ZmCER1,a putative ECERIFERUM 1 protein in maize,functions in cuticular wax biosynthesis and bulliform cell development

The cuticular wax,acting as the ultimate defense barrier,is essential for the normal morphogenesis of plant organs.Despite this importance,the connection between wax composition and leaf development has not been thoroughly explored.In this study,we characterized a new maize mutant,ragged leaf4(rgd4),which exhibits crinkled and ragged leaves starting from the sixth leaf stage.The phenotype of rgd4 is conferred by ZmCER1,which encoding an aldehyde decarbonylase involved in wax biosynthesis.ZmCER1 function deficient mutant displayed reduced cuticular wax density and disordered bulliform cells(BCs),while ZmCER1 overexpressing plants exhibited the opposite effects,indicating that ZmCER1 regulates cuticular wax biosynthesis and BCs development.Additionally,as the density of cuticular wax increased,the water loss rate of detached leaf decreases,suggesting that ZmCER1 is positively correlated with plant drought tolerance.

Material basis and pharmacodynamic mechanism of YangshenDingzhi granules in the intervention of viral pneumonia:Based on serum pharmacochemistry and network pharmacology

Background:YangshenDingzhi granules(YSDZ)are clinically effective in preventing and treating COVID-19.The present study elucidates the underlying mechanism of YSDZ intervention in viral pneumonia by employing serum pharmacochemistry and network pharmacology.Methods:The chemical constituents of YSDZ in the blood were examined using ultraperformance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry(UPLC-Q-Exactive Orbitrap MS).Potential protein targets were obtained from the SwissTargetPrediction database,and the target genes associated with viral pneumonia were identified using GeneCards,DisGeNET,and Online Mendelian Inheritance in Man(OMIM)databases.The intersection of blood component-related targets and disease-related targets was determined using Venny 2.1.Protein-protein interaction networks were constructed using the STRING database.The Metascape database was employed to perform enrichment analyses of Gene Ontology(GO)functions and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathways for the targets,while the Cytoscape 3.9.1 software was utilized to construct drug-component-disease-target-pathway networks.Further,in vitro and in vivo experiments were performed to establish the therapeutic effectiveness of YSDZ against viral pneumonia.Results:Fifteen compounds and 124 targets linked to viral pneumonia were detected in serum.Among these,MAPK1,MAPK3,AKT1,EGFR,and TNF play significant roles.In vitro tests revealed that the medicated serum suppressed the replication of H1N1,RSV,and SARS-CoV-2 replicon.Further,in vivo testing analysis shows that YSDZ decreases the viral load in the lungs of mice infected with RSV and H1N1.Conclusion:The chemical constituents of YSDZ in the blood may elicit therapeutic effects against viral pneumonia by targeting multiple proteins and pathways.

Automation 5.0: The Key to Systems Intelligence and Industry 5.0

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.

Large-scale evaluation of pea(Pisum sativum L.)germplasm for cold tolerance in the field during winter in Qingdao

As a cool-season crop, pea(Pisum sativum L.) can tolerate frost at the vegetative stage but experiences yield loss when freezing stress occurs at the reproductive stage. Cold-tolerance improvement of pea varieties is important for stable yield and expansion of the winter pea planting area. Under natural low-temperature conditions during winter in Qingdao,Shandong, China, we evaluated the cold tolerance of 3672 pea germplasm accessions in the field and categorized them as displaying high resistance(214), moderate resistance(835), or susceptibility(2623). The highly and moderately resistant genotypes were validated in the following year. We found that genotypes from the winter production region showed higher cold tolerance than genotypes from the spring production region. The accessions identified as having high levels of cold tolerance are recommended as potential genetic resources in cold-tolerance breeding of pea.

Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine learning.To summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel intelligence.First,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous driving.Second,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical structure.Third,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning performance.Key issues remained in virtual-to-real are discussed.Furthermore,the future research directions from the viewpoint of parallel learning are suggested.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Recoloring tomato fruit by CRISPR/Cas9-mediated multiplex gene editing

