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Based on the principle of weight superposition, a modified Madelung model is proposed. By combining the signal delay response characteristics (SDRC), a dynamic model and its inverse model are established to describe and compensate for the rate‐dependent hysteresis phenomenon of the piezoelectric actuators (PEAs). The effectiveness of the proposed method is verified by experiments, and the results show that the hysteresis nonlinearity of the PEA is basically eliminated, with the normalized root‐mean‐square error of 0.64% under the excitation of multi‐frequency superposition signal. Based on the principle of weight superposition, a modified Madelung model is proposed. By combining the signal delay response characteristics (SDRC), a dynamic model and its inverse model are established to describe and compensate for the rate‐dependent hysteresis phenomenon of the piezoelectric actuators (PEAs). The effectiveness of the proposed method is verified by experiments.

In order to realize the process of player feature extraction and classification from multi-frequency frame-changing football match images more quickly, and complete the tactical plan that is more conducive to the game, this paper puts forward a method for analyzing and judging the tactics of women’s football team based on Convolutional Neural Network (CNN). By extracting the players’ performance in recent training and competition from continuous video frame data, a multi-dimensional vector input data sample is formed, and CNN is used to analyze the players’ hidden ability before the game and the players’ mistakes in different positions on the field to cope with different football schedules. Before the formal test, 10 games of 2021–2022 UEFA Women’s Champions League were randomly selected and intercepted to train the CNN model. The model showed excellent accuracy in the classification of image features of various football moves and goal angles, and the overall classification accuracy of each category exceeded 95%. The accuracy of classifying a single match is above 88%, which highlights the reliability and stability of the model in identifying and classifying women’s football matches. On this basis, the test results show that: according to the analysis of players’ personal recessive ability before the game, after model image recognition and comparison, the difference between the four scores of players’ personal recessive ability with CNN mode and the manual score of professional coaches was smaller, and the numerical difference was within the minimum unit value, and the numerical calculation results were basically the same. According to the analysis of players’ mistakes in different positions on the field, CNN was used to monitor the real-time mistakes. It was found that the two players in the forward position made the highest mistakes, and they were replaced by substitute players at 73.44 min and 65.28 min after the team scored and kept the ball, respectively. After the substitute players played, the team’s forward position mistake rate decreased obviously. The above results show that CNN technology can help players get personal recessive ability evaluation closer to professional evaluation in a shorter time, and help the coaching team to analyze the real-time events better. The purpose of this paper is to help the women’s football team complete the pre-match tactical training, reduce the analysis time of players’ mistakes in the game, deal with different opponents in the game and improve the winning rate of the game.

Triboelectric nanogenerators (TENGs), a type of promising micro/nano energy source, have been arousing tremendous research interest since their inception and have been the subject of many striking developments, including defining the fundamental physical mechanisms, expanding applications in mechanical to electric power conversion and self-powered sensors, etc. TENGs with a superior surface charge density at the interfaces of the electrodes and dielectrics are found to be crucial to the enhancement of the performance of the devices. Here, an overview of recent advances, including material optimization, circuit design, and strategy conjunction, in developing TENGs through surface charge enhancement is presented. In these topics, different strategies are retrospected in terms of charge transport and trapping mechanisms, technical merits, and limitations. Additionally, the current challenges in high-performance TENG research and the perspectives in this field are discussed. Tactics for modulating the surface charge density of TENGs by material optimization are summarized. Strategies for manufacturing ultra-high electrode charge density TENGs utilizing advanced circuit designs are demonstrated. The synergistic effects of material optimization and advanced circuit design are presented. Current challenges in the field of TENGs are discussed.

Background This study aimed to identify clinical factors and develop a predictive model for pathological complete response (pCR) and major pathological response (MPR) in non-small cell lung cancer (NSCLC) patients receiving neoadjuvant chemotherapy combined with immune checkpoint inhibitors (ICIs). Methods Cases meeting inclusion criteria were divided into high- and low-risk groups according to 75 clinical indicators based on tenfold LASSO selection. Logistic regression was employed to analyze both pCR and MPR. The accuracy of the nomograms was assessed using the time-dependent area under the curve (AUC). Results A total of 297 patients from four multiple centers were included in the study, with 212 assigned to the training set and 85 to the testing set. The AUC was determined for the prediction of pCR (training: 0.97; testing: 0.88) and MPR (training: 0.98; testing: 0.81). Significant associations were observed between the preoperative tumor maximum diameter, preoperative tumor maximum standardized uptake value (SUV max ), changes in tumor SUV max , percentage of tumor reduction, baseline total prostate-specific antigen (TPSA) and pathological response ( P  < 0.001). Conclusions The combined application of clinical indicators including non-invasive tumor imaging and hematology can help clinicians to obtain a higher ability to predict NSCLC patient’s pathological remission, and the effect is better than that of clinical factors alone. These findings could help guide personalized treatment strategies in this patient population. Graphical abstract

Aiming to address the fundamental limitation of fixed activation functions that constrain network expressiveness in existing deep deblurring models, in this pioneering study, we introduced Kolmogorov–Arnold Networks (KANs) into the field of full-color/RGB image deblurring, proposing the Kolmogorov–Arnold and Transformer Network (KT-Deblur) framework based on dynamically learnable activation functions. This framework overcomes the constraints of traditional networks’ fixed nonlinear transformations by employing adaptive activation regulation for different blur types through KANs’ differentiable basis functions. Integrated with a U-Net architecture within a generative adversarial network framework, it significantly enhances detail restoration capabilities in complex scenarios. The innovatively designed Unified Attention Feature Extraction (UAFE) module combines neighborhood self-attention with linear self-attention mechanisms, achieving synergistic optimization of noise suppression and detail enhancement through adaptive feature space weighting. Supported by the Fast Spatial Feature Module (FSFM), it effectively improves the model’s ability to handle complex blur patterns. Our experimental results demonstrate that the proposed method outperforms existing algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics across multiple standard datasets, achieving an average PSNR of 41.25 dB on the RealBlur-R dataset, surpassing the latest state-of-the-art (SOTA) algorithms. This model exhibits strong robustness, providing a new paradigm for image-deblurring network design.

