Modelling for understanding of COVID-19 spread and design of an isolation room in a hospital

We have modelled the transmission of coronavirus 2019 (COVID-19) in the isolation room of a patient suffering from severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) at the Royal Brompton Hospital (RBH) in London. An adaptive mesh computational fluid dynamics (CFD) model was used for simulations of 3D spatial distribution of SARS CoV 2 in the room. The modelling set-up is based on data collected in the room during the patient stay. A number of numerical experiments have been carried out to provide an optimal design layout of the overall isolation room. Our focus has been on (1) the location of the air extractor and filtration rates; (2) the bed location of the patient; and (3) consideration of the health and safety of the staff working area

Prioritising actions for improving classroom air quality based on the analytic hierarchy process: case studies in China and the UK

The air quality in classrooms significantly impacts school children’s health and learning performance. It has been reported worldwide that classroom air quality does not meet the required standard and actions are pledged for improvement. However, it poses a challenge for decision-making in terms of prioritising taking-up measures. The aim of this study is to propose a method of identifying the action measures for improving classroom air quality and prioritising them. Case studies in the UK and China were conducted, and the key measures were identified through literature studies, open-ended questionnaire surveys, and workshop discussions, which are classified into three categories: B1, policy; B2, technology; and B3, information sharing. The analytical hierarchy process (AHP) is applied in the prioritisation of the action measures. A total of 138 teachers and parents from China and the UK participated in this case study. The genetic algorithm-optimised Hadamard product (GAOHP) method is applied to justify the consistency ratio (CR) within the required threshold value in order to ensure the consistency of the subjective perception and the accuracy of comparative weights. The results show that item B2, technology, is the most desired measure by both Chinese and British parents and teachers, despite the deviation from the optimal choice in China and the UK. Among the proposed action measures, the UK respondents strongly expected air purifiers with natural ventilation as opposed to their Chinese counterparts preferring to share the real-time status of classroom air quality. Our work will provide strong support for the subsequent selection of indoor air quality improvement strategies for schools

Numerical and Data-Driven Modelling in Coastal, Hydrological and Hydraulic Engineering

This special issue aims at exploring advanced numerical techniques for real-time prediction and optimal management in coastal and hydraulic engineering [...

Synergistic and protective effect of atorvastatin and amygdalin against histopathological and biochemical alterations in Sprague-Dawley rats with experimental endometriosis

Abstract The aim of the present study was to evaluate the protective effects of combined atorvastatin and amygdalin in a rat model of endometriosis. Tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), matrix metalloproteinase-2 (MMP-2) and MMP-9 levels in the peritoneal fluid were determined. The expression of TNF-α, IL-6, MMP-2, and MMP-9 mRNA, and the levels of lipid peroxidation, reduced glutathione (GSH), superoxide dismutase (SOD), catalase, and glutathione peroxidase (Gpx) were measured. Histopathological analysis was also conducted. The results showed that peritoneal TNF-α, IL-6, MMP-2, and MMP-9 levels were reduced by > 50%, and mRNA expression was decreased. Lipid peroxidation was considerably reduced, while GSH, SOD, Gpx, and catalase levels increased by > 40%. Reductions in leukocyte infiltration and fibrosis following treatment were also observed. Thus, our study suggested that combined treatment consisting of atorvastatin and amygdalin attenuates endometriosis. A detailed investigation of molecular mechanism of atorvastatin and amygdalin in endometriosis is needed

Sediment source analysis using the fingerprinting method in a small catchment of the Loess Plateau, China

Purpose This paper aims to use the composite fingerprinting method to reconstruct the environmental history after the Grain-for-Green Project and to provide effective sediment management and soil erosion-control strategies. Materials and methods This study used a composite fingerprinting method based on 45 geochemical properties and a mixing model to investigate sediment core changes in the sediment source in an agricultural catchment with little native vegetation. The samples consisted of 77 source samples (i.e., gully, grassland, forest, cropland, and fallow land) and five sediment cores. Genetic algorithm (GA) optimization has been recently used to find the best optimum source contribution to sediments. Results and discussion The results demonstrate that gully is the main sediment source in this catchment, constituting 34.7 %, followed by cropland (28.2 %), forest (21.5 %), grassland (12.7 %), and fallow land (2.9 %). However, the relative contribution of each source type was variable in all five sediment cores. The sediment that derived from grassland was relatively stable in the five cores. The relative contribution of forest was higher in the downstream portion of the check dam and lower in the upstream portion and gradually increased in the direction of the runoff pathway. As the forest matured, the sediment that derived from the forest gradually decreased. Changes in the hydro-ecological environment would lead to the leaf litter and understory being poorly developed and the soil being bare in the forest, making it more vulnerable to erosion. Conclusions Reforestation and fallow are the key ecological strategies for reducing soil erosion. However, at the beginning of the Grain-for-Green Project, the young forest contributed 21.5 % of the sediment, indicating that natural fallow may be a better-designed sediment management and soil erosion-control strategy.<br style=" font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; " /

Partial Least Squares Regression for Determining the Control Factors for Runoff and Suspended Sediment Yield during Rainfall Events

