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Objective: To explore the double psychosocial threats of the COVID-19 pandemic, targeted behavior toward Chinese Americans, and the correlates to their mental health. Methods: A quantitative, cross-sectional, and descriptive design was utilized by using a purposive convenience sample of 301 Chinese Americans over the age of 18 residing in the United States. Online data collection was conducted through the social media platform WeChat from April 8–21, 2021. Descriptive statistical analysis was used for the participants' demographic characteristics, Multidimensional Scale of Perceived Social Support (MSPSS), Double Threat Situations, COVID-19 Racial Discrimination, and General Anxiety Disorder-7 (GAD-7). Stepwise logistic regression was conducted to verify predictors for anxiety levels by GAD-7. Results: In this sample (N = 301), 127 (42.19%) were male and 174 (57.81%) were female. The average age was 41.67 (SD = 5.89). Among MSPSS subscales, social support from family (MSPSS-Fam, 79.73%, n = 240) and social support from significant others (MSPSS-SO, 73.75%, n = 222) were high. 231 (76.74%) reported threats due to their Chinese ethnic background during the COVID-19 outbreak. Predictors for the high anxiety level by GAD-7 were COVID-19 racial discrimination from the local community (OR = 0.47, 95% CI = 0.39–0.71, p < 0.001), media/online (OR = 0.36, 95% CI = 0.26–0.53, p < 0.001), the perceived threat from the COVID-19 virus (OR = 0.33, 95% CI = 0.23–0.51, p < 0.001) and Perceived racism threat from Chinese background related to COVID-19 (OR = 0.31, 95% CI = 0.21–0.49, p < 0.001). Conclusions: COVID-19 double-threats (The virus and racial discrimination) situations are significantly related to the high level of anxiety among Chinese Americans. The sense of belonging and social perceptions of Chinese immigrants is closely related to public health problems in Western societies and needs to be addressed at all levels. Our findings call for the attention of healthcare workers to specific racism double-threatened situations and high mental health risks, as well as direct and indirect ethnic discrimination that Chinese Americans are experiencing during this pandemic, the long-term influences and effective coping ways related to this issue should be explored in further research. © 2023, Tech Science Press. All rights reserved.
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Herein, refined LaxCa0.89-xSr0.11MnO3 (LCSMO, x = 0.65, 0.68, 0.71 and 0.74) films were prepared through the sol-gel spin-coating. The influence of La3+ content on the structural properties of LCSMO films was investigated by X-ray diffraction and Atomic force microscope, demonstrating that LCSMO films can grow well on SrTiO3 (00l) substrate. Besides, X-ray photoemission spectroscopy verified the double exchange (DE) effect was weakened with La3+ dopant. The La3+ doping and interconnected grains boundaries (GBs) led to the weakening DE effect and GBs scattering, respectively. Due to superior GBs connectivity, the resistivity of LCSMO films was less than 7.1 x 10(-4) Omega.cm at low temperature of 100 K. Importantly, it is an effective control method to keep the temperature (T-k) corresponding to temperature coefficient of resistivity (TCR) at room temperature with Sr2+ content as constant in LCSMO films. At x = 0.71, the peak TCR value was found to be 8.84%/K and corresponding T-k was 283.15 K. These results are beneficial for advanced application of uncooling infrared bolometer.
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The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances.
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The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances. © 2023 ACM.
