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Super oil and gas basins provide the energy foundation for social progress and human development. In the context of climate change and carbon peak and carbon neutrality goals, constructing an integrated energy and carbon neutrality system that balances energy production and carbon reduction becomes crucial for the transformation of such basins. Under the framework of a green and intelligent energy system primarily based on “four news”, new energy, new electricity, new energy storage, and new intelligence, integrating a “super energy system” composed of a huge amount of underground resources of coal, oil, gas and heat highly overlapping with abundant wind and solar energy resources above ground, and a regional intelligent energy consumption system with coordinated development and utilization of fossil energy and new energy, with a carbon neutrality system centered around carbon cycling is essential. This paper aims to select the traditional oil and gas basins as “super energy basins” with the conditions to build world-class energy production and demonstration bases for carbon neutrality. The Ordos Basin has unique regional advantages, including abundant fossil fuel and new energy resources, as well as matching CO2 sources and sinks, position it as a carbon neutrality “super energy basin” which explores the path of transformation of traditional oil and gas basins. Under the integrated development concept and mode of “coal + oil + gas + new energy + carbon capture, utilization and storage (CCUS)/carbon capture and storage (CCS)”, the carbon neutrality in super energy basin is basically achieved, which enhance energy supply and contribute to the carbon peak and carbon neutrality goals, establish a modern energy industry and promote regional green and sustainable development. The pioneering construction of the world-class carbon neutrality “super energy system” demonstration basin in China represented by the Ordos Basin will reshape the new concept and new mode of exploration and development of super energy basins, which is of great significance to the global energy revolution under carbon neutrality.

ABSTRACT: Intestinal microbial metabolism has an important impact on the health of laying hens, and microbes are also important hosts for ARGs. However, the relationship between intestinal microbes and antibiotic resistance in laying hens is unclear. In this study, a slaughtering experiment, an in vitro fermentation experiment and a single-bacteria culture experiment were carried out, and metagenomic and metabolomic analyses were used to investigate the relationships between microbial metabolism and the antibiotic resistome in the cecum of laying hens. The results showed that there were different types of ARGs in the intestines of laying hens, and the risk scores of the ARGs tended to decrease with growth stage. A total of 1142 metagenome-assembled genomes (MAGs) were obtained, and Escherichia coli was found to be the dominant ARG host, carrying 62 ARGs. Metabolomics revealed that indole and its derivatives, such as indole-3-lactic acid, were negatively correlated with a variety of ARGs. Moreover, in vitro fermentation experiment and single-bacteria culture experiment demonstrated that indole-3-lactic acid reduced the abundance and risk of multiple ARGs in the intestine and inhibited the growth of the ARG host Escherichia coli. In the context of high concern about intestinal microbial metabolism and antibiotic resistance, this is the first study to focus on the relationship between intestinal microbial metabolism and antibiotic resistance in laying hens. These findings have important implications for healthy farming and antibiotic resistance control.

Abstract Self-esteem plays a crucial role in the psychological development of college students. Based on four-wave longitudinal data, this study empirically investigated the longitudinal trajectories of self-esteem, related predictors, and the impact on depression in a sample of Chinese college students. Piecewise growth mixture modeling was utilized to identify potential self-esteem trajectories. Furthermore, multinomial logistic regression models were employed along with the Kruskal‒Wallis test to analyze the factors that influence self-esteem development trajectories and assess variations in depression. The results showed that college students’ self-esteem trajectories can be divided into three categories: slowly decreasing (81.04%), rising then falling (16.94%), and falling then rising (2.02%). Various personal factors (e.g. ethnicity, family residence, self-reported health status, extroversion) and socioeconomic factors (e.g. closeness with classmates, family economic level, family social status, and the mother’s educational level) can influence the classification of students’ self-esteem development trajectories. Moreover, notable differences in depression were observed among students in different self-esteem trajectory groups. The results emphasize the imperative of implementing interventions to enhance self-esteem among college students.

A fundamental vision technique for industrial robots involves the six degrees of freedom pose estimation of target objects from a single image. However, the direct estimation of the six degrees of freedom object pose solely from a single image is subject to limited accuracy. Various refinement approaches have been proposed to improve the accuracy by utilizing rendered images from a 3D model. Nevertheless, balancing speed and accuracy in an industrial setting remains a challenge for these methods. In this study, we propose a novel six degrees of freedom pose refinement approach centered around matching real image patches. Unlike previous approaches, our method does not rely on a 3D model, resulting in increased speed and accuracy. In the offline phase, we construct an offline database using image patches obtained from real images. During the inference phase, our method initially identifies the image patch within the offline database that is closest to the initial pose. Subsequently, we refine the six degrees of freedom pose by matching the corresponding image patches from the offline database. Experimental results indicate that our six degrees of freedom pose refinement method achieves real-time capability with a frame rate of 71 Frames Per Second (FPS), along with high precision. When the threshold is set to 0.5% of the object diameter, the average distance of dots score on the test data surpasses 70%. Moreover, experiments involving gripping and assembling tasks on an industrial robot demonstrate the ability of our method to autonomously select appropriate grasping angles and positions in real time. It further generates suitable motion paths, ultimately ensuring production efficiency.

The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model—the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.

Nowadays, more people are choosing to use cloud storage services to save space and reduce costs. To enhance the durability and persistence, users opt to store important data in the form of multiple copies on cloud servers. However, outsourcing data in the cloud means that it is not directly under the control of users, raising concerns about security and integrity. Recent research has found that most existing multicopy integrity verification schemes can correctly perform integrity verification even when multiple copies are stored on the same Cloud Service Provider (CSP), which clearly deviates from the initial intention of users wanting to store files on multiple CSPs. With these considerations in mind, this paper proposes a scheme for synchronizing the integrity verification of copies, specifically focusing on strongly privacy Internet of Things (IoT) electronic health record (EHR) data. First, the paper addresses the issues present in existing multicopy integrity verification schemes. The scheme incorporates the entity Cloud Service Manager (CSM) to assist in the model construction, and each replica file is accompanied with its corresponding homomorphic verification tag. To handle scenarios where replica files stored on multiple CSPs cannot provide audit proof on time due to objective reasons, the paper introduces a novel approach called probability audit. By incorporating a probability audit, the scheme ensures that replica files are indeed stored on different CSPs and guarantees the normal execution of the public auditing phase. The scheme utilizes identity-based encryption (IBE) for the detailed design, avoiding the additional overhead caused by dealing with complex certificate issues. The proposed scheme can withstand forgery attack, replace attack, and replay attack, demonstrating strong security. The performance analysis demonstrates the feasibility and effectiveness of the scheme.