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Lithium (Li) metal anodes have received sustained attention due to their remarkable theoretical specific capacities and low electrochemical potentials. However, the nucleation and growth of Li dendrites during cycling have hindered their application in Li metal batteries (LMBs). As an indispensable battery component, separators offer an ideal platform for mitigating Li dendrites. Herein, an ion‐management membrane (IMM) concept utilizing polydopamine (PDA)‐modified polyetherimide track‐etched membranes (PEITEMs) is proposed. The PDA@PEITEM‐based IMM features vertically aligned uniform electronegative nanochannels, serving as ion distributors and “Li‐ion guides” to simultaneously smooth ion concentration fluctuations and accelerate Li+ selective conduction. The IMM's unique structural and chemical properties yield exceptional ionic conductivity (0.73 mS cm−1) and high Li+ transfer number (0.80) while minimizing Li+ concentration fluctuations. When employed in Li/Cu cells, the IMM facilitates a Coulombic efficiency exceeding 96% over 100 cycles at 0.5 mA cm−2. Moreover, it extends the cycle lifespan of Li/Li cells to 1 200 h at 1.0 mA cm−2. For Li/LiFePO4 cells, this approach enables a specific capacity of 146 mAh g−1, maintaining capacity retention of 79.84% after 1 000 cycles. This novel strategy for constructing free‐standing functional separators is convenient, efficient, and scalable, providing valuable insights into multifunctional separators for dendrite‐free LMBs. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) possess an inherent three-order structure, prompting increased interest in extracting 3D features. Tensor analysis and low-rank representations, notably truncated higher-order SVD (T-HOSVD), have gained prominence for this purpose. However, determining the optimal order and addressing sensitivity to changes in data distribution remain challenging. To tackle these issues, this paper introduces an unsupervised Superpixelwise Multiscale Adaptive T-HOSVD (SmaT-HOSVD) method. Leveraging superpixel segmentation, the algorithm identifies homogeneous regions, facilitating the extraction of local features to enhance spatial contextual information within the image. Subsequently, T-HOSVD is adaptively applied to the obtained superpixel blocks for feature extraction and fusion across different scales. SmaT-HOSVD harnesses superpixel blocks and low-rank representations to extract 3D features, effectively capturing both spectral and spatial information of HSIs. By integrating optimal-rank estimation and multiscale fusion strategies, it acquires more comprehensive low-rank information and mitigates sensitivity to data variations. Notably, when trained on subsets comprising 2%, 1%, and 1% of the Indian Pines, University of Pavia, and Salinas datasets, respectively, SmaT-HOSVD achieves impressive overall accuracies of 93.31%, 97.21%, and 99.25%, while maintaining excellent efficiency. Future research will explore SmaT-HOSVD's applicability in deep-sea HSI classification and pursue additional avenues for advancing the field. [ABSTRACT FROM AUTHOR]

In this study, an accurate tool is provided for the evaluation of the effect of joint motion effect on gait stability. This quantitative gait evaluation method relies exclusively on the analysis of data acquired using acceleration sensors. First, the acceleration signal of lower limb motion is collected dynamically in real-time through the acceleration sensor. Second, an algorithm based on improved dynamic time warping (DTW) is proposed and used to calculate the gait stability index of the lower limbs. Finally, the effects of different joint braces on gait stability are analyzed. The experimental results show that the joint brace at the ankle and the knee reduces the range of motions of both ankle and knee joints, and a certain impact is exerted on the gait stability. In comparison to the ankle joint brace, the knee joint brace inflicts increased disturbance on the gait stability. Compared to the joint motion of the braced side, which showed a large deviation, the joint motion of the unbraced side was more similar to that of the normal walking process. In this paper, the quantitative evaluation algorithm based on DTW makes the results more intuitive and has potential application value in the evaluation of lower limb dysfunction, clinical training and rehabilitation. [ABSTRACT FROM AUTHOR]

Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model's suitability in the original scene, we present a method called feature disentangling and domain shifting (FDDS) for domain adaptation. FDDS utilizes sample information from both the source and target domains, employing a non-linear disentangling approach and incorporating learnable weights to dynamically separate content and style features. Additionally, we introduce a lightweight component known as the domain shifter into the network architecture. This component allows for classification performance to be maintained in both the source and target domains while consuming moderate overhead. The domain shifter uses the attention mechanism to enhance the ability to extract network features. Extensive experiments demonstrated that FDDS can effectively disentangle features with clear feature separation boundaries while maintaining the classification ability of the model in the source domain. Under the same conditions, we evaluated FDDS and advanced algorithms on digital and road scene datasets. In the 19 classification tasks for road scenes, FDDS outperformed the competition in 11 categories, particularly showcasing a remarkable 2.7% enhancement in the accuracy of the bicycle label. These comparative results highlight the advantages of FDDS in achieving high accuracy in the target domain. [ABSTRACT FROM AUTHOR]

Underwater vision technology is of great significance in marine investigation. However, the complex underwater environment leads to some problems, such as color deviation and high noise. Therefore, underwater image enhancement has been a focus of the research community. In this paper, a new underwater image enhancement method is proposed based on a generative adversarial network (GAN). We embedded the channel attention mechanism into U-Net to improve the feature utilization performance of the network and used the generator to estimate the parameters of the simplified underwater physical model. At the same time, the adversarial loss, the perceptual loss, and the global loss were fused to train the model. The effectiveness of the proposed method was verified by using four image evaluation metrics on two publicly available underwater image datasets. In addition, we compared the proposed method with some advanced underwater image enhancement algorithms under the same experimental conditions. The experimental results showed that the proposed method demonstrated superiority in terms of image color correction and image noise suppression. In addition, the proposed method was competitive in real-time processing speed. [ABSTRACT FROM AUTHOR]

ABSTRACT A 717-bp 5'-flanking regulatory sequence of the metallothionein gene in Crassostrea hongkongensis (ChMT) was isolated using anchored polymerase chain reaction. Several regulatory elements embedded in the flanking sequence, including [...]

ABSTRACT Metallothioneins (MT) play important roles in trace metal homeostasis as well as excess metal detoxification in all organisms. They have been acknowledged as the most valid biomarker to indicate [...]