Fruit color is an important horticultural trait,which greatly affects consumer preferences.In tomato,fruit color is determined by the accumulation of different pigments,such as carotenoids in the pericarp and f lavonoids in the peel,along with the degradation of chlorophyll during fruit ripening.Since fruit color is a multigenic trait,it takes years to introgress all color-related genes in a single genetic background via traditional crossbreeding,and the avoidance of linkage drag during this process is difficult.Here,we proposed a rapid breeding strategy to generate tomato lines with different colored fruits from red-fruited materials by CRISPR/Cas9-mediated multiplex gene editing of three fruit color-related genes(PSY1,MYB12,and SGR1).Using this strategy,the red-fruited cultivar‘Ailsa Craig’has been engineered to a series of tomato genotypes with different fruit colors,including yellow,brown,pink,light-yellow,pink-brown,yellow-green,and light green.Compared with traditional crossbreeding,this strategy requires less time and can obtain transgene-free plants with different colored fruits in less than 1 year.Most importantly,it does not alter other important agronomic traits,like yield and fruit quality.Our strategy has great practical potential for tomato breeding and serves as a reference for improving multigene-controlled traits of horticultural crops.

Parallel Control for Continuous-Time Linear Systems:A Case Study

In this paper,a new parallel controller is developed for continuous-time linear systems.The main contribution of the method is to establish a new parallel control law,where both state and control are considered as the input.The structure of the parallel control is provided,and the relationship between the parallel control and traditional feedback controls is presented.Considering the situations that the systems are controllable and incompletely controllable,the properties of the parallel control law are analyzed.The parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely controllable.Finally,numerical simulations are carried out to demonstrate the effectiveness and applicability of the present method.Index Terms-Continuous-time linear systems,digital twin,parallel controller,parallel intelligence,parallel systems.

Degradation of Extraction from Seaweed and Its Complex with Rare Earths for Organophosphorous Pesticides

On the basis of several experiments carried out in China, it was proved that both seaweed liquid fertilizer and rare earth （RE） could promote the growth of crops and increase their yield. The effects of extraction from seaweed and its complex with RE on the degradation of organophosphorous pesticides and the yield of vegetables were investigated. The resuits showed that the extract and its complex with RE could degrade organophosphorous pesticides in neutral solvent. The residues of the pesticides treated by the extract decreased by 96.88 %, 52.30%, 49.52%, and 22.88 %, respectively, for chlorpyrifos, dichlorvos, omethoate, and dimethoate, and those by the complex decreased by 95.99%, 54.23%, 48.79%, and 25.66%, respectively, when compared with the control. The residues of chlorpyrifos and dimethoate in spinach sprayed with the complex were decreased by 90.64% and 76.56%, respectively, compared with those in spinach from control plots when the interval between spraying and sampling was 8 d. The fresh weight of brassica chinensis and cabbage increased by 28.62% and 18.72%, and their dry weight increased by 44.49% and 14.74%, respectively, compared with those of the controls. The chlorpyrifos and dimethoate residues in brasscia chinese were decreased by 36.36% and 50.00%, respectively, and their rate of decrease in cabbage was 40.00% and 75.00%, respectively, on 5th day after spraying with the complex, when compared with those in the vegetable from control. These results suggest that this complex can increase the agricultural productivity and reduce the use of pesticide residues in the production of vegetables.

Generative Adversarial Networks:Introduction and Outlook

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea.The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution.Since their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application fields.Then, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

Cyber-physical-social System in Intelligent Transportation

A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it's a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail. © 2014 Chinese Association of Automation.

Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.

From Parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of Systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data sharing.In summary, we point out the development directions of quality inspection under metaverse tide.

Social Media Based Transportation Research: the State of the Work and the Networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified. © 2014 Chinese Association of Automation.

SSR genetic linkage map construction of pea(Pisum sativum L.) based on Chinese native varieties

Simple sequence repeat(SSR)markers have previously been applied to linkage mapping of the pea(Pisum sativum L.)genome.However,the transferability of existing loci to the molecularly distinct Chinese winter pea gene pool was limited.A novel set of pea SSR markers was accordingly developed.Together with existing SSR sequences,the genome of the G0003973(winter hardy)×G0005527(cold sensitive)cross was mapped using 190 F2individuals.In total,157 SSR markers were placed in 11 linkage groups with an average interval of 9.7 cM and total coverage of 1518 cM.The novel markers and genetic linkage map will be useful for marker-assisted pea breeding.

Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane discontinuities.For road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant challenges.However,an effective and fast solution for these problems has remained elusive.To solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed.The proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary points.To demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road conditions.Comprehensive experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions.Meanwhile,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.

Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion.Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise.In traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent agents.By introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction.Through the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is analyzed.Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

Training and Testing Object Detectors With Virtual Images

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations.A virtual dataset named Parallel Eye is built, which can be used for several computer vision tasks. Then, by training the DPM(Deformable parts model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining Parallel Eye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.