Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin's central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin's development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.

Characterization and Source Apportionment of PM in Handan—A Case Study during the COVID-19

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10;the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. © 2023 by the authors.

Delivering Monoclonal Antibody Infusions to Novel Outpatient Settings

In November 2020, monoclonal antibody infusions became the first available treatment for outpatients with Covid-19. The logistics of administering the drug, however, necessitated novel approaches to health care delivery to maximize the effectiveness in Geisinger's patient community. To overcome these challenges, Geisinger quickly set up a process to identify the patients at highest risk and to proactively reach out to them for treatment scheduling. For most patients, an ambulatory clinic was the appropriate setting for infusions. For patients living in a skilled nursing facility or a residential facility for the developmentally disabled, Geisinger deployed mobile units to deliver care treatment to them. Additionally, to serve imprisoned patients, the health system arranged for secure access to select ambulatory clinics at designated times. Using this agile approach, nearly 3,000 patients have been treated by Geisinger since monoclonal antibody treatments were first granted Emergency Use Authorization by the FDA. In this article, the authors describe how Geisinger designed and executed this innovative approach to care delivery. © Massachusetts Medical Society.

Digital twin for healthy indoor environment: A vision for the post-pandemic era

Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

Effective multi-step ahead container throughput forecasting under the complex context

Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making. © 2023 John Wiley & Sons Ltd.

Improving Sentiment Prediction of Textual Tweets Using Feature Fusion and Deep Machine Ensemble Model

Widespread fear and panic has emerged about COVID-19 on social media platforms which are often supported by falsified and altered content. This mass hysteria creates public anxiety due to misinformation, misunderstandings, and ignorance of the impact of COVID-19. To assist health professionals in addressing this epidemic more appropriately at the onset, sentiment analysis can potentially help the authorities for devising appropriate strategies. This study analyzes tweets related to COVID-19 using a machine learning approach and offers a high-accuracy solution. Experiments are performed involving different machine and deep learning models along with various features such as Word2vec, term-frequency, term-frequency document frequency, and feature fusion of both feature-generating approaches. The proposed approach combines the extra tree classifier and convolutional neural network and uses feature fusion to achieve the highest accuracy score of 99%. The proposed approach obtains far better results than existing sentiment analysis approaches. © 2023 by the authors.

Neurological complications of COVID-19.

Corona Virus Disease 2019 (COVID-19) has caused several pandemic peaks worldwide due to its high variability and infectiousness, and COVID-19 has become a long-standing global public health problem. There is growing evidence that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) frequently causes multi-organ injuries and more severe neurological manifestations. Therefore, increased awareness of possible neurological complications is beneficial in preventing and mitigating the impact of long-term sequelae and improving the prognostic outcome of critically ill patients with COVID-19. Here, we review the main pathways of SARS-CoV-2 neuroinvasion and the potential mechanisms causing neurological damage. We also discuss in detail neurological complications, aiming to provide cutting-edge basis for subsequent related basic research and clinical studies of diagnosis and treatment.

Application of low dose radiation therapy in the treatment of COVID-19 pneumonia.

With the global pandemic of COVID-19, cytokine storms in critical patients with pneumonia is really a problem and need to be solved immediately.Low dose radiation therapy (LDRT) has been temporarily used to treat pneumonia.In the past decades, researchers were dedicated to clarify the biological mechanism of LDRT.LDRT plays a unique role in the suppression of inflammation, preliminary outcomes have been acquired in critical patients with COVID-19 pneumonia, and radiotherapy community is paying attention to this treatment strategy.This review summarizes the application of LDRT in pneumonia, its biological mechanism, the result of LDRT in COVID-19 pneumonia, the existing problems and prospective in clinic.Copyright © 2021 Chinese Medical Association

Research progress in high value-added utilization technology of waste plastics

The low degradability of waste plastics will continue to pollute the environment, and the spread of the COVID-19 has exacerbated the use and accumulation of plastics, and thus the efficient treatment of waste plastic resources has become an urgent technical problem to be solved. By analyzing several mainstream waste plastics treatment technologies, it was clear that resourceful and high value-added utilization technology was the most competitive and environmentally friendly waste plastics treatment route in the market. The research progress of high value-added utilization technology of waste plastics at home and abroad in recent years were reviewed. The development and variation of conventional thermal cracking technology were discussed. Through this route, the highest yield of waste plastics into fuel products can reach 97%—98%. It was pointed out that the conversion of waste plastics into jet fuel, high value-added chemicals and functional materials for special applications through chemical, catalytic and biological technologies was the mainstream research direction and development trend in this field. Among them, the yield of conversion to high value-added monomer could reach more than 97%, so as to realize the upgrading of plastic waste from the primary treatment stage of "waste clearance” to "turning waste into use” and "turning waste into treasure”, and help China achieve the goal of "double carbon”。. © 2023 Chemical Industry Press. All rights reserved.

