Hot Spot Tracker: Detecting and Visualizing the Types of Spatiotemporal Hot Spots of COVID-19 in the United States

The high transmission rate of COVID-19 and the number of populations whose commute or recreational activities span across multiple counties/cities on a daily basis warrant serious attention to outbreaks in nearby areas beyond the administrative boundaries of counties/cities. We respond to these challenges by developing models using space-time cluster analysis to identify the hot spots and creating a dashboard that provides interactive visualizations of the results. The models use a Getis-Ord Gi statistic to identify clusters based on county-level daily confirmed cases, and Mann-Kendall tests combined with an adapted rule-based process to classify the type of the clusters. The space-time cluster analysis clearly displays the developing trends in the disease transmission patterns. The dashboard supports state- and county-level queries and expandable information. The dashboard displays the statistically tested spatiotemporal trends of COVID-19 breakout in concise and straightforward graphics. © 2023 Taylor and Francis Group, LLC.

The relationship between inflammatory diet score and cancer-specific outcomes: Systematic review and meta-analysis

Cancer remains one of the most prevalent diseases in the United States and a leading cause of death. Large prospective studies have found significant correlations between dietary intake and cancer. Chronic inflammation promotes pro-cancer inflammatory environments and nutrition can influence inflammation, with the intake of certain food items increasing inflammatory biomarkers. The objective of this research was to explore the relationship between inflammatory diet score measured by the Dietary Inflammatory index and all-cause mortality, cancer-specific mortality, and cancer recurrence among cancer survivors. Web of Science, Medline, CINHAL, and PsycINFO databases were searched to collect potentially eligible sources that focus on dietary inflammation and cancer outcomes. All sources were uploaded to Covidence software and screened by two independent blinded reviewers. The quality of the sources was assessed using the Newcastle Ottawa scale and relevant data was extracted and transferred to the Comprehensive Meta Analysis software and a random effects model was used to perform meta-analysis. Of the 1444 studies imported into the Covidence software, 13 passed all the screening stages and were included in the final analysis. Eight studies reported on pre-diagnosis diet while five others reported on postdiagnosis diet. Five studies reported on colorectal cancer, four on breast cancer, two on ovarian cancer, one on endometrial cancer and one on prostate cancer. Meta-analysis of the studies found that being in the highest postdiagnosis DII score indicating pro-inflammatory diet significantly increased the risk of all-cause death among cancer survivors by 33.5% (HR = 1.335, 95% CI = 1.049, 1.698, n = 6). Analysis did not show a statistically significant association between DII score and cancer mortality or recurrence (HR = 1.097, 95% CI = 0.939, 1.281, n = 6). Analysis by cancer subtype found a significant correlation between postdiagnosis DII score and all-cause mortality among the breast cancer survivors (HR = 1.335, 95% CI = 1.041, 1.711, n = 3) though there were no significant associations between DII and the outcomes of interest from the other cancer types. The meta-analysis concludes that being in the highest postdiagnosis DII score group significantly increased the risk of all-cause death among cancer survivors. This suggests that risk of all-cause mortality could be reduced for cancer survivors by consuming more anti-inflammatory food components and reducing consumption of pro-inflammatory foods. These findings also warrant more research in this field to clarify the relationship between dietary inflammation as measured by the DII and cancer outcomes, particularly cancer-specific mortality.

The Effect of FOXP3+Regulatory T Cells on Infectious and Inflammatory Diseases

CD4+CD25+FOXP3+regulatory T cells (Tregs) contribute to the maintenance of immune homeostasis and tolerance in the body. The expression levels and functional stability of FOXP3 control the function and plasticity of Tregs. Tregs critically impact infectious diseases, especially by regulating the threshold of immune responses to pathogenic microorganisms. The functional regulatory mechanism and cell-specific surface markers of Tregs in different tissues and inflammatory microenvironments have been investigated in depth, which can provide novel ideas and strategies for immunotherapies targeting infectious diseases.Copyright © 2021. All rights reserved.

[Performance of screening of contacts of COVID-19 cases in same flight].

