Search SciRate

While numerous indices of inter-coder reliability exist, Krippendorff's \alpha and Cohen's {kappa have long dominated in communication studies and other fields, respectively. The near consensus, however, may be near the end. Recent theoretical and mathematical analyses reveal that these indices assume intentional and maximal random coding, leading to paradoxes and inaccuracies. A controlled experiment with one-way golden standard and Monte Carlo simulations supports these findings, showing that {kappa and \alpha are poor predictors and approximators of true intercoder reliability. As consensus on a perfect index remains elusive, more authors recommend selecting the best available index for specific situations (BAFS). To make informed choices, researchers, reviewers, and educators need to understand the liberal-conservative hierarchy of indices, i.e., which indices produce higher or lower scores. This study extends previous efforts by expanding the math-based hierarchies to include 23 indices and constructing six additional hierarchies using Monte Carlo simulations. These simulations account for factors like the number of categories and distribution skew. The resulting eight hierarchies display a consistent pattern and reveal a previously undetected paradox in the Ir index.

Inverse problems are prevalent in both scientific research and engineering applications. In the context of Bayesian inverse problems, sampling from the posterior distribution is particularly challenging when the forward models are computationally expensive. This challenge escalates further when the posterior distribution is multimodal. To address this, we propose a Gaussian process (GP) based method to indirectly build surrogates for the forward model. Specifically, the unnormalized posterior density is expressed as a product of an auxiliary density and an exponential GP surrogate. In an iterative way, the auxiliary density will converge to the posterior distribution starting from an arbitrary initial density. However, the efficiency of the GP regression is highly influenced by the quality of the training data. Therefore, we utilize the iterative local updating ensemble smoother (ILUES) to generate high-quality samples that are concentrated in regions with high posterior probability. Subsequently, based on the surrogate model and the mode information that is extracted by using a clustering method, MCMC with a Gaussian mixed (GM) proposal is used to draw samples from the auxiliary density. Through numerical examples, we demonstrate that the proposed method can accurately and efficiently represent the posterior with a limited number of forward simulations.

2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials.

The use of Green's function in quantum many-body theory often leads to nonlinear eigenvalue problems, as Green's function needs to be defined in energy domain. The $GW$ approximation method is one of the typical examples. In this article, we introduce a method based on the FEAST eigenvalue algorithm for accurately solving the nonlinear eigenvalue $G_0W_0$ quasiparticle equation, eliminating the need for the Kohn-Sham wavefunction approximation. Based on the contour integral method for nonlinear eigenvalue problem, the energy (eigenvalue) domain is extended to complex plane. Hypercomplex number is introduced to the contour deformation calculation of $GW$ self-energy to carry imaginary parts of both Green's functions and FEAST quadrature nodes. Calculation results for various molecules are presented and compared with a more conventional graphical solution approximation method. It is confirmed that the Highest Occupied Molecular Orbital (HOMO) from the Kohn-Sham equation is very close to that of $GW$, while the Least Unoccupied Molecular Orbital (LUMO) shows noticeable differences.

One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management.

The propagation of millimeter wave (MMW) and sub-terahertz (THz) signals through plasma sheaths is a critical concern for maintaining communication with hypersonic vehicles, yet the impact of complex plasma structures on these high-frequency channels remains insufficiently understood. In this work, we aim to characterize the transmission properties of MMW and sub-THz waves through plasma sheaths with various density profiles and ripple structures, addressing the gap in knowledge regarding the effects of plasma inhomogeneities on signal propagation. We employ an approach combining Inductively Coupled Plasma (ICP) data with transfer matrix methods (TMM) to model propagation through both flat and rippled plasma layers. Our findings reveal that ripple structures in plasma sheaths significantly affect channel performance, with periodic ripples reducing cutoff frequency and introducing frequency-selective behavior, while random ripples cause more unpredictable transmission characteristics. Our results explore the impact of the arrangement of plasma density layers and the parameters of ripple structures (period and amplitude) on channel transmission, group velocity dispersion, and angular dependence of wave propagation. These results provide crucial insights for the design and optimization of communication systems for hypersonic vehicles, potentially enabling the development of adaptive technologies capable of maintaining reliable communication in complex plasma environments.

High-throughput reaction condition (RC) screening is fundamental to chemical synthesis. However, current RC screening suffers from laborious and costly trial-and-error workflows. Traditional computer-aided synthesis planning (CASP) tools fail to find suitable RCs due to data sparsity and inadequate reaction representations. Nowadays, large language models (LLMs) are capable of tackling chemistry-related problems, such as molecule design, and chemical logic Q\&A tasks. However, LLMs have not yet achieved accurate predictions of chemical reaction conditions. Here, we present MM-RCR, a text-augmented multimodal LLM that learns a unified reaction representation from SMILES, reaction graphs, and textual corpus for chemical reaction recommendation (RCR). To train MM-RCR, we construct 1.2 million pair-wised Q\&A instruction datasets. Our experimental results demonstrate that MM-RCR achieves state-of-the-art performance on two open benchmark datasets and exhibits strong generalization capabilities on out-of-domain (OOD) and High-Throughput Experimentation (HTE) datasets. MM-RCR has the potential to accelerate high-throughput condition screening in chemical synthesis.

The global linear behaviors of 2/1 DTM in the collisional regime are investigated based on a concisely resistive drift-MHD model. Besides DTM, extra normal modes including EDW and SAW are coupled together and destabilized in different parameter regimes by considering resistivity in this system. The EVP approach is applied for solving the eigenstate spectra with the distribution of all unstable solutions. It is found that in the small EDD frequency (omega_*e) regime, DTM growth rate agrees well with local theory that is reduced with increasing omega_*e. However, when omega_*e exceeds a critical threshold omega_*crit, the strongly linear coupling between DTM and other discretized EDW instabilities happens so that the free energies from current and pressure channels can be released together and thus enhance the DTM, of which growth rate increases with increasing omega_*e and deviates from local theory results qualitatively. Correspondingly, a cross-scale mode structure forms with mixed polarization, namely, phi perturbation is dominated by electrostatic polarized short-wavelength oscillation as EDW instability character, and A_para perturbation remains typical tearing mode solution of Alfvenic polarized macroscopic structure. Within omega_*e > omega_*crit, the additional IDD causes phi oscillating structure to shift towards small density gradient domain, which cancels the extra drive from ion channel and thus DTM growth rate is insensitive to IDD frequency. Compared to EDD effects, the IDD effect alone with zero-omega_*e only leads to the stabilization of RTM that shows agreements between global simulation and local theory, which is no longer the condition for DTM regime. These results are useful for clarifying the DTM global properties with underlying physics mechanisms, which occurs in the regime of omega_*e >> gamma_c that is relevant to nowadays tokamak discharges with hot plasmas.

The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15\sigma$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes.

