Concentration Distribution Research Articles

Effect of phase exchange kinetics on Taylor dispersion of chemically reactive solutes in an oscillatory magnetohydrodynamics flow between two parallel plates

The study of kinetic sorptive effects on the transport phenomena of reactive solute has numerous real-world applications, including in the industrial and environmental sectors. These kinds of investigations become more realistic when an oscillatory pressure gradient with both the reversible and irreversible reactions at the channel walls is considered in a magnetohydrodynamics flow. In the past, Ng and Yip [J. Fluid Mech. 446, 321–345 (2001)] studied the effect of sorptive phase exchange at boundaries on the solute transport phenomena in an open-channel flow using Mei's multiple-scale homogenization technique. They considered fluid flows without magnetic field and boundary absorption. This work uses the above-mentioned method to investigate the phase exchange kinetics of Taylor dispersion phenomena in a two-dimensional magnetohydrodynamics fluid flowing through a parallel channel. The paper discusses how various parameters and dimensionless numbers, such as the Hartmann, oscillatory Reynolds, and Damkohler, affect the flow velocity, transport coefficient, multi-dimensional concentration distributions, and transverse variation rate. Due to the strong magnetic field, the flow velocity and Taylor dispersivity are adversely affected and conspicuously reduced. Additionally, for large Damkohler numbers, the total dispersion coefficient and the Taylor dispersion coefficient both decrease. However, the longitudinal concentration distribution rises with the Hartmann number and partition coefficient. It is worth noting that in the presence of unequal boundary absorption, there is no occurrence of transverse symmetry in solute concentration at any given time. Controlling various processes of tracer dispersion in environmental systems, especially water purification and the chemical industry, may benefit from these intriguing findings.

China's “coal-to-gas” policy had large impact on PM1.0 distribution during 2016–2019

Particulate matter with an aerodynamic diameter of less than 1 μm (PM1.0) can be extremely hazardous to human health, so it is imperative to accurately estimate the spatial and temporal distribution of PM1.0 and analyze the impact of related policies on it. In this study, a stacking generalization model was trained based on aerosol optical depth (AOD) data from satellite observations, combined with related data affecting aerosol concentration such as meteorological data and geographic data. Using this model, the PM1.0 concentration distribution in China during 2016–2019 was estimated, and verified by comparison with ground-based stations. The coefficient of determination (R2) of the model is 0.94, and the root-mean-square error (RMSE) is 8.49 μg/m3, mean absolute error (MAE) is 4.10 μg/m3, proving that the model has a very high performance. Based on the model, this study analyzed the PM1.0 concentration changes during the heating period (November and December) in the regions where the “coal-to-gas” policy was implemented in China, and found that the proposed “coal-to-gas” policy did reduce the PM1.0 concentration in the implemented regions. However, the lack of natural gas due to the unreasonable deployment of the policy in the early stage caused the increase of PM1.0 concentration. This study can provide a reference for the next step of urban air pollution policy development.

Analytical solutions for unsteady fractional maxwell nanofluid flow and mass transfer between two rigid spheres with periodic oscillations

In this paper, we develop axisymmetric squeeze flow and mass transfer of incompressible Maxwell nanofluid between two rigid spheres. The position of the boundary between the two rigid spheres varies periodically with time and space, and the unsteady magnetic field is considered. Analytical solutions are derived for velocity and concentration by using the method of Laplace integral variation. In addition, the radial velocity and concentration distributions are imaged and the relevant parameters are analyzed.

