Effect of Different Factors on the Quality of Life and Sequelae of COVID-19 Patients after Discharge

Background The continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains an international public health emergency, resulting in a significant global disease burden. The long-term effects of SARS-CoV-2 infection in humans and the long-term prognosis of patients with coronavirus disease 2019 (COVID-19) after discharge remain unclear. We aimed to assess the quality of life (QoL) and sequelae in patients with COVID-19 after discharge from the hospital by conducting multiple follow-up visits to understand the long-term effects of SARS-CoV-2 on patients' health and its possible influencing factors. Methods COVID-19 patients discharged from Huoshenshan Hospital (Wuhan, China) between February 15 and April 5, 2020, were followed up at 6, 9, and 12 months after discharge. They completed questionnaires on postdischarge QoL and sequelae under the guidance of medical staff with professional training. The demographic and clinical characteristics of the COVID-19 patients were analyzed using descriptive statistics. A generalized estimating equation model was used to analyze the QoL-related factors. The χ2 test (or Fisher exact test) and multivariate logistic regression analysis were used to analyze the sequelae and influencing factors. Results A total of 175 patients participated in at least 1 follow-up visit, and 120 completed all 3 follow-up visits. Patients diagnosed with severe and critically ill COVID-19 had worse mental conditions (χ2 = 7.653, P = 0.022) than those with the nonsevere type (not severe or critical) and were more likely to feel fatigued (χ2 = 4.836, P = 0.028). Female patients had a higher risk of sleep disturbance (χ2 = 10.026, P = 0.002) and dyspnea (χ2 = 5.672, P = 0.017) and had more difficulty returning to their original work and life (χ2 = 8.922, P = 0.003) than male patients. Patients with diabetes had a worse appetite (χ2 = 4.669, P = 0.031) and were more prone to sleep disturbance (χ2 = 4.417, P = 0.036) after discharge. The proportion of patients with at least 1 sequela increased from 29.76% (50/168) at 6 months to 51.11% (69/135) at 9 months (χ2 = 14.305, P < 0.001). Compared with the nonsevere type, patients diagnosed with severe and critically ill COVID-19 had an odds ratio (OR) of 4.325 (95% confidence interval [CI], 1.215-15.401) for memory decline. Female patients had an OR of 4.632 (95% CI, 1.716-12.501) for joint or muscle pain. Patients with hypertension had an OR of 3.014 (95% CI, 1.193-7.615) for joint or muscle pain. Conclusion One year after discharge, there were still some patients with varying degrees of decline in QoL and sequelae, which occurred in all follow-up visits. Moreover, QoL and sequelae after discharge were related to sex, clinical classification of COVID-19, and underlying diseases. © Wolters Kluwer Health, Inc. All rights reserved.

A Medical Device That Can Be Used for Psychotherapy in the Metaverse

In 2020, a new coronavirus swept the world, and the advent of this disease has a huge impact on our social and economic development. Due to the limited medical resources and regional differences, this model of virtual medicine becomes more valuable. In this paper, we create a virtual medical space based on a metaverse in order to investigate whether the medical model can be freely transformed between virtual and reality. In this process, I first describe different scenarios of virtual medical care in mixed reality, and then we use one of them as an example to develop a medical device. Then we designed the software and hardware of the product and performed the user experience, it includes the interaction and usage scenarios that affect the user. Finally, this medical device will be demonstrated by user experience and feedback. © 2023 The authors and IOS Press.

A Potent Recombinant Polyclonal Antibody Therapeutic for Protection Against New SARS-CoV-2 Variants of Concern.

