Two-dimensional Distribution Research Articles

Sociocultural attitudes in the self-regulation of youth's ideas about the future

The article contains the results of the third stage of the study, revealing the stability and differentiating effect of habitus (mental and modern features of national character, life positions) and migration attitudes on defining the goals that are significant for the future of the country. Based on the comparative analysis of two-dimensional distributions using the χ² statistics, it was revealed that both habitus, ascending to mental traits (love of the fatherland, mercy and compassion, love of neighbor), traditional life positions (collectivism and selflessness), and habitus, genetically associated with modern traits of national character (the desire to defend human rights, openness to everything foreign), and an active life position are stable. Habitus with a traditional value basis sharply divides the expressed carriers of the attitude and those who deny its presence in their choice of goals. Traditional attitudes underlie the construction of a more definite subjective reality of the future than modern ones. The most obvious differentiating effect is recorded in the links between the attitude of love for the fatherland and the social significance of a strong family for the future of the country; love for one's neighbor, mercy and compassion, honor, dignity, selflessness and accessible qualified health care. The semantic content of these attitudes is converted into care for neighbors and empathy for those far away, mainly concerns the satisfaction of the vital needs of health and life. They are also joined by the modern attitude of defending human rights in relation to a comfortable living space as a parameter of the image of the future. The least stable in regulating ideas about the goals of the country's future were shown by the traditional attitude of suspicion towards foreigners and modern attitudes towards the country as a place of residence, demonstrative compassion, rational and pragmatic attitude towards others and individualism. In this case, unformed attitudes have a regulating effect, which explains their instability. The attitudes of young people towards emigration, compared to attitudes towards sedentarization and internal migration, are characterized by a devaluation of key socially significant goals and values (strong family, work, education), but greater support for an active civil society as the most complete expression of agency, the possibility of real influence on the political process and making political decisions.

Machine Learning-Based Spectral Reconstruction for Equivalence Ratio Measurement in Premixed Air-Methane Flames Using RGB Imaging

ABSTRACT This paper introduced a machine learning-based method for reconstruction spectral information from RGB images for measurements of the equivalence ratio (Φ) of premixed air-methane flames. Digital color cameras capture color and spatial information of hydrocarbon premixed flames in the visible band. The color of the flame is the result of spectral integration of chemiluminescence in the visible wavelength band, and direct use for flame equivalence ratio measurements would result in large errors due to low spectral resolution. The mapping function was used to reconstruct the spectral characteristics of the premixed methane flame for the RGB image, which were built by three types of machine learning methods: support vector regression (SVR), random forest (RF) regression, and Levenberg-Marquardt backpropagation neural network (LM-BPNN), respectively. Finally, LM-BPNN-based reconstructed spectral features are used for the development of Φ measurement model, which measurement error below 0.01, accurately measures the equivalence ratio of premixed methane flames within the range of 0.73 to 1.47. Further analyze the two-dimensional spatial distribution of flame equivalence ratio.

Behavior of sodium dodecyl sulfate at the gas–liquid interface based on the coupling of temperature and calcium chloride concentration

Coal spontaneous combustion disasters frequently occur during deep coal mining, resulting in significant losses. Water-based foam has been shown to effectively inhibit coal spontaneous combustion disasters. The temperature of coal seam depths is a key factor influencing the stability and water retention capacity of foam. Inorganic salts, as a foam additive, have a notable impact on the structure of the bubble film. Here, the influence of temperature and calcium chloride concentration on the gas–liquid interface of sodium dodecyl sulfate (SDS) was further investigated using molecular dynamic simulations. The results indicate that calcium chloride strengthens the interfacial adsorption barrier and decreases the diffusion coefficient of water, which improves foam stability. Meanwhile, Ca2+ is concentrated in the outer Helmholtz plane of the Stern layer, while Na+ is concentrated in the inner Helmholtz plane. The preferential coordination of Ca2+ further induces the expulsion of Na+. The hydration environment of Na+ is weakened by the electrostatic shielding effect of the Ca2+ layer. Furthermore, temperature and CaCl2 concentration exhibit a synergistic effect, influencing the adsorption structure of SDS at the interface. Temperature and CaCl2 cause the SDS head group to orient more perpendicularly to the interface. Therefore, the two-dimensional distribution of SDS in the XY plane exhibits regions of aggregation, diffusion, and vacant sites. With changes in temperature and Ca2+ concentration, the proportion and number density of vacant sites gradually stabilize. SDS forms highly ordered aggregates at the air–liquid interface, which in turn enhances the stability of the foam film.