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Introduction: Transient alterations in patient's quality of life (QOL) following endoscopic endonasal approach skull base surgery (EEA) are inevitable despite substantial progress in techniques and equipment over the last two decades. We have prospectively evaluated patient-reported QOL at our institution using multiple metrics, to better understand the sensitivity of such testing and evaluate for risk factors for worsened quality of life after EEA. Method(s): Prospective, review-board-approved, single-institution cohort study of patients undergoing EEA surgery between 2019 and 2022 (enrollment was paused March to September 2020 due to COVID-19 research restrictions). Patient-reported global PROMIS-29 and sinonasal-specific ASK-Nasal 12 metrics were obtained prior to and at routine intervals after EEA. Result(s): We enrolled 90 patients with baseline and follow-up data available. Average age was 50 years and there was a 58:32 female:male predominance. Most procedures involved at least transsellar (N = 73) surgery, although numerous expanded anatomic compartments (total N = 61) were also accessed. Pituitary adenoma was the most common pathology treated, although a wide range of others were represented. PROMIS-29 evaluation of global QOL demonstrated an expected worsening in symptoms within physical function (+2.9), social interaction (+1.1), and pain (+0.4) metrics at 2-weeks postoperatively compared with baseline;averages in these domains returned to baseline or were improved by 6 months. Anxiety and depression symptom domains were immediately improved as early as 2-weeks after surgery (-1.2 and -0.8, respectively), remaining improved compared with baseline 6 months after surgery. Sinonasal-specific QOL demonstrated expected worsening at 2 weeks postoperative (average sum ASK-Nasal 12 score 21, versus 9.7 baseline, p < 0.05) but returned to baseline at 6 months (average 9.2, p = NS). Subgroup analysis revealed that patients with functional pituitary adenoma (FPA) reported worse baseline global QOL in every PROMIS-29 domain, but similar baseline sinonasal-specific QOL, when compared with the entire cohort. FPA patients reported more absolute improvement in every domain of PROMIS-29 global QOL than did the cohort average at 6 months post-surgery (average change across all PROMIS-29 symptom domains at 6 months -1.96 for FPA, versus -1.2 for all patients, p < 0.05). Discussion(s): We prospectively assessed patient-reported global and sinonasal-specific QOL after EEA at a tertiary center using modern techniques. The PROMIS-29 global QOL metric has not been previously utilized in this patient group;expected postoperative alterations in physical and social function and pain were found and these resolved within six months of surgery. Patient symptoms in Anxiety and Depression QOL domains immediately improved at two weeks postoperatively despite objectively worse reported sinonasal QOL in the same time interval;this implies patients are strongly relieved to have completed surgery even if still suffering sinonasal QOL alterations in the early perioperative period. Patients with functional pituitary tumors have, not surprisingly, worse baseline global QOL than do average EEA patients;nevertheless, functional tumor patients also show more absolute improvement in QOL after surgery. (Figure Presented).
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The outbreak of COVID-2019 has revealed significant challenges in the field of public health security worldwide, especially in rural regions where public infrastructure is poor and public health security is insufficient. In this study, a new type of gas conditioner that combines the functions of sterilization, insect repelling, and carbon-rich fertilization is proposed considering the characteristics of rural an-ti-epidemic measures. The conditioner unit is based on gas-solid adsorption and solid chlorine dioxide technology. The unit adopts the method of combining four-bed electric swing adsorption carbon capture combined with a pressure swing adsorption nitrogen generation cycle method to enrich CO2 and N2 . At the same time, solid chlorine dioxide is applied to the adsorbent to achieve sterilization. Based on test data and simulation models, we analyzed the effects of the temperature difference on the adsorption and desorption temperatures in the car-bon capture cycle, and the outlet flow rate. We also analyzed the effects of the adsorption pressure in the nitrogen generation cycle on the separation performance (recovery rate, purity, and productivity) and energy-efficient performance (specific energy consumption, minimum separation work, second-law efficiency) of the gas conditioner. The results show that the CO2 recovery rate, purity, and produc-tivity all increase with temperature difference;the purity of N2 decreases with an increase in outlet flow rate, and increases with the ad-sorption pressure;the N2 recovery rate and productivity improve with an increase in outlet flow rate and adsorption pressure;the specific energy consumption of the system increases with the increase in the adsorption pressure, and decreases with an increase in the temperature difference and the outlet flow rate;the second law efficiency shows the opposite trend to the specific energy consumption. The simulation results show that when the temperature difference is 105 K, the outlet flow rate is 0. 01 m/ s, and the adsorption pressure is 1 MPa, the purity of CO2 and N2 both reach the maximum, which are 80. 6% VOL and 97. 05% VOL, respectively. The specific energy consumption of the system was 2. 13 MJ/ kg, the efficiency of the second law was 4. 71%, and the energy efficiency performance was better. Even under © 2021 The Author(s).