Emerging variants of SARS-CoV-2 possess mutations that prevent antibody therapeutics from maintaining anti-viral binding and neutralizing efficacy. Monoclonal antibodies (mAb) shown to neutralize Wuhan-Hu-1 SARS-CoV-2 (ancestral) strain have reduced potency against newer variants. Plasma derived polyclonal hyperimmune drugs have improved neutralization breadth compared to mAbs, but lower titers against SARS-CoV-2 require higher dosages for treatment. We previously developed a highly diverse, recombinant polyclonal antibody therapeutic anti-SARS-CoV-2 immunoglobulin hyperimmune (rCIG). Compared to plasma-derived standard (NIBSC-20/130) or mAb SAD-S35, rCIG has improved neutralization of SARS-CoV-2 across World Health Organization (WHO) variants; however, its potency was reduced against some variants relative to ancestral, in particular omicron. Omicron-specific antibody sequences were enriched from yeast expressing rCIG-scFv antibodies and exhibited increased binding and neutralization to omicron BA.2 while maintaining binding and neutralization to the ancestral strain. Polyclonal antibody libraries such as rCIG can be utilized to develop antibody therapeutics against present and future SARS-CoV-2 threats.

COVID-19 and Long-Term Economic Growth

This article investigates the impact of the COVID-19 pandemic on the long-term economic growth of South Africa. We embed an epidemiological model in a modified Solow–Swan model and explore various channels such as morbidity, mortality, unemployment, loss of school days and capital accumulation. We demonstrate that COVID-19 will lower the average annual growth rate of GDP per capita of South Africa by 0.07 percentage points in the next four decades, a 25 per cent decline relative to the no-COVID benchmark. We show that human capital losses due to school closures account for more than half of the economic slowdown. © 2023 The Authors. The Australian Economic Review published by John Wiley & Sons Australia, Ltd on behalf of The University of Melbourne, Melbourne Institute: Applied Economic & Social Research, Faculty of Business and Economics.

Synthesis and enzymatic inhibition effects of thiazolidinedione 3C-like protease inhibitors

The 3C-like protease (also known as Mpro) plays a key role in SARS-CoV-2 replication and has similar substrates across mutant coronaviruses, making it an ideal drug target. We synthesized 19 thiazolidinedione derivatives via the Knoevenagel condensations and Mitsunobu reactions as potential 3C-like protease inhibitors. The activity of these inhibitors is screened in vitro by employing the enzymatic screening model of 3C-like protease using fluorescence resonance energy transfer. Dithiothreitol is included in the enzymatic reaction system to avoid non-specific enzymatic inhibition. Active inhibitors with diverse activity are found in this series of compounds, and two representative inhibitors with potent inhibitory activity are highlighted. © The Author(s) 2023.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.

Dignity therapy effects by race: Chaplain and nurse implementation in pragmatic, multisite steppedwedge randomized control trial

Aim: As a brief psychotherapy for individuals facing mortal threat, Dignity Therapy (DT) effects on spiritual outcomes are unknown, especially as an intervention to support cancer health equity for racial minority patients. Our study aim was to compare usual outpatient palliative care and such care along with nurse-led or chaplain-led DT groups for main effects on dignity impact and the interaction of DT with race. Method(s): We conducted the 4-step, stepped-wedge randomized control trial at 4 NCI designated cancer centers and 2 academic cancer centers across the United States. Half of the sites were randomized to chaplain-led DT and half to nurse-led DT. Of the 645 recruited cancer patients (age >= 55 years) receiving outpatient palliative care, 579 (59% female, mean age 66.4+/-7.4 years, 78% White, 77% Christian religion, 62% stage 4 cancer) provided data for intent-totreat analysis. Over 6 weeks, patients completed pretest/posttest measures including the Dignity Impact Scale (DIS, primary outcome) ranging from low impact of 7 to highest impact of 35. In step 1-3, study procedures were completed in person. In step 4 (during the COVID-19 pandemic), when all sites were providing the intervention, study procedures were completed via Zoom. We used multiple imputation and regression analysis adjusting for pretest DIS, study site, and study step. Result(s): Of the 579 patients, 317 were in the DT group and 262 in the usual care group. The vast majority of the sample was White (n=448) along with 103 Blacks, 5 Asians, 2 Pacific Islanders, 1 Native American, 13 other races (all minorities were combined as Other Race), and 7 were missing race data. At pretest, the mean DIS score was 24.3+/-4.3 in the DT group and 25.9+/-4.3 in the usual care group. Adjusting for pretest DIS scores, study site, and study step, the chaplain-led (beta=1.7, p=.02) and nurse-led (beta=2.1, p=.005) groups reported significantly higher posttest DIS scores than the usual care groups. Adjusting for age, gender, race, education, and income, the effect on DIS scores remained significant for both DT groups. We then examined the interaction between race and DT with the entire sample and observed that the interaction was not significant (p=.73) and the sizes of DT effects were similar for White (beta=1.9, p=.005) and the Other Race (beta=1.6, p=.055) patients. Conclusion(s): Whether led by chaplains or nurses, DT was effective in improving dignity impact for older adult outpatient palliative care patients with cancer. DT, a patient-centered approach, has promise as an intervention to improve health equity in support of dignity for racial minorities. This rigorous trial of DT is a landmark step in gero-oncology palliative care and spiritual health services research focused on cancer health equity.

