Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

This study surveyed 253 truck drivers and found that many drivers scored poorly on the Stanford and Epworth sleepiness scales indicating that they may not be as alert as they should be while driving. Moreover, those who rated the air in their truck cabins as fresh reported less irritation to their eyes, noses, throats, and skin, scored better in both sleepiness scales, and reported fewer sleep-related medical symptoms. Finally, the results of the ordinal logistic model indicate that drivers' perceptions of the air quality in their truck cabins are significantly related to their alertness during a trip

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

This study surveyed 253 truck drivers and found that many drivers scored poorly on the Stanford and Epworth sleepiness scales indicating that they may not be as alert as they should be while driving. Moreover, those who rated the air in their truck cabins as fresh reported less irritation to their eyes, noses, throats, and skin, scored better in both sleepiness scales, and reported fewer sleep-related medical symptoms. Finally, the results of the ordinal logistic model indicate that drivers' perceptions of the air quality in their truck cabins are significantly related to their alertness during a trip

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports

Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports. In contrast to standard global multi-modality alignment methods, we employ a local alignment module for fine-grained representation. Furthermore, a cross-modality conditional reconstruction module is designed to interchange information across modalities in the training phase by reconstructing masked images and reports. For reconstructing long reports, a sentence-wise prototype memory bank is constructed, enabling the network to focus on low-level localized visual and high-level clinical linguistic features. Additionally, a non-auto-regressive generation paradigm is proposed for reconstructing non-sequential reports. Experimental results on five downstream tasks, including supervised classification, zero-shot classification, image-to-text retrieval, semantic segmentation, and object detection, show the proposed method outperforms other state-of-the-art methods across multiple datasets and under different dataset size settings. The code is available at https://github.com/QtacierP/PRIOR.Comment: Accepted by ICCV 202

Technical Note: Melt Dispersion Technique for Preparing Paraffin Wax Microspheres for Cellulose Encapsulation

A practical and convenient approach for making paraffin wax microspheres with a melt dispersion technique was reported in this study. Surfactants were melted in water by water bath and then added to a flask after the wax was completely melted with stirring. Paraffin wax microspheres were generated by cooling. The obtained microspheres exhibited uniform diameters in the range of 10-60 μm observed with a scanning electrical microscope and were mainly dependent on the surfactant ratio. Encapsulated microcrystalline cellulose particles with the previously mentioned conditions were also generated and demonstrated the possibility of encapsulating microcrystalline cellulose with some acceptable agglomeration, although some encapsulated individually. Encapsulation of cellulose could be beneficial if agglomeration could be minimized and the encapsulated microcapsules could be dispersed during blending for wood composites manufacture

YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.Comment: 11 pages, 10 figures, submitted to IEEE Transactions on Medical Imaging (TMI

A hybrid active contour segmentation method for myocardial D-SPECT images

Ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a three-dimensional myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artefacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighbourhood centre. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy (RSF) model and local image fitting energy (LIF) model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation

Boosting output performance of contact-separation mode triboelectric nanogenerators by adopting discontinuity and fringing effect: experiment and modelling studies

Triboelectric nanogenerators (TENGs) are promising self-powering supplies for a diverse range of intelligent sensing and monitoring devices, especially due to their capability of harvesting electric energy from low frequency and small-scale mechanical motions. Inspired by the fact that contact-separation mode TENGs with small contact areas harvest high electrical outputs due to fringing effect, this study employed discontinuity on the dielectric side of contact-separation mode TENGs to promote fringing electric fields for the enhancement of electrical outputs. The results reveal that the TENGs with more discontinuities show higher overall electric performance. Compared to pristine TENGs, the TENGs with cross discontinuities increased the surface charge by 50% and the power density by 114%. However, one should avoid generating discontinuities on tribonegative side of TENGs using metal blade within a positive-ion atmosphere due to the neutralization through electrically conductive metal blade. The computational simulation validated that the TENGs with discontinuities obtained higher electrical outputs, and further investigated the effect of discontinuity gap size and array distance on TENGs performance. This study has provided a promising method for the future design of TENGs using discontinuous structures.Comment: 23 pages, 8 figure