Density Information Research Articles

Ionisation of atoms determined by kappa refinement against 3D electron diffraction data.

Conventional refinement strategies used for three-dimensional electron diffraction (3D ED) data disregard the bonding effects between the atoms in a molecule by assuming a pure spherical model called the Independent Atom model (IAM) and may lead toan inaccurate or biased structure. Here we show that it is possible to perform a refinement going beyond theIAM with electron diffraction data. We perform kappa refinement which models charge transfers between atoms while assuming a spherical model. We demonstrate the procedure by analysing five inorganic samples; quartz, natrolite, borane, lutecium aluminium garnet, and caesium lead bromide. Implementation of kappa refinement improved the structure model obtained over conventional IAM refinements and provided information on the ionisation of atoms. The results were validated against periodic DFT calculations. The work presents an extension of the conventional refinement of 3D ED data for a more accurate structure model which enables charge density information to be extracted.

From megabytes to molecules: The usage of DNA computing for storage and its implications on the future

It is more important now than ever to develop more effective data storage techniques in the constantly advancing field of computers. Due to their limits in terms of durability, information density, and physical space requirements, current storage technologies, such as magnetic and optical media, are finding it difficult to keep up with the global demand for data storage, which is expected to exceed 175 trillion gigabytes by 2025. Nature’s own method of storing genetic information for a long time, deoxyribonucleic acid (DNA), provides great data density, stability, and endurance, making it a viable solution to address this issue. Through a comprehensive analysis of existing literature, this paper aims to provide a nuanced understanding of DNA computing’s current state - mainly its trajectory towards becoming a viable alternative to traditional storage methods - highlighting the history, current developments/challenges, and future prospects.

Two-Dimensional Nanoarchitectonics of End-on Oligomers with Versatile Structures and Tunable Negative Differential Resistance.

Maximizing the molecular information density requires simultaneously functionalizing distinct monomers and their coupling connections. However, current synthesis generally focuses on distinct monomers rather than coupling reactions because the multistep reactions significantly escalate the synthetic complexity in an exponential increase. Here, we report the two-dimensional nanoarchitectures (2DNs) of end-on oligomers, with versatile molecular structures and negative differential resistance (NDR), synthesized by programmed and surface-initiated step electrosynthesis based on the simultaneous utilization of six reactions including cross- and homocouplings. The resulting vertically end-on oligomers, with similar values in thickness and molecular length, as crystalline 2DNs, exhibit subnanometer uniformity, ultrahigh compressive modulus of 40 GPa, and low-bias NDR at 0.13 V with an ultralow power consumption of down to 0.05 nW/μm2. This highly controlled electrosynthesis provides a unique dimension to enhance the structural diversity of molecular 2D nanomaterials for high-density and low-power consumption electronics.

An Innovative Path Planning Algorithm for Complex Obstacle Environments with Adaptive Obstacle Density Adjustment: AODA-PF-RRT*

To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.

What personal factors are associated with osteoporosis, fragility fracture, and osteopenia? A population-level analysis using the United Kingdom Biobank

PurposeOsteopenia, osteoporosis, and fragility fractures pose a major public health concern. Population-level clinical and biopsychosocial data may uncover modifiable risk factors to target when developing whole person approaches to managing these conditions. The purpose of this study was to identify personal risk factors associated with osteoporosis, fragility fractures, and osteopenia from the United Kingdom Biobank (UKB) – a large population-level database. MethodsWe performed a cross-sectional study using the UKB to evaluate the association between 39 systematically selected explanatory variables with a diagnosis of osteopenia, osteoporosis, or fragility fracture. Bivariate analysis was performed followed by multivariable logistic regression adjusting for multicollinearity using covariance testing. ResultsOf 502,507 patients in the UKB, 40,657 had complete bone mineral density information from DEXA scans, and 32,193 had sustained a fragility fracture in the previous five years. In multivariable regression, increased time spent watching television (OR 1.15), living in an area with a high index of deprivation (OR 1.14), infrequent visits from friends and family (OR 1.09), experiencing symptoms of anxiety (OR 1.09), experiencing symptoms of depression (OR 1.08), and decreased exercise frequency (OR 1.03), were associated with increased risk of osteoporosis. Decreased exercise frequency (OR 1.27), increased BMI (OR 1.2), living in an area with a high index of deprivation (OR 1.11), and decreased salary (OR 1.10) were associated with increased risk of fragility fracture. Symptoms of anxiety (OR 1.15), living in an area with a high index of deprivation (OR 1.13), and increased time spent watching television (OR 1.11), living alone (OR 1.08), and symptoms of depression (OR 1.06), were associated with increased risk of osteopenia (p < 0.05 for all variables). ConclusionAnalysis of population-level datasets reveal a range of modifiable mental, social, and lifestyle/behavioral health factors that can inform multidisciplinary team-based care, including strategies that respond to psychosocial concerns and sustaining healthy lifestyles and behaviors in patients experiencing osteoporosis, fragility fracture, and osteopenia. Future work should assess the impact of integrated, whole person management programs for these conditions on longitudinal outcomes.

