Soil vapor extraction in sandy soils: influence of airflow rate

Airflow rate is one of the most important parameters for the soil vapor extraction of contaminated sites, due to its direct influence on the mass transfer occurring during the remediation process. This work reports the study of airflow rate influence on soil vapor extractions, performed in sandy soils contaminated with benzene, toluene, ethylbenzene, xylene, trichloroethylene and perchloroethylene. The objectives were: (i) to analyze the influence of airflow rate on the process; (ii) to develop a methodology to predict the remediation time and the remediation efficiency; and (iii) to select the most efficient airflow rate. For dry sandy soils with negligible contents of clay and natural organic matter, containing the contaminants previously cited, it was concluded that: (i) if equilibrium between the pollutants and the different phases present in the soil matrix was reached and if slow diffusion effects did not occur, higher airflow rates exhibited the fastest remediations, (ii) it was possible to predict the remediation time and the efficiency of remediation with errors below 14%; and (iii) the most efficient remediation were reached with airflow rates below 1.2 cm3 s 1 standard temperature and pressure conditions.

Remediation efficiency of vapour extraction of sandy soils contaminated with cyclohexane: influence of air flow rate, water and natural organic matter content

Abstract This work reports the analysis of the efficiency and time of soil remediation using vapour extraction as well as provides comparison of results using both, prepared and real soils. The main objectives were: (i) to analyse the efficiency and time of remediation according to the water and natural organic matter content of the soil; and (ii) to assess if a previous study, performed using prepared soils, could help to preview the process viability in real conditions. For sandy soils with negligible clay content, artificially contaminated with cyclohexane before vapour extraction, it was concluded that (i) the increase of soil water content and mainly of natural organic matter content influenced negatively the remediation process, making it less efficient, more time consuming, and consequently more expensive; and (ii) a previous study using prepared soils of similar characteristics has proven helpful for previewing the process viability in real conditions.

Study of the voltammetric behaviour of metam and its application to an amperometric flow system

The electrochemical behaviour of the pesticide metam (MT) at a glassy carbon working electrode (GCE) and at a hanging mercury drop electrode (HMDE) was investigated. Different voltammetric techniques, including cyclic voltammetry (CV) and square wave voltammetry (SWV), were used. An anodic peak (independent of pH) at +1.46 V vs AgCl/Ag was observed in MTaqueous solution using the GCE. SWV calibration curves were plotted under optimized conditions (pH 2.5 and frequency 50 Hz), which showed a linear response for 17–29 mg L−1. Electrochemical reduction was also explored, using the HMDE. A well defined cathodic peak was recorded at −0.72 V vs AgCl/ Ag, dependent on pH. After optimizing the operating conditions (pH 10.1, frequency 150 Hz, potential deposition −0.20 V for 10 s), calibration curves was measured in the concentration range 2.5×10−1 to 1.0 mg L−1 using SWV. The electrochemical behaviour of this compound facilitated the development of a flow injection analysis (FIA) system with amperometric detection for the quantification of MT in commercial formulations and spiked water samples. An assessment of the optimal FIA conditions indicated that the best analytical results were obtained at a potential of +1.30 V, an injection volume of 207 μL and an overall flow rate of 2.4 ml min−1. Real samples were analysed via calibration curves over the concentration range 1.3×10−2 to 1.3 mg L−1. Recoveries from the real samples (spiked waters and commercial formulations) were between 97.4 and 105.5%. The precision of the proposed method was evaluated by assessing the relative standard deviation (RSD %) of ten consecutive determinations of one sample (1.0 mg L−1), and the value obtained was 1.5%.

On the development of an automatic voice pleasantness classification and intensity estimation system

In the last few years, the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems, the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how pleasant is a voice from a perceptual point of view when the final application is a speech based interface. In this paper we present an objective definition for voice pleasantness based on the composition of a representative feature subset and a new automatic voice pleasantness classification and intensity estimation system. Our study is based on a database composed by European Portuguese female voices but the methodology can be extended to male voices or to other languages. In the objective performance evaluation the system achieved a 9.1% error rate for voice pleasantness classification and a 15.7% error rate for voice pleasantness intensity estimation.

Optimal controllers with complex order derivatives

This paper studies the optimization of complex-order algorithms for the discrete-time control of linear and nonlinear systems. The fundamentals of fractional systems and genetic algorithms are introduced. Based on these concepts, complexorder control schemes and their implementation are evaluated in the perspective of evolutionary optimization. The results demonstrate not only that complex-order derivatives constitute a valuable alternative for deriving control algorithms, but also the feasibility of the adopted optimization strategy.