Objective: To understand the performance of 2019-nCoV nucleic acid detection in screening of contacts of COVID-19 cases in same flights and provide evidence for the effective screening of persons at high risk for the infection in domestic flights. Methods: The information of passengers who took same domestic flights with COVID-19 cases in China from April 1, 2020 to April 30, 2022 were retrospectively collected,and χ2 test was used to analyze positive nucleic acid detection rates in the passengers in different times before the onsets of the index cases, in different seat rows and in epidemic periods of different 2019-nCoV variants. Results: During the study period, a total of 433 index cases were identified among 23 548 passengers in 370 flights. Subsequently, 72 positive cases of 2019-nCoV nucleic acid were detected in the passengers, in whom 57 were accompanying persons of the index cases. Further analysis of the another 15 passengers who tested positive for the nucleic acid showed that 86.67% of them had onsets or positive detections within 3 days after the diagnosis of the index cases, and the boarding times were all within 4 days before the onsets of the index cases. The positive detection rate in the passengers who seated in first three rows before and after the index cases was 0.15% (95%CI: 0.08%-0.27%), significantly higher than in the passengers in other rows (0.04%, 95%CI: 0.02%-0.10%, P=0.007),and there was no significant difference in the positive detection rate among the passengers in each of the 3 rows before and after the index cases (P=0.577). No significant differences were found in the positive detection rate in the passengers, except the accompanying persons, among the epidemics caused by different 2019-nCoV variants (P=0.565). During the Omicron epidemic period, all the positive detections in the passengers, except the accompanying persons, were within 3 days before the onset of the index cases. Conclusions: The screening test of 2019-nCoV nucleic acid can be conducted in the passengers took the same flights within 4 days before the onsets of the index cases on board. Passengers who seated within 3 rows from the index cases can considered as the close contacts at high risk for 2019-nCoV, for whom screening should be conducted first and special managements are needed. The passengers in other rows can be classified as general risk persons for screening and management.

Factors Associated with Hospitalization with Symptomatic Coronavirus Disease 2019 among Pregnant Individuals: A Multicenter Retrospective Cohort Study

Background: Pregnant individuals are at increased risk of coronavirus disease 2019 (COVID-19) hospitalization and death, and primary and booster COVID-19 vaccination is recommended for this population. Method(s): Among a cohort of pregnant individuals who received prenatal care at 3 healthcare systems in the United States, we estimated the cumulative incidence of hospitalization with symptomatic COVID-19 illness. We also identified factors associated with COVID-19 hospitalization using a multivariable Cox proportional hazards model with pregnancy weeks as the timescale and a time-varying adjustor that accounted for severe acute respiratory syndrome coronavirus 2 circulation;model covariates included site, age, race, ethnicity, insurance status, prepregnancy weight status, and selected underlying medical conditions. Data were collected primarily through medical record extraction. Result(s): Among 19 456 pregnant individuals with an estimated due date during 1 March 2020-28 February 2021, 75 (0.4%) were hospitalized with symptomatic COVID-19. Factors associated with hospitalization for symptomatic COVID-19 were Hispanic ethnicity (adjusted hazard ratio [aHR], 2.7 [95% confidence interval {CI}, 1.3-5.5]), Native Hawaiian or Pacific Islander race (aHR, 12 [95% CI, 3.2-45.5]), age <25 years (aHR, 3.1 [95% CI, 1.3-7.6]), prepregnancy obesity (aHR, 2.1 [95% CI, 1.1-3.9]), diagnosis of a metabolic disorder (aHR, 2.2 [95% CI, 1.2-3.8]), lung disease excluding asthma (aHR, 49 [95% CI, 28-84]), and cardiovascular disease (aHR, 2.6 [95% CI, 1.5-4.7]). Conclusion(s): Although hospitalization with symptomatic COVID-19 was uncommon, pregnant individuals should be aware of risk factors associated with severe illness when considering COVID-19 vaccination. Copyright © 2022 Published by Oxford University Press on behalf of Infectious Diseases Society of America. This work is written by (a) US Government employee(s) and is in the public domain in the US.

[Latent period and incubation period with associated factors of COVID-19 caused by Omicron variant].

Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infections and 335 symptomatic infections in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q1, Q3) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid test within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18-49 years old, the latent period [exp(ß)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(ß)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0-17 years old were prolonged. The latent period [exp(ß)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(ß)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be a influencing factor of the latent period and incubation period.

