The paper presents a framework that through structured analysis of accident reports explores the differences between practice and academic literature as well amongst organizations regarding their views on human error. The framework is based on the hypothesis that the wording of accident reports reflects the safety thinking and models that have been applied during the investigation, and includes 10 aspects identified in the state-of-the-art literature. The framework was applied to 52 air accident reports published by the Dutch Safety Board (DSB) and 45 ones issued by the Australian Transport Safety Bureau (ATSB) from 1999 to 2014. Frequency analysis and statistical tests showed that the presence of the aspects in the accident reports varied from 32.6% to 81.7%, and revealed differences between the ATSB and the DSB approaches to human error. However, in overall safety thinking have not changed over time, thus, suggesting that academic propositions might have not yet affected practice dramatically.
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Market competition and global financial uncertainty have been the principal drivers that impel aviation companies to proceed to budget cuts, including decreases in salary and work force levels, in order to ensure viability and sustainability. Under the concepts of Maslow and Herzberg’s motivation theories, the current paper unfolds the influence of employment cost fluctuations on an aviation organization’s accidents attributed to human error. This study exploited financial and accident data over a period of 13 years, and explored if rates of accidents attributed to human errors of flight, maintenance and ramp crews, correlate with the average employment expenditures (N=13). In addition, the study took into account the relationship between average task load (ratio of flying hours per employee) and accident rates related to human error since task load, as part of total workload, is a constraint of modern complex systems. The results revealed strong correlations amongst accident rates linked to human error with the average employment costs and task load. The use of more specific data per aviation organizational department and professional group may further validate the results of this study. Organizations that seek to explore the 2 association between human error and employment budget and task load might appropriately adapt the approach proposed.
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Background To gain insight into the role of plantar intrinsic foot muscles in fall-related gait parameters in older adults, it is fundamental to assess foot muscles separately. Ultrasonography is considered a promising instrument to quantify the strength capacity of individual muscles by assessing their morphology. The main goal of this study was to investigate the intra-assessor reliability and measurement error for ultrasound measures for the morphology of selected foot muscles and the plantar fascia in older adults using a tablet-based device. The secondary aim was to compare the measurement error between older and younger adults and between two different ultrasound machines. Methods Ultrasound images of selected foot muscles and the plantar fascia were collected in younger and older adults by a single operator, intensively trained in scanning the foot muscles, on two occasions, 1–8 days apart, using a tablet-based and a mainframe system. The intra-assessor reliability and standard error of measurement for the cross-sectional area and/or thickness were assessed by analysis of variance. The error variance was statistically compared across age groups and machines. Results Eighteen physically active older adults (mean age 73.8 (SD: 4.9) years) and ten younger adults (mean age 21.9 (SD: 1.8) years) participated in the study. In older adults, the standard error of measurement ranged from 2.8 to 11.9%. The ICC ranged from 0.57 to 0.97, but was excellent in most cases. The error variance for six morphology measures was statistically smaller in younger adults, but was small in older adults as well. When different error variances were observed across machines, overall, the tablet-based device showed superior repeatability. Conclusions This intra-assessor reliability study showed that a tablet-based ultrasound machine can be reliably used to assess the morphology of selected foot muscles in older adults, with the exception of plantar fascia thickness. Although the measurement errors were sometimes smaller in younger adults, they seem adequate in older adults to detect group mean hypertrophy as a response to training. A tablet-based ultrasound device seems to be a reliable alternative to a mainframe system. This advocates its use when foot muscle morphology in older adults is of interest.
