Supervised framework for COVID-19 classification and lesion localization from chest CT

Abstract Background: Quick and precise identification of people suspected of having COVID-19 plays a key function in imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but also helps in the development of an improved health system. Building a deep-learning framework for automated identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic. Aim: To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and localization of swellings. Methods: In all cases, subjects’ chest areas were segmented by means of a pre-trained U-Net; the segmented 3D chest areas were submitted as inputs to a 3D deep neural network to forecast the likelihood of infection with COVID-19; the swellings were restricted by joining the initiation areas within the classification system and the unsupervised linked elements. A total of 499 3D CT scans were utilized for training worldwide and 131 3D CTscans were utilized for verification. Results: The algorithm took only 1.93 seconds to process the CT amount of a single affected person using a special graphics processing unit (GPU). Interesting results were obtained in terms of the development of societal challenges and better health policy. Conclusions: The deep-learning model can precisely forecast COVID-19 infectious probabilities and detect swelling areas in chest CT, with no requirement for training swellings. The easy-to-train and high-functioning deep-learning algorithm offers a fast method to classify people affected by COVID-19, which is useful to monitor the SARS-CoV-2 epidemic. [Ethiop. J. Health Dev. 2020; 34(4):235-242] Key words: COVID-19, CT scan, deep learning, neural network, DeCoVNet, RT-PCR, computed tomograph

Determinants of Finnish High Technology Exports : An Application of Gravity Model

This paper places its focus on the largest 10 high-tech importers of Finland with the objective to investigate the determinants of Finnish high-tech exports during the period of 1996 and 2010. The Gravity Theory acts as the theoretical foundation of the study and panel data will be adopted to carry out OLS regression and fixed effects regression models. Six clusters of determinant have been introduced to the basic gravity empirical model respectively, seeking to identify categories which have the greatest effects on Finnish high-tech exports. The research finds that the variables related to information cost, labour market and high technological level are more impactful than other factors. An adjusted gravity empirical model with more than 70 per cent explanatory power is subsequently presented. The empirical results manifest that a 1 per cent increase in GDP of Finland’s major trading partners contributes to 0.35 per cent growth in Finnish high-tech exports while a 1 per cent rise in bilateral distance between Finland and its exporting destinations leads to a decline of 0.8 per cent in Finnish high-tech exports. Other independent variables included in this empirical equation are estimated to be statistically significant as well. A case study has been introduced to illustrate certain aspects of the paper as well. The adjusted gravity model together with the Reveal Comparative Advantage (RCA) index are employed to analyse the export potentials of high-tech commodities of China for Finland. The study shows that the high technology export potential is not great, and has the requisites for Finland to develop export potential of products that have comparative advantages over China’s and explore new high-tech commodities

Preparation and Characterization of Superhydrophobic Modification of Polyvinylidene Fluoride Membrane by Dip-Coating

The superhydrophobicity polyvinylidene fluoride (PVDF) membranes were modified via reducing surface energy by dip-coating perfluoroalkyl methacrylic copolymer (Zonyl 8740) onto the membranes prepared on mat glass. The chemical component of the unmodified and modified PVDF membranes surface was investigated by ATR-FTIR. Morphology and hydrophobicity of the unmodified and modified PVDF membranes were examined by scanning electronic microscopy and water contact angle, respectively. The effects of concentration of Zonyl 8740, coating time, conditions of heat treatment on hydrophobic capability of PVDF membranes were investigated. The results showed that the water contact angle increased from 141˚ to 151˚ by the dip-coating modification, therefore getting superhydrophobic PVDF membrane. Moreover, the porosity and the morphology of modified PVDF membrane were unchanged by the dip-coating modification. This results suggested that the hydrophobicty stability of the modified PVDF membrane was also good

Simultaneous Horizontal and Vertical Oscillation of a Quiescent Filament observed by CHASE and SDO

In this paper, we present the imaging and spectroscopic observations of the simultaneous horizontal and vertical large-amplitude oscillation of a quiescent filament triggered by an EUV wave on 2022 October 02. Particularly, the filament oscillation involved winking phenomenon in Ha images and horizontal motions in EUV images. Originally, a filament and its overlying loops across AR 13110 and 13113 erupted with a highly inclined direction, resulting in an X1.0 flare and a non-radial CME. The fast lateral expansion of loops excited an EUV wave and the corresponding Moreton wave propagating northward. Once the EUV wavefront arrived at the quiescent filament, the filament began to oscillate coherently along the horizontal direction and the winking filament appeared concurrently in Ha images. The horizontal oscillation involved an initial amplitude of 10.2 Mm and a velocity amplitude of 46.5 km/s, lasting for 3 cycles with a period of 18.2 minutes and a damping time of 31.1 minutes. The maximum Doppler velocities of the oscillating filament are 18 km/s (redshift) and 24 km/s (blueshift), which was derived from the spectroscopic data provided by CHASE/HIS. The three-dimensional velocity of the oscillation is determined to be 50 km/s at an angle of 50 to the local photosphere plane. Based on the wave-filament interaction, the minimum energy of the EUV wave is estimated to be 2.7 10 20 J. Furthermore, this event provides evidence that Moreton wavesshould be excited by the highly inclined eruptions

Properties of a Small-scale Short-duration Solar Eruption with a Driven Shock

Large-scale solar eruptions have been extensively explored over many years. However, the properties of small-scale events with associated shocks have been rarely investigated. We present the analyses of a small-scale short-duration event originating from a small region. The impulsive phase of the M1.9-class flare lasted only for four minutes. The kinematic evolution of the CME hot channel reveals some exceptional characteristics including a very short duration of the main acceleration phase ($<$ 2 minutes), a rather high maximal acceleration rate ($\sim$50 km s$^{-2}$) and peak velocity ($\sim$1800 km s$^{-1}$). The fast and impulsive kinematics subsequently results in a piston-driven shock related to a metric type II radio burst with a high starting frequency of $\sim$320 MHz of the fundamental band. The type II source is formed at a low height of below $1.1~\mathrm{R_{\odot}}$ less than $\sim2$ minutes after the onset of the main acceleration phase. Through the band split of the type II burst, the shock compression ratio decreases from 2.2 to 1.3, and the magnetic field strength of the shock upstream region decreases from 13 to 0.5 Gauss at heights of 1.1 to 2.3 $~\mathrm{R_{\odot}}$. We find that the CME ($\sim4\times10^{30}\,\mathrm{erg}$) and flare ($\sim1.6\times10^{30}\,\mathrm{erg}$) consume similar amount of magnetic energy. The same conclusion for large-scale eruptions implies that small- and large-scale events possibly share the similar relationship between CMEs and flares. The kinematic particularities of this event are possibly related to the small footpoint-separation distance of the associated magnetic flux rope, as predicted by the Erupting Flux Rope model.Comment: 20 pages, 16 figure

When Dijkstra meets vanishing point: a stereo vision approach for road detection

International audienceIn this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u-and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI dataset. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point and road regions very accurately and robustly. It can achieve promising performance