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As a globally distributed cereal, wheat is an essential part of the daily human dietary structure. Various changes in nutrient composition and starch structure can reflect the quality of wheat. In this study, we carried out a series of measurements to reveal the levels of wheat quality during long-term storage. We found that the deterioration of wheat was apparent after two years of storage: (1) the content of fatty acid increased from 12.47% to 29.02%; (2) the malondialdehyde content increased to 37.46%; (3) the conductivity significantly increased from 35.71% to 46.79%; and (4) other indexes, such as the amylopectin content, peak viscosity, and disintegration rate, increased noticeably during storage. Moreover, SEM images revealed a certain degree of damage on the surface of starch granules, and an X-ray diffraction (XRD) analysis showed A-type crystalline starch of wheat. Additionally, FTIR spectra suggested that the ratio of amylose and amylopectin decreased with a decreasing content of amylose and increasing content of amylopectin. The ratio of amylose and amylopectin can lead to variations in wheat machining characteristics. Therefore, wheat should be kept at an average of 20 °C with safe water content for less than two years to maintain reasonable quality.

The main substances of rice are starches, which vary their metabolism during storage. We conducted a series of tests including rice physicochemical properties, edible quality, starch content and chain length distribution along with starch structure variation to disclose the shift of rice quality by observing the changes of rice during storage. The results showed that: (1) the rice deterioration occurred as time passed, and the germination rate decreased from 70.8% to 29.4% during the storage; (2) fatty acid values increased significantly during long-term storage; (3) electrical conductivity increased as time passed; and (4) the two-year-storage rice showed significantly decreased viscosity and edible quality after sensory evaluation, decreased hardness and damaged surface area of starch granules as storage time passed. Additionally, the damaged surface area of starch granules increased with storage time. Fourier transform infrared spectroscopy (FTIR) showed that the short-range order and spiral degree of rice starch first decreased in the first year and then increased over the storage time. Furthermore, X-ray diffraction showed that the main starch of rice was A-type crystalline. Meanwhile, apparent amylose content increased from 31.00% to 33.85%, then decreased to 31.75%. The peak viscosity reduced from 2735.00 mPa·s to 2163.67 mPa·s and the disintegration value was brought down from 1377.67 mPa·s to 850.33 mPa·s. Based on the results, rice should not be stored for more than 2 years under suitable granary conditions to maintain it at a good quality.

The docking mechanism is a key component for on-orbit refueling technology. In this paper, the design and analysis of a novel floating docking mechanism for on-orbit berthing-based refueling is presented. Compared with traditional berthing and docking, the berthing here is high in success rate and low in impact, which is accomplished by stretching out a docking subassembly instead of pulling back the client spacecraft. However, the berthing also has two problems: initial deviations between two spacecraft and an additional force generated by a hard alloy refueling pipe, which both seriously affect the docking operation. Thus, the docking mechanism is designed to have alignment abilities and decrease the additional force as much as possible. Based on the principles above, we introduced spring pins and a helical refueling pipe to design a light, compact, and simple docking mechanism. To further reduce the additional force, we proposed an elliptical-helical pipe and analyzed its mechanical properties. Finally, simulations and experiments were conducted to validate the proposed mechanism. The results show that the proposed mechanism with an elliptical-helical pipe has a high tolerance for linear and angular misalignment and superior dynamic performance during docking.

Aiming at the requirements of titanium alloy holes in aircraft industry, the 3-DOF cutting stability and surface quality optimization of parallel kinematic manipulator (PKM) are studied. The variation of natural frequencies with the end-effector position of the PKM is analyzed. The cutting force model of titanium alloy helical milling based PKM is developed, and the cutting force coefficients are identified. The prediction model for 3-DOF the stability of helical milling based on the PKM is established through a Semi-Discrete method, and the stability lobes are obtained. The correctness of the stability lobes is verified by subjecting the cutting force signal to time-frequency transformation and roughness detection. The step-cutter is used for machining process improvement to enhance the stability domain. The method proposed in this paper can provide a reference for further optimization of the prediction and optimization of the milling process of difficult-to-process materials based on PKM in the future.