Semi-Supervised Multi-View Fusion for Identifying CAP and COVID-19 With Unlabeled CT Images

Recently under the condition of reducing nucleic acid testing for COVID-19 in large population, the computer-aided diagnosis with the chest computed tomography (CT) image has become increasingly important in differential diagnosis of community-acquired pneumonia (CAP) and COVID-19. In prac-tice, there usually exist a mass of unlabeled CT images, especially in regions without adequate medical resources, and the existing diagnosis methods cannot take advantage of the useful information among them. Therefore, it is practical and urgent need to develop a computer-aided diagnosis model that can effectively exploit both labeled and unlabeled samples. To this end, in this paper, we pro -pose a semi-supervised multi-view fusion method for the diagnosis of COVID-19. It explores both the discriminative features from labeled samples and the structure information from unlabeled samples and fuses multi-view features extracted from CT images, including image feature, statistical feature, and lesions specific feature, for improving the diagnostic performance. Specifically, in the proposed model, we utilize semi-supervised learning technique with pairwise constraint regularization to learn the model with both labeled samples and unlabeled samples. Simultaneously, we employ low-rank multi-view constraint to capture latent comple-mentary information among different features from CT images. Experimental results show that the proposed method outperforms the state-of-the-art methods in differential diagnosis of CAP vs. COVID-19.

Computer-Aided Diagnosis of COVID-19 Radiography using deep transfer deep learning model TLDeNet

Computer-Aided Diagnosis (CAD) is applied in the medical analysis of X-ray images widely. Due to the COVID-19 pandemic, the speed of COVID-19 detection is slow, and the workforce is scarce. Therefore, we have an idea to use CAD to diagnose COVID-19 and effectively respond to the pandemic. Recent studies show that convolutional neural network (CNN) is an appropriate technique for medical image classification. However, CNN is more suitable for datasets with many images, such as ImageNet. Medical image classification relies on doctors to label medical images, so obtaining large-scale medical image data sets is a time-consuming, costly, and unrealistic task. The method of data augmentation for a limited medical dataset can be used to increase the number of images. However, this technology will produce many repeated images, which will easily lead to the overfitting problem of CNN. In the case of a limited number of radiological images, transfer learning is a practical and effective method which can help us overcome the overfitting problem of ordinary CNN by transferring the pre-Trained models on large datasets to our tasks. The proposed model is DenseNet based deep transfer learning model (TLDeNet) to identify the patients into three classes: COVID-19, Normal or Pneumonia. We then analyzed and assessed the performance of our model on COVID-19 X-ray testing images by performing extensive experiments. It is finally demonstrated that the proposed model is superior to other deep transfer learning models according to comparative analyses. The Grad-Cam method is finally applied to interpret the convolutional neural network, revealing that our proposed model focuses on the similar region of the X-ray images as doctors. © 2022 IEEE.

Multi-hop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and can not be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. Firstly, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Secondly, the cluster heads are selected according to the energy and location factors in the clusters, and a reasonable cluster head replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of cluster heads. Finally, a multi-hop routing mechanism between the cluster heads and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9% and 162.2% compared with IGWO, ACA-LEACH and DEAL in the monitoring area of 300m ×300m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. IEEE

Compensatory travel in the post COVID-19 pandemic era: How does boredom stimulate intentions?

