Extended immersive learning environment: a hybrid remote/virtual laboratory

This paper presents a collaborative virtual learning environment, which includes technologies such as 3D virtual representations, learning and content management systems, remote experiments, and collaborative learning spaces, among others. It intends to facilitate the construction, management and sharing of knowledge among teachers and students, in a global perspective. The environment proposes the use of 3D social representations for accessing learning materials in a dynamic and interactive form, which is regarded to be closer to the physical reality experienced by teachers and students in a learning context. A first implementation of the proposed extended immersive learning environment, in the area of solid mechanics, is also described, including the access to theoretical contents and a remote experiment to determine the elastic modulus of a given object.These instructions give you basic guidelines for preparing camera-ready papers for conference proceedings. Use this document as a template if you are using Microsoft Word 6.0 or later. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further. Define all symbols used in the abstract. Do not cite references in the abstract.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.

Board-level prototype validation: a built-in controller and extended BST architecture

Prototype validation is a major concern in modern electronic product design and development. Simulation, structural test, functional and timing debug are all forming parts of the validation process, although very often addressed as dissociated tasks. In this paper we describe an integrated approach to board-level prototype validation, based on a set of mandatory/optional BST instructions and a built-in controller for debug and test, that addresses the late mentioned tasks as inherent parts of a whole process

Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

Automated synthesis procedure of RF discrete tuning differential capacitance circuits

The paper presents a RFDSCA automated synthesis procedure. This algorithm determines several RFDSCA circuits from the top-level system specifications all with the same maximum performance. The genetic synthesis tool optimizes a fitness function proportional to the RFDSCA quality factor and uses the epsiv-concept and maximin sorting scheme to achieve a set of solutions well distributed along a non-dominated front. To confirm the results of the algorithm, three RFDSCAs were simulated in SpectreRF and one of them was implemented and tested. The design used a 0.25 mum BiCMOS process. All the results (synthesized, simulated and measured) are very close, which indicate that the genetic synthesis method is a very useful tool to design optimum performance RFDSCAs.

A fractional calculus perspective in the evolutionary design of combinational circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

Hybrid thermoset polymeric composites – an Innovation towards sustainability

In this study, a new waste management solution for thermoset glass fibre reinforced polymer (GFRP) based products was assessed. Mechanical recycling approach, with reduction of GFRP waste to powdered and fibrous materials was applied, and the prospective added-value of obtained recyclates was experimentally investigated as raw material for polyester based mortars. Different GFRP waste admixed mortar formulations were analyzed varying the content, between 4% up to 12% in weight, of GFRP powder and fibre mix waste. The effect of incorporation of a silane coupling agent was also assessed. Design of experiments and data treatment was accomplished through implementation of full factorial design and analysis of variance ANOVA. Added value of potential recycling solution was assessed by means of flexural and compressive loading capacity of GFRP waste admixed mortars with regard to unmodified polymer mortars. The key findings of this study showed a viable technological option for improving the quality of polyester based mortars and highlight a potential cost-effective waste management solution for thermoset composite materials in the production of sustainable concrete-polymer based products.

Management Driven Hybrid Multicast Framework for Content Aware Networks

The need for better adaptation of networks to transported flows has led to research on new approaches such as content aware networks and network aware applications. In parallel, recent developments of multimedia and content oriented services and applications such as IPTV, video streaming, video on demand, and Internet TV reinforced interest in multicast technologies. IP multicast has not been widely deployed due to interdomain and QoS support problems; therefore, alternative solutions have been investigated. This article proposes a management driven hybrid multicast solution that is multi-domain and media oriented, and combines overlay multicast, IP multicast, and P2P. The architecture is developed in a content aware network and network aware application environment, based on light network virtualization. The multicast trees can be seen as parallel virtual content aware networks, spanning a single or multiple IP domains, customized to the type of content to be transported while fulfilling the quality of service requirements of the service provider.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

Integrated Management of IaaS Resources

This paper proposes and reports the development of an open source solution for the integrated management of Infrastructure as a Service (IaaS) cloud computing resources, through the use of a common API taxonomy, to incorporate open source and proprietary platforms. This research included two surveys on open source IaaS platforms (OpenNebula, OpenStack and CloudStack) and a proprietary platform (Parallels Automation for Cloud Infrastructure - PACI) as well as on IaaS abstraction solutions (jClouds, Libcloud and Deltacloud), followed by a thorough comparison to determine the best approach. The adopted implementation reuses the Apache Deltacloud open source abstraction framework, which relies on the development of software driver modules to interface with different IaaS platforms, and involved the development of a new Deltacloud driver for PACI. The resulting interoperable solution successfully incorporates OpenNebula, OpenStack (reuses pre-existing drivers) and PACI (includes the developed Deltacloud PACI driver) nodes and provides a Web dashboard and a Representational State Transfer (REST) interface library. The results of the exchanged data payload and time response tests performed are presented and discussed. The conclusions show that open source abstraction tools like Deltacloud allow the modular and integrated management of IaaS platforms (open source and proprietary), introduce relevant time and negligible data overheads and, as a result, can be adopted by Small and Medium-sized Enterprise (SME) cloud providers to circumvent the vendor lock-in problem whenever service response time is not critical.

