Recovery of antioxidant gene expression in sacred lotus (Nelumbo nucifera Gaertn.) embryonic axes enhances tolerance to extreme high temperature

Sacred lotus (Nelumbo nucifera Gaertn.) seed is long-living and have various stress-resistance characteristics. We investigated the protecting mechanisms of lotus seeds against extreme high temperature by comparison of expression patterns of antioxidant genes in embryonic axes between exposure and non-exposure to extreme high temperature. It was shown that viability of seeds did not severely decline after exposure to 90°C for 24 h. Germination and growth were inhibited and H2O2 was accumulated at high level in the lotus embryonic axes germinated after heat treatment. Transcriptional levels of superoxide dismutase (SOD), ascorbate peroxidase (APX), peroxidase (POD), glutathione peroxidase (GPX) and thioredoxin-dependent peroxidase (TPX) encoding genes were induced to rise at late germination stage. Transcriptional levels of APX, POD, GPX and alternative oxidase (AOX) encoding genes were also immediately stimulated and up-regulated after heat treatment. These results suggest that the embryonic axes of sacred lotus maintain a protective and recovery mechanism from heat damage during and after exposure to extreme high temperature. Furthermore, the recovery of antioxidant gene expression enhanced tolerance to extreme high-temperature stress in sacred lotus.Keywords: Antioxidant gene, high temperature, seed germination, Nelumbo nucifera Gaert

FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly

While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation. In response, we introduce FocalDreamer, a framework that merges base shape with editable parts according to text prompts for fine-grained editing within desired regions. Specifically, equipped with geometry union and dual-path rendering, FocalDreamer assembles independent 3D parts into a complete object, tailored for convenient instance reuse and part-wise control. We propose geometric focal loss and style consistency regularization, which encourage focal fusion and congruent overall appearance. Furthermore, FocalDreamer generates high-fidelity geometry and PBR textures which are compatible with widely-used graphics engines. Extensive experiments have highlighted the superior editing capabilities of FocalDreamer in both quantitative and qualitative evaluations.Comment: Project website: https://focaldreamer.github.i

Decorative Art of Doors and Windows in Southern Jiangxi: Taking Bailu Ancient Village as an Example

China, as a country with 56 ethnic groups, has different geographical location, historical development and national customs. Residents in different places have incorporated different cultural characteristics when building local houses. This differentiated cultural and artistic feature is a reflection of the cultural connotation in a specific age and region. However, cultural protection is not always perfect. Many details are missed in this torrent of conservation culture, and door and window decorations are in this position. When collecting data, it is found that there is very little research on door and window decoration in Jiangxi. In this study, taking the doors and windows of Bailu Ancient Village as an example, combined with the cultural history of door and window decoration in southern Jiangxi, the artistic essence and historical value of the decorative patterns of houses were discussed. After searching a large number of documents, a field investigation was conducted in Bailu Ancient Village. Through direct observation of doors and windows and asking local residents about their understanding of door and window decoration, the history, current situation and future of door and window decoration in Bailu Ancient Village were described in detailed and predicted roughly

Real-Time Risk Assessment for Road Transportation of Hazardous Materials Based on GRU-DNN with Multimodal Feature Embedding

In this paper, a gated recurrent unit–deep neural network (GRU-DNN) model integrated with multimodal feature embedding (MFE) is developed to evaluate the real-time risk of hazmat road transportation based on various types of data for contributing factors. MFE was incorporated into the framework of a deep learning model in which discrete variables, continuous variables, and images were uniformly embedded. GRU is a pre-trained sub-model, and the DNN is able to directly use the relative structure and weights of the GRU, improving the poor classification and recognition results due to insufficient samples. Additionally, the model is trained and validated based on hazmat road transportation database consisting of 2100 samples with 20 real-time contributing factors and four risk levels in China. The accuracy (ACC), precision (PR), recall (RE), F1-score (F1), and areas under receiver-operating-characteristic curves (AUC) of the proposed model and other commonly used models are compared as performance measurements in numerical examples. Finally, Carlini & Wagner attack and three defenses of adversarial training, dimensionality reduction and prediction similarity are proposed in the training to improve the robustness of the model, alleviating the impact of noise and error on small-sized samples. The results demonstrate that the average ACC of the model reaches 93.51% and 87.6% on the training and validation sets, respectively. The prediction of accidents resulting in injury is the most accurate, followed by fatal accidents. Combined with the RE of 89.0%, the model exhibits excellent performance. In addition, the proposed model outperforms other widely used models based on the overall comparisons of ACC, AUC, F1 and PR-RE curve. Finally, prediction similarity can be used as an effective approach for robustness improvement, with the launched adversarial attacks being detected at a high success rate

Simulation-Based Optimization for the Operation of Toll Plaza at Car Park Exit with Mixed Types of Tollbooths and Waiting-Time-Dependent Service

This study presents an approach of simulation-based optimization to the operation of the toll plaza at the car park exit. We first propose a simulation model, as the representation of the queueing system for the toll plaza with mixed-type customers and servers where the service time is dependent on the waiting time of customer. Then, a simulation-based integer programming model is developed to design more traffic-efficient yet cost-effective operation schemes. It is decomposed by a rolling horizon approach into subproblems which are all solved via the Kriging metamodel algorithm. A numerical example is presented to illustrate the model and offer insight on how to achieve traffic efficiency and cost-effectiveness

