Dynamic QoS adaptation of inter- dependent task sets in cooperative embedded systems

Due to the growing complexity and dynamism of many embedded application domains (including consumer electronics, robotics, automotive and telecommunications), it is increasingly difficult to react to load variations and adapt the system's performance in a controlled fashion within an useful and bounded time. This is particularly noticeable when intending to benefit from the full potential of an open distributed cooperating environment, where service characteristics are not known beforehand and tasks may exhibit unrestricted QoS inter-dependencies. This paper proposes a novel anytime adaptive QoS control policy in which the online search for the best set of QoS levels is combined with each user's personal preferences on their services' adaptation behaviour. 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 as the algorithms are given more time to run, with a minimum overhead when compared against their traditional versions.

Dynamic Approach and Testbed for Small and Medium Players Simulation in Smart Grid Environments

The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of consumers must be carefully done. This paper proposes a system which simulates small and medium players. The system is essential to produce tests and studies about the active participation of small and medium players in the Smart Grid environment. When comparing to similar systems, the advantages comprise the capability to deal with three types of loads – virtual, contextual and real. It can have several loads optimization modules and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper describes the system and all its capabilities.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Resource Scheduling in Residential Microgrids Considering Energy Selling to External Players

The operation of distribution networks has been facing changes with the implementation of smart grids and microgrids, and the increasing use of distributed generation. The specific case of distribution networks that accommodate residential buildings, small commerce, and distributed generation as the case of storage and PV generation lead to the concept of microgrids, in the cases that the network is able to operate in islanding mode. The microgrid operator in this context is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The present paper proposes a methodology for the energy resource scheduling considering power flow issues and the energy buying and selling from/to the main network in each bus of the microgrid. The case study uses a real distribution network with 25 bus, residential and commercial consumers, PV generation, and storage.

Perception of organizational politics and the effect of authenticity and affective commitment on deviance and task performance in contact centers

This study aimed to understand employees’ reactions to organizational politics in Contact Centers. Drawing from a sample of 187 supervisor-employee dyads, we studied the relationship between employees’ perceptions of organizational politics and supervisor-rated task performance and deviance, and mediation effects by authenticity at work and affective commitment. Results indicate that workers tend to react to workplace politics with deviant behavior and worse task performance. We found that the relationship between perceived politics and task performance was mediated by authenticity. The relationship between perceived politics and supervisor-rated deviance was mediated by affective commitment to the organization. Implications for management are discussed.

Expression und funktionelle Relevanz von Zwei-Poren K+-Kanälen der Task-Familie in thalamischen Schaltneutronen

Thalamus, thalamocortical relay neurons, TASK-channels, Two-Pore-K+-channels, HCN-channels, Halothane, Muscarin, Bupivacaine, Spermine, computer modelling

Firing activities of auditory cortical neurons during categorical task performance in behaving monkeys

Category, frequency contour, monkey, auditory cortex, neuron, spike

A decision-making Fokker-Planck model in computational neuroscience

Minimal models for the explanation of decision-making in computational neuroscience are based on the analysis of the evolution for the average firing rates of two interacting neuron populations. While these models typically lead to multi-stable scenario for the basic derived dynamical systems, noise is an important feature of the model taking into account finite-size effects and robustness of the decisions. These stochastic dynamical systems can be analyzed by studying carefully their associated Fokker-Planck partial differential equation. In particular, we discuss the existence, positivity and uniqueness for the solution of the stationary equation, as well as for the time evolving problem. Moreover, we prove convergence of the solution to the the stationary state representing the probability distribution of finding the neuron families in each of the decision states characterized by their average firing rates. Finally, we propose a numerical scheme allowing for simulations performed on the Fokker-Planck equation which are in agreement with those obtained recently by a moment method applied to the stochastic differential system. Our approach leads to a more detailed analytical and numerical study of this decision-making model in computational neuroscience.

Experimental manipulation of colony genetic diversity had no effect on short-term task efficiency in the Argentine ant Linepithema humile

Genetic diversity might increase the performance of social groups by improving task efficiency or disease resistance, but direct experimental tests of these hypotheses are rare. We manipulated the level of genetic diversity in colonies of the Argentine ant Linepithema humile, and then recorded the short-term task efficiency of these experimental colonies. The efficiency of low and high genetic diversity colonies did not differ significantly for any of the following tasks: exploring a new territory, foraging, moving to a new nest site, or removing corpses. The tests were powerful enough to detect large effects, but may have failed to detect small differences. Indeed, observed effect sizes were generally small, except for the time to create a trail during nest emigration. In addition, genetic diversity had no statistically significant impact on the number of workers, males and females produced by the colony, but these tests had low power. Higher genetic diversity also did not result in lower variance in task efficiency and productivity. In contrast to genetic diversity, colony size was positively correlated with the efficiency at performing most tasks and with colony productivity. Altogether, these results suggest that genetic diversity does not strongly improve short-term task efficiency in L. humile, but that worker number is a key factor determining the success of this invasive species.

