Modeling COVID-19 spread using multi-agent simulation with small-world network approach.

BACKGROUND: The rapid global spread of COVID-19 has seriously impacted people's daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems. METHODS: We propose a spatiotemporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents' social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved. RESULTS: Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating 1 hundred times. Additionally, the model simulates and compares different "city closure" scenarios. The results showed that implementing a 'lockdown' 10 days earlier would lead to the peak number of infections occurring 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. DISCUSSION: Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction.

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R2 is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM2.5 predictions. In addition, it evaluates different machine learning approaches in PM2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.

The regulatory roles of DDIT4 in TDCIPP-induced autophagy and apoptosis in PC12 cells.

Tris (1,3-dichloro-2-propyl) phosphate (TDCIPP) is a commonly used organophosphate-based flame retardant and can bio-accumulate in human tissues and organs. As its structure is similar to that of neurotoxic organophosphate pesticides, the neurotoxicity of TDCIPP has raised widespread concerns. TDCIPP can increase neuronal apoptosis and induce autophagy. However, its regulatory mechanism remains unclear. In this study, we found that the expression upregulation of the DNA Damage-Inducible Transcript 4 (DDIT4) protein, which might play essential roles in TDCIPP-induced neuronal autophagy and apoptosis, was observed in TDCIPP-treated differentiated rat PC12 cells. Furthermore, we determined the protective effect of the DDIT4 suppression on the autophagy and apoptosis induced by TDCIPP using Western blot (WB) and Flow cytometry (FACS) analysis. We observed that TDCIPP treatment increased the DDIT4, the autophagy marker Beclin-1, and the microtubule-associated protein light chain 3-II (LC3II) expressions and decreased the mTOR phosphorylation levels. Conversely, the suppression of DDIT4 expression increased the p-mTOR expression and decreased cell autophagy and apoptosis. Collectively, our results revealed the function of DDIT4 in cell death mechanisms triggered by TDCIPP through the mTOR signaling axis in differentiated PC12 cells. Thus, this study provided vital evidence necessary to explain the mechanism of TDCIPP-induced neurotoxicity in differentiated PC12 cells.

Probing the electronic and ionic transport in topologically distinct redox-active metal-organic frameworks in aqueous electrolytes.

Three topologically distinct zirconium-based metal-organic frameworks (Zr-MOFs) constructed from redox-innocent linkers, MOF-808, defective UiO-66, and CAU-24, are synthesized, and the spatially dispersed redox-active manganese sites are post-synthetically immobilized on the hexa-zirconium nodes of these Zr-MOFs. The crystallinity, morphology, porosity, manganese loading, and bulk electrical conductivity of each material are studied. The redox-hopping-based electrochemical reaction between the installed Mn(III) and Mn(IV) occurring within the thin films of these MOFs in aqueous electrolytes is investigated, in the presence of various concentrations of Na2SO4 in the electrolytes. Cyclic voltammetry is used to qualitatively study the redox-hopping process, and chronoamperometry is used to quantify the electrochemically active fractions of manganese sites within the MOF thin film as well as the values of apparent diffusivity for the redox-hopping process. By adjusting the concentration of Na2SO4 in the electrolyte, the rate-determining step for the redox-hopping process can be tuned from ionic transport to electronic transport, and the Mn-decorated MOF-808, which possesses the largest pore size, can achieve the highest value of apparent diffusivity. Findings here shed light on the selection of Zr-MOF as well as the choice of electrolyte concentration for the applications of MOFs in supercapacitors and electrocatalysis relying on such redox-hopping processes in aqueous electrolytes.

Photochemical Vapor Generation for Colorimetric Speciation of Inorganic Selenium.