GPR Data Augmentation Methods by Incorporating Domain Knowledge

Deep learning has significantly improved the recognition efficiency and accuracy of ground-penetrating radar (GPR) images. A significant number of weight parameters need to be specified, which requires lots of labeled GPR images. However, obtaining the ground-truth subsurface distress labels is challenging as they are invisible. Data augmentation is a predominant method to expand the dataset. The traditional data augmentation methods, such as rotating, scaling, cropping, and flipping, would change the GPR signals’ real features and cause the model’s poor generalization ability. We proposed three GPR data augmentation methods (gain compensation, station spacing, and radar signal mapping) to overcome these challenges by incorporating domain knowledge. Then, the most state-of-the-art model YOLOv7 was applied to verify the effectiveness of these data augmentation methods. The results showed that the proposed data augmentation methods decrease loss function values when the training epochs grow. The performance of the deep learning model gradually became stable when the original datasets were augmented two times, four times, and eight times, proving that the augmented datasets can increase the robustness of the training model. The proposed data augmentation methods can be used to expand the datasets when the labeled training GPR images are insufficient

Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression

Pavement condition prediction plays a vital role in pavement maintenance. Many prediction models and analyses have been conducted based on long-term pavement condition data. However, the condition evaluation for road sections can hardly support daily routine maintenance. This paper uses high-frequency pavement distress data to explore the relationship between distress initiation, weather, and geometric factors. Firstly, a framework is designed to extract the initial time of pavement distress. Weather and geometric data are integrated to establish a pavement distress initiation dataset. Then, the time-lag cross-correlation analysis methods were utilized to explore the relationship between distress initiation and environmental factors. In addition, the logistic regression model is used to establish the distress initiation prediction model. Finally, Akaike information criterion (AIC), Bayesian information criterions (BIC), and areas under receiver operating characteristic curves (AUC) of logistic regression models with or without time-lag variables are compared as performance measurements. The results show that pavement distress initiation is susceptible to weather factors and location relationships. Daily total precipitation, minimum temperature, and daily average temperature have a time delay effect on the initiation of the pavement distress. Distress initiation is negatively correlated with the distance from the nearby intersection and positively correlated with adjacent distresses. The weather factors, considering the time-lag effect, can improve the model performance of the distress initiation prediction model and provide support for emergency management after severe weather

Hydration behavior and strength development of supersulfated cement prepared by calcined phosphogypsum and slaked lime

The effect of the content of calcined phosphogypsum (CPG) and slaked lime (SL) on the fluidity, setting time, and compressive strength of supersulfated cement (SSC) were investigated. The hydration mechanism of the SSC containing CPG and SL was examined through electrical resistivity, hydration heat, and chemical shrinkage measurements and thermodynamic modelling was performed to predict the phase assemblages. The results indicate that SSC with CPG delayed the early age hydration and prolongs its induction period. The effect of CPG on delaying hydration was apparent in SSC with 5 % SL. CPG in SSC promotes the formation of ettringite (AFt), while excess SL inhibits it. With the increase of CPG, massive gypsum is produced, leading to the rapid setting and a decrease in fluidity. Excess AFt contributes to expansion and cracking, resulting in a decrease in compressive strength. With the incorporation of SL, the pH value of the pore solution increases, which is beneficial to the stabilization of AFt, thereby increasing the compressive strength. The compressive strength of SSC containing 10 % CPG and 10 % SL reached up to 35.2 MPa after 180 d. The study reveals the mechanism of the effect of CPG and SL on SSC hydration and provides theoretical guidance for preparing SSC for engineering applications.This is a manuscript of an article published as Liao, Yishun, Jinxin Yao, Fang Deng, Hao Li, Kejin Wang, and Shengwen Tang. "Hydration behavior and strength development of supersulfated cement prepared by calcined phosphogypsum and slaked lime." Journal of Building Engineering (2023): 108075. doi:https://doi.org/10.1016/j.jobe.2023.108075. Posted with Permission. Copyright © 2023 Elsevier B.V. CC BY-NC-N

ESGN : efficient stereo geometry network for fast 3D object detection

Fast stereo based 3D object detectors have made great progress recently. However, they suffer from the inferior accuracy. We argue that the main reason is due to the poor geometry-aware feature representation in 3D space. To solve this problem, we propose an efficient stereo geometry network (ESGN). The key in our ESGN is an efficient geometry-aware feature generation (EGFG) module. Our EGFG module first uses a stereo correlation and reprojection module to construct multi-scale stereo volumes in camera frustum space, second employs a multi-scale bird’s eye view (BEV) projection and fusion module to generate multiple geometry-aware features. In these two steps, we adopt deep multi-scale information fusion for discriminative geometry-aware feature generation, without any complex aggregation networks. In addition, we introduce a deep geometry-aware feature distillation scheme to guide stereo feature learning with a LiDAR-based detector. The experiments are performed on the classical KITTI dataset. On KITTI test set, our ESGN outperforms the fast state-of-art-art detector YOLOStereo3D by 5.14% on mAP3d at 62ms. To the best of our knowledge, our ESGN achieves a best trade-off between accuracy and speed. We hope that our efficient stereo geometry network can provide more possible directions for fast 3D object detection