Pricing vulnerable reset options under stochastic volatility jump diffusion model using 3-D FFT

. Under the comprehensive model of assets which satisfy the triple conditions of stochastic jump, stochastic volatility, and stochastic interest rate, the pricing problem of reset options with default risk is investigated. First, two-dimensional log-normal distribution and radical process with reverting property are applied to describe sudden jump of assets and time-varying characteristics of volatility and interest rate, respectively. Further, through the principle of risk neutral pricing with the characteristic function method, we establish the joint characteristic function related to the options. Second, according to measure transform and payoff function decomposition, the analytical pricing and hedging share formulas of vulnerable reset options are given. Third, 3-D fast Fourier transform is constructed to obtain a fast and asymptotic solution of option prices. Finally, the accuracy and stability of 3-D fast Fourier transform is analyzed through numerical examples. The experimental results show that the proposed method can solve the complex pricing problem of vulnerable reset options more efficiently.

Enhanced thermal storage performance with non-linear porosity distribution in copper foam-PCM composites

Phase change materials (PCMs) are increasingly utilized in thermal energy storage systems due to their high energy density and capability to maintain a constant temperature during phase transitions. However, the low thermal conductivity of PCMs poses a significant challenge, often mitigated by embedding PCMs within high-conductivity metal foams. While linear and layered porosity distributions in metal foams have been extensively studied, the potential benefits of non-linear porosity distributions remain underexplored. This study aims to bridge this gap by numerically investigating the effects of non-linear porosity distributions of copper foam on the melting behavior and thermal performance of palmitic acid. The paper thoroughly explains how different melting regimes influence the optimal choice of porosity distribution. Using the Enthalpy-Porosity approach and the Local Thermal Non-Equilibrium model, various positive and negative porosity gradients in both x and y directions were examined. The results demonstrate that positive porosity gradients significantly improve the melting rate and energy storage performance. Specifically, a positive gradient in the x-direction reduced the melting time by 10.4 %, while a positive gradient in the y-direction achieved a 16.74 % reduction compared to uniform porosity configuration. Building on these findings, the study then explores two-dimensional (2D) porosity distribution optimization, leading to further enhancements. The optimized 2D configuration achieved a 22.67 % reduction in melting time and a 32.38 % increase in the average energy storage rate compared to the uniform porosity scenario.

Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement

Laboratory core nuclear magnetic resonance (NMR) relaxation measurements offer geological information, including rock porosity and oil saturation, relevant to logging. When core samples drilled from wells are exposed to air, the fluids within their pores inevitably dissipate. This phenomenon may lead to discrepancies between the results of nuclear magnetic resonance relaxation experiments and the actual situation underground. To deeply explore the impact of fluid dissipation on NMR core analysis experimental results, a series of simulated dissipation experiments were designed under constant temperature and humidity conditions. Variations in one-dimensional and two-dimensional NMR measurement results of oil-saturated samples were examined under varying crude oil viscosities and dissipation times. The experimental results indicate that as exposure time increases, the T2 distribution of oil-saturated cores decreases, and the amplitude of the T2 distribution peaks decreases. Both oil and water relaxation components show a decreasing trend; however, the dissipation rate of the bounding water component significantly exceeds that of the crude oil component. By employing two-dimensional NMR relaxation time distribution fluid quantitative analysis technology, the relationship between the dissipation rates of various phase fluids and exposure time during the stable dissipation stage was analyzed. This offers a reference for adjusting the oil saturation of exposed cores based on NMR measurements.

