Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted of 178 pupils divided over seven classes. In agreement with the literature, we found that all four kindergarten tests were related to speed of word recognition in grade 1. We also performed a multiple regression analysis with a set of background variables and the four kindergarten tests. The model explained 53% of the variance in speed of word recognition. However, only letter knowledge and naming speed for digits had a significant direct effect. Our conclusion is, nevertheless, that all four kindergarten tests should be used to identify children at risk for reading problems.
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This study explores how journalists in highspeed newsrooms gather information, how gathering activities are temporally structured and how reliability manifests itself in information-gathering activities.
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RTs in conversation, with average gaps of 200 msec and often less, beat standard RTs, despite the complexity of response and the lag in speech production (600 msec or more). This can only be achieved by anticipation of timing and content of turns in conversation, about which little is known.Using EEGand an experimental task with conversational stimuli, we show that estimation of turn durations are based on anticipating the way the turn would be completed.We found a neuronal correlate of turn-end anticipation localized in ACC and inferior parietal lobule, namely a beta-frequency desynchronization as early as 1250 msec, before the end of the turn.We suggest that anticipation of the other’s utterance leads to accurately timed transitions in everyday conversations.
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Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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Within this study the aim is to measure running workload and relevant running technique key points on varying cadence in recreational runners using a custom build sensor system ‘Nodes’. Seven participants ran on a treadmill at a self-chosen comfortable speed. Cadence was randomly guided by a metronome using 92%, 96%, 100%, 104%, and 108% of the preferred cadence in 2-min trials. Workload was measured by collecting the heart rate and the rating of perceived exertion (RPE 1 to 10) scores. Heart rate data shows that the 100% cadence trial was most economical with a relative heart rate of 99.2%. The 108% cadence trial had the lowest relative RPE score with 96.2%. The sample rate of the Nodes system during this experiment was too low to analyze the key points. Three requirements are proposed for the further engineering of a wearable running system, (i) sampling frequency of minimal 50 Hz, (ii) step-by-step analysis, and (iii) collecting workload in the heart rate and RPE.
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The COVID-19 pandemic has accelerated remote working and working at the office. This hybrid working is an indispensable part of today's life even within Agile Software Development (ASD) teams. Before COVID-19 ASD teams were working closely together in an Agile way at the office. The Agile Manifesto describes 12 principles to make agile working successful. These principles are about working closely together, face-to-face contact and continuously responding to changes. To what extent does hybrid working influence these agile principles that have been indispensable in today's software development since its creation in 2001? Based on a quantitative study within 22 Dutch financial institutions and 106 respondents, the relationship between hybrid working and ASD is investigated. The results of this research show that human factors, such as team spirit, feeling responsible and the ability to learn from each other, are the most decisive for the success of ASD. In addition, the research shows that hybrid working creates a distance between the business organization and the IT department. The findings are valuable for Managers, HR professionals and employees working in the field of ASD as emphasizing and fostering Team Spirit, Learning Ability, and a Sense of Responsibility among team members can bolster the Speed of ASD.
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Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.
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From the introduction: "There are two variants of fronto-temporal dementia: a behavioral variant (behavioral FTD, bvFTD, Neary et al. (1998)), which causes changes in behavior and personality but leaves syntax, phonology and semantics relatively intact, and a variant that causes impairments in the language processing system (Primary Progessive Aphasia, PPA (Gorno-Tempini et al., 2004). PPA can be subdivided into subtypes fluent (fluent but empty speech, comprehension of word meaning is affected / `semantic dementia') and non-fluent (agrammatism, hesitant or labored speech, word finding problems). Some identify logopenic aphasia as a FTD-variant: fluent aphasia with anomia but intact object recognition and underlying word meaning."
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Advanced technology is a primary solution for the shortage of care professionals and increasing demand for care, and thus acceptance of such technology is paramount. This study investigates factors that increase use of advanced technology during elderly care, focusing on current use of advanced technology, factors that influence its use, and care professionals’ experiences with the use. This study uses a mixed-method design. Logfiles were used (longitudinal design) to determine current use of advanced technology, questionnaires assessed which factors increase such use, and in-depth interviews were administered to retrieve care professionals’ experiences. Findings suggest that 73% of care professionals use advanced technology, such as camera monitoring, and consult clients’ records electronically. Six of nine hypotheses tested in this study were supported, with correlations strongest between performance expectancy and attitudes toward use, attitudes toward use and satisfaction, and effort expectancy and performance expectancy. Suggested improvements for advanced technology include expanding client information, adding report functionality, solving log-in problems, and increasing speed. Moreover, the quickest way to increase acceptance is by improving performance expectancy. Care professionals scored performance expectancy of advanced technology lowest, though it had the strongest effect on attitudes toward the technology.
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Introduction: Previous longitudinal studies indicate that physical activity (PA) significantly declines from primary-to secondary school, and report both changes in individual and environmental determinants of PA. In order to understand this transition and to prevent this negative trend, it is important to gather contextually rich data on possible mechanisms that drive this decline. Therefore, the aim of this study was to investigate changes of PA patterns in transition between primary and secondary school, and to add domain-specific insights of how, where, and when these changes occur. Methods: In total, 175 children participated in a 7-day accelerometer- and Global Positioning System (GPS) protocol at their last year of primary and their first year of secondary school. GPS data-points were overlaid with Geographical Information Systems (GIS) data using ArcGIS 10.1 software. Based on the GPS locations of individual data-points, we identified child’s PA at home, school, local sports grounds, shopping centers, and other locations. Also, trips in active and passive transport were identified according to previously validated GPS speed-algorithms. Longitudinal multi-level linear mixed models were fitted adjusting for age, gender, meteorological circumstances, and the nested structure of days within children and children within schools. Outcome measures were minutes spent in light PA and moderate-to-vigorous PA, specified for the time-segments before school, during school, after school and weekend days. Results: Total PA significantly declined from primary to secondary school. Although transport-related PA increased before- and during school, decreases were found for especially afterschool time spent at sports grounds and transport-related PA during weekends.
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