The concept, structure and mechanism of industrial digital twin systems

Digital twin was originally and firstly researched in manufacturing, and is now widely and maturely used for application in industries. Recent theoretical studies and technical applications mainly focus on the definition of the digital twin, the integration of design and manufacture based on digital twin, and the fault diagnosis and operation maintenance of products on the basis of the digital twin. There are very few studies on the characteristics, system composition, logical architecture, and system operation mode of digital twin systems applied on industrial products. To fill the gap above, this paper proposes the concept of industrial Digital Twin System (iDTS) and summarizes the typical characteristics of iDTS, based on the comparison of different types of digital twin systems, including people-centered thinking, "human-machine-environment" mutual fusion, high fidelity of the system and complexity of twin model. iDTS integrates these characteristics with the functional structure composed of physical layer, perception layer, twin layer, application layer, and control layer. In addition, the maturity model of iDTS is developed to describe the maturity stage of iDTS, along with the life cycle progress of products or systems. Lastly, this paper analyzes four iDTS operation modes and verifies the feasibility of the proposed iDTS with typical cases for specific industrial application scenarios

An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately

Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms

Fabrication of gallic acid electrochemical sensor based on interconnected Super-P carbon black@mesoporous silica nanocomposite modified glassy carbon electrode

The efficient and accurate detection of gallic acid (GA) is particularly important for human health. Herein, a simple and low-cost ultrasonic-assisted strategy was proposed for the preparation of multifunctional nanocomposite of Super-P carbon black (SPCB) nanoparticles and mesoporous silica nanoparticles (MSNs), which was used to modify the glassy carbon electrode (GCE) for the fabrication of GA electrochemical sensor (SPCB@MSNs/GCE). SPCB with pearl-chain nanostructure presented an interconnected highly conductive carbon network, which significantly improved the charge transfer efficiency. MSNs with spherical morphology and mesoporous structure exhibited uniform particle dispersion and high active surface area, which remarkably improved the surface accumulation effect of sensing electrode towards GA. Moreover, the excellent electrical conductivity of SPCB compensated for the non-conductive property of MSNs. Under the optimized condition, the electrochemical response of SPCB@MSNs/GCE sensor had a good linear relationship with GA concentration (0.5–10 μM), and the corresponding limit of detection could reach up to 4.911 nM. The fabricated nanohybrid sensor showed good reproducibility, repeatability and anti-interference property. Moreover, the satisfactory GA recoveries and RSD values were achieved for the electrochemical detection of GA in tea samples. This work provides an important reference for the highly sensitive analysis of GA in the field of food safety

Identification of MicroRNAs in Response to Different Day Lengths in Soybean Using High-Throughput Sequencing and qRT-PCR

<div><p>MicroRNAs (miRNAs) are short, non-coding single-strand RNA molecules that play important roles in plant growth, development and stress responses. Flowering time affects the seed yield and quality of soybean. However, the miRNAs involved in the regulation of flowering time in soybean have not been reported until recently. Here, high-throughput sequencing and qRT-PCR were used to identify miRNAs involved in soybean photoperiodic pathways. The first trifoliate leaves of soybean that receive the signal of light treatment were used to construct six libraries (0, 8, and 16 h under short-day (SD) treatment and 0, 8, and 16 h under long-day (LD) treatment). The libraries were sequenced using Illumina Solexa. A total of 318 known plant miRNAs belonging to 163 miRNA families and 81 novel predicted miRNAs were identified. Among these, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h, showed differences in abundance between LD and SD treatments. Furthermore, the results of GO and KEGG analyses indicated that most of the miRNA targets were transcription factors. Seven miRNAs at 0 h, 23 miRNAs (including four novel predicted miRNAs) at 8 h, 16 miRNAs (including one novel predicted miRNA) at 16 h and miRNA targets were selected for qRT-PCR analysis to assess the accuracy of the sequencing and target prediction. The results indicated that the expression patterns of the selected miRNAs and miRNA targets showed no differences between the qRT-PCR and sequencing results. In addition, 23 miRNAs at 0 h, 65 miRNAs at 8 h and 83 miRNAs at 16 h responded to day length changes in soybean, including six novel predicted miRNAs at 8 h and six novel predicted miRNAs at 16 h. These results provided an important molecular basis to understand the regulation of flowering time through photoperiodic pathways in soybean.</p></div

Size distribution of the clean tags after preliminary analysis of the sequencing mapped to the database named miRBase.

<p>(A) The size distribution of the clean tags. The clean tags could be mapped to the soybean genome, but were not observed among known noncoding RNAs through BLAST against the Rfame (a database of noncoding RNAs). These results were compared with the miRBase (a database of known miRNAs). (B) The size distribution of the tags of the six libraries mapped to the miRBase.</p

qRT-PCR validation of some miRNAs and their targets identified by Solexa sequencing.

<p>qRT-PCR reactions were used to verify the sequencing and the target predictions. (A) The result of the qRT-PCR validation of some miRNAs were identified through sequencing. The expression was represented as the ratios of the expression under SD treatment to that under LD treatment, and the 5SrRNA was used as a control. (B) The results of the qRT-PCR validation of the target predictions for miR159e-3p. The expression was presented as the ratio of expression under SD and LD treatment, and the 18SrRNA acted as a beta-actin (C) The result of the qRT-PCR validation of the target predictions for miR156a. (D) The result of qRT-PCR validation of the target predictions for miR160. (E) The results of qRT-PCR validation of the target predictions for miR395 and miR408. (F) The result of qRT-PCR validation of some miRNAs identified by sequencing.</p

Differential expression analysis of soybean miRNAs identified using Solexa sequencing.

<p>The software IDE6 was used to analyze the known and novel predicted miRNAs obtained through high-throughput sequencing. Panels <b>a</b> to <b>f</b> show LD to SD abundance differences for time points A to C.</p

The novel soybean miRNAs verified using qRT-PCR.

<p>The novel soybean miRNAs verified using qRT-PCR.</p

COG functional classification of consensus sequences in soybean.

<p>COG analysis was used to further evaluate the completeness of the transcriptome and the effectiveness of the annotation process.</p