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.

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Abstract
Biological processes are mediated by protein-protein interactions (PPI) that have been studied using different methodologies, and organized as centralized repositories - PPI databases. The data stored in the different PPI databases only overlaps partially. Moreover, some of the repositories are dedicated to a species or subset of species, not all have the same functionalities, or store data in the same format, making comparisons between different databases difficult to perform. Therefore, here we present EvoPPI (http://evoppi.i3s.up.pt), an open source web application tool that allows users to compare the protein interactions reported in two different interactomes. When interactomes belong to different species, a versatile BLAST search approach is used to identify orthologous/paralogous genes, which to our knowledge is a unique feature of EvoPPI.

Abstract
The next evolution in cellular communications will not only improve upon the performance of previous generations, but also represent an unparalleled expansion in the number of services and use cases. One of the foundations for this evolution is the design of highly flexible, versatile, and resource-/power-efficient hardware components. This paper proposes and evaluates an FPGA-oriented baseband processing architecture suitable for communication scenarios such as non-contiguous carrier aggregation, centralized Cloud Radio Access Network (C-RAN) processing, and 4G/5G waveform coexistence. Our system is upgradeable, resource-efficient, cost-effective, and provides support for three 5G waveform candidates. Exploring Dynamic Partial Reconfiguration (DPR), the proposed architecture expands the design space exploration beyond the available hardware resources on the Zynq xc7z020 through hardware virtualization. Additionally, Dynamic Frequency Scaling (DFS) allows for run-time adjustment of processing throughput and reduces power consumption up to 88%. The resource overhead for DPR and DFS is residual, and the reconfiguration latency is two orders of magnitude below the control plane latency requirements proposed for 5G communications.

Abstract
Hashtags have become a crucial social media tool. The categorization of posts in a simple and informal way helps to spread the content through the web. At the same time, it enables users to easily find messages within a specific topic. However, the flexibility provided to use and create a hashtag carries some problems. Equivalent expressions, like synonyms, are handled like entirely different words. On the other hand, the same hashtag may refer to different topics. In this paper, we present TORHID (Topic Relevant Hashtag Identification), a method that employs topic modeling with the purpose of retrieving and identifying hashtags relevant to a specific topic in Twitter streams, starting from a seed hashtag and resorting to a classifier to remove non relevant hashtags. The result is a network of hashtags related to the seed, that we can use to deepen the initial search.