COMPUTER SIMULATION OF THE PESTICIDE DEPOSITION DISTRIBUTION IN HORIZONTAL DIRECTION SPRAY

Abstract: The objective of this study is taken to realize pesticide precision spray of fruit trees and the other crops and reduce the deposition losses outside the canopy when the real time sensing technology was used in the pesticide target spray. In this paper the Pesticide solution deposition distribution experiments were conducted with two different volume median diameter (VMD) hollow cone nozzles fixed in horizontal direction, to investigate the influence of spray pressure and spray ground speed on the spray deposition region. The probability distribution model of the pesticide deposition was constructed based on the experiments, and the pesticide spray distribution range was simulated by using Matlab statistic toolbox. The simulation result showed that the spray pressure and the ground speed had the great influence on the maximum spray distance. With the increase of the spray speed, the spray deposition distribution range decreases gradually, when the nozzle 200 is under the speed above 1.20km/h and nozzle 300 is under the speed above 2.22km/h, the deposition range was reduced greatly. So the computer simulations make a reference for the choice of the spray control parameters

Improving Multi-Task Generalization via Regularizing Spurious Correlation

Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One possible reason that hurts generalization is spurious correlation, i.e., some knowledge is spurious and not causally related to task labels, but the model could mistakenly utilize them and thus fail when such correlation changes. In MTL setup, there exist several unique challenges of spurious correlation. First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other. Second, the confounder between task labels brings in a different type of spurious correlation to MTL. We theoretically prove that MTL is more prone to taking non-causal knowledge from other tasks than single-task learning, and thus generalize worse. To solve this problem, we propose Multi-Task Causal Representation Learning framework, aiming to represent multi-task knowledge via disentangled neural modules, and learn which module is causally related to each task via MTL-specific invariant regularization. Experiments show that it could enhance MTL model's performance by 5.5% on average over Multi-MNIST, MovieLens, Taskonomy, CityScape, and NYUv2, via alleviating spurious correlation problem.Comment: Published on NeurIPS 202

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user feedback. While there have been many research advances made in academia, deploying these methods in production is very difficult and very few improvements have been made in industry. One challenge is that these methods often hurt overall performance; additionally, they could be complex and expensive to train and serve. In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost. We first find that the predictions of user preferences are biased under long-tail distributions. The bias comes from the differences between training and serving data in two perspectives: 1) the item distributions, and 2) user's preference given an item. Most existing methods mainly attempt to reduce the bias from the item distribution perspective, ignoring the discrepancy from user preference given an item. This leads to a severe forgetting issue and results in sub-optimal performance. To address the problem, we design a novel Cross Decoupling Network (CDN) (i) decouples the learning process of memorization and generalization on the item side through a mixture-of-expert architecture; (ii) decouples the user samples from different distributions through a regularized bilateral branch network. Finally, a new adapter is introduced to aggregate the decoupled vectors, and softly shift the training attention to tail items. Extensive experimental results show that CDN significantly outperforms state-of-the-art approaches on benchmark datasets. We also demonstrate its effectiveness by a case study of CDN in a large-scale recommendation system at Google.Comment: Accepted by KDD 2023 Applied Data Science (ADS) trac

Smyd1b_tv1, a Key Regulator of Sarcomere Assembly, Is Localized on the M-Line of Skeletal Muscle Fibers

12 páginas, 7 figurasBackground Smyd1b is a member of the Smyd family that plays a key role in sarcomere assembly during myofibrillogenesis. Smyd1b encodes two alternatively spliced isoforms, smyd1b_tv1 and smyd1b_tv2, that are expressed in skeletal and cardiac muscles and play a vital role in myofibrillogenesis in skeletal muscles of zebrafish embryos. Methodology/Principal Findings To better understand Smyd1b function in myofibrillogenesis, we analyzed the subcellular localization of Smyd1b_tv1 and Smyd1b_tv2 in transgenic zebrafish expressing a myc-tagged Smyd1b_tv1 or Smyd1b_tv2. The results showed a dynamic change of their subcellular localization during muscle cell differentiation. Smyd1b_tv1 and Smyd1b_tv2 were primarily localized in the cytosol of myoblasts and myotubes at early stage zebrafish embryos. However, in mature myofibers, Smyd1b_tv1, and to a small degree of Smyd1b_tv2, exhibited a sarcomeric localization. Double staining with sarcomeric markers revealed that Smyd1b_tv1was localized on the M-lines. The sarcomeric localization was confirmed in zebrafish embryos expressing the Smyd1b_tv1-GFP or Smyd1b_tv2-GFP fusion proteins. Compared with Smyd1b_tv1, Smyd1b_tv2, however, showed a weak sarcomeric localization. Smyd1b_tv1 differs from Smyd1b_tv2 by a 13 amino acid insertion encoded by exon 5, suggesting that some residues within the 13 aa insertion may be critical for the strong sarcomeric localization of Smyd1b_tv1. Sequence comparison with Smyd1b_tv1 orthologs from other vertebrates revealed several highly conserved residues (Phe223, His224 and Gln226) and two potential phosphorylation sites (Thr221 and Ser225) within the 13 aa insertion. To determine whether these residues are involved in the increased sarcomeric localization of Smyd1b_tv1, we mutated these residues into alanine. Substitution of Phe223 or Ser225 with alanine significantly reduced the sarcomeric localization of Smyd1b_tv1. In contrast, other substitutions had no effect. Moreover, replacing Ser225 with threonine (S225T) retained the strong sarcomeric localization of Smyd1b_tv1. Conclusion/Significance Together, these data indicate that Phe223 and Ser225 are required for the M-line localization of Smyd1b_tv1.This research was supported by research grant No IS-8713-08 from the Israel Binational Agricultural Research and Development Fund, the United States (BARD), and an intercenter collaboration grant (Du-Fang) from University of Maryland Biotechnology Institute. (http://www.bard-isus.com/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Potassium content prediction model of citrus leaves in different phenological period