Optical tweezer arrays of laser-cooled and individual controlled particles have revolutionized the atomic, molecular and optical physics, and they afford exquisite capabilities for applications in quantum simulation of many-body physics, quantum computation and quantum sensing. Underlying this development is the technical maturity of generating scalable optical beams, enabled by active components and high numerical aperture objective. However, such a complex combination of bulk optics outside the vacuum chamber is very sensitive to any vibration and drift. Here we demonstrate the generation of 3*3 static tweezer array with a single chip-scale multifunctional metasurface element in vacuum, replacing the meter-long free space optics. Fluorescence counts on the camera validates the successfully trapping of the atomic ensemble array. Further, we discuss the strategy to achieve low scattering and crosstalk, where a metasurface design featuring dual-wavelength independent control is included. Our results, together with other recent development in integrated photonics for cold atoms, could pave the way for compact and portable quantum sensors and simulators in platforms of neutral atom arrays.

The cycling performance of lithium-ion batteries is closely related to the expansion effect of anode materials during charge and discharge processes. Studying the mechanical field evolution of anode materials is crucial for evaluating battery per-formance. Here, we propose a phase-sensitive ultra-high spatial resolution optical frequency domain reflectometry tech-nique, in which the test fiber is embedded into the anode of a lithium-ion battery to monitor the mechanical evolution of the anode material during cycling. We investigated the strain evolution of the anode material under different loading levels and used this method to infer the morphological changes of the material. Furthermore, combining this with battery capacity in-formation provides a new approach for assessing the performance of lithium-ion batteries.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry into this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, fibers, and correlators), as well as new data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed, and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

The abdominal electrocardiogram (AECG) gives a safe and non-invasive way to monitor fetal well-being during pregnancy. However, due to the overlap with maternal ECG (MECG) as well as significant external noise, it is challenging to extract weak fetal ECG (FECG) using surface electrodes. In this study, we introduce a novel periodic progressive FastICA peel-off (PPFP) method for noninvasive extraction of weak surface FECG signals, leveraging the two-step FastICA method and a peel-off strategy from the progressive FastICA peel-off (PFP) approach. Specifically, for ECG signals, the periodic constrained FastICA that integrates ECG signal characteristics enables precise extraction of MECG and FECG spike trains. Additionally, a peel-off strategy incorporating SVD waveform reconstruction ensures comprehensive identification of subtle source signals. The performance of the proposed method was examined on public datasets with reference, synthetic data and clinical data, with an F1-scores for FECG extraction on public dataset of 99.59%, on synthetic data with the highest noise level of 99.50%, which are all superior to other comparative methods. Furthermore, clearly periodic and physiologically consistent FECG signals were extracted from clinically collected data. The results indicates that our proposed method has potential and effectiveness to separate MECG and weak FECG from multichannel AECG with high precision in high noise condition, which is of vital importance for ensuring the safety of both the fetus and the mother, as well as the advancement of artificial intelligent clinical monitoring.

On-chip structured light, with potentially infinite complexity, has emerged as a linchpin in the realm of integrated photonics. However, the realization of arbitrarily tailoring a multitude of light field dimensions in complex media remains a challenge1, Through associating physical light fields and mathematical function spaces by introducing a mapping operator, we proposed a data-driven inverse design method to precisely manipulate between any two structured light fields in the on-chip high-dimensional Hilbert space. To illustrate, light field conversion in on-chip topological photonics was achieved. High-performance topological coupling devices with minimal insertion loss and customizable topological routing devices were designed and realized. Our method provides a new paradigm to enable precise manipulation over the on-chip vectorial structured light and paves the way for the realization of complex photonic functions.

Light shift and vapor-cell temperature shift are the two most significant factors dominating the long-term instability of compact atomic clocks. Due to the different physical mechanisms, there is not yet a solution that can effectively suppress the frequency shifts induced by these two effects. Here, we propose a 'resonance-offset' locking approach that compensates for the two physical frequency shifts. In this approach, the additional offset locking shift can effectively counteract the atomic resonance shifts arising from changes in vapor-cell temperature and light power, reducing the net impact on the clock's frequency to nearly zero. We have demonstrated this strategy on the 778 nm Rubidium two-photon optical frequency standard, successfully compensating for light shift and cell-temperature shift, respectively. This general method is particularly appealing for compact vapor-cell microwave and optical atomic clocks designed for the excellent stability rather than accuracy.

The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively.

Optical skyrmions are an emerging class of structured light with sophisticated particle-like topologies with great potential for revolutionizing modern informatics. However, the current generation of optical skyrmions involves complex or bulky systems, hindering their development of practical applications. Here, exploiting the emergent "lab-on-fiber" technology, we demonstrate the design of a metafiber-integrated photonic skyrmion generator. We not only successfully generated high-quality optical skyrmions from metafibers, but also experimentally verified their remarkable properties, such as regulability and topological stability with deep-subwavelength features beyond the diffraction limits. Our flexible and fiber-integrated optical skyrmions platform paves the avenue for future applications of topologically-enhanced remote super-resolution microscopy and super-robust information transfer.

Droplet impact behavior has attracted much attention recently due to its academic significance and diverse industrial applications. This study employs the lattice Boltzmann method to simulate the impact of a droplet on a hydrophobic plate featuring a square orifice. Unlike previous studies, the chemical property of the orifice considered in this work is not homogeneous but heterogeneous, and its cross-sectional wettability changes from hydrophobicity to hydrophilicity. The study first validates the numerical method against experimental data, and then investigates in detail the influences of the Weber number, wettability difference, and pore size. According to the numerical results, we observed that the evolutionary stages of the impinging droplet always include the spreading phase and the rebounding phase, while whether there exists the splitting phase, it depends on the combined effect of the wettability difference and the Weber number. The impact behavior of droplets is analyzed by evaluating the underlying mechanisms such as kinetic energy, surface energy, viscous dissipation energy, and pressure. It is interesting to note that the existence of wettability-patterned pore tends to promote adhesion of droplets on the plate, resulting in the droplet impact behaviors are largely different from that for the case of homogeneous pore. Additionally, a phase diagram is constructed for various Weber numbers and pore sizes, revealing that the dynamic behavior of droplets is determined by the competition among dynamic pressure, capillary pressure, and viscous pressure losses. These insights from numerical studies guide the development of innovative solid substrates capable of manipulating droplet motion.

The synthesis of solar methanol through direct CO2 hydrogenation using solar energy is of great importance in advancing a sustainable energy economy. In this study, non-precious NiZn intermetallic/ZnO catalyst is reported to catalyze the hydrogenation of CO2 to methanol using sunlight irradiation (1sun). The NiZn-ZnO interface is identified as the active site to stabilize the key intermediates of HxCO*. At ambient pressure, the NiZn-ZnO catalyst demonstrates a methanol production rate of 127.5 umol g-1h-1 from solar driven CO2 hydrogenation, with a remarkable 100% selectivity towards methanol in the total organic products. Notably, this production rate stands as the highest record for photothermic CO2 hydrogenation to methanol in continuous-flow reactors with sunlight as the only requisite energy input. This discovery not only paves the way for the development of novel catalysts for CO2 hydrogenation to methanol but also marks a significant stride towards a full solar-driven chemical energy storage.

