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The paper mainly studies the deformation monitoring of tunnel engineering based on 3D laser scanning. First, the principle and characteristics of 3D laser scanning technology are expounded, which lays a theoretical foundation for the research. Then, the flow and main technologies of tunnel engineering deformation monitoring are analysed. Among them, the general process includes four stages: preliminary preparation, point cloud data capture, point cloud data processing and deformation analysis. The main technologies include point and data pre-processing technology and tunnel axis fitting. Finally, combined with the case, deformation monitoring is implemented to analyse the deformation of the tunnel in detail.

The low-edge rotation, intrinsically ELM-free, and improved confinement wide-pedestal quiescent H-mode (QH-mode), discovered in DIII-D tokamak, has pedestal widths exceeding the EPED-kinetic-ballooning mode (KBM) model scaling typically by at least 25%. Ion-scale ( ${k_y}{\rho _s}$ < 1) microturbulence and its role in setting the pedestal structure is investigated using the radially local $\delta f$ gyrokinetic code CGYRO. The electromagnetic trapped electron mode (TEM) is unstable at the pedestal top, while plasma beta ( ${\beta _e})$ is ∼60% below the KBM onset threshold and the electron temperature gradient mode is found to be unstable in the peak gradient region. Nonlinear simulation reveals that the ion-scale turbulence could produce electron energy flux consistent with the flux inferred from power balance at the pedestal top, with a reasonable variation of the local $E \times B$ shearing rate; and the local neoclassical transport from NEO is dominant over the simulated turbulent transport in the ion energy flux channel. The simulated ion-scale turbulence produces much lower electron energy flux than inferred from experiment in the pedestal peak gradient region. A correction to the EPED-KBM pedestal width scaling is obtained based on the two-dimensional scan of pedestal top plasma beta ( ${\beta _e})$ and normalized electron density and temperature scale lengths, $a/{L_{{n_e}}}$ , $a/{L_{{T_e}}}$ using CGYRO linear simulations. Mode transitions among TEM, micro-tearing mode, ion-temperature gradient mode and KBM, are observed in the 2D scan at the pedestal top. A fixed normalized growth rate for these drift-type modes is taken to determine the pedestal width scaling, which shows good consistency with the QH experimental database on pedestal heights and widths. The onset of KBM instabilities and the local E × B shear suppression criterion set the lower and upper limit for the pedestal width of standard QH-mode, wide-pedestal QH-mode and type-I ELMy H mode. A potentially higher and wider pedestal is expected from the new scaling of pedestal width. This work presents an improved understanding of the ion-scale micro-turbulence of wide-pedestal QH-mode and sheds light on a promising scenario for future reactors, including ITER and beyond.

The finned surface can increase the specific surface area, which could enhance the boiling efficiency of the working medium to realize the enhancement of heat transfer performance. This research adopted a step punching method to form a radial microgroove structure on the surface of a copper plate. The scanning electron microscopy analysis indicates that when the pressed metal is in the deformation range, it will flow under extrusion and is subject to the reaction force from the metal outside the deformation range, so a micro-finned structure nearby the punching tool is formed. And the deformation area of the formed microgroove is higher than the workpiece’s primitive surface. The relationships of the punching interference length L i with θ c , a c , and α pt are analyzed; and the influence of forming parameters on the groove surface morphology as well as the relationships among these parameters are discussed. When the punching depth a c is 0.3 mm and the punching feeding angle θ c is 2°–4°, the finned surface of the radial V-shaped microgrooves can be obtained.

This study proposes a cooperative lane-changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane-changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane-Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle's longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane-changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle's lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014m, 0.5631m, and -0.7601m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability., Author(s): Zhengwu Wang [1,2]; Jian Xiang (corresponding author) [1,2]; Jie Wang [1,2]; Zhibo Gao [1,2]; Tao Chen [2]; Hao Li [2]; Rui Mao [3] 1. Introduction Lane-changing is one of [...]

