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"Li, Xiaoli"
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Guide to the wildlife of southwest China
A field guide to wildlife found in Southwest China. The guide includes 92 mammal species and 31 pheasant species (and 10 domestic species) found in the region. For each species we include the relevant body measurements and conservation status, descriptions of ecology and natural history, a map of their distribution, and images of the animal and its track and sign (if available). The book is prefaced with an overview of the ecology of the region and there are short introductions for some groups of species.-- Provided by publisher.
Book
A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images
The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.
Journal Article
Deep-Learning-Guided Student Classroom Action Understanding for Preschool Education
A deep architecture for enhancing students’ action recognition is proposed to improve preschool education. This paper seamlessly combines the teaching objectives, teaching scope, teaching implementation, and breeding evaluation status of preschool breeding practice theory. We attempt to solve the problem of effective preschool teaching, based on which we propose the simple adaptation strategies. We further evaluate the practice of preschool breeding and its effectiveness. In this way, civilized and high-quality preschool talents will be cultivated, and preschool educational experiences will be promoted. In the method of promoting the preschool culture of weak-aged children, owing to the problem that the traditional action recognition algorithm can indicate the specific students’ actions, an action recognition method based on the combination of deep integration and human skeleton representation is proposed. First, the connected spatial locations and constraints are fed into a long-short-specified recall (LSTM) mode with a spatially and temporally aware algorithm which is designed to obtain spatiotemporal feature and highly separable deep joint features. Afterward, a new mechanism is introduced to resolve keyframes as well as the joints. Finally, based on the two-stream deep architecture, the effective discrimination of similar actions is achieved by integrating the color and shape features into the skeleton features by designing the deep model. Extensive experiments have demonstrated that, compared with the mainstream algorithms, this method can effectively distinguish students’ action types in the classroom of homogeneous preschool children. Thus, we can substantially improve the efficiency of preschool teaching.
Journal Article
Construction of Business Model of Unmanned Economy Under Digital Technology
In the ever-evolving landscape of modern business, the integration of advanced technologies is paramount for optimizing operations, ensuring efficiency, and staying competitive. The business model for the unmanned economy comprises the challenges related to supply chain and logistics. This paper proposed an architecture of LM-LSTM (Linear Regression and Long Short-Term Memory) model within the context of the Unmanned business model. The proposed model uses the mandami based fuzzy rule for the computation of the unmanned economy. Within the mandami fuzzy linear regression model is adopted for the computation and estimation of the variables related to the unmanned economy. The objective is to provide a comprehensive analysis of the impact of this predictive modeling system on various dimensions of the business. Through the generated rules the LSTM model is utilized for the classification and computation of the features related to supply chain, forecast demand and other parameters in an unmanned economy. The examination of 10 unmanned products in Chinese products are evaluated. The findings of LM-LSTM stated that Sales forecasting, one of the critical aspects of any business, has seen a remarkable improvement in accuracy, with an average Mean Absolute Error (MAE) of 3.00%. This accuracy ensures that products are produced and stocked according to actual demand, preventing costly overstocking or stockouts. The inventory management process has been streamlined, with tailored strategies for each product category. This adaptation has resulted in reduced stockouts, efficient parts sourcing, and minimal overstock situations. Supply chain optimization has significantly reduced lead times, enhancing customer satisfaction through timely product deliveries. Customer behavior analysis, facilitated by LMLSTM, has led to a notable increase in sales across the product range, with an average increase of 91%. This enhanced customer engagement is coupled with substantial cost savings, with an overall reduction of 118%. Downtime has been minimized, contributing to smoother operations and improved customer service.
Journal Article
Two bHLH Transcription Factors, bHLH34 and bHLH104, Regulate Iron Homeostasis in Arabidopsis thaliana
The regulation of iron (Fe) homeostasis is critical for plant survival. Although the systems responsible for the reduction, uptake, and translocation of Fe have been described, the molecular mechanism by which plants sense Fe status and coordinate the expression of Fe deficiency-responsive genes is largely unknown. Here, we report that two basic helix-loop-helix-type transcription factors, bHLH34 and bHLH104, positively regulate Fe homeostasis in Arabidopsis (Arabidopsis thaliana). Loss of function of bHLH34 and bHLH104 causes disruption of the Fe deficiency response and the reduction of Fe content, whereas overexpression plants constitutively promote the expression of Fe deficiency-responsive genes and Fe accumulation. Further analysis indicates that bHLH34 and bHLH104 directly activate the transcription of the Ib subgroup bHLH genes, bHLH38/39/100/101. Moreover, overexpression of bHLH101 partially rescues the Fe deficiency phenotypes of bhlh34bhlh104 double mutants. Further investigation suggests that bHLH34, bHLH104, and bHLH105 (IAA-LEUCINE RESISTANT3) function as homodimers or heterodimers to nonredundantly regulate Fe homeostasis. This work reveals that plants have evolved complex molecular mechanisms to regulate Fe deficiency response genes to adapt to Fe deficiency conditions.