Based on reflectance spectra, the potassium (K) content prediction model was established to realize non-destructive testing of K content in citrus trees. Field experiments were conducted on 117 planted Luogang citrus trees in the Crab Village, and the data was collected on fresh and healthy citrus leaves in four dominant phenological periods. The hyper-spectrometer ASD FieldSpec3 and the flame photometry were used to detect spectral reflectance data and K-contents, respectively. A series of experiments were conducted to analyze the sensitive frequency band of K-contents and the modeling regularity of prediction in different phonological periods. The results show that there is frequency drift of K-contents relevant sensitive band in different phenological periods. Compared with MLR, SVR and PLS, better prediction results can be obtained based on K-contents relevant sensitive frequency band. The R2 of 0.994 and the mean square error of 0.120 with mean relative error of 1.33% are obtained in SVR model on validation set, which illuminates that SVR can well predict K-contents in whole growth periods based on reflectance spectra, regardless of frequency drift and the discrepant model performance

Design of soil moisture sensor based on phase-frequency characteristics of RC networks

Dielectric-based methods are widely used due to their non-destruction, efficiency and accuracy. The capacitance of the probe on the sensor is affected by the soil moisture. Therefore the mathematical model can be built between the capacitance of the sensor and the soil moisture. In this paper, a new soil water content sensor based on the phase-frequency characteristic of RC network is proposed. The sensor consists of four parts, that is a VHF oscillator, a phase-detecting circuit, a first-order RC low-pass circuit, and a probe. The VHF oscillator outputs a frequency-specified f* signal to drive the RC network, and the capacitor C of the first-order RC low-pass network is replaced by the capacitance of the probe of the sensor. Moreover, the changes of capacitance of the probe brought by the change of the soil moisture will cause a significant change in the phase-frequency response of the RC network. The AD8302 phase-detector is used to measure the change of the phase-frequency response of the RC network by converting the phase angle of the RC network to a voltage signal. Thus, the relationship between the soil moisture content and the output voltage signal can be built to estimate water content in soil. Compared with existing published works on the theoretical implementation which has low accuracy and sensitivity of the sensor, the proposed sensor is optimized by the following steps: 1) The measurement equivalent circuit model of the first-order RC low-pass circuit along with the input equivalent circuit of AD8302 is built; 2) The relationship between the output voltage signal of AD8302 with the phase-frequency response of the measurement equivalent circuit with a specified frequency f and the resistor R of RC network is derived; 3) Formulating the optimization problem by maximizing the integration of change of the output voltage of AD8302 in the entire predefined variation range of the capacitor C of the RC circuit, 1×10-12 F<C< 1×10-8 F, subjecting to f and R; 4) Solving the objective function by Genetic Algorithm (GA) to obtain the optimal f*=1.9412×108 Hz and R*=13.1 Ω, making the sensor achieve the highest sensitivity and accuracy of the measurement of the changes of C due to the variations of the water content in soil. Experiments on the sensor are divided in the following two steps. First, the sensor is calibrated in a series of tested solution with different equivalent soil gravimetric water content, and the gravimetric water content prediction model is built as y=-79133x3-18141x2-1418x+0.5926 with the coefficients of determination R2=0.9889. Second, the sensor is evaluated in the soil samples with different gravimetric water content. The maximum prediction and average errors are 4.58% and 1.63%, respectively

Hyperspectral estimation model of total phosphorus content for citrus leaves

Field experiments were conducted on 117 planted Luogang citrus trees in the crab village of Guangzhou. 234 pairs of data sample were collected in two different development stages, respectively, germination period and fruit picking period. Hyperspectral reflection data was used as high-dimensional vector description. Phosphorus content measured by chemical method as true label and to predict the phosphorus content of citrus leaves. Two mainstream multivariate regression analysis algorithms, partial least squares and support vector regression, were used for modeling and prediction after various preprocessing on spectral reflectance data. Calibration and validation sets were used to evaluate the predictive performance of model. Two regression analysis methods respectively achieved coefficient of determination of 0.905 and 0.881, the MSE of 0.005 and 0.004, the mean relative error of 0.0264 and 0.0312, respectively. The experimental results showed that it is an effective way to predict phosphorus level based on hyperspectral reflection data