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Developing suitable cathodes of sodium‐ion batteries (SIBs) with robust electrochemical performance and industrial application potential is crucial for the commercialization of large‐scale stationary energy storage systems. Layered sodium transition metal oxides, Na_xTmO₂(Tm representing transition metal), possessing considerable specific capacity, high operational potential, facile synthesis, cost‐effectiveness, and environmentally friendly characteristics, stand out as viable cathode materials. Nevertheless, the prevailing challenge of air‐induced degradation in most Na_xTmO₂significantly increases costs associated with production, storage, and transportation, coupled with a rapid decay in reversible capacity. This inherent obstacle inevitably impedes the advancement and commercial viability of SIBs. To address this challenge, it is essential to decode the chemistry of degradation caused by air exposure and develop protective strategies accordingly. In this review, a comprehensive and in‐depth understanding of the fundamental mechanisms associated with air‐induced degradation is provided. Additionally, the current state‐of‐the‐art effective protective strategies are explored and discuss the corresponding sustainability and scalability features. This review concludes with an outlook on present and future research directions concerning air‐stable cathode materials, offering potential avenues for upcoming investigations in advancing alkali metal layered oxides.

We propose a diffusion approximation method to the continuous-state Markov decision processes that can be utilized to address autonomous navigation and control in unstructured off-road environments. In contrast to most decision-theoretic planning frameworks that assume fully known state transition models, we design a method that eliminates such a strong assumption that is often extremely difficult to engineer in reality. We first take the second-order Taylor expansion of the value function. The Bellman optimality equation is then approximated by a partial differential equation, which only relies on the first and second moments of the transition model. By combining the kernel representation of the value function, we design an efficient policy iteration algorithm whose policy evaluation step can be represented as a linear system of equations characterized by a finite set of supporting states. We first validate the proposed method through extensive simulations in 2 D obstacle avoidance and 2.5 D terrain navigation problems. The results show that the proposed approach leads to a much superior performance over several baselines. We then develop a system that integrates our decision-making framework with onboard perception and conduct real-world experiments in both cluttered indoor and unstructured outdoor environments. The results from the physical systems further demonstrate the applicability of our method in challenging real-world environments.

Arid ecosystems are known to be sensitive to climate change. The Jornada Basin in the USA, as one representative of arid land, has suffered from land degradation in recent decades. In order to disentangle the climate–vegetation feedback, we analyzed the vegetation dynamics under the effects of climate change via a mathematical model based on the reaction–diffusion mechanism. Using this model, we conducted a sensitive analysis of climate factors and concluded that the ecosystem might experience a catastrophic shift with the climatic deterioration. We considered the non-local interaction term to explain the competition among plants. Additionally, the PLR (power law range) metric was used to quantify the extent of the degradation and to compare the results of the vegetation patterns from the remote sensing data and the simulations. From the results, this model could simulate the trends of land degradation in this area. We found that the land degradation could be mainly attributed to climate changes in recent years. This approach suggests that vegetation patterns can provide hints as to whether the ecosystem is approaching desertification. These results can help with mapping vulnerable arid areas around the world through model simulation and satellite images.