Notes

A deliberate review and meta-analysis from the success regarding individual-level interventions to reduce work stress ideas amongst instructors.
Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs.

Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis.

The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.
The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.
Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice.

We applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset wers as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
Deep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. BMH-21 order The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
Physical dormancy (hard seed) occurs in most species of Leguminosae family and has great consequences not only for ecological adaptation but also for agricultural practice of these species. A rapid, nondestructive and on-site screening method to detect hard seed within species is fundamental important for maintaining seed vigor and germplasm storage as well as understanding seed adaptation to various environment. In this study, the potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds in
,
,
,
,
, and
. Principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) methods were applied to classify hard and soft seeds according to their morphological features and spectral traits.

The performance of discrimination model via multispectral imaging analysis was varied with species. For
,
, and
, an excellent classification could be achieved in an independent validation data set. LDA model had the best calibration and validation abilities with the accuracy up to 90% for
. SVM got excellent seed discrimination results with classification accuracy of 91.67% and 87.5% for
and
, respectively. However, both LDA and SVM model failed to discriminate hard and soft seeds in
,
, and
.

Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.
Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.
Walnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process.

In this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratis well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.
The fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.
Health professionals are especially affected by various stressors in their daily work, such as a high workload, physical and emotional challenges. The aim of this study was to develop and test the validity, reliability and usability of an observation-based instrument designed to assess work stressors in the healthcare sector.

Using a cross sectional design, 110 health professionals were observed during one entire shift by an external observer. Factor analysis was used to test construct validity, Cronbach's alpha to test internal consistency and correlations using Kendall's Tau were computed to test for convergent validity.

For 9 out of 10 tested scales the results showed a one-factor solution for all observation scales (explained variance ranged from 55.5 to 80.2%), satisfactory reliability (Cronbach's alpha between .67 and .92), sufficient usability and satisfactory convergent validity.

The newly developed STRAIN-EOS, an observation-based assessment tool designed to assess stressors specifically in the healthcare sector, was shown to be potentially useful.
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