The concept of the Daily Urban System (DUS) has gained relevance over the past decades as the entity to examine and explain the functionality of the urban landscape. Daily Urban Systems are usually defined and measured by the strength of commuter or shopper flows between the nodes of the system. It is important to realize that these Daily Urban Systems are the accumulated pattern of individuals making frequent, recurring trips to other localities than their own. Understanding the microeconomic decisions behind these spatial interactions will help in assessing the functional and spatial structure of DUS. In this paper is explored how, based on Dutch empirical data, the individual household’s spatial interactions shape the daily urban system and how the destination of these interactions correlates with personal and spatial variables and motives for interaction. The results show that the occurrence of non-local spatial interactions can be explained by the size-based Christallerian hierarchy of the localities of residence, but that it is the regional population – or market potential – that explains and moderates the sorting of households and the intensity and direction of their spatial interactions in the DUS, matching agglomeration theory.
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Usually the whole is the sum of its parts (added linearly): someone is happy and travelling, so happy travelling. Someone walks across a motionless and stable bridge and thus moves forward. But when hundreds of people walk together, the bridge may start to wobble quite suddenly. Both systems - the pedestrian and the bridge - are coupled into one new pattern, in which the whole is more than the sum of its parts. Such discontinuous shifts can be observed everywhere: in horses, the shift from walk to trot to canter (3 patterns); from ice to water to steam. Psychological measures - IQ, emotional stability, planning skills are marginal at best. It is argued that our behaviour is often determined by such dynamic systems.
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From the article: The ‘Axiomatic Design Methodology’ uses ‘Axioms’ that cannot be proven nor derived from physical phenomena. The axioms serve as guidelines for the design process of products and systems. The latest contribution was the addition of the ‘Complexity Axiom’ in 1999. However, the underlying theory of complexity did not get much traction by designers and their managers yet. It emphasises difficulties in the design, not primarily focussing on solutions. The ‘Theory of Complexity’ is converted to a ‘Theory of Maturity’ in this paper. It is supported with a graphical way to plot maturity as it develops. It visualises the results in a way that can be understood by all entities in a company, engineers, managers, and executives. Understanding the maturity of a system enables selection of the right measures to control it. Visualisation enables communication between the interacting parties. If successful development trajectories are understood, eventually from earlier experience, even better corrective actions can be applied. The method appears an affirmative way to graphically represent progression in design, thus presenting advances in a positive context. Though positively presented, it is not the case that the method hides problems; presumed and legitimate project progression can be quite different, which challenges the designer to understand the process. In this way, the method sends out a continuous warning to stay critical on design choices made.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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In order to study education and development, researchers can choose among a plethora of methods. The Merriam-Webster dictionary tells us that “method” means: a procedure or process for attaining an object …such as …a systematic procedure, technique, or mode of inquiry employed by or proper to a particular discipline or art “ or “a way, technique, or process of or for doing something”, or “a body of skills or techniques”. Methods proper to the scientific study of education and development cover a very broad range of procedures, ranging from how to formulate and ask questions, how to design studies for answering such questions, how to perform such studies in real-world contexts, how to extract data and how to process them, how to relate processed data to answers on questions, how to communicate such questions and answers, and how to apply them to real world activities aimed at promoting education and development. This body of methods is customarily termed “methodology”, which is a concept that includes the methods themselves but also our understanding of their relationships and their rational and scientific justification. Let us call this body of methods and the justifications “Integrative methodology”. Researchers often tend to see this integrative methodology as a more or less autonomous set of good practice prescriptions. This view is consistent with practices of academic training in which methodology courses are offered separate from courses on disciplinarian contents, e.g. courses on development or educational science. As a consequence of this autonomy oriented view of methodology, scientific questions regarding development and education tend to be framed in terms of the available or habitual methods. For instance, we readily transform or translate concrete questions about the influence of some particular educational intervention in terms of a statistically significant difference between 2 representative samples that systematically differ in only one variable or feature of interest, which, in this case, is the intervention. Almost every word in this translation carries the heavy burden of methodological principles, concepts and presuppositions: “statistically”, “significant”, “difference”, “representative”, “sample”, “systematically”, “variable”, and “intervention”. And all these principles, concepts and presuppositions are taken from this autonomous body of integrative methodology, which forms our indisputable cookbook of good practices, outside of which no good — scientific — practices exist. The answers to questions that are shaped by this independent body of methodology will then contribute to existing theories of development and education. In this sense, it is the (allegedly) independent methodology that informs theory.In this chapter, we will move against this current practice and make the — apparently deeply obvious — claim that it must be theory that informs the questions and the way we shall answer these questions. That is, it must be theory – that is, your body of justified knowledge about a particular phenomenon – that informs, influences and determines methodology, that is, the whole of methods, procedures and instruments that you use to study that phenomenon. . The sort of theory that should inform integrative methodology must be an integrative theory, that is to say a theory consisting of a consistent set of general principles and concepts shaping the domains of inquiry, which in this particular case are the related domains of development and education
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The final of a four-part series that explores Dynamic Systems Theory (DST). DST has taught us that phase transitions are based on self-organization and that they are not predictable a priori. They can be described, but not explained. This is well established in physics, but not at all in the social sciences and everyday life. It also takes a lot of practice to learn to see dynamic systems in everyday life. A few examples are given.