A ratiometric fluorescent sensing foil for high resolution 2D pH measurement based on a novel hydroxy-pyrene green fluorophore

The pH of environmental systems plays a crucial role in determining pollutant behavior, necessitating the development of effective tools for real-time monitoring. This study introduces a novel series of lipophilic HPTS derivatives, developed through a two-step synthesis route, designed as pH-sensitive dyes, characterized by high fluorescence intensity, photostability, dual excitation/single emission, and significant Stokes shifts. We engineered self-ratiometric pH-sensing planar optode foils and investigated the impact of carbon chain length on foil durability. These foils demonstrate reliable quantitative pH detection across a range of 6.5–9.5 using commercial imaging systems and maintain exceptional photostability with minimal ionic interference. Notably, foils constructed with HPTS(DPA)3 derivatives, where the substituent carbon chain length is longer than six carbons show no significant leakage under varying pH conditions. The practical application of the foil was validated by mapping the two-dimensional pH distribution in the soil rhizosphere around rice roots at a resolution of 17 megapixels, demonstrating the potential for environmental monitoring applications.

Study on the effect of pore structure changes induced by freeze-thaw-wetting-drying cycles on water retention characteristics of compacted loess

Deterioration of earthworks (including compacted soil collapse and loss of suction) as a result of climate change has had a significant negative impact on engineering. To explore the water retention characteristics and microstructural evolution of loess after freeze-thaw-wetting-drying (FTWD) cycles. In this paper, the soil water characteristic curve (SWCC) of unsaturated loess in the full suction range is obtained by using the filter paper method (FPM) and vapor equilibrium technique (VET). Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) were used to test the two-dimensional images and pore size distribution (PSD) curves of the specimens. The results of the study showed that the SWCC of compacted loess before and after FTWD cycles showed an unimodal pattern. Compacted loess has inter-aggregate pores and intra-aggregate pores, and the PSD curves show a bimodal pattern. The FTWD cyclic action reduces the water retention capacity of loess in the low to medium suction range (0kPa-288kPa). Inter-aggregate pore porosity decreases with an increasing number of FTWD cycles. Finally, to easily predict the SWCC of loess under the condition of pore variation, a prediction model was established in the double-porosity media framework, considering the changes of inter-aggregate pores and intra-aggregate pores. The rationality and applicability of the present model were verified by experimental data. The present model can reasonably predict the water retention properties of compacted clays with unimodal or bimodal type SWCC under microscopic pore changes, which is suitable for the engineering application of compacted clays.

Definition of phosphatidylinositol 4,5-bisphosphate distribution by freeze-fracture replica labeling.

Phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] is a phospholipid essential for plasma membrane functions, but its two-dimensional distribution is not clear. Here, we compared the result of sodium dodecyl sulfate-treated freeze-fracture replica labeling (SDS-FRL) of quick-frozen cells with the actual PtdIns(4,5)P2 content and the results obtained by fluorescence biosensor and by labeling of chemically-fixed membranes. In yeast, enrichment of PtdIns(4,5)P2 in the membrane compartment of Can1 (MCC)/eisosome, especially in the curved MCC/eisosome, was evident by SDS-FRL, but not by fluorescence biosensor, GFP-PLC1δ-PH. PtdIns(4,5)P2 remaining after acute ATP depletion and in the stationary phase, 30.0% and 56.6% of the control level, respectively, was not detectable by fluorescence biosensor, whereas the label intensity by SDS-FRL reflected the PtdIns(4,5)P2 amount. In PC12 cells, PtdIns(4,5)P2 was observed in a punctate pattern in the formaldehyde-fixed plasma membrane, whereas it was distributed randomly by SDS-FRL and showed clustering after formaldehyde fixation. The results indicate that the distribution of PtdIns(4,5)P2 can be defined most reliably by SDS-FRL of quick-frozen cells.