Dissipative Non-Slip MHD Nanofluid Flow with Variable Viscousity and Thermal Conductivity in the Presence of Arrhenius Chemical Reaction

This research investigates the intricate dynamics of dissipative non-slip magnetohydrodynamic (MHD) nanofluid flow, characterized by variable viscosity and thermal conductivity, under the influence of an Arrhenius chemical reaction. The inclusion of the Arrhenius chemical reaction adds complexity through heat generation or absorption, impacting temperature and concentration gradients. The study is motivated by the extensive applications of nanofluids in engineering and industrial processes, where precise control of heat and mass transfer is critical. We develop a comprehensive mathematical model that incorporates the variable properties of the nanofluid, the effects of the Lorentz force due to the applied magnetic field, and the temperature-dependent reaction rates dictated by the Arrhenius equation. The formulated governing equations were non-dimensionalised to identify the flow governing parameters. Finite Element Method (FEM), grid generation, solution algorithms, and post-processing to analyse velocity, temperature, and concentration distributions were used to obtain the numerical methods to solve fluid flow problems based on the Navier-Stokes equations, involving concepts of discretization. pdsolve subpackage in Maple 2023 was used to numerically solve PDEs with specific initial and boundary conditions, incorporating the plot and display commands for graphical analysis, and the results are presented and discussed. The findings reveal that the interplay between parameters like Hartmann number, Darcy parameter, and heat generation or absorption profoundly influences flow behaviour and thermal characteristics. The reactivity parameter is crucial, dictating the rate of chemical reactions and affecting system dynamics. This research enhances understanding of the interdependencies among fluid properties, chemical reactions, and external parameters in nanofluid flows.

Assessing the impact of mining on cyclic and linear methylsiloxane distribution in Tibetan soils: Source contribution and transport pattern

The pervasive presence of methylsiloxanes (MSs), comprising linear and cyclic congeners, in the environment poses significant ecological risks, yet the understanding of their transport mechanisms and deposition patterns remains limited. This study analyzed the concentrations of 12 linear-MSs (L3–L14) and 7 cyclic-MSs (D3–D9) in 29 surface soil samples collected across varying altitudes (3726 to 4863 m) near the Jiama mining sector in Tibet, aiming to investigate the distribution and transport dynamics of MSs from the emission source. The distribution of total MS concentration (ranging from 50.1 to 593 ng/g) showed a remarkable correlation with proximity to the mining site, suggesting the emergent source of mining activities for the MSs in the remote environment of the Tibetan Plateau. Employing the innovative model of robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), the analysis predicted that the mining operations contributing 57.1 % of the total soil MSs, would significantly surpass contributions from traffic emissions (14.7 %), residential activities (13.2 %), and the environmental factor of total organic matter content (14.9 %). The Boltzmann equation effectively modeled the distribution pattern of soil MSs, highlighting atmospheric transport and gravitational settling as key distribution mechanisms. However, linear-MSs exhibited longer transport distances than cyclic-MSs and were more profoundly affected by prevailing wind directions, suggesting their differential environmental behaviors and risks. Our study underscored that the mining sector possibly emerged as a significant source of Tibetan MSs, and provided insights into the transport and fate of MSs in remote, high-altitude environments. The findings emphasize the need for targeted pollution control strategies to mitigate the environmental footprint of mining activities in Tibet and similar regions.

Elucidating the influence of the lower and upper microbiological limits: When is a 3-class sampling plan useful to test for pathogens in food?

The potential advantages of using 3-class sampling plans in the management of pathogens in food has been discussed for years. Using a 3-class plan, some lots would be accepted if, among a total number of samples n, the pathogen was detected in no more than a predefined number of samples c (such as c = 1) exceeding a microbiological limit m, and no sample was at a level above a predefined maximal concentration M. We built upon and extended beyond published methodologies and developed a web application to evaluate the performance of 2-class and 3-class sampling plans. We showed that there is little benefit of a 3-class plan over a 2-class plan with equal values for m, n and c under the classical assumption of a lognormal distribution of bacteria concentration in the lot with a standard deviation of 0.8 log cfu/g, for m value set to “absence in 25g” and M set to values > 1 cfu/g. In this situation, the lot will almost always be rejected based on the criterion “>c detected samples” and will almost never (or seldom) be rejected based on only the criterion “at least one sample > M cfu/g”. However, the benefit of a 3-class plan is realized when the distribution of pathogens is not a simple lognormal but includes a proportion of samples contaminated beyond the lognormal distribution, at levels > M. When this is the case, the performance of a 3-class plan relative to the 2-class plan with equal values of m, n, and c improves, then becomes essentially equivalent, as the proportion of highly contaminated samples increases. The behavior of 3-class plan may be useful in such situations. While such distributions of pathogens have been observed across lots in the food supply, better characterization of within-lot contamination distribution in food is needed to better evaluate the performance of sampling plans.