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From the article: Manufacturing technology can improve the turnover of a company if it enables fast market introduction for volume production. Reconfigurable equipment is developed to meet the growing demand for more agile production. Modular reconfiguration, defined as changing the structure of the machine, enables larger variation of products on a single manufacturing system; these solutions are called Reconfigurable Manufacturing Systems (RMS). The quality of RMS, and the required resources to bring it to reliable production, is largely determined by a swift execution of the reconfiguration process. This paper proposes a method to compare alternatives for the ways to implement reconfiguration. Three classes of reconfiguration are defined to distinguish the impact of the proposed alternatives. The procedure uses a recently introduced index method for development of RMS process modules. This index method is based on the Axiomatic Design theory. Weighing factors are used to calculate the resources and lead time needed to implement the reconfiguration process. Application of the method leads to quick comparison of alternatives in the early stage of development. Successful execution of the method was demonstrated for the manufacturing process of a 3D measuring probe.
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There are three volumes in this body of work. In volume one, we lay the foundation for a general theory of organizing. We propose that organizing is a continuous process of ongoing mutual or reciprocal influence between objects (e.g., human actors) in a field, whereby a field is infinite and connects all the objects in it much like electromagnetic fields influence atomic and molecular charged objects or gravity fields influence inanimate objects with mass such as planets and stars. We use field theory to build what we now call the Network Field Model. In this model, human actors are modeled as pointlike objects in the field. Influence between and investments in these point-like human objects are explained as energy exchanges (potential and kinetic) which can be described in terms of three different types of capital: financial (assets), human capital (the individual) and social (two or more humans in a network). This model is predicated on a field theoretical understanding about the world we live in. We use historical and contemporaneous examples of human activity and describe them in terms of the model. In volume two, we demonstrate how to apply the model. In volume 3, we use experimental data to prove the reliability of the model. These three volumes will persistently challenge the reader’s understanding of time, position and what it means to be part of an infinite field. http://dx.doi.org/10.5772/intechopen.99709
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Innovations are required in urban infrastructures due to the pressing needs for mitigating climate change and prevent resource depletion. In order to address the slow pace of innovation in urban systems, this paper analyses factors involved in attempts to introduce novel sanitary systems. Today new requirements are important: sanitary systems should have an optimal energy/climate performance, with recovery of resources, and with fewer emissions. Anaerobic digestion has been suggested as an alternative to current aerobic waste water treatment processes. This paper presents an overview of attempts to introduce novel anaerobic sanitation systems for domestic sanitation. The paper identifies main factors that contributed to a premature termination of such attempts. Especially smaller scale anaerobic sanitation systems will probably not be able to compete economically with traditional sewage treatment. However, anaerobic treatment has various advantages for mitigating climate change, removing persistent chemicals, and for the transition to a circular economy. The paper concludes that loss avoidance, both in the sewage system and in the waste water treatment plants, should play a key role in determining experiments that could lead to a transition in sanitation. http://dx.doi.org/10.13044/j.sdewes.d6.0214 LinkedIn: https://www.linkedin.com/in/karel-mulder-163aa96/
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To improve people’s lives, human-computer interaction researchers are increasingly designing technological solutions based on behavior change theory, such as social comparison theory (SCT). However, how researchers operationalize such a theory as a design remains largely unclear. One way to clarify this methodological step is to clearly state which functional elements of a design are aimed at operationalizing a specific behavior change theory construct to evaluate if such aims were successful. In this article, we investigate how the operationalization of functional elements of theories and designs can be more easily conveyed. First, we present a scoping review of the literature to determine the state of operationalizations of SCT as behavior change designs. Second, we introduce a new tool to facilitate the operationalization process. We term the tool blueprints. A blueprint explicates essential functional elements of a behavior change theory by describing it in relation to necessary and sufficient building blocks incorporated in a design. We describe the process of developing a blueprint for SCT. Last, we illustrate how the blueprint can be used during the design refinement and reflection process.
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