Automated Analysis of Human Performance Data could help to understand and possibly predict the performance of the human. To inform future research and enable Automated Analysis of Human Performance Data a systematic mapping study (scoping study) on the state-of-the-art knowledge is performed on three interconnected components(i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis. Using a systematic method of Kitchenham and Charters for performing the systematic mapping study, resulted in a comprehensive search for studies and a categorisation the studies using a qualitative method. This systematic mapping review extends the philosophy of Shyr and Spisic, and Knuth and represents the state-of-art knowledge on Human Performance,Monitoring Human Performance and Automated Data Analysis
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PURPOSE: Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCTs) to investigate moderators of exercise intervention effects on cancer-related fatigue.METHODS: We used individual patient data from 31 exercise RCTs worldwide, representing 4,366 patients, of whom 3,846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z-score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test.RESULTS: Exercise interventions had statistically significant beneficial effects on fatigue (β= -0.17 [95% confidence interval (CI) -0.22;-0.12]). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (βdifference= -0.18 [95%CI -0.28;-0.08]). Supervised interventions with a duration ≤12 weeks showed larger effects on fatigue (β= -0.29 [95% CI -0.39;-0.20]) than supervised interventions with a longer duration. CONCLUSIONS: In this individual patient data meta-analysis, we found statistically significant beneficial effects of exercise interventions on fatigue, irrespective of demographic and clinical characteristics. These findings support a role for exercise, preferably supervised exercise interventions, in clinical practice. Reasons for differential effects in duration require further exploration.
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Illicit data markets have emerged on Telegram, a popular online instant messaging application, bringing together thousands of users worldwide in an unregulated exchange of sensitive data. These markets operate through vendors who offer enormous quantities of such data, from personally identifiable information to financial data, while potential customers bid for these valuable assets. This study describes how Telegram data markets operate and discusses what interventions could be used to disrupt them. Using crime script analysis, we observed 16 Telegram meeting places encompassing public and private channels and groups. We obtained information about how the different meeting places function, what are their inside rules, and what tactics are employed by users to advertise and trade data. Based on the crime script, we suggest four feasible situational crime prevention measures to help disrupt these markets. These include taking down the marketplaces, reporting them, spamming and flooding techniques, and using warning banners. This is a post-peer-review, pre-copyedit version of an article published in Trends in organized crime . The final authenticated version is available online at https://doi.org/10.1007/s12117-024-09532-6
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In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.
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Application of data analysis in dairy factories can help reduce the variation in quality and the chance on product deviations during storage substantially.
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Introduction: In the Netherlands, Diagnostic Reference Levels (DRLs) have not been based on a national survey as proposed by ICRP. Instead, local exposure data, expert judgment and the international scientific literature were used as sources. This study investigated whether the current DRLs are reasonable for Dutch radiological practice. Methods: A national project was set up, in which radiography students carried out dose measurements in hospitals supervised by medical physicists. The project ran from 2014 to 2017 and dose values were analysed for a trend over time. In the absence of such a trend, the joint yearly data sets were considered a single data set and were analysed together. In this way the national project mimicked a national survey. Results: For six out of eleven radiological procedures enough data was collected for further analysis. In the first step of the analysis no trend was found over time for any of these procedures. In the second step the joint analysis lead to suggestions for five new DRL values that are far below the current ones. The new DRLs are based on the 75 percentile values of the distributions of all dose data per procedure. Conclusion: The results show that the current DRLs are too high for five of the six procedures that have been analysed. For the other five procedures more data needs to be collected. Moreover, the mean weights of the patients are higher than expected. This introduces bias when these are not recorded and the mean weight is assumed to be 77 kg. Implications for practice: The current checking of doses for compliance with the DRLs needs to be changed. Both the procedure (regarding weights) and the values of the DRLs should be updated.
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INTRODUCTION: In the Netherlands, Diagnostic Reference Levels (DRLs) have not been based on a national survey as proposed by ICRP. Instead, local exposure data, expert judgment and the international scientific literature were used as sources. This study investigated whether the current DRLs are reasonable for Dutch radiological practice.METHODS: A national project was set up, in which radiography students carried out dose measurements in hospitals supervised by medical physicists. The project ran from 2014 to 2017 and dose values were analysed for a trend over time. In the absence of such a trend, the joint yearly data sets were considered a single data set and were analysed together. In this way the national project mimicked a national survey.RESULTS: For six out of eleven radiological procedures enough data was collected for further analysis. In the first step of the analysis no trend was found over time for any of these procedures. In the second step the joint analysis lead to suggestions for five new DRL values that are far below the current ones. The new DRLs are based on the 75 percentile values of the distributions of all dose data per procedure.CONCLUSION: The results show that the current DRLs are too high for five of the six procedures that have been analysed. For the other five procedures more data needs to be collected. Moreover, the mean weights of the patients are higher than expected. This introduces bias when these are not recorded and the mean weight is assumed to be 77 kg.IMPLICATIONS FOR PRACTICE: The current checking of doses for compliance with the DRLs needs to be changed. Both the procedure (regarding weights) and the values of the DRLs should be updated.
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In dit artikel wordt het door Twigg et al. (2011) uitgevoerde onderzoek kritisch bekeken. In dit onderzoek is gekeken naar de relatie tussen de verpleegkundige bezetting en verbetering van verpleegkundig sensitieve uitkomsten. De onderzoekers constateren een positieve causale relatie maar onderbouwen dat niet door de gepresenteerde resultaten. Daarnaast wordt er geen aandacht geschonken aan andere contextuele factoren (zoals multidisciplinaire samenwerking) die van invloed zijn op de uitkomsten. Geconcludeerd kan worden dat de relatie tussen de verpleegkundige bezetting minder duidelijk is dan de onderzoekers concluderen.
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Corporate reputation is an intangible resource that is closely tied to an organization’s success but measuring it and to derive actions that can improve the reputations can be a long and expensive journey for an organization. In the available literature, corporate reputation is primarily measured through surveys, which can be time and cost intensive. This paper uses online reviews on the web as the source for a machine-learning driven aspect-based sentiment analysis that can enable organizations to evaluate their corporate reputation on a fine-grained level. The analysis is done unsupervised without organizations needing to manually label datasets. Using the insights generated through the analysis, on one hand, organizations can save costs and time to measure corporate reputation, and, on the other hand, it provides an in-depth analysis that splits the overall reputation into multiple aspects, with which organizations can identify weaknesses and in turn improve their corporate reputa tion. Therefore, this research is relevant for organizations aiming to understand and improve their corporate reputation to achieve success, for example, in form of financial performance, or for organizations that help and consult other organizations on their journeys to increased success. Our approach is validated, evaluated and illustrated with Trustpilot review data.
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Background—Self-management interventions are widely implemented in care for patients with heart failure (HF). Trials however show inconsistent results and whether specific patient groups respond differently is unknown. This individual patient data meta-analysis assessed the effectiveness of self-management interventions in HF patients and whether subgroups of patients respond differently. Methods and Results—Systematic literature search identified randomized trials of selfmanagement interventions. Data of twenty studies, representing 5624 patients, were included and analyzed using mixed effects models and Cox proportional-hazard models including interaction terms. Self-management interventions reduced risk of time to the combined endpoint HF-related all-0.71- in Conclusions—This study shows that self-management interventions had a beneficial effect on time to HF-related hospitalization or all-cause death, HF-related hospitalization alone, and elicited a small increase in HF-related quality of life. The findings do not endorse limiting selfmanagement interventions to subgroups of HF patients, but increased mortality in depressed patients warrants caution in applying self-management strategies in these patients.
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