The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation measures, and maintaining biodiversity. However, there are two main issues with current fish detection algorithms. First, the lighting conditions underwater are significantly different from those on land. In addition, light scattering and absorption in water trigger uneven illumination, color distortion, and reduced contrast in images. The accuracy of detection algorithms can be affected by these lighting variations. Second, the wide variation of fish species in shape, color, and size brings about some challenges. As some fish have complex textures or camouflage features, it is difficult to differentiate them using current detection algorithms. To address these issues, we propose a fish detection algorithm—FishDet-YOLO—through improvement in the YOLOv8 algorithm. To tackle the complexities of underwater environments, we design an Underwater Enhancement Module network (UEM) that can be jointly trained with YOLO. The UEM enhances the details of underwater images via end-to-end training with YOLO. To address the diversity of fish species, we leverage the Mamba model's capability for long-distance dependencies without increasing computational complexity and integrate it with the C2f from YOLOv8 to create the Mamba-C2f. Through this design, the adaptability in handling complex fish detection tasks is improved. In addition, the RUOD and DUO public datasets are used to train and evaluate FishDet-YOLO. FishDet-YOLO achieves mAP scores of 89.5% and 88.8% on the test sets of RUOD and DUO, respectively, marking an improvement of 8% and 8.2% over YOLOv8. It also surpasses recent state-of-the-art general object detection and underwater fish detection algorithms. [ABSTRACT FROM AUTHOR]

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new data set called Fish52 (multi-scene fish data set, a data set containing 52 fish species) was constructed, on which the model achieved an mAP (mean average precision is a key measure of model performance) of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish data set) and Fish30 data set (a data set containing 30 fish species) and tested them on the F4k (Fish4Knowledge (F4K) is a data set focused on fish detection and identification) and FishNet data set (it is a data set containing 94,532 images from 17,357 aquatic species). The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet data sets reaches 91.72%, 98.7%, 88.6%, and 81.5%, respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses a high generalization ability and reaches advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and a new module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection head module, introducing classes with independent parameters and regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar targets in other environments and offers assistance for the construction of smart fisheries and digital fisheries. [ABSTRACT FROM AUTHOR]

Introduction: Patients with renal insufficiency are more prone to postoperative complications (PCs). Studies have shown that minor changes in serum creatinine (SCr), immediately post-surgery, can aid in assessing patients' renal function. This study aimed to explore the relationship between the changes in SCr and PCs in patients with gastric cancer (GC). Materials and methods: We prospectively collected data regarding the SCr of 530 GC patients, within 2 weeks before surgery and within 24 hours after surgery in our hospital (2014-2016). The patients were divided into three groups according to the level of SCr change after surgery: reduced (<10%), normal (10%), and elevated (>10%) creatinine groups. Univariate and multivariate logistic analysis were performed to evaluate its correlation with short-term PCs in the patients. The R language was used to construct a nomogram. Results: 83, 217, and 230 patients were assigned to the elevated, reduced, and normal SCr groups, respectively. Multivariate analysis showed that the reduced and elevated SCr groups were independently associated with the occurrence of PCs and severe postoperative complications (SPCs), respectively. Additionally, postsurgical SCr change, age, hypoalbuminemia, total gastrectomy, combined resection, and laparoscopy, were independently related to PCs. Combining the above influential factors, the predictive model can distinguish patients with PCs more reliably (c-index is 0.715). Conclusion: Post-surgery, reduced SCr is a protective factor for PCs, while elevated serum creatinine is an independent risk factor for SPCs. Our nomogram can identify GC patients with high risks of PCs. [ABSTRACT FROM AUTHOR]

The excessive application of nitrogen in tea plantations leads to severe soil acidification and N2O emission boosting. To promote sustainable agriculture, nanoparticles (NPs) have emerged as alternative fertilizers, but their effects on soil nitrification and greenhouse gas emissions in tea plantations remain unclear. In this study, the effects of NP type (ZnO-NPs and Fe2O3-NPs) and dose (0, 1, 10, and 100 mg·kg−1) on soil N2O emissions were investigated via a lab incubation trial. Soil pH, ammonium, and nitrate changes were also monitored during the incubation period. The abundance of functional genes related to nitrification and denitrification processes was analyzed as well. The results showed that ZnO-NPs led to a decrease in N2O emissions. The reduction effect was stronger with increasing dose and resulted in a 33% reduction at an addition rate of 100 mg·kg−1. The cumulative N2O emissions had significantly positive correlations with NH4+-N and NO3−-N. ZnO-NP addition showed a significantly negative effect on Ammonia-Oxidizing Archaea (AOA) but a positive effect on Ammonia-Oxidizing Bacteria (AOB) gene abundance. In contrast, Fe2O3-NPs showed an insignificant impact on N2O emissions and soil N content, as well as nitrification–denitrification gene abundance, regardless of different doses. These results imply that the application of ZnO-NPs may inhibit nitrification through the retarding of AOA activity. This study provided us with a potential practice to reduce N2O emissions in tea plantations by applying ZnO-NPs, but the efficiency of this reduction needs further examination under ambient conditions before field application. [ABSTRACT FROM AUTHOR]

