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Highlights: Introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and tissue. Highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices in bone tissue engineering. Emphasize the significance of simulating the target tissue's electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics. The incidence of large bone defects caused by traumatic injury is increasing worldwide, and the tissue regeneration process requires a long recovery time due to limited self-healing capability. Endogenous bioelectrical phenomena have been well recognized as critical biophysical factors in bone remodeling and regeneration. Inspired by bioelectricity, electrical stimulation has been widely considered an external intervention to induce the osteogenic lineage of cells and enhance the synthesis of the extracellular matrix, thereby accelerating bone regeneration. With ongoing advances in biomaterials and energy-harvesting techniques, electroactive biomaterials and self-powered systems have been considered biomimetic approaches to ensure functional recovery by recapitulating the natural electrophysiological microenvironment of healthy bone tissue. In this review, we first introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and tissue. Next, we highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices and their implementation in bone tissue engineering. Finally, we emphasize the significance of simulating the target tissue's electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics for bone repair strategies. [ABSTRACT FROM AUTHOR]

Light‐sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3‐D imaging of large‐scale biological tissues and 4‐D (space + time) imaging of small live animals. The large data associated with LSFM require efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML‐directed LSFM is an emerging area for multiorgan imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multiscale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep‐tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multiorgan imaging to tumor diagnosis. In addition, this review will address AI/ML‐directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post‐imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research. [ABSTRACT FROM AUTHOR]

Room‐temperature sodium‐sulfur (RT Na−S) batteries, noted for their low material costs and high energy density, are emerging as a promising alternative to lithium‐ion batteries (LIBs) in various applications including power grids and standalone renewable energy systems. These batteries are commonly assembled with glass fiber membranes, which face significant challenges like the dissolution of polysulfides, sluggish sulfur conversion kinetics, and the growth of Na dendrites. Here, we develop an amorphous two‐dimensional (2D) iron tin oxide (A‐FeSnOx) nanosheet with hierarchical vacancies, including abundant oxygen vacancies (Ovs) and nano‐sized perforations, that can be assembled into a multifunctional layer overlaying commercial separators for RT Na−S batteries. The Ovs offer strong adsorption and abundant catalytic sites for polysulfides, while the defect concentration is finely tuned to elucidate the polysulfides conversion mechanisms. The nano‐sized perforations aid in regulating Na ions transport, resulting in uniform Na deposition. Moreover, the strategic addition of trace amounts of Ti3C2 (MXene) forms an amorphous/crystalline (A/C) interface that significantly improves the mechanical properties of the separator and suppresses dendrite growth. As a result, the task‐specific layer achieves ultra‐light (~0.1 mg cm−2), ultra‐thin (~200 nm), and ultra‐robust (modulus=4.9 GPa) characteristics. Consequently, the RT Na−S battery maintained a high capacity of 610.3 mAh g−1 and an average Coulombic efficiency of 99.9 % after 400 cycles at 0.5 C. [ABSTRACT FROM AUTHOR]

Early‐stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non‐invasive and cost‐effective detection method for early‐stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on‐skin sensor with low electrode‐skin contact impedance for early‐stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on‐skin sensor to reduce electrode‐skin contact impedance by increasing double‐layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention‐based deep learning algorithm is introduced to differentiate impedance signals associated with early‐stage NAFLD in high‐fat‐diet‐fed low‐density lipoprotein receptor knockout (Ldlr−/−) mice compared to healthy controls. The integration of an adhesive, soft on‐skin sensor with low electrode‐skin contact impedance and the attention‐based deep learning algorithm significantly enhances the detection accuracy for early‐stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non‐invasive approach for early‐stage NAFLD detection and display a strategy for improved early detection through on‐skin electronics and deep learning. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving software environment, the graphical user interface (GUI) plays a crucial role in providing intuitive, user-friendly interaction. However, traditional intrusive GUI testing methods often face challenges such as interrupting user workflows, requiring significant manual effort and insufficient test scenario coverage. Non-intrusive testing methods, such as Robotic Process Automation (RPA), offer a solution to validate GUI functionality without modifying the application's code or affecting the user experience. RPA systems automate repetitive tasks by simulating user interactions, becoming valuable tools in GUI testing. However, challenges like limited computational resources, time constraints, or restricted exploration capabilities may limit the efficiency of individual RPA agents, thus restricting coverage and effectiveness. To address this issue, this study explores the performance of a single RPA agent versus an RPA cluster under different testing conditions, using three popular testing methods: Monkey, Stoat and Q-testing. Experimental results indicate that an RPA cluster outperforms a single RPA in GUI coverage and error detection, making a significant contribution to the field of non-intrusive GUI exploration testing. The findings of this study provide directions for future research to ensure the delivery of high-quality mobile applications. [ABSTRACT FROM AUTHOR]

