ABSTRACT
Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.
ABSTRACT
Purpose: The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns. Design/methodology/approach: By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. Findings: The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment. Research limitations/implications: The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency. Practical implications: Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit. Social implications: Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns. Originality/value: So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude. © 2023, Emerald Publishing Limited.
ABSTRACT
Worldwide, up to 8.8 million excess deaths/year have been attributed to air pollution, mainly due to the exposure to fine particulate matter (PM). Traffic-related noise is an additional contributor to global mortality and morbidity. Both health risk factors substantially contribute to cardiovascular, metabolic and neuropsychiatric sequelae. Studies on the combined exposure are rare and urgently needed because of frequent co-occurrence of both risk factors in urban and industrial settings. To study the synergistic effects of PM and noise, we used an exposure system equipped with aerosol generator and loud-speakers, where C57BL/6 mice were acutely exposed for 3d to either ambient PM (NIST particles) and/or noise (aircraft landing and take-off events). The combination of both stressors caused endothelial dysfunction, increased blood pressure, oxidative stress and inflammation. An additive impairment of endothelial function was observed in isolated aortic rings and even more pronounced in cerebral and retinal arterioles. The increase in oxidative stress and inflammation markers together with RNA sequencing data indicate that noise particularly affects the brain and PM particularly affects the lungs. Noise also increased levels of circulating stress hormones adrenaline and noradrenaline, while PM increased levels of circulating cytokines CD68 and MCP-1. The combination of both stressors has additive adverse effects on the cardiovascular system that are based on PM-induced systemic inflammation and noise-triggered stress hormone signaling. We demonstrate an additive upregulation of ACE-2 in the lung, suggesting that there may be an increased vulnerability to COVID-19 infection. The data warrant further mechanistic studies to characterize the propagation of primary target tissue damage (lung, brain) to remote organs such as aorta and heart by combined noise and PM exposure.Copyright © 2023
ABSTRACT
Society must understand, model, and forecast infectious disease transmission patterns in order to prevent pandemics. Mathematical models and computer technology may help us better understand the pandemic and create more systematic and effective infection management strategies. This study offers a novel perspective through a compartmental model that incorporates fractional calculus. The first scenario is based on proportional fractional definitions, considering compartmental individuals of susceptible, moving susceptible, exposed, infected, hospitalized, and recovered. Through an extension of this derivative, they decimated the model to integer order. We extended the deterministic model to a stochastic extension to capture the uncertainty or variance in disease transmission. It can develop an appropriate Lyapunov function to detect the presence and uniqueness of positive global solutions. Next, we discuss how the epidemic model might have become extinct. In our theoretical study, we demonstrated that a sufficiently outrageous amount of noise can cause a disease to become extinct. A modest level of noise, on the other hand, promotes the persistence of diseases and their stationary distribution. The Khasminskii method was used to determine the stationary distribution and ergodicity of the model.
ABSTRACT
OBJECTIVE: To observe total leisure noise (TLN) exposure and to investigate determinants of risky TLN exposure among adolescents and young adults over a ten-year observation period. DESIGN: OHRKAN is a longitudinal study with five equidistantly distributed questionnaires (waves) over ten years. Risky TLN exposure was defined as exceeding ≥85dB(A) averaged over 40h per week. To identify determinants of risky TLN exposure longitudinally, generalised estimating equations were applied. STUDY SAMPLE: A subgroup (n = 661; mean age 25.6 years in the fifth wave; 58.4% female) of the closed cohort study OHRKAN was analysed. Included participants took part in the fifth wave prior to the study break due to COVID-19. RESULTS: Analysis of participants' data from all five waves showed that risky TLN exposure was highest during the second wave (72.0%), when participants were aged 17-19 years, and thereafter steadily declined. Among young adults, attendance at discotheques and private parties, especially, caused very high exposure. Determinants of risky TLN exposure were wave time point, male gender, a higher level of education, and smoking. CONCLUSIONS: As TLN exposure is highest among older adolescents, prevention programs should target younger teenagers and be tailored to the identified risk groups. The risk from private parties should be addressed.