{\mathrm{B}\mathrm{a}\mathrm{F}\mathrm{e}}_2{\mathrm{S}}_3 and {\mathrm{B}\mathrm{a}\mathrm{F}\mathrm{e}}_2{\mathrm{S}\mathrm{e}}_3 are the only two quasi-one-dimensional iron-based compounds that become superconductors under pressure. Interestingly, these two compounds exhibit different symmetries and properties. While more detailed and recent studies on {\mathrm{B}\mathrm{a}\mathrm{F}\mathrm{e}}_2{\mathrm{S}\mathrm{e}}_3 using single crystals have advanced the field towards a more universal description of this family, such a study is still lacking for the compound {\mathrm{B}\mathrm{a}\mathrm{F}\mathrm{e}}_2{\mathrm{S}}_3. Here, we present a detailed study of the crystalline and magnetic structures performed on single crystals using X-ray and neutron diffraction. We demonstrate a polar structure at room temperature within the Cm2m space group, followed by a structural transition at T_S = 130 K to the polar Pb{2}_{1}m space group. This space group remains unchanged across the magnetic transition at T_N = 95 K, revealing multiferroic characteristics with a weak magnetoelastic coupling. The determined magnetic structure is monoclinic (P_{a}m), with non-collinear Fe magnetic moments, tilted from the rung axis. This reexamination of the temperature-dependent properties of {\mathrm{B}\mathrm{a}\mathrm{F}\mathrm{e}}_2{\mathrm{S}}_3 provides new insights into the physics of this system from multiple key perspectives.

Metamaterials, engineered materials with exceptional electromagnetic properties not found in nature, have emerged as a revolutionary technology for enhancing conventional electrically-fed antenna performance across multiple critical parameters. This paper comprehensively reviews the application of metal-based metamaterials as the core enabling technology for optimizing electrically-fed antenna characteristics. Key performance improvements demonstrated include: (i) Gain enhancement using zero-index, epsilon-near-zero (ENZ), mu-near-zero metamaterials, or metamaterial lenses/superstrates, achieving gains up to 22.4 dBi and directivity improvements; (ii) Bandwidth expansion overcoming the inherent narrow bandwidth limitations of traditional patch antennas, with techniques yielding bandwidths exceeding 64% and operational ranges covering 4−9.1 GHz; (iii) Isolation improvement in multiple input multiple output (MIMO) systems crucial for 5G/6G, utilizing metamaterial walls or decoupling structures to achieve isolation >28 dB and mutual coupling suppression exceeding 36 dB; (iv) Antenna miniaturization leveraging complementary double-negative metamaterials, capacitive loading, or slow-wave structures to achieve up to 60.7% size reduction while maintaining or enhancing performance; and (v) Radiation efficiency Improvement, particularly addressing challenges like losses in graphene plasmonic antennas using engineered ENZ metamaterial substrates. The paper details innovative designs such as Fabry−Pérot resonator antennas with chessboard metamaterial superstrates, hybrid metasurface arrays, and pattern-reconfigurable zeroth-order resonance metasurface antennas. Future prospects highlight the integration of metamaterials and electrically-fed antennas with 6G, terahertz technology, AI-driven adaptive beamforming, and conformal applications in stealth and autonomous systems, despite ongoing challenges in cost-effective manufacturing and electromagnetic control complexity. Metamaterials are conclusively positioned as a pivotal technology for next-generation high-performance, multifunctional, and miniaturized antenna systems.

Artificial neurons are essential for constructing neuromorphic networks, enabling information computation with high parallelism and efficiency akin to the human brain. Nonetheless, artificial synapses fabricated from conventional silicon-based materials exhibit limited adaptability in complex biological environments, making it challenging for them to replicate the intricate synaptic plasticity of biological synapses. In this work, Pt/La_0.5Sr_0.5CoO₃(LSCO)/BST:CeO₂/LSCO parallel-plate capacitor structure devices with capacitive-coupled memristive effects were fabricated. The current−voltage curves of the device under different voltages were investigated, revealing a remarkable evolution of the device from capacitive-dominated responses to pure memory behaviors. The pure memristive state of the device was utilized to simulate biological synaptic functions, while the capacitive-coupled memristive effects serve as the linchpin for emulating biological neuronal functionalities. By integrating these devices into bespoke circuits, leaky integrate-and-fire (LIF) functions are faithfully replicated, encompassing the precise mimicry of leakage, spatiotemporal integration, and firing processes. Moreover, a spiking neural network (SNN) based on the devices was constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 91%. The research findings thus herald a significant stride in neuromorphic computing, proffering a potent avenue for architecting brain-inspired computational frameworks.

Nanophotonic is an emerging frontier that explores the interaction between light and matter at the nanoscale. The design of ultra-compact and high-performance photonic devices remains a central challenge in this field. Conventional forward design approaches, which often rely on empirical knowledge, are computationally demanding and offer limited flexibility. The advent of deep learning has enabled inverse design strategies that start from desired optical responses. This review first outlines the background of traditional photonic device design and introduces machine learning techniques, particularly deep learning, for modeling nanostructures. It then examines the application of advanced optimization methods, including topology optimization and genetic algorithms, in the design of devices such as meta-lens, meta-gratings, and planar beam splitters. These examples highlight the integration of optoelectronics with artificial intelligence. The interplay between light manipulation and intelligent algorithms is accelerating advancements in optical design, imaging, communications, and data analysis, opening new avenues for interdisciplinary innovation.