To promote tourism recovery in the post-COVID-19 pandemic era, it is critical to understand the psychological factors that either boost or suppress travel demands. However, little is known about the underlying psychological mechanism that affects compensatory travel intention. Therefore, by scrutinizing the roles that autonomous self -motivation, sensation seeking, and perceived susceptibility to COVID-19 play, this study conducted two scenario -based experiments (N = 223 + 200) to explore the psychological mechanism and boundary conditions behind the influence of boredom on compensatory travel intention. The findings reveal that people are more likely to generate compensatory travel intention when there is a higher level of boredom during the COVID-19 pandemic due to their desire for sensation seeking. This effect is magnified when people adopt autonomous self-motivating strategies. However, for people with high (vs. low) perceived susceptibility to COVID-19, a high level of boredom evokes lower compensatory travel intention through sensation seeking.

The Effects of Intervention Strategies for COVID-19 Transmission Control on Campus Activity

University is one of the most likely environments for the cluster infection due to the long-time close contact in house and frequent communication. It is critical to understand the transmission risk of COVID-19 under various scenario, especially during public health emergency. Taking the Tsinghua university's anniversary as a representative case, a set of prevention and control strategies are established and investigated. In the case study, an alumni group coming from out of campus is investigated whose activities and routes are designed based on the previous anniversary schedule. The social closeness indicator is introduced into the Wells-Riley model to consider the factor of contact frequency. Based on the anniversary scenario, this study predicts the number of the infected people in each exposure indoor location (including classroom, dining hall, meeting room and so on) and evaluates the effects of different intervention measures on reducing infection risk using the modified Wells-Riley model, such as ventilation, social distancing and wearing mask. The results demonstrate that when applying the intervention measure individually, increasing ventilation rate is found to be the most effective, whereas the efficiency of increased ventilation on reducing infection cases decreases with the increase of the ventilation rate. To better prevent COVID-19 transmission, the combined intervention measures are necessary to be taken, which show the similar effectiveness on the reduction of infected cases under different initial infector proportion. The results provide the insights into the infection risk on university campus when dealing with public health emergency and can guide university to formulate effective operational strategies to control the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effect of COVID-19 Pandemic on Acute Coronary Syndrome Clinical Practice Patterns: Findings from a Multicenter Clinician Survey in China

Background: The coronavirus disease 2019 (COVID-19) pandemic has severely affected healthcare systems around the world. This study aimed to investigate the perceptions of cardiologists regarding how the COVID-19 pandemic has affected the clinical practice patterns for acute coronary syndrome (ACS). Method(s): A multicenter clinician survey was sent to 300 cardiologists working in 22 provinces in China. The survey collected demographic information and inquired about their perceptions of how the COVID-19 pandemic has affected ACS clinical practice patterns. Result(s): The survey was completed by 211 (70.3%) cardiologists, 82.5% of whom were employed in tertiary hospitals, and 52.1% reported more than 10 years of clinical cardiology practice. Most respondents observed a reduction in ACS inpatients and outpatients in their hospitals during the pandemic. Only 29.9% of the respondents had access to a dedicated catheter room for the treatment of COVID-19-positive ACS patients. Most respondents stated that the COVID-19 pandemic had varying degrees of effect on the treatment of acute ST-segment elevation myocardial infarction (STEMI), acute non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Compared with the assumed non-pandemic period, in the designed clinical questions, the selection of coronary interventional therapy for STEMI, NSTEMI, and unstable angina during the COVID-19 pandemic was significantly decreased (all p < 0.05), and the selection of pharmacotherapy was increased (all p < 0.05). The selection of fibrinolytic therapy for STEMI during the pandemic was higher than in the assumed non-pandemic period (p < 0.05). Conclusion(s): The COVID-19 pandemic has profoundly affected ACS clinical practice patterns. The use of invasive therapies significantly decreased during the pandemic period, whereas pharmacotherapy was more often prescribed by the cardiologists. Copyright: © 2022 The Author(s).