Nomogram for Predicting Viral Shedding Time of SARS-CoV-2 Delta Variant in Patients with COVID-19: A Retrospective Study

Background The coronavirus disease 2019 (COVID-19) cases continue to rise, and the demand for medical treatment and resources in healthcare systems surges. Assessing the viral shedding time (VST) of patients with COVID-19 can facilitate clinical decision making. Although some studies have been conducted on the factors affecting the VST of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), few prediction models are currently available. Methods This retrospective study included the consecutive patients with COVID-19 admitted to Xi'an Chest Hospital in Shaanxi, China, for treatment between December 19, 2021 and February 5, 2022. The clinical data of the patients were extracted from their electronic medical records. Combining significant factors affecting the VST, a nomogram was developed to predict the VST of the SARS-CoV-2 Delta variant in patients with COVID-19. Results We included 332 patients in this study. The average VST was 21 d. VST was significantly prolonged in patients with severe clinical symptoms, sore throat, old age, long time from onset to diagnosis, and an abnormal white blood cell count. Consequently, we developed a nomogram prediction model using these 5 variables. The concordance index (C-index) of this nomogram was 0.762, and after internal validation using bootstrapping (1000 resamples), the adjusted C-index was 0.762. The area under the nomogram's receiver operator characteristic curve showed good discriminative ability (0.965). The calibration curve showed high consistency. The VST was prolonged in the group with lower model fitting scores according to the Kaplan-Meier curve (χ2=286, log-rank P < 0.001). Conclusions We developed a nomogram for predicting VST based on 5 easily accessible factors. It can effectively estimate the appropriate isolation period, control viral transmission, and optimize clinical strategies. © Wolters Kluwer Health, Inc. All rights reserved.

EXERCISE CAPACITY IS REDUCED IN HIV INDEPENDENT OF SARS-CoV-2 INFECTION

Background: Reduced exercise capacity occurs as a post-acute sequela of COVID-19 ("PASC" or "Long COVID"). Cardiopulmonary exercise testing (CPET) is the gold standard for measuring exercise capacity and identifying reasons for exercise limitations. Only one prior study used CPET to examine exercise limitations among people living with HIV (PLWH). Extending our prior findings in PASC, we hypothesized that PLWH would have a greater reduction in exercise capacity after SARS-CoV-2 co-infection due to chronotropic incompetence (inability to increase heart rate). Method(s): We performed CPET within a COVID recovery cohort that included PLWH (NCT04362150). We evaluated associations of HIV and prior SARS-CoV- 2 infection with or without PASC with: (1) exercise capacity (peak oxygen consumption, VO2) and (2) adjusted heart rate reserve (AHRR, marker of chronotropic incompetence) using linear regression with adjustment for age, sex, and body mass index. Result(s): We included 83 participants (median age 54, 35% female, 10% hospitalized, 37 (45%) PLWH) who underwent CPET at 16 months (IQR 14-17) after SARS-CoV-2 infection. Among PLWH (median duration living with diagnosed HIV 21 years (IQR 15-28), all virally suppressed on antiretroviral therapy), 14 (39%) had not had SARS-CoV-2 infection, 12 (32%) had prior SARSCoV- 2 infection without PASC, and 11 (30%) had PASC (Long COVID symptoms at CPET). Median CD4 count was 608 (370-736) and CD4/CD8 ratio 0.92 (0.56-1.27). Peak VO2 was reduced among PLWH compared to individuals without HIV with an achieved exercise capacity only 80% vs 99% (p=0.005, Fig.), a difference in peak VO2 of 5.5 ml/kg/min (95%CI 2.7-8.2, p< 0.001). Exercise capacity did not vary by SARS-CoV-2 infection among PLWH (p=0.48 for uninfected vs infected;p=0.25 for uninfected vs no PASC;p=0.32 no PASC vs PASC). Chronotropic incompetence was present in 38% of PLWH vs 11% without HIV (p=0.002), and AHRR (normal >80%) was significantly reduced among PLWH vs individuals without HIV (60% vs 83%, p< 0.0001, Fig.). Heart rate response varied by SARSCoV- 2 status among those with HIV: namely, 3/14 (21%) without SARS-CoV-2, 4/12 (25%) with SARS-CoV-2 without PASC, and 7/11 (64%) with PASC (p=0.04 PASC vs no PASC). Among PLWH, CD4 count, CD4/CD8 ratio, and hsCRP were not associated with peak VO2 or AHRR. Conclusion(s): Exercise capacity is reduced among PLWH, with no differences by SARS-CoV-2 infection or PASC. Chronotropic incompetence may be a mechanism of reduced exercise capacity among PLWH. (Figure Presented).

