In biomechanical joint-motion analyses, the continuous motion to be studied is often approximated by a sequence of finite displacements, and the Finite Helical Axis(FHA) or "screw axis" for each displacement is estimated from position measurements on a number of anatomical or artificial landmarks. When FHA parameters are directly determined from raw (noisy) displacement data, both the position and the direction of the FHA are ill-determined, in particular when the sequential displacement steps are small. This implies, that under certain conditions, the continuous pathways of joint motions cannot be adequately described. The purpose of the present experimental study is to investigate the applicability of smoothing (or filtering)techniques, in those cases where FHA parameters are ill-determined. Two different quintic-spline smoothing methods were used to analyze the motion data obtained with Roentgenstereophotogrammetry in two experiments. One concerning carpal motions in a wrist-joint specimen, and one relative to a kinematic laboratory model, in which the axis positions are a priori known. The smoothed and nonsmoothed FHA parameter errors were compared. The influences of the number of samples and the size of the sampling interval (displacement step) were investigated, as were the effects of equidistant and nonequidistant sampling conditions and noise invariance
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Here, we delve into Demand Forecasting via Machine Learning, dissecting how to predict future demand using time-sensitive data. Westveer highlights key forecasting models, from the basic Simple Exponential Smoothing to the advanced SARIMA, applied to an electricity production dataset. The session, encapsulating the essence of data-driven forecasting, culminates in a compelling three-year predictive outlook, illustrating the transformative potential of machine learning in strategic planning and decision-making.
VIDEO
The American company Amazon has made headlines several times for monitoring its workers in warehouses across Europe and beyond.1 What is new is that a national data protection authority has recently issued a substantial fine of €32 million to the e-commerce giant for breaching several provisions of the General Data Protection Regulation (gdpr) with its surveillance practices. On 27 December 2023, the Commission nationale de l’informatique et des libertés (cnil)—the French Data Protection Authority—determined that Amazon France Logistique infringed on, among others, Articles 6(1)(f) (principle of lawfulness) and 5(1)(c) (data minimization) gdpr by processing some of workers’ data collected by handheld scanner in the distribution centers of Lauwin-Planque and Montélimar.2 Scanners enable employees to perform direct tasks such as picking and scanning items while continuously collecting data on quality of work, productivity, and periods of inactivity.3 According to the company, this data processing is necessary for various purposes, including quality and safety in warehouse management, employee coaching and performance evaluation, and work planning.4 The cnil’s decision centers on data protection law, but its implications reach far beyond into workers’ fundamental right to health and safety at work. As noted in legal literature and policy documents, digital surveillance practices can have a significant impact on workers’ mental health and overall well-being.5 This commentary examines the cnil’s decision through the lens of European occupational health and safety (EU ohs). Its scope is limited to how the French authority has interpreted the data protection principle of lawfulness taking into account the impact of some of Amazon’s monitoring practices on workers’ fundamental right to health and safety.
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From an evidence-based perspective, cardiopulmonary exercise testing (CPX) is a well-supported assessment technique in both the United States (US) and Europe. The combination of standard exercise testing (ET) [i.e. progressive exercise provocation in association with serial electrocardiograms (ECGs), haemodynamics, oxygen saturation, and subjective symptoms] and measurement of ventilatory gas exchange amounts to a superior method to: (i) accurately quantify cardiorespiratory fitness (CRF), (ii) delineate the physiologic system(s) underlying exercise responses, which can be applied as a means to identify the exercise-limiting pathophysiological mechanism(s) and/or performance differences, and (iii) formulate function-based prognostic stratification. Cardiopulmonary ET certainly carries an additional cost as well as competency requirements and is not an essential component of evaluation in all patient populations. However, there are several conditions of confirmed, suspected, or unknown aetiology where the data gained from this form of ET is highly valuable in terms of clinical decision making.1
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From an evidence-based perspective, cardiopulmonary exercise testing (CPX) is a well-supported assessment technique in both the United States (US) and Europe. The combination of standard exercise testing (ET) (ie, progressive exercise provocation in association with serial electrocardiograms [ECG], hemodynamics, oxygen saturation, and subjective symptoms) and measurement of ventilatory gas exchange amounts to a superior method to: 1) accurately quantify cardiorespiratory fitness (CRF), 2) delineate the physiologic system(s) underlying exercise responses, which can be applied as a means to identify the exercise-limiting pathophysiologic mechanism(s) and/or performance differences, and 3) formulate function-based prognostic stratification. Cardiopulmonary ET certainly carries an additional cost as well as competency requirements and is not an essential component of evaluation in all patient populations. However, there are several conditions of confirmed, suspected, or unknown etiology where the data gained from this form of ET is highly valuable in terms of clinical decision making
