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The isothermal hot forming of high-elasticity titanium alloy thin-walled components often suffers from significant elastic loss. This study investigates the novel Ti–35Nb–2Zr–0.3O (Ti3523) high-elasticity titanium alloy, focusing on the effects of rapid resistance heating (RRH) compared to traditional muffle furnace heating (MFH) on its microstructure and mechanical properties via EBSD and tensile tests. The results show that RRH at a high heating rate (~45 °C/s) effectively suppresses grain coarsening and minimizes dislocation annihilation. Following air cooling, the yield strength of the alloy decreased to 503.01 MPa for RRH-treated specimens, while 405.49 MPa for MFH-treated specimens compared to the original cold-rolled sheet with a yield strength of 754.25 MPa. Additionally, RRH promoted a higher martensitic α′′ transition, leading to lower elastic modulus (40.62 GPa). After aging treatment, the RRH-treated specimens exhibited precipitation of a high-strength α phase, leading to significant improvement of yield strength (755.63 MPa) and elastic modulus (70.39 GPa). The elastic performance of RRH-treated specimens ( Ur = 4.056 MPa) was better than that of the MFH-treated specimens ( Ur = 3.333 MPa) and close to the performance of the original sheet ( Ur = 4.577 MPa). With the identical heating method (RRH), higher heating rates can preserve the high elasticity of the original sheet. Building upon these findings, the hot forming process of the Ti3523 alloy was further explored. The results revealed that dynamic recrystallization process occurs more completely in the alloy after forming and aging under the RRH process, leading to a 70.72% increase in resilience modulus compared to the original cold-rolled sheet. Due to the dynamic recrystallization, the dislocation density decreased from 6.94×10 14 /m 2 to 6.63×10 14 /m 2 and the proportion of dynamically recrystallized grains increased from 28% to 48.1% after aging treatment. This rapid heating and high-temperature forming method offers a promising technical route for manufacturing advanced aerospace components.

Lymphatic metastases are closely associated with tumor relapse and reduced survival in colorectal cancer (CRC). How tumor cells disseminate within the lymphatic network remains largely unknown. Here, we analyze the subclonal structure of 94 tumor samples, covering the primary tumors, lymph node metastases (LNMs), and liver metastases from 10 CRC patients. We portray a high-resolution lymphatic metastatic map for CRC by dividing LNMs into paracolic, intermediate, and central subgroups. Among the 61 metastatic routes identified, 38 (62.3%) are initiated from the primary tumors, 22 (36.1%) from LNMs, and 1 from liver metastasis (1.6%). In 5 patients, we find 6 LNMs that reseed 2 or more LNMs. We summarize 3 diverse modes of metastasis in CRC and show that skip spreading of tumor cells within the lymphatic network is common. Our study sheds light on the complicated metastatic pattern in CRC and has great clinical implications. Lymphatic metastases are closely associated with tumor relapse and reduced survival in colorectal cancer (CRC). Here, the authors analysed the primary tumours, lymph node metastasis and liver metastasis of ten CRC patients and reveal co-existence of diverse modes of metastasis in the same patient.

Tiny object detection remains a formidable challenge in the field of computer vision. There are many factors that influence tiny object detection performance. In this paper, we focus primarily on the following two aspects. First, due to diminutive size and inappropriate label assignment strategy, tiny objects yield significantly fewer positive samples than larger objects, resulting in weakened supervisory signals during backpropagation and model training. Second, most existing detectors directly combine the classification loss and bounding box regression loss during training. Some improvement methods focus exclusively on either classification or localization, leading to potential discrepancies in which predictions exhibit precise localization but incorrect classifications or accurate classifications with imprecise localization. To address these issues, we propose a novel Joint Optimization Loss (JOL) that dynamically assigns optimal weights to each training sample, enabling joint optimization of both the classification and regression losses. Notably, JOL integrates seamlessly with most mainstream detectors and loss functions without requiring alterations to network architectures. Extensive experiments conducted on five benchmark datasets demonstrate the superior performance of our approach, achieving AP improvements of 1.7 and 1.5 points on the AI-TOD and SODA-D datasets, respectively, compared to the state-of-the-art method.