Gold nanoparticles (AuNPs) are widely used as optical probes in colorimetric detection, thanks to their high molar extinction coefficient. However, sample matrixes of high salinity or strong acidity/alkalinity often break the electrostatic repulsion of AuNPs suspension, or/and the surface functionality of AuNPs, causing strong and unfavorable interferences. Photochemical vapor generation (PVG) is an efficient technique for the sample matrix separation. Besides, it possesses distinct features of green reducing reagent, reduced interferences from concomitant elements, and direct speciation by the assistance of photocatalyst. Herein, we developed a photochemical vapor generation (PVG) method for the green and direct speciation analysis of inorganic selenium (i.e., Se(IV) and Se(VI)), by colorimetric or visual monitoring of unmodified AuNPs. The generated Se species from PVG were directed into the AuNPs solution for a reaction to take place, which produced a specific new absorption band at 600 nm for detection. The experimental parameters, including the concentration of organic acid, the sample flow rate, the concentration of AuNPs, and the flow rate of carries gas, were optimized in detail. Under optimized conditions, the limits of detection (LOD) for Se(IV) and Se(VI) were 0.007 and 0.006 µg mL-1 by UV-vis detection, respectively. It is worth mentioning that 0.08 µg mL-1 Se can induce an obvious color change, which can be directly observed with the naked eye. Relative standard deviations (RSDs) of 4.5% and 4.3% were obtained from seven replicate measurements of 0.15 µg mL-1 Se(IV) and Se(VI) standard solution, respectively. The developed assay has been successfully applied for the speciation of Se in a dietary supplement sample and environmental water samples including lake water, seawater, simulated water reference materials, and tap water.

Enhanced Photochemical Vapor Generation for the Determination of Bismuth by Inductively Coupled Plasma Mass Spectrometry.

An enhanced photochemical vapor generation (PVG) sample introduction procedure is developed for the determination of trace Bi with inductively coupled plasma mass spectrometry (ICP MS) by the addition of iron. Gas chromatography mass spectrometry (GC-MS) reveals that (CH3)3Bi is the major component of the volatile Bi species formed in the presence of 20% (v/v) acetic acid, 5% (v/v) formic acid, and 60 µg mL-1 Fe3+ under UV irradiation. The addition of Fe3+ not only largely increases the PVG efficiency of Bi3+ but also accelerates the reaction kinetics of photochemical reduction of Bi3+. The analytical sensitivity was enhanced 30-fold using PVG for sample introduction compared to that for direct solution nebulization detection by ICP MS detection. Furthermore, the proposed method shows much better tolerance of interference from Cu2+ and Ni2+ than that from conventional hydride generation (HG). Under the optimized conditions, a detection limit of 0.3 ng L-1 was obtained for Bi by ICP MS determination. The relative standard deviation (RSD) was 2.5% for seven replicate measurements of 0.5 ng mL-1 Bi3+ standard solution. The proposed method has been successfully applied for the determination of Bi in environmental samples, including water samples, and certified reference material of soil (GSS-1) and sediments (GSD-5a and GSD-10) with satisfying results.

Two-Dimensional Metal-Organic Frameworks/Epoxy Composite Coatings with Superior O2/H2O Resistance for Anticorrosion Applications.

Corrosion protection technology plays a crucial role in preserving infrastructure, ensuring safety and reliability, and promoting long-term sustainability. In this study, we combined experiments and various analyses to investigate the mechanism of corrosion occurring on the epoxy-based anticorrosive coating containing the additive of two-dimensional (2D) and water-stable zirconium-based metal-organic frameworks (Zr-MOFs). By using benzoic acid as the modulator for the growth of the MOF, a 2D MOF constructed from hexazirconium clusters and BTB linkers (BTB = 1,3,5-tri(4-carboxyphenyl)benzene) with coordinated benzoate (BA-ZrBTB) can be synthesized. By coating the BA-ZrBTB/epoxy composite film (BA-ZrBTB/EP) on the surface of cold-rolled steel (CRS), we found the lowest coating roughness (RMS) of BA-ZrBTB/EP is 2.83 nm with the highest water contact angle as 99.8°, which represents the hydrophobic coating surface. Notably, the corrosion rate of the BA-ZrBTB/EP coating is 2.28 × 10-3 mpy, which is 4 orders of magnitude lower than that of the CRS substrate. Moreover, the energy barrier for oxygen diffusion through BA-ZrBTB/EP coating is larger than that for epoxy coating (EP), indicating improved oxygen resistance for adding 2D Zr-MOFs as the additive. These results underscore the high efficiency and potential of BA-ZrBTB as a highly promising agent for corrosion prevention in various commercial applications. Furthermore, this study represents the first instance of applying 2D Zr-MOF materials in anticorrosion applications, opening up new possibilities for advanced corrosion-resistant coatings.