Muon Induced X-ray Emission (MIXE) was discovered by Chinese physicist Zhang Wenyu as early as 1947, and it can conduct non-destructive elemental analysis inside samples. Research has shown that MIXE can retain the high efficiency of direct imaging while benefiting from the low noise of pinhole imaging through encoding holes. The related technology significantly improves the counting rate while maintaining imaging quality. The sphere encoding technology effectively solves the imaging blurring caused by the tilting of the encoding system, and successfully images micrometer sized X-ray sources. This paper will combine MIXE and X-ray sphere coding imaging techniques, including ball coding and zone plates, to study the method of non-destructive deep structure imaging of ICF targets and obtaining sub element distribution. This method aims to develop a new method for ICF target detection, which is particularly important for inertial confinement fusion. At the same time, this method can be used to detect and analyze materials that are difficult to penetrate or sensitive, and is expected to solve the problem of element resolution and imaging that traditional technologies cannot overcome. It will provide new methods for the future development of multiple fields such as particle physics, material science, and X-ray optics.

With the rapid development of Kesterite Cu2ZnSn(S, Se)4 solar cells in the past few years, how to achieve higher cost-performance ratio has become an important topic in the future development and industrialization of this technology. Herein, we demonstrate an all-solution route for the cell fabrication, in particular targeting at the solution processed window layer comprised of ZnO nanoparticles/Ag nanowires. A multi-interface engineering strategy assisted by organic polymers and molecules is explored to synergistically improve the film deposition, passivate the surface defects and facilitate the charge transfer. These efforts help us achieve high-performance and robust Kesterite solar cells at extremely low time and energy costs, with efficiency records of 14.37% and 13.12% being realized in rigid and flexible Kesterite solar cells, respectively. Our strategy here is also promising to be transplanted into other solar cells with similar geometric and energy band structures, helping reduce production costs and shorten the production cycle (i.e. increasing production capacity) of these photovoltaic industries.

Atomic disorder, a widespread problem in compound crystalline materials, is a imperative affecting the performance of multi-chalcogenide Cu2ZnSn(S, Se)4 (CZTSSe) photovoltaic device known for its low cost and environmental friendliness. Cu-Zn disorder is particularly abundantly present in CZTSSe due to its extraordinarily low formation energy, having induced high-concentration deep defects and severe charge loss, while its regulation remains challenging due to the contradiction between disorder-order phase transition thermodynamics and atom-interchange kinetics. Herein, through introducing more vacancies in the CZTSSe surface, we explored a vacancy-assisted strategy to reduce the atom-interchange barrier limit to facilitate the Cu-Zn ordering kinetic process. The improvement in the Cu-Zn order degree has significantly reduced the charge loss in the device and helped us realize 15.4% (certified at 14.9%) and 13.5% efficiency (certified at 13.3%) in 0.27 cm2 and 1.1 cm2-area CZTSSe solar cells, respectively, thus bringing substantial advancement for emerging inorganic thin-film photovoltaics.

Active optical waveguides combine light source and waveguides together in an individual component, which are essential for the integrated photonic chips. Although 1D luminescent materials based optical waveguides were extensively investigated, 2D waveguides allow photons to flow within a plane and serve as an ideal component for the ultracompact photonic circuits. Nevertheless, light guiding in 2D planar structures normally relies on the precise control of molecular orientation, which is complicated and low yield. Here, we report a strategy to guide polarized light in 2D microflakes by making use of the out-of-plane (OP) orientation of self-trapped excitons in as-synthesized 2D perovskite microplates. A space confined crystallization method is developed to synthesize 2D perovskite microflakes with dominated broad self-trapped excitons emission at room temperature, which are highly OP orientated with a percentage of the OP component over 85%. Taking advantages of the negligible absorption coefficient and improved coupling efficiency of OP orientated self-trapped exciton emission to the planar waveguide mode of the as-synthesized perovskite microflakes, we have achieved a broadband polarized light guiding with a full width at half maximum over 120 nm. Our findings provide a promising platform for the development of ultracompact photonic circuits.

The dielectric properties of environmental surfaces, including walls, floors and the ground, etc., play a crucial role in shaping the accuracy of terahertz (THz) channel modeling, thereby directly impacting the effectiveness of communication systems. Traditionally, acquiring these properties has relied on methods such as terahertz time-domain spectroscopy (THz-TDS) or vector network analyzers (VNA), demanding rigorous sample preparation and entailing a significant expenditure of time. However, such measurements are not always feasible, particularly in novel and uncharacterized scenarios. In this work, we propose a new approach for channel modeling that leverages the inherent sensing capabilities of THz channels. By comparing the results obtained through channel sensing with that derived from THz-TDS measurements, we demonstrate the method's ability to yield dependable surface property information. The application of this approach in both a miniaturized cityscape scenario and an indoor environment has shown consistency with experimental measurements, thereby verifying its effectiveness in real-world settings.

We propose a theoretical scheme for dipole exchange-induced grating (DEIG) based on a hybrid system consisting of ultra-cold Rubidium ($^{87}$Rb) atomic ensemble and movable Rydberg spin atoms. The optical response of the grating appears as a superposition of three- and four-level configurations, similar to the cooperative optical nonlinear effect caused by the dipole blockade effect. However, such Rydberg atomic grating uniquely responds to the spatial positions of spin atoms, offering a novel approach to dynamically control electromagnetically induced gratings (EIG) except for input probe intensity.

Sulfide Kesterite Cu2ZnSnS4 (CZTS), a nontoxic and low-cost photovoltaic material, has always being facing severe charge recombination and poor carrier transport, resulting in the cell efficiency record stagnating around 11% for years. Gradient bandgap is a promising approach to relieve these issues, however, has not been effectively realized in Kesterite solar cells due to the challenges in controlling the gradient distribution of alloying elements at high temperatures. Herein, targeting at the Cd alloyed CZTS, we propose a pre-crystallization strategy to reduce the intense vertical mass transport and Cd rapid diffusion in the film growth process, thereby realizing front Cd-gradient CZTS absorber. The Cd-gradient CZTS absorber, exhibiting downward bending conduction band structure, has significantly enhanced the minority carrier transport and additionally improved band alignment and interface property of CZTS/CdS heterojunction. Ultimately, we have achieved a champion total-area efficiency of 13.5% (active-area efficiency: 14.1%) in the cell and in particular a high open-circuit voltage of >800 mV. We have also achieved a certified total-area cell efficiency of 13.16%, realizing a substantial step forward for the pure sulfide Kesterite solar cell.

