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Cold-atom absolute gravimeters are widely used for measuring the acceleration of gravity, yet their sensitivity is often limited by ground vibrations. Existing vibration compensation algorithms struggle to strike a balance between search accuracy and computational efficiency and are prone to local optima. Here, we propose an improved whale optimization algorithm (IWOA) to address these issues. By combining Logistic-LHS (Latin hypercube sampling) chaotic initialization, adaptive adjustment, and a Gaussian mutation operator to prevent premature convergence, IWOA achieves higher search efficiency and superior sensitivity than traditional algorithms. The method is validated through multiple simulation studies and further assessed experimentally on the NIM-AGRb-1 cold-atom gravimeter system. The results show that IWOA reduces the uncertainty of the fitted phase parameter by 66%. The Pearson correlation between atomic transition probability and the calculated phase increases to a maximum of 0.98, and the gravity sensitivity improves to 47 μGal/Hz when the evolution time T is 80 ms.

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios.

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.

This study addresses the key scientific problem of urban safety in complex, hazard-inducing geological environments by focusing on representative towns in the Three Gorges Reservoir area. Through the integrated use of field investigations, numerical simulations, and multivariate statistical analysis, we developed a comprehensive model for assessing geological safety risk in reservoir-area towns. A four-tier deep safety evaluation system was constructed for two types of hazard-inducing geological environments, and a classification scheme for shallow susceptibility was proposed. On this basis, a five-tier integrated urban geological safety risk evaluation model was established that combines deep safety level, engineering sensitivity, shallow susceptibility, and prevention difficulty. The model exhibited strong performance (pseudo R2 ≥ 0.914, p < 0.001) and indicates that risk is predominantly moderate (Grade III), with 85.7% of the 21 representative areas in Wushan, Fengjie, and other “2 + 4” towns falling into this category. Overall, the results provide an operational tool to support disaster risk reduction, risk-informed land-use governance, and resilient infrastructure planning, thereby contributing to sustainable urban development in reservoir-side mountainous regions.

It has long been desired to achieve mechanical enhancement and structural health monitoring by introducing carbon nanotubes (CNTs) into traditional carbon fiber (CF) composites. Herein, the initiation of micro-damage and crack propagation has been investigated by utilizing in situ electrical resistance changes in interlaminar hybrid CNT network/CF composites during the shear loading process. The results show a clear relationship between the crack propagation and the electrical resistance response particularly when approaching the failure of the single-layer CNT network hybrid composites. Furthermore, the chemically modified CNT network exhibits evident enhancement on main mechanical properties of the CF composites, superior to the thermoplastic toughening method. The characterizations manifest that the multiscale interlayered CNT/CF structure can simultaneously resist the crack propagation along both the in-plane direction and the cross-plane direction, which consequently enhances the flexural and compressive strengths of the composite material. This discovery provides a novel idea for the potential application of CNT network/CF hybrid composites in the integration of mechanical reinforcement and structural health monitoring, namely, that the CNT network acts not only as a reinforcing phase but also as a sensor for the structural health monitoring of the composites.

Surface free energy is an important parameter in surface and interface properties of fiber reinforced polymer composite. The BET (Brunauer, Emmett, and Teller) surface area and surface energy of the sample can be obtained by Inverse Gas Chromatography (IGC) based on the adsorption principle. In this paper, surface energy of carbon fiber bundle was tested by means of IGC under different conditions to find reliable test parameters. The main parameters involved include length, mass, and packing density of sample, target fractional surface coverage, flow rate, and maximum elution time. It is demonstrated that IGC has the advantages of simple sample preparation, stable test data, high automation, and high sensitivity for carbon fiber. Among all test conditions, packing density and flow rate have the greatest influences on the experimental results. The optimized test parameters are suitable for various kinds of carbon fiber bundles, including polyacrylonitrile-based and pitch-based carbon fibers with different tensile properties and tow sizes. Moreover, IGC can acutely characterize the surface properties of carbon fibers after carbon nanotube modification and heat treatment, which are hard to carry out using contact angle method.