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This paper aims to describe how we change our mind. As any human knows from his or her own experience, this does not always come naturally to us. This dissertation therefore aims to find fault in Seneca’s assertion: not only to err is human, but it is also human to persist in this mistake despite evidence to the contrary.In this paper I will argue that changing one’s mind is regulated through emotions, building on Damasio’s thoughts that emotions are not a luxury, but essential to rational thinking and normal behavior. His landmark book “Descarte’s Error“ (Damasio and Sutherland 1996) inspired the title of the current work. This research has been triggered by my experience in industry, where I have been lucky enough to have collaborated with many talented, friendly and rather stubborn people for over 20 years.Referentiede Boer, R.J. (2011), Seneca's Error: The Intervening Effect of Emotions on Mental Model Preservation , Symposium on Human Factors for Future Aviation, Schiphol Oost, The Netherlands
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The study of human factors in forensic science informs our understanding of the interaction between humans and the systems they use. The Expert Working Group (EWG) on Human Factors in Forensic DNA Interpretation used a systems approach to conduct a scientific assessment of the effects of human factors on forensic DNA interpretation with the goal of recommending approaches to improve practice and reduce the likelihood and consequence of errors. This effort resulted in 44 recommendations. The EWG designed many of these recommendations to improve the production, interpretation, evaluation, documentation, and communication of DNA comparison results.
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The Annual Conference on the Human Factor in Cybercrime is a small and specialised scientific event that aims to bring together scholars from around the world to present their research advances to a select audience. Its dynamic and linear format favours group discussions since all contributions are heard by all the attendants. This, together with its tailored social scheme, promotes interaction between members, which—in turn—leads to new collaborations. However, it has not yet been analysed whether the design of the conference actually encourages varied participation and fosters collaborative networks among its participants. The purpose of this chapter is to assess participation in the 2018 and 2019 editions to determine whether this is the case. Using descriptive analyses, here we show how participation in the conference has varied and examine the composition of the collaboration networks among the participants. The results show an increased and more diverse participation in the 2019 meeting along with a greater presence of stakeholders. Furthermore, the findings reveal that members of previously established organisations play an important role in cohering the network. Yet few connections exist between academia and practice. A further analysis of the strengths and weaknesses identified in the two editions of the conference serves to elaborate a series of recommendations for future editions.
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In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone footage to aid FRs in tasks such as locating victims. Detecting small-scale objects, particularly humans from high altitudes, poses a challenge for AI systems. We present first steps of introducing and evaluating a series of YOLOv8 Convolutional Neural Networks (CNNs) for human detection from drone images. The models are fine-tuned on a created drone image dataset of the Dutch Fire Services and were able to achieve a 53.1% F1-Score, identifying 439 out of 825 humans in the test dataset. These preliminary findings, validated by an incident commander, highlight the promising utility of these models. Ongoing efforts aim to further refine the models and explore additional technologies.
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A substantial amount of studies have addressed the influence of sound on human performance. In many of these, however, the large acoustic differences between experimental conditions prevent a direct translation of the results to realistic effects of room acoustic interventions. This review identifies those studies which can be, in principle, translated to (changes in) room acoustic parameters and adds to the knowledge about the influence of the indoor sound environment on people. The review procedure is based on the effect room acoustics can have on the relevant quantifiers of the sound environment in a room or space. 272 papers containing empirical findings on the influence of sound or noise on some measure of human performance were found. Of these, only 12 papers complied with this review's criteria. A conceptual framework is suggested based on the analysis of results, positioning the role of room acoustics in the influence of sound on task performance. Furthermore, valuable insights are pre- sented that can be used in future studies on this topic. Whi le the influence of the sound environment on performance is clearly an issue in many situations, evidence regarding the effectiveness of strategies to control the sound environment by room acoustic design is lacking and should be a focus area in future studies.
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This research focuses on exit choices within SMEs. In this study, “exit choice” refers to the decision to opt for either liquidation or sale of the firm. The predictions focus on human-capital and firm-resource variables. The hypotheses are tested on a set of 158 owners of small firms, the majority of which are micro-firms with 0–9 employees. The results of a series of binominal logistic regression analyses show that firm-resource characteristics (previous sales turnover, the firm’s independence from its owner, and firm size), together with one aspect of the owner’s specific human capital (the owner’s acquisition experience), predict exit choice. The conclusions have been made with caution, as the dataset is relatively small and the number of predictors is limited.
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