Vortex pinning is a crucial factor that determines the critical current of practical superconductors and enables their diverse applications. However, the underlying mechanism of vortex pinning has long been elusive, lacking a clear microscopic explanation. Here using high-resolution scanning tunneling microscopy, we studied single vortex pinning induced by point defect in layered FeSe-based superconductors. We found the defect-vortex interaction drives low-energy vortex bound states away from EF, creating a "mini" gap that effectively lowers the system energy and enhances pinning. By measuring the local density-of-states, we directly obtained the elementary pinning energy and estimated the pinning force via the spatial gradient of pinning energy. The results are consistent with bulk critical current measurement. Furthermore, we show that a general microscopic quantum model incorporating defect-vortex interaction can naturally capture our observation. It suggests that the local pairing near pinned vortex core is actually enhanced compared to unpinned vortex, which is beyond the traditional understanding that non-superconducting regions pin vortices. Our study thus unveils a general microscopic mechanism of vortex pinning in superconductors, and provides insights for enhancing the critical current of practical superconductors.

For transportation hubs, leveraging pedestrian flows for commercial activities presents an effective strategy for funding maintenance and infrastructure improvements. However, this introduces new challenges, as consumer behaviors can disrupt pedestrian flow and efficiency. To optimize both retail potential and pedestrian efficiency, careful strategic planning in store layout and facility dimensions was done by expert judgement due to the complexity in pedestrian dynamics in the retail areas of transportation hubs. This paper introduces an attention-based movement model to simulate these dynamics. By simulating retail potential of an area through the duration of visual attention it receives, and pedestrian efficiency via speed loss in pedestrian walking behaviors, the study further explores how design features can influence the retail potential and pedestrian efficiency in a bi-directional corridor inside a transportation hub. Project webpage: https://danruili.github.io/AttentionMove

Single-photon terahertz (THz) detection is one of the most demanding technology for a variety of fields and could lead to many breakthroughs. Although its significant progress has been made in the last two decades, operating it at room temperature still remains a great challenge. Here, we demonstrate, for the first time, the room temperature THz detector at single-photon levels based on nonlinear wave mixing in thermal Rydberg atomic vapor. The low-energy THz photons are coherently upconverted to the high-energy optical photons via a nondegenerate Rydberg state involved six-wave-mixing process, and therefore, the single-photon THz detection is achieved by a conventional optical single-photon counting module. The noise equivalent power of such a detector is reached to be 9.5*10^-19 W/Hz^1/2, which is more than four orders of magnitude lower than the state-of-the-art room temperature THz detectors. The optimum quantum efficiency of the whole wave-mixing process is about 4.3% with 40.6 dB dynamic range, and the maximum conversion bandwidth is 172 MHz, which is all-optically controllable. The developed fast and continuous-wave single-photon THz detector at room temperature operation has a great potential to be portable and chip-scale, and could be revolutionary for a wide range of applications in remote sensing, wireless communication, biomedical diagnostics, and quantum optics.

Non-line-of-sight (NLOS) data transmission through surface reflection is pivotal for enhancing the reach and efficiency of terahertz (THz) communication systems. However, this innovation also introduces significant eavesdropping risks, exacerbated by the complex bistatic scattering effects during adverse weather conditions like rain. This work delves into the assessment of the vulnerabilities of NLOS THz communication channels to eavesdropping under simulated rain conditions using metallic wavy surfaces (MWS). The observation reveals the feasibility of successful signal interception under these conditions, highlighting a prevalent security concern for outdoor terahertz communication networks utilizing NLOS channels to broaden coverage. This insight underscores the critical need for addressing and mitigating potential eavesdropping threats to ensure secure and reliable terahertz communications in varied environmental conditions.

Rare-earth (Re)Ba2Cu3O7-x (ReBCO) no-insulation (NI) coil is widely concerned due to its excellent electromagnetic and thermal properties. However, the presence of the turn-to-turn shunts in NI coils leads to that complexity of numerical simulation is increased. In this paper, a modified J model is proposed and the corresponding explicit-implicit hybrid algorithm is designed to calculate NI coils. The numerical results are in good agreement with the experimental data and the circuit model. The homogenization model is also proposed to simulate the large-scale NI coils in the background magnets. The modified J model has good accuracy and fast calculation speed, which can also be used to solve electromagnetic fields of insulation coils efficiently.

In the evolving domain of wireless communication, the investigation on terahertz (THz) frequency spectrum, spanning 0.1 to 10 THz, has become a critical focus for advancing ultra-high-speed data transmission technologies. The effective deployment of THz wireless communication techniques mandates a complete study of channel performance under various atmospheric conditions, such as rain, fog, cloud, haze, and notably, snow. These environmental elements significantly impact the design of the protocol stack, ranging from physical-layer signal processing to application design and strategic network planning. An in-depth understanding of channel propagation and fading characteristics in real-world environments, especially over ultra-wide bandwidths, is crucial. This work presents a comprehensive measurement-based and theoretical investigation of line-of-sight (LoS) THz channel performance in snowy conditions. It methodically examines both the empirical and predicted aspects of channel power and bit-error-ratio (BER). The effects of snowfall rate, carrier frequency, ambient temperature, and relative humidity on channel performance are analyzed and discussed. Our findings demonstrate that snowy conditions not only amplify power loss but also induce rapid fluctuations in the power levels of the THz channel. Notably, our results reveal an absence of significant multipath effects in these scenarios. This insight highlights the need for further research into the dynamics of snowflake movement and their interaction with THz transmission paths.

Advanced microwave technologies constitute the foundation of a wide range of modern sciences, including quantum computing, microwave photonics, spintronics, etc. To facilitate the design of chip-based microwave devices, there is an increasing demand for state-of-the-art microscopic techniques capable of characterizing the near-field microwave distribution and performance. In this work, we integrate Josephson junctions onto a nano-sized quartz tip, forming a highly sensitive microwave mixer on-tip. This allows us to conduct spectroscopic imaging of near-field microwave distributions with high spatial resolution. Leveraging its microwave-sensitive characteristics, our Josephson microscope achieves a broad detecting bandwidth of up to 200 GHz with remarkable frequency and intensity sensitivities. Our work emphasizes the benefits of utilizing the Josephson microscope as a real-time, non-destructive technique to advance integrated microwave electronics.

This study introduces the Lower Ricci Curvature (LRC), a novel, scalable, and scale-free discrete curvature designed to enhance community detection in networks. Addressing the computational challenges posed by existing curvature-based methods, LRC offers a streamlined approach with linear computational complexity, making it well-suited for large-scale network analysis. We further develop an LRC-based preprocessing method that effectively augments popular community detection algorithms. Through comprehensive simulations and applications on real-world datasets, including the NCAA football league network, the DBLP collaboration network, the Amazon product co-purchasing network, and the YouTube social network, we demonstrate the efficacy of our method in significantly improving the performance of various community detection algorithms.

