A decision support system for a waste collection vehicle routing problem

The selective collection of municipal solid waste for recycling is a very complex and expensive process, where a major issue is to perform cost-efficient waste collection routes. Despite the abundance of commercially available software for fleet management, they often lack the capability to deal properly with sequencing problems and dynamic revision of plans and schedules during process execution. Our approach to achieve better solutions for the waste collection process is to model it as a vehicle routing problem, more specifically as a team orienteering problem where capacity constraints on the vehicles are considered, as well as time windows for the waste collection points and for the vehicles. The final model is called capacitated team orienteering problem with double time windows (CTOPdTW).We developed a genetic algorithm to solve routing problems in waste collection modelled as a CTOPdTW. The results achieved suggest possible reductions of logistic costs in selective waste collection.

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

Eco-efficient self-compacting concrete with reduced Portland cement content and high volume of fly ash and metakaolin

The eco-efficient, self-compacting concrete (SCC) production, containing low levels of cement in its formulation, shall contribute for the constructions' sustainability due to the decrease in Portland cement use, to the use of industrial residue, for beyond the minimization of the energy needed for its placement and compaction. In this context, the present paper intends to assess the viability of SCC production with low cement levels by determining the fresh and hardened properties of concrete containing high levels of fly ash (FA) and also metakaolin (MK). Hence, 6 different concrete formulations were produced and tested: two reference concretes made with 300 and 500 kg/m3 of cement; the others were produced in order to evaluate the effects of high replacement levels of cement. Cement replacement by FA of 60% and by 50% of FA plus 20% of MK were tested and the addition of hydrated lime in these two types of concrete were also studied. To evaluate the self-compacting ability slump flow test, T500, J-ring, V-funnel and L-box were performed. In the hardened state the compressive strength at 3, 7, 14, 21, 28 and 90 days of age was determined. The results showed that it is possible to produce low cement content SCC by replacing high levels of cement by mineral additions, meeting the rheological requirements for self-compacting, with moderate resistances from 25 to 30 MPa after 28 days.

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

New control algorithm for single-phase series active power filter

This paper presents a single-phase Series Active Power Filter (Series APF) for mitigation of the load voltage harmonic content, while maintaining the voltage on the DC side regulated without the support of a voltage source. The proposed series active power filter control algorithm eliminates the additional voltage source to regulate the DC voltage, and with the adopted topology it is not used a coupling transformer to interface the series active power filter with the electrical power grid. The paper describes the control strategy which encapsulates the grid synchronization scheme, the compensation voltage calculation, the damping algorithm and the dead-time compensation. The topology and control strategy of the series active power filter have been evaluated in simulation software and simulations results are presented. Experimental results, obtained with a developed laboratorial prototype, validate the theoretical assumptions, and are within the harmonic spectrum limits imposed by the international recommendations of the IEEE-519 Standard.

A new and efficient synthesis of N3-cycloalkylamino-6,8-diaminopurines

[Excerpt] The purine core is a privileged scaffold in medicinal chemistry and the biological relevance of purine derivatives makes them attractive targets in the preparation of combinatorial libraries.1,2 In particular, there is a great interest in the synthesis of 8-substituted purines due to their important potential as antiviral and anticancer agents.3 Reports on 8-aminopurines are limited and general methods to obtain these purine derivatives are still needed.4 Cyclic amines and hydrazines are key structural motifs in various bioactive agents.5 Here we report a novel, efficient and inexpensive method for the synthesis of 6,8-diaminopurines 4 incorporating cycloalkylamino substituents at N3position of the purine ring. (...)

