Multi-scale distribution of coal fractures based on CT digital core deep learning

In order to realize high-precision and high-efficiency identification of multi-scale distribution characteristics of coal fractures, carry out the study of multi-scale distribution characteristics identification methods based on CT digital core deep learning. Industrial CT scanning system is used to collect a large number of coal original CT digital core information array, the CT digital core information array is converted into a two-dimensional gray-scale image and then it is divided into square images of different scales and the image brightness is enhanced to different levels as training samples, Finally, the construction and optimization of model parameters of AlexNet, ResNet-18, GoogLeNet and Inception-V3 models for the identification of CT-containing fractures are realized by Matlab platform. Study the recognition accuracy and verification accuracy of different model training under different number of training samples; Study the accuracy, calculation efficiency and training time of different models for images with different scales and brightness under the same training sample, obtain the optimal model for calculating the fractal dimension of two-dimensional CT images with fractures, then, the fractal distribution characteristics of each fracture image are calculated according to the statistical method of box-counting dimension, compared with the traditional binarization method and human eye recognition method, The applicability of the multi-scale distribution characteristics identification method of coal fractures based on CT digital core deep learning is verified. The result shows: ① ResNet-18 model is the optimal model for calculating the fractal dimension of two-dimensional CT images with cracks when the image sample is brightness 4 and the scale is 3.5 mm to 21 mm, the model has high accuracy and short training time in calculating the fractal dimension of two-dimensional CT fracture images. ② Compared with the traditional binarization method, the multi-scale recognition method of coal fracture based on CT digital core deep learning has the advantages of fast speed, high accuracy and is not easily affected by impurities in coal

Intramuscular vitamin A injection in newborn lambs enhances antioxidant capacity and improves meat quality

IntroductionVitamin A (VA) and its metabolite, retinoic acid (RA) possess several biological functions. This report investigated whether neonatal intramuscular VA injection affected antioxidative activity and meat quality in longissimus dorsi (LD) muscle of lambs.MethodsLambs were injected with 0 (control) or 7,500 IU VA palmitate into the biceps femoris muscle on day 2 after birth. At 3, 12, and 32 weeks of age, blood samples were collected in the jugular vein for serum levels of RA and muscle samples were collected in the biceps femoris for analysis of relative mRNA expression of enzyme contributors to retinoid metabolism. All animals were harvested at 32 weeks of age and muscle samples were collected to explore the role of VA on the meat quality and antioxidant capacity of lambs.Results and discussionOur results indicated that VA increased the redness, crude protein, and crude fat (p < 0.05), without affecting moisture, ash, and amino acid composition in LD muscle (p > 0.05). In addition, VA increased catalase (CAT) activity and decreased malondialdehyde (MDA) levels in LD muscle (p < 0.05). Meanwhile, greater levels of CAT and NRF2 mRNA and protein contents with VA treatment were observed in LD muscle (p < 0.05), partly explained by the increased level of RA (p < 0.05). Collectively, our findings indicated that VA injection at birth could improve lamb meat quality by elevating the redness, crude protein, crude fat, and antioxidative capacity in LD muscle of lambs

A Novel Discrete Fruit Fly Optimization Algorithm for Intelligent Parallel Test sheets Generation

Parallel test sheet generation (PTSG) is a NP-hard combinational optimization problem, in which test sheet generation algorithm with high quality and efficiency is the core technology. Basic fruit fly optimization algorithm (FOA) has the defects of easily relapsing into local optimal and low convergence precision when solving PTSG problem. In this paper, a novel discrete fruit fly optimization algorithm is proposed to solve the PTSG problem, in which a discrete osphesis searching operator based on the problem-specific knowledge is designed to help the FOA escaping from being trapped in local minima. To evaluate the performance of the proposed algorithm, the simulation experiments were conducted using a series of item banks with different scales. The superiority of the proposed algorithm is demonstrated by comparing it with the particle swarm optimization algorithm and differential evolution algorithm

Using artificial intelligence to improve identification of nanofluid gas–liquid two-phase flow pattern in mini-channel

This work combines fuzzy logic and a support vector machine (SVM) with a principal component analysis (PCA) to create an artificial-intelligence system that identifies nanofluid gas-liquid two-phase flow states in a vertical mini-channel. Flow-pattern recognition requires finding the operational details of the process and doing computer simulations and image processing can be used to automate the description of flow patterns in nanofluid gas-liquid two-phase flow. This work uses fuzzy logic and a SVM with PCA to improve the accuracy with which the flow pattern of a nanofluid gas-liquid two-phase flow is identified. To acquire images of nanofluid gas-liquid two-phase flow patterns of flow boiling, a high-speed digital camera was used to record four different types of flow-pattern images, namely annular flow, bubbly flow, churn flow, and slug flow. The textural features extracted by processing the images of nanofluid gas–liquid two-phase flow patterns are used as inputs to various identification schemes such as fuzzy logic, SVM, and SVM with PCA to identify the type of flow pattern. The results indicate that the SVM with reduced characteristics of PCA provides the best identification accuracy and requires less calculation time than the other two schemes. The data reported herein should be very useful for the design and operation of industrial applications