Deep learning-based image analysis with RTFormer network for measuring 2D crystal size distribution during cooling crystallization of β form L-glutamic acid

In this paper, a deep learning-based image analysis method is proposed for in-situ measurement of two-dimensional (2D) crystal size distribution during the cooling crystallization process of β form L-glutamic acid (β-LGA). Firstly, an image quality assessment strategy is presented for in-situ snapshotted crystal images to distinguish different crystallization stages, followed by image enhancement for the snapshotted images in each stage to facilitate analysis. Then, an edge-guided network based on the RTFormer network is developed to acquire precise crystal image segmentation and boundary location, thus improving the identification accuracy on crystal image boundary and its internal body. The network performance is further enhanced by using hyperparameter optimization and a class balance strategy. Subsequently, another identification strategy is developed to distinguish agglomerated and overlapped crystal images, so as to acquire more individual crystals for statistical measurement. Finally, the 2D size of each crystal is calculated based on the major axis and maximum inscribed circle of its segmented image. Experiments on measuring the 2D size distributions of crystal populations during β-LGA crystallization process are performed to verify the accuracy and efficiency of the proposed measurement method.

Modeling and Analysis of IGBT Based on Two-Dimensional Charge Distribution

Modeling and Analysis of IGBT Based on Two-Dimensional Charge Distribution

Assessing the toroidal radiation distribution at Wendelstein 7-X by combining Gaussian Process Tomography and field line mapping

Radiative cooling is one of the main heat dissipation channels for magnetically confined fusion plasmas. In stellarators power exhaust control is complicated by the three-dimensional radiation distribution. Using a Gaussian Process Tomography routine, the two-dimensional radiation distribution in Wendelstein 7-X is reconstructed from the line-integrated bolometer data of one poloidal cross-section. These experimental tomograms are mapped to the field of view of the newly installed divertor bolometer cameras and compared to the toroidally separated local measurements to assess the toroidal radiation distribution. We observe peaking of the plasma radiated power density in the divertor region and steep toroidal gradients, especially at low radiated power fraction (up to ×8 increase in the emissivity along a ∼2m long flux tube). These results suggest the presence of significant toroidal radiation asymmetries within a stellarator half-module.

Research on the random generation model of 2D irregular aggregates in concrete based on fast grid region division method and its ground penetrating radar characteristics

Concrete can be regarded as a composite material with aggregates embedded in hardened slurries. The size, shape and distribution of aggregates directly affect multiple properties of concrete, including mechanical properties, ultrasonic field characteristics of concrete and ground penetrating radar (GPR) wave field characteristics. Therefore, conducting in-depth research on the random generation model of two-dimensional irregular aggregates is particularly important. This article introduces a fast grid region division method (FGRDM), which is an efficient reconstruction algorithm designed for two-dimensional concrete aggregate models. The algorithm takes into consideration the random micro-aggregate structure of concrete. A two-dimensional random distribution model of irregular aggregates in concrete was successfully established by generating concrete models with different particle size distributions and aggregate contents. Furthermore, the finite difference time domain method was used to analyze the GPR wave field of the random model. The significant influence of aggregate shape and distribution on the GPR wave field was discussed. Compared with existing two-dimensional irregular aggregate random generation models, the FGRDM proposed in this paper avoids computational redundancy and generates irregular aggregates at an increasingly fast speed. In the analysis of GPR data, electromagnetic waves are greatly affected by the shape and distribution of aggregates, highlighting the critical role of two-dimensional random distribution models in GPR data analysis. The paper uses FGRDM to generate random irregular aggregates to simulate the distribution of aggregates in actual concrete. Different diameter glass fiber reinforced polymer (GFRP) bars are inserted into the actual concrete and the numerical model to investigate the effect of aggregate size on the recognition of different diameter GFRP bars. The simulation results are generally consistent with the experimental results.