Denoiser Learning for Infrared and Visible Image Fusion.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance. To this end, a novel information measurement method is proposed to achieve IR and VI fusion. Its core idea is to guide a generator in achieving image fusion by learning the denoisers. Specifically, by using denoisers to restore fusion images with different noise interference to source images, a mutual competition relationship is formed between denoisers, which helps the generator thoroughly explore the data specificity of the source images and guide it to achieve more accurate feature representation. In addition, a semantic adaptive measurement loss function is proposed to constrain the generator, which fuses semantic information adaptively by considering the semantic information density of different source images. The results of quantitative and qualitative experiments have shown that the proposed method can achieve a higher quality information fusion and has a faster fusion speed on three public datasets when compared with advanced methods.

Joint measurement of cosmic-ray muons and seismic waves at laboratory scale

SUMMARY Current geophysical exploration methods face challenges in accurately determining gas saturation levels and elastic constants with adequate spatial resolution. Seismic wave velocity is a critical physical property in these techniques, but it introduces uncertainties because of its composite nature involving density and two elastic constants (e.g. bulk and shear modulus), which exhibit a trade-off relationship. We propose a novel approach that integrates cosmic-ray muon detection with seismic exploration to independently resolve P- and S-wave velocities into their constituent elastic constants and densities. First, we utilized a fluid substitution approach based on Gassmann's model to illustrate the benefits of incorporating density information in predicting gas saturation levels in pores. This supports the advantage of decomposing seismic wave velocity into density and two elastic constants. Second, to validate the applicability and performance of the proposed method, which involves separating seismic wave velocity into density and two types of elastic constants, muon and ultrasonic data were collected in laboratory experiments on two different targets: an acrylic block and an aluminium block. Upon muon observation, a relationship is established to convert muon flux into density length, considering the characteristics of the building housing the laboratory and the direction of muon arrival at specific positions within the building. Although there is potential for enhancing the accuracy of the derived physical properties such as density, bulk modulus, and shear modulus, the feasibility of this method has been successfully demonstrated at the laboratory scale.

Modality-specific adaptive scaling and attention network for cross-modal retrieval

There are huge differences in data distribution and feature representation of different modalities. How to flexibly and accurately retrieve data from different modalities is a challenging problem. The mainstream common subspace methods only focus on the heterogeneity gap, and use a unified method to jointly learn the common representation of different modalities, which can easily lead to the difficulty of multi-modal unified fitting. In this work, we innovatively propose the concept of multi-modal information density discrepancy, and propose a modality-specific adaptive scaling method incorporating prior knowledge, which can adaptively learn the most suitable network for different modalities. Secondly, for the problem of efficient semantic fusion and interference features, we propose a multi-level modal feature attention mechanism, which realizes the efficient fusion of text semantics through attention mechanism, explicitly captures and shields the interference features from multiple scales. In addition, to address the bottleneck of cross-modal retrieval task caused by the insufficient quality of multimodal common subspace and the defects of Transformer structure, this paper proposes a cross-level interaction injection mechanism to fuse multi-level patch interactions without affecting the pre-trained model to construct higher quality latent representation spaces and multimodal common subspaces. Comprehensive experimental results on four widely used cross-modal retrieval datasets show the proposed MASAN achieves the state-of-the-art results and significantly outperforms other existing methods.

