Two-dimensional Data Research Articles

Numerical Modeling of Tsunamis Generated by Subaerial, Partially Submerged, and Submarine Landslides

Using the existing two-dimensional experimental data and Open-source Fields Operation and Manipulation (OpenFOAM) software, this study performs a comprehensive comparative analysis of three types of landslide-generated tsunamis (subaerial, partially submerged, and submarine). The primary objective was to assess whether numerical simulations can accurately reproduce the experimental results of each type and to compare the predictive equations of the tsunami amplitudes derived from experimental and simulated data. The mesh size and dynamic viscosity parameters were initially optimized for a specific partially submerged landslide tsunami scenario and then applied across a broader range of experimental scenarios. Most of the simulated wave amplitudes remained within the 50% error margin, although significant discrepancies were observed between landslide types. When focusing on the crest amplitude of the first wave, the simulations of subaerial landslides least deviated from the experimental data, with a mean absolute percentage error of approximately 20%, versus approximately 40% for the partially submerged and submarine landslides. The predictive equations derived from the simulations closely matched those from the experimental data, confirming that OpenFOAM can effectively capture complex landslide–tsunami dynamics. Nonetheless, variations in the coefficients related to slope angles highlight the need for further calibration to enhance the simulation fidelity.

RS-Net: Hyperspectral Image Land Cover Classification Based on Spectral Imager Combined with Random Forest Algorithm

Recursive neural networks and transformers have recently become dominant in hyperspectral (HS) image classification due to their ability to capture long-range dependencies in spectral sequences. Despite the success of these sequential architectures, mainstream deep learning methods primarily handle two-dimensional structured data. However, challenges such as the curse of dimensionality, spectral variability, and confounding factors in hyperspectral remote sensing images limit their effectiveness, especially in remote sensing applications. To address this issue, this paper proposes a novel land cover classification algorithm that integrates random forests with a spectral transformer network structure (RS-Net). Firstly, this paper presents a combination of the Gramian Angular Field (GASF) and Gramian Angular Difference Field (GADF) algorithms, which effectively maps the multidimensional time series constructed for each pixel onto two-dimensional image features, enabling precise extraction and recognition in the backend network algorithms and improving the classification accuracy of land cover types. Secondly, to capture the relationships between features at different scales, this paper proposes a SpectralFormer network architecture using the Context and Structure Encoding (CASE) module to effectively learn dependencies between channels. This architecture enhances important features and suppresses unimportant ones, thereby addressing the semantic gap and improving the recognition capability of land cover features. Finally, the final prediction results are determined by a voting mechanism from the Random Forest algorithm, which synthesizes predictions from multiple decision trees to enhance classification stability and accuracy. To better compare the performance of RS-Net, this paper conducted extensive experiments on three benchmark HS datasets obtained from satellite and airborne imagers, comparing various classic neural network models. Surprisingly, the RS-Net algorithm achieves high performance and efficiency, offering a new and effective tool for land cover classification.

Ultrafast T1-T1ρ NMR for Correlating Different Motional Regimes of Molecules.

Nuclear magnetic resonance (NMR) relaxation times provide detailed information about molecular motions and local chemical environments. Longitudinal T1 relaxation time is most often sensitive to relatively fast, nano- to picosecond ranges of molecular motion. Rotating frame T1ρ relaxation time reflects a much slower, micro- to millisecond range of motion, and the motional regime can be tuned by changing spin-lock field strength. Conventional methods for measuring T1 and T1ρ relaxation times are time-consuming, since experiments must be repeated many times with incremented magnetization recovery or decay delay. In this work, we introduce two novel and efficient NMR methods to correlate the T1 and T1ρ relaxation times. The first method, IR-SPICY, combines the conventional T1 inversion recovery (IR) with the single-scan T1ρ detection-based spin-lock cycle (SPICY). The second method, ultrafast (UF) IR-SPICY, allows measurement of whole two-dimensional T1-T1ρ correlation data in a single scan, in a couple of seconds, based on spatial encoding of the T1 dimension. We demonstrate the performance of the methods by studying relaxation of water in porous silica and hydrogel samples, latter acting as a model of the articular cartilage extracellular matrix. The methods allow correlating different molecular motional regimes, potentially providing unprecedented information about various chemical and biochemical systems, such as structures and fluid dynamics in porous materials, macromolecular changes in tissues, and protein dynamics. One to three orders of magnitude shortened experiment time enable the studies of changing or degrading samples. Furthermore, the single-scan approach may significantly facilitate the use of modern nuclear-spin hyperpolarization techniques to enhance the sensitivity of T1-T1ρ measurements by several orders of magnitude.

