Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
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In the Netherlands an innovative programme for early detection of chronic obstructive pulmonary disease (COPD) in primary care among patients aged 40–70 years has been evaluated in both an effect study and a pilot implementation study. Health-care providers identified four obstacles for successful implementation of a COPD early detection programme. This Brief Communication describes the most important results of a qualitative study using in-depth interviews.
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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 recent years, the fight against terrorism and political violence has focused more on anticipating the threats that they pose. Therefore, early detection of ideas by local professionals has become an important part of the preventive approach in countering radicalization. Frontline workers who operate in the arteries of society are encouraged to identify processes toward violent behavior at an early stage. To date, however, little is known about how these professionals take on this screening task at their own discretion. Research from the Netherlands suggests that subjective assessment appears to exist. In this article, we argue that the absence of a clear norm for preliminary judgments affects prejudice or administrative arbitrariness, which may cause side effects due to unjustified profiling.
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Standard SARS-CoV-2 testing protocols using nasopharyngeal/throat (NP/T) swabs are invasive and require trained medical staff for reliable sampling. In addition, it has been shown that PCR is more sensitive as compared to antigen-based tests. Here we describe the analytical and clinical evaluation of our in-house RNA extraction-free saliva-based molecular assay for the detection of SARS-CoV-2. Analytical sensitivity of the test was equal to the sensitivity obtained in other Dutch diagnostic laboratories that process NP/T swabs. In this study, 955 individuals participated and provided NP/T swabs for routine molecular analysis (with RNA extraction) and saliva for comparison. Our RT-qPCR resulted in a sensitivity of 82,86% and a specificity of 98,94% compared to the gold standard. A false-negative ratio of 1,9% was found. The SARS-CoV-2 detection workflow described here enables easy, economical, and reliable saliva processing, useful for repeated testing of individuals.
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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Little is known about which self-management behaviors have the highest potential to influence exacerbation impact in COPD patients. We aimed to reach expert consensus on the most relevant set of self-management behaviors that can be targeted and influenced to maximize reduction of exacerbation impact. Materials and methods A 2-round Delphi study was performed using online surveys to rate the relevance and feasibility of predetermined self-management behaviors identified by literature and expert opinion. Descriptive statistics and qualitative analyses were used. Results An international expert panel reached consensus on 17 self-management behaviors focusing on: stable phase (n=5): pharmacotherapy, vaccination, physical activity, avoiding stimuli and smoking cessation; periods of symptom deterioration (n=1): early detection; during an exacerbation (n=5): early detection, health care contact, self-treatment, managing stress/anxiety and physical activity; during recovery (n=4): completing treatment, managing stress/anxiety, physical activity and exercise training; and after recovery (n=2): awareness for recurrent exacerbations and restart of pulmonary rehabilitation. Conclusion This study has provided insight into expert opinion on the most relevant and feasible self-management behaviors that can be targeted and influenced before, during and after an exacerbation to exert the highest magnitude of influence on the impact of exacerbations. Future research should focus at developing more comprehensive patient-tailored interventions supporting patients in these exacerbation-related self-management behaviors.
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Bespreking academisch proefschrift N. Boonstra (RU Groningen). Dit proefschrift heeft als centraal thema de vroegtijdige onderkenning van de eerste psychotische symptomen bij psychiatrische patiënten. Een centrale behandeldoelstelling hierbij is om de duur van de onbehandelde psychose (Duration of Untreated Psychosis, afgekort DUP) zo kort mogelijk te Iaten zljn. De DUP verwljst naar de tijd die verstrljkt tussen de eerste manifestatie van psychotische symptomen en het moment waarop een hlerblj passende behandeling start. Een kortere DUP blijkt samen te hangen met een betere prognose van de ziekte, zich ultend in een vroegere en betere remissie, minder psychotische terugval, minder cognitleve achteruitgang, minder psychotische symptomen en beter sociaal functioneren.
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Background: Given that relapse is common in patients in remission from anxiety and depressive disorders, relapse prevention is needed in the maintenance phase. Although existing psychological relapse prevention interventions have proven to be effective, they are not explicitly based on patients’ preferences. Hence, we developed a blended relapse prevention program based on patients’ preferences, which was delivered in primary care practices by mental health professionals (MHPs). This program comprises contact with MHPs, completion of core and optional online modules (including a relapse prevention plan), and keeping a mood and anxiety diary in which patients can monitor their symptoms. Objective: The aims of this study were to provide insight into (1) usage intensity of the program (over time), (2) the course of symptoms during the 9 months of the study, and (3) the association between usage intensity and the course of symptoms. Methods: The Guided E-healTh for RElapse prevention in Anxiety and Depression (GET READY) program was guided by 54 MHPs working in primary care practices. Patients in remission from anxiety and depressive disorders were included. Demographic and clinical characteristics, including anxiety and depressive symptoms, were collected via questionnaires at baseline and after 3, 6, and 9 months. Log data were collected to assess the usage intensity of the program. Results: A total of 113 patients participated in the study. Twenty-seven patients (23.9%) met the criteria for the minimal usage intensity measure. The core modules were used by ≥70% of the patients, while the optional modules were used by <40% of the patients. Usage decreased quickly over time. Anxiety and depressive symptoms remained stable across the total sample; a minority of 15% (12/79) of patients experienced a relapse in their anxiety symptoms, while 10% (8/79) experienced a relapse in their depressive symptoms. Generalized estimating equations analysis indicated a significant association between more frequent face-to-face contact with the MHPs and an increase in both anxiety symptoms (β=.84, 95% CI .39-1.29) and depressive symptoms (β=1.12, 95% CI 0.45-1.79). Diary entries and the number of completed modules were not significantly associated with the course of symptoms.
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Background: Medically unexplained physical symptoms (MUPS) are a leading cause of reduced work functioning. It is not known which factors are associated with reduced work functioning in people with moderate MUPS. Insight in these factors can contribute to prevention of reduced work functioning, associated work-related costs and in MUPS becoming chronic. Therefore, the aim of this study was to identify which demographic and health-related factors are associated with reduced work functioning, operationalized as impaired work performance and absenteeism, in people with moderate MUPS. Methods: Data of 104 participants from an ongoing study on people with moderate MUPS were used in this cross-sectional study. Ten independent variables were measured at baseline to determine their association with reduced work functioning: severity of psychosocial symptoms (four domains, measured with the Four-Dimensional Symptom Questionnaire), physical health (RAND 36-Item Health Survey), moderate or vigorous physical activity (Activ8 activity monitor), age, sex, education level and duration of complaints. Two separate multivariable linear regression analyses were performed with backward stepwise selection, for both impaired work performance and absenteeism. Results: Absenteeism rate rose with 2.5 and 0.6% for every increased point on the Four-Dimensional Symptom Questionnaire for domain 'depression' (B = 0.025, SE = 0.009, p = .006) and domain 'somatization' (B = 0.006, SE = 0.003, p = .086), respectively. An R2 value of 0.118 was found. Impaired work performance rate rose with 0.2 and 0.5% for every increased point on the Four-Dimensional Symptom Questionnaire for domain 'distress' (B = 0.002, SE = 0.001, p = .084) and domain 'somatization' (B = 0.005, SE = 0.001, p < .001), respectively. An R2 value of 0.252 was found. Conclusions: Severity of distress, probability of a depressive disorder and probability of somatization are positively associated with higher rates of reduced work functioning in people with moderate MUPS. To prevent long-term absenteeism and highly impaired work performance severity of psychosocial symptoms seem to play a significant role. However, because of the low percentage of explained variance, additional research is necessary to gain insight in other factors that might explain the variance in reduced work functioning even better.
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