Experimental study on the characteristics of buoyant gas flow produced by vertical gas leakage in tunnels under natural ventilation

In recent years, there have been reports of gas leakage accidents during the transportation of gas tankers in tunnels, potentially leading to destructive explosions. However, the overall distribution of gas concentration in the entire tunnel has not been experimentally investigated. This study presents a series of brine-water experiments to explore the characteristics of buoyant gas flow in tunnel leakage accidents. The gas distribution is categorized into a stably stratified longitudinal current and a source region. Gas concentration in the tunnel is correlated with the dimensionless reduced gravity, which increases with the source buoyancy flux but decreases with the source release velocity. A model is proposed to estimate the maximum gas concentration of the longitudinal current. The spatial range of the explosive region is influenced by the source buoyancy flux and the leakage velocity. However, the propagation velocity of the longitudinal current is primarily controlled by the source buoyancy flux, with the source momentum having a negligible effect. The thickness of the longitudinal gas flow, δ, linearly decreases from the source region to the tunnel ends. A dimensionless parameter, λ, is introduced to characterize δ. When λ < 1.25, δ rapidly increases with λ and then remains approximately constant when λ exceeds 1.25. This study contributes to a better understanding of buoyant gas flow in tunnel leakage accidents.

Assessment of Occupational Exposure to Inhalable Aerosols in an Instant Powdered Food Manufacturing Plant in Norway

BackgroundIn the food manufacturing industry, exposure to inhalable aerosols contributes to respiratory illnesses such as occupational asthma and rhinitis. However, there is a lack of comprehensive exposure assessment studies. This study evaluated occupational exposure to inhalable aerosols in an instant powdered food manufacturing plant during work operations involving dried food and powders. MethodsIn total, 50 workers from an instant powdered food manufacturing plant were recruited. Personal inhalable aerosol exposure measurements were taken for both full-shift and task-based activities. The concentrations of inhalable aerosols were analyzed to identify any variation within and across departments, as well as between seasons, handedness, and sex. ResultsIn total, 134 personal air samples were collected, and the particulate mass was determined gravimetrically. The concentrations of inhalable aerosols ranged from 0.1 to 27 mg/m3 for full-shift exposure measurements and 3.1 to 73 mg/m3 for task-based measurements. Statistically significant differences in mean aerosol concentrations were found across departments (A:B p < 0.001, A:C p < 0.05, B:C p < 0.001) and between seasons (p < 0.001). ConclusionThis study revealed high exposure to inhalable aerosols among workers, particularly those involved in manual weighing, mixing, and adding powders. The significant differences between departments highlight the specific activities contributing to increased inhalable aerosol concentrations. Seasonal variations were also evident, with autumn showing higher concentrations of inhalable aerosols in all departments compared with summer. These findings emphasize the importance of understanding the distribution of aerosol concentrations across different work tasks and departments, particularly during different seasons.

Investigating Mass Segregation of the Binary Stars in the Open Cluster NGC 6819

We search for mass segregation in the intermediate-aged open cluster NGC 6819 within a carefully identified sample of probable cluster members. Using photometry from Gaia, Pan-STARRS, and the Two Micron All Sky Survey as inputs for a Bayesian statistics software suite, BASE-9, we identify a rich population of (photometric) binaries and derive posterior distributions for the cluster age, distance, metallicity, and reddening, as well as star-by-star photometric membership probabilities, masses, and mass ratios (for binaries). Within our entire sample, we identify 2632 cluster members and 777 binaries. We then select a main-sequence “primary sample” with 14.85 <G < 19.5, containing 1342 cluster members and 250 binaries with mass ratios q > 0.5, to investigate mass segregation. Within this primary sample, we find the binary radial distribution is significantly shifted toward the cluster center as compared to the single stars, resulting in a binary fraction that increases significantly toward the cluster core. Furthermore, we find that within the binary sample, more massive binaries have more centrally concentrated radial distributions than less massive binaries. The same is true for the single stars. We verify the expectation of mass segregation for this stellar sample in NGC 6819 through both relaxation time arguments and by investigating a sophisticated N-body model of the cluster. Importantly, this is the first study to investigate mass segregation of the binaries in the open cluster NGC 6819.

