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Exploring the relationships between plant phenotypes and genetic information requires advanced phenotypic analysis techniques for precise characterization. However, the diversity and variability of plant morphology challenge existing methods, which often fail to generalize across species and require extensive annotated data, especially for 3D datasets. This paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors. It extends the 2D segmentation model SAM (Segment Anything Model) to 3D through a multi-view strategy. RGB image sequences captured from multiple viewpoints are used to reconstruct 3D plant point clouds via multi-view stereo. HQ-SAM (High-Quality Segment Anything Model) segments leaves in 2D, and the segmentation is mapped to the 3D point cloud. An incremental fusion method based on confidence scores aggregates results from different views into a final output. Evaluated on a custom peanut seedling dataset, the method achieved point-level precision, recall, and F1 scores over 0.9 and object-level mIoU and precision above 0.75 under two IoU thresholds. The results show that the method achieves state-of-the-art segmentation quality while offering zero-shot capability and generalizability, demonstrating significant potential in plant phenotyping.

After the outbreak of the severe acute respiratory syndrome (SARS) in the world in 2003, human coronaviruses (HCoVs) have been reported as pathogens that cause severe symptoms in respiratory tract infections. Recently, a new emerged HCoV isolated from the respiratory epithelium of unexplained pneumonia patients in the Wuhan seafood market caused a major disease outbreak and has been named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus causes acute lung symptoms, leading to a condition that has been named as “coronavirus disease 2019” (COVID-19). The emergence of SARS-CoV-2 and of SARS-CoV caused widespread fear and concern and has threatened global health security. There are some similarities and differences in the epidemiology and clinical features between these two viruses and diseases that are caused by these viruses. The goal of this work is to systematically review and compare between SARS-CoV and SARS-CoV-2 in the context of their virus incubation, originations, diagnosis and treatment methods, genomic and proteomic sequences, and pathogenic mechanisms.

Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods—such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)—struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.

Conductor galloping is a low-frequency, large-amplitude vibration phenomenon induced by the combined action of ice accretion and wind loads, which poses severe threats to power grid safety. For 500 kV transmission lines in Xinjiang, a strain-displacement relationship based on elastic catenary theory was developed, establishing a nonlinear dynamic model for galloping of ice-coated bundled conductors with torsional stiffness. Employing D-shaped, fan-shaped, and crescent-shaped ice-accreted bundle conductors, this work investigates the influence of wake effects and wind attack angles on aerodynamic characteristics at varying spanwise lengths. Subsequently, aerodynamic loads were computed based on the derived aerodynamic coefficients, enabling galloping response analysis of ice-accreted bundle conductors. The results demonstrate that the proposed methodology facilitates efficient analysis of galloping amplitude in ice-accreted transmission conductors, and the three-dimensional numerical model significantly improves the computational accuracy of aerodynamic forces on bundle conductors. The flow field around ice-coated bundled conductors exhibits pronounced periodicity, governed collectively by wake effects, wind attack angle, ice accretion geometry, ice thickness, and wind velocity. Additionally, spacer bars with articulated connections suppress conductor galloping more effectively.

Robust hydrogels offer a candidate for artificial skin of bionic robots, yet few hydrogels have a comprehensive performance comparable to real human skin. Here, we present a general method to convert traditional elastomers into tough hydrogels via a unique radiation-induced penetrating polymerization method. The hydrogel is composed of the original hydrophobic crosslinking network from elastomers and grafted hydrophilic chains, which act as elastic collagen fibers and water-rich substances. Therefore, it successfully combines the advantages of both elastomers and hydrogels and provides similar Young’s modulus and friction coefficients to human skin, as well as better compression and puncture load capacities than double network and polyampholyte hydrogels. Additionally, responsive abilities can be introduced during the preparation process, granting the hybrid hydrogels shape adaptability. With these unique properties, the hybrid hydrogel can be a candidate for artificial skin, fluid flow controller, wound dressing layer and many other bionic application scenarios. Robust hydrogels offer a promising solution for the development of artificial skin for bionic robots, yet few hydrogels have a comprehensive performance comparable to real human skin. Here, the authors present a general method to convert traditional elastomers into tough hydrogels via a unique radiation-induced penetrating polymerization method.

