FeDETC) incorporated in a latex rubber matrix and works as a trap for NO, which is detectable by Electron Paramagnetic Resonance (EPR). We explored the optimization of our sensors changing systematically two fabrication parameters: the latex rubber matrix temperature of polymerization and FeDETC concentration inside the matrix. The sensor was prepared in four different temperatures: 4, 10, 20 and 40°C. The FeDETC concentration was also varied from 0.975 to 14.8 mM. We observed a variation of the EPR signals from the sensors prepared at different conditions. We found a high stability of the EPR response from our sensor, 40 days at RT. The best sensor was made with a latex rubber matrix polymerized at 10°C and with a FeDETC concentration of 14.8 mM. In vivo tests show good biocompatibility of our sensor. © 2007 Asian Network for Scientific Information.

Network node with wireless and wired interfaces: Nios II processor and uClinux to development of a NCAP embedded (IEEE 1451.1) with two interfaces, wireless (IEEE 1451.5) and wired (IEEE p1451.2)

This paper presents a NCAP embedded on DE2 kit with Nios II processor and uClinux to development of a network gateway with two interfaces, wireless (ZigBee) and wired (RS232) based on IEEE 1451. Both the communications, wireless and wired, were developed to be point-to-point and working with the same protocols, based on IEEE 1451.0-2007. The tests were made using a microcomputer, which through of browser was possible access the web page stored in the DE2 kit and send commands of control and monitoring to both TIMs (WTIM and STIM). The system describes a different form of development of the NCAP node to be applied in different environments with wired or wireless in the same node. © 2011 IEEE.

Development of heterogeneous node network using microcontroller with protocols CAN and Zigbee

In this paper was proposed the development of an heterogeneous system using the microcontroller (AT90CANI28) where the protocol model CAN and the standard IEEE 802.15.4 are connected. This module is able to manage and monitor sensors and actuators using CAN and, through the wireless standard 802.15.4, communicate with the other network modules. © 2011 IEEE.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Estimação da porcentagem de flúor em alumina fluoretada proveniente de uma planta de tratamento de gases por meio de um sensor virtual neural

As indústrias têm buscado constantemente reduzir gastos operacionais, visando o aumento do lucro e da competitividade. Para alcançar essa meta, são necessários, dentre outros fatores, o projeto e a implantação de novas ferramentas que permitam o acesso às informações relevantes do processo de forma precisa, eficiente e barata. Os sensores virtuais têm sido aplicados cada vez mais nas indústrias. Por ser flexível, ele pode ser adaptado a qualquer tipo de medição, promovendo uma redução de custos operacionais sem comprometer, e em alguns casos até melhorar, a qualidade da informação gerada. Como estão totalmente baseados em software, não estão sujeitos a danos físicos como os sensores reais, além de permitirem uma melhor adaptação a ambientes hostis e de difícil acesso. A razão do sucesso destes tipos de sensores é a utilização de técnicas de inteligência computacional, as quais têm sido usadas na modelagem de vários processos não lineares altamente complexos. Este trabalho tem como objetivo estimar a qualidade da alumina fluoretada proveniente de uma Planta de Tratamento de Gases (PTG), a qual é resultado da adsorção de gases poluentes em alumina virgem, via sensor virtual. O modelo que emula o comportamento de um sensor de qualidade de alumina foi criado através da técnica de inteligência computacional conhecida como Rede Neural Artificial. As motivações deste trabalho consistem em: realizar simulações virtuais, sem comprometer o funcionamento da PTG; tomar decisões mais precisas e não baseada somente na experiência do operador; diagnosticar potenciais problemas, antes que esses interfiram na qualidade da alumina fluoretada; manter o funcionamento do forno de redução de alumínio dentro da normalidade, pois a produção de alumina de baixa qualidade afeta a reação de quebra da molécula que contém este metal. Os benefícios que este projeto trará consistem em: aumentar a eficiência da PTG, produzindo alumina fluoretada de alta qualidade e emitindo menos gases poluentes na atmosfera, além de aumentar o tempo de vida útil do forno de redução.

Cross-layer optimizations for multimedia distribution over Wireless Multimedia Sensor Networks and Flying Ad-Hoc Networks with quality of experience support

The proliferation of multimedia content and the demand for new audio or video services have fostered the development of a new era based on multimedia information, which allowed the evolution of Wireless Multimedia Sensor Networks (WMSNs) and also Flying Ad-Hoc Networks (FANETs). In this way, live multimedia services require realtime video transmissions with a low frame loss rate, tolerable end-to-end delay, and jitter to support video dissemination with Quality of Experience (QoE) support. Hence, a key principle in a QoE-aware approach is the transmission of high priority frames (protect them) with a minimum packet loss ratio, as well as network overhead. Moreover, multimedia content must be transmitted from a given source to the destination via intermediate nodes with high reliability in a large scale scenario. The routing service must cope with dynamic topologies caused by node failure or mobility, as well as wireless channel changes, in order to continue to operate despite dynamic topologies during multimedia transmission. Finally, understanding user satisfaction on watching a video sequence is becoming a key requirement for delivery of multimedia content with QoE support. With this goal in mind, solutions involving multimedia transmissions must take into account the video characteristics to improve video quality delivery. The main research contributions of this thesis are driven by the research question how to provide multimedia distribution with high energy-efficiency, reliability, robustness, scalability, and QoE support over wireless ad hoc networks. The thesis addresses several problem domains with contributions on different layers of the communication stack. At the application layer, we introduce a QoE-aware packet redundancy mechanism to reduce the impact of the unreliable and lossy nature of wireless environment to disseminate live multimedia content. At the network layer, we introduce two routing protocols, namely video-aware Multi-hop and multi-path hierarchical routing protocol for Efficient VIdeo transmission for static WMSN scenarios (MEVI), and cross-layer link quality and geographical-aware beaconless OR protocol for multimedia FANET scenarios (XLinGO). Both protocols enable multimedia dissemination with energy-efficiency, reliability and QoE support. This is achieved by combining multiple cross-layer metrics for routing decision in order to establish reliable routes.

Body and Didactics. Possible directions of international research

The paper investigates the role of the body in didactics. It looks up for points of contact between the functional sceneries of the classroom and some recent approaches, such like simplexity, neurosciences and enactivism. The two experiments presented they aim to demonstrate the importance of body awareness to improve the didactic quality. The first experience used a SenseWear Armband that provided data about the energetic expenditure of a teacher during diff erent activities in a lesson. Th e second experiment relied on a neurofeedback device integrated to a sensor, it detected body temperature with the aim of understanding the role of the body in the process of self-regulation-learning and management of attention and arousal.

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

Análise de pathway e network em tecido adiposo in vivo e o papel da vitamina D na adipogênese in vitro

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)