Purpose – Against the background of current leadership theory, this research paper analyses and compares the leadership approaches of two outstanding leaders: Daniel Vasella, chairman of the leading Swiss pharmaceutical organization Novartis and Ricardo Semler, owner of the Brazilian conglomerate Semco. In contrast to many rather abstract, unpractical and pointlessly theoretical papers on leadership this analysis provides a more applied view of leadership by means of the life history approach delivering insight into both leaders’ development and leader personality. Methodology/approach – First, this paper locates the ideas and practices associated with the term “leadership” as a concept through theories that have developed over time and shows how the practices of leading can be derived and understood through chosen theories. Based on this, the specific characteristics and career paths of both leaders are presented and compared so that a final analysis of their leadership approach can be done. The paper is based on secondary sources such as peer-reviewed business journals and literature on leadership. Information about both leaders and their approach to leadership is gathered mainly from published interviews with them. Additional information on Semler is taken from his autobiography. Conclusions – It is difficult to identify an “essence” of leadership, whether that takes the form of personality characteristics or traits, charisma, the ability to transform people or organizations or a brain function. All presented theories of leadership seem to have their raison d’être. Both Vasella and Semler apply a combination of different attitudes and behaviours that characterize their leadership style containing elements of transformational, charismatic, ethical, servant and authentic leadership.
MULTIFILE
The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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New technologies or approaches are being widely developed and proposed to be deployed in real energy systems to improve desired objectives; however, supporting decision making processes to select best solutions in terms of performance and efficiently following cost-benefit analysis require some sort of scientific evidence based tools. These tools should be reliable, robust, and capable of demonstrating the behaviour and impact of newly developed devices or algorithms in different pre- defined scenarios. Therefore, new approaches and technologies need to be tested and verified using a safe laboratory test environment.This report is about the development and realisation of some major tools and reliable methods to calculate risks and opportunities for integrating of new energy resources into the European electricity grid. Hanze University Groningen and Politecnico di Torino worked together within the STORE&GO project sharing laboratories, knowledge, hardware facilities and researchers for the realisation of the characterisation and mathematical modelling of renewable resources. Needed to realize a stable and reliable environment for remote physical hardware in the loop simulations.For this realisation we started with the local characterisation of a PV-Field and a PEM electrolyser at Entrance Groningen by logging and measuring the electric behaviour and specific device parameters to integrate and convert these into working mathematical models of a PV-Field and electrolyser prosumer. After testing and evaluating these models by comparing the results with the real-time measurements, these test and modelling is also realised from the remote laboratory in Torino. To achieve dynamical physical hardware we also realised dynamic mathematical model(s) with real-time functionality to interact directly with the remote electrolyser. To connect both the laboratories with full duplex communication functionalities between physical hardware and models we have also realized a network which is able to share network resources on both local and remote sites.
DOCUMENT
Climate change adaptation has influenced river management through an anticipatory governance paradigm. As such, futures and the power of knowing the future has become increasingly influential in water management. Yet, multiple future imaginaries co-exist, where some are more dominant that others. In this PhD research, I focus on deconstructing the future making process in climate change adaptation by asking ‘What river imaginaries exist and what future imaginaries dominate climate change adaptation in riverine infrastructure projects of the Meuse and Magdalena river?’. I firstly explore existing river imaginaries in a case study of the river Meuse. Secondly, I explore imaginaries as materialised in numerical models for the Meuse and Magdalena river. Thirdly, I explore the integration and negotiation of imaginaries in participatory modelling practices in the Magdalena river. Fourthly, I explore contesting and alternative imaginaries and look at how these are mobilised in climate change adaptation for the Magdalena and Meuse river. Multiple concepts stemming from Science and Technology Studies and Political Ecology will guide me to theorise the case study findings. Finally, I reflect on my own positionality in action-research which will be an iterative process of learning and unlearning while navigating between the natural and social sciences.
In the last decade, the concept on interactions between humans, animals and their environment has drastically changed, endorsed by the One Health approach that recognizes that health of humans and animals are inextricably linked. Consideration of welfare of livestock has increased accordingly and with it, attention into the possibilities to improve livestock health via natural, more balanced nutrition is expanding. Central to effects of healthy nutrition is an optimal gastrointestinal condition which entails a well-balanced functional local immune system leading to a resilient state of well-being. This project proposal, GITools, aims to establish a toolbox of in vitro assays to screen new feed ingredients for beneficial effects on gastrointestinal health and animal well-being. GITools will focus on pig and chicken as important livestock species present in high quantities and living in close proximity to humans. GITools builds on intestinal models (intestinal cell lines and stem cell-derived organoids), biomarker analysis, and in vitro enzymatic and microbial digestion models of feed constituents. The concept of GITools originated from various individual contacts and projects with industry partners that produce animal feed (additives) or veterinary medicines. Within these companies, an urgent need exists for straightforward, well-characterized and standardized in vitro methods that provide results translatable to the in vivo situation. This to replace testing of new feed concepts in live animal. We will examine in vitro methods for their applicability with feed ingredients selected based on the availability of data from (previous) in vivo studies. These model compounds will include long and short chain fatty acids, oligosaccharides and herbal-derived components. GITools will deliver insights on the role of intestinal processes (e.g. dietary hormone production, growth of epithelial cells, barrier function and innate immune responses) in health and well-being of livestock animals and improve the efficiency of testing new feed products.
English: This living lab aims to support the creation, development and implementation of next generation concepts for sustainable healthcare logistics, with special attention for last mile solutions. Dutch healthcare providers are on the verge of a transition towards (more) sustainable business models, spurred by e.g., increasing healthcare costs, ongoing budget cuts, tight labor market conditions and increasing ecological awareness. Consequently, healthcare providers need to improve and innovate their business model and underlying logistics concept(s). Simultaneously, many cities are struggling with congestion in traffic, air quality and liveability in general. This calls for Last Mile Logistics (LML) concepts that can address challenges like effective and efficient resource planning, scheduling and utilization and, particularly, sustainability goals. LML can reduce environmental and social impact by decreasing emissions, congestion and pollution through effectively consolidating in-flows of goods and providing innovative solutions for care, wellbeing and related services. The research and initiatives in the living lab will address the following challenges: reducing the ecological footprint, reducing (healthcare-related) costs, improving service quality, decreasing loneliness of frail citizens and improving the livability of urban areas (reducing congestion and emissions). Given the scarcity and fragmentation of knowledge on healthcare logistics in organizations the living lab will also act as a learning community for (future) healthcare- and logistics professionals, thereby supporting the development of human capital. By working closely with related stakeholders and using a transdisciplinary research approach it is ensured that the developed knowledge and solutions deliver a contribution to societal challenges and have sound business potential.