A novel and efficient approach to new N3-substituted-6,8-diaminopurines

[Excerpt] Purine nucleobases are fundamental biochemicals in living organisms. They have been a valuable inspiration for drug design once they play several key roles in the cell.1 To the best of our knowledge, reported routes to 8-aminopurines are still scarce due to the difficulty in introducing amino groups in this position of the purine ring. Here we report a novel, inexpensive and facile synthetic method to generate N3,N6-disubstituted-6,8-diaminopurines. In our research group, a number of substituted purines have been obtained from a common imidazole precursor, the 5-amino-4-cyanoformimidoyl imidazole 1. Recently, a comprehensive study on the reactivity of imidazoles 1 with nucleophiles under acidic conditions led us to develop experimental methods to incorporate primary amines into the cyanoformimidoyl group.2 (...)

A new and efficient synthesis of benzoimidazopurine derivatives

[Excerpt] Purine nucleobases are essential biomolecules in living organisms. Playing several key roles in the cell, they have been a significant inspiration for drug design.1 Benzimidazole nucleus is an important pharmacophore in the development of molecules with pharmaceutical or biological interest. Benzimidazoles have been reported to display significant pharmacological activities such as antiulcer, antifungal, antiparkinson, anticancer and antibiotic.2 Fused structures incorporating these two scaffolds might be important for medicinal chemistry and, to the best of our knowledge, there are no reports of these systems in the literature. In particular, benzo[4,5]imidazo[2,1]purines seem to be novel and must be important target molecules in the heterocyclic synthesis. (...)

Cost-efficient one-part alkali-activated mortars with low global warming potential for floor heating systems applications

Increasing building energy efficiency is one the most cost-effective ways to reduce emissions. The use of thermal insulation materials mitigates heat loss in buildings, therefore minimising heat energy needs. In recent years, several papers were published on the subject of foam alkali-activated cements with enhanced thermal conductivity. However, on those papers cost analysis was strangely avoided. This paper presents experimental results on one-part alkali-activated cements. It also includes global warming potential assessment and cost analysis. Foam one-part alkali-activated cements cost simulations considering two carbon dioxide social costs scenarios are also included. The results show that one-part alkali-activated cements mixtures based on 26%OPC + 58.3%FA + 8%CS + 7.7%CH and 3.5% hydrogen peroxide constitute a promising cost-efficient (67 euro/m3), thermal insulation solution for floor heating systems. This mixture presents a low global warming potential of 443 KgCO2eq/m3. The results confirm that in both carbon dioxide social cost scenarios the mixture 26 OPC + 58.3 FA + 8 CS + 7.7 CH with 3.5% hydrogen peroxide foaming agent is still the most cost efficient.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

An efficient chronic unpredictable stress protocol to induce stress-related responses in C57BL/6 mice

Exposure to chronic stress can have broad effects on health ranging from increased predisposition for neuropsychiatric disorders to deregulation of immune responses. The chronic unpredictable stress (CUS) protocol has been widely used to study the impact of stress exposure in several animal models and consists in the random, intermittent, and unpredictable exposure to a variety of stressors during several weeks. CUS has consistently been shown to induce behavioral and immunological alterations typical of the chronic stress-response. Unfortunately C57BL/6 mice, one of the most widely used mouse strains, due to the great variety of genetically modified lines, seem to be resistant to the commonly used 4-week-long CUS protocol. The definition of an alternative CUS protocol allowing the use of C57BL/6 mice in chronic stress experiments is a need. Here, we show that by extending the CUS protocol to 8?weeks is possible to induce a chronic stress-response in C57BL/6 mice, as revealed by abrogated body weight gain, increased adrenals weight, and an overactive hypothalamic-pituitary-adrenal axis with increased levels of serum corticosterone. Moreover, we also observed stress-associated behavioral alterations, including the potentiation of anxious-like and depressive-like behaviors and a reduction of exploratory behavior, as well as subtle stress-related changes in the cell population of the thymus and of the spleen. The present protocol for C57BL/6 mice consistently triggers the spectrum of CUS-induced changes observed in rats and, thus, will be highly useful to researchers that need to use this particular mouse strain as an animal model of neuropsychiatric disorders and/or immune deregulation related to CUS.