We demonstrate a simple and highly sensitive optical fiber relative humidity (RH) sensor based on a long-period fiber grating (LPFG) coated with polyethylene glycol (PEG)/polyvinyl alcohol (PVA) composite films. The resonance wavelength of the LPFG is sensitive to environmental humidity due to the change in effective refractive index caused by the strong surface absorption and desorption of the porous PEG/PVA coatings. The sensor is sensitive in a wide range from 50% to 95% RH, with a highest sensitivity of 2.485 nm/%RH in the range 50–75% RH. The proposed RH sensor has the advantages of compact size, good reversibility, and stability, which makes it attractive for high-humidity environments.

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.

Protective layers are essential for Si-based photocathodes to achieve long-term stability. The conventionally used inorganic protective layers, such as TiO 2 , need to be free of pinholes to isolate Si from corrosive solution, which demands extremely high-quality deposition techniques. On the other hand, organic hydrophobic protective layers suffer from the trade-off between current density and stability. This paper describes the design and fabrication of a discontinuous hybrid organic protective layer with controllable surface wettability. The underlying hydrophobic layer induces the formation of thin gas layers at the discontinuous pores to isolate the electrolyte from Si substrate, while allowing Pt co-catalyst to contact the electrolyte for water splitting. Meanwhile, the surface of this organic layer is modified with hydrophilic hydroxyl groups to facilitate bubble detachment. The optimized photocathode achieves a stable photocurrent of 35 mA/cm 2 for over 110 h with no trend of decay. Preparation of inorganic protective layers for photoelectrodes requires high-quality deposition techniques. Here, the authors report a spin-coated organic protective layer that enables Si photocathodes to realize stable solar water splitting.

Probiotics are considered to play an crucial role in the treatment of high-fat diet (HFD)-induced lipid metabolic diseases, including metabolic syndrome (MS). This study aimed to investigate the effects of Lactobacillus plantarum S9 on MS in HFD-fed rats, and to explore the underlying role of probiotics in the treatment of MS. Sprague-Dawley rats were fed with HFD for 8 weeks, followed by the treatment of L. plantarum S9 for 6 weeks, and The body weight and blood glucose level of rats were detected on time. The results showed that L. plantarum S9 significantly decreased the body weight gain, Lee’s index, and liver index. Additionally, L. plantarum S9 reduced the levels of serum lipids and insulin resistance. L. plantarum S9 also decreased the levels of alanine aminotransferase (ALT) and aspartate transaminase (AST) in liver. Moreover, the serum levels of MS-related inflammatory signaling molecules, including lipopolysaccharide (LPS) and tumor necrosis factor-α (TNF-α), were significantly elevated. Western blot analysis showed that L. plantarum S9 inhibited the activation of nuclear factor-κB (NF-κB) pathway, decreased the expression level of Toll-like receptor 4 (TLR4), suppressed the activation of inflammatory signaling pathways, and reduced the expression levels of inflammatory factors in HFD-fed rats. Moreover, it further decreased the ratios of p-IκBα/IκBα, p-p65/NF-κB p65, and p-p38/p38. In summary, L. plantarum S9, as a potential functional strain, prevents or can prevent onset of MS.

The efficient use of renewable X/γ-rays or accelerated electrons for chemical transformation of CO 2 and water to fuels holds promise for a carbon-neutral economy; however, such processes are challenging to implement and require the assistance of catalysts capable of sensitizing secondary electron scattering and providing active metal sites to bind intermediates. Here we show atomic Cu-Ni dual-metal sites embedded in a metal-organic framework enable efficient and selective CH 3 OH production (~98%) over multiple irradiated cycles. The usage of practical electron-beam irradiation (200 keV; 40 kGy min −1 ) with a cost-effective hydroxyl radical scavenger promotes CH 3 OH production rate to 0.27 mmol g −1  min −1 . Moreover, time-resolved experiments with calculations reveal the direct generation of CO 2 •‒ radical anions via aqueous electrons attachment occurred on nanosecond timescale, and cascade hydrogenation steps. Our study highlights a radiolytic route to produce CH 3 OH with CO 2 feedstock and introduces a desirable atomic structure to improve performance. Most approaches for CH 3 OH production focus on thermochemical, electrolytic, and photolytic processes. Here the authors report a radiolytic route to produce CH 3 OH from CO 2 and water by atomic Cu-Ni dual sites embedded in a metal-organic framework.