Spatiotemporal control encompasses a variety of techniques for producing laser pulses with dynamic intensity peaks that move independently of the group velocity. This controlled motion of the intensity peak offers a new approach to optimizing laser-based applications and enhancing signatures of fundamental phenomena. Here, we demonstrate spatiotemporal control with a plasma optic. A chirped laser pulse focused by a plasma lens exhibits a moving focal point, or "flying focus," that can travel at an arbitrary, predetermined velocity. Unlike currently used conventional or adaptive optics, a plasma lens can be located close to the interaction region and can operate at an orders of magnitude higher, near-relativistic intensity.

The Terahertz (THz) frequency band offers a wide range of bandwidths, from tens to hundreds of gigahertz (GHz) and also supports data speeds of several terabits per second (Tbps). Because of this, maintaining THz channel reliability and efficiency in adverse weather conditions is crucial. Rain, in particular, disrupts THz channel propagation significantly and there is still lack of comprehensive investigations due to the involved experimental difficulties. This work explores how rain affects THz channel performance by conducting experiments in a rain emulation chamber and under actual rainy conditions outdoors. We focus on variables like rain intensity, raindrop size distribution (RDSD), and the channel's gradient height. We observe that the gradient height (for air-to-ground channel) can induce changes of the RDSD along the channel's path, impacting the precision of modeling efforts. To address this, we propose a theoretical model, integrating Mie scattering theory with considerations of channel's gradient height. Both our experimental and theoretical findings confirm this model's effectiveness in predicting THz channel behavior in rainy conditions. This work underscores the necessary in incorporating the variation of RDSD when THz channel travels in scenarios involving ground-to-air or air-to-ground communications.

Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and subtracted to calculate the amplitude and phase of the LM. A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT. An approach called Power Multiple Time Scale (PMTS) has been developed with outstanding regressing effect in multiple frequency ranges. Three models have been progressed based on PMTS NNs. PMTS could fit the brn=0 on the LM detectors with little errors both in time domain and frequency domain. The n>0 pick-up brn>0 generated by resonant magnetic perturbations (RMPs) can be obtained after subtracting the extracted brn=0. This new method uses only one LM instead of 4 LM detectors to extract brn=0. Therefore, the distribution of the LM detectors can also be optimized based on this new method.

In the previous studies, the ultraviolet light thermoreflectance (UV-TDTR) signal from bulk semiconducting samples cannot be well explained by a thermal models based on the assumption that the heat is both absorbed and probed near the surface. A thermoreflectance (TDTR) technique was developed to directly excite semiconductors using UV-TDTR. At \lambda = 400nm, the photon energy is much greater than most semiconducting bandgaps, potentially allowing semiconducting transducers to absorb light within about 10 nm of the surface, potentially enabling direct measurements of semiconductor-semiconductor interfaces. The thermoreflectance coefficient for some direct-bandgap semiconducting materials was measured. Thermal transport models were fitted to thermorefectance data collected from the system. The differences in the signal for semiconductors with different doping and nanostructure features were also studied, which should change the recombination rate. No significant changes to the signal were observed.

In the field of integrated sensing and communication, there's a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous system's decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for high-speed conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was collected for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method's effectiveness in high-speed conditions, suggesting terahertz frequencies' potential in future sensing and communication applications.

In the evolving landscape of terahertz communication, the behavior of channels within indoor environments, particularly through glass doors, has garnered significant attention. This paper comprehensively investigates terahertz channel performance under such conditions, employing a measurement setup operational between 113 and 170 GHz. Analyzing scenarios frequently induced by human activity and environmental factors, like door movements, we established a comprehensive theoretical model. This model seamlessly integrates transmission, reflection, absorption, and diffraction mechanisms, leveraging the Fresnel formula, multi-layer transmission paradigm, and knife-edge diffraction theory. Our experimental results and theoretical predictions harmoniously align, revealing intricate dependencies, such as increased power loss at higher frequencies and larger incident angles. Furthermore, door interactions, whether opening or oscillations, significantly impact the terahertz channel. Notably, door edges lead to a power blockage surpassing the transmission loss of the glass itself but remaining inferior to metallic handle interferences. This paper's insights are pivotal for the design and fabrication of terahertz communication systems within indoor settings, pushing the boundaries of efficient and reliable communication.

We consider the steady state limiting current that can be carried by an infinite periodic array of thin electron sheets spaced by period p in a planar diode of gap voltage V and gap separation d. Our primary assumptions are (1) electron motion is restricted by an infinite magnetic field to the direction normal to the electrode surfaces, (2) all electrons are emitted from the cathode with initial kinetic energy Ein, and (3) electron motion is non-relativistic. The limiting current density, averaged over a period and normalized to the classical 1D Child-Langmuir (CL) current density (including a factor that accounts for non-zero Ein), is found to depend only on the two dimensionless parameters p/d and Ein/eV. This average limiting current density is computed from the maximum current density for which the iterative solution of a non-linear integral equation converges. Numerical results and empirical curve fits for the limiting current are presented, together with an analysis as p/d and Ein/eV approach zero or infinity, in which cases previously published results are recovered. Our main finding is that, while the local anode current density within each electron sheet is infinite in our model (that is, it exceeds the classical 1D CL value by an 'infinite' factor), the period average anode current density is in fact still bounded by the classical 1D CL value. This study therefore provides further evidence that the classical 1D Child-Langmuir current density is truly a fundamental limit that cannot be circumvented.

Most theoretical analysis for lifetime distribution explains origins of specific distribution based on independent failure. We develop a unified framework encompassing different lifetime distribution for failure-coupled network systems. We find three types of system lifetime distributions emerged from competence between system size N and failure coupling strength $\phi$. System lifetime distribution can be describe by modified Weibull model, which degenerates into Gompertz model when N dominates and exponential distribution when $\phi$ dominates. We derive asymptotic lifetime distribution. Specially, we derive a fundamental equation of thermodynamics for failure-coupled systems. Our study will help design highly reliable systems.

Kesterite Cu2ZnSn(S, Se)4 (CZTSSe) solar cell has emerged as one of the most promising candidates for thin-film photovoltaics. However, severe charge losses occurring at the grain boundaries (GBs) of Kesterite polycrystalline absorbers has hindered the improvement of cell performance. Herein, we report a redox reaction strategy involving palladium (Pd) to eliminate atomic vacancy defects such as VSn and VSe in GBs of the Kesterite absorbers. We demonstrate that PdSex compounds could form during the selenization process and distribute at the GBs and the absorber surfaces; thereby aid in the suppression of Sn and Se volatilization loss and inhibiting the formation of VSn and VSe defects. Furthermore, Pd(II)/Pd(IV) serves as a redox shuttle, i.e., on one hand, Pd(II) captures Se vapor from the reaction environment to produce PdSe2, on the other hand, PdSe2 provides Se atoms to the Kesterite absorber by being reduced to PdSe, thus contributing to the elimination of pre-existing VSe defects within GBs. These effects collectively reduce defects and enhance the p-type characteristics of the Kesterite absorber, leading to a significant reduction in charge recombination loss within the cell. As a result, high-performance Kesterite solar cells with a total-area efficiency of 14.5% have been achieved. This remarkable efficiency increase benefited from the redox reaction strategy offers a promising avenue for the precise regulation of defects in Kesterite solar cells and holds generally significant implications for the exploration of various other photovoltaic devices.

