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Self-healing triboelectric nanogenerators (TENGs), which incorporate self-healing materials capable of recovering their structural and functional properties after damage, are transforming the field of artificial skin by effectively addressing challenges associated with mechanical damage and functional degradation. This review explores the latest advancements in self-healing TENGs, emphasizing material innovations, structural designs, and practical applications. Key materials include dynamic covalent polymers, supramolecular elastomers, and ion-conductive hydrogels, which provide rapid damage recovery, superior mechanical strength, and stable electrical performance. Innovative structural configurations, such as layered and encapsulated designs, optimize triboelectric efficiency and enhance environmental adaptability. Applications span healthcare, human–machine interfaces, and wearable electronics, demonstrating the immense potential for tactile sensing and energy harvesting. Despite significant progress, challenges remain in scalability, long-term durability, and multifunctional integration. Future research should focus on advanced material development, scalable fabrication, and intelligent system integration to unlock the full potential of self-healing TENGs. This review provides a comprehensive overview of current achievements and future directions, underscoring the pivotal role of self-healing TENGs in artificial skin technology. [ABSTRACT FROM AUTHOR]

Energy-saving buildings have become a topical issue and widely used the energy-saving ventilation design, which aims to maximize the use of natural ventilation while reducing demand on mechanical ventilation. In this research, natural ventilation performance of two different semi-enclosed building layouts, "O" type and "L-L" type, are studied by numerical simulation. According to the simulation results, in the "O" type, the maximum indoor air velocity occurs in building A (orientation facing the inflow direction). The minimum indoor air velocity occurs in building C (orientation facing building A). In the "L-L" type, the maximum indoor air velocity occurs in building A (orientation facing the inflow direction). The minimum indoor air velocity occurs in buildings B and D (orientation parallel to the inflow direction). And air flow field simulation results show that, the "L-L" type exhibits better than the "O" type in natural ventilation. Furthermore, simulation results show that the natural ventilation performance in the room is influenced not only by the building orientation, but also by the window size.

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model. [ABSTRACT FROM AUTHOR]

Two-dimensional transition metal borides (MBenes), particularly TiB, hold promise as electrocatalysts for CO2-related reactions. However, their bifunctional catalytic performance for reversible Li2CO3 conversion in Li–CO2 batteries remains inferior to that of Ru-based catalysts. We addressed this issue by introducing tensile strain and doping late transition metal atoms (Mn, Fe, Co, Ni, Cu) into the basal plane of a TiB MBene/graphene heterostructure. Spin-polarized density functional theory (DFT) calculations revealed that the Cu-doped TiB/graphene catalyst (Cu/Ti17B18/G) exhibits an ultralow CO2 reduction and evolution overpotential of 0.66 V, enhancing Li2CO3 nucleation and reversible conversion with carbon products. This improvement is attributed to weakened adsorption of O-containing intermediates on the Cu-doped surface, facilitated by the down-shifted d-band center and increased antibonding state occupancy. Consequently, Cu/Ti17B18/G emerges as a promising bifunctional electrocatalyst for Li–CO2 batteries, outperforming pristine TiB/G and other reported catalysts. Furthermore, its bifunctional activity can be further improved by applying x-direction tensile strain. Molecular dynamics simulations combined with explicit solvent models further confirmed the catalytic durability and stability of Cu/Ti17B18/G in solution. This work provides valuable atomic-scale insights for exploring advanced Li–CO2 battery catalysts. [ABSTRACT FROM AUTHOR]

To introduce the randomness of a physical process to students, a chaotic pendulum experiment was opened in East China University of Science and Technology (ECUST) on the undergraduate level in the physics department. It was shown chaotic motion could be initiated through adjusting the operation of a chaotic pendulum. By using the data of the angular displacements of chaotic motion, random binary numerical arrays can be generated. To check the randomness of generated numerical arrays, the NIST Special Publication 800-20 method was adopted. As a result, it was found that all the random arrays which were generated by the chaotic motion could pass the validity criteria and some of them were even better than the quality of pseudo-random numbers generated by a computer. Through the experiments, it is demonstrated that chaotic pendulum can be used as an efficient mechanical facility in generating random numbers, and can be applied in teaching random motion to the students.

