Abstract Hafnium (Hf) oxide-based ferroelectric materials have emerged as a transformative platform for next-generation non-volatile memory and advanced computing technologies. This review comprehensively examines the development, challenges, and applications of HfO 2 ferroelectrics, emphasizing their CMOS compatibility, scalability, and robust polarization at nanoscale dimensions. Breakthroughs in doping strategies, stress engineering, and VO control have stabilized the metastable orthorhombic phase, enabling high-performance devices such as ferroelectric RAM (FeRAM), ferroelectric field-effect transistors (FeFETs), and ferroelectric tunnel junctions (FTJs). These devices offer ultrafast switching, low power consumption, and multi-level storage, driving innovations in neuromorphic computing, in-memory processing, and cryogenic systems; nonetheless, they face ongoing challenges in reliability, such as fatigue and imprint effects, and scalability at sub-5 nm technology nodes. Emerging frontiers, such as wurtzite-structured nitrides (e.g., AlScN) and antiferroelectric ZrO 2 -based systems, have garnered significant attention due to their exceptionally high remanent polarization and promising potential for enhanced endurance, respectively. Further addressing the reliability issues of these emerging ferroelectric materials and the challenges associated with large-scale integration processes through interdisciplinary efforts will unlock the full potential of ferroelectric technologies, positioning them as pivotal enablers of post-Moore computing architectures and sustainable AI-driven applications.

There is a problem in the field of early automatic detection of forest fire smoke that due to low concentration or tiny size, some smoke is difficult to capture. This article proposes a multiscale convergence coordinated pyramid network (MCCPN) with mixed attention and Fast-robust NMS (MMFNet) for the fast detection of forest fire smoke. First, an MCCPN is designed, which combines a dual-attention feature pyramid network and a coordinated convergence module. It improves the detection rate of targets of different sizes. Second, a mixed attention module is designed to focus more on the smoke in the image and enhance the extraction of horizontal and vertical features of smoke. Then, a Fast-robust nonmaximum suppression is proposed to accelerate the convergence of bounding boxes and increase the accuracy of the prediction box. Finally, a forest fire detection system of the Internet of Things using MMFNet is built. The experimental results show that our method achieves 80.72% AP, 88.52% AP50, 83.45% AP75, 46.88% AR, and 154 FPS, which is superior to the state-of-art forest fire smoke detection methods.

The time-band approximation model on flight operations recovery problems is constructed based on the time-band network, and the object of its' mathematical representation can capture approximated delay costs and cancellation costs. But the calculation of delay costs associated with the actual flying time in China, the actual flying time should not be a fixed time but a random time around the planned flying time. In this paper, we describe the approximated delay costs considering the random flying time around the planned flying time. Subsequently, we give out the time-band approximation model on flight operations recovery with the random flying time. Finally, we design a set of practical numerical experiments. The results demonstrate that the time-band approximation model on flight operations recovery with the random flying time may be more efficient than the time band approximation model on flight operations recovery with the planned flying time.