The Insecticidal Activity of Secondary Metabolites Produced by Streptomyces sp. SA61 against Trialeurodes vaporariorum (Hemiptera: Aleyrodidae).

Trialeurodes vaporariorum Westwood poses a significant threat to vegetable and ornamental crops in temperate zones, resulting in notable reductions in yield and substantial economic burdens. In order to find compounds with high insecticidal activity against T. vaporariorum, five compounds were isolated and identified from the crude extract of Streptomyces sp. SA61. These include three new polyketides, named strekingmycins F-H (1-3); one new diterpenoid, named phenalinolactone CD8 (4); and one known compound, strekingmycin A (5). Their structures were analyzed using high-resolution electrospray ionization mass spectrometry and one-dimensional and two-dimensional nuclear magnetic resonance spectroscopy data and by comparing them with previously reported data. The insecticidal activities of compounds 1-5 against T. vaporariorum were evaluated. Among them, compound 5 exhibited the highest insecticidal activity, with an LC50 of 6.949 mg/L against T. vaporariorum at 72 h using the leaf-dip method. Lower insecticidal activities were found in compounds 1-4, with LC50 values of 22.817, 19.150, 16.981 and 41.501 mg/L, respectively. These data indicate that strekingmycin could be a potential candidate for a novel insecticide to control T. vaporariorum.

Searching for Continuous n-Clusters with Boolean Reasoning

A bicluster consists of a subset of rows and columns of a given matrix, whose intersection defines the region (bicluster) of values of precisely defined condition. Through the decades, a variety of biclustering techniques have been successfully developed. Recently, it was proved that many possible patterns defined in two-dimensional data could be found with the application of Boolean reasoning. The provided theorems showed that any existing pattern in the data could be unequivocally encoded as an implicant of a proper Boolean function. Moreover, a prime implicant of that function encoded the inclusion-maximal (non-extendable) pattern. On the other hand, the definition of some two-dimensional patterns may be easily extended to three-dimensional patterns (triclusters) as well as to any number of dimensions (n-clusters). This paper presents a new approach for searching for three- and higher-dimensional simple patterns in continuous data with Boolean reasoning. Providing the definition of the Boolean function for this tasks, it is shown that the similar correspondence—implicants encode patterns, and prime implicants encode inclusion-maximal patterns—has a strong mathematical background: the proofs of appropriate theorems are also presented in this paper.

Two-dimensional distribution experiment of ‘lattice diffraction’ using ultrasound in an educational laboratory

Abstract A device that can study the two-dimensional distribution of ultrasonic diffraction on lattice structures was developed for educational laboratories, enabling students to design the size, shape and dimension of diffraction obstacles independently. In this experiment, metal balls were used to simulate atomic lattice structures, and the two-dimensional diffraction distribution data of simple cubic, body-centered cubic, and hexagonal close-packed structure were collected. The results were compared with the sound field simulation of COMSOL, and a good consistency was found. By observing the diffraction results of complex structures, students can gain a better understanding of the concept of diffraction.

