The Effect of the Crosstalk between Photoperiod and Temperature on the Heading-Date in Rice

Photoperiod and temperature are two important environmental factors that influence the heading-date of rice. Although the influence of the photoperiod on heading has been extensively reported in rice, the molecular mechanism for the temperature control of heading remains unknown. This study reports an early heading mutant derived from tissue culture lines of rice and investigates the heading-date of wild type and mutant in different photoperiod and temperature treatments. The linkage analysis showed that the mutant phenotype cosegregated with the Hd1 locus. Sequencing analysis found that the mutant contained two insertions and several single-base substitutions that caused a dramatic reduction in Hd1mRNA levels compared with wild type. The expression patterns of Hd1 and Hd3a were also analyzed in different photoperiod and temperature conditions, revealing that Hd1 mRNA levels displayed similar expression patterns for different photoperiod and temperature treatments, with high expression levels at night and reduced levels in the daytime. In addition, Hd1 displayed a slightly higher expression level under long-day and low temperature conditions. Hd3a mRNA was present at a very low level under low temperature conditions regardless of the day-length. This result suggests that suppression of Hd3a expression is a principle cause of late heading under low temperature and long-day conditions

A rapid identification model of mine water inrush based on PSO-XGBoost

Mine water inrush is one of the main threats to mine safety production. Rapid analysis of the cause of water inrush and accurate identification of water inrush source are the key steps of mine water inrush disaster control. In order to effectively prevent and control mine water inrush disaster and identify mine water inrush source accurately and quickly, a mine water inrush source identification model (PSO-XGBoost) based on particle swarm optimization algorithm (PSO) and limit gradient lifting regression tree (XGBoost) was proposed. The efficiency and accuracy of water inrush source identification were further improved by the efficient parameter global search model, and the model was successfully applied to the Laohutai mine in Fushun coal field, Liaoning Province to verify the practicability of the model. Based on the spectral data of 40 groups of water samples from Laohutai mine, the original spectral data were preprocessed by multiple scattering correction, smoothing denoising, standardization and principal component analysis, and the training set and test set were divided according to the ratio of 7∶3 according to stratified random sampling. Secondly, the individual optimal value and the global optimal value of particles are initialized, and PSO is used to iteratively optimize seven parameters of XGBoost algorithm, such as learning_rate, n_estimatiors, max_depth, etc., to construct the classification and recognition model under the optimal parameter combination. To further investigate the superiority of the model, the average discrimination accuracy and log loss value were selected as evaluation indexes to compare the classification recognition results of PSO-XGBoost model with PSO-SVM and PSO-RF models, while the generalization ability of each model was evaluated by 100 repetitions of cross-validation. The comparison results showed that the average discrimination accuracies of XGBoost, PSO-SVM, PSO-RF and PSO-XGBoost models for the test set data were 87.76%, 87.56%, 91.67% and 91.67%, respectively. For repeated cross-validation, the average accuracy of XGBoost, PSO-SVM, PSO-RF, and PSO-XGBoost models were 87.76%, 87.56%, 90.63%, and 93.18%, respectively, with corresponding log-loss averages of 0.5453, 0.5460, 0.5623, and 0.4534, respectively. Comprehensive analysis of evaluation indexes shows that PSO-XGBoost model has higher discrimination accuracy and better generalization ability in mine water inrush source identification

Effects of the seedling tray overlapping for seed emergence mode on emergence characteristics and growth of rice seedlings

Seedling mode plays a crucial role in the rice production process, as it significantly affects the growth and development of seedlings. Among the various seedling modes, the seedling tray overlapping for seed emergence mode (STOSE mode) has been demonstrated to be effective in enhancing seedling quality. However, the impact of this mode on the germination and growth of seeds with varying plumpness remains uncertain. To investigate the effect of the STOSE mode on seedling emergence characteristics, growth uniformity, and nutrient uptake of seeds with varying plumpness levels, we conducted a study using super early rice Zhongzao 39 (ZZ39) as the test material. The seeds were categorized into three groups: plumped, mixed, and unplumped. The results indicated that the STOSE mode significantly improved the seedling rate for all types of seeds in comparison to the seedling tray nonoverlapping for seed emergence mode (TSR mode). Notably, the unplumped seeds exhibited the most pronounced enhancement effect. The soluble sugar content of the seeds increased significantly after 2 days of sowing under the STOSE mode, whereas the starch content exhibited a significant decrease. Furthermore, the STOSE mode outperformed the TSR mode in several aspects including seedling growth uniformity, aboveground dry matter mass, root traits, and nutrient uptake. Overall, the STOSE mode not only promoted the germination and growth of plumped and mixed seeds but also had a more pronounced impact on unplumped seeds

