Abstract
The advances in wireless communications are still very limited when intended to be used on Underwater Communication Systems mainly due to the adverse proprieties of the submarine channel to the acoustic and radio frequency (RF) waves propagation. This work describes the development and characterization of a polyvinylidene difluoride ultrasound transducer to be used as an emitter in underwater wireless communications. The transducer has a beam up to 10 degrees x 70 degrees degrees and a usable frequency band up to 1 MHz. The transducer was designed using Finite Elements Methods and compared with real measurements. Pool trials show a transmitting voltage response (TVR) of approximately 150 dB re mu Pa/V@1 m from 750 kHz to 1 MHz. Sea trials were carried in Ria Formosa, Faro (Portugal) over a 15 m source-receiver communication link. All the signals were successfully detected by cross-correlation using 10 chirp signals between 10 to 900 kHz.

Abstract

Abstract
Faster Internet connections are breaking most of the geographic barriers. At the same time, the huge digital content that have been generated in last years is motivating new forms of digital piracy. We know that piracy of copyrighted digital material has a huge impact on countries' economy, being a major issue for the whole society and not only for content creators. The purpose of this paper is to investigate digital piracy intention. For that purpose, we have expanded the framework of the theory of planned behavior using the utility theory, the deterrence theory and other relevant constructs. Using data from students of a Portuguese university and high school, a sample of 590 questionnaires has been collected. Two models were developed and analyzed using structural equation modeling. The first considers the full sample (Full Model), while the second considers only those who had pirated (Pirate Model). The pirate model confirmed the existence of a significant and strong relation between past behavior and intention toward digital piracy.

Abstract
The enhancement of tubular structures such as vessels in medical images has been addressed in the past, aiming for easier extraction and or visualization of such structures by professionals. Some literature methodologies propose vesselness measures whose design is motivated by local properties of vascular networks and how these influence the eigenvalues of the Hessian matrix. However, past work fails to combine properly the scale-space and neighborhood information, thus leading to the proposal of suboptimal vesselness measures. In this paper, we show that a shallow convolutional neural network is able to learn more optimal embedding spaces from the eigenvalue analysis at different scales, thus leading to a stronger vessel enhancement. Additionally, we also show that such a system maintains one of the biggest advantages of Hessian-based vesselness measures, which is the robustness to data with varying statistics.

Abstract
Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. Starting from a particular lock-free hash map data structure, named Lock-Free Hash Tries (LFHT), we focus on solving the problem of memory reclamation without losing the lock-freedom property. We propose an approach that explores the characteristics of the LFHT structure in order to achieve efficient memory reclamation with low and well-defined memory bounds. Experimental results show that our approach obtains better results when compared with other state-of-the-art memory reclamation methods and provides a competitive and scalable hash map implementation, if compared to lock-based implementations.

Abstract
The work reported in this paper jointly addresses two major challenges in modern power systems: 1) systematically maximizing wind power generation (WPG) utilization under worst-case uncertainty and 2) employing mixed integer-nonlinear programming (MINLP) in the co-optimization of variable reactance devices (VRD) and transmission switching (TS) in an AC optimal power flow problem (ACOPF). The first challenge is solved by proposing an interval based robust approach to identify the worst-case WPG uncertainty. Similarly, to overcome the second challenge, a tri-level decomposition algorithm is used to decompose the MINLP representation into one consisting of one mixed-integer linear programming (MILP) and two nonlinear programming (NLPs) problems. Finally, the effectiveness and efficiency of the proposed model is shown by analysing results from testing on the modified RTS-96 system.