Dali County is located in the eastern Weihe Basin where frequent ground fissure disasters occur. Dali ground fissures are macroscopically part of the ground fissure group in the Weihe Basin and have caused serious damage to infrastructure and agricultural production in this area. In this paper, 25 ground fissures in Dali County are identified through investigation, and two typical ground fissures in Fengcun and Weizhuang are selected for research. Through field investigation, trench excavation, geophysical exploration, soil water content tests and collapsibility coefficient tests, the basic features, shallow structural characteristics, deep structural characteristics and genetic mechanisms of the two ground fissures are revealed. The strike of the Fengcun ground fissure is 45°, almost parallel to the Shuangquan-Linyi Fault; the length is 2 km, and the profile is ladder-like and extends deep with synsedimentary characteristics. The Weizhuang ground fissures are circular in plan view with a length of 315 m and trumpet-shaped in profile; they disappear in the paleosol layer. The mechanism of the Fengcun ground fissure is related to basin extension and fault activity. The ground fissures are the surface reflections of deep faults. The mechanism of the Weizhuang ground fissures is related to loess collapsibility and seepage. The Malan loess has differential settlement in collapsible and noncollapsible areas, and ground fissures are the boundary line for differential settlement of loess. Finally, based on the genetic mechanisms of ground fissures, Dali ground fissures can be divided into two types: tectonic and nontectonic.

Graphene films, fabricated by chemical vapor deposition (CVD) method, have exhibited superiorities in high crystallinity, thickness controllability, and large‐scale uniformity. However, most synthesized graphene films are substrate‐dependent, and usually fragile for practical application. Herein, a freestanding graphene film is prepared based on the CVD route. By using the etchable fabric substrate, a large‐scale papyraceous freestanding graphene fabric film (FS‐GFF) is obtained. The electrical conductivity of FS‐GFF can be modulated from 50 to 2800 Ω sq−1 by tailoring the graphene layer thickness. Moreover, the FS‐GFF can be further attached to various shaped objects by a simple rewetting manipulation with negligible changes of electric conductivity. Based on the advanced fabric structure, excellent electrical property, and high infrared emissivity, the FS‐GFF is thus assembled into a flexible device with tunable infrared emissivity, which can achieve the adaptive camouflage ability in complicated backgrounds. This work provides an infusive insight into the fabrication of large‐scale freestanding graphene fabric films, while promoting the exploration on the flexible infrared camouflage textiles. A freestanding graphene film is reported by etching graphene‐coated substrate with fabric structure. Moreover, the large‐scale papyraceous freestanding graphene fabric film (FS‐GFF) can be further attached to various shaped substrates and showing a superior electrical conductivity. Meanwhile, the FS‐GFF based infrared camouflage flexible textile device exhibits superior infrared camouflage performance.

The 3D organization of chromatin plays a fundamental role in gene regulation, cellular function, and disease mechanisms. However, current experimental techniques, such as Hi‐C, remain costly and labor‐intensive, limiting their application in large‐scale and disease‐related studies. To address this challenge, ChromNet is presented, a multi‐task learning framework that integrates epigenetic signals across diverse cell types to enable high‐precision prediction of chromatin architecture. By incorporating noise perturbation and auxiliary classification tasks, ChromNet improves the identification of topologically associating domains (TADs) and cell‐type‐specific chromatin structures, demonstrating superior generalization performance. Notably, ChromNet accurately predicts chromatin interactions in acute myeloid leukemia (AML) samples by leveraging epigenetic signals from both normal and diseased cells, highlighting its potential for studying disease‐associated chromatin remodeling. Across multiple key benchmarks, ChromNet consistently outperforms existing models, providing a robust and cost‐effective solution for large‐scale chromatin conformation studies. This framework enables the exploration of chromatin structural variations across both cell types and disease states, offering new insights into the relationship between 3D genome architecture and gene regulation. ChromNet enables accurate cross‐cell‐type prediction of chromatin architecture from DNA and epigenetic profiles alone, capturing both conserved and cell‐type‐specific (including disease‐associated) structural features. Its noise‐perturbed multi‐task learning design enhances generalization, enabling precise reconstruction of 3D genome organization even when Hi‐C data for the target cell type are unavailable.