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In the context of global sustainable development, energy waste and comfort deficiencies in rural living environments have attracted increasing attention. Promoting low-carbon buildings and improving residential comfort have become urgent challenges. This study focuses on the rural areas of Xuzhou, where Computational Fluid Dynamics (CFD) simulations were conducted using PHOENICS software to assess the wind environment performance of various courtyard layout configurations. Typical courtyard forms and their key design parameters were identified through field surveys and measurements. Based on the prevailing wind characteristics in Xuzhou, four influential factors—courtyard enclosure form, gate position, roof slope, and fence height—were systematically evaluated through simulation. The optimization strategies derived from the analysis were subsequently applied to residential renovation practices, aiming to enrich the approaches to rural habitat enhancement and provide a reference framework for the climate-responsive design of rural dwellings in the Xuzhou region.

Magnetic flux leakage (MFL) testing is a critical non-destructive testing (NDT) method for ensuring the safety of ferromagnetic storage and transportation equipment. However, existing research has predominantly focused on weak or saturated magnetization states, leaving the characteristic laws and physical mechanisms of defect signals under supersaturated magnetization conditions unclear. To address this gap, this paper systematically investigates the MFL signal evolution mechanism and develops a high-precision quantitative identification method for defects under supersaturated magnetization conditions through finite element simulation, theoretical modeling, and experimental validation. First, a three-dimensional (3D) finite element model for MFL testing is established using COMSOL Multiphysics. The regulatory effects of key parameters—sensor lift-off value, defect burial depth, length, and depth—on the peak values and distribution characteristics of axial and radial MFL signals are revealed, a signal peak characterization model for each parameter and their adjusted R2 is obtained via fitting, and the detection capability of the detector for defects with different shapes is simultaneously verified. Furthermore, actual detection is conducted on three crack defects of different sizes, and the analysis results indicate that the characterization models of each parameter obtained from the simulation exhibit high accuracy. The results show that MFL signal intensity under supersaturated magnetization conditions is significantly enhanced compared to that under saturated magnetization conditions. Furthermore, to improve defect length measurement accuracy, a signal correction method based on the midpoint of extreme values of the second derivative of axial signals is proposed. By compensating for peak offsets caused by factors like magnetic field diffusion, this method reduces the maximum defect length identification error from 14.25% (pre-correction) to below 0.3%. This study elucidates the coupling influence mechanism of multi-physical parameters on MFL signals under supersaturated magnetization conditions. The proposed high-precision signal correction method provides a novel theoretical basis and technical approach for the accurate quantification and inversion of defects in complex operating conditions.

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. Additionally, the SVM is used instead of the Softmax classifier to classify the fused features of vibration and sound, compensating for the poor diagnostic performance and generalization ability of the CNN network in small sample data scenarios. To further enhance the fault diagnosis performance of the SVM, the SABO is introduced for hyperparameter optimization of the SVM classifier. An HVCB fault test platform was established to train and test the model with limited data. The experimental results show that, compared with the multi-channel CNN-SVM and the CNN model based on unimodal signals, the proposed multi-channel CNN-SABO-SVM model improves the accuracy by 2.66% and 10.66%, respectively, and effectively addresses the challenge of circuit breaker fault diagnosis with limited samples.

Real-time road damage detection and assessment is crucial to ensure road safety. Traditional road damage detection methods mostly rely on manual labor, which is not only inefficient, but it is also difficult to guarantee its reliability. In this study, a road damage detection model, MED-YOLOv8s, based on YOLOv8s is proposed. MobileNetv3 is adopted as the backbone of the detection algorithm, which reduces the number of parameters and the number of computations in the process of feature extraction, enabling the model to achieve a good balance between the detection speed and the detection accuracy. The introduction of the ultralightweight attention mechanism, ECA, adapts the optimization of the correlation of channels to improve the model generalization performance. In addition, replacing the standard convolution with the DW convolution in the 21st layer of the network not only eliminates part of the redundant feature maps but also better extracts the correlation information between the feature maps. In this study, we also discuss the influence of the mix-up data augmentation weight parameter on the detection effect of the model. The experimental results show that the mAP@0.5 of the MED-YOLOv8s model proposed in this study is 95.2%, which is 1.1% higher than that of the original model, and at the same time, the calculation amount of the model is reduced by 46.2%. This method not only improves the detection accuracy but also greatly reduces the model complexity, providing a reference for subsequent model migration.

The regular detection of weld seams in large-scale special equipment is crucial for improving safety and efficiency, and this can be achieved effectively through the use of weld seam tracking and detection robots. In this study, a wall-climbing robot with integrated seam tracking and detection was designed, and the wall climbing function was realized via a permanent magnet array and a Mecanum wheel. The function of weld seam tracking and detection was realized using a DeepLabv3+ semantic segmentation model. Several optimizations were implemented to enhance the deployment of the DeepLabv3+ semantic segmentation model on embedded devices. Mobilenetv2 was used to replace the feature extraction network of the original model, and the convolutional block attention module attention mechanism was introduced into the encoder module. All traditional 3×3 convolutions were substituted with depthwise separable dilated convolutions. Subsequently, the welding path was fitted using the least squares method based on the segmentation results. The experimental results showed that the volume of the improved model was reduced by 92.9%, only being 21.8 Mb. The average precision reached 98.5%, surpassing the original model by 1.4%. The reasoning speed was accelerated to 21 frames/s, satisfying the real-time requirements of industrial detection. The detection robot successfully realizes the autonomous identification and tracking of weld seams. This study remarkably contributes to the development of automatic and intelligent weld seam detection technologies.

Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the straight-line driving, longitudinal climbing, and lateral climbing processes of the chassis were theoretically analyzed, and the critical parameters that affect the normal climbing of the chassis were calculated. Meanwhile, the multi-body dynamics model of the tracked chassis was established by using the software SolidWorks 2020 and RecurDyn 2023, and its climbing and obstacle crossing performance were analyzed. The relevant motion parameters of the tracked chassis suitable for longitudinal and transverse slopes in hilly and mountainous areas were obtained, and field tests were conducted on the tracked chassis to verify the reliability of the simulation model. According to the simulation results, the tracked chassis achieves ultimate slope angles of 28° longitudinally and 23° laterally. It demonstrates the capability to navigate 140 mm high ridges and 250 mm wide trenches smoothly, while its straight-line driving offset rate conforms to prevailing agricultural machinery industry standards. Field test results indicated that the tracked chassis achieved a maximum longitudinal climbing angle of 26°. The relative error of less than 8% between the experimental and simulated data confirms a strong correlation. The maximum offset rate for straight-line travel is 1.95%, meeting the requirements of the agricultural machinery industry standards. The test verified the feasibility of the dynamic model of the crawler chassis of the silage harvester, providing a theoretical basis and technical support for the optimal design of the crawler chassis of the self-propelled silage harvester in hilly and mountainous areas.

Underground spaces offer significant potential for sustainable urban development, particularly in cold climate regions where surface thermal fluctuations are extreme. However, optimizing the wind–heat environmental performance of such spaces remains insufficiently explored, especially in relation to spatial morphology. This study addresses this gap by investigating how underground spatial configurations influence thermal comfort and ventilation efficiency. Six representative spatial prototypes—fully enclosed, single-side open, double-side open, central atrium, wind tower, and earth kiln—were constructed based on common underground design typologies. Computational fluid dynamics (CFD) simulations were conducted to evaluate airflow patterns and thermal responses under winter and summer conditions, incorporating relevant geotechnical properties into the boundary setup. The results indicate that deeper burial depths enhance thermal stability, while central atrium and wind tower prototypes offer the most balanced performance in both ventilation and heat regulation. These findings provide valuable design guidance for climate-responsive underground developments and contribute to the interdisciplinary integration of building physics, spatial design, and geotechnical engineering.

Background The age of glioma plays a unique role in prognosis. We hypothesized that age is not positively correlated with survival prognosis and explored its exact relationship. Methods Glioma was identified from the SEER database (between 2000 and 2018). A multivariate Cox proportional regression model and restricted cubic spline (RCS) plot were used to assess the relationship between age and prognosis. Results A total of 66465 patients with glioma were included. Hazard ratios (HR) for ten-year by age: 0–9 years, HR 1.06 (0.93–1.20); 10–19 years: reference; 20–29 years, HR 0.90 (0.82–1.00); 30–39 years, HR 1.14 (1.04–1.25); 40–49 years, HR 2.09 (1.91–2.28); 50–59 years, HR 3.48 (3.19–3.79); 60–69 years, HR 4.91 (4.51–5.35);70–79 years, HR 7.95 (7.29–8.66); 80–84 years, HR 12.85 (11.74–14.06). After adjusting for covariates, the prognosis was not positively correlated with age. The smooth curve of RCS revealed this non-linear relationship: HR increased to 10 years first, decreased to 23 years, reached its lowest point, and became J-shaped. Conclusion The relationship between age and glioma prognosis is non-linear. These results challenge the applicability of current age groupings for gliomas and advocate the consideration of individualized treatment guided by precise age.

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities.

To address the negative impacts in recovering large areas of residual plastic film from corn stubble in the Hexi irrigation area—such as the residual film containing substantial amounts of soil, corn stubble, and corn straw, and high power consumption during the operation process—in this study, a combined operation machine was designed for corn straw crushing and residual film recovery. The machine consisted of a double-wing, single-blade shovel for lifting the film and cutting corn stubble, a corn straw-crushing and returning device for reducing the residual film impurity rate, an eccentric teeth shifting cylinder for picking up residual film, a device for shifting residual film, and a collection device for bundling residual film. The key components of the combined operation machine were designed based on an agronomic model for corn planting and the mechanized operation requirements in the Hexi irrigation area. The optimal combination of operating parameters was devised based on theoretical calculations and single- and multifactor simulation tests. The results showed that when the angle of entry of the film-lifting shovel was 25.14°, the rotational speed of the eccentric teeth shifting cylinder was 80.96 rpm, and the forward velocity of the machine was 4.03 km/h, while the rate of recovery of residual film was 92.56%. The field test showed that the residual film contained 16.65% impurities, and the qualified rate of corn straw crushing was 88.51%, with a relative error of 0.65% from the optimized value. The experimental results provide theoretical support and a design reference for research on the mechanized recycling of residual film in large areas of corn stubble.