Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Plant electrical signals are physiological signals within the plant body that respond to both external and internal stimuli. Using plant electrical signals as an effective indicator for evaluating the plant growth status is a new theory and method to study the relationship between environmental factors affecting plant growth and plant growth responses. This novel proposed approach is advantageous in terms of response sensitivity and accuracy. Therefore, automation and intelligence of agricultural plant cultivation can be realized and implemented by monitoring the changes in the patterns of plant electrical signals. This paper investigated rapeseed plants in three groups and evaluated the effect of soil water content as the controlled environmental variable. The plant electrical signals under different soil water contents were collected for wavelet packet noise reduction processing. The plant electrical signals were analyzed from three aspects: time domain, frequency domain, and wavelet packet decomposition. The mean, root mean square, standard deviation in the time domain, the power spectral entropy (PSE) of the centroid frequency (SCF) in the frequency domain, and the electrical signal energy in the wavelet packet decomposition were used as the eigenvectors required for classification. The external plant morphological data and the rapeseed growth under different soil water contents were collected to establish plant water stress evaluation indexes. By this, the optimal water demand gradient for rapeseed growth was obtained. The plant water stress evaluation index was used to verify the classification effect of electrical signals, combined with the plants’ electrical signal changes under different water stress conditions to comprehensively evaluate the growth status of plants under different soil water contents. Finally, a support vector machine (SVM) and a particle swarm optimized support vector machine (PSO-SVM) were used to classify the water stress status of plants and establish prediction model the relationship between plant growth status and plant electrical signals under different soil water contents. The results showed that the plant water stress state classification model accuracy based on SVM was 90.83%, and the mean square error MSE was 0.175, while the accuracy of the plant water stress state classification model based on PSO-SVM was 94.3167%, and the mean square error was 0.1646. The classification experiments results show that the water stress of plants and the classification of plant growth status can be realized utilizing electrical signal analysis, with the support vector machine classification model after particle swarm optimization being more accurate. This method lays the foundation for the realization of automation in agricultural plant cultivation and monitoring through plant electrical signals

Multi-objective parameter optimization strategy based on engine coordinated control for improving shifting quality

In order to address the poor shifting quality issue of a certain model of heavy-duty vehicles, a multi-objective parameter optimization strategy based on engine coordinated control is proposed. This strategy aims to improve shifting quality by reducing the sliding friction work and impact during the shifting process. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to perform multi-objective optimization on the coordinated control parameters, which include external control torque of the engine, start of fuel cut-off timing, and duration of fuel cut-off. By comparing the performance of different parameter combinations in terms of sliding friction work and impact, the optimal parameter combination is determined. Through bench testing verification, it has been demonstrated that utilizing the optimized parameters for engine coordinated control during the torque phase of the shifting process can significantly enhance shifting quality. This strategy provides an effective solution for addressing shifting quality issues

First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high

An adaptive LIC based geographic flow field visualization method by means of rotation distance

Geographic visualization is essential for explaining and describing spatiotemporal geographical processes in flow fields. However, due to multi-scale structures and irregular spatial distribution of vortices in complex geographic flow fields, existing two-dimensional visualization methods are susceptible to the effects of data accuracy and sampling resolution, resulting in incomplete and inaccurate vortex information. To address this, we propose an adaptive Line Integral Convolution (LIC) based geographic flow field visualization method by means of rotation distance. Our novel framework of rotation distance and its quantification allows for the effective identification and extraction of vortex features in flow fields effectively. We then improve the LIC algorithm using rotation distance by constructing high-frequency noise from it as input to the convolution, with the integration step size adjusted. This approach allows us to effectively distinguish between vortex and non-vortex fields and adaptively represent the details of vortex features in complex geographic flow fields. Our experimental results show that the proposed method leads to more accurate and effective visualization of the geographic flow fields

Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful

Genome-Wide Analysis of Sugar Transporters Identifies the gtsA Gene for Glucose Transportation in Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 possesses an extraordinary number of transporters which confer this rhizosphere bacterium with the sophisticated ability to metabolize various carbon sources. However, sugars are not a preferred carbon source for P. stutzeri A1501. The P. stutzeri A1501 genome has been sequenced, allowing for the homology-based in silico identification of genes potentially encoding sugar-transport systems by using established microbial sugar transporters as a template sequence. Genomic analysis revealed that there were 10 sugar transporters in P. stutzeri A1501, most of which belong to the ATP-binding cassette (ABC) family (5/10); the others belong to the phosphotransferase system (PTS), major intrinsic protein (MIP) family, major facilitator superfamily (MFS) and the sodium solute superfamily (SSS). These systems might serve for the import of glucose, galactose, fructose and other types of sugar. Growth analysis showed that the only effective medium was glucose and its corresponding metabolic system was relatively complete. Notably, the loci of glucose metabolism regulatory systems HexR, GltR/GtrS, and GntR were adjacent to the transporters ABCMalEFGK, ABCGtsABCD, and ABCMtlEFGK, respectively. Only the ABCGtsABCD expression was significantly upregulated under both glucose-sufficient and -limited conditions. The predicted structure and mutant phenotype data of the key protein GtsA provided biochemical evidence that P. stutzeri A1501 predominantly utilized the ABCGtsABCD transporter for glucose uptake. We speculate that gene absence and gene diversity in P. stutzeri A1501 was caused by sugar-deficient environmental factors and hope that this report can provide guidance for further analysis of similar bacterial lifestyles

An enhanced vector-free allele exchange (VFAE) mutagenesis protocol for genome editing in a wide range of bacterial species

Abstract Vector-free allele exchange (VFAE) is a newly developed protocol for genome editing in Pseudomonas species. Although several parameters have been determined to optimize the procedures for obtaining a stable and high-frequency mutation, numerous false-positive clones still appear on the plate, which increases the difficulty of finding the desired mutants. It has also not been established whether this protocol can be used for genome editing in other bacterial species. In the current study, the protocol was modified to dramatically decrease the occurrence of false-positive colonies using Pseudomonas stutzeri A1501 as a model strain. This improvement was reached by increasing the occurrence of circular-DNA cassettes of the correct size. Furthermore, the enhanced protocol was used to construct mutants in both the gram-negative Escherichia coli BL21 and gram-positive Bacillus subtilis 168 strains. The protocol works well in both strains, yielding ideal results with a low percentage of false-positive colonies. In summary, the enhanced VFAE mutagenesis protocol is a potential tool for use in bacterial genome editing

MOESM2 of An enhanced vector-free allele exchange (VFAE) mutagenesis protocol for genome editing in a wide range of bacterial species

Additional file 2. Schematic representation and comparison of the original and modified VFAE procedures

The Sigma Factor AlgU Regulates Exopolysaccharide Production and Nitrogen-Fixing Biofilm Formation by Directly Activating the Transcription of pslA in Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501, a plant-associated diazotrophic bacterium, prefers to conform to a nitrogen-fixing biofilm state under nitrogen-deficient conditions. The extracytoplasmic function (ECF) sigma factor AlgU is reported to play key roles in exopolysaccharide (EPS) production and biofilm formation in the Pseudomonas genus; however, the function of AlgU in P. stutzeri A1501 is still unclear. In this work, we mainly investigated the role of algU in EPS production, biofilm formation and nitrogenase activity in A1501. The algU mutant ΔalgU showed a dramatic decrease both in the EPS production and the biofilm formation capabilities. In addition, the biofilm-based nitrogenase activity was reduced by 81.4% in the ΔalgU mutant. The transcriptional level of pslA, a key Psl-like (a major EPS in A1501) synthesis-related gene, was almost completely inhibited in the algU mutant and was upregulated by 2.8-fold in the algU-overexpressing strain. A predicted AlgU-binding site was identified in the promoter region of pslA. The DNase I footprinting assays indicated that AlgU could directly bind to the pslA promoter, and β-galactosidase activity analysis further revealed mutations of the AlgU-binding boxes drastically reduced the transcriptional activity of the pslA promoter; moreover, we also demonstrated that AlgU was positively regulated by RpoN at the transcriptional level and negatively regulated by the RNA-binding protein RsmA at the posttranscriptional level. Taken together, these data suggest that AlgU promotes EPS production and nitrogen-fixing biofilm formation by directly activating the transcription of pslA, and the expression of AlgU is controlled by RpoN and RsmA at different regulatory levels