A method of mechanism analysis about a traditional chinese medicine prescription on a disease based on PageRank algorithm and network pharmacology

Network pharmacology is a method to study the mechanism of a Traditional Chinese Medicine (TCM) prescription on a disease. However, most articles using network pharmacology to study the mechanism did not combine the weight information of herbs, the weight information of targets of disease, and the interaction information between targets together. We propose a method, network pharmacology combined with two iterations of PageRank algorithm, to make use of these information. It takes prescription-disease system as a whole, calculates PageRank score of targets in the prescription-disease system, which means an importance in the system, and the score is used to rank the analysis results of GO and KEGG pathway which help us to analyze the mechanism of a prescription on a disease. At last, we use two prescription-disease pairs which have been proved effectiveness in clinical trials: Qingfei Paidu Decoction on COVID-19, and FuFang DanShen Diwan on Coronary Heart Disease, and find that the results of our method are consistent with some results of clinical trials.Copyright © 2021

A grid management system for COVID-19 antigen detection based on image recognition

Objective: To develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by "health information technology" and "neural network image recognition", so as to give full play to the advantages of "grid management". This system is applied to the normalized prevention and control of COVID-19 epidemic. Method(s): The model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. Result(s): The proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. Conclusion(s): Antigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.Copyright © 2023 The Authors

Integration of a hybrid scan approach and in-house high-resolution MS2 spectral database for charactering the multicomponents of Xuebijing Injection

Xuebijing (XBJ) Injection is a reputable patent Chinese medicine widely used to cure sepsis, among the Chinese ″Three Medicines and Three Prescriptions″ solution to fight against COVID-19. We were aimed to achieve the comprehensive multicomponent characterization from the single drugs to traditional Chinese medicine (TCM) formula, by integrating powerful data acquisition and the in-house MS2 spectral database searching. By ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS), a hybrid scan approach (HDMSE-HDDDA) was developed, while the HDMSE data for five component drugs and 56 reference compounds were acquired and processed to establish an in-house MS2 spectral database of XBJ. Good resolution of the XBJ components was accomplished on a Zorbax Eclipse Plus C18 column within 24 min, while a fit-for-purpose HDMSE-HDDDA approach was elaborated in two ionization modes for enhanced MS2 data acquisition. XBJ MS2 spectral library was thus established on the UNIFITM platform involving rich structure-related information for the chemicals from five component drugs. We could identify or tentatively characterize 294 components from XBJ, involving 81 flavonoids, 51 terpenoids, 42 phthalides, 40 organic acids, 13 phenylpropanoids, seven phenanthrenequinones, six alkaloids, and 54 others. In contrast to the application of conventional MS1 library, this newly established strategy could demonstrate superiority in the accuracy of identification results and the characterization of isomers, due to the more restricted filtering/matching criteria. Conclusively, the integration of the HDMSE-HDDDA hybrid scan approach and the in-house MS2 spectral database can favor the efficient and more reliable multicomponent characterization from single drugs to the TCM formula. © 2022 The Author(s)

Key Generation with Ambient Audio

Digital Contact Tracing (DCT) has been proposed to limit the spread of COVID-19, allowing for targeted quarantine of close contacts. The protocol is designed to be lightweight, broad-casting limited-time tokens over Bluetooth Low Energy (BLE) beacons, allowing receivers to record contacts pseudonymously. However, currently proposed protocols have vulnerabilities that permit an adversary to perform massive surveillance or cause significant numbers of false-positive alerts. In this paper, we present AcousticMask, which encrypts broadcast messages using a key derived from the audio signal present at each device with sufficient security levels. Our results show that a receiver sharing the same social space as a sender will hear all of the sender's ephemeral IDs (EphIDs) with Hamming distance at most 3, which can be decrypted at the rate of 10 Hz on a Raspberry Pi 4, while achieving a security factor of over 2108against attackers in our testing set, showing AcousticMask is lightweight for DCT and provides sufficient security levels to protect user's privacy. © 2022 IEEE.

Identification of shared genes and possible therapeutic target between systemic lupus erythematosus and COVID-19

Background/purpose: Coronavirus disease 2019 (COVID-19) has led to a rapid increase in mortality worldwide. Systemic lupus erythematosus (SLE) was a high-risk factor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) infection, Whereas the molecular mechanisms underlying SLE and CVOID-19 are not well understood. This study aims to discover the common molecular mechanisms and genetic biomarkers of SLE and COVID-19, providing new ideas for the treatment of COVID-19. Method(s): RNA sequencing data of peripheral blood mononuclear cells (PBMC) from 6 SLE datasets and 8 COVID-19 datasets were obtained from the GEO database. Highly related modular genes associated with COVID-19 and SLE were identified by weighted gene co-expression network analysis (WGCNA). The differentially expressed genes (DEGs) between patients and healthy controls (HCs) were identified by the limma package. Common shared DEGs from COVID-19 and SLE were identified. Cytoscape and MCODE plugin were utilized for exploring the protein-protein interaction network (PPI) and identifying shared hub genes. Potential biological functions and pathways were also explored from the common DEGs. For better analysis of detailed biological mechanisms, both xCell algorithm and the cMap in CLUE (https://clue.io/) were utilized for discovering immune cell infiltration and predicting potential drugs that negatively regulate the highly expressed genes. Result(s): With identified 498 up-regulated common DEGs in SLE and COVID-19 related genes, total 11 and 13 gene modules of SLE and COVID-19 were identified espectively After overlapping differential genes, the final intersection gene set contains 218 genes. The PPI, especially the functional subnet module consists of upregulated genes by MCODE showed a great deal IFN related genes involved in the regulation of immunity. GO biological processes also showed possible functions were defense response to virus and mitotic cell cycle. Moreover, changes of most immune cells were strongly consistent between SLE and COVID-19. CDK inhibitors identified may be more likely to inhibit two diseases. Conclusion(s): Our study examined in detail the common molecular mechanisms of SLE and COVID-19, in which cellular response to cytokine stimulus, like regulating IFN, which might be the key target of both diseases. CDK is associated with the progression of SLE and COVID-19, which may be the potential therapeutic drug for SLE patients with COVID-19 infection.

