Control strategies used by experienced marine navigators: observation of verbal conversations during navigation training

This article investigates the relationship between the control situation framework, as presented by Bjørkli et al. (Cogn Technol Work 9:67–80, 2007), and observed operator action and communication in high-speed craft operation in the Norwegian Royal Navy. The reported data include two series of events during sailing in the Norwegian archipelago where uncertainty of ship position, challenges of manoeuvring and navigation strategies are addressed. The results indicate that the navigators perform actions and communicate in accordance with the key features of the control situation framework, and thus adapt to the interrelation between control possibilities and control requirements. The navigators were guided by explicit knowledge of the functional characteristics of psychical and temporal dynamics, which were actualized in manoeuvring. It indicates that the control situation framework can be used to describe the models the operators use.     

Predicting Articulated Human Motion from Spatial Processes

We present a probabilistic interpretation of inverse kinematics and extend it to sequential data. The resulting model is used to estimate articulated human motion in visual data. The approach allows us to express the prior temporal models in spatial limb coordinates, which is in contrast to most recent work where prior models are derived in terms of joint angles. This approach has several advantages. First of all, it allows us to construct motion models in low dimensional spaces, which makes motion estimation more robust. Secondly, as many types of motion are easily expressed in spatial coordinates, the approach allows us to construct high quality application specific motion models with little effort. Thirdly, the state space is a real vector space, which allows us to use off-the-shelf stochastic processes as motion models, which is rarely possible when working with joint angles. Fourthly, we avoid the problem of accumulated variance, where noise in one joint affects all joints further down the kinematic chains. All this combined allows us to more easily construct high quality motion models. In the evaluation, we show that an activity independent version of our model is superior to the corresponding state-of-the-art model. We also give examples of activity dependent models that would be hard to phrase directly in terms of joint angles.     

Efficient search based on content similarity over self-organizing P2P networks

The advent of the World Wide Web has made an enormous amount of information available to everyone and the widespread use of digital equipment enables end-users (peers) to produce their own digital content. This vast amount of information requires scalable data management systems. Peer-to-peer (P2P) systems have so far been well established in several application areas, with file-sharing being the most prominent. The next challenge that needs to be addressed is (more complex) data sharing, management and query processing, thus facilitating the delivery of a wide spectrum of novel data-centric applications to the end-user, while providing high Quality-of-Service. In this paper, we propose a self-organizing P2P system that is capable to identify peers with similar content and intentionally assign them to the same super-peer. During content retrieval, fewer super-peers need to be contacted and therefore efficient similarity search is supported, in terms of reduced network traffic and contacted peers. Our approach increases the responsiveness and reliability of a P2P system and we demonstrate the advantages of our approach using large-scale simulations.     

Musculoskeletal complaints among nurses related to patient handling tasks and psychosocial factors--based on logbook registrations

The aims were to evaluate the inter-method reliability of a registration sheet for patient handling tasks, to study the day-to-day variation of musculoskeletal complaints (MSC) and to examine whether patient handling tasks and psychosocial factors were associated with MSC.Nurses (n = 148) fulfilled logbooks for three consecutive working days followed by a day off. Low back pain (LBP), neck/shoulder pain (NSP), knee pain (KP), psychosocial factors (time pressure, stress, conscience of the quality of work) and patient transfers and care tasks were reported.The logbook was reliable for both transfer and care tasks. The numbers of nurses reporting MSC and the level of pain increased significantly during the three working days (15%-30% and 17%-37%, respectively) and decreased on the day off. Stress and transfer task were associated with LPB and transfer tasks were associated with KP.Our results confirm a relationship between work factors and MSC and indicate that logs could be one way to obtain a better understanding of the complex interaction of various nursing working conditions in relation to MSC.     

Latent classification models for binary data

One of the simplest, and yet most consistently well-performing set of classifiers is the naïve Bayes models (a special class of Bayesian network models). However, these models rely on the (naïve) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naïve Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.     

Response of structures to planar blast loads – A finite element engineering approach

Design and validation of structures against blast loads are important for modern society in order to protect and secure its citizen. Since it is a challenge to validate and optimise protective structures against blast loads using full-scale experimental tests, we have to turn our attention towards advanced numerical tools like the finite element method. Several different finite element techniques can be used to describe the response of structures due to blast loads. Some of these are: (1) a pure Lagrangian formulation, (2) an initial Eulerian simulation (to determine the load) followed by a Lagrangian simulation (for the structural response) and (3) a hybrid technique that combines the advantages of Eulerian and Lagrangian methods to have a full coupling between the blast waves and the deformation of the structure. Ideally, all blast simulations should be carried out using the fully coupled Eulerian–Lagrangian approach, but this may not be practical as the computational time increases considerably when going from a pure Lagrangian to a fully coupled Eulerian–Lagrangian simulation. A major goal in this study is to investigate if a pure Lagrangian formulation can be applied to determine the structural response in a specified blast load problem or if more advanced approaches such as the fully coupled Eulerian–Lagrangian approach is required for reliable results. This is done by conducting numerical simulations of an unprotected 20 ft ISO container exposed to a blast load of 4000 kg TNT at 120 m standoff distance using the three different approaches presented above. To validate and discuss the results, the simulated response of the container is compared to available data from a full-scale blast test under such conditions.     

Improving convergence of Iterative Feedback Tuning

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.     

Systematic review of the relationship between quick returns in rotating shift work and health-related outcomes

A systematic literature search was carried out to investigate the relationship between quick returns (i.e. 11.0 hours or less between two consecutive shifts) and outcome measures of health, sleep, functional ability and work–life balance. A total of 22 studies published in 21 articles were included. Three types of quick returns were differentiated (from evening to morning/day, night to evening, morning/day to night shifts) where sleep duration and sleepiness appeared to be differently affected depending on which shifts the quick returns occurred between. There were some indications of detrimental effects of quick returns on proximate problems (e.g. sleep, sleepiness and fatigue), although the evidence of associations with more chronic outcome measures (physical and mental health and work–life balance) was inconclusive.

Practitioner Summary: Modern societies are dependent on people working shifts. This study systematically reviews literature on the consequences of quick returns (11.0 hours or less between two shifts). Quick returns have detrimental effects on acute health problems. However, the evidence regarding effects on chronic health is inconclusive.     

Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response

In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.