Technical Challenges in Evaluating Southern China’s Forage Germplasm Resources

The present status of the collection, conservation and utilisation of the pasture germplasm in tropical and subtropical zones in China is reviewed. The Tropical Pasture Research Centre (TPRC) of Chinese Academy of Tropical Agricultural Sciences (CATAS) has been engaged in this research since the 1940s. A low temperature gene bank, an in vitro plant library and a nursery station have been established. In total, 5890 indigenous fodder materials belonging to 478 species, 161 genera and 12 families have been surveyed and collected in South China; 1130 exotic materials belonging to 87 species, 42 genera of grasses and legumes have been introduced and conserved; 3769 materials from 301 species, 127 genera, 12 families have been conserved in the seed bank; 482 materials belonging to 6 species, 6 genera, 3 families have been propagated in vitro, and 388 materials belonging to 10 species, 8 genera, 3 families grown in the plant preservation nursery. Suggestions are made for developing and utilising southern Chinese grassland resources

360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network

To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera's narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance.Comment: 6 pages, 9 figure

The sustainable cycle between lean production and auditing practices and its efficiency in improving supplier relationships and green supply chains

This study explores why a sustainable cycle is induced when manufacturers implement auditing in combination with lean production. Furthermore, it verifies whether this sustainable cycle enhances process integration and risk resilience, thereby allowing the manufacturer to build strong cooperation with suppliers, which further produces a positive effect on the green supply chain. Sociotechnical systems theory is our theoretical basis, and calculating Spearman’s rank correlation coefficient and estimating PLS regressions are the main methods used. The results show that the implementation of auditing induces two driving forces: internal responsibility and the ability to respond to emergencies. These two forces drive suppliers to actively and positively cooperate with lean practices to ensure that the effect of those practices is strengthened. Moreover, stronger lean practices also produce two feedback forces – expanded tolerance for auditing and expanded acceptance of auditing interventions – that strengthen auditing practices. As a result, the mutually continuous strengthening of lean production and auditing practices is produced, which further becomes a sustainable cycle. This cycle can continue to enhance process integration and increase risk resilience, build strong cooperation with suppliers, and improve the green supply chain

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

MFTR-Net: A Multi-Level Features Network with Targeted Regularization for Large-Scale Point Cloud Classification

There are some irregular and disordered noise points in large-scale point clouds, and the accuracy of existing large-scale point cloud classification methods still needs further improvement. This paper proposes a network named MFTR-Net, which considers the local point cloud’s eigenvalue calculation. The eigenvalues of 3D point cloud data and the 2D eigenvalues of projected point clouds on different planes are calculated to express the local feature relationship between adjacent point clouds. A regular point cloud feature image is constructed and inputs into the designed convolutional neural network. The network adds TargetDrop to be more robust. The experimental result shows that our methods can learn more high-dimensional feature information, further improving point cloud classification, and our approach can achieve 98.0% accuracy with the Oakland 3D dataset

Technical challenges in evaluating southern China’s forage germplasm resources

The present status of the collection, preservation and utilization of pasture germplasm in tropical and subtropical zones in China is reviewed. The Tropical Pasture Research Centre (TPRC) of the Chinese Academy of Tropical Agricultural Sciences (CATAS) has been engaged in this research since the 1940s. A low-temperature gene bank, an in-vitro plant library and a nursery station have been established. In total, 5890 indigenous fodder accessions belonging to 478 species, 161 genera and 12 families have been surveyed and collected in South China; 1130 exotic accessions belonging to 87 species and 42 genera of grasses and legumes have been introduced and are preserved. In the seed bank, 3769 accessions from 301 species, 127 genera and 12 families are maintained; in the form of in-vitro culture, 482 accessions belonging to 6 species, 6 genera and 3 families are preserved; and in the plant preservation nursery 388 accessions belonging to 10 species, 8 genera and 3 families. A list of 12 forage legume and 9 grass cultivars released by CATAS during 1991-2011 is presented and suggestions are made for developing and utilizing southern Chinese grassland germplasm resources.</p

Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners

Self-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of education modernization. Behavior data of online learning platforms are an important carrier to reflect the learners’ initiative to plan, monitor, and regulate their learning process. Self-regulated learning (SRL) is one of the important skills to achieve learning goals and is an essential means to ensure the quality of online learning. However, there are still great challenges in studying the types and sequential patterns of learners’ self-regulated learning behaviors in online environments. In addition, for higher education, the defects of the traditional education mode are increasingly prominent, and self-regulated learning (SRL) has become an inevitable trend. Based on Zimmerman’s self-regulation theory model, this paper first classifies learning groups using the hierarchical clustering method. Then, lag sequence analysis is used to explore the most significant differences in SRL behavior and its sequence patterns among different learning groups. Finally, the differences in academic achievement among different groups are discussed. The results are as follows: (1) The group with more average behavior frequency tends to solve online tasks actively, presenting a “cognitive oriented” sequential pattern, and this group has the best performance; (2) the group with more active behavior frequency tends to improve in the process of trial and error, showing a “reflective oriented” sequence pattern, and this group has better performance; (3) the group with the lowest behavior frequency tends to passively complete the learning task, showing a “negative regulated” sequence pattern, and this group has poor performance. From the aspects of stage and outcome of self-regulated learning, the behavior sequence and learning performance of online learning behavior mode are compared, and the learning path and learning performance of different learning modes are fully analyzed, which can provide reference for the improvement of online learning platform and teachers’ teaching intervention