Assessing Mechanical Properties and Response of Expansive Soft Rock in Tunnel Excavation: A Numerical Simulation Study.

This study investigates the dynamics of moisture absorption and swelling in soft rock during tunnel excavation, emphasizing the response to support resistance. Utilizing COMSOL numerical simulations, we conduct a comparative analysis of various strength criteria and non-associated flow rules. The results demonstrate that the Mohr-Coulomb criterion combined with the Drucker-Prager model under compressive loads imposes stricter limitations on water absorption and expansion than when paired with the Drucker-Prager model under tensile loads. Restricted rock expansion leads to decreased horizontal displacement and ground uplift, increased displacement in the tunnel's bottom arch, and significantly reduced displacement in the top arch. The study also considers the effects of shear dilation, burial depth, and support resistance on the stress and displacement of the surrounding rock. Increased shear dilation angles correlate with greater rock expansion, resulting in increased horizontal displacement and ground uplift. The research study concludes that support resistance is critical in limiting the movement of the tunnel's bottom arch and impacting the stability of the surrounding rock. Additionally, the extent of rock damage during the excavation of expansive soft rock tunnels is found to be minimal. Overall, this study provides valuable insights into the processes of soft rock tunnel excavation and contributes to the development of more efficient support systems.

Two-dimensional metal-organic framework for post-synthetic immobilization of graphene quantum dots for photoluminescent sensing.

Immobilization of graphene quantum dots (GQDs) on a solid support is crucial to prevent GQDs from aggregation in the form of solid powder and facilitate the separation and recycling of GQDs after use. Herein, spatially dispersed GQDs are post-synthetically coordinated within a two-dimensional (2D) and water-stable zirconium-based metal-organic framework (MOF). Unlike pristine GQDs, the obtained GQDs immobilized on 2D MOF sheets show photoluminescence in both suspension and dry powder. Chemical and photoluminescent stabilities of MOF-immobilized GQDs in water are investigated, and the use of immobilized GQDs in the photoluminescent detection of copper ions is demonstrated. Findings here shed the light on the use of 2D MOFs as a platform to further immobilize GQDs with various sizes and distinct chemical functionalities for a range of applications.

Immobilization of europium and terbium ions with tunable ratios on a dispersible two-dimensional metal-organic framework for ratiometric photoluminescence detection of D2O.

A two-dimensional zirconium-based metal-organic framework (2D Zr-MOF), ZrBTB (BTB = 1,3,5-tri(4-carboxyphenyl)benzene), is used as a platform to simultaneously immobilize terbium ions and europium ions with tunable ratios on its hexa-zirconium nodes by a post-synthetic modification. The crystallinity, morphology, porosity and photoluminescence (PL) properties of the obtained 2D Zr-MOFs with various europium-to-terbium ratios are investigated. With the energy transfer from the excited BTB linker to the installed terbium ions and the energy transfer from terbium ions to europium ions, a low loading of immobilized europium ions and a high loading of surrounding terbium ions in the 2D Zr-MOF result in the optimal PL emission intensities of europium; this phenomenon is not observable for the physical mixture of both terbium-installed ZrBTB and europium-installed ZrBTB. The role of installed terbium ions as efficient mediators for the energy transfer from the excited BTB linker to the installed europium ion is confirmed by quantifying PL quantum yields. As a demonstration, these materials with modulable PL characteristics are applied for the ratiometric detection of D2O in water, with the use of the stable emission from the BTB linker as the reference. With the strong emission of immobilized europium ions and the good dispersity in aqueous solutions, the optimal bimetal-installed ZrBTB, Eu-Tb-ZrBTB(1 : 10), can achieve the sensing performance outperforming those of the terbium-installed ZrBTB, europium-installed ZrBTB and the physical mixture of both.