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Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R-2 of 0.81 and RMSE of 3.45 mu g/m(3) in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R-2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.
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Background: Geriatric patients with COVID-19 have had poor clinical outcomes globally, especially during the first wave of the pandemic. In Taiwan, the first wave of the COVID-19 pandemic occurred from May to July 2021. This retrospective study aimed to compare the characteristics and outcomes between geriatric and younger patients with COVID-19 infection. Methods: A total of 257 confirmed COVID-19 cases who were hospitalized from May to June 2021 were included. Their characteristics and outcomes, including in-hospital mortality, use of mechanical ventilation, and hospital stay, were collected for analysis. Results: There were 98 elderly patients (aged >= 65 years, median, 72.5 (interquartile range, 69.0-78.0) years) and 159 younger patients (aged < 65 years, median 55.0 (46.0-60.0) years). The elderly patients had a significantly higher Charlson comorbidity score (4.0 (3.0-5.0) vs. 1.0 (1.0-2.0), p < 0.001), and significantly higher D-dimer, procalcitonin, ferritin, and creatinine levels, but lower albumin level than the younger patients. The elderly group also had higher in-hospital mortality (7.1% vs. 1.9%, p < 0.05), were more likely to develop severe disease (83.7% vs. 67.9%, p < 0.01), and had a longer hospital stay (15.0 (11.0-23.0) vs. 12.0 (9.0-16.5) days, p < 0.001). Nevertheless, the elderly patients did not have a higher risk of using high-flow nasal cannulas (17.3% vs. 15.1%, p = 0.63) or mechanic ventilation (23.5% vs. 17.0%, p = 0.20). Conclusion: Elderly COVID-19 patients had significant higher risks of severe disease, mortality, and lon-ger duration of hospitalization, possible due higher rates of comorbidities and pro-inflammatory status. Copyright (c) 2022, Taiwan Society of Geriatric Emergency & Critical Care Medicine.
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To survive increasingly uncertain and competitive markets, technology and capitalintensive semiconductor companies need to be more agile, responsive and flexible than ever before. This study investigates the impact financial flexibility on firm performance within Taiwan???s semiconductor industry and whether the impact on FP differs depending on the semiconductor industry characteristics. Using quantile regression analysis, data from semiconductor companies listed on the Taiwan Stock Exchange during the COVID-19 shock was investigated. The results evidence an inverted U-shaped relationship between FF and FP in the lower and median return on equity quantiles of the semiconductor industry. For the asset heavy business model companies, FF has a concave impact on FP for IC-design and IC-manufacturing companies but not the semiconductor companies. For the asset light business model companies, FF has a concave impact on FP in the lower and median quantiles for semiconductor companies, in the upper quantiles for IC-design companies and in all except the 90th quantile for IC-manufacturing companies. The results of this research significantly contribute to extant literature as with such specific knowledge regarding the impact of FF on FP, managers are able to make decisions based on a firm???s individual FF-FP relationship and identify the most lucrative business trajectory.
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The greatest threat to global health is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) currently. COVID-19 was declared as a global pandemic on March 11, 2020. For this highly contagious disease, the way of human-to-human transmission has forced us to implement large-scale COVID-19 testing worldwide. On February 21, 2021, 120 million people have already undergone COVID-19 testing. The large scale of COVID-19 testing has driven innovation in strategies, technologies, and concepts for managing public health testing. It is an unprecedented global testing program. In this study, we describe the role of COVID-19 testing while establishing a comprehensive and validated research dataset that includes data from 189 countries and 893 regions between August 8, 2019, and March 3, 2021. Through our analysis, we observed that the more COVID-19 testings provided, the more confirmed cases were detected. The availability of large-scale COVID-19 testing is indispensable to fully control the outbreak, as it is the main way to cut off the source of COVID-19 transmission. Then we used this dataset to predict the COVID-19 detection capabilities of each country by Machine Learning, Ensemble Learning, and Broad Learning System. Experimental results show that Broad Learning System significantly outperformed the Machine Learning. The R2 of predicted the ability of the COVID-19 testing can reach 0.999921. © 2021 IEEE.
