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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 [...].

[Scale Effects of Landscape Pattern on Water Quality in Dongjiang River Source Watershed].

Based on water quality monitoring data and land use data, Dongjiang River source watershed water quality variation characteristics from 2017 to 2019 and the relationships between the landscape pattern of the Dongjiang River source watershed and water quality were analyzed using spatial analysis, correlation analysis, and redundancy analysis. The results showed that:① the water quality of the Dongjiang River source watershed improved overall, but the total nitrogen pollution was still severe. As of 2019, the annual average concentration of total nitrogen in all sampling points exceeded the type Ⅲ water quality standard. ② At the landscape level, water quality was positively correlated with landscape shape index, number of patches, and Shannon's diversity index but negatively correlated with largest patch index and aggregation index. Considering the land use type, construction land was the primary source of total nitrogen and total phosphorus. Landscape shape index and number of forest patches were positively correlated with NH4+-N. The number of forest patches was positively correlated with total phosphorus. However, the largest patch index and aggregation index of the forest were negatively correlated with total phosphorus. The number of patches of grassland was positively correlated with total phosphorus. The aggregation index of grassland was negatively correlated with total phosphorus. ③ It is suggested that management departments should focus on the optimization of the landscape pattern within the 2000 m buffer zone of monitoring points. The reasonable allocation of urban sewage-centralized treatment facilities, strengthening management of abandoned mining areas, improvement of intensive treatment of cultivated land, and construction of shelterbelt in areas where cultivated land is concentrated are beneficial for the water quality of Dongjiang River source watershed.

A Chemical Damage Creep Model of Rock Considering the Influence of Triaxial Stress.

In order to accurately describe the characteristics of each stage of rock creep behavior under the combined action of acid environment and true triaxial stress, based on damage mechanics, chemical damage is connected with elastic modulus; thus, the damage relations considering creep stress damage and chemical damage are obtained. The elastic body, nonlinear Kelvin body, linear Kelvin body, and viscoelastic-plastic body (Mogi-Coulomb) are connected in series, and the actual situation under the action of true triaxial stress is considered at the same time. Therefore, a damage creep constitutive model considering the coupling of rock acid corrosion and true triaxial stress is established. The parameters of the deduced model are identified and verified with the existing experimental research results. The yield surface equation of rock under true triaxial stress is obtained by data fitting, and the influence of intermediate principal stress on the creep model is discussed. The derived constitutive model can accurately describe the characteristics of each stage of true triaxial creep behavior of rock under acid environment.

A Quantitative Study of Micro and Macro Mechanical Parameters Based on the PFC3D Flat-Joint Model.

The flat-joint model, which constructs round particles as polygons, can suppress rotation after breakage between particles and simulate more larger compression and tension ratios than the linear parallel-bond model. The flat-joint contact model was chosen for this study to calibrate the rock for 3D experiments. In the unit experiments, the triaxial unit was loaded with flexible boundaries, and the influence of each microscopic parameter on the significance magnitude of the macroscopic parameters (modulus of elasticity E, Poisson's ratio ν, uniaxial compressive strength UCS, crack initiation strength σci, internal friction angle φ and uniaxial tensile strength TS) was analysed by ANOVA (Analysis of Variance) in an orthogonal experimental design. Among them, Eƒ, kƒ has a significant effect on E; Cƒ and kƒ have a significant effect on ν; Cƒ, σƒ and kƒ have a significant effect on UCS; Cƒ; σƒ and Eƒ have a significant effect on TS; Rsd has a significant effect on σci; and φf, Eƒ, kƒ, µƒ, and σƒ have a significant effect on φ. Regressions were then carried out to establish the equations for calculating the macroscopic parameters of the rock material so that the three-dimensional microscopic parameters of the PFC can be quantitatively analysed and calculated. The correctness of the establishment of the macroscopic equations was verified by comparing the numerical and damage patterns of uniaxial compression, Brazilian splitting, and triaxial experiments with those of numerical simulation units in the chamber.

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.

Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed.

The ecological and environmental quality of Dongjiang river watershed has great influence on Guangdong, Hong Kong and Macao. The landscape ecological risk assessment model could effectively monitor and assess environmental quality. In this study, spatial autocorrelation and geographic detector methods were used to explore the spatial characteristics of landscape ecological risk and their driving factors in the Dongjiang river watershed for four decades. The results showed that the ecological risks of Dongjiang River Source Watershed are mainly classified as low and intermediate, which are distributed in the hilly regions and the marginal mountainous regions at the junction of the Xunwu and Dingnan counties. From 1980 to 2018, the area of regions with the low ecological risk increased by 587.01 km 2. The size of regions with moderate, high and severe ecological risk decreased by 165.6 km 2, 258.82 km2 and 162.58 km2, respectively. Moreover, landscape ecological risk values exhibited an apparent spatial dependency, and high-risk areas cluster together. Among influencing factors, population density has the most significant impact on the change of landscape ecological risk in the Dongjiang river watershed, followed by elevation (DEM), human interface, vegetation index (NDVI), and urbanization level. However, the interaction of driving factors has a greater impact on the ecological risk of the Dongjiang river watershed than a single driving factor. The research provides good knowledge for environmental quality management, and the proposed methods can be used for other regions.