Omnidirectional simulation analysis of thermo-mechanical coupling mechanism in inertia friction welding of Ni-based superalloy

The coupling between heat and pressure is the kernel of inertia friction welding (IFW) and is still not fully understood. A novel 3D fully coupled finite element model based on a plastic friction pair was developed to simulate the IFW process of a Ni-based superalloy and reveal the omnidirectional thermo-mechanical coupling mechanism of the friction interface. The numerical model successfully simulated the deceleration, deformation processes, and peak torsional moments in IFW and captured the evolution of temperature, contact pressure, and stress. The simulated results were validated through measured thermal history, optical macrography, and axial shortening. The results indicated that interfacial friction heat was the primary heat source, and plastic deformation energy only accounted for 4% of the total. The increase in initial rotational speed and friction pressure elevated the peak temperature, reaching a maximum of 1525.5 K at an initial rotational speed of 2000 r/min and friction pressure of 400 MPa. The interface heat generation could form an axial temperature gradient exceeding 320 K/mm. The radial inhomogeneities of heat generation and temperature were manifested in a concentric ring distribution with maximum heat flux and temperature ranging from 2/5 to 2/3 radius. The radial inhomogeneities were caused by increasing linear velocity along the radius and an opposite distribution of contact pressure, which could reach 1.7 times the set pressure at the center. The circumferential inhomogeneity of thermo-mechanical distribution during rotary friction welding was revealed for the first time, benefiting from the 3D model. The deflection and transformation of distribution in contact pressure and Mises stress were indicators of plastic deformation and transition of quasi-steady state welding. The critical Mises stress was 0.5 times the friction pressure in this study. The presented modeling provides a reliable insight into the thermo-mechanical coupling mechanism of IFW and lays a solid foundation for predicting the microstructures and mechanical properties of inertia friction welded joints.

Treatment failure is a key factor in the development of Helicobacter pylori resistance.

Helicobacter pylori eradication failure influences its antibiotic resistance. This study aimed to evaluate the effect of previous treatment failures on it, including the changes in the antibiotic resistance rates, minimal inhibitory concentration (MIC) distributions, and resistance patterns. This single-center retrospective study included 860 primary isolates and 247 secondary isolates. Antibiotic susceptibility testing was performed for amoxicillin, metronidazole, clarithromycin, levofloxacin, furazolidone, tetracycline, and rifampicin. The demographic data and detailed regimens were collected. The primary resistance rates to amoxicillin, metronidazole, clarithromycin, levofloxacin, tetracycline, rifampin, and furazolidone were 5.93%, 83.84%, 28.82%, 26.28%, 0.35%, 1.16%, and 0%, while secondary were 25.10%, 92.31%, 79.76%, 63.16%, 1.06%, 3.19%, and 0%, respectively. The resistance rates to amoxicillin, metronidazole, clarithromycin, and levofloxacin increased significantly with the number of treatment failures accumulated, and showed a linear trend. The proportion of primary and secondary multidrug-resistant (MDR) isolates were 17.79% and 63.16%, respectively. The MIC values of amoxicillin, clarithromycin, and levofloxacin were elevated significantly with medication courses increased. The prevalence of amoxicillin, clarithromycin, levofloxacin, and metronidazole resistance would increase rapidly following first-line treatment failure, as well as the MIC values of them. Clinicians should pay great attention to the first-line treatment to cure H. pylori infection successfully.

