Abstract
The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future. © 2008-2011 IEEE.

Abstract
In this paper, a mixed-integer linear programing (MIL?) model for the traffic behavior of plug-in electric vehicles (PEVs) in a multi energy system (MES) is proposed. It is assumed that two micro-MESs are covering two traffic zones with different consumption patterns. The difference between these two micro-MESs is not only the different multi energy demand (MED) they provide, but also different PEV traffic pattern that travel in these two micro-MESs. The PEVs traffic pattern and their behavior in using parking lot (PL) or charging station (CS) as their charging places is integrated in the MES operation model. The results demonstrate an improved strategy of the MES operator in using its components, such as combined heat and power (CHP) unit and auxiliary boiler (AB), in response to extra added load of the PEVs. The stochastic behavior of the PEVs is implemented in the model through various scenarios of arrival and departure. © 2016 IEEE.

Abstract
Human capital is identified as one of the main determinants of economic growth and plays an important role in the technological progress of countries. Nevertheless, existing studies have to some extent neglected the importance of human capital in the growth process via the interaction it can have with a country's industrial specialization. Additionally, the emphasis is mainly placed on supply-side determinants, while demand-side factors are neglected, particularly the relevance of the processes of structural change. Thus, using a growth model which integrates variables from both the supply side and demand side, we assess the direct and indirect effects of human capital on economic growth, including in the latter the interaction of human capital with the industrial specialization of countries. Based on dynamic panel data estimations, we found that human capital and the countries' productive specialization dynamics are crucial factors for economic growth. Moreover, the interaction between human capital and structural change in high knowledge-intensive industries impacts significantly on economic growth. However, the sign of this effect depends on the type of country and the period of analysis. Specifically, over a longer time span (1960-2011) and for more highly developed (OECD) countries, the impact of the interaction between human capital and structural change is positive. When we also include transition and Mediterranean countries over a shorter time period (1990-2011), we find that human capital significantly and positively impacts on the countries' economic growth but the effect of human capital via specialization in high-tech and knowledge-intensive activities is negative. The latter result indicates that the lack of industrial structures able to properly integrate highly educated individuals into the productive system leads countries to experience disappointing economic returns.

Abstract
We present Symbiosis: a concurrency debugging technique based on novel differential schedule projections (DSPs). A DSP shows the small set of memory operations and dataflows responsible for a failure, as well as a reordering of those elements that avoids the failure. To build a DSP, Symbiosis first generates a full, failing, multithreaded schedule via thread path profiling and symbolic constraint solving. Symbiosis selectively reorders events in the failing schedule to produce a nonfailing, alternate schedule. A DSP reports the ordering and dataflow differences between the failing and nonfailing schedules. Our evaluation on buggy real-world software and benchmarks shows that, in practical time, Symbiosis generates DSPs that both isolate the small fraction of event orders and dataflows responsible for the failure and report which event reorderings prevent failing. In our experiments, DSPs contain 90% fewer events and 96% fewer dataflows than the full failure-inducing schedules. We also conducted a user study that shows that, by allowing developers to focus on only a few events, DSPs reduce the amount of time required to understand the bug's root cause and find a valid fix.

Abstract
In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

Abstract
We propose a mathematical model to study the water usage for the irrigation of given farmland to guarantee that the field crop is kept in a good state of preservation. This problem is formulated as an optimal control problem. The lack of analytic solution leads us to turn to numerical methods to solve the problem numerically. We then apply necessary conditions of optimality to validate the numerical solution. To deal with the high degree of unpredictability of water inflow due to weather, we further propose a replan strategy and we implement it.