Journal Article
Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy
The contents of hemicellulose, cellulose and lignin are important for moso bamboo processing in biomass energy industry. The feasibility of using near infrared (NIR) spectroscopy for rapid determination of hemicellulose, cellulose and lignin was investigated in this study. Initially, the linear relationship between bamboo components and their NIR spectroscopy was established. Subsequently, successive projections algorithm (SPA) was used to detect characteristic wavelengths for establishing the convenient models. For hemicellulose, cellulose and lignin, 22, 22 and 20 characteristic wavelengths were obtained, respectively. Nonlinear determination models were subsequently built by an artificial neural network (ANN) and a least-squares support vector machine (LS-SVM) based on characteristic wavelengths. The LS-SVM models for predicting hemicellulose, cellulose and lignin all obtained excellent results with high determination coefficients of 0.921, 0.909 and 0.892 respectively. These results demonstrated that NIR spectroscopy combined with SPA-LS-SVM is a useful, nondestructive tool for the determinations of hemicellulose, cellulose and lignin in moso bamboo.
Journal Article
bHLH transcription factor bHLH115 regulates iron homeostasis in Arabidopsis thaliana
Iron (Fe) deficiency is a limiting factor for the normal growth and development of plants, and many species have evolved sophisticated systems for adaptation to Fe-deficient environments. It is still unclear how plants sense Fe status and coordinate the expression of genes responsive to Fe deficiency. In this study, we show that the bHLH transcription factor bHLH115 is a positive regulator of the Fe-deficiency response. Loss-of-function of bHLH115 causes strong Fe-deficiency symptoms and alleviates expression of genes responsive to Fe deficiency, whereas its overexpression causes the opposite effect. Chromatin immunoprecipitation assays confirmed that bHLH115 binds to the promoters of the Fe-deficiency-responsive genes bHLH38/39/100/101 and POPEYE (PYE), which suggests redundant molecular functions with bHLH34, bHLH104, and bHLH105. This is further supported by the fact that the bhlh115-1 mutant was complemented by overexpression of any of bHLH34, bHLH104, bHLH105, and bHLH115. Further investigations determined that bHLH115 could interact with itself and with bHLH34, bHLH104, and bHLH105. Their differential tissue-specific expression patterns and the severe Fe deficiency symptoms of multiple mutants supported their non-redundant biological functions. Genetic analysis revealed that bHLH115 is negatively regulated by BRUTUS (BTS), an E3 ligase that can interact with bHLH115. Thus, bHLH115 plays key roles in the maintenance of Fe homeostasis in Arabidopsis thaliana.
Journal Article
Rapidly and exactly determining postharvest dry soybean seed quality based on machine vision technology
The development of machine vision-based technologies to replace human labor for rapid and exact detection of agricultural product quality has received extensive attention. In this study, we describe a low-rank representation of jointly multi-modal bag-of-feature (JMBoF) classification framework for inspecting the appearance quality of postharvest dry soybean seeds. Two categories of speeded-up robust features and spatial layout of L*a*b* color features are extracted to characterize the dry soybean seed kernel. The bag-of-feature model is used to generate a visual dictionary descriptor from the above two features, respectively. In order to exactly represent the image characteristics, we introduce the low-rank representation (LRR) method to eliminate the redundant information from the long joint two kinds of modal dictionary descriptors. The multiclass support vector machine algorithm is used to classify the encoding LRR of the jointly multi-modal bag of features. We validate our JMBoF classification algorithm on the soybean seed image dataset. The proposed method significantly outperforms the state-of-the-art single-modal bag of features methods in the literature, which could contribute in the future as a significant and valuable technology in postharvest dry soybean seed classification procedure.
Journal Article
Efficient modified stabilized invariant energy quadratization approaches for phase-field crystal equation
The phase-field crystal equation is a sixth-order nonlinear parabolic equation and can be applied to simulate various phenomena such as epitaxial growth, material hardness, and phase transition. We propose a series of efficient modified stabilized invariant energy quadratization approaches with unconditional energy stability for the phase-field crystal model. Firstly, we propose a more suitable positive preserving function strictly in square root and consider a modified invariant energy quadratization (MIEQ) approach. Secondly, a series of efficient and suitable functionals
H
(
ϕ
) in square root are considered and the modified stabilized invariant energy quadratization (MSIEQ) approaches are developed. We prove the unconditional energy stability and optimal error estimates for the semi-discrete schemes carefully and rigorously. A comparative study of classical IEQ, MIEQ, and MSIEQ approaches is considered to show the accuracy and efficiency. Finally, we present various 2D numerical simulations to demonstrate the stability and accuracy.
Journal Article
Classification of English Translation Teaching Models based on Multiple Intelligence Theory
In order to improve the quality of English translation teaching, this paper combines the theory of multiple intelligence to classify the English translation teaching process. Moreover, this paper adopts Fisher’s discriminant method and Bayesian discriminant method to classify the English translation teaching samples. In order to improve the discrimination accuracy of the extreme learning machine algorithm, this paper applies the particle swarm optimization extreme learning machine algorithm to the research on the classification of English translation teaching samples and proposes an intelligent English classification teaching model based on the actual situation of English translation teaching. In addition, this paper verifies the system model proposed in this paper by evaluating the teaching method. The research shows that the classification model of English translation teaching mode based on the theory of multiple intelligence proposed in this paper has a certain effect, which can promote the effect of English translation teaching.
Journal Article