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COVID-19 trend prediction helps policymakers to handle disease situations. Therefore, it is necessary to predict the pandemic spread trend for prevention and control. The traditional infectious disease model is established according to the transmission characteristics of the disease. However, the trend prediction method of the traditional infectious disease model ignores considering the actual prevention and control situation, resulting in inaccurate models. To address this problem, this paper uses the ARIMA model to predict the spreading trend. First, we download the pandemic data from the website, compare the pandemic situation in different countries and select the United States as the research object. Second, the time series forecasting method is used to analyze the characteristics of the experimental data set. Finally, we use the ARIMA model to analyze the confirmed cases of COVID-19 in the United States and predict the spreading trend. To verify the effectiveness of the ARIMA model, we compare it with the prophet model and random forest model, evaluate the model performance with mean absolute scaled error, symmetric mean absolute percentage error, and root mean squared error. The experimental results illustrate that the ARIMA model significantly outperforms baselines by obtaining the three values of 0.14,9.97, 22316.57, respectively. The empirical results based on the pandemic spreading prediction in the United States show that the model has good applicability and accuracy. © 2022 IEEE.
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Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.
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The COVID-19 pandemic created an unprecedented shortage of personal protective equipment (PPE) for healthcare workers-especially respirators. In response to a lack of commercial respirator equipment, a multidisciplinary prototyping hackathon was held and the key components required to develop an inexpensive, scalable "COVID-19 reusable elastomeric respirator" (RER-19) were identified. Available hospital supplies were assessed based on their published technical specifications to meet each of the key component requirements. The fully assembled prototype was then validated through user testing, and volunteers underwent standard fit testing with cardiopulmonary monitoring while wearing the RER-19 in a small pilot study. Multiple social media platforms were then used to disseminate educational information on respirator assembly, use, and maintenance. Here, we present our institution's initial experience with prototyping to meet a specific healthcare challenge, in combination with prompt dissemination of information to educate and empower healthcare workers in the face of a critical PPE shortage during an unprecedented and evolving pandemic.
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OBJECTIVES: This study aims to examine the rates of anxiety, depression, and posttraumatic stress disorder (PTSD) after hospital discharge among COVID-19 survivors and to determine the associated risk factors. METHODS: Adult COVID-19 survivors discharged from hospitals between March 2020 and March 2021 were asked to complete a questionnaire at 4 weeks after discharge. The Chinese version of the 22-item Impact of Event Scale - Revised (IES-R) was used to measure symptoms of PTSD. The 9-item Patient Health Questionnaire (PHQ-9) was used to assess symptoms of major depressive disorder. The 7-item Generalised Anxiety Disorder Scale (GAD-7) was used to measure symptoms of generalised anxiety disorder. The rates of anxiety, depression, and PTSD among discharged patients were determined, as were associations between psychosocial factors and outcome measures and predictors for moderate-tosevere symptoms of anxiety, depression, and PTSD. RESULTS: 96 men and 103 women aged 18 to 81 years returned the completed questionnaire. 12.1% to 20.1% of them reported symptoms of PTSD, anxiety, or depression. Higher symptom severity was associated with higher perceived life threat, lower emotional support, lower disease severity upon admission, and longer hospital stay. Women had more PTSD symptoms than men, particularly when knowing someone under quarantine. CONCLUSION: COVID-19 survivors with higher perceived life threat, lower emotional support, lower disease severity upon admission, and longer hospital stay were associated with higher severity of symptoms of PTSD, anxiety, and depression. Timely intervention should provide to at-risk survivors.