Integration of physics principles with data-driven methods has attracted great attention in recent few years. In this study, a physics-informed dynamic mode decomposition (piDMD) method, where the mass conservation law is integrated with a purely data-driven DMD method, is developed for fast prediction of the spatiotemporal dynamics of solid volume fraction distribution in bubbling fluidized beds. Assessment of the prediction ability using both piDMD and DMD is performed using the CFD-DEM results as the benchmark: Both DMD and piDMD can predict the short-term behaviour of solid volume fraction reasonably well, but piDMD outperforms the DMD in both qualitative and quantitative comparisons; With respect to their long-term ability, the piDMD-based prediction of the instantaneous solid volume fraction distributions is qualitatively correct although the accuracy needs to be improved, and the predicted time-averaged radial and axial profiles are satisfactory; Whereas the DMD-based prediction of instantaneous snapshots and time-averaged results is completely nonphysical. Present study provides a fast and relatively accurate method for predicting the hydrodynamics of gas-solid flows.

The performance of holographic multiple-input multiple-output (MIMO) communications, employing two-dimensional (2-D) planar antenna arrays, is typically compromised by finite degrees-of-freedom (DOF) stemming from limited array size. The DOF constraint becomes significant when the element spacing approaches approximately half a wavelength, thereby restricting the overall performance of MIMO systems. To break this inherent limitation, we propose a novel three-dimensional (3-D) antenna array that strategically explores the untapped vertical dimension. We investigate the performance of MIMO systems utilizing 3-D arrays across different multi-path scenarios, encompassing Rayleigh channels with varying angular spreads and the 3rd generation partnership project (3GPP) channels. We subsequently showcase the advantages of these 3-D arrays over their 2-D counterparts with the same aperture sizes. As a proof of concept, a practical dipole-based 3-D array, facilitated by an electromagnetic band-gap (EBG) reflecting surface, is conceived, constructed, and evaluated. The experimental results align closely with full-wave simulations, and channel simulations substantiate that the DOF and capacity constraints of traditional holographic MIMO systems can be surpassed by adopting such a 3-D array configuration.

Pedestrian studies in retail areas are critical for comfort and convenience in transportation facility designs. But existing literature lacks detailed empirical observations that focus on pedestrian speed variations and their mechanisms in front of stores. This paper bridges this gap by analyzing 1193 pedestrian trajectories in front of a convenience store located in a metro station. The results show that the store imposes a non-uniform slowing effect on the pedestrian flow. The spatial distribution and the lower walking speed of consumers and gazing pedestrians jointly contribute to such an effect while avoiding behaviors between pedestrians play little role. The findings complement the existing empirical observations and lay a foundation for realistic pedestrian modeling in retail areas.

Two-dimensional layered ReX2 (X = Se, S) has attracted researcher's great interest due to its unusual in-plane anisotropic optical and electrical properties and great potential in polarization-sensitive optoelectronic devices, while the clean, energy-saving, and ecological synthesis of highly-crystalline ReSe2 with controlled domains remains challenging yet promising. Here, we develop a novel space-confined solid-phase approach for the growth of high-quality two-domain 1T'-ReSe2 with tunable optoelectronic properties by using pure Re powder film as Re precursor. The results show that ReSe2 can be grown at a temperature as low as 550 oC in a small-tube-assisted space-confined reactor, with its size and shape well-tailored via temperature control. A solid-phase two-domain ReSe2 growth mechanism is proposed, as evidenced by combining in-situ optical monitoring, ex-situ electron microscope and elemental mapping, and polarized optical imaging. Moreover, we have fabricated two-domain ReSe2 transistors, which exhibit switchable transport behavior between n-type and ambipolar character via grain boundary orientation control. This modulation phenomenon is attributed to the different doping levels between the grain boundary and the single domain. Furthermore, the as-fabricated two-domain ReSe2 photodetectors exhibit a highly gate-tunable current on-off ratio (with a maximum value of ~8.2x10^3), a polarization-sensitive photo-response, and a high-speed response time (~300 us), exceeding most of the previously reported ReX2 photodetectors. Our work thus provides a new, low-consumption, energy-saving growth strategy toward high-quality, domain-controlled ReX2 for highly tunable and high-performance optoelectronics.

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized information encompassing specific network structure and task. Here we design an active learning architecture to predict node influence quantitively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multi-scale individualized information. This brings two intuitive advantages: (1) discovering potential high-influence but weak-connected nodes that are usually ignored in existing methods, (2) improving the influence maximization strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. Additionally, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the influence maximization problem. This architecture holds great potential for applications in graph mining and prediction tasks.

In this study, a dual-interrogation (DI) method was used to suppress the light shift in the Rb 778 nm 5S1/2-5D5/2 two-photon transition (TPT) optical frequency standard. The approach used an auxiliary system to calibrate the light shift of the primary system in real time to mitigate the absolute light shift and suppress the sensitivity of the system to the optical power. Results show that after using the DI method, the absolute light shift and light-power sensitivity of the system were reduced by a factor of 10. The proposed method will improve the accuracy of the Rb 778 nm TPT optical frequency standard and increase the mid- and long-term stability. The method can also be applied to other vapor-cell atomic frequency standards that experience light shifts.

Accelerated muon beams have been considered for next-generation studies of high-energy lepton-antilepton collisions and neutrino oscillations. However, high-brightness muon beams have not yet been produced. The main challenge for muon acceleration and storage stems from the large phase-space volume occupied by the beam, derived from the muon production mechanism through the decay of pions from proton collisions. Ionization cooling is the technique proposed to decrease the muon beam phase-space volume. Here we demonstrate a clear signal of ionization cooling through the observation of transverse emittance reduction in beams that traverse lithium hydride or liquid hydrogen absorbers in the Muon Ionization Cooling Experiment (MICE). The measurement is well reproduced by the simulation of the experiment and the theoretical model. The results shown here represent a substantial advance towards the realization of muon-based facilities that could operate at the energy and intensity frontiers.

