There are certain requirements for the mechanical properties of paper or paper-based materials during their service. However, existing experimental and theoretical studies of size effects indicate that the mechanical properties of specimens with different sizes are not identical. Therefore, it is necessary to clarify the size effect of their mechanical properties to ensure their safety. The kraft paper, which is easy to obtain and has high strength, was selected to carry out uniaxial tensile tests on with different sizes. And then the size effects of mechanical properties and their dispersions were determined. By analyzing the deformation characteristics and macro-microscopic damage features of different sizes specimens, the intrinsic mechanism of the size effect on the mechanical properties of kraft paper was revealed. Finally, a size effect model of mechanical properties for kraft paper was established. The study finds that the nominal tensile strength and nominal peak strain of kraft paper first increase and then decrease as the sizes increases. This is because the cut edge fibers cause the edges of the kraft paper specimens to have weaker stress and strain capabilities, and the proportion of weakened edges in small-sized specimens is relatively large, leading to an increasing size effect on the nominal tensile strength and nominal peak strain. In large-sized specimens, the proportion of weakened edges can be ignored, and the internal fracture process zone plays a dominant role, leading to a decreasing size effect on the nominal tensile strength and nominal peak strain. The established edge-internal fracture process zone size effect model can well describe the size effect on the nominal tensile strength and nominal peak strain of kraft paper. However, this model cannot capture the nonlinear characteristics of the increasing size effect stage for the nominal tensile strength and nominal peak strain.

The changes in clouds are complex and diverse, playing a significant role in weather forecast and disaster warning, and affecting our daily lives. The observation of clouds is mainly carried out through radar, remote sensing satellites, and all-sky imagers. The recorded cloud images are divided into radar cloud images, satellite cloud images, and ground-based cloud images, all of which are indispensable parts of cloud observation. With the development of machine learning in multiple fields, it has gradually been applied to cloud segmentation and has made great progress. Through extensive research on literature and achievements in related fields, machine learning cloud segmentation was divided into three types: cloud segmentation methods based on neural networks, cloud segmentation methods based on transfer learning, and cloud segmentation methods based on lightweight models. The methods proposed in recent years for each type were compared, and improvement methods for different problems in cloud segmentation were further summarized. Several improvement schemes were provided for reference.

There are various evaluation methods for the effectiveness of asphalt pavement grouting repair, most of which rely on on-site testing of a single indicator. Up to now, there are few related reports in the comprehensive evaluation of multiple indicators. In order to comprehensively understand the detection methods of asphalt pavement grouting repair effect and promote the development and application from single index evaluation to multi index evaluation methods, the research status of relative methods were summarized including drilling core method, grouting plug gauge method, deflection detection method, modulus inverse algorithm, ground penetrating radar method, and transient Rayleigh surface wave method in the detection of grouting filling quality, pavement strength, pavement modulus, compaction degree, void ratio, and grouting repair degree and other indicators. Furthermore, drawing on the current research status in the fields of tunnel grouting and goaf grouting, the application prospects of fuzzy analytic hierarchy process, grey correlation degree method were analyzed, as well as subjective and objective weighting cloud model in the evaluation of grouting repair effect of internal diseases in asphalt pavement, and looks forward to the development trend of precision, objectivity, and informatization in the evaluation of grouting repair effect of asphalt pavement were forecasted. Diversified method guidance is provided for evaluating the grouting repair effect of internal diseases in asphalt pavement.