Analysis of the prevalence of common respiratory pathogens in children in Beijing in 2020

Objective To investigate the changes in epidemiological characteristics of common respiratory pathogens in children in Beijing during COVID-19 epidemic.Methods A total of 9 728 serum samples were collected from cases of acute respiratory infections in Beijing Children′s Hospital from January 2020 to December 2020.Indirect immunofluorescence antibody test was performed to detect IgM antibodies against eight common respiratory pathogens and the test results were statistically analyzed.The eight common respiratory pathogens were influenza virus A (FluA), influenza virus B (FluB), respiratory syncytial virus (RSV), adenovirus (ADV), parainfluenza virus (PIV), Mycoplasma pneumoniae (Mp), Chlamydia pneumoniae (Cp) and Legionella pneumophila (Lp).Results The detection rate of respiratory pathogens in 9 728 cases was 41.71% (4 058/9 728) and respiratory viruses (FluA, FluB, RSV, ADV and PIV) accounted for 46.18% (2 343/5 074) of all detected pathogens.Mp, FluB and FluA accounted for 84.73% (4 299/5 074)of all detected pathogens, and the detection rates were 24.27% (2 361/9 728), 11.49% (1 118/9 728) and 8.43% (820/9 728), respectively.There were 846 cases positive for two kinds of pathogens, and the most common co-infection was Mp and FluB.The detection rates in male and female were 37.56% (2 089/5 562) and 47.26% (1 969/4 166), respectively.There were significant differences in the total detection rate and the positive rates of PIV and Mp between different sexes (P<0.05).The detection rate in school-age children (6-12 years old) was the highest (52.26%, 1 535/2 937).The detection rates of respiratory pathogens in different months ranged from 30.12% (203/674) to 49.81% (268/538) with higher rates in autumn and winter [42.45% (1 304/3 072) and 43.29% (1 618/3 738)].The detection rates of FluA and FluB were higher in summer [11.46% (195/1 701)] and winter [14.63% (547/3738)], respectively.Most of RSV infection occurred in summer [1.35% (23/1 701)], and Mp could be detected all year round, especially in winter and spring [27.21% (1 017/3 738) and 25.64% (312/1 217)].The detection rate of respiratory pathogens in outpatient group was higher than that in inpatient group [46.48% (1 583/3 406) vs 39.15% (2 475/6 322)].The detection rate in severe cases was 26.10% (71/272).The detection rates of total pathogens, FluB and Mp were higher in outpatients than in inpatients and the differences were statistically significant (P<0.05).The detection rates of FluA, PIV and ADV were higher in inpatients than in outpatients and the differences were statistically significant (P < 0.05).The detection rates of total pathogens, FluB and Mp in mild cases were significantly higher than those in severe cases and the differences were statistically significant (P<0.05).The detection rate of RSV in severe cases was significantly higher than that in mild cases and the difference was statistically significant (P<0.05).Conclusions The protective measures taken during the period of regular prevention and control of COVID-19 epidemic could better prevent the spread of respiratory viruses, having a certain impact on the population susceptible to respiratory pathogens and typical seasonal patterns, but had little effect on the prevention and control of Mp.New protective measures needed to be studied to prevent Mp infection in children during epidemical season.

Estimating Spatiotemporal Contacts Between Individuals in Underground Shopping Streets Based on Multi-Agent Simulation

Coronavirus disease 2019 (COVID-19) has exposed the public safety issues. Obtaining inter-individual contact and transmission in the underground spaces is an important issue for simulating and mitigating the spread of the pandemic. Taking the underground shopping streets as an example, this study aimed to verify commercial facilities' influence on the spatiotemporal distribution of inter-individual contact in the underground space. Based on actual surveillance data, machine learning techniques are adopted to obtain utilizers' dynamics in underground pedestrian system and shops. Firstly, an entropy maximization approach is adopted to estimate pedestrians' origin-destination (OD) information. Commercial utilization behaviors at different shops are modeled based on utilizers' entering frequency and staying duration, which are obtained by re-identifying individuals' disappearances and appearances at storefronts. Based on observed results, a simulation method is proposed to estimate utilizers' spatiotemporal contact by recreating their space-time paths in the underground system. Inter-individual contact events and exposure duration are obtained in view of their space-time vectors in passages and shops. A social contact network is established to describe the contact relations between all individuals in the whole system. The exposure duration and weighted clustering coefficients were defined as indicators to measure the contact degree of individual and the social contact network. The simulation results show that the individual and contact graph indicators are similar across time, while the spatial distribution of inter-individual contact within shops and passages are time-varying. Through simulation experiments, the study verified the effects of self-protection and commercial type adjustment measures.

