Abstract
This paper proposes a decision support model to optimize small players' negotiations in multiple alternative/complementary market opportunities. The proposed model endows players with the ability to maximize their gains in electricity market negotiations. The proposed approach is integrated in a multi-agent simulation platform, which enables experimenting different market configurations, thus facilitating the assessment of the impact of negotiation outcomes in distinct electricity markets. The proposed model is directed to supporting the actions of small players in a transactive energy environment. Therefore, the experimental findings include negotiations in local markets, negotiations through bilateral contracts, and the participation in wholesale markets (through aggregators). The validation is performed using real data from the Iberian market, and results show that by planning market actions considering the expected prices in different market opportunities, small players are able to improve their benefits from market negotiations.

Abstract
IEEE 802.11 Wireless Networks are getting more and more popular at university campuses, enterprises, shopping centers, airports and in so many other public places, providing Internet access to a large crowd openly and quickly. The wireless users are also getting more dependent on WiFi technology and therefore demanding more reliability and higher performance for this vital technology. However, due to unstable radio conditions, faulty equipment, and dynamic user behavior among other reasons, there are always unpredictable performance problems in a wireless covered area. Detection and prediction of such problems is of great significance to network managers if they are to alleviate the connectivity issues of the mobile users and provide a higher quality wireless service. This paper aims to improve the management of the 802.11 wireless networks by characterizing and modeling wireless usage patterns in a set of anomalous scenarios that can occur in such networks. We apply time-invariant (Gaussian Mixture Models) and time-variant (Hidden Markov Models) modeling approaches to a dataset generated from a large production network and describe how we use these models for anomaly detection. We then generate several common anomalies on a Testbed network and evaluate the proposed anomaly detection methodologies in a controlled environment. The experimental results of the Testbed show that HMM outperforms GMM and yields a higher anomaly detection ratio and a lower false alarm rate.

Abstract
An optical fiber Fabry-Perot (FP) for relative humidity (RH) sensing is proposed. The FP cavity is fabricated by splicing a short length of hollow silica tube in a single mode fiber. The fiber is then coated with a polyvinylidene fluoride (PVDF) thin film to work as a mirror. The fabrication process of the FP interferometer with a dip coating process in a PVDF/dimethyl formamide solution is presented. The pattern fringes of the FP suffer a wavelength shift due to the change in the PVDF's refractive index with the ambient RH variation. A short overview of the cavity's formation and stability is presented. The RH response of the FPI cavity is tested. The sensor presented a sensitivity of 32.54 pm/%RH at constant temperature and -15.2 pm/degrees C for temperature variation.

Abstract
Although selection menus are widely used for interaction, their use on 3D virtual reality applications needs to be objectively assessed. The focus of this study is to evaluate a traditional panel and a radial menu in two distinct virtual environment placements (i.e. fixed on the wall and following the users' hands). Fifty-one participants used two different menus of the four possible combinations. To evaluate the menus' effectiveness and efficiency, we measured usability (System Usability Scale Questionnaire), user satisfaction (After-Scenario Questionnaire), time to finish the tasks (in seconds) and the number of unnecessary steps (errors) performed by the users. Overall results showed a clear preference for the traditional panel menu type and the fixed wall placement of the menu. We conclude that all menu types perform well, despite different user preferences, and that fixing the menu to the wall gives users a better overview of both the menu and the virtual environment, improving their ability to perceive their actions on the menu.

Abstract
We propose the use of Temporal Convolutional Networks for audio-based beat tracking. By contrasting our convolutional approach with the current state-of-the-art recurrent approach using Bidirectional Long Short-Term Memory, we demonstrate three highly promising attributes of TCNs for music analysis, namely: i) they achieve state-of-the-art performance on a wide range of existing beat tracking datasets, ii) they are well suited to parallelisation and thus can be trained efficiently even on very large training data; and iii) they require a small number of weights. © 2019 IEEE

Abstract
To be competitive, companies must constantly innovate, and having efficient and well-managed supply chains is undoubtedly an important success factor. In the case of spare parts manufacturing, supply chain management is a very complex and arduous task. Quite often, spare parts have to be produced for products that have been on the market for very long, with the need to keep a large and varied stocks to ensure supply service level. With an increasing investment in the development and applications, the Additive Manufacturing (AM) technology can yield significant benefits to spare parts manufacturing. AM allows the production of parts with a high level of customization, without the need for setups, and helps to decrease costs, inventory levels and lead time. This new reality creates numerous challenges, forcing the design reformulation of traditional supply chains, and leading to an allocation of the production of certain types of parts downstream. This paper proposes a simulation model to address the use of the 3D printing technology on the supply chain of an elevator maintenance service provider. The simulation model allows the assessment of new supply chain designs, measuring their performance, thus avoiding the need of experimenting new solutions in the real system. © 2019, IEOM Society International.