Angiopteris nodosipetiolata Ting Wang tris, H.F.Chen & Y.H.Yan, a new fern of Marattiaceae, is described and illustrated. Morphologically, A. nodosipetiolata is similar to A. chingii with more than one naked pulvinus on the stipe and numerous jointed hairs on the undersides of the mature pinnae. However, the pinnae of A. nodosipetiolata are lanceolate and can reach up to 4–6 pairs, whereas they are elliptic and occur in 2–3 pairs in A. chingii. Phylogenetic and genetic distance analysis, based on the plastid genomes, also indicates that A. nodosipetiolata is not closely related to A. chingii. Currently, there are ca. 500 mature individuals in Gulinqing Nature Reserve and we suggest A. nodosipetiolata should be categorised as an Endangered (EN) species according to the criteria of IUCN. [ABSTRACT FROM AUTHOR]

The emergence of connected autonomous vehicles (CAVs) has the potential to dramatically increase road capacity, and the provision of dedicated lanes for CAVs is a common management strategy to ensure that CAVs take appropriate priority over other vehicles. However, existing models of CAV lane management do not perform well at low CAV market penetration rates. Therefore, a novel model is proposed to address this shortcoming in this paper, and qualified human‐driven vehicles (HDVs) are allowed to enter the CAV priority lane when there is a low CAV penetration rate. A theoretical model is proposed to examine the impacts of three managed lane strategies under mixed traffic and then compare three lane management strategies under different CAV penetration rates and traffic demands. The results show that these lane management strategies can improve throughput, reduce travel time, increase speed, and reduce the variance of the distribution of vehicle speeds under different penetration rates and traffic demands. [ABSTRACT FROM AUTHOR]

PAP2 (Production of anthocyanin segment 2) encodes MYB transcription factor, and regulates anthocyanin synthesis by forming MYB-bHLH-WD40 complex with bHLH and WD40. In this study we cloned PAP2 gene in Brassica, and preliminarily verified the function of PAP2, providing a reference for further understanding the regulatory mechanism of anthocyanin synthesis in Brassica. Two and one copies of PAP2 gene were cloned from Brassica carinata and Brassica nigra, respectively, and named as BcaB.PAP2, BcaC.PAP2, BniB.PAP2. These three genes are composed of 3 exons and 2 introns, encoding 247 amino acids. Based on the PAP2 gene sequences cloned in this study and other reported PAP2 gene sequences of Brassica, three pairs of primers were designed to detect the PAP2 gene of A, B and C genomes respectively, and the genomic origin of PAP2 of six species in the U's Triangle of Brassica could be distinguished by allele specific PCR. The BjuB.PAP2 gene from the B genome of Brassica juncea was selected to construct an overexpression vector and transformed into Arabidopsis thaliana. The transgenic Arabidopsis leaves turned purple, indicating that PAP2 gene is regulating anthocyanin synthesis. After shading treatment for 10 days, the purple color of Brassica carinata lightened, and the anthocyanin content decreased by 41.22%. Quantitative PCR and transcriptomic studies showed that the expression of BcaB.PAP2 and BcaC.PAP2 in the leaves of shading plants decreased. The expression of structural genes for anthocyanin synthesis, such as chalcone synthase gene (CHS), chalcone isomerase gene (CHI), flavanone 3-hydroxylase gene (F3H), dihydroflavonol reductase gene (DFR), anthocyanin synthase gene (ANS), flavonoid 3-glucosyltransferase gene (UFGT) and other genes were also decreased. This study completed the cloning of PAP2 gene of Brassica carinata and Brassica nigra, and carried out their evolutionary analysis. Allele specific PCR primers were designed according to the sequences of PAP2 gene cloned in this experiment and other reported genes to provide molecular markers for the genomic transmission identification of PAP2 gene in Brassica interspecific hybrids. Through the transformation of BjuB.PAP2 gene into Arabidopsis plants, it was found that PAP2 gene was involved in the accumulation of anthocyanins. It was found that the expression of BcaPAP2 gene and anthocyanin synthesis structure gene were induced by light after shading. This study cloned the PAP2 gene in Brassica and preliminarily verified its function, providing a reference for further understanding the regulatory mechanism of anthocyanin synthesis in Brassica plants. [ABSTRACT FROM AUTHOR]