基于Self-X认知制造网络实现认知大规模个性化定制--一种工业知识图谱及图嵌入技术使能的途径

1.Introduction Facilitated by cutting-edge information and communication technologies(ICTs),smart manufacturing is emerging as an overwhelming wave,reforming global manufacturing systems[1].The ubiquitous connection and interoperability of manufacturing resources can be achieved through the Industrial Internet of Things(IIoT)[2,3],and the generated high volume,velocity,variety。

makeTwin:A reference architecture for digital twin software platform

Digital twin is currently undergoing a significant transformation from the conceptual and theoretical research phase to the implementation and application phase.However,a universally adaptable research and development platform for digital twin is critically needed to meet the development requirements.Specifically,a publicly accessible simulation,testing,and validation platform which can support digital twin model building,data processing,algorithm design,configuration,etc.,is urgently required for researchers.Furthermore,for developers from the industry,a lowcode development platform that can offer a customizable suite of functions such as model creation,data management,protocol configuration,and visualization is urgently needed.Meanwhile,for enterprise users,there is a lack of an application management platform that can be configured and migrated for various application scenarios,functions,and modes.Therefore,based on the system research of digital twin theories and key technologies by the authors(such as the five-dimension digital twin model,digital twin modeling and digital twin data theory,digital twin standards,and so on),a digital twin software platform reference architecture,namely make Twin,is proposed and designed,as well as its ten core functions.The workflow of the make Twin and the interaction mechanisms among its core functions are described.Finally,a digital twin application system for a chemical fiber textile shop floor(CFTS)which was developed according to make Twin,is introduced,which validates the proposed reference architecture.

Quantitative Trait Loci for Panicle Layer Uniformity Identified in Doubled Haploid Lines of Rice in Two Environments

Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid （DH） population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus （QTL） for three related traits of paniclelayer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniforrnity in two locations：Hangzhou （HZ） and Hainan （HN）. A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH-4 and qTPH-8 were co-located with the QTLs for qLPH-4 and qLPH-8, which were only significant in the HZ environment, whereas the qTPH-6 and qLPH-6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD-10.1 and qPLD-10.2, were closely linked to qTPH-10, and they might have been at the same locus. One QTL, qPLD-3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD-3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD-3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice.

Starch RVA profile parameters of rice are mainly controlled by Wx gene

Quantitative trait locus (QTLs) mapping for rapid visco analyser (RVA) profile parameters has been carried out by using a double haploid (DH) population derived from a cross between indica variety Zhai-Ye-Qing 8 and j’aponica variety Jing-Xi 17 and its genetic linkage map. The results indicate that the segregation of the RVA profiles is continually distributed a-mong the DH lines, and some DH lines show transgressive segregation for all the parameters.

Real-time task processing method based on edge computing for spinning CPS

Spinning production is a typical continuous manufacturing process characterized by high speed and uncertain dynamics. Each manufacturing unit in spinning production produces various real-time tasks, which may affect production efficiency and yam quality if not processed in time. This paper presents an edge computing- based method that is different from traditional centralized cloud computation because its decentralization characteristics meet the high-speed and high-response requirements of yam production. Edge computing nodes, real-time tasks, and edge computing resources are defined. A system model is established, and a real-time task processing method is proposed for the edge computing scenario. Experimental results indicate that the proposed real-time task processing method based on edge computing can effectively solve the delay problem of real-time task processing in spinning cyber-physical systems, save bandwidth, and enhance the security of task transmission.

Mapping QTLs for heading synchrony in a doubled haploid population of rice in two environments

Simultaneous heading of plants within the same rice variety, also refer to heading synchrony, is an important factor that affects simultaneous ripening of the variety. Understanding of the genetic basis of heading synchrony may contribute to molecular breeding of rice with simultaneous heading and ripening. In the present study, a doubled haploid （DH） population, derived from a cross between Chunjiang 06 and TN1 was used to analyze quantitative trait locus （QTL） for heading synchrony related traits, i.e., early heading date （EHD）, late heading date （LHD）, heading asynchrony （HAS）, and tiller number （PN）. A total of 19 QTLs for four traits distributed on nine chromosomes were detected in two environments. One QTL, qHAS-8 for HAS, explained 27.7% of the phenotypic variation, co-located with the QTLs for EHD and LHD, but it was only significant under long-day conditions in Hangzhou, China. The other three QTLs, qHAS-6, qHAS-9, and qHAS-10, were identified under short-day conditions in Hainan, China, each of which explained about 11% of the phenotypic variation. Two of them, qHAS-6 and qHAS-9, were co-located with the QTLs for EHD and LHD. Two QTLs, qPN-4 and qPN-5 for PN, were detected in Hangzhou, and qPN-5 was also detected in Hainan. However, none of them was co-located with QTLs for EHD, LHD, and HAS, suggesting that PN and HAS were controlled by different genetic factors. The results of this study can be useful in marker assisted breeding for improvement of heading synchrony.