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Fallen trees enter the adjacent stream and are carried away downstream by the current. As the stream joins another one, the complex hydrodynamics near their confluence make the movement of wood hard to predict. These woods may accumulate near the confluence resulting in backwater and subsequent potential flooding. A laboratory study was conducted to investigate the movement and accumulation behavior of individual pieces of wood near the confluence. The characteristics of wood (i.e., the length, diameter, and density) and the hydraulic conditions (i.e., the discharge ratio and the release distance) were varied in this investigation. It was found that the wooden pieces released from the tributary got occasionally trapped in the flow separation zone of the confluence, whereupon they were mainly trapped by a clockwise vortex and continued to stay driven by a reverse cluster of currents within this zone. The accumulation probability of wood was mainly related to its length, the discharge ratio and the release distance. The effect of wood diameter and density within the tested parameters was negligible. The probability increased with an increase in the discharge ratio as well as a decrease in the release distance. The longer pieces had a higher probability of being trapped, whereas for those exceeding some critical value, the probability was nearly the same, or dropped sharply. A generalized model for wood accumulation near the confluence was developed for practical application. These findings carry significant implications for river management, particularly in preventing the risk of flooding caused by wood blockage. Key Points Conducting a laboratory study to investigate the transport, accumulation and trapping mechanism of wood near the confluence Evaluating the wood accumulation probability depending on different wood characteristics and confluence hydrodynamic conditions Wood released from the tributary may be trapped by the clockwise vortex and thus accumulate in the separation zone

Background Semantic segmentation of brain tumors plays a critical role in clinical treatment, especially for three-dimensional (3D) magnetic resonance imaging, which is often used in clinical practice. Automatic segmentation of the 3D structure of brain tumors can quickly help physicians understand the properties of tumors, such as the shape and size, thus improving the efficiency of preoperative planning and the odds of successful surgery. In past decades, 3D convolutional neural networks (CNNs) have dominated automatic segmentation methods for 3D medical images, and these network structures have achieved good results. However, to reduce the number of neural network parameters, practitioners ensure that the size of convolutional kernels in 3D convolutional operations generally does not exceed 7 × 7 × 7 , which also leads to CNNs showing limitations in learning long-distance dependent information. Vision Transformer (ViT) is very good at learning long-distance dependent information in images, but it suffers from the problems of many parameters. What’s worse, the ViT cannot learn local dependency information in the previous layers under the condition of insufficient data. However, in the image segmentation task, being able to learn this local dependency information in the previous layers makes a big impact on the performance of the model. Methods This paper proposes the Swin Unet3D model, which represents voxel segmentation on medical images as a sequence-to-sequence prediction. The feature extraction sub-module in the model is designed as a parallel structure of Convolution and ViT so that all layers of the model are able to adequately learn both global and local dependency information in the image. Results On the validation dataset of Brats2021, our proposed model achieves dice coefficients of 0.840, 0.874, and 0.911 on the ET channel, TC channel, and WT channel, respectively. On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively. Conclusion We propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between the number of model parameters and segmentation accuracy. The code can be found at https://github.com/1152545264/SwinUnet3D .

The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git .

Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays.

Non-coding RNAs (ncRNAs) are crucial molecules that do not encode proteins but play roles in regulating various biological processes. Recent research highlights that ncRNAs not only control gene expression within cells but also facilitate intercellular communication via exosomes and other carriers. This function is vital in the tumor microenvironment (TME). Our review covers the structure and functions of different ncRNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). We examine how these ncRNAs influence tumor initiation and progression. Additionally, we explore their role in promoting tumor growth or immune evasion by modulating the TME. The potential of using these ncRNAs as therapeutic targets or biomarkers for clinical use is also discussed. As our understanding of ncRNAs grows, the development of new therapies based on ncRNAs is anticipated to offer improved treatment options for cancer patients.

Abstract Layahima Navás, 1912 is the most diverse antlion genus of the tribe Acanthoplectrini (Myrmeleontidae: Dendroleontinae) endemic to the Oriental region, currently comprising 12 species. However, the species diversity of this genus is still far from completely explored, and its larval stage is poorly known. Here, we describe four new species of Layahima , i.e., L. haohani sp. nov. , L. qilin sp. nov. , L. pixiu sp. nov. , and L. zhitengi sp. nov. , from Southwest China. Moreover, we describe the larval stages of three Layahima species, i.e., L. chiangi Banks, 1941, L. lhoba Zheng, Badano, Liu, 2023, and L. yangi Wan & Wang, 2006. The precise distribution of L. chiangi , whose type locality was previously unclear, has now been clarified to be exclusively restricted to the Nujiang dry hot river valley around Cawarong, Xizang. The phylogeny of Layahima by adding new species herein reported was inferred based on molecular data. The L. zonata group, once considered monophyletic, was recovered as paraphyletic within Layahima .

