Abstract
In this paper, smart objects embedded production and quality management functions are proposed, to promote accurately support decision-making processes, from the shop floor level up to higher decision-making levels. The proposed functions contribute for different kind of problems solving in production and quality management, such as production planning and control, scheduling, factory supervision, real-time data acquisition and processing, and real-time decision making. The web access at different middleware devices and tools, at different decision levels, along with the use of integrated algorithms and tools, embedded in smart objects, promotes conditions for better decision-making for optimized use of knowledge and resources in production systems. The relevance of the proposed smart objects embedded production and quality management functions has been validated positively in a manufacturing company.

Abstract
Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. © 2015 IEEE.

Abstract
This paper presents a new stochastic multi-layer agent-based decision making model to study the behavior of market participants in the future smart grid. In the agent-based model proposed, wholesale market players are modeled in the first layer. The players include renewable/sustainable power producers, optimizing the bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers include electric vehicle owners and consumers who participate in demand response programs, being modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. According to the high uncertainty of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers, aggregators and retailers providing demand response is considered to confirm the usefulness of the proposed multi-layer model.

Abstract
In some Wireless Sensor Network applications the sensor nodes share the same sensing activity, which means that for a considerable number of applications, not all nodes are required to perform sensing tasks during the network lifetime. Sleep-scheduling approaches can be applied in this scenario, enabling that some nodes turn off their radios, saving energy and bandwidth, as long as there are enough nodes to ensure the required Quality of Service (QoS) of the network. This paper presents a new adaptive approach for QoS and energy management in IEEE 802.15.4 networks, entitled Skip Game. This approach targets a trade-off between increasing the network lifetime and maintaining the QoS of the network, aiming a greater number of nodes to participate in the monitoring application. In order to evaluate the proposed approach, we performed some experiments using the OMNeT++ simulator tool under the MiXiM framework. The results show that the Skip Game outperforms both the traditional Gur Game and Gureen Game approaches in terms of QoS provision and network lifetime.

Abstract
The relationship between FDI and corruption/institutional quality in host countries has been widely analyzed. However, the use of distinct samples and indicators for corruption tends to hinder the interpretation and outcomes of econometric assessments. The aims of this paper are to assess the extent to which the use of distinct proxies for corruption provides diverse evidence regarding the relationship between corruption and FDI, and to assess whether controlling for other indicators of institutional quality reinforces the effect of corruption indicators on FDI inflows. In order to accomplish these goals, we estimate a set of multivariate logistic models using 96 countries over the period 2000 to 2010. The results evidence that using distinct proxies for corruption variables, as well as controlling for other types of the countries' institutional quality, generate distinct outcomes. In isolation, a country's transparency and its citizens' corruption perceptions fail to impact on FDI whereas a bribe-free environment is conducive to FDI inflows. When we control for the human, social and economic development of the countries, the impact of a transparent and bribe-free context on FDI attraction is enhanced. Overall, it is clear that in order to become a large recipient of FDI a country has to guarantee a transparent and bribe-free environment, characterized by low income taxes, high literacy rates and generalized economic freedom (own labor and property control by citizens).

Abstract
This article presents a semantics-based program verification framework for critical embedded real-time systems using the worst-case execution time (WCET) as the safety parameter. The verification algorithm is designed to run on devices with limited computational resources where efficient resource usage is a requirement For this purpose, the framework of abstract-carrying code (ACC) is extended with an additional verification mechanism for linear programming (LP) by applying the certifying properties of duality theory to check the optimality of WCET estimates. Further, the WCET verification approach preserves feasibility and scalability when applied to multicore architectural models. The certifying WCET algorithm is targeted to architectural models based on the ARM instruction set and is presented as a particular instantiation of a compositional data-flow framework supported on the theoretic foundations of denotational semantics and abstract interpretation. The data-flow framework has algebraic properties that provide algorithmic transformations to increase verification efficiency, mainly in terms of verification time. The WCET analysis/verification on multicore architectures applies the formalism of latency-rate (LR.) servers, and proves its correctness in the context of abstract interpretation, in order to ease WCET estimation of programs sharing resources.