Aims: Physical exercise (PE) can accelerate post‐stroke recovery. This study investigated contributions of circRNAs to PE‐induced improvements in post‐stroke neurological function. Methods: Rats subjected to transient middle cerebral artery occlusion were left sedentary or provided running‐wheel access for 4 weeks during recovery. CircRNAs from peri‐infarct cortex were identified by high‐throughput sequencing, and interactions with miRNAs by immunoprecipitation, fluorescence in suit hybridization, and dual‐luciferase reporter assays. In vivo circRNA knockdown was achieved using shRNA‐AAVs and in vitro overexpression by plasmid transfection. Transmission electron microscopy, western blotting, and TUNEL assays were conducted to explore circRNA contributions to endoplasmic reticulum (ER) stress and neuronal apoptosis. CircRNA levels were measured in plasma from stroke patients by qRT‐PCR and associations with neurological scores assessed by Pearson's correlation analysis. Results: PE upregulated circAnks1b, reduced infarct volume, and mitigated neurological dysfunction, while circAnks1b knockdown exacerbated neurological dysfunction and increased infarct size despite PE. CircAnks1b sponged miR‐130b‐5p, thereby disinhibiting Pak2 expression. Conversely, Pak2 downregulation disrupted PE‐mediated protective ER stress, leading to reduced IRE1/XBP1 and heightened apoptosis. Plasma circAnks1b was higher in stroke patients receiving PE than sedentary patients and correlated negatively with neurological scores. Conclusions: CircAnks1b upregulation may be an effective therapeutic strategy for post‐stroke recovery. [ABSTRACT FROM AUTHOR]

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the "domain gap" that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks. [ABSTRACT FROM AUTHOR]

Simple Summary: The Pearl River Estuary (PRE) in China, a highly urbanized coastal area, presents a unique opportunity to study the effects of environmental changes on the phytoplankton com-munity. In September 2018, a field study was conducted to examine how the photosynthetic status and spatial distribution of these organisms varied from freshwater to oceanic waters. Dinophyta and Haptophyta were prevalent in seawater, while Chlorophyta and Cryptophyta dominated from freshwater to estuarine water. Bacillariophyta were found across all regions. Phytoplankton in the mixing zone showed signs of stress due to fluctuating environmental conditions, whereas those in freshwater and oceanic areas appeared more photosynthetically active. Human activities have in-creased nutrient levels in the estuary, leading to higher chlorophyll concentrations and more diverse phytoplankton communities upstream. Understanding these patterns helps us assess the health of coastal ecosystems, which is crucial for managing the impacts of climate change and human development on marine environments. The Pearl River Estuary (PRE) is one of the world's most urbanized subtropical coastal systems. It presents a typical environmental gradient suitable for studying estuarine phytoplankton communities' dynamics and photosynthetic physiology. In September 2018, the maximum photochemical quantum yield (Fv/Fm) of phytoplankton in different salinity habitats of PRE (oceanic, estuarine, and freshwater zones) was studied, revealing a complex correlation with the environment. Fv/Fm of phytoplankton ranged from 0.16 to 0.45, with taxa in the upper Lingdingyang found to be more stressed. Community composition and structure were analyzed using 18S rRNA, accompanied by a pigment analysis utilized as a supplementary method. Nonmetric multidimensional scaling analysis indicated differences in the phytoplankton spatial distribution along the estuarine gradients. Specificity-occupancy plots identified different specialist taxa for each salinity habitat. Dinophyta and Haptophyta were the predominant taxa in oceanic areas, while Chlorophyta and Cryptophyta dominated freshwater. Bacillariophyta prevailed across all salinity gradients. Canonical correlation analysis and Mantel tests revealed that temperature, salinity, and elevated nutrient levels (i.e., NO3−-N, PO43−-P, and SiO32−-Si) associated with anthropogenic activities significantly influenced the heterogeneity of community structure. The spatial distribution of phytoplankton, along with in situ photosynthetic characteristics, serves as a foundational basis to access estuarine primary productivity, as well as community function and ecosystem health. [ABSTRACT FROM AUTHOR]