Real-Time Hand Gesture Detection Based on YOLOv5s

Since the breakout of Corona Virus Disease 2019 (COVID-19), the global fight against influenza has begun. Various technologies have been developed to support the fast-growing contactless service market, and hence contactless services are rapidly becoming a new growth strategy. In particular, the retail service industry most urgently needs contactless service technology. A representative technical case is the self-checkout machine, which can reduce labor costs and provide customer satisfaction. We present a solution in this article. We propose a hand gesture recognition contactless self-checkout system, which is a hand gesture recognition model based on YOLOv5s. The hand gesture recognition mAP (0.5) value reaches 0.995, the mAP (0.5:0.95) value reaches 0.865, and the F1 score is 0.96, together with the accuracy and recall rate is close to 1. Compared with the excellent algorithm YOLOx-s, the FPS value of YOLOv5s can reach 123 (YOLOx-s is 108). In addition, the model can be used to detect recorded static and dynamic hand gestures in real-time. Practical results show that the YOLOv5s can effectively recognize hand gestures and realize the contactless checkout process.

Scalable one-stage multi-view subspace clustering with dictionary learning

The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.

COVID-19 Impact on the Italian Community-based System of Mental Health Care: Reflections and Lessons Learned for the Future

Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the longstanding problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2;(2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling;(3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.

Optimisation of collaborative supply transportation based on traffic road network topology

With the rapid development of China's economy, enterprises need to plan their logistics transportation routes reasonably in advance. This will make the transportation service more efficient. For the supplier's transportation service problem, an analysis method of critical path nodes is provided and a multi-supplier collaborative transportation strategy is designed in this article. First, a model for minimising the transportation cost was established, then a path diagram was simulated and the optimal and alternative transportation paths of suppliers based on the k-shortest path algorithm were calculated. In addition, path node availability during COVID-19 is used as a research context in this article. A multi-stage path analysis method was provided by discussing different cases of critical path nodes, which can make a reasonable selection of paths in a timely and effective manner. Finally, simulations of collaborative transportation for suppliers were performed in three scenarios and the results verified the effectiveness of the collaborative transportation strategy. The proposed collaborative transportation strategy of suppliers not only strengthened the synergistic cooperation among suppliers, but also cultivated the potential customer for suppliers in this article. Furthermore, the strategy could improve the flexibility of the supply chain, maximise the overall efficiency and also provide a new solution for the development of logistics and transportation services.

Scenic Beauty Evaluation of Forests with Autumn-Colored Leaves from Aerial and Ground Perspectives: A Case Study in Qixia Mountain in Nanjing, China

In recent years, on-site visitation has been strictly restricted in many scenic areas due to the global spread of the COVID-19 pandemic. "Cloud tourism”, also called online travel, uses high-resolution photographs taken by unmanned aerial vehicles (UAVs) as the dominant data source and has attracted much attention. Due to the differences between ground and aerial observation perspectives, the landscape elements that affect the beauty of colored-leaved forests are quite different. In this paper, Qixia National Forest Park in Nanjing, China, was chosen as the case study area, and the best viewpoints were selected by combining tourists' preferred viewing routes with a field survey, followed by a scenic beauty evaluation (SBE) of the forests with autumn-colored leaves in 2021 from the aerial and ground perspectives. The results show that (1) the best viewpoints can be obtained through the spatial overlay of five landscape factors: elevation, surface runoff, slope, aspect, and distance from the road;(2) the dominant factors influencing the beauty of colored-leaved forests from the aerial perspective are terrain changes, forest coverage, landscape composition, landscape contrast, the condition of the human landscape, and recreation frequency;and (3) the beauty of the ground perspective of the colored-leaved forests is strongly influenced by the average diameter at breast height (DBH), the dominant color of the leaves, the ratio of the colored-leaved tree species, the canopy width, and the fallen leaf coverage. The research results can provide scientific reference for the creation of management measures for forests with autumn-colored leaves. © 2023 by the authors.

A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

IoT-based smart healthcare system allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the smart healthcare system generates enormous data which contains sensitive personal information. To protect patients’privacy, we propose a secure blockchain-assisted access control scheme for smart healthcare system in fog computing. All the operations of users are recorded on the blockchain by smart contract in order to ensure transparency and reliability of the system. We present a blockchain-assisted Multi-Authority Attribute-Based Encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the blockchain-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient. IEEE

Analysis of Malfunctions & Injuries Attributed to CT Scanners Before and After the Pandemic