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Traditional IMU based PDR systems suffer from rapidly growing drift effects due to the inherent bias of the inertial sensor. Many existing solutions to mitigate this problem use aiding sensors or information as heuristics or map data. We propose a new optimization framework to solve the PDR estimation problem where the sensors biases are explicitly included as state variables and therefore be used to correct for bias effects in the PDR. By using a smoothing approach and exploiting the rigid structure of a MIMU array one can solve for the slowly varying sensor biases. This paper presents the method and gives an exemplary result of a walking trial. Good agreements in the position and orientation with an optical reference system were found. Moreover, accelerometer and gyroscope biases could be estimated accordingly. Further research includes the performance of more experiments under various conditions such that a more quantitative evaluation can be obtained. In addition, an exploration of a (pseudo) realtime filter version would be valuable such that the system can be applied online.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research evidence. A synthesis of available research will establish consistency in the current knowledge base on valuer judgement, identify future research opportunities and support decision-making policy by educational and regulatory stakeholders how to cope with judgement bias. This article therefore, provides a systematic review of empirical research on real estate valuer judgement over the last 30 years. Based on a number of inclusion and exclusion criteria, we have systematically analysed 32 relevant papers on valuation judgement bias. Although we find some consistency in evidence, we also find the underlying research to be biased; the methodology adopted is dominated by a quantitative approach; research context is skewed by timing and origination; and research evidence seems fragmented and needs replication. In order to obtain a deeper understanding of valuation judgement processes and thus extend the current knowledge base, we advocate more use of qualitative research methods and scholars to adopt an interpretative paradigm when studying judgement behaviour.
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In large organizations, innovation activities often take place in separate departments, centers, or studios. These departments aim to produce prototypes of solutions to the problems of operational business owners. However, too often these concepts remain in the prototype stage: they are never implemented and fall into what is popularly termed the Valley of Death. A design approach to innovation is presented as a solution to the problem. However, practice shows that teams that use design nevertheless encounter implementation challenges due to the larger infrastructure of the organization they are part of. This research aims to explore which organizational factors contribute to the Valley of Death during design innovation. An embedded multiple case study at a large heritage airline is applied. Four projects are analyzed to identify implementation challenges. A thematic data analysis reveals organizational design, departmental silos, and dissimilar innovation strategies contribute to the formation of, and encounters with, the Valley of Death. Arising resource-assignment challenges that result from these factors are also identified. Materialization, user-centeredness, and holistic problem framing are identified as design practices that mitigate encounters with the Valley of Death, thus leading to projects being fully realized. https://doi.org/10.1111/dmj.12052 LinkedIn: https://www.linkedin.com/in/christine-de-lille-8039372/
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Many students persistently misinterpret histograms. This calls for closer inspection of students’ strategies when interpreting histograms and case-value plots (which look similar but are diferent). Using students’ gaze data, we ask: How and how well do upper secondary pre-university school students estimate and compare arithmetic means of histograms and case-value plots? We designed four item types: two requiring mean estimation and two requiring means comparison. Analysis of gaze data of 50 students (15–19 years old) solving these items was triangulated with data from cued recall. We found five strategies. Two hypothesized most common strategies for estimating means were confirmed: a strategy associated with horizontal gazes and a strategy associated with vertical gazes. A third, new, count-and-compute strategy was found. Two more strategies emerged for comparing means that take specific features of the distribution into account. In about half of the histogram tasks, students used correct strategies. Surprisingly, when comparing two case-value plots, some students used distribution features that are only relevant for histograms, such as symmetry. As several incorrect strategies related to how and where the data and the distribution of these data are depicted in histograms, future interventions should aim at supporting students in understanding these concepts in histograms. A methodological advantage of eye-tracking data collection is that it reveals more details about students’ problem-solving processes than thinking-aloud protocols. We speculate that spatial gaze data can be re-used to substantiate ideas about the sensorimotor origin of learning mathematics.
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