Abstract
This paper presents a Photovoltaic (PV) power conversion model and a forecasting approach which uses spatial dependency of variables along with their temporal information. The power produced by a PV plant is forecasted by a PV conversion model using the predictions of three weather variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed. The predictions are accomplished using a spatio-temporal algorithm that exploits the sparsity of correlations between time series data of different meteorological stations in the same region. The performances of the forecasting algorithm as well as the PV conversion model are investigated using real data recorded at various locations in Italy. The comparisons with various benchmark methods show the effectiveness of the proposed approaches over short-term forecasts.

Abstract
The wide spread of food outlets has become an environmental and sanitation infrastructure problem, due to Fats, Oils and Grease (FOG). A grease box is used at the industrials facilities to collect the FOG, in a specific time window, while its quality is good for recycling (e.g., biodiesel) and it is economically valuable. After this period, it will be disposed at a cost. For the proper management of the grease boxes, it is necessary to know the quantity of FOG inside the boxes, which is a major problem, as the boxes are sealed and permanently filled with water. The lack of homogeneity of the FOG renders it not detectable by current probes for level detection in liquids. In this article, the design, development and testing of a set of probes for FOG level measurement, based on the principles used in sensors for the detection of liquids inside containers, is described. The most suitable probe, based on the capacitance principle, together with the necessary hardware and software modules for data acquisition and transmission, was developed and tested. After the development phase, the probe was integrated on a metropolitan system for FOG collection and grease box management in partnership with a grease box management company.

Abstract

Abstract
In the last decade, companies have become tend virtualized thanks to the use of outsourcing, the workforce has become more distributed, workplaces are increasingly distributed and outsourced and increasingly digital employees, with the philosophy of work anytime and anywhere. This development has, and has had a very big impact on mobile platforms and infrastructure, on the one hand, the adoption of the BYOD concept and the use of mobile devices and on the other hand, through the use of cloud computing will have profound implications in the way the technologies are and will be used as well as on the interaction between individuals and these technologies. By applying for a questionnaire and subsequent analysis showed that organizations have a lack of understanding, almost complete, the implications and consequences of using mobile devices and the cloud in organizations. A result of the Focus Group performed we propose the 4CM model.

Abstract
When designing clinical trials for testing novel cardiovascular therapies, it is highly relevant to understand what a given technology can provide in terms of information on the physiologic status of the heart and vessels. Ultrasound imaging has traditionally been the modality of choice to study the cardiovascular system as it has an excellent temporal resolution; it operates in real-time; it is very widespread and - not unimportant - it is cheap. Although this modality is mostly known clinically as a two-dimensional technology, it has recently matured into a true three-dimensional imaging technique. In this review paper, an overview is given of the available ultrasound technology for cardiac chamber quantification in terms of volume and function and evidence is given why these parameters are of value when testing the effect of new cardiovascular therapies.

Abstract
When defining the layout for a production line, it is necessary to assign tasks to workstations, so that the work is performed in a feasible sequence and approximately equal amounts of time are needed at each workstation, a process called line balancing. Therefore, the need for balancing production lines involves the distribution of sequential activities for jobs in order to allow high labour and equipment utilisation and minimise the idle time. Line balancing problems are complex to treat, being used distinct methodologies to perform it. This paper describes an application for line balancing using two genetic algorithms (the first obtains solutions to the problem and the second optimises those solutions), associated with a graphical interface for the problem data input and visualisation of results. Results demonstrate advantages over heuristic methods as it is possible to obtain more than one solution and it is more practical to use the developed application. Copyright © 2016 Inderscience Enterprises Ltd.