Abstract:Two-dimensional semiconductors are identified by the International Roadmap for Devices and Systems as key candidate materials for future sub-1nm nodes, owing to their atomic-scale thickness, smooth surface without dangling bonds and capability to suppress short-channel effects. Focusing on the current status of the full-chain development of two-dimensional semiconductors from basic materials science to system-level integration, the intrinsic physical advantages over traditional silicon-based materials and the progress in preparation processes were systematically analyzed. The latest progress and technical bottlenecks of core process modules including contact resistance engineering, gate dielectric integration and device architecture evolution of two-dimensional semiconductor transistors were reviewed in detail. Meanwhile, the development trajectory from early single-transistor verification to large-scale integrated circuits was traced comprehensively, and the collaborative challenges among materials, processes and design during the integration process were analyzed. The unique potential of two-dimensional semiconductors in emerging paradigms such as in-sensor computing, neuromorphic computing and van der Waals heterogeneous integration is further discussed.

Abstract:To promote technological innovation in ultrafast spectroscopy, broaden its interdisciplinary applications and address the associated research challenges, this paper provides a systematic review of the current development status in ultrafast spectroscopic techniques. The fundamental definition of ultrafast spectroscopy is illustrated. The principles of key techniques including ultrafast pulse sources, pump-probe spectroscopy, two-dimensional coherent spectroscopy, and near-filed optics are introduced. By combining research examples such as charge transfer in organic photovoltaics, electron transfer in molecular chemical reactions, exciton fine energy level structure in quantum-confined materials, and lipid molecular photoswitches, the application scopes and unique advantages of various spectroscopic techniques are discussed in detail. The emerging opportunities and future challenges in the development of ultrafast spectroscopy are also summarized.

Abstract:With the growing demand for nanometer-scale precision in scientific research and industrial production, compliant nanopositioning technology capable of generating and controlling motion with nanometer-level precision has emerged as a key enabler for high-end equipment. The positioning range and resonant frequency determine the workspace and dynamic response of nanopositioning systems. However, there exists an inherent trade-off between them, making it difficult to optimize both simultaneously. This paper focuses on the compliant nanopositioning technologies for achieving long range and high resonant frequency. From the perspectives of system configuration and key performance indicators, the intrinsic mechanism underlying the trade-off between positioning range and resonant frequency was introduced. Furthermore, the efforts and practices of researchers worldwide in addressing the trade-off, including the design of nanopositioners based on different actuation methods, as well as related control issues and control strategies, were reviewed. The current research status was analyzed, and future development trends were discussed, providing useful references for the advancement of high-end equipment and precision instruments in our country.

Abstract:High dynamic range imaging aims to recover the luminance and color information of real-world scenes, thereby overcoming the common problems of highlight saturation and shadow detail loss in conventional sensor imaging. It has now been extended to applications such as autonomous driving and virtual reality/augmented reality. However, artifact removal in dynamic scenes remains a central challenge. To address this issue, this paper systematically reviewed the related datasets and evaluation metrics, comprehensively summarized the major research advances, and further analyzed the inherent causes of imaging model deficiencies and the current technical bottlenecks. It also compared and analyzed the performance differences among existing state-of-the-art methods from the perspectives of model generalization ability, computational complexity, and inference time. Building on recent development trends, the paper further identified three levels of important research directions, namely fundamental core challenges, key performance optimization, and frontier technology exploration, with the aim of providing a useful reference for both academic research and engineering practice.

Abstract:A systematic review of hyperspectral underwater target detection under complex water conditions was presented from three perspectives: imaging mechanism, characteristic modeling, and algorithm design. Starting from the underwater hyperspectral imaging mechanism, existing methods were categorized into five groups: spectral prediction, spectral restoration, band selection, pixel classification, and feature construction. Their differences and connections were compared in terms of mechanism-consistent modeling, distortion correction, representational robustness, and interpretability. Analysis showed that current methods exhibited distinct characteristics in prior dependency, information utilization, and cross-scene adaptability, and the technical approaches are evolving from mechanism-oriented analysis toward mechanism-data synergy, as well as the integration of generative modeling and feature construction. On this basis, the major challenges in environmental adaptability, reliability modeling, and generalization were further summarized, and future directions were discussed, including differentiable physical modeling, uncertainty characterization, and cross-scene generalization mechanisms.

