In order to reduce the weight of electric vehicle suspension systems, improve structural strength and stiffness, and lower manufacturing costs. This study is based on the continuum topology optimization method to optimize the structure design of the lower control arm. First, taking the minimization of overall flexibility as the optimization objective, the sub objectives are normalized based on the compromise programming method, and a comprehensive mathematical model of the objectives is constructed. Subsequently, the Analytic Hierarchy Process was used to determine the weight coefficients for each operating condition, and an optimization plan was obtained by combining ANSYS finite element analysis. Finally, the optimized conceptual model is reconstructed into a 3D solid on the NX platform. The optimization results show that the overall weight of the optimized lower control arm has been reduced by 15.1%, and the structural stiffness and strength performance have been significantly improved. The topology optimization design of the lower control arm not only provides an effective technical solution for the lightweight design of electric vehicle suspension systems, but also takes into account manufacturing feasibility, bringing energy‐saving and economic benefits to large‐scale production.

This paper designed a kind of automatic conveying and stamping machine, which was mainly used for drawing thin sheet metal. UGNX software was used to design the motion law of the mechanism and the simulation mechanism. The time displacement diagram obtained by theoretical calculation and simulation was compared to verify the correctness of theoretical analysis and the rationality of the design. The comparison shows that the device meets the requirements of theoretical calculation. The device has the characteristics of simple structure, high efficiency and easy operation, which provides a reference for the subsequent realization of conveying and stamping devices with different requirements.

Microalgae biophotovoltaic (BPV) systems convert solar energy into electricity through photosynthetic electron transfer (PET) and have emerged as a promising solar-integrated bioenergy technology. However, high-density tubular arrays suffer from the “canyon effect” at low solar angles and from photoinhibition under peak irradiance (>450 W/m²), where non-photochemical quenching (NPQ) and reactive oxygen species (ROS) dissipate bioelectric potential as heat. To address this, a hysteresis-based dual-mode PID controller with hysteresis switching is proposed within an optical–mechanical–biological co-optimization framework, integrating array self-shading, nonlinear microalgal photoresponse, and tracking parasitic losses. In low-light mode, the system actively tracks the sun to minimize shading; in peak-light mode, it defocuses the incident angle to limit irradiance near the saturation threshold, mitigating the risk of photoinhibition. Structural control boundaries, including tube spacing and connecting rod length, are determined numerically. Under the nominal clear-sky design day, the defocusing mode reduces the daily exposure of the culture to irradiance above the saturation threshold (450 W/m²) from 5.28 h to 3.08 h. Numerical simulations indicate a daily net energy yield improvement of +14.9% over continuous dual-axis tracking and +6.3% over the latitude-based fixed-tilt baseline under idealized clear-sky design-day conditions. These values are simulation-derived estimates; experimental validation with a physical prototype is required before the framework can be interpreted as a validated system-level performance gain.

In the current Industrial Internet of Things (IIoT) environment, data security for product lifecycle management is greatly challenged, particularly in scenarios involving vertical multi-level Bill of Materials (BOM) deep nesting and lifecycle dynamic evolution. The traditional case-bounding model, in large-scale deployment, easily leads to rule expansion and an increase in database I/O overhead, thus causing authorization lag, authority boundary ambiguity and other problems. To address these limitations, this paper proposes a Decoupled Hybrid Access Resolution (DHAR) framework. The framework separates static organizational roles from dynamic lifecycle constraints, and the complexity of authorization configuration is reconstructed from case-dependent growth into an object-instance-independent bounded structure; combined with the state-based pre-filtering mechanism and memory cache strategy, redundant recursive query is reduced. Experiments on increasing BOM depths show that, under a 20-layer topology, DHAR reduces average access latency from 285.8 ms to 1.3 ms. Under a 20-layer BOM with 1000 concurrent requests, DHAR maintains an average latency of 5.2 ms, while compressing the authorization rule set from millions to hundreds. These results indicate that, within the studied vertical multi-level BOM setting, DHAR improves response performance while preserving data consistency and strengthening protection against unauthorized modification.