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Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock. The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel. An in situ microseismic (MS) monitoring system is established in the plateau soft tock tunnel. This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation, which agree well with field convergence deformation results. The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results. The results demonstrate that: (1) Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section. As excavation continues, the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding; (2) Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass, vertical tension fractures (account for 45%) exist in deep rockmass, which play a crucial role in controlling the macroscopic failure of surrounding rock; and (3) Based on the radiated MS energy, a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock. The model depicted varying degrees of damage, and three high damage zones were identified. Generally, the depth of high damage zone ranged from 4 m to 12 m. This study may be a valuable reference for the warning and controlling of large deformations in similar projects.

A major engineering challenge in construction projects lies in the stability assessment of rocky slopes with faults. The left bank slope of the Jinchuan Hydropower Station in the outlet field is notably influenced by the steeply inclined faults and rock joints, resulting in highly complex geological conditions and raising critical concerns regarding slope stability throughout construction and operation. The evolution mechanism of rock mass from fracture to deformation during slope excavation and unloading is determined using microseismic (MS) monitoring and numerical simulation. Results show that the MS activity can effectively reveal the influence of slope construction disturbance on geological structure damage and structural plane activation. Specifically, the g7 fault is a crucial factor influencing the failure mode of the left bank slope of the Jinchuan Hydropower Station in the outlet field. The comparison of monitoring data, numerical simulation, and in-situ failure demonstrates consistency between the deformation area of rock mass and the damage area defined by MS monitoring, confirming the accuracy of MS monitoring and numerical simulation results. Overall, the results can serve as a basis for the later-stage construction of the dam foundation slope at the Jinchuan Hydropower Station and provide guidance for the stability assessment of similar slope engineering projects.

Abstract Background The expression of biological traits is modulated by genetics as well as the environment, and the level of influence exerted by the latter may vary across characteristics. Photosynthetic traits in plants are complex quantitative traits that are regulated by both endogenous genetic factors and external environmental factors such as light intensity and CO2 concentration. The specific processes impacted occur dynamically and continuously as the growth of plants changes. Although studies have been conducted to explore the genetic regulatory mechanisms of individual photosynthetic traits or to evaluate the effects of certain environmental variables on photosynthetic traits, the systematic impact of environmental variables on the dynamic process of integrated plant growth and development has not been fully elucidated. Results In this paper, we proposed a research framework to investigate the genetic mechanism of high-dimensional complex photosynthetic traits in response to the light environment at the genome level. We established a set of high-dimensional equations incorporating environmental regulators to integrate functional mapping and dynamic screening of gene‒environment complex systems to elucidate the process and pattern of intrinsic genetic regulatory mechanisms of three types of photosynthetic phenotypes of Populus simonii that varied with light intensity. Furthermore, a network structure was established to elucidate the crosstalk among significant QTLs that regulate photosynthetic phenotypic systems. Additionally, the detection of key QTLs governing the response of multiple phenotypes to the light environment, coupled with the intrinsic differences in genotype expression, provides valuable insights into the regulatory mechanisms that drive the transition of photosynthetic activity and photoprotection in the face of varying light intensity gradients. Conclusions This paper offers a comprehensive approach to unraveling the genetic architecture of multidimensional variations in photosynthetic phenotypes, considering the combined impact of integrated environmental factors from multiple perspectives.

Inspired by the maxillary mouthparts of longicorn beetles, four types of bionic cutters were designed in this research to address the prevalent issues of high cutting resistance and severe stubble damage encountered during alfalfa harvesting. Finite element simulation was utilized to assess the structural integrity and cutting performance of these bionic cutters. Additionally, bench tests were conducted on a homemade stem-cutting force measurement and control rig to evaluate their effectiveness. The results indicated: (1) the bionic cutters achieved a reduction in maximum equivalent force ranging from 20.9% to 49.2% and a decrease in maximum deformation from 31.4% to 64.1% compared to conventional cutters; (2) the maximum cutting resistance of alfalfa stems was reduced by 28.6%, 43.9%, 52.4%, and 38.6%, significantly enhancing the flatness of the cut surfaces; (3) orthogonal bench tests demonstrated that the type of cutter and the slip-cutting angle significantly influenced the maximum cutting resistance of the stems (p < 0.01), with the optimal configuration being bionic cutter c, a slip-cutting angle of 10°, and a rotational speed of 2600 rpm. In conclusion, bionic cutters demonstrate substantial advantages in reducing maximum cutting resistance and improving the flatness of alfalfa stubble, suggesting their potential for widespread application and adoption.

