In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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De laatste decennia is tijd een strategische concurrentiefactor geworden in de maakindustrie (Demeter, 2013; Godinho Filho et al., 2017a; Gromova, 2020). Naast tijdige levering verwacht de klant ook keuze, maatwerk, hoge kwaliteit en een lage prijs (Siong et al., 2018; Suri, 2020). Om de door de klant gewenste korte doorlooptijd te kunnen realiseren en daarbij ook te voldoen aan zijn andere eisen, zijn flexibiliteit en aanpassingsvermogen essentieel geworden (Godinho Filho et al., 2017b; Siong et al., 2018). Quick Response Manufacturing (QRM) heeft als doel de doorlooptijd te verkorten in productieomgevingen die gekenmerkt worden door een hoge variëteit in producten en maatwerk (Suri, 2020; Siong et al., 2018). QRM kent zijn oorsprong begin jaren negentig van de vorige eeuw (Suri, 2020) en vertoont sterke gelijkenis met lean manufacturing. Het verschil met lean manufacturing is echter dat QRM zich richt op bedrijven in een omgeving met veel productvariatie. Daarnaast heeft QRM nieuwe elementen toegevoegd, zoals Paired-cell Overlapping Loops of Cards with Authorization (POLCA) en Manufacturing Critical Path Time’ (MCT)’ (Godinho Filho et al., 2017b).
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We thank the authors Hayden and Glenn for commenting on our paper ‘The introduction of a nursing guideline on depression at psychogeriatric nursing home wards: Effects on Certified Nurse Assistants’, which was published in the International Journal of Nursing Studies (Verkaik et al., 2011). In our paper we conclude that the introduction of the nursing guideline ‘Depression in dementia’ had a positive, though small, significant effect on the perceived professional autonomy of Certified Nurse Assistants. We found that effects could probably be enlarged if non-Certified Nurse Assistants and nursing helpers were also trained, and managers paid more attention to the necessary conditions for successful introduction.
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In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
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Limited evidence is available about (non)-representativeness of participants in health-promoting interventions. The Dutch Healthy Primary School of the Future (HPSF)-study is a school-based study aiming to improve health through altering physical activity and dietary behaviour, that started in 2015 (registered in ClinicalTrials.gov on14-06-2016, NCT02800616). The study has a response rate of 60%. A comprehensive non-responder analysis was carried out, and responders were compared with schoolchildren from the region and the Netherlands using a cross-sectional design. External sources were consulted to collect non-responder, regional, and national data regarding relevant characteristics including sex, demographics, health, and lifestyle. The Chi-square test, Mann-Whitney U test, or Student's t-test were used to analyse differences.
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Whereas investments in new attractions continue to rise within the theme park indus- try, knowledge regarding the effects of new attractions on theme park performance and attendance remains scarce. In order to predict the impact of new attractions on the performance of European theme parks, this article presents an Attraction Response Matrix (ARM). The Attraction Response Matrix offers an integrated framework in which research into the effects of new attractions can take place in a systematic manner. The ARM attempts to transform post priori knowledge into a priori knowledge by better understanding the impact of a new attraction and its' mediating causes. The main premise of the ARM is: "in situation A, attraction B will most likely have effect C on target audience D." By performing research into the relevant effects within certain cells of the ARM and consecu- tively investigating the relationship between the various cells, a better insight will be gained in the working of new attractions. ARM is based on an extensive ZMET study conducted in The Netherlands.
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Postprandial high glucose and insulin responses after starchy food consumption, associated with an increased risk of developing several metabolic diseases, could possibly be improved by altering food structure. We investigated the influence of a compact food structure; different wheat products with a similar composition were created using different processing conditions. The postprandial glucose kinetics and metabolic response to bread with a compact structure (flat bread, FB) was compared to bread with a porous structure (control bread, CB) in a randomized, crossover study with ten healthy male volunteers. Pasta (PA), with a very compact structure, was used as the control. The rate of appearance of exogenous glucose (RaE), endogenous glucose production, and glucose clearance rate (GCR) was calculated using stable isotopes. Furthermore, postprandial plasma concentrations of glucose, insulin, several intestinal hormones and bile acids were analyzed. The structure of FB was considerably more compact compared to CB, as confirmed by microscopy, XRT analysis (porosity) and density measurements. Consumption of FB resulted in lower peak glucose, insulin and glucose-dependent insulinotropic polypeptide (ns) responses and a slower initial RaE compared to CB. These variables were similar to the PA response, except for RaE which remained slower over a longer period after PA consumption. Interestingly, the GCR after FB was higher than expected based on the insulin response, indicating increased insulin sensitivity or insulin-independent glucose disposal. These results demonstrate that the structure of wheat bread can influence the postprandial metabolic response, with a more compact structure being more beneficial for health. Bread-making technology should be further explored to create healthier products.
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BACKGROUND AND AIM: Functional Capacity Evaluations (FCEs) are used to quantify physical aspects of work capacity. Safety is a critical issue for clinical use of an FCE. Patients with Chronic Low Back Pain (CLBP) are known to report a temporary increase in pain following an FCE, but it is not known whether this increase is a normal pain response to FCE. It is currently unknown how healthy subjects respond to an FCE and whether this should be interpreted as a normal reaction after physical exercise. This study was performed to quantify the intensity, duration, location and nature of the pain response following an FCE in healthy subjects and to compare this pain response with the pain response of patients with CLBP from a previous study.METHODS: A total of 197 healthy working subjects between 20 and 60 years of age volunteered to participate in this study. All subjects performed a 12-item FCE. Pain response was measured by a self-constructed Pain Response Questionnaire (PRQ). Descriptive statistics were used to describe the pain response following an FCE. Mann-Whitney and t-tests were performed to compare the data from this study with data of patients with CLBP from a previous study.RESULTS: About 82% of all subjects reported a pain response following the FCE. The intensity of the pain response after 24 h post FCE was a median of 3.0 on a numeric rating scale (0-10). About 78% of all pain was reducible to muscle soreness. Pain was most often reported in the upper legs (51%), the lower back (38%) the shoulders (37%) and upper arms (36%). Symptoms decreased to pre-FCE levels in a mean of 3 days. The pain response of 2 subjects (1%) lasted for 3 weeks. The intensity and duration of the pain response of healthy subjects was not significantly different from the response of patients with CLBP.CONCLUSION: Pain response of 99% of all subjects who reported a pain response was interpreted as normal. It was concluded that a pain response following an FCE can be expected in healthy subjects and that this pain response is a normal musculoskeletal reaction. The pain response of patients with CLBP resembles the pain response of healthy subjects.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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Although Item Response Theory (IRT) has been recommended for helping advance interprofessional education (IPE) research, its use remains limited. This may be partly explained by potential misconceptions regarding IRT`s “limitation” to cross-sectional data. The aim of this study is to demonstrate how Item Response Theory (IRT) can be applied effectively in before-and-after designs in IPE research. Specifically, a two-week before-after design with survey methodology using the Extended Professional Identity Scale (EPIS), an interprofessional identity measure, was conducted among n = 146 mixed health-science students. Results indicated that EPIS increased significantly before-after intervention by.74 standardised mean differences, t146 = 7.73, p
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