Abstract:In data engineering, the enhancement of processing effectiveness for real-world massive raw data, characterized by being multi-source, heterogeneous, and high-noise, via the application of artificial intelligence methods is currently regarded as a research hotspot. Based on the general research framework of data engineering, the latest research progress in intelligent data engineering methods was systematically reviewed in accordance with the design of three key stages: data cleaning, data linking, and data discovery. Additionally, the principles and effectiveness of the methods related to each key stage were analyzed in detail. Furthermore, an outlook on future research in data engineering is provided in combination with the development trends of intelligence.

Abstract:APN (almost perfect nonlinear) functions, renowned for their optimal differential properties, have become a research focus in the field of cryptographic functions. This paper systematically reviewed the research progress of APN functions: first, it summarized the general methods for generating APN function examples; second, it refined the construction techniques of existing infinite families of APN functions and clarifies their specific constructions; third, it introduced the equivalence classification results of APN function examples and infinite families; fourth, it combed through the research conclusions on the cryptographic properties of APN functions, such as permutation property, algebraic degree, and nonlinearity; fifth, it reviewed some applications of APN functions in coding theory and combinatorial design; Finally, the research prospects of APN functions were prospected. Currently, the construction of APN functions is still dominated by quadratic ones, and no infinite families of polynomials with higher algebraic degree have been found. Major challenges, such as the "big APN problem", remain unsolved. Future research may focus on constructing APN polynomials with non-classical Walsh spectra, discovering APN polynomials with higher degree, among others, and exploring their applications in coding theory and combinatorial design.

Abstract:To meet the experimental requirements for developing wide-speed range aircraft, research on a supersonic continuous variable Mach number wind tunnel with a Mach number range of 3 to 4.5 was conducted. A scheme of a continuous variable Mach number nozzle was proposed, followed by the design of the SCV-WT (supersonic continuous variable Mach number wind tunnel) and the calibration of its flow field. Based on P-M (Prandtl-Meyer) expansion theory, a nozzle with single-degree-of-freedom adjustment was designed to achieve continuous variable Mach number. Numerical calculations and calibration experiments were adopted to verify the variable Mach number scheme. Numerical calculation results show that the flow field of the variable Mach number nozzle is uniform at different Mach number. The nozzle centerline Mach number calibration gave the Mach number RMS, maximum deviation and error. The exit Mach number–expansion angle relation fits Prandtl-Meyer theory. The supersonic continuous wave-damping variable Mach number wind tunnel achieves the design goal of continuously changing the Mach number by controlling the nozzle rotation angle while ensuring a high-quality experimental flow field, providing a simplified scheme for experimental research equipment of supersonic variable Mach number flow.

Abstract:To address the critical challenges where traditional materials in aerospace and high-end advanced equipment are approaching their physical limits and struggling to meet the stringent "Size, Weight, and Power" (SWaP) requirements, this work focuses on the frontier of two-dimensional (2D) materials. It systematically elucidates their application potential, derived from their atomic-level thickness and quantum confinement effects. By comprehensively reviewing the latest advancements in five core areas—stealth and electromagnetic shielding (survivability), high-performance sensing (perception), lightweight protection (defense), high-efficiency energy (logistics/support), and quantum information (computing)—we reveal the intrinsic correlations and mechanisms connecting microscopic properties to macroscopic performance. Furthermore, the key bottlenecks restricting the engineering implementation of 2D materials are analyzed, including wafer-scale high-quality fabrication, long-term stability in extreme environments, and the standardization of testing and evaluation. Based on this analysis, and incorporating emerging technologies such as Artificial Intelligence (AI)-assisted design and heterostructure stacking, we present an outlook for achieving multi-functional integration and intelligent systems based on 2D materials towards the development of next-generation smart equipments. This review aims to provide theoretical support and forward-looking insights for securing a strategic technological edge in the future.

Abstract:In the inference process of mixture-of-experts (MoE) models, matrix operators constitute the primary performance bottleneck, with those in the attention module and expert computation being particularly time-consuming. Although existing approaches have extensively optimized matrix operators on GPUs, the substantial differences between GPU and CPU architectures in memory hierarchy and compute units make these optimizations difficult to transfer directly to CPU platforms. To address this limitation, FlashMatrix is introduced as a matrix-operator optimization scheme tailored for CPUs equipped with Advanced Matrix Extensions (AMX). FlashMatrix incorporates an efficient data layout transformation strategy that avoids additional memory-access overhead caused by layout conversions, and employs a carefully designed micro-kernel for matrix multiplication that achieves an optimal compute-to-memory ratio through effective register reuse. Experimental results show that, compared with the state-of-the-art CPU matrix-computation library oneDNN, FlashMatrix delivers an average 2.5× speedup. For end-to-end inference performance, FlashMatrix achieves a speedup of approximately 1.2×.