A Novel Discrete Fruit Fly Optimization Algorithm for Intelligent Parallel Test sheets Generation

Parallel test sheet generation (PTSG) is a NP-hard combinational optimization problem, in which test sheet generation algorithm with high quality and efficiency is the core technology. Basic fruit fly optimization algorithm (FOA) has the defects of easily relapsing into local optimal and low convergence precision when solving PTSG problem. In this paper, a novel discrete fruit fly optimization algorithm is proposed to solve the PTSG problem, in which a discrete osphesis searching operator based on the problem-specific knowledge is designed to help the FOA escaping from being trapped in local minima. To evaluate the performance of the proposed algorithm, the simulation experiments were conducted using a series of item banks with different scales. The superiority of the proposed algorithm is demonstrated by comparing it with the particle swarm optimization algorithm and differential evolution algorithm

Mechanism of Radical Initiation and Transfer in Class Id Ribonucleotide Reductase Based on Density Functional Theory

Class Id ribonucleotide reductase (RNR) is a newly discovered enzyme, which employs the dimanganese cofactor in the superoxidized state (MnIII/MnIV) as the radical initiator. The dimanganese cofactor of class Id RNR in the reduced state (inactive) is clearly based on the crystal structure of the Fj-β subunit. However, the state of the dimanganese cofactor of class Id RNR in the oxidized state (active) is not known. The X-band EPR spectra have shown that the activated Fj-β subunit exists in two distinct complexes, 1 and 2. In this work, quantum mechanical/molecular mechanical calculations were carried out to study class Id RNR. First, we have determined that complex 2 contains a MnIII-(μ-oxo)2-MnIV cluster, and complex 1 contains a MnIII-(μ-hydroxo/μ-oxo)-MnIV cluster. Then, based on the determined dimanganese cofactors, the mechanism of radical initiation and transfer in class Id RNR is revealed. The MnIII-(μ-oxo)2-MnIV cluster in complex 2 has not enough reduction potential to initiate radical transfer directly. Instead, it needs to be monoprotonated into MnIII-(μ-hydroxo/μ-oxo)-MnIV (complex 1) before the radical transfer. The protonation state of μ-oxo can be regulated by changing the protein microenvironment, which is induced by the protein aggregation and separation of β subunits with α subunits. The radical transfer between the cluster of MnIII-(μ-hydroxo/μ-oxo)-MnIV and Trp30 in the radical-transfer chain of the Fj-β subunit (MnIII/MnIV ↔ His100 ↔ Asp194 ↔ Trp30 ↔ Arg99) is a water-mediated tri-proton-coupled electron transfer, which transfers proton from the ε-amino group of Lys71 to the carboxyl group of Glu97 via the water molecule Wat551 and the bridging μ-hydroxo ligand through a three-step reaction. This newly discovered proton-coupled electron-transfer mechanism in class Id RNR is different from those reported in the known Ia–Ic RNRs. The ε-amino group of Lys71, which serves as a proton donor, plays an important role in the radical transfer

A Precise Simultaneous Sowed Control System for Maize Seed and Fertilizer

To improve the utilization rate of maize seed fertilizer, this study aimed to propose a precise co-sowing control system for the real-time control of the relative position of seed fertilizer during the co-sowing operation. According to the operating speed of the machine, the longitudinal distance between the seed feeder and the outer groove wheel, the height of the seed and fertilizer falling, and the relative position of the seed and fertilizer falling into the soil, the calculation method for the seed and fertilizer falling into the soil was obtained, the precise co-seeding model of the seed fertilizer was constructed, the control algorithm of the precise co-seeding of the seed fertilizer was designed, and the hardware system and software system were designed. Based on the hardware structure and working principle of the motor drive seeding and fertilization control system, a functional circuit based on the STM32F103ZET6 single-chip microcomputer (Zhengdianyuanzi (Guangzhou) Technology Co., Ltd., Guangzhou, China) was built. When the system is working, the satellite speed measurement module collects the operating speed of the machine, the encoder feeds back the motor speed in real time, a Hall sensor detects the time interval between fertilizer and seed discharge at the point of discharge, and the PID algorithm is applied to make the speed regulation system regulate the motor speed and position and adjust the speed and position of the seed discharge tray and fertilizer on the outer slot wheel in real time. The relative position of seed and fertilizer in the soil can be controlled accurately in the process of sowing fertilizer. The test results showed that when the feed speed was 2, 3, and 4 km·h−1, and the grain spacing was 20, 25, and 30 cm, respectively, the seed fertilizer alignment was better and met the requirements of precise sowing, improving fertilizer utilization rate

Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged