Toward Immune Buildings: Lessons Learned from the COVID-19 Pandemic and Its Aftermath

The COVID-19 (SARS-CoV-2) pandemic has not yet ended. The COVID-19 pandemic has been the worst human health crisis after the Second World War [1]. Over 90% of SARS-CoV-2 cross-infections occur in confined spaces (e.g., buildings and transportation cabins), which reveals that confined environments and their associated air systems are vulnerable to infectious disease transmission. Evidence of SARS-CoV-2 indoor transmission has been documented, such as in residential buildings [2], hospitals [3], restaurants [4], offices [5], airport terminals [6], aircraft cabins [7], high-speed trains [8], buses [9], and ships [10], etc. Vaccination has played a crucial role in dampening the SARS-CoV-2 risk; however, it still may not be able to terminate the pandemic in the near future due to the possibility of continuous virus variation. In addition to improving the vaccination rate and vaccine effectiveness, strategies that can reduce the human exposure to various infectious pathogens are pressingly needed, both during and after the pandemic. The experience obtained and lessons learned from one event will always provide great insights into the handling of similar ones in the future. &nbsp;</p

Spreading of cfr-Carrying Plasmids among Staphylococci from Humans and Animals

The multidrug resistance gene cfr mediates resistance to multiple antimicrobial agents, including linezolid. Plasmids are the preferred vector for the dissemination of cfr. However, the presence and transmission of cfr-carrying plasmids among staphylococci from humans and animals have rarely been studied. Here, we investigated the presence of the cfr gene in 2,250 staphylococci of human clinical origin collected in Zhejiang, China, in 1998 to 2021 and in 3,329 porcine staphylococci preserved in our laboratories. The cfr gene was detected in 38 human isolates; its presence in Staphylococcus haemolyticus and Staphylococcus cohnii in 2003 was earlier than that identified in 2005, and Staphylococcus capitis (n = 30) was the predominant species. The cfr-carrying fragment in 38 isolates exhibited >99% nucleotide sequence similarity to plasmid pLRSA417 (39,504 bp), which was identified in 2015 and originated from a human clinical methicillin-resistant Staphylococcus aureus isolate from Zhejiang, China. The cfr-carrying plasmids in 18 MinION-sequenced staphylococci ranged in size from 32,697 bp to 43,457 bp. Fifteen plasmids were identical to pLRSA417, except for the inversion of an 8.4-kb segment comprising IS256-aacA/aphD-ISEnfa4_1-cfr-ISEnfa4_2, while the remaining 3 plasmids exhibited slightly different structures. Among the 114 cfr-positive staphylococci from pigs, pLRSA417-like plasmids were detected in 3 isolates. Intraspecies and interspecies conjugation occurred in human-derived pLRSA417-like plasmids. The presence of pLRSA417-like plasmids in staphylococci from multiple geographic regions and different hosts implied the possible transmission of the respective isolates between humans and animals

Shear-Wave Velocity Prediction Method via a Gate Recurrent Unit Fusion Network Based on the Spatiotemporal Attention Mechanism

AbstractCompression-wave velocity and shear-wave velocity are important elastic parameters describing deeply tight sandstone. Limited by cost and technical reasons, the conventional logging data generally lack shear-wave velocity. In addition, the existing rock physics theory is difficult to accurately establish the rock physics models due to the complex pore structure of tight sandstone reservoir. With the rapid development of the artificial intelligence, the attention mechanism that can increase the sensitivity of the network to important characteristics has been widely used in machine translation, image processing, and other fields, but it is rarely used to predict shear-wave velocity. Based on the correlation between the shear-wave velocity and the conventional logging data in the spatiotemporal direction, a gate recurrent unit (GRU) fusion network based on the spatiotemporal attention mechanism (STAGRU) is proposed. Compared with the convolutional neural network (CNN) and gate recurrent unit (GRU), the network proposed can improve the sensitivity of the network to important spatiotemporal characteristics using the spatiotemporal attention mechanism. It is analyzed that the relationship between the spatiotemporal characteristics of the conventional logging data and the attention weights of the network proposed to verify the rationality of adding the spatiotemporal attention mechanism. Finally, the training and testing results of the STAGRU, CNN, and GRU networks show that the prediction accuracy and generalization of the network proposed are better than those of the other two networks

Integrated inverse design of ventilation for an aircraft cabin

Cabin ventilation is crucial for maintaining thermal comfort and air quality for passengers and crew. The genetic algorithm, proper orthogonal decomposition (POD), and adjoint method have been proposed to inversely design the cabin ventilation. However, each method has its cons and pros. This paper proposed to integrate the above three methods in cascades. The genetic algorithm was applied first in the first stage to roughly circumscribe the ranges of design parameters. Then POD was applied in the next stage to further narrow the ranges and estimate the optimal parametric sets for each design criterion. The estimated optimal design from POD was supplied to the adjoint method for fine tuning. The air-supply parameters of a five-row aircraft cabin were inversely designed to achieve the minimum absolute value of the predicted mean vote (PMV) and the minimum averaged mean age of air. The results showed that the integrated method was able to improve the design stage by stage. The integrated method has superior advantages to assure the optimal design while minimizing the computing expense

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Toward Immune Buildings: Lessons Learned from the COVID-19 Pandemic and Its Aftermath

The COVID-19 (SARS-CoV-2) pandemic has not yet ended [...