This review explores porous architecture engineering in SIBs, analyzing performance enhancement through structural design, synthesis, and porosity optimization.

As the world pivots to a greener paradigm, Shanghai emerges as an archetype in the sustainable urban transit narrative, particularly through the aggressive expansion and refinement of its electric vehicle (EV) charging infrastructure. This scholarly article provides a comprehensive examination of the current state of charging infrastructure in Shanghai, highlighting the challenges that the existing infrastructure may face in light of the burgeoning electric vehicle market. This paper delves into the strategic development approaches adopted by Shanghai to address these challenges, particularly emphasizing the expansion of high-power charging infrastructure to meet the anticipated increase in future electric vehicle charging demands. It also discusses the implementation of co-construction and sharing models, the enhancement of interconnectivity and standardized management of charging facilities, and the continuous improvement and strengthening of infrastructure construction and operations. Furthermore, this article explores the implementation of time-of-use electricity pricing policies and the ongoing conduct of demand response activities, which are instrumental in creating conditions for vehicle-to-grid interaction. The aim of our presentation is to foster a keen understanding among policymakers, industry stakeholders, and urban planners of the mechanisms necessary to effectively navigate the emerging electric vehicle market, thereby encouraging harmonious development between metropolises and transportation systems. Future research endeavors should delve into the realms of fast-charging technologies, intelligent operation and maintenance of charging infrastructure, and vehicle-to-grid interaction technologies. These areas of study are pivotal in fostering the harmonious development of electric vehicles (EVs) and their charging infrastructure, thereby aligning with the dual objectives of advancing urban transportation systems and sustainable green city development. The findings presented herein offer valuable insights for policymakers, urban planners, and industry leaders, guiding them in crafting informed strategies that not only address the immediate needs of the EV market but also lay the groundwork for a scalable and resilient charging infrastructure, poised to support the long-term vision of sustainable urban mobility.

In the emerging Industrial Internet of Things (IIoT) applications, federated learning (FL) enables model training without the need to transmit raw data directly. Nevertheless, transmitting model parameters could still reveal private information. To further protect local model parameters, differential privacy combined with FL (DPFL) has been introduced. Nonetheless, adding noise in DPFL can severely impact model performance, especially in non-independent and identically distributed (non-iid) data scenarios typical of IIoT environments. It is necessary to carefully balance privacy preservation and utility. In this article, we propose a robust DPFL scheme leveraging local model partition and sparsification (namely, FedMPS) for heterogeneous IIoT scenarios. The local model is divided into a shared part, which is sparsified before adding noise to mitigate its impact, and a private part that remains on the client. We provide a theoretical analysis of the privacy guarantees. Extensive experiments on common datasets, including Fashion-MNIST, CIFAR-10, and CIFAR-100, demonstrate that the proposed approach achieves a better privacy-utility tradeoff, with a 10%–20% improvement compared to baseline methods, and performs well especially in non-iid scenarios.

The control problem of underactuated mechatronic systems is one of the key representatives of complex nonlinear dynamical systems. Starting from the dynamical structures of underactuated systems is usually one of the most direct and effective ways to design controllers. However, controllers developed for specific dynamic models are often difficult to be directly generalized to other underactuated systems. Moreover, due to the lack of control inputs, designing robust controllers for unmatched disturbances (acting on unactuated states) remains a challenging problem. Therefore, based on the Euler-Lagrange dynamics of multi-input-multi-output (MIMO) underactuated systems, this paper gives four coordinate transformations according to different configurations of the inertia matrix, which extends the Olfati transformation to some extent and finally unifies underactuated systems into normal forms. A sliding manifold and an adaptive neural network sliding mode controller are developed with the derived normal forms, which improves transient performance and avoids the chattering problem in traditional sliding mode controllers by combining an estimation error-driven adaptive law, a high-order sliding-mode differentiator, and the super-twisting algorithm. More importantly, the stability is guaranteed by Lyapunov techniques even in the presence of both persistent matched disturbances and asymptotically vanishing unmatched disturbances. Furthermore, the proposed control strategy is applied to overhead cranes and tower cranes, whose superior control performance is verified by hardware experiments.