Purpose: The aim of this study was to investigate the effect of possible late effects of cancer treatment (physical complaints, fatigue, and cognitive complaints) and of two job resources (autonomy and supportive leadership style) on future burnout complaints, among employees living 2–10 years beyond breast cancer diagnosis.Methods: Data at T1 (baseline questionnaire) and at T2 (9 months later) were collected in 2018 and 2019 (N = 287). These data were part of a longitudinal study among Dutch speaking workers with a cancer diagnosis 2–10 years ago. All complaints and job resources were self-reported. Longitudinal multivariate regression analyses were executed, controlling for years since diagnosis, living with cancer (recurrence or metastasis), and other chronic or severe diseases. Mediation by baseline burnout complaints was considered.Results: A higher level of fatigue and cognitive complaints at baseline (T1) resulted in higher future burnout complaints (at T2), with partial mediation by baseline burnout complaints. No effect of physical complaints at T1 was observed. Higher levels of autonomy or a supportive leadership style resulted in lower burnout complaints, with full mediation by baseline burnout complaints. Buffering was observed by autonomy in the relationship of cognitive complaints with future burnout complaints. No moderation was observed by supportive leadership.Conclusion: The level of burnout complaints among employees 2–10 years beyond breast cancer diagnosis may be an effect of fatigue or cognitive complaints, and awareness of this effect is necessary. Interventions to stimulate supportive leadership and autonomy are advisable, the latter especially in the case of cognitive complaints.
BACKGROUND:The number of workers who have previously undergone a cancer treatment is increasing, and possible late treatment effects (fatigue, physical and cognitive complaints) may affect work ability.OBJECTIVE:The aim of the study was to investigate the impact of late treatment effects and of job resources (autonomy, supportive leadership style, and colleagues’ social support) on the future work ability of employees living 2–10 years beyond a breast cancer diagnosis.METHODS:Data at T1 (baseline questionnaire) and at T2 (9 months later) were collected in 2018 and 2019 (N = 287) among Dutch-speaking workers with a breast cancer diagnosis 2–10 years ago. Longitudinal regression analyses, controlling for years since diagnosis, living with cancer (recurrence or metastasis), other chronic or severe diseases, and work ability at baseline were executed.RESULTS:Higher levels of fatigue and cognitive complaints at baseline predicted lower future work ability. The three job resources did not predict higher future work ability, but did relate cross-sectionally with higher work ability at baseline. Autonomy negatively moderated the association between physical complaints and future work ability.CONCLUSIONS:Fatigue and cognitive complaints among employees 2–10 years past breast cancer diagnosis need awareness and interventions to prevent lower future work ability. Among participants with average or high levels of physical complaints, there was no difference in future work ability between medium and high autonomy. However, future work ability was remarkably lower when autonomy was low.
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.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
ADHD, normalisering en demedicalisering, stepped diagnosisDruk en Dwars is onderdeel van de Academische Werkplaats ADHD. Samen met de RuG en zes gemeentes voeren we het pakket Druk en Dwars uit. Dit bestaat uit: uitvoering van en onderzoek naar ouderbegeleiding, leerkrachtbegeleiding en voorlichting
