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.

Impact of the Skill Shortage on the Construction Supply Chain Performance in Australia

The purpose of this paper is to investigate how the skill shortage impacts the performance of the construction supply chain in Australia. The study has adopted a quantitative research method. The quantitative data were collected by conducting a survey of employees who work in construction companies in Australia. A regression analysis was used to analyze the data from 113 respondents. The findings of the study reveal that the construction sector in Australia has high labour costs, but workers are still thinking they are not getting paid enough and cannot invest more in themselves to improve their skills. There is a lack of academic and vocational training programs offered to them. Insufficient recruitment and incentive policies are also main barriers to attract talents to the construction industry in Australia. The situation became more serious during the COVID-19 period due to the lockdowns, lack of skilled migrants, and Government working visa policies. The study implies that firms should have a deeper understanding of the reasons for the skill shortage. Firms also need to devise strategies for hiring the right talent. Further, it was found that quality talent can come from the local or foreign markets. More effective selection criteria should be designed so that the best-fit approach can be implemented. © 2023 by the authors. Licensee C.E.J, Tehran, Iran.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods. © 2022 IEEE.

Effectiveness and Controversy of Convalescent Plasma Therapy for Coronavirus Disease 2019 Patients

Since the coronavirus disease 2019 (COVID-19) began to spread, it remains pandemic worldwide. The European Medicines Agency's human medicines committee and Food and Drug Administration have only granted a conditional marketing authorization for remdesivir to treat COVID-19. It is essential to apply other valuable treatments. Convalescent plasma (CP), donated by persons who have recovered from COVID-19, is the cellular component of blood that contains specific antibodies. Therefore, to determine the feasibility of CP for COVID-19, the effectiveness and controversy are discussed in depth here. It is suggested that CP plays a certain role in the treatment of COVID-19. As a treatment, it may have its own indications and contraindications, which need to be further discussed. Meanwhile, it is critical to establish a standard procedure for treatment from CP collection, preservation, transport, to transfusion, and conduct some large sample randomized controlled trials to confirm the transfusion dosage, appropriate time, frequency, and actively prevent adverse outcomes that may occur. © 2022 Journal of Bone and Joint Surgery Inc.. All rights reserved.

Federated Learning for Smart Healthcare: A Survey

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

Individual Surveillance Around Parked Aircraft at Nighttime: Thermal Infrared Vision-Based Human Action Recognition

The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action;a convolutional neural network-based module for spatial feature extraction;a triple-layer convolutional long short-term memory network for temporal feature extraction;and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods.

Recent advances in nanozymes for combating bacterial infection

Bacterial infection is a major threat to public health around the world. Currently, antibiotics remain the most extensive mode of medical treatment for bacterial infection. However, the overuse and misuse of antibiotics have exacerbated the emergence of antibiotic-resistant strains, especially during the COVID-19 pandemic. In addition, the improper and excessive use of biocides and disinfectants has a catastrophic impact on antibiotic management plans worldwide. Therefore, there is an urgent need for alternative antibacterial treatments to alleviate this crisis. In recent years, nanozymes have become promising new antibacterial agents because of their broad-spectrum antibacterial activity, less drug resistance, and high stability. This review focuses on the classification of nanozymes and research progress of nanozymes as antibacterial agents, as well as perspectives for future research in this field.

Effects of wearing personal protective equipment during COVID-19 pandemic on composition and diversity of skin bacteria and fungi of medical workers.