The study of the single-particle erosion mechanism is essential to understand the material removal mechanism in the non-contact polishing process and ultimately ensure the high-efficiency, non-damage, and ultra-smooth processing of optical glass. In this study, the theoretical model of smoothed particle hydrodynamics (SPH) is established to reveal the dynamic removal process of a single particle impacting the optical glass. The single-particle erosion mechanisms, which include ductile–brittle transition, crack initiation, and propagation, are discussed in detail through theoretical simulation. A series of particle impact experiments are designed to validate the correctness of the SPH model. The experimental data show good agreement with the simulation results in terms of the depth and width of the eroded craters. Thereafter, the SPH simulation is conducted by studying the effect of various impact parameters, such as impact speed, impact angle, and abrasive diameter, on the material removal process. With the gradual increase of impact velocity and particle size, the material removal mode changes from plastic removal to brittle removal. Although the large impact velocity and particle size increase the material removal rate, they lead to the occurrence of brittle removal and reduce the surface and sub-surface quality. When the impact angle is between 45° and 75°, the material removal rate is the largest, and the increase of the material removal rate does not cause damage to the subsurface layer of the material.

Salsola collina Pall has a long history of being used as a traditional medicine to treat hypertension, headache, insomnia, constipation and vertigo. However, only a few biologically active substances have been identified from S. collina. Here, the shoots and roots of S. collina, namely L-Sc and R-Sc, were studied. The primary and secondary metabolites were investigated using ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). A total of 637 putative metabolites were identified and these metabolites were mainly classified into ten different categories. Correlation analysis, hierarchical clustering analysis, principal component analysis and orthogonal partial least squares discriminant analysis of metabolites showed that the L-Sc samples could be clearly separated from the R-Sc samples. Differential accumulated metabolite analysis revealed that most of differential primary metabolites were significantly lower in the L-Sc than in the R-Sc. Conversely, the major differential secondary metabolites had higher levels in the L-Sc than in the R-Sc. Further analysis indicated that the flavonoids were the major putative antioxidant components and most of putative antioxidant components exhibited higher relative concentrations in the L-Sc than the R-Sc. These results improve our understanding of metabolite accumulation and provide a reference for the study of medicinal value in S. collina.

Robot control based on visual information perception is a hot topic in the industrial robot domain and makes robots capable of doing more things in a complex environment. However, complex visual background in an industrial environment brings great difficulties in recognizing the target image, especially when a target is small or far from the sensor. Therefore, target recognition is the first problem that should be addressed in a visual servo system. This paper considers common complex constraints in industrial environments and proposes a You Only Look Once Version 2 Region of Interest (YOLO-v2-ROI) neural network image processing algorithm based on machine learning. The proposed algorithm combines the advantages of YOLO (You Only Look Once) rapid detection with effective identification of ROI (Region of Interest) pooling structure, which can quickly locate and identify different objects in different fields of view. This method can also lead the robot vision system to recognize and classify a target object automatically, improve robot vision system efficiency, avoid blind movement, and reduce the calculation load. The proposed algorithm is verified by experiments. The experimental result shows that the learning algorithm constructed in this paper has real-time image-detection speed and demonstrates strong adaptability and recognition ability when processing images with complex backgrounds, such as different backgrounds, lighting, or perspectives. In addition, this algorithm can also effectively identify and locate visual targets, which improves the environmental adaptability of a visual servo system

This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.

The rearrangement of the transfection (RET) gene, which mediates the functions of the ganglion in the gastrointestinal tract, plays an important role in the development of the gastrointestinal nervous system. Therefore, the RET gene is a potential factor influencing animal body measurement. The aim of this study was to reveal the significant genetic variations in the bovine RET gene and investigate the relationship between genotypes and body measurement in two Chinese cattle breeds (Qinchuan and Nanyang cattle). In this study, two SNPs (c.1407A>G and c.1425C>G) were detected in the exon 7 of RET gene by sequencing. For the SNP1 and SNP2, the GG genotype was significantly associated with body height, hip height, and chest circumference in Qinchuan cattle (p < 0.05). Individuals with an AG-CC genotype showed the lowest value of all body measurement in both breeds. Our results demonstrate that the polymorphisms in the bovine RET gene were significantly associated with body measurement, which could be used as DNA marker on the marker-assisted selection (MAS) and improve the performance of beef cattle.

