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The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance. [ABSTRACT FROM AUTHOR]

Compared to hyperspectral trackers that adopt the "pre-training then fine-tuning" training paradigm, those using the "pre-training then prompt-tuning" training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt template design, thus failing to bridge the domain gap between hyperspectral data and pre-trained models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability and require re-training for the different spectral bands of hyperspectral data, leading to the inefficient use of computational resources. In order to address the aforementioned problems, we propose a spectral similarity prompt learning approach for hyperspectral object tracking (SPTrack). First, we introduce a spectral matching map based on spectral similarity, which converts 3D hyperspectral data with different spectral bands into single-channel hotmaps, thus enabling cross-spectral domain generalization. Then, we design a channel and position attention-based feature complementary prompter to learn blended prompts from spectral matching maps and three-channel images. Extensive experiments are conducted on the HOT2023 and IMEC25 data sets, and SPTrack is found to achieve state-of-the-art performance with minimal computational effort. Additionally, we verify the cross-spectral domain generalization ability of SPTrack on the HOT2023 data set, which includes data from three spectral bands. [ABSTRACT FROM AUTHOR]

COVID‐19 is mainly characterized by respiratory disorders and progresses to multiple organ involvement in severe cases. With expansion of COVID‐19 and SARS‐CoV‐2 research, correlative liver injury has been revealed. It is speculated that COVID‐19 patients exhibited abnormal liver function, as previously observed in the SARS and MERS pandemics. Furthermore, patients with underlying diseases such as chronic liver disease are more susceptible to SARS‐CoV‐2 and indicate a poor prognosis accompanied by respiratory symptoms, systemic inflammation, or metabolic diseases. Therefore, COVID‐19 has the potential to impair liver function, while individuals with preexisting liver disease suffer from much worse infected conditions. COVID‐19 related liver injury may be owing to direct cytopathic effect, immune dysfunction, gut−liver axis interaction, and inappropriate medication use. However, discussions on these issues are infancy. Expanding research have revealed that angiotensin converting enzyme 2 (ACE2) expression mediated the combination of virus and target cells, iron metabolism participated in the virus life cycle and the fate of target cells, and amino acid metabolism regulated immune response in the host cells, which are all closely related to liver health. Further exploration holds great significance in elucidating the pathogenesis, facilitating drug development, and advancing clinical treatment of COVID‐19‐related liver injury. This article provides a review of the clinical and laboratory hepatic characteristics in COVID‐19 patients, describes the etiology and impact of liver injury, and discusses potential pathophysiological mechanisms. Significance statement: This review will be beneficial to understand the relationship between COVID‐19 and liver injury, concentrate on direct cellular effects, gut−liver axis, and immune response between them and provide some ideas for clinical medication and drug development. [ABSTRACT FROM AUTHOR]

Alloying high‐cost Pt with transition metals has been considered as an effective route to synthesize the electrocatalysts with low Pt loading and excellent activity towards oxygen reduction reaction (ORR) under acid solution. The galvanic replacement method, as featured with efficiency and simplicity, is widely reported to produce Pt‐based bimetallic alloys and thereby declare the significance of reductive transition metal precursor on the enhancement of ORR performance. Herein, mix‐phased Cu−Cu2O precursor was applied to prepare carbon black supported highly dispersed PtCu alloy nanoparticles (PtCu/C). The proper Cu−Cu2O ratios can exactly facilitate the generation of small sized PtCu alloy nanoparticles with regulated bimetallic content. Meanwhile, the Cu2O phase is revealed to benefit the electron transfer from Pt to Cu and thus improve the intrinsic activity of Pt active sites. And the metallic Cu can favor the promotion of electrochemical active surface area. Consequently, the as‐prepared PtCu/C behaves impressive ORR activity with half‐wave potential of 0.88 V (vs. RHE) and mass activity of 0.49 A cm−2 mgPt−1 at 0.8 V, which is 9.8 times of commercial Pt/C catalysts. Our work will offer helpful advices for the development and regulation of novel Pt‐based alloy materials towards diverse electrocatalysis. [ABSTRACT FROM AUTHOR]

Introduction: Expressive flexibility, or the ability to both up‐ and down‐regulate emotional expressions in social interactions, is thought as an indicator and a consequence of healthy interpersonal relationships. The present longitudinal study examined bidirectional associations between expressive flexibility and friendship quality in early adolescence. Since prior research found inconsistent results regarding the adaptiveness of expressive flexibility, which indicated the necessity to consider individual variability in the process, we further tested the potential moderating effect of social anxiety in the links from expressive flexibility to friendship quality. Methods: Participants from two junior high schools in eastern China (N = 274; 50.4% female; Mage = 13.56) were surveyed at three time points with 6‐month intervals. Expressive flexibility, friendship quality, and social anxiety were all assessed via self‐reported scales. Results: According to the cross‐lagged model results, friendship quality significantly predicted increased expressive flexibility over time. Conversely, the longitudinal association from expressive flexibility to friendship quality was not significant, but the interaction between expressive flexibility and social anxiety significantly predicted later friendship quality. Further analyses via the Johnson–Neyman technique revealed that expressive flexibility only positively predicted friendship quality for adolescents with lower levels of social anxiety. Conclusion: Our results suggest that expressive flexibility is not always socially adaptive, so practical interventions that aim to improve youths' social adjustment via expressive flexibility training might need to consider the role of individual characteristics. [ABSTRACT FROM AUTHOR]

Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weakened as the depth of network increases. In this paper, we propose a dense dual-attention network for LF image SR. Specifically, we design a view attention module to adaptively capture discriminative features across different views and a channel attention module to selectively focus on informative information across all channels. These two modules are fed to two branches and stacked separately in a chain structure for adaptive fusion of hierarchical features and distillation of valid information. Meanwhile, a dense connection is used to fully exploit multi-level information. Extensive experiments demonstrate that our dense dual-attention mechanism can capture informative information across views and channels to improve SR performance. Comparative results show the advantage of our method over state-of-the-art methods on public datasets. [ABSTRACT FROM AUTHOR]