SAIC-GM-Wuling (China)

ABSTRACT The pursuit of sustainable growth and ecological balance has become a central policy priority for OECD nations. While prior studies examined economic, technological, and environmental determinants of sustainability, the joint effect of macroeconomic policies, technological innovation, and institutional uncertainty remains underexplored. This study addressed this gap by investigating how monetary policy, fiscal policy, economic complexity, energy transition, green technology, and ESG uncertainty jointly influence green growth and ecological footprint over the period 2003 to 2022 in 17 OECD nations. To account for nonlinear and asymmetric relationships, advanced econometric methods, including quantile‐on‐quantile regression (QQ) and cross‐quantilogram (CQ) methods are employed. The results show that all six factors significantly promote green growth while reducing ecological footprint, indicating their complementary role in driving sustainable transformation. The results suggest the importance of integrating coordinated macroeconomic management, innovation‐driven technological strategies, and transparent ESG practices to support a low‐carbon transition. Policymakers are encouraged to implement green‐oriented monetary and fiscal policies, foster technological efficiency, and strengthen institutional accountability to achieve long‐term ecological stability. This study contributes to the literature by linking macroeconomic, technological, and environmental dimensions within a unified green growth framework, providing robust empirical evidence to guide policy design and promote sustainable development across OECD nations.

ABSTRACT This study systematically evaluated the performance regulation mechanisms of three inorganic nanoparticles (1–4 wt%)—titanium dioxide (TiO 2 ), zinc oxide (ZnO), and silicon carbide (SiC)—in alkali‐treated straw fiber/polypropylene (PP) composites based on polypropylene. All three nanoparticles were melt‐blended with fibers and PP in industrial‐grade powder form to achieve uniform dispersion, followed by injection molding. The results showed that different nanoparticles exhibited different reinforcement mechanisms: TiO 2 mainly regulated crystallization behavior and improved wear resistance through heterogeneous nucleation; ZnO significantly improved toughness through interfacial energy dissipation and grain refinement; and SiC improved load‐bearing capacity due to its high hardness. Performance tests showed that 4 wt% SiC increased tensile strength by 12.2%, while 3 wt% ZnO achieved an elongation at break of 7.25%, exhibiting the best toughness enhancement effect. Combined with microstructure and tribological analysis, this study revealed the structure–property relationship between nanoparticle type, dispersion state, and interfacial structure. Comprehensive performance test results show that 3 wt% TiO 2 and 3 wt% ZnO can achieve the best balance between mechanical properties and wear resistance, while the amount of SiC added should be controlled between 1 and 3 wt% to avoid interface failure.

The ZD6 turnout switch machine is an important part of rail transit signaling equipment. It is one of the most widely used turnout switch machine models to switch train tracks. Any abnormality in its operation could result in serious consequences, such as train derailment. To achieve condition-based maintenance of ZD6 turnout switch machines, this paper proposes a multimodal lightweight end-to-end deep learning framework for anomaly detection in ZD6 turnout switch machines. The input data for this framework are multimodal, comprising two-dimensional time-frequency diagrams obtained by a short-time Fourier transform of the armature current of a ZD6 turnout switch machine series-excited direct current motor, as well as one-dimensional data manually extracted from the armature current characteristics. This framework uses partial convolution and depthwise separable convolution to achieve lightweight processing. SimAM’s parameter-free attention mechanism enhances the network’s feature extraction capabilities. Experimental verification based on a dataset collected from the armature current of two actual ZD6-D and ZD6-G turnout switch machines demonstrates that the proposed anomaly detection framework performs well and has high application value.

The growing demand for high power density in electric vehicle traction motors presents significant challenges for thermal management. This study investigates an oil-cooled permanent magnet synchronous motor (PMSM) equipped with an integrated encircling spray cooling system. A combined CFD approach, using the finite volume method (FVM) and the moving particle semi-implicit (MPS) method, is employed to capture oil flow behavior and determine local convective heat transfer coefficients. These results are coupled into a three-dimensional finite element thermal model to predict motor temperature distribution under various operating conditions. Experimental steady-state tests confirm the simulation accuracy, with a maximum temperature deviation of 2.78 °C. The findings demonstrate that the proposed cooling strategy effectively reduces winding and stator temperatures while highlighting the need to improve oil distribution uniformity for further reliability enhancement.