Search Results (2,839)

The lower sub-member of Member 2, Dongying Formation (Paleogene) in the HHK Depression hosts an extensively developed thin-bedded beach-bar system characterized by favorable source rock conditions and reservoir properties, indicating significant hydrocarbon exploration potential. Integrating drilling cores, wireline log interpretations, three-dimensional seismic data, geochemical analyses, and palynological data, this study investigates the sedimentary characteristics, sandbody distribution patterns, controlling factors, and genetic model of this lacustrine beach-bar system. Results reveal the following: (1) widespread thin-bedded beach-bar sandbodies dominated by fine-grained sandstones and siltstones, exhibiting wave ripples and low-angle cross-bedding; (2) two vertical stacking patterns, Type A, thick mudstone intervals intercalated with laterally continuous thin sandstone layers, and Type B, composite sandstones comprising thick sandstone units overlain by thin sandstone beds, both demonstrating significant lateral continuity; (3) three identified microfacies: bar-core, beach-core, and beach-margin facies; (4) key controls on sandbody development: paleoenvironmental evolution establishing the depositional framework, secondary fluctuations modulating depositional processes, strike-slip extensional tectonics governing structural zonation, paleobathymetry variations and paleotopography controlling distribution loci, and provenance clastic influx regulating scale and enrichment (confirmed by detrital zircon U-Pb dating documenting a dual provenance system). Collectively, these findings establish a sedimentary model for a thin-bedded beach-bar system under the strike-slip extensional tectonic framework. Full article

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article

Loess Plateau is the region with the most concentrated loess distribution and the deepest loess soil layer in the world, and it is facing serious problems of soil erosion and ecological degradation. The nano carbon modification of soil surface properties is a novel strategy for soil improvement and enhancing the soil’s capacity to sequester carbon, which has been extensively researched. However, the mechanisms underlying the influence of carbon surface structure on the efficacy of loess soil remediation remain unclear. Herein, graphene oxide (GO) with a unique two-dimensional structure and adjustable surface properties was optimized as a model carbon filler to investigate the modification effect on loess. As a result, the addition amount of 0.03% GO significantly reduced the disintegration amount of loess, but, if inhibited for a long time, the disintegration effect would weaken. The highly reduced GO can delay the loess disintegration rate due to its enhanced hydrophobicity, but the inhibitory effect fails over a long period of time. After adjusting the reduce degree with a 50% SA (sodium ascorbate), the water-holding capacity of the modified soil in the high suction range is enhanced. This study reveals the synergistic mechanism of the sheet structure and surface properties of GO on the water stability of loess, providing a reference for the prevention and control of soil erosion and ecological restoration in the Loess Plateau. Full article

We revisit the problem of random search walks in the two-dimensional (2D) space between concentric absorbing annuli, in which a searcher performs random steps until finding either the inner or the outer ring. By considering step lengths drawn from a power-law distribution, we obtain the exact analytical result for the search efficiency $\eta$ in the ballistic limit, as well as an approximate expression for $\eta$ in the regime of searches starting far away from both rings, and the scaling behavior of $\eta$ for very small initial distances to the inner ring. Our numerical results show good overall agreement with the theoretical findings. We also analyze numerically the absorbing probabilities related to the encounter of the inner and outer rings and the associated Shannon entropy. The power-law exponent marking the crossing of such probabilities (equiprobability) and the maximum entropy condition grows logarithmically with the starting distance. Random search walks inside absorbing annuli are relevant, since they represent a mean-field approach to conventional random searches in 2D, which is still an open problem with important applications in various fields. Full article

A meshless, quasi-convex reproducing kernel particle framework for three-dimensional steady-state thermomechanical coupling problems is presented in this paper. A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. The quasi-convex reproducing kernel approximation proposed by Wang et al. to construct almost positive reproducing kernel shape functions with relaxed monomial reproducing conditions is applied to improve the positivity of the thermal matrixes in the final discreated equations. Two numerical examples are given to verify the effectiveness of the developed method. The numerical results show that the solutions obtained by the quasi-convex reproducing kernel particle method agree well with the analytical ones, with a slightly better-improved numerical accuracy than the element-free Galerkin method and the reproducing kernel particle method. The effects of different parameters, i.e., the scaling parameter, the penalty factor, and node distribution on computational accuracy and efficiency, are also investigated. Full article

Gas voids inevitably form during the solidification of phase change materials (PCMs) due to volumetric contraction and thus deteriorate the thermal conductivity of solidified PCMs. In this work, the gas void morphology and distribution in solidified pure paraffin within a cubic thermal energy storage unit are experimentally studied. The three-dimensional structure of the solidified pure paraffin is reconstructed via computed tomography (CT) scanning with a resolution of up to 25 µm. Four distinct morphological types of gas voids are found, including irregular elliptical gas voids, elongated “needle-like” gas voids, micro gas voids, and large circular gas voids. The formation mechanisms of each type are analyzed. The morphology and distribution of gas voids indicate that the solidified pure paraffin structure is anisotropic. The effective thermal conductivity (ETC) of this solid–gas structure is numerically evaluated using lattice Boltzmann simulations, and a two-term power equation is fitted. The results show that the ETC in the vertical direction is significantly lower than in the horizontal direction and the ETC could be reduced by as much as 31.5% due to the presence of gas voids. Full article

This study presents a numerical comparison between Direct numerical simulation (DNS) and the standard κ-ε turbulence model to evaluate natural convection in a two-dimensional, differentially heated, air-filled cavity over the Rayleigh number range 10³ to 10¹⁰. The objective is to assess the predictive capabilities of both methods across laminar and turbulent regimes, with a particular emphasis on the quantitative comparison of thermal characteristics under high Rayleigh number conditions. The Navier–Stokes and energy equations were solved using the finite element method with Boussinesq approximation, employing refined meshes near the hot and cold walls to resolve thermal and velocity boundary layers. The results indicate that for Ra ≤ 10⁶, the κ-ε model significantly underestimates temperature gradients, maximum velocities, and average Nusselt numbers, with errors up to 19.39%, due to isotropic assumptions and empirical formulation. DNS, in contrast, achieves global energy balance errors of less than 0.0018% across the entire range. As Ra increases, the κ-ε model predictions converge to DNS, with Nusselt number deviations dropping below 1.2% at Ra = 10¹⁰. Streamlines, temperature profiles, and velocity distributions confirm that DNS captures flow dynamics more accurately, particularly near the wall vortices. These findings validate DNS as a reference solution for high-Ra natural convection and establish benchmark data for assessing turbulence models in confined geometries Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

The inherent limitations of ordinary cement mortar—characterized by its high brittleness and low flexibility—result in a diminished load-bearing capacity, predisposing concrete pavements to cracking. A novel approach has been proposed to enhance material performance by incorporating emulsified asphalt and latex into ordinary cement mortar, aiming to improve the flexibility and durability of concrete pavements effectively. To further validate the feasibility of this proposed approach, a series of comprehensive experimental investigations were conducted, with corresponding conclusions detailed herein. As outlined below, the flexibility properties of the modified cement mortar were systematically evaluated at curing durations of 3, 7, and 28 days. The ratio of flexural to compressive strength can be increased by up to 38.9% at 8% emulsified asphalt content at the age of 28 days, and by up to 50% at 8% latex content. The mechanism of emulsified asphalt and latex-modified cement mortar was systematically investigated using a suite of analytical techniques: X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TG-DTG), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). Through comprehensive analyses of microscopic morphology, hydration products, and elemental distribution, the enhancement in cement mortar toughness can be attributed to two primary mechanisms. First, Ca²⁺ ions combine with the carbonyl groups of emulsified asphalt to form a flexible film structure during cement hydration, thereby reducing the formation of brittle hydrates. Second, active functional groups in latex form a three-dimensional network, regulating internal expansion-contraction tension in the modified mortar and extending its service life. Full article

This article uses FLUENT to construct a two-dimensional axisymmetric numerical model of a cryogenic hydrogen charging pipeline. By loading with initial temperature gradient and transient initial pressure disturbance, the basic characteristics of low-temperature hydrogen Taconis thermoacoustic oscillation are calculated, including temperature, heat flux density distribution, pressure amplitude, and frequency. The instability boundary of hydrogen TAO is also obtained. The results show that (1) the temperature distribution and flow characteristics of the gas inside the pipeline exhibit significant periodic changes. In the first half of the oscillation period, the cold-end gas moves towards the end of the pipeline. Low-viscosity cold hydrogen is easily heated and rapidly expands. In the second half of the cycle, the expanding cold gas pushes the hot-end gas to move towards the cold end, forming a low-pressure zone and causing gas backflow. (2) Thermoacoustic oscillation can also cause additional thermal leakage on the pipeline wall. The average heat flux during one cycle is 1150.1 W/m² for inflow and 1087.7 W/m² for outflow, with a net inflow heat flux of 62.4 W/m². (3) The instability boundary of the system is mainly determined by the temperature ratio of the cold and hot ends α, temperature gradient β, and length ratio of the cold and hot ends ξ. Increasing the pipe diameter and minimizing the pipe length can effectively weaken the amplitude of thermoacoustic oscillations. This study provides theoretical support for predicting thermoacoustic oscillations in low-temperature hydrogen transport pipeline systems and offers insights for system stability control and design verification. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Instead of measuring the distance between two points with a positive real number, determining the degree to which the distance between these two points is close, not close, or uncertain allows for more detailed measurement. Recently, researchers have overcome this grading problem by using probability distribution functions, along with fuzzy, intuitionistic fuzzy, and neutrosophic sets. This study pioneers neutrosophic quadruple metric spaces as a powerful new tool for quantifying distances under complex, multi-dimensional uncertainty. It provides a comprehensive mathematical structure, including topology, convergence theory, and completeness, and handles both symmetric and asymmetric cases, generalising previous neutrosophic metric results. For this purpose, neutrosophic quadruple metric spaces were derived from neutrosophic metric spaces in order to better model situations involving uncertainty. Also, we generalised the findings obtained with the neutrosophic metric to the quadruple neutrosophic metric. Full article

This work presents a high-performance novel photodetector based on two-dimensional boron nitride (BN) nanosheets functionalized with gold nanoparticles (Au NPs), offering ultra-broadband photoresponse from 0.25 to 5.9 μm. Operating in both photovoltaic and photoconductive modes, the device features rapid response times (<0.5 ms), high responsivity (up to 1015 mA/W at 250 nm and 2.5 V bias), and thermal stability up to 100 °C. The synthesis process involved CO₂ laser exfoliation of hexagonal boron nitride, followed by gold NP deposition via RF sputtering and thermal annealing. Structural and compositional analyses confirmed the formation of a three-dimensional network of atomically thin BN nanosheets decorated with uniformly distributed gold nanoparticles. This architecture facilitates plasmon-enhanced absorption and efficient charge separation via heterojunction interfaces, significantly boosting photocurrent generation across the deep ultraviolet (DUV), visible, near-infrared (NIR), and mid-infrared (MIR) spectral regions. First-principles calculations support the observed broadband response, confirming bandgap narrowing induced by defects in h-BN and functionalization by gold nanoparticles. The device’s self-driven operation, wide spectral response, and durability under elevated temperatures underscore its strong potential for next-generation broadband, self-powered, and wearable sensing and optoelectronic applications. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article