Greenness visibility in urban living environments as pathway to promote health and well-being: Mapping spatial differentiation in Flanders (Belgium) based on viewshed analysis

A substantial body of evidence links green space to positive health outcomes. Beyond the availability of dedicated green spaces such as parks and the overall presence of vegetation and trees, visual exposure to greenery has also been shown to be important for well-being. Enhancing visual access to greenery may thus represent a pathway to promoting health and well-being. However, at regional or national scales, insights into disparities in visual access to greenery remain scarce. Most research and policy efforts rely on two-dimensional (top-down) data, which often fails to accurately capture the amount of greenery visible from individual perspectives, such as from within homes.This study builds on the work of Labib et al. [20], who developed a high-resolution (5 m grid) Viewshed Greenness Visibility Index (VGVI) for the Greater Manchester area (UK). By introducing technical improvements that significantly increased computational efficiency, we extended this approach to create a comprehensive VGVI map for Flanders, the northern region of Belgium. The resulting map enabled a broad assessment of VGVI disparities across Flanders. Our findings reveal evidence of distributive environmental injustice, as demonstrated by a strong negative correlation (Spearman's Rho = -0.48) between neighbourhood deprivation levels and VGVI scores. In Flanders, residents of more deprived neighbourhoods are exposed to significantly lower levels of visible greenery.We further explored the differences and similarities between traditional top-down greenness mapping and VGVI, which provides an eye-level perspective. Although a strong positive correlation was observed at the regional scale (Pearson's r = 0.80), this relationship weakens at the local level. Our findings suggest that VGVI analysis is particularly valuable at smaller geographic scales, making it especially relevant for research or spatial policies that address disparities in individual living conditions or target specific locations, such as schools, nurseries, hospitals, retirement homes, and community centres.

Using two- and three-dimensional landmark data to examine the shape variation of Elliptio complanata from two heavily impounded rivers in northern New York (USA)

ABSTRACT Despite vast improvements in the quantification of shape variation with geometric morphometric methods, evaluating morphological variation in unionids has remained difficult due to the lack of measurable landmarks on shells and their overall morphological plasticity. In this study, we supplement traditional two-dimensional landmark data with three-dimensional landmark data. Using photogrammetry, we generate three-dimensional models that are landmarked. This approach adds a new, third dimension to capture shape changes, providing a more detailed understanding of the shell topography. With this method, we explore the differences in shell shape along river stretches of two heavily impounded rivers in northern New York (USA). We ask whether isolation by distance, presence of dams and/or difference in land use around sampling localities impact the shell morphologies of Elliptio complanata populations. Our data suggest that, although separated for almost a century, these unionid populations do not show a pattern of increased morphological difference with distance. But we find that broad, rounded morphs that are posteriorly extended occur more frequently in reservoir environments while unaltered river habitats have a higher number of slim and more elongated variants.

Research on prediction of yellow flesh peach firmness using a novel acoustic real-time detection device and Vis/NIR technology

Firmness is a critical indicator for predicting fruit ripeness, optimal harvest date, and shelf life. In this study, a novel fruit acoustic real-time detection prototype device and a conventional visible near-infrared (Vis/NIR) spectroscopy real-time detection device were used to collect acoustic and spectral signals from yellow flesh peaches to jointly predict their firmness. The acoustic and optical signals were generated into one- and two-dimensional feature data by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT) and Gramian angular field (GAF) data processing methods. Based on these data, a variety of yellow flesh peach firmness prediction models were constructed in this study, including partial least square (PLS), support vector regression (SVR), Swin Transformer (SwinT), and SwinT-PLS/SVR. The experimental results showed that the SwinT-PLS model based on the fusion of competitive adaptive re-weighted sampling (CARS)-acoustic image features and CARS-Vis/NIR spectral features showed the best prediction performance (R2P = 0.951, the RMSEP = 0.443 N/mm, RPDP = 4.339), and the prediction performance is significantly higher than that of the prediction model based on single acoustic and Vis/NIR spectral data. The method proposed can fast, non-destructively, accurately predict fruit firmness and has excellent prospects for commercial real-time fruit sorting applications.