Soft-label recover based label-specific features learning

Presently, multi-label classification algorithms are mainly based on positive and negative logical labels, which have achieved good results. However, logical labeling inevitably leads to the label misclassification problem. In addition, missing labels are common in multi-label datasets. Recovering missing labels and constructing soft labels that reflect the mapping relationship between instances and labels is a difficult task. Most existing algorithms can only solve one of these problems. Based on this, this paper proposes a soft-label recover based label-specific features learning (SLR-LSF) to solve the above problems simultaneously. Firstly, the information entropy is used to calculate the confidence matrix between labels, and the membership degree of soft labels is obtained by combining the label density information. Secondly, the membership degree and confidence matrix are combined to construct soft labels, and this process not only solves the problem of missing labels but also obtains soft labels with richer semantic information. Finally, in the process of learning specific label features for soft labels. The local smoothness of the labels learned through stream regularization is complemented by the global label correlation, thus improving the classification performance of the algorithm. To demonstrate the effectiveness of the proposed algorithm, we conduct comprehensive experiments on several datasets.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

Linking agricultural water-food-environment nexus with geographical indication products using geographic information

Geographical Indication (GI) agricultural products play a crucial role in modern agriculture due to their distinct regional characteristics and their significance in the water-food-environment nexus. This study utilizes ArcGIS 10.2 to create a comprehensive geographical dataset of GI products in Jiangsu Province, incorporating various data types, including digital elevation models (DEM), hydrological data, road density, and socioeconomic information. We employed GeoDetector software to analyze these datasets, aiming to uncover the spatial distribution and influencing factors of GI products. Our findings indicate that there are significant concentrations of GI products in coastal and urban areas, especially in regions such as Suzhou, Yancheng, Nantong, and Suqian. These areas exhibit notable diversity in their GI products: Suqian, Yancheng, Nantong, and Taizhou are prominent in both crop cultivation and aquaculture, while Nantong stands out as a hotspot for livestock-based products. Additionally, natural factors such as topography, landforms, water resources, and climate are found to significantly shape the spatial distribution of GI products. These natural influences are complemented by socioeconomic factors, including population density and economic activity, which contribute to the regional specialization and development of GI products. Furthermore, this study explores the complexities of water management, investigating how local climate and water resource availability affect agricultural practices and product quality. We also assess the environmental impacts of agricultural activities, emphasizing the critical need for sustainable practices to maintain ecological balance and ensure the longevity of GI agricultural systems.

An interval uncertainty modeling method based on information granulation and improved multidimensional parallelepiped

Interval uncertainty modeling is essential for non-probabilistic reliability analysis of uncertain structures in the context of small samples, which confronts the difficulties of obtaining marginal intervals and characterizing variables correlation. A novel method for interval uncertainty modeling based on information granulation and improved multidimensional parallelepiped is proposed in this paper. Firstly, the method for constructing marginal intervals of uncertain variables is presented by integrating the up-sampling method based on kernel density estimation and information granulation theory (KDE-IG). Secondly, a relatively simple correlation coefficient expression based on the correlation angle is defined, and an improved multidimensional parallelepiped modeling method is proposed by optimizing the correlation coefficient within the marginal intervals. Additionally, two evaluation indexes are proposed to evaluate the robustness and accuracy of the constructed model. Finally, the applicability and effectiveness of the proposed method are demonstrated through two examples.

A novel Deep High-level Concept-mining Jointing Hashing Model for unsupervised cross-modal retrieval

Unsupervised cross-modal hashing has achieved great success in various information retrieval applications owing to its efficient storage usage and fast retrieval speed. Recent studies have primarily focused on training the hash-encoded networks by calculating a sample-based similarity matrix to improve the retrieval performance. However, there are two issues remain to solve: (1) The current sample-based similarity matrix only considers the similarity between image-text pairs, ignoring the different information densities of each modality, which may introduce additional noise and fail to mine key information for retrieval; (2) Most existing unsupervised cross-modal hashing methods only consider alignment between different modalities, while ignoring consistency between each modality, resulting in semantic conflicts. To tackle these challenges, a novel Deep High-level Concept-mining Jointing Hashing (DHCJH) model for unsupervised cross-modal retrieval is proposed in this study. DHCJH is able to capture the essential high-level semantic information from image modalities and integrate into the text modalities to improve the accuracy of guidance information. Additionally, a new hashing loss with a regularization term is introduced to avoid the cross-modal semantic collision and false positive pairs problems. To validate the proposed method, extensive comparison experiments on benchmark datasets are conducted. Experimental findings reveal that DHCJH achieves superior performance in both accuracy and efficiency. The code of DHCJH is available at Github.

GRNet:Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling.