Remote sensing target recognition has always been an important topic of image analysis, which has significant practical value in computer vision. However, remote sensing targets may be largely occluded by obstacles due to the long acquisition distance, which greatly increases the difficulty of recognition. Shape, as an important feature of a remote sensing target, plays an important role in remote sensing target recognition. In this paper, an occluded shape recognition method based on the local contour strong feature richness (contour pixel richness, contour orientation richness, and contour distance richness) to the walking minimum bounding rectangle (MBR) is proposed for the occluded remote sensing target (FEW). The method first obtains the local contour feature richness by using the walking MBR; it is a simple constant vector, which greatly reduces the cost of feature matching and increases the speed of recognition. In addition, this paper introduces the new concept of strong feature richness and uses the new strategy of constraint reduction to reduce the complex structure of shape features, which also speeds up the recognition speed. Validation on a self-built remote sensing target shape dataset and three general shape datasets demonstrate the sophisticated performance of the proposed method. FEW in this paper has both higher recognition accuracy and extremely fast recognition speed (less than 1 ms), which lays a more powerful theoretical support for the recognition of occluded remote sensing targets.

Mesenchymal stem cells (MSCs) are a type of pluripotent adult stem cell with strong self-renewal and multi-differentiation abilities. Their excellent biological traits, minimal immunogenicity, and abundant availability have made them the perfect seed cells for treating a wide range of diseases. After more than 60 years of clinical practice, metformin is currently one of the most commonly used hypoglycaemic drugs for type 2 diabetes in clinical practice. In addition, metformin has shown great potential in the treatment of various systemic diseases except for type 2 diabetes in recent years, and the mechanisms are involved with antioxidant stress, anti-inflammatory, and induced autophagy, etc. This article reviews the effects and the underlying mechanisms of metformin on the biological properties, including proliferation, multi-differentiation, and aging, of MSCs in vitro and in vivo with the aim of providing theoretical support for in-depth scientific research and clinical applications in MSCs-mediated disease treatment.

The application of diffusion models in the field of remote sensing image generation has significantly improved the performance of generation algorithms. However, existing methods still exhibit certain limitations, such as the inability to generate images with rich texture details and minimal geometric distortions in a controllable manner. To address these shortcomings, this work introduces an innovative remote sensing image generation algorithm, OP-Gen, which is guided by textual descriptions and OpenStreetMap (OSM) images. OP-Gen incorporates two information extraction branches: ControlNet and OSM-prompt (OP). The ControlNet branch extracts structural and spatial information from OSM images and injects this information into the diffusion model, providing guidance for the overall structural framework of the generated images. In the OP branch, we design an OP-Controller module, which extracts detailed semantic information from textual prompts based on the structural information of the OSM image. This information is subsequently injected into the diffusion model, enriching the generated images with fine-grained details, aligning the generated details with the structural framework, and thus significantly enhancing the realism of the output. The proposed OP-Gen algorithm achieves state-of-the-art performance in both qualitative and quantitative evaluations. The qualitative results demonstrate that OP-Gen outperforms existing methods in terms of structural coherence and texture detail richness. Quantitatively, the algorithm achieves a Fréchet inception distance (FID) of 45.01, a structural similarity index measure (SSIM) of 0.1904, and a Contrastive Language-Image Pretraining (CLIP) score of 0.3071, all of which represent the best performance among the current algorithms of the same type.

Promoters play a crucial role in regulating gene transcription. However, our understanding of how genetic variants influence alternative promoter selection is still incomplete. In this study, we implement a framework to identify genetic variants that affect the relative usage of alternative promoters, known as promoter usage quantitative trait loci (puQTLs). By constructing an atlas of human puQTLs across 49 different tissues from 838 individuals, we have identified approximately 76,856 independent loci associated with promoter usage, encompassing 602,009 genetic variants. Our study demonstrates that puQTLs represent a distinct type of molecular quantitative trait loci, effectively uncovering regulatory targets and patterns. Furthermore, puQTLs are regulating in a tissue-specific manner and are enriched with binding sites of epigenetic marks and transcription factors, especially those involved in chromatin architecture formation. Notably, we have also found that puQTLs colocalize with complex traits or diseases and contribute to their heritability. Collectively, our findings underscore the significant role of puQTLs in elucidating the molecular mechanisms underlying tissue development and complex diseases. In this study, the authors create an atlas of promoter usage QTLs across 49 tissues from 838 individuals, identifying 602,009 genetic variants associated with promoter usage, offering insights into the genetic regulation of transcription.

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data. Intronic polyadenylation (IPA) can produce transcripts with truncated coding regions and has been implicated in diverse biological processes and diseases. Here, the authors present a computational method for the accurate delineation of IPA events using RNA-sequencing data.