Ergonomic glove pattern drafting method for hand assistive devices: considering 3D hand dimensions and finger mobility

Recently, interest has surged in glove-type assistive devices for relieving hand muscle stiffness caused by brain lesions. This study aims to develop an ergonomic method for drafting glove patterns intended for hand-assistive devices. To facilitate pattern development, we acquired three-dimensional (3D) scan data from the four hemiplegic patients while their hands were in a relaxed posture, which was subsequently transformed into two-dimensional (2D) data. Based on the 3D shape data, we analyzed the finger joint range of motion (ROM) and change ratio of skin surface length resulting from flexion and extension movements of the paralyzed hand. Incisions were strategically applied to regions displaying significant variations in these parameters. These flattened 2D patterns were then integrated into revised pattern blocks to enhance the shading data related to the 3D shape, resulting in the development of four glove patterns. We found that gloves prototyped using this innovative pattern-drafting method did not impede joint ROM when worn. Changes in clothing pressure inside the glove at the joints corresponded to the bending angles of the fingers, and the pressure did not exceed the discomfort threshold during hand flexion and extension movements. Importantly, participants provided positive subjective feedback concerning the comfort of the gloves. Our findings yield fundamental data for developing a foundational glove design for hand-assisted devices for patients with paralysis, achieved through the utilization of this novel ergonomic glove pattern-drafting method.

Total organic carbon content estimation for mixed shale using Xgboost method and implication for shale oil exploration

In this study, the Xgboost method is employed for TOC estimation in mixed carbonate and siliciclastic shale from the Hashan area, Junggar Basin. The results show that this approach is effective for TOC estimation in this area although the model performance is not very excellent with a correlation coefficient of 0.54 between measured TOC and predicted TOC values, likely due to a small samples dataset. Therefore, the PCA method is applied to debase dimension of well log data from five dimensional to two-dimensional data, which enhances the correlation coefficient between the predicted and measured TOC from 0.54 to 0.68. Based on the model, the isopleth maps of TOC distributions in Fengcheng Formation were redrawn showing two shale oil exploration targets, which likely correspond to two depositional centers of this strata. All the same, the model in this work provides reliable data for shale oil evaluation in the study area and a good example under similar geological setting.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

Non-unique machine learning mapping in data-driven Reynolds-averaged turbulence models

Recent growing interest in using machine learning for turbulence modeling has led to many proposed data-driven turbulence models in the literature. However, most of these models have not been developed with overcoming non-unique mapping (NUM) in mind, which is a significant source of training and prediction error. Only NUM caused by one-dimensional channel flow data has been well studied in the literature, despite most data-driven models having been trained on two-dimensional flow data. The present work aims to be the first detailed investigation on NUM caused by two-dimensional flows. A method for quantifying NUM is proposed and demonstrated on data from a flow over periodic hills and an impinging jet. The former is a wall-bounded separated flow, and the latter is a shear flow containing stagnation and recirculation. This work confirms that data from two-dimensional flows can cause NUM in data-driven turbulence models with the commonly used invariant inputs. This finding was verified with both cases, which contain different flow phenomena, hence showing that NUM is not limited to specific flow physics. Furthermore, the proposed method revealed that regions containing low strain and rotation or near pure shear cause the majority of NUM in both cases—approximately 76% and 89% in the flow over periodic hills and impinging jet, respectively. These results led to viscosity ratio being selected as a supplementary input variable (SIV), demonstrating that SIVs can reduce NUM caused by data from two-dimensional flows and subsequently improve the accuracy of tensor-basis machine learning models for turbulence modeling.

Elucidation of AsANS controlling pigment biosynthesis in Angelica sinensis through hormonal and transcriptomic analysis.