Background This study evaluates and compares ChatGPT-4.0, Gemini Pro 1.5(0801), Claude 3 Opus, and Qwen 2.0 72B in answering dental implant questions. The aim is to help doctors in underserved areas choose the best LLMs(Large Language Model) for their procedures, improving dental care accessibility and clinical decision-making. Methods Two dental implant specialists with over twenty years of clinical experience evaluated the models. Questions were categorized into simple true/false, complex short-answer, and real-life case analyses. Performance was measured using precision, recall, and Bayesian inference-based evaluation metrics. Results ChatGPT-4 exhibited the most stable and consistent performance on both simple and complex questions. Gemini Pro 1.5(0801)performed well on simple questions but was less stable on complex tasks. Qwen 2.0 72B provided high-quality answers for specific cases but showed variability. Claude 3 opus had the lowest performance across various metrics. Statistical analysis indicated significant differences between models in diagnostic performance but not in treatment planning. Conclusions ChatGPT-4 is the most reliable model for handling medical questions, followed by Gemini Pro 1.5(0801). Qwen 2.0 72B shows potential but lacks consistency, and Claude 3 Opus performs poorly overall. Combining multiple models is recommended for comprehensive medical decision-making.

Andrographolide (ADG) is a typical poorly water-soluble drug, and a co-amorphous strategy was used here to improve its aqueous solubility. Co-amorphous systems of ADG and amino acids with a 1:1 molar ratio were screened via the neat ball milling method. L-lysine (Lys) and L-tryptophan (Trp) can be used as co-formers with ADG, forming a co-amorphous phase, which was confirmed by powder X-ray diffraction, IR and Raman spectroscopy. ADG-Trp showed poor solubility at 37 °C, which was close to that of raw ADG (0.08 mg·mL−1). ADG-Lys showed unexpectedly enhanced solubility, at 0.5 mg·mL−1 in the media of water and PBS (pH 7.4) and 0.3 mg·mL−1 in the medium of HCl buffer (pH 1.2) at 37 °C. ADG-Lys showed good storage stability for 5 months, but its thermal stability was poor and it could recrystallize at 100 °C. Compared with ADG-Trp, ADG-Lys has weaker hydrogen bonding interactions and stronger hydrophobic interactions related to ADG molecules, which might cause the unusual enhancement in solubility. To our knowledge, ADG-Lys prepared in this work shows the maximum ADG content (70 wt.%) and the highest ADG solubility among the reported ADG amorphous solid dispersions and co-amorphous systems.

Salt lake brine contains abundant rubidium resources; however, the separation of rubidium from brine with a high K content remains a significant challenge in metallurgical processes and materials science. In this study, PAN-KCuFC-PEG particles were synthesized by phase transformation, using hydrophilic polyacrylonitrile (PAN) as the skeleton structure, potassium cupric ferricyanide (KCuFC) as the active component and water-soluble polymer polyethylene glycol (PEG) as the pore regulator. Characterization revealed that the addition of PEG increased the pore volume of PAN-KCuFC-PEG by 63% and the BET surface area by 172%. KCuFC powder was uniformly dispersed in PAN-KCuFC-PEG, and its crystal structure remained stable after loading. In static adsorption experiments, the maximum adsorption capacity of PAN-KCuFC-PEG for Rb+ reached 190 mg/g. The adsorption behavior followed a pseudo-second-order kinetic model, with the rate jointly controlled by external diffusion, intraparticle diffusion, and chemical reaction. In the column experiment, PAN-KCuFC-PEG was used to adsorb Qarhan Salt Lake brine (K: 26,000 mg/L, Rb: 65 mg/L). NH4Cl was employed for elution and desorption of PAN-KCuFC-PEG. During the adsorption–desorption process, the separation factor between Rb and K reached 160, the desorption rate reached 96.6%, and the overall yield was 68.3%. The enrichment and separation of Rb were successfully achieved.