Dreams that appear to predict future events that could not have been anticipated through any known inferential processes have been reported for centuries, and dreams that appear to anticipate the death of an acquaintance or loved one are particularly common. Such reports become more suggestive of genuine precognition if there are no natural cues (such as an illness) to an impending death and if the time interval between the dream and the subsequent death is brief. Most reports are difficult to evaluate because we dream many times each night but typically remember and report only a salient subset of our dreams. Thus we cannot assess whether the time interval between a death-related dream and the death of the dream character is brief or lengthy because we have no control set of non-death-related dreams to which its time interval can be compared. The study reported here provides just such a control set by comparing deathrelated and non-death-related dreams featuring the same set of dream characters who died after the dreams occurred. These were drawn from the author's own dream journal in which he has recorded his nightly dreams for nearly twenty-five years. The mean time interval between death-related dreams and the person's subsequent death was significantly shorter than the time interval between non-death-related dreams and his or her death, t(11) = 3.30, p =.004, one-tailed. Cases in which death-related dreams occurred after the characters had died are also considered. Seven of the cases are discussed in detail.
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Introduction: Retrospective studies suggest that a rapid initiation of treatment results in a better prognosis for patients in the emergency department. There could be a difference between the actual medication administration time and the documented time in the electronic health record. In this study, the difference between the observed medication administration time and documentation time was investigated. Patient and nurse characteristics were also tested for associations with observed time differences. Methods: In this prospective study, emergency nurses were followed by observers for a total of 3 months. Patient inclusion was divided over 2 time periods. The difference in the observed medication administration time and the corresponding electronic health record documentation time was measured. The association between patient/nurse characteristics and the difference in medication administration and documentation time was tested with a Spearman correlation or biserial correlation test. Results: In 34 observed patients, the median difference in administration and documentation time was 6.0 minutes (interquartile range 2.0-16.0). In 9 (26.5%) patients, the actual time of medication administration differed more than 15 minutes with the electronic health record documentation time. High temperature, lower saturation, oxygen-dependency, and high Modified Early Warning Score were all correlated with an increasing difference between administration and documentation times. Discussion: A difference between administration and documentation times of medication in the emergency department may be common, especially for more acute patients. This could bias, in part, previously reported time-to-treatment measurements from retrospective research designs, which should be kept in mind when outcomes of retrospective time-to-treatment studies are evaluated.
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Het schatten van het tijdstip van overlijden is een van de taken van een forensisch arts. De mate van ontbinding, het proces van verval van het lichaam, is een van de methoden die wordt gebruikt om het tijdstip van overlijden te bepalen. Het is echter onbekend hoe betrouwbaar deze methode is. Om dit te toetsen zijn vijftien casussen geselecteerd uit in totaal 1534 lijkschouwingen op basis van vooropgestelde criteria. Deze vijftien casussen zijn voorgelegd aan tien ervaren forensisch artsen met de vraag om het postmortale interval te schatten. Dit onderzoek toont aan dat er weinig overeenstemming is tussen de forensisch artsen. Het gebruik van ontbinding alleen om het tijdstip van overlijden te bepalen wordt daarom ontraden.
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Stroke is the second most common cause of death and the third leading cause of disability worldwide,1,2 with the burden expected to increase during the next 20 years.1 Almost 40% of the people with stroke have a recurrent stroke within 10 years,3 making secondary prevention vital.3,4 High amounts of sedentary time have been found to increase the risk of cardiovascular disease,5–11 particularly when the sedentary time is accumulated in prolonged bouts.12–15 Sedentary behavior, is defined as “any waking behavior characterized by an energy expenditure ≤1.5 Metabolic Equivalent of Task (METs) while in a sitting, reclining or lying posture”.16,17 Studies in healthy people, as well as people with diabetes and obesity, have shown that reducing the total amount of sedentary time and/or breaking up long periods of uninterrupted sedentary time, reduces metabolic risk factors associated with cardiovascular disease.6,9,10,12–15 Recent studies have shown that people living in the community after stroke spend more time each day sedentary, and more time in uninterrupted bouts of sedentary time compared to age-matched healthy peers.18–20 Reducing sedentary time and breaking up long sedentary bouts with short bursts of activity may be a promising intervention to reduce the risk of recurrent stroke and other cardiovascular diseases in people with stroke. To develop effective interventions, it is important to understand the factors associated with sedentary time in people with stroke. Previous studies have found associations between self-reported physical function after stroke and total sedentary time, but inconsistent results with regards to the relationship of age, stroke severity, and walking speed with sedentary time.20,21 These results are from secondary analyses of single-site observational studies, not powered to address associations, and inconsistent in the methods used to determine waking hours; thus making direct comparisons between studies difficult.20,21 Individual participant data pooling, with consistent processing of wake time data, allows novel exploratory analyses of larger datasets with greater power. By pooling all available individual participant data internationally, this study aimed to comprehensively explore the factors associated with sedentary time in community-dwelling people with stroke. Specifically, our research questions were: (1) What factors are associated with total sedentary time during waking hours after stroke? (2) What factors are associated with time spent in prolonged sedentary bouts during waking hours?