Angelica sinensis, a traditional Chinese medicinal plant, has been primarily reported due to its nutritional value. Pigmentation in this plant is an important appearance trait that directly affects its commercial value. To understand the mechanism controlling purpleness in A. sinensis, hormonal and transcriptomic analyses were performed in three different tissues (leave, root and stem), using two cultivars with contrasting colors. The two-dimensional data set provides dynamic hormonal and gene expression networks underpinning purpleness in A. sinensis. We found abscisic acid as a crucial hormone modulating anthocyanin biosynthesis in A. sinensis. We further identified and validated 7 key genes involved in the anthocyanin biosynthesis pathway and found a specific module containing ANS as a hub gene in WGCNA. Overexpression of a candidate pigment regulatory gene, AsANS (AS08G02092), in transgenic calli of A. sinensis resulted in increased anthocyanin production and caused purpleness. Together, these analyses provide an important understanding of the molecular networks underlying A. sinensis anthocyanin production and its correlation with plant hormones, which can provide an important source for breeding.

Study on rapid prediction of flow field in a knudsen compressor based on multi-fidelity reduced-order models

The safe and stable operation of a hydrogen Knudsen compressor is essential for transporting hydrogen in microfluidic systems. This study uses proper orthogonal decomposition to identify the coherent structures within the hydrogen flow field during non-equilibrium evolution. A long short-term memory neural network is then used to create a multi-fidelity reduced-order model, connecting two-dimensional and three-dimensional data to uncover transient flow mechanisms and enable rapid flow field prediction. The results show that the coherent structures of hydrogen flow, representing the most energetic modes, retain 99% of the flow energy and significantly influence the evolution of Poiseuille and thermal transpiration flows during non-equilibrium processes. The multi-fidelity reduced-order model effectively captures hydrogen transient flow and instabilities at various stages, achieving a 99.4% reduction in computational time while maintaining a maximum relative error of 0.53%. This approach facilitates the rapid prediction and control of flow states during hydrogen transport.

A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning.

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management.

Investigation on concentration detection of turbid solution based on hemisphere sample cell and multidimensional spectroscopy

The study developed a hemisphere sample cell and constructed a multidimensional spectroscopy acquisition system to enhance the accuracy of detecting turbid solution with scattering properties. The system simultaneously captures absorption and scattering information from the tested samples. Monte Carlo simulation was employed to model the transmission light intensity distribution data from sample cells of various shapes. It was found that the hemisphere sample cell increases the dimensionality of transmission light intensity information and enables the acquisition of more scattering-related data. Furthermore, 46 samples of intralipid-20% solution with varying concentrations were examined. Models were constructed using partial least squares (PLS) regression with one-dimensional and two-dimensional light intensity distribution data. The results indicate that the model using two-dimensional light intensity distribution data significantly outperforms the model using one-dimensional data, reducing the root mean square error by 39.96% and increasing the correlation coefficient by 0.332%. Experimental results demonstrate that the multidimensional spectroscopic modeling method employing the hemisphere sample cell can significantly enhance the accuracy and speed of detecting chemical composition concentrations in turbid solution.

Tunable optical matrix convolution of 20-Gbit/s QPSK 2-D data with a kernel using optical wave mixing.

Compared to its electronic counterpart, optically performed matrix convolution can accommodate phase-encoded data at high rates while avoiding optical-to-electronic-to-optical (OEO) conversions. We experimentally demonstrate a reconfigurable matrix convolution of quadrature phase-shift keying (QPSK)-encoded input data. The two-dimensional (2-D) input data is serialized, and its time-shifted replicas are generated. This 2-D data is convolved with a 1-D kernel with coefficients, which are applied by adjusting the relative phase and amplitude of the kernel pumps. Time-shifted data replicas (TSDRs) and kernel pumps are coherently mixed using nonlinear wave mixing in a periodically poled lithium niobate (PPLN) waveguide. To show the tunability and reconfigurability of this approach, we vary the kernel coefficients, kernel sizes (e.g., 2 × 1 or 3 × 1), and input data rates (e.g., 6-20 Gbit/s). The convolution results are verified to be error-free under an applied: (a) 2 × 1 kernel, resulting in a 16-quadrature amplitude modulation (QAM) output with an error vector magnitude (EVM) of ∼5.1-8.5%; and (b) 3 × 1 kernel, resulting in a 64-QAM output with an EVM of ∼4.9-5.5%.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.