Multivariate statistics are commonly used to identify the factors that control the dynamics of runoff or sediment yields during hydrological processes. However, one issue with the use of conventional statistical methods to address relationships between variables and runoff or sediment yield is multicollinearity. The main objectives of this study were to apply a method for effectively identifying runoff and sediment control factors during hydrological processes and apply that method to a case study. The method combines the clustering approach and partial least squares regression (PLSR) models. The case study was conducted in a mountainous watershed in the Three Gorges Area. A total of 29 flood events in three hydrological years in areas with different land uses were obtained. In total, fourteen related variables were separated from hydrographs using the classical hydrograph separation method. Twenty-nine rainfall events were classified into two rainfall regimes (heavy Rainfall Regime I and moderate Rainfall Regime II) based on rainfall characteristics and K-means clustering. Four separate PLSR models were constructed to identify the main variables that control runoff and sediment yield for the two rainfall regimes. For Rainfall Regime I, the dominant first-order factors affecting the changes in sediment yield in our study were all of the four rainfall-related variables, flood peak discharge, maximum flood suspended sediment concentration, runoff, and the percentages of forest and farmland. For Rainfall Regime II, antecedent condition-related variables have more effects on both runoff and sediment yield than in Rainfall Regime I. The results suggest that the different control factors of the two rainfall regimes are determined by the rainfall characteristics and thus different runoff mechanisms

A quantitative evaluation model of outdoor dynamic thermal comfort and adaptation: a year-long longitudinal field study

The understanding of human outdoor thermal comfort demand and thermal adaptation contributes to sustainable urban design as well as city resilience in the context of human health and wellbeing. Humans' past thermal experience influence their outdoor thermal comfort. However, the quantitative relationship between the past thermal experience and outdoor thermal comfort is still not clear. This study aims to reveal quantitative relations of the impact of people's past thermal experience on adaptive thermal comfort and to develop a new outdoor adaptive thermal comfort model. A year-long longitudinal questionnaire survey along with a combination of outdoor thermal environment campaigns was carried out in Chongqing, China. It began on August 15, 2020, and finished on August 19, 2021. Through the analysis of 2240 valid responses to the questionnaire survey, the outdoor thermal adaptation characteristic and dynamic thermal comfort evaluation of the respondents were revealed. The results show that the quantified temperature of past outdoor thermal experience is the quadratic correlation with the thermal sensitivity coefficient and deviation constant, and the linear correlated with outdoor thermal demands. Based on the quantitative analysis, a new outdoor adaptive thermal comfort model has been developed as a function of the exponentially weighted sum of historical mean air temperature series (MeanTrm). The outdoor adaptive thermal comfort zones by 80% and 90% satisfactions thereby have been first drawn based on the Universal Thermal Climate Index (UTCI). The study developed a methodology for the evaluation of dynamic outdoor thermal comfort which can be used for different climate regions

A Hybrid Data‐Driven and Data Assimilation Method for Spatiotemporal Forecasting: PM2.5 Forecasting in China

Abstract Spatiotemporal forecasting involves generating temporal forecasts for system state variables across spatial regions. Data‐driven methods such as Convolutional Long Short‐Term Memory (ConvLSTM) are effective in capturing both spatial and temporal correlations, but they suffer from error accumulation and accuracy loss as forecasting time increases due to the nonlinearity and uncertainty in physical processes. To address this issue, we propose to combine data‐driven and data assimilation (DA) methods for spatiotemporal forecasting. The accuracy of the data‐driven ConvLSTM model can be improved by periodically assimilating real‐time observations using the ensemble Kalman filter (EnKF) approach. This proposed hybrid ConvLSTM‐EnKF method is demonstrated through PM2.5 forecasting in China, which is a challenging task due to the complexity of topographical and meteorological conditions in the region, the need for high‐resolution forecasting over a large study area, and the scarcity of observations. The results show that the ConvLSTM‐EnKF method outperforms conventional methods and can provide satisfactory operational PM2.5 forecasts for up to 1 month with spatially averaged RMSE below 20 μg/m3 and correlation coefficient (R) above 0.8. In addition, the ConvLSTM‐EnKF method shows a substantial reduction in CPU time when compared to the commonly used NAQPMS‐EnKF method, up to three orders of magnitude. Overall, the use of data‐driven models provides efficient forecasts and speeds up DA. This hybrid ConvLSTM‐EnKF is a novel operational forecasting technique for spatiotemporal forecasting and is used in real spatiotemporal forecasting for the first time

Simultaneous Determination of Six Active Components in Danggui Kushen Pills via Quantitative Analysis of Multicomponents by Single Marker

In this paper, a valid evaluation method for the quality control of Danggui Kushen pills (DKP) has been established based on quantitative analysis of multicomponents by single marker (QAMS). Gallic acid, matrine, oxymatrine, catechin, ferulic acid, and rutin were selected as the indexes for quality evaluation of DKP. The analysis was achieved on an Agilent ZORBAX SB-C18 column (250  mm × 4.6  mm, 5 μm) via gradient elution. Gallic acid was used as internal standard to determine the relative correction factors (RCF) between gallic acid and other five constituents in DKP. The contents of those components were calculated at the same time. The accuracy of QAMS method was verified by comparing the contents of six components calculated by external standard (ES) method with those of the QAMS method. It turned out that there was no significant difference between the quantitative results of QAMS method and external standard method. The proposed QAMS method was proved to be accurate and feasible according to methodological experiments, which provided an accurate, efficient, and economical approach for quality evaluation of DKP