Histological, Physiological and Transcriptomic Analysis Reveal Gibberellin-Induced Axillary Meristem Formation in Garlic (Allium sativum)

The number of cloves in a garlic bulb is controlled by axillary meristem differentiation, which directly determines the propagation efficiency. Our previous study showed that injecting garlic plants with gibberellins (GA3) solution significantly increased clove number per bulb. However, the physiological and molecular mechanism of GA-induced axillary bud formation is still unknown. Herein, dynamic changes in histology, phytohormones, sugars and related genes expression at 2, 4, 8, 16 and 32 days after treatment (DAT) were investigated. Histological results indicated two stages (axillary meristem initiation and dormancy) were in the period of 0–30 days after GA3 treatment. Application of GA3 caused a significant increase of GA3 and GA4, and the downregulation of AsGA20ox expression. Furthermore, the change trends in zeatin riboside (ZR) and soluble sugar were the same, in which a high level of ZR at 2 DAT and high content of soluble sugar, glucose and fructose at 4 DAT were recorded, and a low level of ZR and soluble sugar arose at 16 and 32 DAT. Overall, injection of GA3 firstly caused the downregulation of AsGA20ox, a significant increase in the level of ZR and abscisic acid (ABA), and the upregulation of AsCYP735 and AsAHK to activate axillary meristem initiation. Low level of ZR and soluble sugar and a high level of sucrose maintained axillary meristem dormancy

An Evaluating Method with Combined Assigning-Weight Based on Maximizing Variance

This paper proposes a combined assigning-weight approach to determine attribute weights in the multiattribute decision problems. The approach combines subjective weights and objective weights of attributes based on maximizing variance. Objective weights are determined by rough set method and subjective weights by Analytic Hierarchy Process. This new combination method may integrate the merits of both subjective and objective weighting methods. Empirical study shows that the new method can lead to more reasonable weighting results and decision

Evaluating Risks of Mergers & Acquisitions by Grey Relational Analysis Based on Interval-Valued Intuitionistic Fuzzy Information

Purpose. The purpose of our research is to explore a new grey relational analysis method when information of decision making is interval-valued, intuitionistic, fuzzy, and uncertain in risk analysis of Mergers & Acquisitions. Design/Methodology/Approach. We proposed a new method to evaluate risks of Mergers & Acquisitions. The process of our method is to determine the positive and negative ideal solutions of interval-valued intuitional fuzzy uncertain language firstly. Then, calculate grey relational grades of every evaluating value for positive or negative ideal solutions. Third, determine the weights of attributes by a linear programming model if part of attribute information is known. Fourth, calculate grey relational grades of each alternative for the positive or negative ideal solutions. Lastly, calculate relative grey relational grades and sort the alternatives. Findings. Our case analysis demonstrated that the new grey relational analysis is an effective tool to evaluate the risks of Mergers & Acquisition when information of decision making is interval-valued, intuitionistic, fuzzy, and uncertain. At the same time, we also bring forward the steps of evaluation. Originality/Value. Because risks of Mergers & Acquisitions decide its success or failure to some extent, it is very important to evaluate them by feasible and available method. However, the information of risks is fuzzy and uncertain usually. The new grey relational analysis based on Interval-Valued Intuitionistic Fuzzy Information does not only evaluate risks of Mergers & Acquisitions but also can be widely applied to similar problems of decision making in other fields

Prediction of passenger flow on the highway based on the least square suppoert vector machine / Mažiausių kvadratų atraminių vektorių metodo taikymas keleivių srautui greitkelyje prognozuoti /

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway. Santrauka Atraminių vektorių metodas (Support Vector Machine – SVM) yra skaičiuojamasis metodas, paremtas statistikos teorija, struktūriniu požiūriu mažinant riziką. SVM metodas, palyginti su kitais metodais, yra patikimesnis metodas, nes juo remiantis galima išspręsti realias problemas, esant įvairioms sąlygoms. Tyrimams naudojama SVM metodo regresijos teorija ir sukuriamas regresinis modelis, kuris grindžiamas mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector Machine – LS-SVM). Straipsnio autoriai prognozuoja keleivių srautą Hangdžou (Kinija) greitkelyje 2000–2008 m. Gauti rezultatai rodo, kad regresinis LS-SVM modelis yra labai tikslus ir patikimas, todėl gali būti efektyviai taikomas keleivių srautams prognozuoti greitkeliuose. Резюме Метод опорных векторов (Support Vector Machine – SVM) – это набор аналогичных алгоритмов вида «обучение с учителем», использующихся для задач классификации и регрессионного анализа. Метод SVM принадлежит к семейству линейных классификаторов. Основная идея метода SVM заключается в переводе исходных векторов в пространство более высокой размерности и поиске разделяющей гиперплоскости с максимальным зазором в этом пространстве. Алгоритм работает в предположении, что чем больше разница или расстояние между параллельными гиперплоскостями, тем меньше будет средняя ошибка классификатора. В сравнении с другими методами метод SVM более надежен и позволяет решать проблемы с различными условиями. Для исследования был использован метод SVM и регрессионный анализ, затем создана регрессионная модель, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector Machine – LS-SVM). Авторы прогнозировали пассажирский поток на автомагистрали Ханчжоу (Китай) в 2000–2008 гг. Полученные результаты показывают, что регрессионная модель LS-SVM является надежной и может быть применена для прогнозирования пассажирских потоков на других магистралях. Reikšminiai žodžiai: atraminių vektorių metodas (SVM), mažiausių kvadratų atraminių vektorių metodas (LS-SVM), statistinė teorija, regresinis modelis, keleivių srautas, prognozė Ключевые слова: метод опорных векторов (SVM), метод опорных векторов с квадратичной функцией потерь (LS-SVM), статистика, регрессионная модель, пассажирский поток, прогно