The nonlinear optical effects arising from the interaction between ultrafast lasers and metasurfaces have become a significant focus of photonics research. Among these effects, high-order harmonic generation is regarded as an effective pathway toward realizing extreme ultraviolet light sources and attosecond pulse generation. High-harmonic generation (HHG) based on metasurfaces also offers new strategies for the design of integrated light sources. During this process, the driving conditions for metasurfaces are distinct from those for gas media and bulk materials. Clarifying the physical mechanisms governing the interaction of light with media during HHG, as well as the relationship between these mechanisms and structural parameters, is essential; this understanding also holds great application potential in the research and development of new materials and microscopic dynamics characterization. Here, we review recent advancements in HHG from metasurfaces, focusing on the effects of different resonance modes, structural symmetries, and molecular arrangements in the medium on harmonic yield, polarization control, and the precise manipulation of spatiotemporal distribution. Finally, we discuss improvement strategies for metasurfaces with the aim of more effectively mitigating damping in nonlinear processes, and explore future applications of metasurface-based HHG in frontier fields.

We present a novel method, termed discontinuity calculus, for computing discontinuities of complex functions. This framework enables a systematic investigation of both analytic continuation and the topological structure of Riemann surfaces. We apply this calculus to analyze the analytic continuation of partial-wave amplitudes in two-body coupled-channel scattering problems and discuss their uniformization of the corresponding Riemann surfaces. This methodology offers new perspectives and tools for analyzing coupled-channel scattering problems in quantum scattering theory.

Electric fields can induce topological phase transitions from normal insulators to topological insulators, offering a promising pathway for designing low-power electronic devices based on two-dimensional topological insulators with tunable and practical functionalities. In this work, we demonstrate a dynamically tunable topological insulator realized in van der Waals (vdW) heterostructures composed of two-dimensional transition metal dichalcogenides — specifically, a CrS₂/CrSe₂ junction. Remarkably, this system exhibits significant electric-field tunability of its topological properties. First-principles calculations reveal that an external electric field induces a band inversion at a critical field strength of −2.0 eV/nm, signaling the emergence of nontrivial topological phases. These topological characteristics remain robust, as confirmed by van der Waals-corrected DFT-D2 calculations. Furthermore, we systematically explore strain engineering as an additional control parameter. Under 1.45% biaxial strain, the band inversion threshold shifts significantly to −0.3 eV/nm while maintaining a finite bandgap of 9.6 meV. This dual tunability, achieved through both electric fields and mechanical strain, establishes the CrS₂/CrSe₂ heterostructure as a versatile platform for developing topological field-effect transistors and other quantum devices requiring dynamic control of edge conduction states.

Typical suspended one-dimensional waveguides, tapered optical fibers (TOFs), combine sub-wavelength optical confinement with mechanical flexibility, enabling applications in optomechanics, quantum optics, atomic physics and sensing. However, the intrinsic mechanical modes (IMMs) of TOF, including flexural, longitudinal, and torsional modes, remain underexplored. Through analytical formulations and simulations, we comprehensively and explicitly explore the classification of various mechanical modes of a TOF. The characteristics of various TOF IMMs are investigated in detail, including frequencies, displacements, and vibration patterns. Novel degenerate IMMs are confirmed, and these modes result from dynamic coupling between the modes in the tapered and waist regions. It is also revealed that the IMMs are strongly geometry-dependent. The frequencies scale with the length and diameter of the waist, whereas linear tapers exhibit stronger energy localization than exponential tapers. The elaborate design and engineering of TOFs allow for precise mechanical resonance tuning and seamless integration with nanophotonic systems, positioning them as a versatile platform for quantum precision metrology and advanced optomechanical technologies.

Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP searches to be conducted in a clean environment, and such searches can reach their full physics potential when combined with machine learning (ML) techniques. This experimental study, utilizing comprehensive full simulation data samples, focuses on LLP searches resulting from Higgs decay in e^{+}{e}^{-}\to ZH. We demonstrate that, by employing deep neural network approaches the LLP signal efficiency can be improved up to 95% for an LLP mass around 50 GeV and a lifetime of approximately 1 nanosecond, while rejecting all SM backgrounds. Furthermore, the signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches a state-of-art limit of 1.0\times {10}^{-6} with a statistics of 4\times {10}^6 Higgs.

Shock waves are typical multiscale phenomena in nature and engineering, inherently driven by both hydrodynamic and thermodynamic non-equilibrium (HNE and TNE) effects. However, the underlying mechanisms governing these non-equilibrium processes remain incompletely understood. In this study, we advance the discrete Boltzmann method (DBM) to adequately capture higher-order non-equilibrium. To reveal the dominant mechanism and offer mutual interpretation between macroscopic and TNE quantities, we derive analytical solutions for distribution functions and TNE measures of various orders via Chapman−Enskog analysis. Using argon shock structures as a case study, the DBM results show agreement with experimental, direct simulation Monte Carlo, and analytical data across two levels: (i) macroscopic interface profiles and thickness, and (ii) mesoscopic-level distribution functions and TNE indicators. Key findings include: (i) a Mach-number-induced two-stage behavior that manifests not only in shock width and smoothness but also in the peak of the distribution function, and (ii) a shift in the compressibility-dominated region from the outflow to inflow side with increasing Mach number. Beyond classical hydrodynamics, we analyze the dominant TNE mechanisms with analysis perspectives including the distribution function, types of TNE quantities, and TNE at different non-equilibrium orders. This study reveals how various orders and types of non-equilibrium effects govern shock wave structure, offering mesoscopic insight into kinetic behavior and laying a theoretical foundation for constructing physically robust non-equilibrium models.