Measuring pore sizes of nonwoven geotextiles by X-ray CT images: a comparative evaluation

This paper focuses on evaluating the pore sizes of four nonwoven geotextile specimens with different pore size distribution techniques. The paper introduces a bubble analysis technique to find the two-dimensional (2D) pore size distribution of geotextile specimens. A new method is also introduced to determine the three-dimensional (3D) constriction sizes of nonwoven geotextiles from X-ray CT images. The digital image concept and image processing methods for the 2D image analysis and 3D constriction size measurement of geotextile specimens are explained. Finally, the paper compares the 3D constriction sizes with those measured from 2D image analysis, mercury intrusion porosimetry, manufacturer-reported opening size values based on the wet sieving method, and pore sizes calculated from theoretical equations. The results show that the largest pore opening sizes measured from the 3D constriction size method are in good agreement with the theoretical equations. The values measured from the wet sieving method were found to be the smallest, indicating the limitations of the wet sieving method to determine the largest pore opening size of nonwoven geotextiles. As it is the constriction size which must control filtration behaviour, the 3D constriction size method is recommended for assessing the reference porometry.

Numerical investigation of the effective receptive field and its relationship with convolutional kernels and layers in convolutional neural network

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Experimental and numerical evaluation of soil water and salt dynamics in a corn field with shallow saline groundwater and crop-season drip and autumn post-harvest irrigations

In areas with shallow saline groundwater, soil salts inevitably accumulate in the root zone during the growth period due to irrigation and upward movement of salts from the groundwater. In Northern China, autumn irrigation (AIR) with large amounts of water is commonly employed post-harvest to mitigate soil salt stress on crop growth in the subsequent year. Optimizing the total irrigation depth during both crop-growth and non-growth periods is challenging because of the movement of soil salts, which is influenced by their two-dimensional distribution around drippers and the impact of the winter freeze-thaw cycles, significantly affecting water flow and solute transport during winter. In this study, the HYDRUS-1D and HYDRUS-2D models were integrated and calibrated using experimental data collected from 2021 to 2023 in China's Ordos south bank irrigation area. This model integration was conducted to assess soil water and salt dynamics during the non-growth and corn-growth periods under different irrigation strategies: a) AIR with high (AH) and low (AL) irrigation depths, and b) drip irrigation (DIR) with high (DH), medium (DM), and low (DL) irrigation depths. The results indicated that HYDRUS effectively modeled the electrical conductivity of the saturation paste extract (ECe) across different irrigation strategies, yielding an average coefficient of determination (R2) and the root mean square errors (RMSE) of 0.87 and 0.53 dS m−1, respectively. Generally, ECe increased during the growth period with DIR and decreased during the non-growth period with AIR. For the 0–40 cm soil layer, ECe decreased by 5.7 % and 12 % for every 100 mm increase in the AIR and DIR depths, respectively. Compared with the AHDM and AHDL treatments, reducing an AIR depth and increasing a DIR depth resulted in lower ECe in the 0–40 cm layer during the growth period and higher crop yield (CY) and irrigation water productivity (WPI). Specifically, the average ECe in the 0–40 cm layer decreased by 4.8 % during the growth period in the ALDH treatment compared to the AHDM treatment, and CY and WPI increased by 7.2 % and 10.3 %, respectively. Additionally, the irrigation strategy was the most effective in reducing ECe when AIR accounted for 35 % of the total irrigation. This study suggested that combining low AIR and high DIR could enhance water and field productivity.