Based on the underground powerhouse of Shuangjiangkou hydropower station, Octree theory is adopted to define the indices of the microseismic (MS) spatial aggregation degree and the deviation values of MS count and energy. The relationship between the MS multiple parameters and surrounding rock mass instability is established from three aspects: time, space, and strength. Supplemented by the center frequency of the signal evolution characteristics, A fuzzy comprehensive evaluation model and the evolution trend of the MS event center frequency are constructed to quantitatively describe the early warning state of the surrounding rock mass instability. The results show that the multilevel tree structure and voxels generated based on the Octree theory fit relatively well with the set of MS points in three-dimensional space. The fuzzy comprehensive evaluation model based on MS spatial aggregation and MS count and energy deviation values enables three-dimensional visualization of the potential damage area and damage extent of the surrounding rock mass. The warning time and potential damage zone quantified are highly consistent with the characteristics of MS precursors, with wide recognition and field investigation results, which fully validate the rationality and applicability of the proposed method. These findings can provide references for the early warning of surrounding rock mass instability in similar underground engineering.

In response to the issues of low-velocity zones and non-uniform jet velocity distribution in the airflow field of traditional air-assisted orchard sprayers, an arc-shaped air outlet suitable for axial-flow air-assisted systems is designed. This article employs the method of CFD numerical simulation and experimental verification to compare and analyze the internal flow field of the air-assisted system and validates the reliability of the numerical simulation results through calculation error and chi-square test. The wind speed of the cross-section is measured at different distances from the outlet, and the distribution characteristics of the outflow field wind speed before and after the structural optimization of the air-assisted system are compared. The horizontal distribution of fog droplets is collected using a fog collection chamber. The experimental results show that the design of the arc-shaped outlet increases the average wind speed of the annular outlet from 14.95 m/s to 18.20 m/s and reduces the proportion of low-speed area from 20.83% to 0.71%. When the rounded corner radius of the air outlet is 50 mm, optimal parameters are attained. The maximum error between the simulated and experimental values is 9.52%. At a significance level of 0.05, the χ2 value is 0.252, indicating that the simulated values follow the distribution of the actual measurement values. On the cross-sections located at distances of 0.5, 0.75, 1, 1.25, and 1.5 m from the air outlet, the wind speed distribution with no arc-shaped air outlets exhibits a “low left and high right” type, tending to shift towards the right as a whole. Fog droplets also display a drift tendency towards the right side. The wind speed distribution with arc-shaped air outlets shows a symmetric “high in the middle and low on the sides” type. Fog droplets concentrate in the central position. The optimized air-assisted system can reduce the air field’s low-flow area, increase the airflow distribution uniformity, improve the average wind speed at the outlet, and decrease fog droplet drift. This provides a reference for the structural design of air-assisted systems in current orchard sprayers of the same type.

Objective: To investigate the accuracy and repeatability of the software that we developed in measuring the adolescent idiopathic scoliosis (AIS) Cobb angle. Methods: Fifty patients with AIS were included in the study. Three independent surveyors measured the Cobb angle of coronal main curve, thoracic kyphosis angle (TK), thoracolumbar kyphosis angle (TLK) and lumbar kyphosis angle (LL) by using protractor, scoliosis screening software and picture archiving and communication system (PACS). The measurement was repeated after an interval of 2 weeks. The measurement time of software method and protractor method were compared by Paired t-test. Taking the Cobb angle measurement results of the PACS as the reference standard. The repeatability of the surveyor and the consistency between the surveyors are compared by the intraclass correlation coefficient (ICC). Results: Among the 50 patients, there were 18 males and 32 females, aged from 10 to 18 years (mean age of 14.4 years). The time of measuring Cobb angle by software method was significantly shorter than that by protractor method (94.7 ​± ​8.5s versus 217.9 ​± ​24.1s, t ​= ​−60.536, P ​ ​0.05). The reliability analysis showed that the three surveyors used software to measure the Cobb angle of the coronal main curve, TK, TLK and LL were all in good consistency (ICC>0.90). The repeatability and consistency of the software method used by the three surveyors were higher than that of the protractor method (ICC> 0.90). Conclusions: Compared with traditional protractor, the smartphone scoliosis screening software has shorter time, higher efficiency, higher accuracy and repeatability in measuring Cobb angle of AIS, so as to widely screen the diagnosis of scoliosis patients.

