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Background Respiratory problems in chronic obstructive pulmonary disease (COPD) patients are prone to poor sleep quality, anxiety and depression, but they are often ignored by medical staff and patients themselves. Understanding the sleep quality, anxiety and depression symptoms of patients with COPD and their influencing factors can help to improve the quality of life of patients, however, there are few relevant studies. Objective To investigate the sleep quality, anxiety and depression of patients with COPD and explore the influencing factors. Methods Convenience sampling was used to recruit COPD patients who visited the Department Respiratory Diseases and Critical Care Medicine of seven hospitals in Anhui Province from August to November 2022. Demographic characteristics, dyspnea grade, airflow restriction severity, risk of malnutrition, physical activity level, vaccination, and acute exacerbations in the last 1 year were collected. The Pittsburgh Sleep Quality Index (PSQI) and the Hospital Anxiety and Depression Scale (HADS) were used to evaluate the sleep quality and anxiety and depression symptoms of COPD patients, and the influencing factors were analyzed by multivariate Logistic regression. Results A total of 248 patients with COPD were investigated, among which the incidence of poor sleep quality was 60.9% (151/248), only 8.9% (22/248) of the patients thought they had good sleep quality, and more than half of the patients actually had insufficient sleep time (54.4%) or low sleep efficiency (60.1%). 29.4% (73/248) of COPD patients had at least one of the symptoms of anxiety or depression, of which 35 had anxiety symptoms, 61 had depression symptoms, and 23 had both anxiety and depression symptoms. Multivariate Logistic regression analysis showed that older ages (OR=1.052, 95%CI=1.018-1.086) and risk of malnutrition (OR=3.393, 95%CI=1.855-6.206) were risk factors for poor sleep quality in COPD patients. Regular physical activity was a protective factor for poor sleep quality in COPD patients (OR=0.450, 95%CI=0.242-0.834) (P

The main research direction of hardware trojan detection is based on the side channel analysis. This paper proposes an optimized support vector machine(SVM) classification method to improve the accuracy and speed when analyzing the data for detection hardware trojan. Firstly, the principal component analysis is used for data dimension reduction. Then, genetic algorithm is used to accelerate the selection of optimal penalty coefficient and kernel function parameters. Finally, the hardware trojan detection classifier is built with SVM. Experimental results show that the optimized SVM method improves the detection speed and accuracy, and can effectively detect hardware trojans with the area of 0.1%,the accuracy can be increased by 15.6%，and the time consumption can be reduced by 98.1%.

Despite the large emission of chlorinated volatile organic compounds (CVOCs) into the atmosphere, the ultimate fate of these compounds remains largely unknown. Herein, we explore the photochemical conversion of an important class of CVOCs, namely chlorobenzene (CB), on mineral α-Fe2O3 particulates under atmospheric relevant conditions. A series of chamber reactions composed of the CB with/without SO2 or NO2 are performed, followed by in situ diffuse reflectance infrared Fourier transform spectroscopy measurements and density functional theory calculations. We show that CB can be considerably degraded by α-Fe2O3 under light irradiation, whereas the reaction is markedly suppressed by adding SO2 or NO2 owing to their competitive adsorption and surface acidification. In particular, we discover that CB can be ultimately converted into polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) under dark state or light irradiation, suggesting a possible origin of atmospheric PCDD/Fs from this overlooked photochemical source. [ABSTRACT FROM AUTHOR]