A State-Dependent Elasto-Plastic Model for Hydrate-Bearing Cemented Sand Considering Damage and Cementation Effects.

In order to optimize the efficiency and safety of gas hydrate extraction, it is essential to develop a credible constitutive model for sands containing hydrates. A model incorporating both cementation and damage was constructed to describe the behavior of hydrate-bearing cemented sand. This model is based on the critical state theory and builds upon previous studies. The damage factor Ds is incorporated to consider soil degradation and the reduction in hydrate cementation, as described by plastic shear strain. A computer program was developed to simulate the mechanisms of cementation and damage evolution, as well as the stress-strain curves of hydrate-bearing cemented sand. The results indicate that the model replicates the mechanical behavior of soil cementation and soil deterioration caused by impairment well. By comparing the theoretical curves with the experimental data, the compliance of the model was calculated to be more than 90 percent. The new state-dependent elasto-plastic constitutive model based on cementation and damage of hydrate-bearing cemented sand could provide vital guidance for the construction of deep-buried tunnels, extraction of hydrocarbon compounds, and development of resources.

Mapping the social stock and spatiotemporal distribution of high-tech minerals from wasted mobile phones in China: 2001-2019.

In the twenty-first century, mobile phones have become one of the most indispensable electronic products in the international community. The pollution of wasted mobile phones has become an urgent problem worldwide and needs special attention. In this paper, we applied the consumption and usage method to calculate the high-tech mineral elements in China from 2001 to 2019. To analyze the spatial distribution of per capita high-tech minerals in China, we proposed a model (3D GHM) through which a 3D grid of high-tech minerals in wasted mobile phones can be obtained in 1 km resolution. The results showed that the total amount of wasted mobile phones in China from 2001 to 2019 was 8.6 billion, with a growth rate of 1026.7% in 2019 compared with 2001. Moreover, the spatiotemporal distribution of wasted mobile phones is characterized by more in the east and less in the west. The total amount of cobalt, palladium, antimony, beryllium, neodymium, praseodymium, and platinum in wasted mobile phones from 2001 to 2019 reached 42,422.4 tons. Based on our results, we proposed a system for efficient collecting and recycling of wasted mobile phones in China.

Special Issue: Advancement of Functionalized Mineral Materials and Rock.

Mineral materials have historically been extensively utilised in human society, and they hold significant prominence in various domains such as military, aerospace, electronics, and environmental conservation [...].

High-resolution mapping and evolution of steel stocks and waste in civil buildings: a case study of Changsha, China.

Systematic estimation of steel stocks and waste in urban areas and analysis of its historical evolution pattern is crucial for urban buildings steel recycling and environmental sustainability. However, it is a challenging task to collect big data from different sources and estimate accurately with high resolution. In this study, we proposed a novel hybrid approach (GMB model) to estimate building steel stocks and the annual waste rate through combining Geographic Information System, Material Flow Analysis, and Big Data Mining techniques. We estimated the civil-building steel stocks and amount of waste in Changsha urban area from 1985 to 2020 based on the GMB model, and analyzed the historical evolution pattern of steel stocks by using standard deviation ellipse and kernel density. The results showed that the cumulative steel stock in civil buildings grew from 0.66 million tons in 1985 to 8.26 million tons in 2020. The amount of waste increased by 2557 times. The spatiotemporal analysis showed that variations in distribution of the steel stocks are mainly concentrated in the central city, indicating a "central-peripheral" distribution, with a southward trend in the standard deviation ellipse and a southeast-northwest direction in the center of gravity of the steel stocks. There is low-high and high-low spatial aggregation patterns. We also compared the experimental results with the observed data to determine the feasibility of the GMB model. Our study can promote the management of steel resources recycling and aid to achieve the green and low-carbon goals in sustainable development policies.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms.

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Spatiotemporal transmission of infectious particles in environment: A case study of Covid-19.