Living bioelectronics, which integrate electronic devices with living biological systems, have the potential to revolutionize disease treatment and monitoring. Traditional electronic devices are limited in their ability to interact with biological tissues, but recent advancements have allowed for the development of living biointerfaces that can modulate immune responses and monitor disease states. These biointerfaces incorporate genetically programmed bacteria and hydrogel structures to detect and process signals, enabling applications such as chemical detection on the skin and long-term diagnostics of intestinal diseases. Living bioelectronics also show promise in the treatment of psoriasis, with devices that can record physiological signals and deliver therapeutic treatments. However, there are practical challenges to overcome, such as limited wireless functionality, power consumption, breathability concerns, and motion artifacts. Despite these challenges, living bioelectronics offer a novel approach to disease treatment and have the potential to be applied to a wide range of inflammatory conditions. [Extracted from the article]

Plasma etching treatment is an effective strategy to improve the electrocatalytic activity, but the improvement mechanism is still unclear. In this work, a nitrogen‐doped carbon nanotube‐encased iron nanoparticles (Fe@NCNT) catalyst is synthesized as the model catalyst, followed by plasma etching treatment with different parameters. The electrocatalytic activity improvement mechanism of the plasma etching treatment is revealed by combining the physicochemical characterizations and electrochemical results. As a result, highly active metal–nitrogen species introduced by nitrogen plasma etching treatment are recognized as the main contribution to the improved electrocatalytic activity, and the defects induced by plasma etching treatment also contribute to the improvement of the electrocatalytic activity. In addition, the prepared catalyst also demonstrates superior ORR activity and stability than the commercial Pt/C catalyst. [ABSTRACT FROM AUTHOR]

Since the initial discovery of Ti3C2 a decade ago, there has been a significant surge of interest in 2D MXenes and MXene‐based composites. This can be attributed to the remarkable intrinsic properties exhibited by MXenes, including metallic conductivity, abundant functional groups, unique layered microstructure, and the ability to control interlayer spacing. These properties contribute to the exceptional electrical and mechanical performance of MXenes, rendering them highly suitable for implementation as candidate materials in flexible and wearable energy storage devices. Recently, a substantial number of novel research has been dedicated to exploring MXene‐based flexible materials with diverse functionalities and specifically designed structures, aiming to enhance the efficiency of energy storage systems. In this review, a comprehensive overview of the synthesis and fabrication strategies employed in the development of these diverse MXene‐based materials is provided. Furthermore, an in‐depth analysis of the energy storage applications exhibited by these innovative flexible materials, encompassing supercapacitors, Li‐ion batteries, Li–S batteries, and other potential avenues, is conducted. In addition to presenting the current state of the field, the challenges encountered in the implementation of MXene‐based flexible materials are also highlighted and insights are provided into future research directions and prospects. [ABSTRACT FROM AUTHOR]

The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to identify metabolically active oxidized low‐density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning‐directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL‐rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six‐point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL‐rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Seawater electrolysis holds great promise for hydrogen production in the future, while the development of anodic catalysts has been severely hampered by the side‐reaction, chloride evolution reaction. In this work, nano‐flower‐cluster structured CoO@FeSe2/CF catalysts are synthesized via a scalable electrodeposition technique, and the performance is systematically studied. The oxygen evolution reaction (OER) overpotential of CoO@FeSe2/CF is 267 mV at 100 mA·cm−2, which is significantly lower than that of the IrO2 catalyst (435 mV). Additionally, the catalyst shows high selectivity for OER (97.9%) and almost no loss of activity after a durability test for 1100 h. The impressive performance is attributed to the dense rod‐like structure with abundant active centers after electrochemical activation and the in‐situ generated CoOOH and FeOOH that improve the catalytic activity of the catalyst. The synergistic effect induced by the non‐uniform structure endows the catalyst with excellent stability. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia technology, personnel verification systems have become increasingly important in the security field and identity verification. However, unimodal verification systems have performance bottlenecks in complex scenarios, thus triggering the need for multimodal feature fusion methods. The main problem with audio–visual multimodal feature fusion is how to effectively integrate information from different modalities to improve the accuracy and robustness of the system for individual identity. In this paper, we focus on how to improve multimodal person verification systems and how to combine audio and visual features. In this study, we use pretrained models to extract the embeddings from each modality and then perform fusion model experiments based on these embeddings. The baseline approach in this paper involves taking the fusion feature and passing it through a fully connected (FC) layer. Building upon this baseline, we propose three fusion models based on attentional mechanisms: attention, gated, and inter–attention. These fusion models are trained on the VoxCeleb1 development set and tested on the evaluation sets of the VoxCeleb1, NIST SRE19, and CNC-AV datasets. On the VoxCeleb1 dataset, the best system performance achieved in this study was an equal error rate (EER) of 0.23% and a detection cost function (minDCF) of 0.011. On the evaluation set of NIST SRE19, the EER was 2.60% and the minDCF was 0.283. On the evaluation set of the CNC-AV set, the EER was 11.30% and the minDCF was 0.443. These experimental results strongly demonstrate that the proposed fusion method can significantly improve the performance of multimodal character verification systems. [ABSTRACT FROM AUTHOR]