A fully stabilized soliton microcomb is critical for many applications of optical frequency comb based on microresonators. However, the current approaches for full frequency stabilization require either external acousto-optic or electro-optic devices or auxiliary lasers and multiple phase-locked loops, which compromises the convenience of the system. This study explores a compact atomic referenced fully stabilized soliton microcomb that directly uses a rubidium atomic optical frequency reference as the pump source, and complements the repetition rate (7.3 GHz) of the soliton microcomb was phase-locked to an atomic-clock-stabilized radio frequency (RF) reference by mechanically tuning the resonance of the optical resonator. The results demonstrate that the stability of the comb line (0.66 THz away from the pump line) is consistent with that of the Rb87 optical reference, attaining a level of approximately 4 Hz @100 s, corresponding to the frequency stability of 2E-14 @100 s. Furthermore,the frequency reproducibility of the comb line was evaluated over six days and it was discovered that the standard deviation (SD) of the frequency of the comb line is 10 kHz, resulting in a corresponding absolute deviation uncertainty of 1.3E-10, which is technically limited by the locking range of the soliton repetition rate. The proposed method gives a low-power and compact solution for fully stabilized soliton micorcombs.

The GW approximation has been widely accepted as an ab initio tool for calculating defect levels with many-electron effect included. However, the GW simulation cost increases dramatically with the system size, and, unfortunately, large supercells are often required to model low-density defects that are experimentally relevant. In this work, we propose to accelerate GW calculations of point defects by reducing the simulation cost of the many-electron screening, which is the primary computational bottleneck. The random-phase approximation of many-electron screening is divided into two parts: one is the intrinsic screening, calculated using a unit cell of pristine structures, and the other is the defect-induced screening, calculated using the supercell within a small energy window. Depending on specific defects, one may only need to consider the intrinsic screening or include the defect contribution. This approach avoids the summation of many conductions states of supercells and significantly reduces the simulation time. We have applied it to calculating various point defects, including neutral and charged defects in two-dimensional and bulk systems with small or large bandgaps. The results consist with those from the direct GW simulations, and the agreements are further improved at the dilute-defect limit, which is experimentally relevant but extremely challenging for direct GW simulations. This defect-patched screening approach not only clarifies the roles of defects in many-electron screening but also paves the way to fast screen defect structures/materials for novel applications, including single-photon sources, quantum qubits, and quantum sensors.

HiLo microscopy is a powerful, low-cost, and easily configurable technique for acquiring high-contrast optically-sectioned images. However, traditional HiLo microscopes are based on coherent light sources with diffusive glass plates or incoherent light sources with digital mirror devices (DMD) and spatial light modulators (SLM), which are more expensive. Here, we propose a new low-cost HiLo microscopy technique using MLAs and incoherent LED light sources. We simulated structured illumination (SI) patterns and HiLo image generation based on Fresnel diffraction and incoherent imaging. To observe how MLAs affect HiLo images, we used three common MLAs with specific microlens pitch and numerical aperture (NA) to generate periodic illumination patterns. According to our simulations, using MLAs and incoherent light sources can enhance the image contrast compared with a traditional widefield fluorescence microscope. We found that the MLA NA does not significantly affect HiLo images. A larger lens pitch can bring a higher image contrast. However, there is an optimized lens pitch. If the lens pitch is too high, artefacts are observed in HiLo images. To our knowledge, this is the first numerical study about MLA-based HiLo microscopy. This study can benefit researchers using MLAs and incoherent light sources to configure their low-cost HiLo microscopes.

Nonlinear optical (NLO) imaging has emerged as a promising plant cell imaging technique due to its large optical penetration, inherent 3D spatial resolution, and reduced photodamage, meanwhile exogenous nanoprobes are usually needed for non-signal target cell analysis. Here, we report in-vivo, simultaneous 3D labeling and imaging of potato cell structures using plasmonic nanoprobe-assisted multimodal NLO microscopy. Experimental results show that the complete cell structure could be imaged by the combination of second-harmonic generation (SHG) and two-photon luminescence (TPL) when noble metal silver or gold ions are added. In contrast, without noble metal ion solution, no NLO signals from the cell wall could be acquired. The mechanism can be attributed to noble metal nanoprobes with strong nonlinear optical responses formed along the cell walls via a femtosecond laser scan. During the SHG-TPL imaging process, noble metal ions that cross the cell wall could be rapidly reduced to plasmonic nanoparticles by fs laser and selectively anchored onto both sides of the cell wall, thereby leading to simultaneous 3D labeling and imaging of potato cells. Compared with traditional labeling technique that needs in-vitro nanoprobe fabrication and cell labeling, our approach allows for one-step, in-vivo labeling of plant cells, thus providing a rapid, cost-effective way for cellular structure construction and imaging.

An optoelectronic oscillation method with reconfigurable multiple formats for simultaneous generation of coherent dual-band signals is proposed and experimentally demonstrated. By introducing a compatible filtering mechanism based on stimulated Brillouin scattering (SBS) effect into a typical Phase-shifted grating Bragg fiber (PS-FBG) notch filtering cavity, dual mode-selection mechanisms which have independent frequency and time tuning mechanism can be constructed. By regulating three controllers, the proposed scheme can work in different states, named mode 1, mode 2 and mode 3. At mode 1 state, a dual single-frequency hopping signals is achieved with 50 ns hopping speed and flexible central frequency and pulse duration ratio. The mode 2 state is realized by applying the Fourier domain mode-locked (FDML) technology into the proposed optoelectrical oscillator, in which dual coherent pulsed single-frequency signal and broadband signal is generated simultaneously. The adjustability of the time duration of the single-frequency signal and the bandwidth of the broadband signal are shown and discussed. The mode 3 state is a dual broadband signal generator which is realized by injecting a triangular wave into the signal laser. The detection performance of the generated broadband signals has also been evaluated by the pulse compression and the phase noise figure. The proposed method may provide a multifunctional radar system signal generator based on the simply external controllers, which can realize low-phase-noise or multifunctional detection with high resolution imaging ability, especially in a complex interference environment.

We consider the limiting current from an emitting patch whose size is much smaller than the anode-cathode spacing. The limiting current is formulated in terms of an integral equation. It is solved iteratively, first to numerically recover the classical one-dimensional Child-Langmuir law, including Jaffe's extension to a constant, nonzero electron emission velocity. We extend to 2-dimensions in which electron emission is restricted to an infinitely long stripe with infinitesimally narrow stripe width, so that the emitted electrons form an electron sheet. We next extend to 3-dimensions in which electron emission is restricted to a square tile (or a circular patch) with an infinitesimally small tile size (or patch radius), so that the emitted electrons form a needle-like line charge. Surprisingly, for the electron needle problem, we only find the null solution for the total line charge current, regardless of the assumed initial electron velocity. For the electron sheet problem, we also find only the null solution for the total sheet current if the electron emission velocity is assumed to be zero, and the total maximum sheet current becomes a finite, nonzero value if the electron emission velocity is assumed to be nonzero. These seemingly paradoxical results are shown to be consistent with the earlier works of the Child-Langmuir law of higher dimensions. They are also consistent with, or perhaps even anticipated by, the more recent theories and simulations on thermionic cathodes that used realistic work function distributions to account for patchy, nonuniform electron emission. The mathematical subtleties are discussed.