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Productivity in construction is relatively low compared to other industries. This is particularly true for labour productivity. Problems that contribute to low labour productivity are often related to unorganised workspace, and inefficient organisation of work, materials and equipment. In terms of time use, site workers spend time on various activities including installing, waiting, walking etc. In lean production terms time use should be value adding and not wasteful or non-value adding. The study reported in this paper has endeavoured to measure the time use and movement applying an automated data system. The case study reflected a limited application to a specific kind of activity, namely doors installation. The study investigated time use and movements based on interviews and on automated detection of workforce. The interviews gave insights in the time build-up of work and value-added time use per day. The automated tracking indicated time intervals and uninterrupted presence of site workers on work locations giving indications of value adding time. The time measurements of the study enable comparison of time use categories of site workers. The study showed the data system calculated the same amounts of productive and value adding time one would expect based on the organisation and characteristics of the work. However, the discussion of the results underlined that the particular characteristics of individual projects and types of team work organisation may well have an impact on productivity levels of workers. More application and comparative studies of projects and further development and extension of the automated data system should be helpful.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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From the article: Abstract Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on the unknown parameters of the adjustment problem. Thus they describe deformation patterns. If deformation is absent, the epochs of the time series are supposed to be related via affine, similarity or congruence transformations. S-basis invariant testing of deformation patterns is treated. The model is experimentally validated by showing the procedure for a point set of 3D coordinates, determined from total station measurements during five epochs. The modelling of two patterns, the movement of just one point in several epochs, and of several points, is shown. Full, rank deficient covariance matrices of the 3D coordinates, resulting from free network adjustments of the total station measurements of each epoch, are used in the analysis.
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Eating rate is a basic determinant of appetite regulation, as people who eat more slowly feel sated earlier and eat less. Without assistance, eating rate is difficult to modify due to its automatic nature. In the current study, participants used an augmented fork that aimed to decelerate their rate of eating. A total of 114 participants were randomly assigned to the Feedback Condition (FC), in which they received vibrotactile feedback from their fork when eating too fast (i.e., taking more than one bite per 10 s), or a Non-Feedback Condition (NFC). Participants in the FC took fewer bites per minute than did those in the NFC. Participants in the FC also had a higher success ratio, indicating that they had significantly more bites outside the designated time interval of 10 s than did participants in the NFC. A slower eating rate, however, did not lead to a significant reduction in the amount of food consumed or level of satiation.These findings indicate that real-time vibrotactile feedback delivered through an augmented fork is capable of reducing eating rate, but there is no evidence from this study that this reduction in eating rate is translated into an increase in satiation or reduction in food consumption. Overall, this study shows that real-time vibrotactile feedback may be a viable tool in interventions that aim to reduce eating rate. The long-term effectiveness of this form of feedback on satiation and food consumption, however, awaits further investigation.
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The aim of this study was to test the inter- and intraobserver reliability of the Physician Rating Scale (PRS) and the Edinburgh Visual Gait Analysis Interval Testing (GAIT) scale for use in children with cerebral palsy (CP). Both assessment scales are quantitative observational scales, evaluating gait. The study involved 24 patients ages 3 to 10 years (mean age 6.7 years) with an abnormal gait caused by CP. They were all able to walk independently with or without walking aids. Of the children 15 had spastic diplegia and 9 had spastic hemiplegia. With a minimum time interval of 6 weeks, video recordings of the gait of these 24 patients were scored twice by three independent observers using the PRS and the GAIT scale. The study showed that both the GAIT scale and the PRS had excellent intraobserver reliability but poor interobserver reliability for children with CP. In the total scores of the GAIT scale and the PRS, the three observers showed systematic differences. Consequently, the authors recommend that longitudinal assessments of a patient should be done by one observer only.
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Pupils with problem behaviours are challenging teachers as well as they are a challenge to teachers to find a way to teach them what curricula prescribe. Especially middle school teachers and those working in schools for special education are con-fronted with pupils with behavioural problems. There, teachers experience hard classes and find it difficult to fit classroom management with the pupils needs. In this paper we focus on two questions: is pullout an effective treatment to handle problem behaviour? do special classes have advantages for pupils who were pulled out or not? First we present a theoretical framework about pullout and we explicit our expectations. Then we describe the methods of our research in schools for special educa-tion during two months for students (N=759) when pulled out. We examined the reason of pulling out and the interactions during the process outside the classroom and the return. Because teachers noticed date and time of the removal, it was possible to use survival analysis to show the effects of the treatment. We found that pullout occurs under quite different circumstances, so the treatment integrity is a problem because deficiency of the intervention leads to repeated pullout. The data also showed that special classes for pupils who are pulled out seem to trigger and/or in-tensify the process itself. So, we conclude that these classes have a contra-productive effect.
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