Hip replacement femoral implants are made of substantial materials that all have stiffness considerably higher than that of bone, which can cause significant bone resorption secondary to stress shielding and lead to severe complications. The topology optimization design method based on the uniform distribution of material micro-structure density can form a continuous mechanical transmission route, which can better solve the problem of reducing the stress shielding effect. A multi-scale parallel topology optimization method is proposed in this paper and a topological structure of type B femoral stem is derived. Using the traditional topology optimization method (Solid Isotropic Material with Penalization, SIMP), a topological structure of type A femoral stem is also derived. The sensitivity of the two kinds of femoral stems to the change of load direction is compared with the variation amplitude of the structural flexibility of the femoral stem. Furthermore, the finite element method is used to analyze the stress of type A and type B femoral stem under multiple conditions. Simulation and experimental results show that the average stress of type A and type B femoral stem on the femur are $14.80$ MPa, $23.55$ MPa, $16.94$ MPa and $10.89$ MPa, $20.92$ MPa, $16.50$ MPa, respectively. For type B femoral stem, the average error of strain is $-1682\mathsf{\mu }\mathsf{\epsilon }$ and the average relative error is $20.3\%$ at the test points on the medial side and the mean error of strain is $1281\mathsf{\mu }\mathsf{\epsilon }$ and the mean relative error is $19.5\%$ at the test points on the outside.

This work focuses on the consensus problem for multi-agent systems (MASs) with actuator failures and time-varying state constraints, and presents a fixed-time self-triggered consensus control protocol. The use of time-varying asymmetrical barrier Lyapunov functions (BLF) avoids the violation of time-varying state constraints in MASs, ensuring stability and safety. Meanwhile, the system’s performance is further enhanced by leveraging the proposed adaptive neural networks (NNs) control method to mitigate the effects of actuator failures and nonlinear disturbances. Moreover, a self-triggered mechanism based on a fixed-time strategy is proposed to reach rapid convergence and conserve bandwidth resources in MASs. The mechanism achieves consensus within a predefined fixed time, irrespective of the system’s initial states, while conserving communication resources. Finally, the proposed method’s effectiveness is confirmed through two simulation examples, encompassing diverse actuator failure scenarios.

This study designed an adaptive neural network (NN) control method for a category of multi-robotic systems with parametric uncertainties. In practical engineering applications, systems commonly face design challenges due to uncertainties in their parameters. Especially when a system’s parameters are completely unknown, the unpredictability caused by parametric uncertainties may increase control complexity, and even cause system instability. To address these problems, an adaptive NN compensation mechanism is proposed. Moreover, using backstepping and barrier Lyapunov functions (BLFs), guarantee that state constraints can be ensured. With the aid of the transform function, systems’ convergence speeds were greatly improved. Under the implemented control strategy, the prescribed time control of multi-robotic systems with parametric uncertainties under the prescribed performance was achieved. Finally, the efficacy of the proposed control strategy was verified through the application of several cases.

The problem of adaptive event-triggered control for uncertain nonlinear systems with full-state constraints was investigated. State constraints can significantly affect system performance, especially when time-varying external disturbances are present, potentially leading to instability. Thus, a fixed-time disturbance observer was designed. It estimated unknown uncertainties within a predetermined time. Meanwhile, an asymmetric barrier Lyapunov function was developed. It ensured the stability of the system state under constraints. Furthermore, to reduce the utilization rate of the system’s communication resources, an adaptive event-triggered control scheme was proposed, and an integrated control method was established to preset the convergence time of the system’s state error, greatly improving the convergence speed. Theoretical analysis and simulations demonstrated the effectiveness of the proposed approach. The results show that the system achieved stable control within a fixed time, even under full-state constraints and external disturbances, while using fewer communication resources.

Conventional fertigation systems suffer from limited cross-regional adaptability, mainly due to unstable fertilizer flow from fixed-aperture units, poor terrain adaptability, and an inadequate response to environmental heterogeneity. This study proposes an autonomous, cross-regional intelligent fertigation system based on an STM32F1 microcontroller and UART communication protocols. The system integrates a mechanically adjustable iris fertilizer delivery unit, a dual-axis fertigation module, a data interconnection unit, and comprehensive control software with dynamic calibration capabilities. Prototype evaluations conducted on both sloped terrain (up to 38°) and flat surfaces demonstrate a stable performance, achieving fertilizer flow control errors below 3%, irrigation deviation under 5%, and fertilization deviation within 2%. Real-time data acquisition, remote monitoring, and intelligent operation are supported by a YOLOv5s-based visual recognition system, which attains an mAP@0.5 of 92.5%. This integrated solution offers a robust approach to precision agriculture across diverse environmental conditions.

AISI 304 stainless steel is widely used as the metallic friction pair in friction pendulum bearings. However, its limited hardness and inadequate wear resistance restrict its application in high-frequency reciprocating sliding condition. In this study, temperature-assisted ultrasonic shot peening (TUSP) was employed to modify the surface of AISI 304 stainless steel. The effect of auxiliary temperature on the evolution of surface microstructure and tribological properties was investigated. The experimental results revealed that the introduction of auxiliary temperature inhibits the transformation of austenite to martensite, while increasing the dislocation density and the depth of the plastic deformation layer. The TUSP-treated sample processed at an auxiliary temperature of 100 °C exhibited a surface hardness of ∼577.3 HV and a plastic deformation depth of ∼200 μm, representing increases of ∼2.6% and ∼60%, respectively, compared with the USP-treated sample. Furthermore, it showed a lower coefficient of friction (COF), accompanied by wear rate reductions of approximately 21.6%, 24.2%, and 9.5% under normal loads of 5, 7.5, and 10 N, respectively. The enhanced tribological performance is mainly due to the high dislocation density, large number of low-angle grain boundaries (LAGBs), thick plastic deformation-affected layer, and uniform surface morphology. This study provides new insights into the TUSP technique aimed at improving the tribological performance of AISI 304 stainless steel.