BACKGROUND: During the COVID-19 pandemic, wearing PPE can induce skin damage such as erythema, pruritus, erosion, and ulceration among others. Although the skin microbiome is considered important for skin health, the change of the skin microbiome after wearing PPE remains unknown. OBJECTIVE: The present study aimed to characterize the diversity and structure of bacterial and fungal flora on skin surfaces of healthcare workers wearing personal protective equipment (PPE) during the COVID-19 pandemic using metagenomic next-generation sequencing (mNGS). METHODS: A total of 10 Chinese volunteers were recruited and the microbiome of their face, hand, and back were analysed before and after wearing PPE. Moreover, VISIA was used to analyse skin features. RESULTS: Results of alpha bacterial diversity showed that there was statistically significant decrease in alpha diversity indice in the skin samples from face, hand, and three sites after wearing PPE as compared with the indice in the skin samples before wearing PPE. Further, the results of evaluated alpha fungal diversity show that there was a statistically significant decrease in alpha diversity indices in the skin samples from hand after wearing PPE as compared with the indices in the skin samples before wearing PPE (P < 0.05). Results of the current study found that the main bacteria on the face, hand, and back skin samples before wearing the PPE were Propionibacterium spp. (34.04%), Corynebacterium spp. (13.12%), and Staphylococcus spp. (38.07%). The main bacteria found on the skin samples after wearing the PPE were Staphylococcus spp. (31.23%), Xanthomonas spp. (26.21%), and Cutibacterium spp. (42.59%). The fungal community composition was similar in three skin sites before and after wearing PPE. CONCLUSION: It was evident that wearing PPE may affect the skin microbiota, especially bacteria. Therefore, it was evident that the symbiotic microbiota may reflect the skin health of medical workers during the COVID-19 pandemic.

Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing

COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes. IEEE

Eosinophilic esophagitis-our clinical experience with orodispersible tablet formulation budesonide (BOT)

Introduction: The prevalence of Eosinophilic Esophagitis (EoE) is increasing. Pharmacological options were limited and practitioners have relied on proton pump inhibitor (PPI) and swallowed 'topical' form of steroids (TS) with limited data on efficacy. The novel orodispersible budesonide tablet formulation (BOT) has been shown to be effective in patients with EoE and approved as the first licensed oral steroid therapy for EoE in the United Kingdom. We set out to assess and present our clinical experience with BOT, assessing duration of therapy and response rates. Aims & Methods: We performed a retrospective cohort analysis of patients (≥ 18 years old) diagnosed with EoE. Basic demographics, index symptoms, associated atopic conditions, pharmacotherapy (PPI, TS, and BOT) including duration and response to treatment was reviewed electronically. Response was based on clinical evaluation by specialist Gastroenterologists with interest in the upper gastrointestinal tract at follow up. Fisher's exact test was used for comparing treatment efficacy. Results: 40 cases of EoE were identified under regular follow up. Basic demographics, symptoms at index presentation and proportion of patients with associated atopic conditions are shown in table 1. 97.5% (n=39/40) received PPI at initial follow up. One patient was asymptomatic. In the treated group, 38.4% (n=15/39) required escalation to TS following diagnosis. 38.4% (n=15/39) was escalated to BOT either directly following PPI with or without prior TS therapy. Ten of these patients had their follow up assessment. 80% (n=8/10) of patients had a good response to BOT. 87.5% (n=7/8) required repeated courses (≥ 12 weeks). One responded after an initial six weeks course. Subgroup analysis within the BOT cohort showed no difference between response rates in patients who had prior TS therapy and those who went direct to BOT from PPI (83.3% vs 75%, p>0.05). Conclusion: Our data suggests a high clinical response rate in patients receiving BOT in line with the results of the eos-1 trial (85% remission rates following 12 weeks treatment)1. Optimal timing with regards to treatment escalation requires further evaluation but our data suggest that prior TS use is not associated with a difference in response rates with BOT. Prospective analysis with clinical-histological and endoscopic assessment is superior but currently challenging due to the COVID-19 pandemic. Clinical evaluation could be enhanced by using a standardised questionnaire for EoE but this will require further evaluation.

Repositioning Molecules of Chinese Medicine to Targets of SARS-Cov-2 by Deep Learning Method

Traditional Chinese medicine has been used to treat and prevent infectious diseases for thousands of years, and has accumulated a large number of effective prescriptions. Deep learning methods provide powerful applications in calculating interactions between drugs and targets. In this study, we try to use the method of deep learning to reposition molecules of Chinese medicines (CMs) and the targets of syndrome coronavirus 2 (SARS-CoV-2). A deep convolution neural network with residual module (DCNN-Res) is constructed and trained on KIBA dataset. The accuracy of predicting the binding affinity of drug-target pairs is 85.33%. By ranking binding affinity scores of 433 molecules in 35 CMs to 6 targets of SARS-Cov-2, DCNN-Res recommends 30 possible repositioning molecules. The consistency between our result and the latest research is 0.827. The molecules in Gancao and Huangqin have a strong binding affinity to targets of SARS-CoV-2, which is also consistent with the latest research.