Task-shifting in the delivery of hormonal contraceptive methods: Validation of a questionnaire and preliminary results.

Guilbert ER, Morin D, Guilbert AC, Gagnon H, Robitaille J, Richardson M. International Journal of Nursing Practice 2011; 17: 315-321 Task-shifting in the delivery of hormonal contraceptive methods: Validation of a questionnaire and preliminary results In order to palliate the access problem to effective contraceptive methods in Quebec, Canada, as well as to legitimate nurses' practices in family planning, a collaborative agreement was developed that allow nurses, in conjunction with pharmacists, to give hormonal contraceptives to healthy women of reproductive age for a 6 month period. Training in hormonal contraception was offered to targeted nurses before they could begin this practice. A questionnaire, based on Rogers's theory of diffusion of innovations, was elaborated and validated to specifically evaluate this phenomenon. Preliminary results show that the translation of training into practice might be suboptimal. The validated questionnaire can now be used to fully understand the set of factors influencing this new practice.

Task Support in Scenario Process: Case LUT 2016

This thesis attempts to find whether scenario planning supports the organizational strategy as a method for addressing uncertainty. The main issues are why, what and how scenario planning fits in organizational strategy and how the process could be supported to make it more effective. The study follows the constructive approach. It starts with examination of competitive advantage and the way that an organization develops strategy and how it addresses the uncertainty in its operational environment. Based on the conducted literature review, scenario methods would seem to provide versatile platform for addressing future uncertainties. The construction is formed by examining the scenario methods and presenting suitable support methods, which results in forming of the theoretical proposition for supporter scenario process. The theoretical framework is tested in laboratory conditions, and the results from the test sessions are used a basis for scenario stories. The process of forming the scenarios and the results are illustrated and presented for scrutiny

Researching Manufacturing Planning and Control system and Master Scheduling in a manufacturing firm.

The objective of this thesis is to research Manufacturing Planning and Control (MPC) system and Master Scheduling (MS) in a manufacturing firm. The study is conducted at Ensto Finland Corporation, which operates on a field of electrical systems and supplies. The paper consists of theoretical and empirical parts. The empirical part is based on weekly operating at Ensto and includes inter-firm material analysis, learning and meetings. Master Scheduling is an important module of an MPC system, since it is beneficial on transforming strategic production plans based on demand forecasting into operational schedules. Furthermore, capacity planning tools can remarkably contribute to production planning: by Rough-Cut Capacity Planning (RCCP) tool, a MS plan can be critically analyzed in terms of available key resources in real manufacturing environment. Currently, there are remarkable inefficiencies when it comes to Ensto’s practices: the system is not able to take into consideration seasonal demand and react on market changes on time; This can cause significant lost sales. However, these inefficiencies could be eliminated through the appropriate utilization of MS and RCCP tools. To utilize MS and RCCP tools in Ensto’s production environment, further testing in real production environment is required. Moreover, data accuracy, appropriate commitment to adapting and learning the new tools, and continuous developing of functions closely related to MS, such as sales forecasting, need to be ensured.

Doubly fed wind turbine performance in variable grid conditions

Wind turbines based on doubly fed induction generators (DFIG) become the most popular solution in high power wind generation industry. While this topology provides great performance with the reduced power rating of power converter, it has more complicated structure in comparison with full-rated topologies, and therefore leads to complexity of control algorithms and electromechanical processes in the system. The purpose of presented study is to present a proper vector control scheme for the DFIG and overall control for the WT to investigate its behavior at different wind speeds and in different grid voltage conditions: voltage sags, magnitude and frequency variations. The key principles of variable-speed wind turbine were implemented in simulation model and demonstrated during the study. Then, based on developed control scheme and mathematical model, the set of simulation is made to analyze reactive power capabilities of the DFIG wind turbine. Further, the rating of rotor-side converter is modified to not only generate active rated active power, but also to fulfill Grid Codes. Results of modelling and analyzing of the DFIG WT behavior under different speeds and different voltage conditions are presented in the work.