This study explores the dynamic transmission of infectious particles due to COVID-19 in the environment using a spatiotemporal epidemiological approach. We proposed a novel multi-agent model to simulate the spread of COVID-19 by considering several influencing factors. The model divides the population into susceptible and infected and analyzes the impact of different prevention and control measures, such as limiting the number of people and wearing masks on the spread of COVID-19. The findings suggest that reducing population density and wearing masks can significantly reduce the likelihood of virus transmission. Specifically, the research shows that if the population moves within a fixed range, almost everyone will eventually be infected within 1 h. When the population density is 50%, the infection rate is as high as 96%. If everyone does not wear a mask, nearly 72.33% of the people will be infected after 1 h. However, when people wear masks, the infection rate is consistently lower than when they do not wear masks. Even if only 25% of people wear masks, the infection rate with masks is 27.67% lower than without masks, which is strong evidence of the importance of wearing a mask. As people's daily activities are mostly carried out indoors, and many super-spreading events of the new crown epidemic also originated from indoor gatherings, the research on indoor epidemic prevention and control is essential. This study provides decision-making support for epidemic preventions and controls and the proposed methodology can be used in other regions and future epidemics.

Time-Dependent Effect of Seepage Force on Initiation of Hydraulic Fracture around a Vertical Wellbore.

Fluid penetration into the rock during hydraulic fracturing has been an essential issue in studying the mechanism of fracture initiation, especially the seepage force caused by fluid penetration, which has an important effect on the fracture initiation mechanism around a wellbore. However, in previous studies, the effect of seepage force under unsteady seepage on the fracture initiation mechanism was not considered. In this study, a new seepage model that can predict the variations of pore pressure and seepage force with time around a vertical wellbore for hydraulic fracturing was established by using the method of separation of variables and the Bessel function theory. Then, based on the proposed seepage model, a new circumferential stress calculation model considering the time-dependent effect of seepage force was established. The accuracy and applicability of the seepage model and the mechanical model were verified by comparison with numerical, analytical and experimental results. The time-dependent effect of seepage force on fracture initiation under unsteady seepage was analyzed and discussed. The results show that when the wellbore pressure is constant, the circumferential stress induced by seepage force increases over time, and the possibility of fracture initiation also increases. The higher the hydraulic conductivity, the lower the fluid viscosity and the shorter the time required for tensile failure during hydraulic fracturing. In particular, when the tensile strength of rock is lower, the fracture initiation may occur within the rock mass rather than on the wellbore wall. This study is promising to provide a theoretical basis and practical guidance for further research on fracture initiation in the future.

Special Issue "Construction Materials and Other Related Materials: Basic Theory, Applied Technology and Advanced Research Methods".

The rapid advancement of science and technology has facilitated the creation of new technologies and techniques, leading to the convergence and diversification of fields [...].

Chemical Corrosion-Water-Confining Pressure Coupling Damage Constitutive Model of Rock Based on the SMP Strength Criterion.

Aiming at the problem of chemical-mechanics-hydro (C-M-H) action encountered by rocks in underground engineering, chemical damage variables, water damage variables, and force damage variables are introduced to define the degree of degradation of rock materials. Stone is selected as the sample for acid corrosion treatment at pH 3, 4, and 7, and a chemical damage factor is defined that coupled the pH value and duration of exposure. Then based on the spatial mobilized plane (SMP) criterion and the Lemaitre strain equivalence hypothesis, this research develops a constitutive model considering rock chemical corrosion-water-confining pressure damage. The proposed damage constitutive model employs the extremum method to ascertain the two Weibull distribution parameters (m and F0) by theoretical derivation and exhibits satisfactory conformity between the theoretical and experimental curves. The damage constitutive model can be consistent in the stress-strain characteristics of the rock triaxial compression process, which verifies the rationality and reliability of the model parameters. The model effectively represents the mechanical properties and damage characteristics of rocks when subjected to the combined influence of water chemistry and confinement. The presented model contributes to a better understanding of tangible rock-engineered structures subjected to chemical corrosion in underwater environments.

The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta.

The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R2 ˃ 0.57), summer (R2 ˃ 0.76) and autumn (R2 ˃ 0.38). However, in winter the composite model outperformed others (R2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41＜R2 ＜0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.