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

The utilization bound of non-preemptive rate-monotonic scheduling in controller area networks is 25%

Consider a distributed computer system comprising many computer nodes, each interconnected with a controller area network (CAN) bus. We prove that if priorities to message streams are assigned using rate-monotonic (RM) and if the requested capacity of the CAN bus does not exceed 25% then all deadlines are met.

Optimal virtual cluster-based multiprocessor scheduling

Scheduling of constrained deadline sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good processor utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and global scheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster are globally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize total processor utilization of individual clusters. In the last part of this paper, we develop new virtual cluster-based scheduling algorithms. For implicit deadline sporadic task systems, we develop an optimal scheduling algorithm that is neither Pfair nor ERfair. We also show that the processor utilization bound of us-edf{m/(2m−1)} can be improved by using virtual clustering. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved processor utilization bounds.

Runtime CRPD management for rate-based scheduling

Temporal isolation is an increasingly relevant con- cern in particular for ARINC-351 and virtualisation- based systems. Traditional approaches like the rate- based scheduling framework RBED do not take into account the impact of preemptions in terms of loss of working set in the acceleration hardware (e.g. caches). While some improvements have been suggested in the literature, they are overly heavy in the presence of small high-priority tasks such as interrupt service routines. Within this paper we propose an approach enabling adaptive assessment of this preemption delay in a tem- poral isolation framework with special consideration of capabilities and limitations of the approach.

Iterative refinement approach for QOS-Aware service configuration

In heterogeneous environments, diversity of resources among the devices may affect their ability to perform services with specific QoS constraints, and drive peers to group themselves in a coalition for cooperative service execution. The dynamic selection of peers should be influenced by user’s QoS requirements as well as local computation availability, tailoring provided service to user’s specific needs. However, complex dynamic real-time scenarios may prevent the possibility of computing optimal service configurations before execution. An iterative refinement approach with the ability to trade off deliberation time for the quality of the solution is proposed. We state the importance of quickly finding a good initial solution and propose heuristic evaluation functions that optimise the rate at which the quality of the current solution improves as the algorithms have more time to run.

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to perform services with specific Quality of Service constraints, particularly in dynamic distributed environments where the characteristics of the computational load cannot always be predicted in advance. Our work addresses this problem by allowing resource constrained devices to cooperate with more powerful neighbour nodes, opportunistically taking advantage of global distributed resources and processing power. Rather than assuming that the dynamic configuration of this cooperative service executes until it computes its optimal output, the paper proposes an anytime approach that has the ability to tradeoff deliberation time for the quality of the solution. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial solution and effectively optimise the rate at which the quality of the current solution improves at each iteration, with an overhead that can be considered negligible.

On the performance limits of slotted CSMA/CA in IEEE 802.15.4 for broadcast transmissions in wireless sensor networks

The IEEE 802.15.4 has been adopted as a communication protocol standard for Low-Rate Wireless Private Area Networks (LRWPANs). While it appears as a promising candidate solution for Wireless Sensor Networks (WSNs), its adequacy must be carefully evaluated. In this paper, we analyze the performance limits of the slotted CSMA/CA medium access control (MAC) mechanism in the beacon-enabled mode for broadcast transmissions in WSNs. The motivation for evaluating the beacon-enabled mode is due to its flexibility and potential for WSN applications as compared to the non-beacon enabled mode. Our analysis is based on an accurate simulation model of the slotted CSMA/CA mechanism on top of a realistic physical layer, with respect to the IEEE 802.15.4 standard specification. The performance of the slotted CSMA/CA is evaluated and analyzed for different network settings to understand the impact of the protocol attributes (superframe order, beacon order and backoff exponent), the number of nodes and the data frame size on the network performance, namely in terms of throughput (S), average delay (D) and probability of success (Ps). We also analytically evaluate the impact of the slotted CSMA/CA overheads on the saturation throughput. We introduce the concept of utility (U) as a combination of two or more metrics, to determine the best offered load range for an optimal behavior of the network. We show that the optimal network performance using slotted CSMA/CA occurs in the range of 35% to 60% with respect to an utility function proportional to the network throughput (S) divided by the average delay (D).

Optimal location of the workpiece in a PKM-based machining robotic cell

Most machining tasks require high accuracy and are carried out by dedicated machine-tools. On the other hand, traditional robots are flexible and easy to program, but they are rather inaccurate for certain tasks. Parallel kinematic robots could combine the accuracy and flexibility that are usually needed in machining operations. Achieving this goal requires proper design of the parallel robot. In this chapter, a multi-objective particle swarm optimization algorithm is used to optimize the structure of a parallel robot according to specific criteria. Afterwards, for a chosen optimal structure, the best location of the workpiece with respect to the robot, in a machining robotic cell, is analyzed based on the power consumed by the manipulator during the machining process.

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms

This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.