Abstract
This study focused on the use of sound playbacks as acoustic deterrents to direct native potamodromous migratory species away from all kind of traps. The effects of two acoustic treatments, a repeated sine sweep up to 2 kHz (sweep-up stimulus) and an intermittent 140 Hz tone, were tested in three fish species native to Iberia: Salmo trutta, Pseudochondrostoma duriense and Luciobarbus bocagei. In contrast with S. trutta, the endemic cyprinids P. duriense and L. bocagei exhibited a strong repulse reaction to the frequency sweep-up sound. The 140 Hz stimulus did not seem to alter significantly the behaviour of any of the studied species. These results highlight the potential of acoustic stimuli as fish behavioural barriers and their application to in situ conservation measures of native Iberian fish populations, to protect them from hydropower dams. In addition, this study shows that acoustic deterrents can be used selectively on target species.

Abstract
It is known that excessive levels of occupational stress affect professionals' technical and non-technical skills and surgeons are no exception. However, very few studies address this problem in neurosurgeons. A system for monitoring cardiovascular strain and autonomic imbalance during intracranial aneurysm procedures is proposed in order to obtain overall cardiac measures from those procedures. Additionally, this study also allows to detect stressful events and compare their impact with the surgeon's own appraisal. Linear and nonlinear heart rate variability (HRV) features were extracted from surgeon's electrocardiogram (ECG) signal using wearable ECG monitors and mobile technology during 10 intracranial aneurysm surgeries with two surgeons. Stress appraisal and cognitive workload were assessed using self-report measures. Findings suggest that the surgeon associated to the main role during the clipping can be exposed to high levels of stress, especially if a rupture occurs (pNN20 = 0%), while the assistant surgeon tends to experience mental fatigue. Cognitive workload scores of one of the surgeons were negatively correlated with AVNN, SDNN, pNN20, pNN50, 1 V, 2 L V, SD2 and CVI measures. Cognitive workload was positively related with stress appraisal, suggesting that more mentally demanding procedures are also assessed as more stressful. Finally, pNN20 seems to better mirror behavior during stress moments than pNN50. Additionally, a sympathovagal excitation occurs in one of the professionals after changing to main role. The present methodology shows potential for the identification of harmful events. This work may be of importance for the design of effective interventions in order to reduce surgeons stress levels. Furthermore, this approach can be applied to other professions.

Abstract
This paper proposes a novel method for solving the Optimal Power Flow (OPF) problem in conditions close to realtime. The linearized cost function of the generating units is used to this end. Besides, the presented linear model is solved using the Consensus Alternating Direction Method of Multipliers (C-ADMM) approach. This technique would provide the possibility of modeling the problem both in centralized and decentralized manners. The suggested method exploits the power flow results obtained from the previous iteration to considerably improve the rate of convergence. As the C-ADMM method uses an iterative technique, Lagrange multipliers, and the norm function, the rate of convergence highly depends upon assigning the initial conditions and the optimality gap. Thus, using the operating points of the previous instant due to being close to the operating point of the current instant would enhance the results. The proposed model has been implemented on two case studies including the Pennsylvania-New Jersey-Maryland (PJM) network to verify the results and the 9-bus system to evaluate the performance of the model for the daily operation. © 2019 IEEE.

Abstract
Security and privacy concerns are challenging the way users interact with devices. The number of devices connected to a home or enterprise network increases every day. Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud; however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The software implementations that are currently deployed in companies should have updates and control, as cybersecurity threats increasingly appearing over time. There is already some research towards solutions and methods to predict new attacks or classify variants of previous known attacks, such as (algorithmic) information theory. This survey combines all relevant applications of this topic (also known as Kolmogorov Complexity) in the security and privacy domains. The use of Kolmogorov-based approaches is resource-focused without the need for specific knowledge of the topic under analysis. We have defined a taxonomy with already existing work to classify their different application areas and open up new research questions.

Abstract
Existing approaches for detecting anomalous events in time-evolving networks usually focus on detecting events involving the majority of the nodes, which affect the overall structure of the network. Since events involving just a small subset of nodes usually do not affect the overall structure of the network, they are more difficult to spot. In this context, tensor decomposition based methods usually beat other techniques in detecting global events, but fail at spotting localized event patterns. We tackle this problem by replacing the batch decomposition with a sliding window decomposition, which is further mined in an unsupervised way using statistical tools. Via experimental results in one synthetic and four real-world networks, we show the potential of the proposed method in the detection and specification of local events.

Abstract