Given the stresses placed on healthcare during the COVID-19 pandemic, and the critical role of computed tomography (CT) scanners in diagnosing cancers and other disorders, this project is designed to investigate the impact of the COVID-19 pandemic on reported malfunctions, injuries (and deaths) attributed to CT scans. Data were extracted from the Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. Yearly numbers of adverse event (AE) reports attributed to CT scanners including malfunctions, injuries, and deaths were recorded for the last 10 years (2012 to 2021). Monthly numbers of reports were also recorded for the 12 months immediately preceding the pandemic (2019/03 to 2020/03), as well as all the months after WHO declared COVID-19 a global pandemic (since 2020/03). It was found that the reported rates of injuries and malfunctions for CT scanners increased during the COVID-19 pandemic. The analysis also revealed unusual trends such as spikes in the malfunction rates from 2015 to 2018 compared to the preceding years, as well as in injuries and deaths. Manufacturers most responsible for these AE spikes included Philips, Superdimension, GE, Siemens, etc. The FDA Recall Database was further mined, and similar trends were identified in the yearly recalls over 2015- 2018, which correlated well with the malfunction rates (less apparent for injuries and deaths). While this project was originally centered around adverse pandemic-related effects on CT scanners, the important pre-pandemic findings warrant further research. These results might help prevent future AEs caused not only by CT scans but also by other medical devices. © 2022, Avestia Publishing. All rights reserved.

A User-POI-Guide Cost Optimization Method for Tourism Planning Considering Social Distance and User Preferences

MaaS (Mobility as a Service) itself has come into common use, and these developments have attracted keen interest from the industry in recent years. MaaS can be applied as a solution to deal with the current situation by considering the social distance. However, due to the time-share mechanism, personal assets are monopolized by specific users for a long time that cannot be shared with other users at the same time. Thus, the sharing economy companies in the tourism industry (e.g., Airbnb Experience and Huber) are in a dilemma of low productivity and high cost. In this research, we propose a new travel guide sharing service that considers the concept of social distance and user preferences. The user side only needs to select simple conditions such as travel time and the number of POIs (Point of Interest) that she/he plans to visit, meanwhile, the guide side simply inputs the POIs that she/he can guide. Furthermore, by analyzing these basic information, our proposed system can recommend the tour guides, scenic spots, and route planning to provide a real-time tour guide plan, which addressed the user's preferences and reduced the face-to-face communication to users in advance. To verify the effectiveness of our proposed method, we also ask 68 users to evaluate our system and analyze the results of questionnaires. © 2023 IEEE Computer Society. All rights reserved.

Impact of the COVID-19 on labour employment in rural China: Can information literacy mitigate the shock?

In the context of the COVID-19 pandemic, most work was done online, the role of information literacy in influencing rural labour employment is worth investigating. Using survey data from 3,225 rural labourers in China, this study builds Item Response Theory and Tobit models, and finds that the pandemic hinders rural labour employment;however, information literacy promotes it and can mitigate the negative impact of the pandemic. We provide evidence for the positive role of information literacy, and policymakers should consider improving the information literacy of the rural labourers. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

[Emphasis on Post-ICU Syndrome].

With the development of technology and medicine, the mortality rate of intensive care unit (ICU) has declined significantly, and more and more professionals in the medical field are also aware that the disability rate of ICU survivors remains high. More than 70% of ICU survivors have Post-ICU Syndrome (PICS), which is mainly manifested by cognitive, physical, and mental dysfunction, which seriously affects the quality of life of survivors and their caregivers. The COVID-19 pandemic has brought a series of problems such as shortage of medical staff, restricted family visits, and lack of personalized care, which have brought unprecedented challenges to the prevention of PICS and the care of patients with severe COVID-19. In the future, the treatment of ICU patients should change from reducing short-term mortality to improving long-term quality of life of patients, from disease-centered to health-centered, and to practice " the health promotion, the prevention, the diagnosis, the control, the treatment, and the rehabilitation " six-in-one concept to promote comprehensive health care with pulmonary rehabilitation.

CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses

There is an ever-urgent need for accessing real-time crowdedness and airflow information for indoor study spaces in universities, for example, to control COVID-19 transmission risk. Even before the pandemic, many students spent valuable time finding suitable study areas with proper lighting, low noise, and ample seating. This paper presents a pilot system, CampusX, which aims to provide students with useful real-time information about study spaces on campus. Our system collects and analyzes environmental data before presenting them to students as useful information. This helps them to select the most suitable study spaces. The main components of this system include a sensor platform, data collection and processing pipelines, networking, and an interactive web-application. © 2023 IEEE.