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La transferencia de tecnología sostenible es compleja para las firmas de construcción. Una posible solución es analizar esa clase de transferencia como una red social ya que, si se identifican las diferentes relaciones entre los actores del sector construcción, es posible evaluar la capacidad de adaptación tecnológica de dichos actores. El objetivo fue evaluar la transferencia de tecnología sostenible entre empresas constructoras internacionales que se dedican a construir vivienda social o accesible. Para esto, se identificaron dos países con capacidad de transferencia de tecnología sostenible (Reino Unido y Estados Unidos) y dos países de menor capacidad tecnológica y con potencial de adaptarse a dichas tecnologías (Brasil y Colombia); posteriormente, se seleccionaron cinco firmas constructoras por cada país, con las cuales se hizo un análisis de redes (brasilbragrado, intensidad, cercanía y densidad), y luego, procesos de simulación. Como resultado se identificó la capacidad de transferencia tecnológica que tienen las empresas latinoamericanas para aceptar y adaptar tecnologías de empresas de países industrializados, y se espera poder desarrollar indicadores de medición de transferencia tecnológica que permitan comprender mejor la complejidad de la vivienda social.

Laura, a very beautiful but also mysterious lady, inspired the famous poet Petrarch for poems, which express ecstatic love as well as deep despair. F. J. Jones-a scientist for literary work-recognized in these changes between love and despair an oscillating behaviour-from 1328 to 1350-which he called Petrarch's emotional cycle. The mathematician S. Rinaldi investigated this cycle and established a mathematical model based on ordinary differential equation: two coupled nonlinear ODEs, reflecting Laura's and Petrarch's emotions for each other, drive an inspiration variable, which coincides with Petrarch's emotional cycle. These ODEs were the starting point for the investigations in two directions: mapping the mathematical model to a suitable modelling concept, and trying to extend the model for love dynamics in modern times (F. Breitenecker et al.). This contribution introduces and investigates a modelling approach for love dynamics and inspiration by means of System Dynamics, for Laura's and Petrarch's emotions as well as for a modern couple in love. In principle, emotions and inspiration emerge from a source and are fading into a sink. But the controlling parameters for increase and decrease of emotion create a broad variety of emotional behaviour and of degree of inspiration, because of the nonlinearities. Experiments including an implementation of this model approach and selected simulations provide interesting case studies for different kinds of love dynamics-attraction, rejection and neglect-stable equilibria and chaotic cycles.

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Smart Grids ideally interconnect intelligent grid members. One big share of grid presence is with buildings. Flexible and grid-friendly buildings would improve grid management and are an important contribution to the integration of renewable energy sources. Classical buildings, however, are passive and not cooperative. This article describes how electro-thermal processes in buildings can be used for demand response and how such intelligent behavior can be enabled via communication technology. Experiments and simulations on typical mid-European buildings were done to estimate the potential time constants. I. MOTIVATIONBuildings are responsible for around forty percent of an economy's energy consumption in western countries [1]. To reach the given emission goals an obvious way is to increase the building's energy efficiency. Typical measures are improved building shell (insulation) or more efficient HVAC (heating, ventilation and air conditioning) equipment. Such efficiency measures immediately and directly reduce the overall energy demand and its related emissions. The typical development -especially observable in the European Union -is to increase efficiency to the maximum (a.k.a. Passive House Level) and in addition to add local renewable energy generation (e.g. photovoltaics, solar thermal, wind, ground water) to cover the remaining energy demand or to even reach "plus-energy" level. In the latter case, the building would generate a positive net energy balance, i.e. generate more than it needs. This positive net energy balance is not to be confused with energy autonomy, since the building is still grid-connected (electric, thermal) and dynamically changes between its roles as energy producer and energy consumer. Local, as well as centralized, renewable energy often has a stochastic behavior: it feeds in whenever the primary resource (solar radiation, wind) is available. By the use of a good prognosis, flexible conventional generation (gas turbine power stations) and potential storage (pump storage power plants) it is possible to keep these fluctuations under control. However, the increasing share of renewable generation and the approaching wave of electric vehicles with its thirst for electricity pose new challenges for our energy system and its generation, transport and distribution capacity. Intelligent buildings can help. "Intelligence" in this context means that the building is equipped with an advanced building automation system (BAS) that • influences energy-relevant equipment and settings like HVAC or windows, • senses energy-relevant information like occupancy, weather, or usage, and that • contains advanced control algorithms that go beyond plain PID (proportional, integral and derivative control function). Such an advanced BAS can use weather forecasts, learn usage profiles or reschedule the operations of building systems in order to meet smart grid requirements (see [2] and [3]). One key ingredient for operating in such a smart way is to know the dynamic behavior of the building. Model-based control ...

Building automation systems record operation data including physical values, system states and operation conditions. This data is stored, but commonly not automatically evaluated. This historic data is the key to efficient operation and to quick recognition of errors and inefficiencies, a potential that is not exploited today. Instead, today the evaluation during operation delivers only alarming in case of system failures. Analysis is commonly done by the facility manager, who uses his experience to interpret data. Methods from data mining and data analysis can contribute to a better understanding of building operation and provide the necessary information to optimize operation, especially in the area of Heating, Ventilation and Air Conditioning (HVAC) systems. Increases in energy efficiency and can be achieved by automated data analysis and by presenting the user energy performance indicators of all relevant HVAC components. The authors take a first step to examine operation data of adsorption chillers using the X-Means algorithm to automate the detection of system states.