The tectonic activities at the bottom and surrounding the sedimentary basin will inevitably lead to changes in the sedimentary filling characteristics and evolution of the basin. These changes serve as records of the sedimentary strata’s response to tectonic activities. Based on logging interpretation and outcrop data, the sedimentary evolution of the Middle and Late Jurassic to Early Cretaceous in the Ordos Basin was analyzed through paleogeography and sedimentary facies restoration. The results indicate that the southern part of the basin developed multi-phase depositional systems, including alluvial fan, river-delta-lake, and desert phases. There was a sedimentary divergence from east to west, with the sedimentary center migrating from east to west. During this period, the basin was primarily influenced by the Yanshan Movement, with strong tectonic activities occurring around it. Consequently, a tectonic pattern emerged within the basin, characterized by uplifting and tilting in the east and sinking and depression in the west. The intense tectonic activities at the western edge of the basin also led to the development of an intralittoral foreland basin at the southwestern edge during the Late Jurassic Fenfanghe Stage. Additionally, multiple sets of molasse formations and unconformity contacts were present during the Late Jurassic and Early Cretaceous periods. Based on these insights, a model map of the Middle and Late Jurassic to Early Cretaceous tectono-sedimentary evolution has been established. The occurrence of these sedimentary events serves as a record of the basin’s response to the early and middle stages of the Yanshan Movement, proving a close relationship between sedimentary evolution and the uplift of the Qinling-Qilian Mountain orogenic belt and the Lvliang Mountains. These insights not only complement and improve the theory of tectonic-sedimentary response in the southern basin during the Yanshan Movement but also have practical significance for the exploration and development of mineral resources such as oil, gas, and uranium in the southern basin.

As a major risk source, slope in open-pit coal mine is one of the main challenges faced by mine safety production. Taking Wanyuan open-pit coal mine in Wuhai City, Inner Mongolia Autonomous Region as an example, based on detailed investigation of slope development characteristics, deformation and failure modes were analyzed, slope radar deformation monitoring was carried out, and early warning models were established, a monitoring and early warning platform was built to achieve risk control. The results show that the slope of Wanyuan open-pit coal mine involves reverse, cross and consequent rock slope and soil slope of dump, and its deformation and failure modes are respectively tensile fracturing, wedge failure, slip-bending and creep (-tensile cracking). There is a possibility of instability under the influence of mining, blasting, rainfall or unreasonable stacking. On this basis, in order to realize the monitoring of slope without blind area, two sets of slope radar were respectively installed at the stable bedrock on the northeast and southwest sides of the mine, and the slope safety grade was divided into four grades: blue (<3 mm/h), yellow (3~8 mm/h), orange (8~15 mm/h) and red (>15 mm/h). Combined with the organizational structure of mining enterprises, the slope safety management system from slope radar deformation monitoring data-early warning model-early warning platform-risk management and control are constructed, which can provide technical reference for the construction of regional coal mine safety production capacity.

Guangxi is located in the South China block, its structure is relatively stable, the seismic activity observed by the instrument is relatively weak. The little observation data leads to weak research on the crustal stress field in the area. In 2019, the Beiliu earthquake sequence occurrence in this area leads to the accumulation of considerable focal mechanism data. The focal mechanism data were collected in Guangxi and its adjacent areas. Using the grid search algorithm, the stress field in overall region and subregions were inverted. The results show as follows. The statistics of the focal mechanism in this area is mainly strike-slip type, and from the distribution of NW to ES, the focal mechanism of the reverse type gradually increases, and the focal mechanism of the normal fault type gradually decreases. The principal compressive stress axis of the overall stress field is NW-SE direction, the principal extensional stress axis is NE-SW direction, both of them are close to horizontal, which representing strike-slip type. The direction of the principal compressive stress axis in the western region is close to the N-E direction, and that in the east gradually changes to the direction of NW-SE, which makes the pattern of the compressive stress direction present a fan-shaped in the overall area. Based on the stress field analysis of subregions: NE-SW and NW-SE faults are easily generated in region-a by primarily of strike-slip type. NNE-SSW and NWW-SEE faults are easily generated in region-b, with NNE-SSW faults more towards reverse strike-slip type and NWW-SEE faults tending towards strike-slip type. Pure azimuth N-S and E-W faults are easily generated in region-c, but its main fault properties are not reflected due to the influence of surrounding fault extensions, with faults mainly of reverse strike-slip and reverse fault types. NW-SE and NE-SW faults are easily generated in region-d, with NW-SE faults more towards reverse fault and NE-SW faults tending towards strike-slip.

