Effect of vibration on the interface properties of welded steel joints and filled concrete in steel pipes.

Concrete-filled steel tubes (CFSTs) have been increasingly utilized in engineering due to their excellent mechanical properties. Ensuring a solid bond between a steel tube and concrete is essential for optimizing their synergistic effect. This study introduces an internally welded steel bar structure within the inner wall of a steel tube to enhance the bond properties at the connection interface. The influence of various configurations of steel bars welded to the inner surface of the tube on the bond strength is investigated considering the impact of vibration on the load-bearing capacity of the component. This study comprises two groups of specimens, one with vibration and one without vibration, for a total of ten specimens. Each group included CFST members with five distinct internal welded steel bar structures. The experimental results, including load-displacement curves and strain data of the steel tube, were used to assess the impact of the internal welded steel bar configurations on the steel-concrete interface. The sliding process is described by correlating test data with curves and observed phenomena. To comprehensively compare the effects of structural dimensions on the bonding and slipping properties of the welded bars, finite element simulations replicating the experimental conditions were carried out using ABAQUS software, and the simulation results agreed with the experimental observations. The study demonstrated that incorporating internal welded steel bars substantially enhances the bond strength of steel pipe-concrete interfaces. While vibration weakens the bond strength in CFST members, internal welded steel bars mitigate this effect. These findings improve the structural performance of CFST structures and their resilience to external vibrations.

GFI-YOLOv8: Sika Deer Posture Recognition Target Detection Method Based on YOLOv8.

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions.

Gellan gum-cellulose hydrogel incorporating with graphene oxide and magnetic nanoparticles as a novel nanocatalyst for the synthesis of dihydropyrano derivatives.

In this project, a highly efficient catalyst with a remarkable yield of over 97 % was developed for the synthesis of dihydropyrano[2,3-c] pyrazole derivatives. A Gellan Gum-Cellulose hydrogel was prepared using Glutaraldehyde as the cross-linker, which served as the matrix for further modifications. Synthesized graphene oxide was then incorporated into the hydrogel structure using a modified Hummers method, enhancing the catalytic properties of the material. To facilitate the separation and recovery of the catalyst, the resulting structure was magnetized, leading to the formation of a magnetic nanocomposite. Even after undergoing four cycles of catalyst recovery, the GG-Cell hydrogel/GO/Fe3O4 nanocomposite retained 90 % of its initial catalytic activity, highlighting its robustness and stability. Detailed physical and chemical analyses were conducted to gain a comprehensive understanding of the synthesized magnetic catalyst, contributing to the advancement of the field of catalysis and holding great potential for various applications in organic synthesis and related fields.

Numerical Simulation of Steel-Reinforced Reactive Powder Concrete Beam Based on Bond-Slip.

In this study, based on the concrete damaged plasticity (CDP) model in the ABAQUS software, various plastic damage factor calculation methods were introduced to obtain CDP parameters suitable for reactive powder concrete (RPC) materials. Combined with the existing tests for the bending performance of steel-reinforced RPC beams, the CDP parameters of the RPC material were input into ABAQUS to establish a finite element model considering the bond and slip between the steel and RPC for numerical simulation. The load-deflection curve obtained by the simulation was compared with the measured curve in the experiment. The results indicated that on the basis of the experimentally measured RPC material eigenvalue parameters, combined with the appropriate RPC constitutive relationship and the calculation method of the plastic damage factor, the numerical simulation results considering the bond-slip were in good agreement with the experimental results with a deviation of less than 10%. Thus, it is recommended to select a gentle compressive stress-strain curve in the descending section, an approximate strengthening model of the tensile stress-strain curve, and to use the energy loss method and Sidoroff's energy equivalence principle to calculate the RPC plastic damage parameters.