Dynamically variable intensity patterns projected using an optofluidic phase-shifter array

We present a concept for the dynamically variable definition of arbitrary intensity distributions using an array of optofluidic phase-shifters. These components consist of cylindrical fluidic surfaces whose interface may be controllably shaped using electrowetting-on-dielectrics actuation. Key to the generation of defined intensity distributions is the ability to calculate the required shape for this phase-shifting interface and to this end we present a novel procedure, to our knowledge, that combines a real-time optimization algorithm with an influence matrix approach to generate the required surface. We illustrate the effectiveness of this approach by reconstructing arbitrary surface profiles with one optofluidic phase-shifting component that is then used to project a desired two-dimensional intensity distribution. Using this approach, we further demonstrate the capability of a two-dimensional phase-shifter array to generate larger dynamically programmable intensity patterns by combining individual intensity distributions.

Study of lepton flavor universality and angular distributions in D→KJ∗(→Kπ)ℓνℓ

The decay of the D meson into multibody final states is a complex process that provides valuable insights into the fundamental interactions within the Standard Model of particle physics. This study focuses on the decay cascade D+→KJ∗ℓ+ν→K±π∓ℓ+ν where the KJ∗ resonance encompasses the K∗(892),K∗(1410),K0∗(1430) states. We employ the helicity amplitude technique to derive the angular distributions for the decay chain, enabling the extraction of one-dimensional and two-dimensional distributions. Utilizing form factors for the D→K∗ transition derived from the quark model, we calculate the differential and integrated partial decay widths, explicitly considering the electron and muon masses. Decay branching fractions are calculated, the ratios of the branching fractions are found to be Br(D+→K∗(892)(→K-π+)μ+νμ)Br(D+→K∗(892)(→K-π+)e+νe)=0.975, Br(D+→K∗(1410)(→K-π+)μ+νμ)Br(D+→K∗(1410)(→K-π+)e+νe)=0.714 and Br(D+→K0∗(1430)(→K+π-)μ+νμ)Br(D+→K0∗(1430)(→K+π-)e+νe)=0.774. Results in this work will serve a calibration for the study of c→s in D meson decays in future and provide useful information towards the understanding of the properties of the K∗ meson, as well as Kπ system.

Modelling of water sorption hysteresis of cement-based materials based on pore microstructure

This paper presents a model for predicting water sorption isotherms of cement-based materials. In this model, hysteresis contributions of interlayer pores and gel/capillary pores are calculated separately. The hysteresis model of gel/capillary pores is achieved in four steps: (i) quantification of the pore network, (ii) determination of the criterion of saturation/desaturation of pores, (iii) derivation of the two-dimensional volumetric density distribution function, (iv) calculation of the real-time water content of gel/capillary pores. The hysteresis of interlayer pores is represented by an empirical model based on experiments. The reliability of the model is verified by comparing the model predictions and the experimental data in the literatures. Furthermore, three subjects were discussed: the influence of pore network on hysteresis, the hysteretic behavior of water in pores, the interaction of hysteresis mechanisms.

Spatio-temporal and multi-mode prediction for blast furnace gas flow

The reasonable and stable distribution of blast furnace (BF) gas flow is the basis for maintaining the smooth operation of BF. Therefore, the accurate detection of the gas flow distribution is essential in the BF ironmaking process due to the direct impact on productivity, stability, and efficiency. However, there is a significant challenge to capture the complex interactions and dynamic changes of the ironmaking process by single predictive mode and two-dimensional (2D) distribution, leading to a lack of flexibility and interpretability in dealing with different abnormalities. To address this issue, a novel spatio-temporal multi-mode approach for three-dimensional (3D) BF gas flow prediction is proposed in this article. First, Pearson correlation analysis is employed to evaluate correlated variables in the spatial dimension. The precise temporal correlations among the multiple variables are matched with mutual information (MI) to extract spatio-temporal variables. Next, the spatio-temporal variables are decomposed utilizing variation mode decomposition (VMD), and the noise is removed with integrated correlation analysis and Fourier transform (FT) to identify and retain the relevant information. Finally, the MI-VMD-Informer is innovatively proposed to establish three different prediction modes based on spatio-temporal features, thus obtaining 2D and 3D gas flow distributions. The superiority of the proposed method is verified by actual BF production data.