Abstract
Power systems are becoming more complex and the need for increased awareness at the lower voltage levels of the distribution grid requires new tools that provide a reliable and accurate estimation of the system state. This paper describes an innovative state estimation method for low voltage (LV) grids that analyses similarities between a real-time snapshot comprising only a subset of smart meters with real-time communications and fully observed system states present in historical data. Real-time estimates of voltage magnitudes are obtained by smoothing the most similar past snapshots with a data-driven methodology that does not relies on full knowledge of the grid topology and electrical characteristics. Moreover, the output of the LV state estimator is a conditional probability distribution obtained with kernel density estimation. The results show highly accurate estimation of voltage magnitude, even in a scenario characterized by a strong integration of photovoltaic (PV) microgeneration.

Abstract
This study presents and applies generalized angular phase space analysis to lower limb joint angles of specific subject during normal and modified gait for discrimination of gait and joint angular movements. Case study of an adult healthy male in-vivo and noninvasive kinematic assessment of skin surface adhesive markers at lower limb was performed at human movement lab during normal gait, stiff knee gait and slow running. Musculoskeletal modeling was performed using AnyGait v.0.92 morphing Twente Lower Extremity Model (TLEM) to match the size and joint morphology of the stick-figure model. Inverse kinematics was performed obtaining hip, knee and ankle joint flexion-extension angular displacements, velocities and accelerations. Generalized phase space analysis was applied to lower limb joint angular displacements, velocities and accelerations. Directional statistics was applied to generalized phase planes with mean direction, resultant length and circular standard deviation assessment. Rayleigh test was employed for directional concentration and coordination assessment, and Watson's U2 goodness of fit test applied to the von Mises distribution. Results point for the importance of subject specific study, generalized joint angular phase space analysis, comparing results with other normalization methods and validation of applied methods with qualitative clinical analysis. © 2018 IEEE.

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.

Abstract
Computer-aided engineering simulations, in particular, Computational Fluid Dynamics, have become a fundamental design and analysis tool in product development. Over time, a demand for larger problem sizes and higher accuracy has led to huge computational workloads requiring extended compute capabilities. Increasing computing capabilities requirements, however, drive a fast-growing power consumption. In order to deal with increasing power demand, hardware and software solutions' reevaluation in terms of power-efficiency becomes of paramount importance. Establishing a power budget and reducing the compute units operating frequency in order to comply with such budget is becoming common practice. However, in the presence of heterogeneous compute units and dynamic workloads, a static and uniform reduction across compute units leads to a potentially severe impact on performance. This paper proposes a run-time heterogeneity-aware power-adaptive schedule that provides power consumption optimization, targeting heterogeneous parallel distributed systems in the context of CFD simulations. The proposed approach is integrated into OpenFOAM computational library and explores power migration and reduction across nodes, considering runtime workload imbalances and node performances. Results reveal not only a substantial reduction in power usage but also significant performance gains relative to the uniform static approach. To the best of authors' knowledge, this is the first implementation and integration of power management solutions in OpenFOAM.

Abstract
Access control is a crucial part of a system’s security, restricting what actions users can perform on resources. Therefore, access control is a core component when dealing with e-Health data and resources, discriminating which is available for a certain party. We consider that current systems that attempt to assure the share of policies between facilities are mostly centralized, being prone to system’s and network’s faults and do not assure the integrity of policies lifecycle. Using a blockchain as store system for access policies we are able to ensure that the different entities have knowledge about the policies in place while maintaining a record of all permission requests, thus assuring integrity, auditability and authenticity. © 2020, Springer Nature Switzerland AG.

Abstract
The Cyber-Physical System (CPS) paradigm promotes the decentralization and distribution of the logic control as well as the integration of cyber and physical counterparts. In parallel, self-organization allows the dynamic and automatic system re-configuration responding to condition and environment changes. Modeling and simulation assume a crucial importance in the design of such complex, distributed, and self-organized systems, in the way that the detected and debugged errors may be corrected before the deployment into the real system, as well different strategies can be tested and evaluated. Agent-based modeling tools are computational frameworks able to analyze, experiment and compare systems populated by cooperative agents, supporting the fast prototyping of agent-based solutions exhibiting self-* properties. In this paper, the NetLogo tool was used to model and simulate the agent-based control layer of a small scale CPS, which control uses self-organization principles. © 2017 IEEE.