Co-enrichment of selenium and cadmium in soils of southern China and its implication for the safe utilisation of selenium-rich lands

Dietary deficiency of selenium (Se) is a global health threat related to low Se concentrations in crops. Southern China has abundant Se-rich land resources, but the cadmium (Cd) pollution problem is prominent, which may lead to Cd exceeding the safety standard in Se-rich crops. Therefore, it is important to delineate Se-rich land without Cd pollution in order to develop green Se-rich agriculture. Based on soil/sediment geochemical survey data covering 2.3 million km2 of southern China, this study analysed the concentration and spatial distribution of Se and Cd, discussed their enrichment mechanisms and proposed suggestions for the safe use of Se-rich land. The results showed that the soil/sediments in southern China were significantly enriched in Se and Cd, and the median values were 0.31 mg·kg−1 and 221 μg·kg−1, which were 1.8 times and 2.5 times the national soil background values, respectively. In addition, a significant positive correlation was observed between Se and Cd, indicating that Cd contamination in Se-rich soils frequently exceeded the pollution limit. According to statistics, Se-rich land accounted for 32.13 % of southern China. However, due to the co-enrichment of Se and Cd, areas without Cd pollution accounted for only one-third of the total Se-rich area. In particular, the areas with co-enrichment of SeCd are primarily distributed in Yunnan, Guizhou, Guangxi, western Hubei, northwestern Zhejiang, western Hunan, southern Hunan and northern Guangdong. The enrichment of Se is predominantly associated with parent rocks (black and carbonate rocks). At the same time, the enrichment of Cd is influenced by the parent rocks and PbZn mineralisation and mining activities. Three recommendations for managing Se-rich land were proposed: imposing restrictions on the utilisation of heavily contaminated Se-rich land for agricultural production, adopting a rational approach towards utilising lightly polluted Se-rich land and actively promoting the development of Se-rich agriculture in uncontaminated Se-rich areas. In the future, it is necessary to develop technologies that simultaneously enhance Se absorption while inhibiting Cd absorption in order to safely exploit Se-rich lands affected by Cd pollution.

Sub-grid Variability and its Impact on Exposure in Regional Scale Air Quality and Integrated Assessment Models: Application of the uEMEP Downscaling Model

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator ’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organizations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

Establishment and Validation of a Blood Test-based Nomogram to Diagnose Patients with AFP-negative HCC.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer death worldwide. Alpha-protein (AFP) is the most widely used blood biomarker for HCC. However, elevated serum AFP is only observed in part of HCC. This study aimed to develop an efficient nomogram model to distinguish patients with alpha- protein-negative HCC and liver cirrhosis. A total of 1130 patients (508 HCC patients + 622 cirrhosis patients) were enrolled in the training cohort. A total of 244 HCC patients and 246 cirrhosis patients were enrolled in the validation cohort. A total of 41 parameters about blood tests were analyzed with logistic regression. The nomogram was based on independent factors and validated both internally and externally. Independent factors were eosinophils %, hemoglobin concentration distribution width, fibrinogen, platelet counts, total bile acid, and mitochondria aspartate aminotransferase. The calibration curve for the probability of HCC showed good agreement between prediction by nomogram and actual observation. The concordance index was 0.851. In the validation cohort, the nomogram distinguished HCC from liver cirrhosis with an area under the curve of receiver operating characteristic of 0.754. This proposed nomogram was an accurate and useful method to distinguish patients with AFP-negative HCC from liver cirrhosis.

Observation and Numerical Prediction of Concentration Distribution at Cast Coating Interface of Solid Pt, Ir, Re Using Liquid Ni-Based Alloys

Observation and Numerical Prediction of Concentration Distribution at Cast Coating Interface of Solid Pt, Ir, Re Using Liquid Ni-Based Alloys

Simulation of thermal-fluid coupling in silicon single crystal growth based on gradient normalized physics-informed neural network

The thermal-fluid coupling phenomenon of silicon melt is significant in the growth process of silicon single crystals. Complex convection affects the temperature and concentration distribution of the silicon melt. Therefore, establishing and solving the thermal-fluid coupling model of silicon melts is crucial to optimizing the crystal growth process and improving crystal quality. Traditional numerical simulation methods have limitations in regard to optimization, control, and real-time monitoring. Physics-Informed Neural Network (PINN) does not require model discretization, after training, it can make predictions quickly, showing potential for industrial applications. However, when solving practical industrial coupling models, PINN often struggles to converge due to large parameter values and significant gaps between solution variables. Moreover, solving the thermal-fluid coupling model with PINN can be treated as a multitask problem, where the gradients of different equations interfere with each other, leading to gradient confusion, slow convergence, or even divergence. Therefore, this paper proposes a gradient normalized PINN (GNPINN) for solving the thermal-fluid coupling model of silicon melt. GNPINN balances the contribution of each task, ensuring a more equitable training speed between different tasks to stabilize the training process of the coupling model. This paper considers the thermal-fluid coupling model of silicon melt under different rotation conditions. GNPINN can accurately and comprehensively capture the complex temperature, velocity, and pressure distribution of silicon melt compared with other methods. Additionally, the experimental results uncover the flow and heat transfer properties of silicon melt, validating the effectiveness and industrial applicability of GNPINN.

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models – Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) – under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond

Effect of an inclined magnetic field on dispersion of solute in a pulsatile flow through a channel of absorptive porous walls

The present investigation gives an insight to comprehend the complex mechanism of species transport through porous walls, which has applications for crude oil refining, oil reservoir engineering, and separation of metal from fluids. The paper analyzes the impact of an inclined magnetic field on mass transport phenomena of solute through an unsteady, viscous, incompressible, and electrically conducting fluid flowing between two parallel plates. Both plates are permeable, and the flow is driven by a periodic pressure gradient. At both channel walls, the first order boundary reaction is applied. The governing time depending advection and diffusion equation is solved numerically based on Aris's method of moments. To determine the axial mean concentration distribution of solute, the first four central moments are used in a Hermite polynomial representation. It is significant to note that the dispersion of tracer is more significant for the low frequencies rather than the high frequencies. The behavior of the dispersion process of the tracer is studied for various flow parameters such as the angle of inclination of the magnetic field (α), Hartmann number (M), absorption parameter (β), suction Reynolds number (R), injection Reynolds number (R′), Womersley number (ω), and dispersion time (t) for both purely oscillatory and combined flows. It is significant to note that with the increment of R, α, and M, the amplitude of the dispersion coefficient of the solute reduces. On the other hand, an opposite phenomenon is observed for R′. It is seen that the transport coefficient moves cyclically with a double frequency period for all values of R and R′. Also, it is found that the peak of the mean concentration distribution enhances with the increment of α and M because the flow velocity decreases.

The Distribution Pattern of Ground Movement and Co‐Seismic Landslides: A Case Study of the 5 September 2022 Luding Earthquake, China

AbstractMajor earthquakes can cause extensive landsliding that poses a major threat to both property and human lives. In addition to co‐seismically triggered ground failure, the earthquake‐affected region remains vulnerable to landslides due to loosened and unstable materials and structures. Many researchers have studied landslide distributions and their controlling factors after earthquakes, but the function of ground motion is unclear. To investigate the connection in a strike‐slip earthquake, we analyzed the 5 September 2022 Luding earthquake (Mw 6.6) in Sichuan Province, China. We interpreted remote‐sensing images to obtain the landslide distribution before and after the earthquake, calculated surface deformation from D‐InSAR data (pre‐ and post‐earthquake), utilized a point‐source model for the focal mechanism inversion, and then constructed a finite fault model for the rupture slip. There are clear differences in the landslide distributions on the two sides of the fault before and after the earthquake. The density of co‐seismic landslides on the west side of the fault exceeded that on the east side. The patterns of surface deformation and ground motion indicated that the areas with larger deformation and motion were associated with more landslides. Furthermore, the landslide size decreased with distance from the fault. A new finding is that co‐seismic landslides induced by strike‐slip earthquakes result in high landslide concentration on both sides of the fault, while previous studies find that co‐seismic landslides triggered by thrust earthquakes present a hanging wall concentrated distribution pattern. These findings contribute to a more comprehensive understanding of the connection between ground movement patterns and landslide distributions.

Novel Tools for Single Comparative and Unified Evaluation of Qualitative and Quantitative Bioassays: SS/PV-ROC and SS-J/PV-PSI Index-ROC Curves with Integrated Concentration Distributions, and SS-J/PV-PSI Index Cut-Off Diagrams.