Nuclear receptor subfamily 6, group A, member 1 (NR6A1), as an important member of the nuclear receptor family, plays an important role in regulating growth, metabolism, and differentiation of embryonic stem cells. For this reason, the NR6A1 gene is considered to be a promising candidate for economic traits and was found to be associated with body size traits in many livestock. However, no studies have been conducted on NR6A1 in donkeys so far. Thus, in this research, we focused on donkeys and identified a 13 bp deletion in intron-1 of the NR6A1 gene among 408 individuals from Guanzhong and Dezhou donkeys using polyacrylamide gel electrophoresis. Three genotypes were identified, namely II, ID, and DD. The association analysis indicated that the body lengths and body heights5f genotype II individuals were significantly different to those of genotype ID in Dezhou donkeys. Conclusively, the 13 bp deletion was associated with growth traits in both Guanzhong donkeys and Dezhou donkeys, indicating that the NR6A1 gene could be a possible candidate gene in marker-assisted selection for donkey breeding programs.

MicroRNAs (miRNAs) are small non-coding RNAs that are critical in post-transcriptional regulation. Macaca mulatta is an important nonhuman primate that is often used in basic and translational researches. However, the annotation of miRNAs in Macaca mulatta is far from complete, and there are no reports of miRNA editing events in Macaca mulatta, although editing may affect the biogenesis or functions of the miRNAs. To improve miRNA annotation and to reveal editing events of miRNAs in Macaca mulatta, we generated 12 small RNA profiles from eight tissues and performed comprehensive analysis of these profiles. We identified 479 conserved pre-miRNAs that have not been reported in Macaca mulatta and 17 species specific miRNAs. Furthermore, we identified 3386 editing sites with significant editing levels from 471 pre-miRNAs after analyzing the 12 self-generated and 58 additional published sRNA-seq profiles from 17 different types of organs or tissues. In addition to 16 conserved A-to-I editing sites, we identified five conserved C-to-U editing sites in miRNAs of Macaca mulatta and Homo sapiens. We also identified 11 SNPs in the miRNAs of Macaca mulatta. The analysis of the potential targets of 69 miRNAs with editing or mutation events in their seed regions suggest that these editing or mutation events severely changed their targets and their potential functions. These results significantly increase our understanding of miRNAs and their mutation/editing events in Macaca mulatta.

Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models.

Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to measure the value of experiences and adjusts the sampling probability of experiences. Although PER can sample valuable experiences according to the TD-error, freshness is also an important character of experiences. It implicitly reflects the potential value of experiences. Fresh experiences are produced by virtue of the current networks and they are more valuable for updating the current network parameters than the past. The sampling of fresh experiences to train the neural networks can increase the learning speed of the agent, but few algorithms can perform this job efficiently. To solve this issue, a novel experience replay method is proposed in this paper. We first define that the experience freshness is negatively correlated with the number of replays. A new hyper-parameter, the freshness discounted factor μ, is introduced in PER to measure the experience freshness. Further, a novel experience replacement strategy in the replay buffer is proposed to increase the experience replacement efficiency. In our method, the sampling probability of fresh experiences is increased by raising its priority properly. So the algorithm is more likely to choose fresh experiences to train the neural networks during the learning process. We evaluated this method in both discrete control tasks and continuous control tasks via OpenAI Gym. The experimental results show that our method achieves better performance in both modes of operation.

Soil organic matter mineralization changed by land-use types is still not clearly understood. In this study, soils from typical land-use types including adjacent plantations of bamboo (Bam), camphor (Cam), and tea (Tea) were chosen to systematically investigate the role of organic carbon components and microbial community compositions in the organic matter mineralization in Ultisol. The mineralization of organic matter followed the sequence Bam < Cam < Tea. The higher carbon contents of labile pools were in the Cam and the Tea than that in the Bam. The carbon content of dissolved organic matter (DOM) showed the order Bam < Cam < Tea, whereas the complexity of chemical structure in DOM followed the opposite trend. The land-use types significant shifted the bacterial and fungal communities, and the relative abundances of bacterial or fungal phyla of Actinobacteria, Acidobacteria, Firmicutes, and Basidiomycota were significantly different among the land-use types. The multivariate regression tree results showed that the total organic carbon and/or the C/N ratio were dominant factors in influencing the bacterial and fungal communities. Moreover, the redundancy analysis results demonstrated that the communities of bacteria and fungi in Bam, Cam, and Tea were tightly linked to the C/N ratio, the pH and the labile pool I carbon, and the DOM, respectively. The Pearson’s correlation results revealed that the mineralization of organic matter was significantly correlated with the organic carbon components, but generally not the microbial community compositions, which implied that the organic carbon components were perhaps the major determinant in controlling the organic matter mineralization in Ultisol.