Addressing the issues of large model parameters and high computational complexity in apple target detection algorithms for complex orchard environments, which hinder application on devices with limited computational resources, an improved and lightweight apple target detection algorithm named YOLOv8n-Apple based on YOLOv8 was proposed. The backbone network, yaniaNet, was introduced to reduce model parameters and complexity. The original C2f module in the model was replaced with the C2fGhost module, which further decreased model parameters by obtaining similar feature maps through fewer convolutional operations. The lightweight upsampling operator CARAFE was utilized to address the issues of semantic loss and excessively small receptive fields associated with traditional upsampling operators. Given that traditional loss functions cannot fully capture the relative position and size differences between targets, the WIoU bounding box was adopted as the regression loss function. A dataset comprising 3 120 images of mature apples in various scenarios, including distant and close views under front-light and backlight conditions, was collected from diverse angles and backgrounds, to mitigate potential dataset uncertainties. The improved apple detection model for orchard environments demonstrated an average detection accuracy of 90%, which was 7.5, 4.8, 2.2, 3.8, and 3.4 percentage points higher than SSD, Faster R-CNN, YOLOv5, YOLOv7, and YOLOv8, respectively. The detection speed reached 286 frames per second, and the model size was reduced to 1.8 MB, representing an improvement of 41 frames per second compared to the original model, while occupying only 60.0% of size.

To address the issues of long stitching time due to numerous mismatched feature points and insufficient stitching accuracy when using all feature points directly in image stitching tasks, an optimized image stitching method combining a matching point increasing strategy with RANSAC(random sample consensus) was proposed. The method initially screened feature points to prevent numerous ineffective samples, thus improving computational efficiency. Then, a progressive sampling strategy was employed to incrementally increase matching points and repeatedly sample for precise results. Finally, the optimal model was obtained by utilizing a new loss function based on root mean square error to filter the results. The experimental results indicate that, without a noticeable increase in time consumption, the interior point rate of the algorithm in this paper is further enhanced, the mean and root mean square errors of feature points have decreased significantly, the accuracy of image stitching is improved, the misalignment phenomenon at the stitching seam is effectively improved, and the stitching errors in image stitching tasks are significantly reduced.

To further meet the plasma heating requirements of EAST (experimental advanced superconducting tokamak) device, NBI (neutral beam injection) system requires higher beam power. Accordingly, the EAST NBI laboratory has developed a 120 keV accelerator for this purpose. The analysis of the whole accelerator needs to be carried out from various perspectives such as beam optics, insulation support, active cooling and materials. Taking into account factors such as plasma parameters, electrostatic lens, voltage resistance between grids, and assembly errors, the numerical simulation program was utilized to beam optics analysis for the slit tetrode ion source accelerator. Beam trajectory, electric field strength distribution, and beam divergence angle were investigated and optimized to preliminarily determine the grid parameters of the new multi-slit accelerator. The accelerator obtains the beam with the minimum divergence angle of 0.6° in the vertical direction with perveance of 1.52 μp, meeting the design criteria for beam current intensity of 60 A and divergence angle below 1° of the ion source.

With the gradual formation of “trunk-branch” linkage multi-level air cargo transportation system, the problem of low turnover efficiency of airport cargo is becoming more and more prominent, and the “trunk-branch” linkage efficient cargo allocation methods have become one of the key technologies to solve the above problems. An integer programming linkage allocation model was established with the maximum loading rate under “trunk-branch” linkage as the optimization objective while simultaneously satisfying constraints such as cabin position, cabin size, center of gravity, and weight for both trunk and branch aircraft. This model was applied based on genetic algorithms to optimize the cargo allocation between the B757-200 trunk aircraft and the ARJ21-700F branch aircraft. It was found that the average loading rate of “trunk-branch” linkage allocation was increased by 5.45% to 72.05% compared with sequential allocation.By demonstrating that the proposed “trunk-branch” linkage allocation method in this study can significantly increase the overall cargo loading capacity, and thus effectively improving the efficiency of cargo turnover at airports, laying a theoretical foundation and providing technical support for the safe and efficient development of the “trunk-branch” linkage multi-level air cargo transportation system.