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COVID-19 , Depressive Disorder, Major , Stress Disorders, Post-Traumatic , Adolescent , Adult , Aged , Aged, 80 and over , Anxiety/epidemiology , Anxiety Disorders/complications , Anxiety Disorders/epidemiology , COVID-19/epidemiology , Depression/epidemiology , Depressive Disorder, Major/complications , Female , Humans , Male , Middle Aged , Stress Disorders, Post-Traumatic/etiology , Survivors , Young AdultABSTRACT
Introduction: Tele-cardiac rehabilitation has demonstrated safety and efficacy in several clinical studies. With the outbreak of COVID-19, the centered-based CR service was totally suspended. To facilitate patients to exercise at home while being monitored. A pilot home-based cardiac tele-rehabilitation program was developed with a structured protocol at Princess Margaret Hospital (PMH) and rolled out from October 2020. Objectives: 1. To minimize the impact of suspension of in-hospital CR service due to outbreak of COVID-19. 2. To evaluate the effects and develop a home-based CR program for remote rehabilitation, based on advanced technological infrastructure and complementary clinical protocols. Methodology: Target patients: Low risk cardiac patients who fulfil the intake criteria, able and willing to use digital monitoring devices including blood pressure machine, smart watch and smart phone. Program design: The program will last for 12 weeks and consists of education, exercise training and relaxation training. Each consenting patient will be given a training kit containing a training log-book, informative educational leaflets and a set of QR codes to access our home-made education, exercise training & relaxation practice videos. Individual phone consultation by multidisciplinary will be scheduled once a week at the first five weeks. Patients can view the video at their own convenience, and then discuss or ask questions during phone follow-up. Individualized exercise will be prescribed according to patients' age, mobility and cardio fitness level. Patients can follow the designated video to do exercise at home. They will be instructed to measure and record their blood pressure, heart rate, and rate perceived exertion (RPE) before and after exercise. Physiotherapist will phone call patient to monitor and coach patients. Evaluation: All patients will undergo a detailed face-to-face assessment at baseline and at 12-week. They are including 6-minute walk test, body mass index (BMI), waist circumference, blood test for lipid profile, etc. In addition, patients will also request to fill in a set of questionnaires to measure the physical activity level, functional performance and psychological fitness. Conclusion: It believes that tele-rehabilitation is a more cost-effective model compared to center-based CR. It enables a new direction for the CR program.
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This study examines agenda-setting in US-China elite newspapers coverage of COVID-19 through topic modeling. It attempts to contribute to studies of media agenda first by demonstrating the relevance of text-mining in agenda-setting research and second by comparing how elite newspapers from different countries choose topics as part of agenda-setting when they report a single event. Topic-modeling the news corpora collected between 15 January 2020 and 15 June 2020 from the four US-China elite newspapers, the study finds that "domestic economy" and "international relations" are the two dominant topics that help shape the agenda in the Chinese newspapers, whereas "family & friends" and "daily life" are the topics playing the same role in the US newspapers. The study argues that such differences may associate with ideological gaps between the two countries in terms of "concepts of development", "media bias" and "views of individualism".
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Purpose: This study is to reconfigure a hierarchical supply chain model utilizing databases and text files to understand future pathways due to COVID-19 pandemic has had a bullwhip effect, disrupting the global supply chain, and a mechanism is needed to address this disruptive event under pandemic uncertainties. Design/methodology/approach: To address this mechanism, this study employs bibliometric analysis and text mining to reconfigure a hierarchical supply chain model under pandemic conditions and associates it with social media to conduct an intuitive visual analysis. Findings: The current academic concerns are related to an overconcentration on risk management and a data-driven approach, generating an enormous gap between the concerns of academics and those of the public. The evidence shows that for both countries with outstanding performance and those that need improvement, the efficiency in terms of preventing the spread of the pandemic should be promoted. Originality/value: This study contributes to (1) reconfiguring a hierarchical supply chain model under pandemic uncertainties and (2) bridging theory and practice by offering comparable interrelated attributes to guide post-COVID-19 strategies in the supply chain. The findings are that the supply management approach and big data are attributes that involve the concerns of world public and academics under pandemic uncertainties. © 2021, Emerald Publishing Limited.