Recent advancements in deep learning have driven the rapid proliferation of deepfake generation techniques, raising substantial concerns over digital security and trustworthiness. Most current detection methods primarily focus on spatial or frequency domain features but show limited effectiveness when dealing with compressed videos and cross-dataset scenarios. Observing that mainstream generation methods use frame-by-frame synthesis without adequate temporal consistency constraints, we introduce the Spatiotemporal Attention 3D Network (STA-3D), a novel framework that combines a lightweight spatiotemporal attention module with a 3D convolutional architecture to improve detection robustness. The proposed attention module adopts a symmetric multi-branch architecture, where each branch follows a nearly identical processing pipeline to separately model temporal-channel, temporal-spatial, and intra-spatial correlations. Our framework additionally implements Spatial Pyramid Pooling (SPP) layers along the temporal axis, enabling adaptive modeling regardless of input video length. Furthermore, we mitigate the inherent asymmetry in the quantity of authentic and forged samples by replacing standard cross entropy with focal loss for training. This integration facilitates the simultaneous exploitation of inter-frame temporal discontinuities and intra-frame spatial artifacts, achieving competitive performance across various benchmark datasets under different compression conditions: for the intra-dataset setting on FF++, it improves the average accuracy by 1.09 percentage points compared to existing SOTA, with a more significant gain of 1.63 percentage points under the most challenging C40 compression level (particularly for NeuralTextures, achieving an improvement of 4.05 percentage points); while for the intra-dataset setting, AUC is enhanced by 0.24 percentage points on the DFDC-P dataset.

Colorectal cancer (CRC) is a common malignancy of the digestive tract associated with high mortality rates and significant invasive properties. Despite advancements in research, a comprehensive understanding of the regulatory mechanisms underlying CRC remains elusive. This study aimed to investigate the potential role of nucleolar protein 6 (NOL6) and its related genes as novel biomarkers for cell proliferation in CRC. The findings of this study could significantly contribute to early diagnosis and more effective therapeutic strategies for CRC. Human CRC cell line HCT116 was cultured under standard conditions. Quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to measure NOL6 expression levels in CRC tissues. Cell proliferation was assessed using the MTT assay, Celigo cell count assay, and colony formation assays, while flow cytometry was employed to evaluate cell apoptosis. Additionally, a transwell migration assay was performed to evaluate CRC cell migration and invasion. Comprehensive proteomic and transcriptomic analyses were performed to identify the downstream genes and pathways affected by NOL6 knockdown. The expression of these genes was further validated by Western blotting. Xenograft mouse models were used to determine the effects of NOL6 on CRC in vivo. Tandem mass tags (TMT)-labeled quantitative proteomic technology and bioinformatic analysis were employed to identify the functional pathway and proteins regulated by NOL6. The Cancer Genome Atlas data analysis revealed a significant upregulation of NOL6 in CRC cells compared with adjacent normal cells. In HCT116 cells, downregulation of NOL6 was associated with decreased proliferation and colony formation, as well as increased apoptosis. Additionally, NOL6 knockdown resulted in a decrease in the weight and volume of tumors in nude mice, suggesting its role in tumorigenesis. TMT and Western blot analyses revealed that NOL6 knockdown suppressed MCM3 and MCM7 expression. This study demonstrated that NOL6 functions as an oncogene that facilitates CRC progression, suggesting its potential role as a therapeutic target for CRC management.

In real-world visual recognition tasks, long-tailed distributions pose a widespread challenge, with extreme class imbalance severely limiting the representational learning capability of deep models. In practice, due to this imbalance, deep models often exhibit poor generalization performance on tail classes. To address this issue, data augmentation through the synthesis of new tail-class samples has become an effective method. One popular approach is CutMix, which explicitly mixes images from tail and other classes, constructing labels based on the ratio of the regions cropped from both images. However, region-based labels completely ignore the inherent semantic information of the augmented samples. To overcome this problem, we propose a saliency-guided local semantic mixing (LSM) method, which uses differentiable block decoupling and semantic-aware local mixing techniques. This method integrates head-class backgrounds while preserving the key discriminative features of tail classes and dynamically assigns labels to effectively augment tail-class samples. This results in efficient balancing of long-tailed data distributions and significant improvements in classification performance. The experimental validation shows that this method demonstrates significant advantages across three long-tailed benchmark datasets, improving classification accuracy by 5.0%, 7.3%, and 6.1%, respectively. Notably, the LSM framework is highly compatible, seamlessly integrating with existing classification models and providing significant performance gains, validating its broad applicability.