The lossy Geometry-based Point Cloud Compression (G-PCC) inevitably impairs the geometry information of point clouds, which deteriorates the quality of experience (QoE) in reconstruction and/or misleads decisions in tasks such as classification. To tackle it, this work proposes GRNet for the geometry restoration of G-PCC compressed large-scale point clouds. By analyzing the content characteristics of original and G-PCC compressed point clouds, we attribute the G-PCC distortion to two key factors: point vanishing and point displacement. Visible impairments on a point cloud are usually dominated by an individual factor or superimposed by both factors, which are determined by the density of the original point cloud. To this end, we employ two different models for coordinate reconstruction, termed Coordinate Expansion and Coordinate Refinement, to attack the point vanishing and displacement, respectively. In addition, 4-byte auxiliary density information is signaled in the bitstream to assist the selection of Coordinate Expansion, Coordinate Refinement, or their combination. Before being fed into the coordinate reconstruction module, the G-PCC compressed point cloud is first processed by a Feature Analysis Module for multiscale information fusion, in which kNN-based Transformer is leveraged at each scale to adaptively characterize neighborhood geometric dynamics for effective restoration. Following the common test conditions recommended in the MPEG standardization committee, GRNet significantly improves the G-PCC anchor and remarkably outperforms state-of-the-art methods on a great variety of point clouds (e.g., solid, dense, and sparse samples) both quantitatively and qualitatively. Meanwhile, GRNet runs fairly fast and uses a smaller-size model when compared with existing learning-based approaches, making it attractive to industry practitioners.

Wave masked autoencoder: An electrocardiogram signal diagnosis model based on wave making strategy

ECG signal labeling is time-consuming and laborious, significantly constraining the performance of deep learning models on ECG diagnosis. Self-supervised learning methods offer a means to mitigate the reliance on annotated ECG data. Nevertheless, the direct transposition of these methods to ECG diagnostics may encounter challenges stemming from deficient domain knowledge and incongruous model architectures. Therefore, a self-supervised learning model that introduces ECG prior knowledge, called wave masked autoencoder (WMAE), is proposed. Firstly, considering that various waves that make up the ECG signal are key features for diagnosing arrhythmias, we design a wave masking strategy as an auxiliary task for self-supervised learning to introduce ECG-related knowledge. Secondly, sliding window slicing is utilized to divide each ECG signal into a series of subsequences, which are mapped into deep features with a convolutional feature mapper to increase the information density of basic semantic units and learn complete waveform representations. In this way, the proposed WMAE can capture robust and transferable ECG signal features from unlabeled data, thereby alleviating the problem of insufficient annotated ECG data and improving the performance of the model on cardiovascular disease diagnosis. Experimental results show that WMAE is significantly better than other benchmark models and has high practical value.

Lossy Image Compression with Stochastic Quantization

Introduction. Lossy image compression algorithms play a crucial role in various domains, including graphics, and image processing. As image information density increases, so do the resources required for processing and transmission. One of the most prominent approaches to address this challenge is color quantization, proposed by Orchard et al. (1991). This technique optimally maps each pixel of an image to a color from a limited palette, maintaining image resolution while significantly reducing information content. Color quantization can be interpreted as a clustering problem (Krishna et al. (1997), Wan (2019)), where image pixels are represented in a three-dimensional space, with each axis corresponding to the intensity of an RGB channel. The purpose of the paper. Scaling of traditional algorithms like K-Means can be challenging for large data, such as modern images with millions of colors. This paper reframes color quantization as a three-dimensional stochastic transportation problem between the set of image pixels and an optimal color palette, where the number of colors is a predefined hyperparameter. We employ Stochastic Quantization (SQ) with a seeding technique proposed by Arthur et al. (2007) to enhance the scalability of color quantization. This method introduces a probabilistic element to the quantization process, potentially improving efficiency and adaptability to diverse image characteristics. Results. To demonstrate the efficiency of our approach, we present experimental results using images from the ImageNet dataset. These experiments illustrate the performance of our Stochastic Quantization method in terms of compression quality, computational efficiency, and scalability compared to traditional color quantization techniques. Conclusions. This study introduces a scalable algorithm for solving the color quantization problem without memory constraints, demonstrating its efficiency on a subset of images from the ImageNet dataset. The convergence speed of the algorithm can be further enhanced by modifying the update rule with alternative methods to Stochastic Gradient Descent (SGD) that incorporate adaptive learning rates. Moreover, the stochastic nature of the proposed solution enables the utilization of parallelization techniques to simultaneously update the positions of multiple quants, potentially leading to significant performance improvements. This aspect of parallelization and its impact on algorithm efficiency presents a topic for future research. The proposed method not only addresses the limitations of existing color quantization techniques but also opens up new possibilities for optimizing image compression algorithms in resource-constrained environments. Keywords: non-convex optimization, stochastic optimization, stochastic quantization, color quantization, lossy compression.

