The current study focuses on the short-term effect of MARIO, a social robot, on quality of life, depression, and perceived social support in persons with dementia (PWD) and evaluates their acceptability of MARIO. Ten PWD in one nursing home took part in a 4-week pilot study, where each participant had up to 12 sessions with MARIO. Sessions comprised engagement in music, news, reminiscence, games, and calendar applications. Standardized questionnaires were administered before and after the 4-week period. Participants had a sustained interest in MARIO during their interactions and an acceptance of MARIO's appearance, sound, and applications. Consequently, participants spent more time socially engaged. No statistically significant differences were found in quality of life, depression, and perceived social support.PWD can engage with a social robot in a real-world nursing home. Future research should incorporate a larger sample and longer intervention period. connect them with their family, their pastimes and the outside world. The initial design of MARIO's applications was also based on four key principles: (i) the applications are individualised, (ii) the applications offer choice, (iii) the applications can prompt the individual and (iv) the applications are simple and intuitive to use. The applications underwent an iterative process of user-driven development, which involved testing several iterations of the applications with PLWD and using their feedback to further refine the applications. This paper presents the results of the final evaluation of MARIO carried out in the X (identifying information) nursing home setting. The aims of the pilot study were (i) to evaluate the acceptability, functionality and usability of MARIO to PLWD in a nursing home, as well as any potential ethical issues, from the perspective of the PLWD interacting with MARIO and the researcher observing the interactions and (ii) to explore the short-term effect of MARIO on quality of life, depression and perceived social support of PLWD. Methods DesignThis study was a single group, pre-post, pilot study. It was carried out in one purposively selected nursing home, containing 100 beds, in rural X (identifying information). Quantitative data was collected from PLWD, on quality of life, depression and social support, at baseline and directly after a four-week intervention period. These outcomes were established to be important for measuring the effect of psychosocial interventions for PLWD (Moniz-Cook et al., 2008). The study received ethical approval from the Research Ethics Committee of X (identifying information).

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Abstract. Smart grid security has many facets, ranging over a spectrum from resisting attacks aimed at supervisory and control systems, to end user privacy concerns while monitored by the utility enterprise. This multi-faceted problem also includes vulnerabilities that arise from deployment of local cyber-physical attacks at a smart metering location, with a potential to a) manipulate the measured energy consumption, and b) being massively deployed aiming at destabilisation. In this paper we study a smart metering device that uses a trusted platform for storage and communication of metering data, and show that despite the hard core security, there is still room for deployment of a second level of defence as an embedded real-time anomaly detector that can cover both the cyber and physical domains.

Abstract-Reconfigurable Radio Systems (RRS), based on Software Defined Radio (SDR) and Mobile Ad-hoc Network (MANET) technologies, offer considerable advantages for military operations, such as increased network survivability and interoperability. The RRS-based Common Tactical Radio System (GTRS), currently in development by the Swedish Armed Forces, is designed for use in diverse geographical settings and for purposes varying from international combat missions to national contingency operations. However, protecting these networks from attacks and safeguarding the carried information against leaks is an ongoing research challenge, especially in combined scenarios where tactical data may flow across organizational boundaries. This paper presents a best-effort approach to Data Leakage Prevention (DLP) for inter-organizational RRS-based networks.The proposed architecture makes use of data mining techniques and an efficient n-dimensional clustering algorithm which has previously been successfully used for real-time anomaly detection in critical infrastructure protection. The DLP architecture is developed as an extension to the GTRS system, modeled and simulated in OPNET™ Modeler. Our results show that common data leaks can be efficiently identified by the proposed scheme, while keeping the important false positive rate at a very low level.