Analyzing the farmers' pro-environmental behavior intention and their rural tourism livelihood in tourist village where its ecological environment is polluted.

This study examines farmers' intentions towards pro-environmental behavior in a famous tourist village in China called Guanshan, whose ecological environment is polluted. By adopting the empirically validated norm activation model (NAM) of Schwartz and merging it with Vroom's expectancy theory, the current research aims to develop a refined framework for understanding the formation of and predicting changes in pro-environmental intention. Field surveys were conducted in Guanshan, which resulted in sample data consisting of 275 valid responses collected by the research team. We develop a refined model, including six latent variables and 24 observational items. The structural equation modeling results show that the final model enjoys a better predictive accuracy of pro-environmental intention than does the original NAM. The study also discovers that the motivational force of expectancy theory significantly influences pro-environmental intention, whose motivational force comes from the impact of valence and expectancy. The pro-environmental intentions of farmers are mainly affected by the environmental personal norm and by a certain degree of personal expectancy. The improvement of farmers' pro-environmental intention needs be promoted in two approaches: the cultivation of personal environmental protection norms and the guidance of producing a desired expectation for pro-environmental intention

Innovation Ability Evaluation.xlsx

data of 30 listed chinese compan

Design and syntheses of functional carbon dioxide-based polycarbonates via ternary copolymerization

Carbon dioxide (CO2) is an important heat-trapping gas, or greenhouse gas, that comes from the extraction and burning of fossil fuels. On the other hand, plastic is widely used for making various products, such as medical devices and packaging bags, but it causes pollution that damages human health and the environment. Therefore, biodegradable plastic materials synthesized by using CO2 is one of the most desirable alternatives of traditional non-biodegradable plastic materials to alleviate anthropogenic CO2 and non-degradable plastic pollution. Particularly, CO2-based poly(propylene carbonate) (PPC) has received increasing attention owing to its good functionalization modification advantages and its potential in the large-scale utilization of CO2. We review the synthetic routes of PPCs through incorporations of different functionalized third monomers to the polymer chains and their effects on properties of newly functionalized PPCs. The results show that the copolymerization of CO2 and propylene oxide (PO) provides a way to utilize excess CO2 for producing biodegradable and environmentally friendly polymerization products (i.e., CO2-based PPC). This offers a novel strategy for the preparation of new and potentially sustainable plastic materials

Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM

Short video hot spot classification is a fundamental method to grasp the focus of consumers and improve the effectiveness of video marketing. The limitations of traditional short text classification are sparse content as well as inconspicuous feature extraction. To solve the problems above, this paper proposes a short video hot spot classification model combining latent dirichlet allocation (LDA) feature fusion and improved bi-directional long short-term memory (BiLSTM), namely the LDA-BiLSTM-self-attention (LBSA) model, to carry out the study of hot spot classification that targets Carya cathayensis walnut short video review data under the TikTok platform. Firstly, the LDA topic model was used to expand the topic features of the Word2Vec word vector, which was then fused and input into the BiLSTM model to learn the text features. Afterwards, the self-attention mechanism was employed to endow different weights to the output information of BiLSTM in accordance with the importance, to enhance the precision of feature extraction and complete the hot spot classification of review data. Experimental results show that the precision of the proposed LBSA model reached 91.52%, which is significantly improved compared with the traditional model in terms of precision and F1 value

Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder

We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of image patches to fit the input form of the DeiT encoder, which was distilled by RegNet. Then, we used the Transformer decoder to generate descriptions. Compared to “CNN + LSTM” models, our proposed model is entirely convolution-free and has high training efficiency. On the Rice2k dataset created by us, the model achieved a 47.3 BLEU-4 score, 65.0 ROUGE_L score, and 177.1 CIDEr score. The extensive experiments demonstrate the effectiveness and the strong robustness of our model. It can be better applied to automatically generate descriptions of similar crop disease characteristics