Abstract

Abstract
Purpose: Police in Europe are facing increased demands and diminished resources, and this is particularly prominent among emergency response officers (EROs) working in poorer countries such as Portugal. Considering that daily stress and limited coping skills can result in detrimental consequences for officers’ health and society welfare, the purpose of this paper is to investigate stress and coping among Portuguese EROs. Design/methodology/approach: EROs completed daily diaries over 11 working days. Each diary entry included an open-ended stressor, coping section and a Likert-type scale to evaluate coping effectiveness. Data were analyzed using inductive and deductive content analysis procedures. The frequency of stressors, coping and coping effectiveness were calculated. Findings: EROs reported facing more operational stressors, particularly public disorder situations. However, gun situations were perceived as the most intense stressor. Emotion-focused coping (i.e. peer support) was more used than problem-focused. Despite variation in coping effectiveness in accordance to stressor experienced, longitudinal analysis suggests that problem-focused coping is more effective. Research limitations/implications: Longitudinal methodologies should contemplate stress appraisal and coping effectiveness in order to fully understand stress and coping. Future studies should employ this methodology at a larger scale and over longer periods. Practical implications: Intervention programs for EROs should be multidimensional, targeting work conditions and resources, stress management, and coping effectiveness. Originality/value: Findings provide strong recommendations for future research and applied implications for stress prevention and effective coping interventions. © 2017, © Emerald Publishing Limited.

Abstract

Abstract
The privacy of information is an increasing concern of software applications users. This concern was caused by attacks to cloud services over the last few years, that have leaked confidential information such as passwords, emails and even private pictures. Once the information is leaked, the users and software applications are powerless to contain the spread of information and its misuse. With databases as a central component of applications that store almost all of their data, they are one of the most common targets of attacks. However, typical deployments of databases do not leverage security mechanisms to stop attacks and do not apply cryptographic schemes to protect data. This issue has been tackled by multiple secure databases that provide trade-offs between security, query capabilities and performance. Despite providing stronger security guarantees, the proposed solutions still entrust their data to a single entity that can be corrupted or hacked. Secret sharing can solve this problem by dividing data in multiple secrets and storing each secret at a different location. The division is done in such a way that if one location is hacked, no information can be leaked. Depending on the protocols used to divide data, functions can be computed over this data through secure protocols that do not disclose information or actually know which values are being calculated. We propose a SQL database prototype capable of offering a trade-off between security and query latency by using a different secure protocol. An evaluation of the protocols is also performed, showing that our most relaxed protocol has an improvement of 5% on the query latency time over the original protocol. © 2017 ACM.

Abstract
A method to detect juxta-pleural nodules with radius smaller than 5mm is presented. The intensity difference between nodules and parenchymal tissue as well as the nodules' natural roundness are exploited. Solid nodules are detected by selecting an appropriate threshold over a sliding window, whereas sub-solid/non-solid nodules are enhanced using multi-scale Laplacian-of-Gaussian filters. The 2D-wise outputs are combined to 3D, producing a final candidate list. False positive reduction is achieved with fixed rules and supervised learning. The achieved sensitivity is 57% with 4 false positives/scan, increasing to 62% if only solid nodules are considered. © 2017 SPIE.

Abstract
In this work, the self-scheduling problem of a power producer in smart grids is addressed using a stochastic programming approach. Different uncertainties are considered as price uncertainties, forced-outage of the unit as well as generation reallocation. The conditional value-at-risk index is used for modeling of risk. The markets considered in this study are bilateral contracts, day-ahead and ancillary services, including spinning reserve and regulation, and spot market decisions, while an incomplete competitive market is considered. In this sense, an innovative method for bilateral contracts is proposed to increase the profit of the market without ignoring any regulatory rules. The Monte Carlo method is implemented together with a reduction scenario process to generate scenarios.