Background: This investigation is both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid test and a study including novel comparative methods for bioassay evaluation and comparison that uses bladder cancer as a useful example. The objective of the paper is not to investigate specific data. It is used only for demonstration, partially to compare ROC methodologies and also to show how both sensitivity/specificity and predictive values can be used in clinical diagnostics and decision making. This study includes ROC curves with integrated cut-off distribution curves for a comparison of sensitivity/specificity (SS) and positive/negative predictive values (PPV/NPV or PV), as well as SS-J index/PV-PSI index-ROC curves and SS-J/PV-PSI index cut-off diagrams (J = Youden, PSI = Predictive Summary Index) for the unified direct comparison of SS-J/PV results achieved via quantitative and/or qualitative bioassays and an identification of optimal separate or unified index cut-off points. Patients and Methods: According to the routine diagnostics, there were 91 patients with confirmed bladder cancer and 1152 patients with no evidence of bladder cancer, leading to a prevalence value of 0.073. This study performed a quantitative investigation of used-up test cassettes from the visual UBC® Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. Interrelations between SS and PV values were evaluated using cumulative distribution analysis (CAD), SS/PV-ROC curves, SS-J/PV-PSI index-ROC curves, and the SS-J/PV-PSI index cut-off diagram. The maximum unified SS-J/PV-PSI index value and its corresponding cut-off value were determined and calculated with the SS-J/PV-PSI index cut-off diagram. Results: The use of SS/PV-ROC curves with integrated cut-off concentration distribution curves provides improved diagnostic information compared to "traditional" ROC curves. The threshold distributions integrated as curves into SS/PV-ROC curves and SS-J/PV-PSI index-ROC curves run in opposite directions. In contrast to the SS-ROC curves, the PV-ROC and the novel PV-PSI index-ROC curves had neither an area under the curve (AUC) nor a range from 0% to 100%. The cut-off level of the qualitative assay was 7.5 µg/L, with a sensitivity of 65.9% and a specificity of 63.3%, and the PPV was 12.4% and the NPV was 95.9%, at a threshold value of 12.5 µg/L. Based on these set concentrations, the reader-based evaluation revealed a graphically estimated 5% increase in sensitivity and a 13% increase in specificity, as compared to the visual qualitative POC test. In the case of predictive values, there was a gain of 8% for PPV and 10% for NPV. The index values and cut-offs were as follows: visual SS-J index, 0.328 and 35 µg/L; visual PV-PSI index, 0.083 and 5.4 µg/L; maximal reader Youden index, 0.0558 and 250 µg/L; and maximal PV-PSI index, 0.459 and 250 µg/L, respectively. The maximum unified SS-J/PV-PSI index value was 0.32, and the cut-off was 43 µg/L. The reciprocal SS-J index correctly detected one out of three patients, while the reciprocal PV-PSI index gave one out of twelve patients a correct diagnosis. Conclusions: ROC curves including cut-off distribution curves supplement the information lost in "traditionally plotted" ROC curves. The novel sets of ROC and index-ROC curves and the new SS/PV index cut-off diagrams enable the simultaneous comparison of sensitivity/specificity and predictive value profiles of diagnostic tools and the identification of optimal cut-off values at maximal index values, even in a unifying SS/PV approach. Because the curves within an SS-J/PV-PSI index cut-off diagram are distributed over the complete cut-off range of a quantitative assay, this field is open for special clinical considerations, with the need to vary the mentioned clinical diagnostic parameters. Complete or partial areas over the x-axis (AOX) can be calculated for summarized quantitative or qualitative effectivity evaluations with respect to single and/or unified SS-J and PV-PSI indices and with respect to single, several, or several unified assays. The SS-J/PV-PSI index-AOX approach is a new tool providing additional joint clinical information, and the reciprocal SS-J indices can predict the number of patients with a correct diagnosis and the number of persons who need to be examined in order to correctly predict a diagnosis of the disease. These methods could be used in applications like medical or plant epidemiology, machine learning algorithms, and neural networks.