In this chapter it is argued that self-direction is currently well above the head of the majority of youngsters and even of many adults. Evidence for this conclusion stems from developmental and brain research. However, for various reasons it is important that people develop the competences that are necessary for self-direction. To what degree is it possible to develop these competences? Are they 'learnable'? What can education contribute?
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.
BACKGROUND: Since 2011, a tailored, interdisciplinary head and neck rehabilitation (IHNR) program, covered by the basic healthcare insurance, is offered to advanced head and neck cancer (HNC) patients in the Netherlands Cancer Institute (NKI). This program is developed to preserve or restore patients' functioning, and to optimize health-related quality of life (HRQoL). It applies an integrated approach to define patients' individual goals and provide rehabilitation care throughout the cancer care continuum. The aim of the current study is to assess the (cost-) effectiveness of the IHNR approach compared to usual supportive care (USC) consisting of monodisciplinary and multidisciplinary care in advanced HNC patients.METHODS: This multicenter prospective observational study is designed to compare (cost-)effectiveness of the IHNR to USC for advanced HNC patients treated with chemoradiotherapy (CRT) or bioradiotherapy (BRT). The primary outcome is HRQoL represented in the EORTC QLQ-C30 summary score. Functional HRQoL, societal participation, utility values, return to work (RTW), unmet needs (UN), patient satisfaction and clinical outcomes are secondary outcomes, assessed using the EORTC QLQ-H&N35, USER-P, EQ-5D-5 L, and study-specific questionnaires, respectively. Both patient groups (required sample size: 64 per arm) are requested to complete the questionnaires at: diagnosis (baseline; T0), 3 months (T1), 6 months (T2), 9 months (T3) and 12 months (T4) after start of medical treatment. Differences in outcomes between the intervention and control group will be analyzed using mixed effects models, Chi-square test and descriptive statistics. In addition, a cost-effectiveness analysis (CEA) will be performed by means of a Markov decision model. The CEA will be performed using a societal perspective of the Netherlands.DISCUSSION: This prospective multicenter study will provide evidence on the effectiveness and cost-effectiveness of IHNR compared to USC. RTW and societal participation, included as secondary outcomes, have not been studied sufficiently yet in cancer rehabilitation. Interdisciplinary rehabilitation has not yet been implemented as usual care in all centers, which offers the opportunity to perform a controlled clinical study. If demonstrated to be (cost-)effective, national provision of the program can probably be advised.TRIAL REGISTRATION: The study has been retrospectively registered in the Netherlands Trial Registry on April 24th 2018 ( NTR7140 ).