Towards Efficient Masked-Face Alignment Via Cascaded Regression

Practical and efficient face alignment has been highly required and widely focused in recent years, especially under the trend of edge computation and real-Time operation. And it is a critical need to deal with masked faces in the context of COVID-19 epidemic. In this paper, we propose a novel cascaded facial landmark detector towards efficient masked face alignment, which we call QCN (Quantized Cascaded Network). QCN consists of three stages: Alignment, estimation and refinement. The alignment stage help to pre-Align the faces to alleviate extreme poses. And the next two stages localize facial landmarks in a coarse-To-fine manner. Thanks to the Network Architecture Search and Quantization techniques, the networks of QCN are designed as efficient as possible. Specifically, QCN occupies 1.75 Mb storage and runs in 84.18 MFLOPs only. Despite costs little computations, the proposed method yields 62.62% AUC (@0.08) on test set of JD-landmark-mask, which achieves 2nd place in the Grand Challenge of 106-point Facial Landmark Localization in ICME2021. © 2021 IEEE.

Adaptive Self-Paced Transfer Learning for COVID-19 Diagnosis with CXR Images

Coronavirus Disease 2019 (COVID-19) has become an unprecedented public health crisis since December of 2019. Compared with real-time reverse transcription polymerase chain reaction (rRT-PCR), the computer-aided diagnosis machine learning algorithm based on medical images can vastly ease the burden on clinicians. Even so, despite existing hundreds of millions of confirmed cases worldwide, there has not been a mature, large scale, high quality, single standard shared image data set yet, which can lead to some problems. For instance, 1) Because the sources of medical images and the collection standards are not guaranteed, features extracted by the neural network may not be very ideal. 2) Due to the small number of samples, some outliers (e.g., blurry medical images, inconspicuous symptoms) may significantly descend the performance of the model. To address these problems, we propose an adaptive self-paced transfer learning (ASPTL) algorithm in this paper. Specifically, inspired by the process of human learning from easy to difficult, we also evaluated the learning difficulty of the samples. Samples with no obvious disease features or wrong labels are relatively difficult to diagnose, and the samples that are easy to diagnose are selected adaptively in the iterative process. In addition, we adopt transfer learning to select easy to learn samples on the pre-trained network by self-paced learning, and gradually fine-tune the pre-trained model in an iterative way. We designed two experiments to validate the ASPTL algorithm's performance on COVID-19. The reult prove the effectiveness on solving mentioned problems. © 2021 IEEE.

Polarization-enhanced p-AlGaN superlattice optimization for GUV LED

AlGaN germicidal ultraviolet (GUV) light emitting diodes (LEDs) are one of the most promising disinfection technologies in fighting the COVID-19 pandemic;however, GUV LEDs are still lacking in efficiency due to low p-type doping efficiency in p-AlGaN. The most successful approach for producing conductive p-type AlGaN is the implementation of a polarization-enhanced short period AlxGa1-xN/ AlyGa1-yN superlattice (SL) structure, which enhances hole injection and reduces device operating voltage. In this report, we investigated different aspects of the superlattice including the AlxGa1-xN and AlyGa1-yN alloy constituent compositions, x and y, period thickness, total thickness, and Mg dopant concentration in terms of LED performance as well as electrical, optical, and morphological characteristics. The polarization-enhanced p-type doping in the AlGaN superlattice was also investigated computationally, giving excellent agreement with experimental results. Highly efficient UVC LEDs (279 nm) with EQE of 2% at 5 A/cm2 were demonstrated. A maximum output power of 5.5 mW (56 mW/mm2) was achieved at 100 mA. IEEE