Study on the Influence of Spatial Attributes on Passengers’ Path Selection at Fengtai High-Speed Railway Station Based on Eye Tracking

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time &gt; density of information &gt; density of key information &gt; diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations.

A Massively Parallel In Vivo Assay of TdT Mutants Yields Variants with Altered Nucleotide Insertion Biases.

Terminal deoxynucleotidyl transferase (TdT) is a unique DNA polymerase capable of template-independent extension of DNA. TdT's de novo DNA synthesis ability has found utility in DNA recording, DNA data storage, oligonucleotide synthesis, and nucleic acid labeling, but TdT's intrinsic nucleotide biases limit its versatility in such applications. Here, we describe a multiplexed assay for profiling and engineering the bias and overall activity of TdT variants with high throughput. In our assay, a library of TdTs is encoded next to a CRISPR-Cas9 target site in HEK293T cells. Upon transfection of Cas9 and sgRNA, the target site is cut, allowing TdT to intercept the double-strand break and add nucleotides. Each resulting insertion is sequenced alongside the identity of the TdT variant that generated it. Using this assay, 25,623 unique TdT variants, constructed by site-saturation mutagenesis at strategic positions, were profiled. This resulted in the isolation of several altered-bias TdTs that expanded the capabilities of our TdT-based DNA recording system, Cell HistorY Recording by Ordered InsertioN (CHYRON), by increasing the information density of recording through an unbiased TdT and achieving dual-channel recording of two distinct inducers (hypoxia and Wnt) through two differently biased TdTs. Select TdT variants were also tested in vitro, revealing concordance between each variant's in vitro bias and the in vivo bias determined from the multiplexed high throughput assay. Overall, our work and the multiplex assay it features should support the continued development of TdT-based DNA recorders, in vitro applications of TdT, and further study of the biology of TdT.

Optimising Text Steganography with Alpha-Based Representative Binary: Analysing Contain Letter Used and Capacity Ratio

Text steganography has become essential for secure communication in the digital age, offering a method to hide messages within seemingly ordinary text. This paper introduces the Alpha-based Representative Binary technique, an advanced feature-based method for text steganography. It utilizes both capital and small letters to represent 2-bit binary values, embedding more information without altering the visual integrity of the text. By assigning different binary values to uppercase and lowercase letters, this technique optimizes the balance between information density and detectability. Embedding 2-bit values strikes an ideal balance, as it allows for a higher capacity of hidden information while maintaining a low profile. This study evaluates the performance of the Alpha-based Representative Binary technique against existing methods like QUAD and One-Flow-2-bit, focusing on two metrics performance: the total character count in the stego text (contain letter used) and the amount of data that can be embedded relative to the cover text size (capacity ratio). The findings underscore the technique's strengths in maintaining secure and undetectable hidden messages within text. This paper highlights the evolution and effectiveness of feature-based methods, emphasizing the need for continued innovation to address detection risks and content modification challenges. By providing a detailed analysis and potential improvements, this paper aims to contribute to the development of more robust and undetectable text steganography systems. The perceptions gained from this paper is expected as valuable for researchers and practitioners in the field of secure communication.

Comparative Study of Chinese Translations of “Gitanjali” based on Natural Language Processing

This paper employs the HanLP natural language processing model to conduct a quantitative text analysis of four Chinese translations of “Gitanjali”. The analysis examines text features such as character and sentence count, lexical properties, lexical density, and high-frequency words. The results indicate that, except for Wu Yan’s translation, the text features of the other translations are relatively similar in terms of data distribution. Wu Yan’s translation has higher character and sentence count, and usage of lexical properties than the other translations, but it has the lowest lexical density, indicating a lower information density. The study also reveals that the choice of lexical properties and high-frequency words in each translation is influenced by the translator’s personal language preferences. Although the types of high-frequency words are relatively fixed, their frequencies vary significantly. Based on these findings, the study further uncovers the intrinsic logic and structural relationships among different text features, offering significant insights for the study of specific literary and translation texts, as well as broader translation and linguistic research. This paper demonstrates the effectiveness of HanLP in literary and translation research, highlighting the potential of natural language processing models in large-scale text analysis.