Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

With the adjustment of agricultural industry structure and the acceleration of land transfer in our country, the appropriate management level of continuous, large scale and intensive farming has remarkably improved, which has put forward a higher requirement for the level of agricultural machinery. According to statistics, the comprehensive mechanization rate of farming, planting and harvesting of major crops in China has exceeded 70% in 2020. However, the development level of mechanization in different provinces and regions is still unbalanced, so is the demand and supply of seasonal agricultural machinery operation. Cross regional operation of agricultural machinery is still a common occurrence. So a scientific, efficient and low-cost agricultural machinery allocation scheme is to be constructed so as to solve the imbalance of development level between regions of agricultural mechanization and the contradiction between supply and demand of agricultural machinery in operation seasons and to realize the rational allocation of agricultural machinery in cross regional operation. The allocation scheme can increase the machinery owners\u27 income by improving the utilization rate and allocation cost of agricultural machinery.This study systematically analyzes and summarizes the allocation mode of agricultural machinery in the reclamation area, constructs relevant data models and solution methods, and ultimately comes to the effective solution of the operation relationship allocation model and path optimization model under different allocation modes of agricultural machinery based on TLBO optimization theory and method, which effectively solves the problem of rational and efficient allocation of agricultural machinery in cross regional operation of agricultural machinery

Optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on Particle Swarm Optimization Algorithm

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 - 1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis

The Secreted Peptide PIP1 Amplifies Immunity through Receptor-Like Kinase 7

In plants, innate immune responses are initiated by plasma membrane-located pattern recognition receptors (PRRs) upon recognition of elicitors, including exogenous pathogen-associated molecular patterns (PAMPs) and endogenous damage-associated molecular patterns (DAMPs). Arabidopsis thaliana produces more than 1000 secreted peptide candidates, but it has yet to be established whether any of these act as elicitors. Here we identified an A. thaliana gene family encoding precursors of PAMP-induced secreted peptides (prePIPs) through an in-silico approach. The expression of some members of the family, including prePIP1 and prePIP2, is induced by a variety of pathogens and elicitors. Subcellular localization and proteolytic processing analyses demonstrated that the prePIP1 product is secreted into extracellular spaces where it is cleaved at the C-terminus. Overexpression of prePIP1 and prePIP2, or exogenous application of PIP1 and PIP2 synthetic peptides corresponding to the C-terminal conserved regions in prePIP1 and prePIP2, enhanced immune responses and pathogen resistance in A. thaliana. Genetic and biochemical analyses suggested that the receptor-like kinase 7 (RLK7) functions as a receptor of PIP1. Once perceived by RLK7, PIP1 initiates overlapping and distinct immune signaling responses together with the DAMP PEP1. PIP1 and PEP1 cooperate in amplifying the immune responses triggered by the PAMP flagellin. Collectively, these studies provide significant insights into immune modulation by Arabidopsis endogenous secreted peptides

Identification and experimental validation of key m6A modification regulators as potential biomarkers of osteoporosis

Osteoporosis (OP) is a severe systemic bone metabolic disease that occurs worldwide. During the coronavirus pandemic, prioritization of urgent services and delay of elective care attenuated routine screening and monitoring of OP patients. There is an urgent need for novel and effective screening diagnostic biomarkers that require minimal technical and time investments. Several studies have indicated that N6-methyladenosine (m6A) regulators play essential roles in metabolic diseases, including OP. The aim of this study was to identify key m6A regulators as biomarkers of OP through gene expression data analysis and experimental verification. GSE56815 dataset was served as the training dataset for 40 women with high bone mineral density (BMD) and 40 women with low BMD. The expression levels of 14 major m6A regulators were analyzed to screen for differentially expressed m6A regulators in the two groups. The impact of m6A modification on bone metabolism microenvironment characteristics was explored, including osteoblast-related and osteoclast-related gene sets. Most m6A regulators and bone metabolism-related gene sets were dysregulated in the low-BMD samples, and their relationship was also tightly linked. In addition, consensus cluster analysis was performed, and two distinct m6A modification patterns were identified in the low-BMD samples. Subsequently, by univariate and multivariate logistic regression analyses, we identified four key m6A regulators, namely, METTL16, CBLL1, FTO, and YTHDF2. We built a diagnostic model based on the four m6A regulators. CBLL1 and YTHDF2 were protective factors, whereas METTL16 and FTO were risk factors, and the ROC curve and test dataset validated that this model had moderate accuracy in distinguishing high- and low-BMD samples. Furthermore, a regulatory network was constructed of the four hub m6A regulators and 26 m6A target bone metabolism-related genes, which enhanced our understanding of the regulatory mechanisms of m6A modification in OP. Finally, the expression of the four key m6A regulators was validated in vivo and in vitro, which is consistent with the bioinformatic analysis results. Our findings identified four key m6A regulators that are essential for bone metabolism and have specific diagnostic value in OP. These modules could be used as biomarkers of OP in the future

Improved capsule routing for weakly labeled sound event detection

Polyphonic sound event detection aims to detect the types of sound events that occur in given audio clips, and their onset and offset times, in which multiple sound events may occur simultaneously. Deep learning-based methods such as convolutional neural networks (CNN) achieved state-of-the-art results in polyphonic sound event detection. However, two open challenges still remain: overlap between events and prone to overfitting problem. To solve the above two problems, we proposed a capsule network-based method for polyphonic sound event detection. With so-called dynamic routing, capsule networks have the advantage of handling overlapping objects and the generalization ability to reduce overfitting. However, dynamic routing also greatly slows down the training process. In order to speed up the training process, we propose a weakly labeled polyphonic sound event detection model based on the improved capsule routing. Our proposed method is evaluated on task 4 of the DCASE 2017 challenge and compared with several baselines, demonstrating competitive results in terms of F-score and computational efficiency

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station

generic side-channel distinguisher based on kolmogorov-smirnov test: explicit construction and practical evaluation

Construction and evaluation of efficient distinguishers with broad generality is one fundamental problem in the area of side-channel cryptanalysis. Due to their capabilities to deal with general correlations, MIA-like distinguishers have received wide attention from academia. In this paper, we conduct a comprehensive comparison investigation of existing MIA-like distinguishers, and then propose a new generic side-channel distinguisher based on partial Kolmogorov-Smirnov test, namely PKS distinguisher. Theoretical analysis and experimental attacks unanimously justify that PKS distinguisher works remarkably well with both linear and non-linear leakage models. Specifically, PKS distinguisher has obvious advantages over existing MIA-like distinguishers in terms of both success rate and guessing entropy. Additionally, lower computational complexity of PKS distinguisher further shows its better applicability than MIA-like distinguishers.National Natural Science Foundation of China 61073178; Natural Science Foundation of Beijing 4112064Construction and evaluation of efficient distinguishers with broad generality is one fundamental problem in the area of side-channel cryptanalysis. Due to their capabilities to deal with general correlations, MIA-like distinguishers have received wide attention from academia. In this paper, we conduct a comprehensive comparison investigation of existing MIA-like distinguishers, and then propose a new generic side-channel distinguisher based on partial Kolmogorov-Smirnov test, namely PKS distinguisher. Theoretical analysis and experimental attacks unanimously justify that PKS distinguisher works remarkably well with both linear and non-linear leakage models. Specifically, PKS distinguisher has obvious advantages over existing MIA-like distinguishers in terms of both success rate and guessing entropy. Additionally, lower computational complexity of PKS distinguisher further shows its better applicability than MIA-like distinguishers