In real-world visual recognition tasks, long-tailed distribution is a pervasive challenge, where the extreme class imbalance severely limits the representation learning capability of deep models. Although supervised learning has demonstrated certain potential in long-tailed visual recognition, these models’ gradient updates dominated by head classes often lead to insufficient representation of tail classes, resulting in ambiguous decision boundaries. While existing Supervised Contrastive Learning variants mitigate class bias through instance-level similarity comparison, they are still limited by biased negative sample selection and insufficient modeling of the feature space structure. To address this, we propose Rebalancing Supervised Contrastive Learning (Reb-SupCon), which constructs a balanced and discriminative feature space during model training to alleviate performance deviation. Our method consists of two key components: (1) a dynamic rebalancing factor that automatically adjusts sample contributions through differentiable weighting, thereby establishing class-balanced feature representations; (2) a prototype-aware enhancement module that further improves feature discriminability by explicitly constraining the geometric structure of the feature space through introduced feature prototypes, enabling locally discriminative feature reconstruction. This breaks through the limitations of conventional instance contrastive learning and helps the model to identify more reasonable decision boundaries. Experimental results show that this method demonstrates superior performance on mainstream long-tailed benchmark datasets, with ablation studies and feature visualizations validating the modules’ synergistic effects.

With the rapid development of remote sensing technology, the application of convolutional neural networks in remote sensing object detection has become very widespread, and some multimodal feature fusion networks have also been proposed in recent years. However, these methods generally do not consider the long-tailed problem that is widely present in remote sensing images, which limits the further improvement of model detection performance. To solve this problem, we propose a novel long-tailed object detection method for multimodal remote sensing images, which can effectively fuse the complementary information of visible light and infrared images and adapt to the imbalance between positive and negative samples of different categories. Firstly, the dynamic feature fusion module (DFF) based on image entropy can dynamically adjust the fusion coefficient according to the information content of different source images, retaining more key feature information for subsequent object detection. Secondly, the instance-balanced mosaic (IBM) data augmentation method balances instance sampling during data augmentation, providing more sample features for the model and alleviating the negative impact of data distribution imbalance. Finally, class-balanced BCE loss (CBB) can not only consider the learning difficulty of specific instances but also balances the learning difficulty between categories, thereby improving the model’s detection accuracy for tail instances. Experimental results on three public benchmark datasets show that our proposed method achieves state-of-the-art performance; in particular, the optimization of the long-tailed problem enables the model to meet various application scenarios of remote sensing image detection.

With the development of unmanned aerial vehicle and satellite technology, the application of tiny object detection in remote sensing images is becoming increasingly widespread. Although significant progress has been made in the accuracy and speed of object detection in recent years, performance declines sharply when general object detectors are applied to tiny objects; one of the main reasons is unsuitable label assignment strategy. Traditional label assignment strategies often rely on fixed thresholds, leading to mismatches between the number of positive samples and object areas. Additionally, most improved methods require setting one or more hyperparameters. In this paper, we propose a dynamic adaptive label assignment strategy (DALA) comprising three modules. First, we calculate the similarity distance to comprehensively evaluate the matching degree between anchors and each ground truth. Then, we use the ratio‐based label assignment strategy to select an appropriate number of positive samples for each object. Finally, we introduce dynamic weighting loss during training to ensure the model pays more attention to tiny objects. Our three modules automatically adapt to different datasets and detectors without any manual hyperparameter settings. Extensive experiments on four widely used datasets demonstrate the excellent performance of our proposed method. Our code will be released soon.

To achieve high-precision trajectory tracking control for an underwater hexapod robot, this paper proposes a hierarchical control architecture. Firstly, a multi-rigid-body dynamic model for the robot is established based on the Newton-Euler method and reasonably simplified. Secondly, a Central Pattern Generator (CPG) network with the Hopf oscillator as its core is designed to generate stable and coordinated crawling gaits. By introducing a steering parameter, a kinematic model connecting the CPG output is constructed. Furthermore, based on this dynamic and kinematic model, an upper-layer Model Predictive Controller (MPC) is designed. The optimized control quantities output by the MPC are mapped into the rhythmic parameters of the CPG network via a transfer function established by fitting experimental data, thus forming the complete MPC-CPG controller. Finally, the proposed method is validated through simulations of circular trajectory tracking. The results show that even in the presence of initial errors, the controller can converge rapidly, with trajectory position error consistently maintained within −0.1 m~0.1 m, and heading angle error confined to the range of −15~15°. The experiments fully demonstrate the effectiveness of the proposed MPC-CPG controller in ensuring trajectory tracking accuracy, motion smoothness, and system stability.