Neural ODE and DAE Modules for Power System Dynamic Modeling

The time-domain simulation is the fundamental tool for power system transient stability analysis. Accurate and reliable simulations rely on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a modified neural ODE module and a neural differential-algebraic equations (DAEs) module for power system dynamic component modeling are proposed. The modules adopt an autoencoder to raise the dimension of state variables, model the dynamics of components with artificial neural networks (ANNs), and keep the numerical integration structure. In the neural DAE module, an additional ANN is used to calculate injection currents. The neural models can be easily integrated into time-domain simulations. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of parameter inference, physics-data-integrated modeling, black-box modeling, etc., and can be easily integrated into power system dynamic simulations. Some simple numerical tests are carried out in the IEEE-39 system and prove the validity and potentiality of the proposed modules.Comment: 8 pages, 4 figures, 1 tabl

An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph

An integrated navigation system coupled with additional sensors can be used in the Micro Unmanned Aerial Vehicle (MUAV) applications because the multi-sensor information is redundant and complementary, which can markedly improve the system accuracy. How to deal with the information gathered from different sensors efficiently is an important problem. The fact that different sensors provide measurements asynchronously may complicate the processing of these measurements. In addition, the output signals of some sensors appear to have a non-linear character. In order to incorporate these measurements and calculate a navigation solution in real time, the multi-sensor fusion algorithm based on factor graph is proposed. The global optimum solution is factorized according to the chain structure of the factor graph, which allows for a more general form of the conditional probability density. It can convert the fusion matter into connecting factors defined by these measurements to the graph without considering the relationship between the sensor update frequency and the fusion period. An experimental MUAV system has been built and some experiments have been performed to prove the effectiveness of the proposed method

Overlapped Tray Seedling Raising Model for Mechanical Transplanting of Rice

With the development of China's social economy, transfer of rural labors, and rise of labor costs, the rice production technologies are changing from manual operation to mechanical operation. The core of mechanical rice production is mechanical planting, while the key to mechanical planting is the seedling raising. On the basis of analyzing and studying the problems and experience of traditional mechanical transplanting of rice, a new overlapped tray seedling raising model was introduced. Main features of this model: substrate seedling raising and overlapped seedling tray. The seedling trays for seeding line are overlapped, and the overlapped trays are moved into the seedling room with controlled temperature and humidity. The temperature of the seedling room is controlled at 30-32℃, and the humidity of the seedling room is controlled at above 90%. After about 48 h, when the seedling height reaches 0.5 cm, move the seedling trays to the nursery. This model consists of one seeding center (seedling raising center) and N nurseries, to realize 1+N seedling raising for mechanical transplanting of rice. One seeding center can provide seedlings for several hundred to several thousand hectares of mechanical transplanting of rice, and provide a new model for social services. This model could improve the quality of seedlings, increase the seedling survival rate by about 20%, reduce the seedling raising costs by 15%-20%, reduce the seedling raising risks, and greatly increase the utilization rate of the site and equipment of seedling raising center

Evaluation Index for IVIS Integration Test under a Closed Condition Based on the Analytic Hierarchy Process

The intelligent vehicle infrastructure system (IVIS) requires systematic testing before being put into large-scale applications. IVIS testing under closed conditions includes stress tests for typical scenarios and extreme scenario strength testing. To extract IVIS integration test indicators under closed conditions, this article constructed a hierarchical framework of IVIS’s evaluation indexes in the stress tests and the strength tests. The hierarchical framework of IVIS stress test evaluation indicators reflect the highway construction area under typical scenarios, and the hierarchical framework of IVIS strength test evaluation indicators reflect the highway merging area under extreme scenarios. Both are based on the test requirements of the stress test and strength test, with safety as the evaluation objective. Second, the analytic hierarchy process (AHP) was used to calculate the weights of the test evaluation indicators of the two scenarios. Finally, the activity-based classification (ABC) method was used after ranking the weight results in order to extract the key factors that have the maximum impact on safety in the scenarios. In this paper, we proved the practicality and feasibility of the AHP-ABC extraction method in the IVIS integration testing evaluation index and guided the development and testing of the IVIS

Evaluation of Climate Change Impacts on the Potential Distribution of Wild Radish in East Asia

Climate change can exert a considerable influence on the geographic distribution of many taxa, including coastal plants and populations of some plant species closely related to those used as agricultural crops. East Asian wild radish, Raphanus raphanistrum subsp. sativus, is an annual coastal plant that is a wild relative of the cultivated radish (R. sativus). It has served as source of genetic material that has been helpful to develop and improve the quality and yield of radish crops. To assess the impact of climate change on wild radish in East Asia, we analyzed its distribution at different periods using the maximum entropy model (MaxEnt). The results indicated that the precipitation of the driest month (bio14) and precipitation seasonality (bio15) were the two most dominant environmental factors that affected the geographical distribution of wild radish in East Asia. The total potential area suitable for wild radish is 102.5574 × 104 km2, mainly located along the seacoasts of southern China, Korea, and the Japanese archipelago. Compared with its current distribution regions, the potentially suitable areas for wild radish in the 2070s will further increase and expand northwards in Japan, especially on the sand beach habitats of Hokkaido. This research reveals the spatiotemporal changes for the coastal plant wild radish under global warming and simultaneously provides a vital scientific basis for effective utilization and germplasm innovation for radish cultivars to achieve sustainable agriculture development