Engineering Design and Evaluation of the Process Evaluation Method of Auto Repair Professional Training in Virtual Reality Environment

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners&rsquo; realization. The teaching evaluation of traditional vocational skill training requires time, workforce, and educational resources. Due to the limitations of experimental conditions, it is easy to ignore the procedural characteristics of skill training and difficult to implement the procedural evaluation. Based on the above problems, combined with virtual reality and the parts of vocationally skilled auto repair training specialty, using machine learning methods, engineering design of process evaluation method for skilled auto repair training, and takes the secondary vocational auto repair specialty as an example, constructs an evaluation index model based on KSA theoretical model, and evaluates three dimensions: knowledge acquisition, skill mastery, and ability cultivation (knowledge, skill, ability, KSA). The experimental verification of the process evaluator is carried out in the theoretical training evaluation auto repair system (TTE) based on virtual reality. The experimental results can effectively evaluate the practical training of students. The research results of this paper provide a new perspective and reference for the learning evaluation of skill-based training majors

Fruit Tree Legume Herb Intercropping Orchard System Is an Effective Method to Promote the Sustainability of Systems in a Karst Rocky Desertification Control Area

Karst rocky desertification control through the conversion of cropland to economic forest is vital for vegetation recovery and the alleviation of distinct contradiction between ecological conservation and economic development. To evaluate the sustainability of orchard systems from the perspectives of ecosystem and economic services, we employed emergy analysis for the comprehensive and quantitative assessment of two orchard system types: (1) mango monoculture (MM) and macadamia monoculture (NM) and (2) mango Vicia angustifolia intercropping (MVI) and macadamia Desmodium intortum intercropping (NDI). In the past, these areas were converted from a maize field (MF) in the southwest karst area of China. Our results showed that, compared to the MF, the total emergy input in monoculture orchards (NM and NM) decreased by 8.99% and 35.25%, and the economic profit (EP) increased by 20,406.57 and 114,406.32 RMB&middot;ha&minus;1, respectively. However, the non-renewable environmental input (energy loss of soil, SOM reduction, and irrigation water) still accounted for 43.25% and 62.01% in the total emergy input. After conversion to orchard legume herb intercropping (MVI and NDI), purchased resource inputs accounted for 86.36% and 68.20% of the total emergy input. Orchard legume herb intercropping further increased the EP, while improving ecosystem services and providing the capability for groundwater recharge, soil conservation, and soil carbon sequestration. The intercropping orchards were relatively sustainable from the view of economic and ecosystem services (EISD &gt; 3.18), due to lower environmental loading ratios (ELR &lt; 1.15), higher emergy yield ratio (EYR &gt; 0.89), and economic output/input ratio (O/I ratio &gt; 2.41). The integrated pest management simulations indicated that, compared to intercropping systems, the renewable percent (R%) and emergy sustainability index (ESI) of the scenario simulations (MVI-O and NDI-O) increased by 17.61% and 10.51%, respectively. These results suggest that integrated pest management is an effective method to improve the short-term sustainability of the orchard system. Therefore, the management of intercropped legume herb within an orchard system is an effective way to achieve sustainable development

Engineering Design and Evaluation of the Process Evaluation Method of Auto Repair Professional Training in Virtual Reality Environment

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners’ realization. The teaching evaluation of traditional vocational skill training requires time, workforce, and educational resources. Due to the limitations of experimental conditions, it is easy to ignore the procedural characteristics of skill training and difficult to implement the procedural evaluation. Based on the above problems, combined with virtual reality and the parts of vocationally skilled auto repair training specialty, using machine learning methods, engineering design of process evaluation method for skilled auto repair training, and takes the secondary vocational auto repair specialty as an example, constructs an evaluation index model based on KSA theoretical model, and evaluates three dimensions: knowledge acquisition, skill mastery, and ability cultivation (knowledge, skill, ability, KSA). The experimental verification of the process evaluator is carried out in the theoretical training evaluation auto repair system (TTE) based on virtual reality. The experimental results can effectively evaluate the practical training of students. The research results of this paper provide a new perspective and reference for the learning evaluation of skill-based training majors