General Electric (Czechia)

The objective of this study was to evaluate the abnormal myocardial function in HFpEF patients with renal dysfunction (RD) and investigate the relationship between renal function and myocardial mechanical characteristics in patients with HFpEF. 134 patients with HFpEF and 32 control subjects were enrolled in our study. Clinical and echocardiography data were collected for offline analysis. Global work index (GWI), global constructive work (GCW), global waste work (GWW), and global work efficiency (GWE) were measured after noninvasive pressure-strain loop analysis. Univariate and multivariate analyses were used to determine the correlation between renal function and myocardial function in patients with HFpEF. In comparison to control subjects, patients with HFpEF tend to have higher GWW (78 [50-115] vs. 108 [65-160] mm Hg%, p < 0.05) and lower GWE (96 [95-97] vs. 95 [92-96] %, p < 0.05), while left ventricular ejection fraction (65.5 ± 3.3 vs. 64.3 ± 4.6%, p < 0.05) was comparable between them. Besides, increased GWW (86 [58-152] vs. 125 [94-187] mm Hg%, p < 0.05) and decreased GWE (96 [93-97] vs. 94 [92-96] %, p < 0.05) were detected in patients with RD compared to those with normal renal function. An independent correlation was found between estimated glomerular filtration rate and GWW after multivariate analysis. More severely impaired myocardial function was detected in HFpEF patients with RD compared to those with normal renal function. Estimated glomerular filtration rate was independently correlated to GWW in patients with HFpEF.

Abstract The creation of a 2D floor plan is an integral part of finishing a building construction. Legal obligations in different states often include submitting a precise floor plan for ownership purposes, as the building needs to be divided between new residents with reasonable precision. Common practice for floor plan generation includes manual measurements (tape or laser) and laser scanning (static or SLAM). In this paper, a novel approach is proposed using spherical photogrammetry, which is becoming increasingly popular due to its versatility, low cost and unexplored possibilities. Workflow is also noticeably faster than other methods, as video acquisition is rapid, on a par with SLAM. The accuracy and reliability of the measurements are then experimentally verified, comparing the results with established methods.

Coronary CT angiography (CCTA) is the only non-invasive imaging technique that reliably depicts the anatomic extent of Coronary Artery Disease (CAD). While occlusion remains a highly predictive indicator of major cardiovascular events (MACE), there is growing evidence that the presence and characteristics of coronary atherosclerosis provide additional prognostic information. In CCTA calcified plaques display high-intensity Hounsfield Units (HU) representative features while more complex representations characterize high-risk soft plaques. As such, accurate identification and quantification is burdensome and time consuming because of the limited temporal, spatial and contrast resolutions of X-ray scanners. Despite the success of deep learning in medical imaging, automatic localization of coronary plaques and especially soft plaques remains a challenging subject in CCTA vessel analysis. For this study, 150 CCTA scans were retrospectively collected. All patients were accepted at triage with minimal to severe CAD suspicion. Selection was carried out with uniform CAD-RADS severity distribution which normally follows an exponential decay function, thus obtaining a higher than normal concentration of plaques. The proposed method outperforms the state of the art for the localization of diverse types of plaques by exploiting the self-attention mechanism of transformers networks to embed contextual features of the coronary tree.

The usability of small devices such as smartphones or interactive watches is often hampered by the limited size of command vocabularies. This paper is an attempt at better understanding how finger identification may help users invoke commands on touch screens, even without recourse to multi-touch input. We describe how finger identification can increase the size of input vocabularies under the constraint of limited real estate, and we discuss some visual cues to communicate this novel modality to novice users. We report a controlled experiment that evaluated, over a large range of input-vocabulary sizes, the efficiency of single-touch command selections with vs. without finger identification. We analyzed the data not only in terms of traditional time and error metrics, but also in terms of a throughput measure based on Shannon's theory, which we show offers a synthetic and parsimonious account of users' performance. The results show that the larger the input vocabulary needed by the designer, the more promising the identification of individual fingers.

A full scale experimental program was conducted to investigate the influence of fluid film turbulence on the performance of the tilting-pad, self-equalizing type thrust bearing. Test results include data on centrally pivoted, 6-pad, 381-mm (15-in.) and 432-mm (17-in.) O.D. bearings operating with shaft speeds up to 10000 rpm and bearing loads ranging up to 3.447 MPa (500 psi). Data collected include bearing metal temperatures, bearing power loss, and observation of the laminar to turbulent transition range.