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Teachers’ mindfulness following a social, emotional andintercultural learning intervention as a support mechanismfor dealing with emotional problems
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Optimizacija parametrov elektro-uporovnega točkovnega varjenja zlitin na osnovi aluminija tipov 6082 in 7075 s pomočjo Taguchijeve metode
Get PDFUvodne raziskave nastanka trojnih prevodnih oksidnih katod LiCo[sub]0.8M[sub]0.2O[sub]2-δ (M = Zr, Ca) za gorivne celice na osnovi trdnega oksida
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Finding a largest-area triangle in a terrain in near-linear time
Get PDFA terrain is an -monotone polygon whose lower boundary is a single line segment. We present an algorithm to find in a terrain a triangle of largest area in time, where is the number of vertices defining the terrain. The best previous algorithm for this problem has a running time of
IPBES TCA Chapter 1. Literature review determining the principles of transformative change / IPBES transformative change assessment
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Let\u27s see if you can hear
Get PDFObjectives: Pupil dilation can serve as a measure of auditory attention. It has been proposed as an objective measure for adjusting hearing aid configurations, and as a measure of hearing threshold in the pediatric population. Here we explore (1) whether the pupillary dilation response (PDR) to audible sounds can be reliably measured in normally hearing infants within their average attention span, and in normally hearing adults, (2) how accurate within-participant models are in classifying PDR based on the stimulus type at various intensity levels, (3) whether the amount of analyzed data affects the model reliability, and (4) whether we can observe systematic differences in the PDR between speech and nonspeech sounds, and between the discrimination and detection paradigms. Design: In experiment 1, we measured the PDR to target warble tones at 500 to 4000 Hz compared with a standard tone (250 Hz) using an oddball discrimination test. A group of normally hearing infants was tested in experiment 1a (n = 36, mean [ME] = 21 months), and a group of young adults in experiment 1b (n = 12, ME = 29 years). The test was divided into five intensity blocks (30 to 70 dB SPL). In experiment 2a (n = 11, ME = 24 years), the task from experiment 1 was transformed into a detection task by removing the standard warble tone, and in experiment 2b (n = 12, ME = 29 years), participants listened to linguistic (Ling-6) sounds instead of tones. Results: In all experiments, the increased PDR was significantly associated with target sound stimuli on a group level. Although we found no overall effect of intensity on the response amplitude, the results were most clearly visible at the highest tested intensity level (70 dB SPL). The nonlinear classification models, run for each participant separately, yielded above-chance classification accuracy (sensitivity, specificity, and positive predictive value above 0.5) in 76% of infants and in 75% of adults. Accuracy further improved when only the first six trials at each intensity level were analyzed. However, accuracy was similar when pupil data were randomly attributed to the target or standard categories, indicating over-sensitivity of the proposed algorithms to the regularities in the PDR at the individual level. No differences in the classification accuracy were found between infants and adults at the group level, nor between the discrimination and detection paradigms (experiment 2a versus 1b), whereas the results in experiment 2b (speech stimuli) outperformed those in experiment 1b (tone stimuli). Conclusions: The study confirms that PDR is elicited in both infants and adults across different stimulus types and task paradigms and may thus serve as an indicator of auditory attention. However, for the estimation of the hearing (or comfortable listening) threshold at the individual level, the most efficient and time-effective protocol with the most appropriate type and number of stimuli and a reliable signal to noise ratio is yet to be defined. Future research should explore the application of pupillometry in diverse populations to validate its effectiveness as a supplementary or confirmatory measure within the standard audiological evaluation procedures
HIDRA3
Get PDFAccurate modeling of sea level and storm surge dynamics with several days of temporal horizons is essential for effective coastal flood responses and the protection of coastal communities and economies. The classical approach to this challenge involves computationally intensive ocean models that typically calculate sea levels relative to the geoid, which must then be correlated with local tide gauge observations of sea surface height (SSH). A recently proposed deep-learning model, HIDRA2 (HIgh-performance Deep tidal Residual estimation method using Atmospheric data, version 2), avoids numerical simulations while delivering competitive forecasts. Its forecast accuracy depends on the availability of a sufficiently long history of recorded SSH observations used in training. This makes HIDRA2 less reliable for locations with less abundant SSH training data. Furthermore, since the inference requires immediate past SSH measurements as input, forecasts cannot be made during temporary tide gauge failures. We address the aforementioned issues using a new architecture, HIDRA3, that considers observations from multiple locations, shares the geophysical encoder across the locations, and constructs a joint latent state that is decoded into forecasts at individual locations. The new architecture brings several benefits: (i) it improves training at locations with scarce historical SSH data, (ii) it enables predictions even at locations with sensor failures, and (iii) it reliably estimates prediction uncertainties. HIDRA3 is evaluated by jointly training on 11 tide gauge locations along the Adriatic. Results show that HIDRA3 outperforms HIDRA2 and the Mediterranean basin Nucleus for European Modelling of the Ocean (NEMO) setup of the Copernicus Marine Environment Monitoring Service (CMEMS) by ∼ 15 % and ∼ 13 % mean absolute error (MAE) reductions at high SSH values, creating a solid new state of the art. The forecasting skill does not deteriorate even in the case of simultaneous failure of multiple sensors in the basin or when predicting solely from the tide gauges far outside the Rossby radius of a failed sensor. Furthermore, HIDRA3 shows remarkable performance with substantially smaller amounts of training data compared with HIDRA2, making it appropriate for sea level forecasting in basins with high regional variability in the available tide gauge data