Analysis of pesticide residues in strawberries and soils by GC-MS/MS, LC-MS/MS and two-dimensional GC-timeof- flight MS comparing organic and integrated pest management farming

This study analysed 22 strawberry and soil samples after their collection over the course of 2 years to compare the residue profiles from organic farming with integrated pest management practices in Portugal. For sample preparation, we used the citrate-buffered version of the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. We applied three different methods for analysis: (1) 27 pesticides were targeted using LC-MS/MS; (2) 143 were targeted using low pressure GC-tandem mass spectrometry (LP-GC-MS/MS); and (3) more than 600 pesticides were screened in a targeted and untargeted approach using comprehensive, two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOF-MS). Comparison was made of the analyses using the different methods for the shared samples. The results were similar, thereby providing satisfactory confirmation of both similarly positive and negative findings. No pesticides were found in the organic-farmed samples. In samples from integrated pest management practices, nine pesticides were determined and confirmed to be present, ranging from 2 μg kg−1 for fluazifop-pbutyl to 50 μg kg−1 for fenpropathrin. Concentrations of residues in strawberries were less than European maximum residue limits.

Sensitive bi-enzymatic biosensor based on polyphenoloxidases–gold nanoparticles–chitosan hybrid film–graphene doped carbon paste electrode for carbamates detection

A bi-enzymatic biosensor (LACC–TYR–AuNPs–CS/GPE) for carbamates was prepared in a single step by electrodeposition of a hybrid film onto a graphene doped carbon paste electrode (GPE). Graphene and the gold nanoparticles (AuNPs) were morphologically characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, dynamic light scattering and laser Doppler velocimetry. The electrodeposited hybrid film was composed of laccase (LACC), tyrosinase (TYR) and AuNPs entrapped in a chitosan (CS) polymeric matrix. Experimental parameters, namely graphene redox state, AuNPs:CS ratio, enzymes concentration, pH and inhibition time were evaluated. LACC–TYR–AuNPs–CS/GPE exhibited an improved Michaelis–Menten kinetic constant (26.9 ± 0.5 M) when compared with LACC–AuNPs–CS/GPE (37.8 ± 0.2 M) and TYR–AuNPs–CS/GPE (52.3 ± 0.4 M). Using 4-aminophenol as substrate at pH 5.5, the device presented wide linear ranges, low detection limits (1.68×10− 9 ± 1.18×10− 10 – 2.15×10− 7 ± 3.41×10− 9 M), high accuracy, sensitivity (1.13×106 ± 8.11×104 – 2.19×108 ± 2.51×107 %inhibition M− 1), repeatability (1.2–5.8% RSD), reproducibility (3.2–6.5% RSD) and stability (ca. twenty days) to determine carbaryl, formetanate hydrochloride, propoxur and ziram in citrus fruits based on their inhibitory capacity on the polyphenoloxidases activity. Recoveries at two fortified levels ranged from 93.8 ± 0.3% (lemon) to 97.8 ± 0.3% (orange). Glucose, citric acid and ascorbic acid do not interfere significantly in the electroanalysis. The proposed electroanalytical procedure can be a promising tool for food safety control.

Integrated biomarker responses of an estuarine invertebrate to high abiotic stress and decreased metal contamination

An integrated chemical-biological effects monitoring was performed in 2010 and 2012 in two NW Iberian estuaries under different anthropogenic pressure. One is low impacted and the other is contaminated by metals. The aim was to verify the usefulness of a multibiomarker approach, using Carcinus maenas as bioindicator species, to reflect diminishing environmental contamination and improved health status under abiotic variation. Sampling sites were assessed for metal levels in sediments and C. maenas, water abiotic factors and biomarkers (neurotoxicity, energy metabolism, biotransformation, anti-oxidant defences, oxidative damage). High inter-annual and seasonal abiotic variation was observed. Metal levels in sediments and crab tissues were markedly higher in 2010 than in 2012 in the contaminated estuary. Biomarkers indicated differences between the study sites and seasons and an improvement of effects measured in C. maenas from the polluted estuary in 2012. Integrated Biomarker Response (IBR) index depicted sites with higher stress levels whereas Principal Component Analysis (PCA) showed associations between biomarker responses and environmental variables. The multibiomarker approach and integrated assessments proved to be useful to the early diagnosis of remediation measures in impacted sites.