A Dynamic Access Control Model Based on Attributes and Intro VAE

Affected by the COVID-19 pandemic, teleworking is becoming more popular, with the exposed attack surface of the internal network expanding. Once outsiders personate accounts or insiders conduct illegal operations, the data security in teleworking with traditional border protection will be broken. Therefore, it is necessary to implement fine-grained and dynamic access control to protect data from malicious access. Attribute-based access control (ABAC) is ideal, where authorization is performed through attributes and rules. On this basis, risk assessment, context awareness, and machine learning are supplemented for dynamic access control. However, these methods have their limitations due to the requirement of sufficient prior knowledge and massive label-classified data. Moreover, it is challenging to obtain the samples of attack behaviors, and the attack behaviors may change frequently to evade detection. In contrast, the normal behaviors are relatively stable except for the update of network services. We propose a dynamic access control model, ABAC-IntroVAE, to address the above issues. ABAC-IntroVAE judges users' requests through rule matching and behavior analysis based on the attributes of the requests. It first filters out requests against the rules by rule matching. Then, the introspective variational autoencoder (IntroVAE) is used for behavior analysis to realize dynamic access decisions. Requests classified as normal can be authorized for access. ABAC-IntroVAE only needs samples of normal requests for training, avoiding the difficult task of collecting massive and frequently changing samples of attack requests. Meanwhile, the IntroVAE model is updated through continual learning to adapt to new-style normal behaviors due to the update of network services. Our experiment study suggests that our proposed ABAC-IntroVAE can effectively perform dynamic access control. It achieves an accuracy of 97.2% in abnormal detection and maintains an accuracy of over 97% through continual learning, despite the addition of new-style user behavior patterns. © 2022 IEEE.

Molecular mechanisms underlying regulatory T cell deficiency and potential drugs in COVID-19

Background/Purpose: The 2019 outbreak of coronavirus disease COVID-19 causes immune system disruption. Recent studies reported that the decrease or depletion of regulatory T cell (Treg) may be responsible for overstimulation of the immune system and lung damage in patients with severe COVID-19. This study aims to find the molecular mechanisms and genetic biomarkers associated with Tregs in COVID-19, providing new ideas for the treatment of COVID-19. Method(s): RNA sequencing data of peripheral blood mononuclear cells (PBMC) from 252 COVID-19 infected patients and 69 healthy controls (HC) were obtained from the GEO database. The Tregs composition of COVID-19 samples was quantified using the CIBERSORT deconvolution method. The differential genes (DEGs) were identified by the limma R package. Gene co-expression network analysis (WGCNA) was used to identify the gene. Differentially expressed Tregs-related genes (DETregRGs) were obtained by intersecting DEGs with the highly related modular genes obtained in the previous step. The potential biological functions and pathways of DETregRGs were then explored. Protein-protein interaction (PPI) networks were subsequently constructed to identify hub genes. In addition, the prediction of small molecule drugs for the potential treatment of COVID-19 was made using the CMap database. Result(s): After the weighted gene co-expression network analysis (WGCNA), the turquoise module was highly correlated with Treg expression and a total of 134 DEGs was identified as DETregRGs. These genes were mainly involved in GO biological processes, such as the inflammatory response, and T cell differentiation of thymus. Then, 11 hub genes (including RPS12, RPL21, RPS3A, CD8B, CD3D, TRAT1, RPS6, CD3E, CD28, RPL3, and CD4) were ranked based on Molecular Complex Detection (MCODE) analysis. The TregRG score of COVID-19 patients showed significantly lower than HC, calculated by the 'singscore' algorithms. After the signature query of the CMap database, the KU-0063794, an mTOR inhibitor ranked second in the negative enrichment score, may restore immune system dysregulation caused by increased Th17 differentiation and decreased Treg differentiation during SARS-CoV- 2 infection. Conclusion(s): Our study examined in detail the molecular mechanisms underlying the inadequacy of Tregs in patients with COVID-19 infection. mTOR inhibitors may improve COVID-19 symptoms by expanding Tregs which may be one of the potential therapeutic methods that need further investigation. (Figure Presented).