Achieving fast and accurate prediction of the fruit mechanical damage area is important to improve the accuracy and efficiency of apple quality grading. In this paper, the spectral data of all samples in the wavelength range from 376 to 1011 nm were collected, the sample set was divided by the physicochemical coeval distance method, and the spectral preprocessing methods were evaluated by establishing a full-wavelength artificial neural network model. The wavelength selection of spectral data was performed by competitive adaptive reweighted sampling, L1 parameter method, and the Pearson correlation coefficient method, and the partial least squares, artificial neural network, and support vector machine (Gaussian kernel) prediction models were established to predict the fruit bruise area size. The surface fitting was performed using the actual apple bruise area, and the regression surface equation of the damage time and damage height of the fruit was established. The results showed that (1) the preprocessing method of first-order difference + SG smoothing can make the prediction model more accurate; (2) the CARS-ANN prediction model has better prediction performance and higher operation efficiency, with the prediction set root mean square error of prediction and R-value of 0.1150 and 0.8675, respectively; (3) the sparrow search algorithm was used to optimize the model, which improved the accuracy of the prediction model. The root mean square error of prediction reached 0.0743 and The R-value reached 0.9739. (4) The relationship between spectral information, bruise area, damage time, and damage degree was obtained by combining the establishment of the fitted surface of the apple bruise area with the prediction model. This study is of application and extension value for the rapid nondestructive prediction of fruit bruise area.

In order to improve the adaptability of sweet cherry sorters to sweet cherries, a full-view-type grading cup for automated sweet cherry sorters was developed based on the physical characteristics of three species of sweet cherries: Tieton, Huang Mi, and Lapins. The structure and working principle of the full-view-type grading cup are described in this paper. The main factors affecting the operating stability of the grading cup were identified in this study by analyzing the mechanical properties of sweet cherries during the conveying and rotating process. According to the Box-Behnken test design method, we took the operating speed, the Young’s modulus, and the friction coefficient of the double-roller supporter as the test factors and we took the success rate as the test index. The operating parameters of the full-view-type grading cup were tested and studied, and the regression model between the test index and the test factors was established. The influence law of each factor on the test index was analyzed, and the test factors were comprehensively optimized according to the regression model. The results showed that when the operating speed was 48 sweet cherries/min; the butadiene rubber (BR) 9000, with a Young’s modulus of 0.012 GPa was selected as the material for making the double-support rollers; and the friction coefficient was designed to be 15, the success rate was 86.0%. The results of this research have provided a theoretical basis for the design and optimization of grading cups for automated sweet cherry sorters.

Aimed at the problems of difficult treatment, unreasonable utilization and serious waste of fruit tree residue, combined with the terrain and planting characteristics of hilly orchards, a self-propelled monorail branch chipper was developed. It can realize long distances and large ranges of crushing operations and debris tiling in the garden. Because the monorail branch chipper adopts the half-empty suspension support method, the moving operations and discontinuous cutting of branches can lead to vibration failures or hazards. In response to this problem, modal analysis of a track system with different numbers of nodes by ANSYS software showed that an increase in the number of track sections decreases the natural frequency of each order under the condition of rigid fixation of the support rods, and weakness of vibration is especially seen in sections 1–4, but after a certain amount, the vibrational change tends to level off. The number of lateral rods should be increased for distal short rail branches of less than four sections to reduce operational and operational vibration. The vibration level test results of field multi-condition and multi-point grinding operations showed that the static vibration amplitude of the crusher is basically the same as that of the mobile state. The vibration amplitude of the chipper is significantly increased when in the states from no-load to grinding operation, and the maximum vibration occurs in the left and right direction of the transverse rail, which provides a theoretical basis and optimization direction for further optimization of the single-track branch chipper operational stability problem.

As the hydropower development strategies of China continue to be implemented, a host of large hydropower projects have been completed or are being constructed in southwest China. During construction of the Jinchuan hydropower station, this study examined the stability of the surrounding rock during the excavation and unloading of hydraulic tunnels under demanding geological conditions. Microseismic (MS) monitoring technology was employed to monitor the deformation and failure of the surrounding rock online and in real time, based on engineering geological data and site surveys. To analyze the stability of the surrounding rock in the spillway tunnel and to study the temporal and spatial evolution characteristics of MS events, source parameter analysis and numerical modeling were performed. The 3D finite-difference numerical modeling software FLAC3D was used to simulate the mechanical response of the surrounding rock during the excavation and unloading of the spillway tunnel and the diversion tunnel. The numerical modeling results were compared with the monitoring results and site surveys to determine the failure mechanisms of the surrounding rock during the construction and unloading of the hydraulic tunnels. The research results can serve as a guide for studying the stability of the surrounding rock in similar hydraulic tunnels.

In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ang, Zhiwei, Wei, Yuepeng, Longlong, Yuliang, Jianping and Lijia.)

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depth-separable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE(Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model's operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ang, Han, Yuepeng, Longlong, Yue and Xiang.)