We introduce a practical and efficient approach for calculating the all-electron full potential bandstructure in real space, employing a finite element basis. As an alternative to the k-space method, the method involves the self-consistent solution of the Kohn-Sham equation within a larger finite system that encloses the unit-cell. It is based on the fact that the net potential of the unit-cell converges at a certain radius point. Bandstructure results are then obtained by performing non-self-consistent calculations in the Brillouin zone. Numerous numerical experiments demonstrate that the obtained valence and conduction bands are in excellent agreement with the pseudopotential k-space method. Moreover, we successfully observe the band bending of core electrons.

Deep neural networks offer an alternative paradigm for modeling weather conditions. The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages. Neural models trained using atmospheric observations, the highest fidelity and lowest latency data, have to date achieved good performance only up to twelve hours of lead time when compared with state-of-the-art probabilistic Numerical Weather Prediction models and only for the sole variable of precipitation. In this paper, we present MetNet-3 that extends significantly both the lead time range and the variables that an observation based neural model can predict well. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 introduces a key densification technique that implicitly captures data assimilation and produces spatially dense forecasts in spite of the network training on extremely sparse targets. MetNet-3 has a high temporal and spatial resolution of, respectively, up to 2 minutes and 1 km as well as a low operational latency. We find that MetNet-3 is able to outperform the best single- and multi-member NWPs such as HRRR and ENS over the CONUS region for up to 24 hours ahead setting a new performance milestone for observation based neural models. MetNet-3 is operational and its forecasts are served in Google Search in conjunction with other models.

The Lobster Eye Imager for Astronomy (LEIA), a pathfinder of the Wide-field X-ray Telescope of the Einstein Probe (EP) mission, was successfully launched onboard the SATech-01 satellite of the Chinese Academy of Sciences on 27 July 2022. In this paper, we introduce the design and on-ground test results of the LEIA instrument. Using state-of-the-art Micro-Pore Optics (MPO), a wide field-of-view (FoV) of 346 square degrees (18.6 degrees * 18.6 degrees) of the X-ray imager is realized. An optical assembly composed of 36 MPO chips is used to focus incident X-ray photons, and four large-format complementary metal-oxide semiconductor (CMOS) sensors, each of 6 cm * 6 cm, are used as the focal plane detectors. The instrument has an angular resolution of 4 - 8 arcmin (in FWHM) for the central focal spot of the point spread function, and an effective area of 2 - 3 cm2 at 1 keV in essentially all the directions within the field of view. The detection passband is 0.5 - 4 keV in the soft X-rays and the sensitivity is 2 - 3 * 10-11 erg s-1 cm-2 (about 1 mini-Crab) at 1,000 second observation. The total weight of LEIA is 56 kg and the power is 85 W. The satellite, with a design lifetime of 2 years, operates in a Sun-synchronous orbit of 500 km with an orbital period of 95 minutes. LEIA is paving the way for future missions by verifying in flight the technologies of both novel focusing imaging optics and CMOS sensors for X-ray observation, and by optimizing the working setups of the instrumental parameters. In addition, LEIA is able to carry out scientific observations to find new transients and to monitor known sources in the soft X-ray band, albeit limited useful observing time available.

The models of $k$-core percolation and interdependent networks (IN) have been extensively studied in their respective fields. A recent study has revealed that they share several common critical exponents. However, several newly discovered exponents in IN have not been explored in $k$-core percolation, and the origin of the similarity still remains unclear. Here, we investigate k-core percolation in random networks. We find that for k-core percolation,the fractality of the giant component fluctuations is manifested by a fractal fluctuation dimension, $\widetilde d_f = 3/4$, within a correlation \emphsize $N'$ that scales as $N' \propto (p-p_c)^{-\widetilde\nu}$, with $\widetilde\nu = 2$, same as found in IN. Indeed, here, $\widetilde\nu \equiv d\cdot \nu'$ and $\widetilde{d}_f \equiv d'_f/d$, where $\nu'$ and $d'_f$ are respectively the same as the correlation \emphlength exponent and the fractal fluctuation dimension observed in $d$-dimensional IN spatial networks. These two new exponents found here for $k$-core percolation demonstrate the same scaling behaviors as found for IN with the same critical exponents, reinforcing the similarity between the two models. Furthermore, we suggest that these two models are similar since both have two types of interactions: short-range (SR) connectivity and long-range (LR) influences. In IN the LR are the influences of dependency links while in k-core we find here that for $k=1$ and $k=2$ the influences are short range while for $k\geq3$ the influence is long range. In addition, analytical arguments for a universal hyper-scaling relation for the fractal fluctuation dimension of the $k$-core giant component and for IN as well as for any mixed-order transition are established.Our analysis enhances the comprehension of k-core percolation and supports the generalization of the concept of fractal fluctuations in mixed-order phase transitions.

Flexible Cu2ZnSn(S, Se)4 (CZTSSe) solar cells take the advantages of environmental friendliness, low cost, and multi-scenario applications, and have drawn extensive attention in recent years. Compared with rigid devices, the lack of alkali metal elements in the flexible substrate is the main factor limiting the performance of flexible CZTSSe solar cells. This work proposes a Rb ion additive strategy to simultaneously regulate the CZTSSe film surface properties and the CdS chemical bath deposition (CBD) processes. Material and chemical characterization reveals that Rb ions can passivate the detrimental Se0 cluster defect and additionally provide a more active surface for the CdS epitaxial growth. Furthermore, Rb can also coordinate with thiourea (TU) in the CBD solution and improve the ion-by-ion deposition of the CdS layer. Finally, the flexible CZTSSe cell fabricated by this strategy has reached a high total-area efficiency of 12.63% (active-area efficiency of 13.2%), with its VOC and FF reaching 538 mV and 0.70, respectively. This work enriches the alkali metal passivation strategies and provides new ideas for further improving flexible CZTSSe solar cells in the future.

The energetic electrons (EEs) generated through auxiliary heating have been found to destabilize various Alfven eigenmodes (AEs) in recent experiments, which in turn lead to the EE transport and degrade the plasma energy confinement. In this work, we propose a global fluid-kinetic hybrid model for studying corresponding kinetic-magnetohydrodynamic (MHD) processes by coupling the drift-kinetic EEs to the Landau-fluid model of bulk plasmas in a non-perturbative manner. The numerical capability of Landau-fluid bulk plasmas is obtained based on a well-benchmarked eigenvalue code MAS [Multiscale Analysis of plasma Stabilities, J. Bao et al. Nucl. Fusion accepted 2023], and the EE responses to the electromagnetic fluctuations are analytically derived, which not only contribute to the MHD interchange drive and parallel current but also lead to the newly kinetic particle compression with the precessional drift resonance in the leading order. The hybrid model is casted into a nonlinear eigenvalue matrix equation and solved iteratively using Newton's method. By calibrating the EE precession frequency against the particle equation of motion in general geometry and applying more realistic trapped particle distribution in the poloidal plane, MAS simulations of EE-driven beta-induced Alfven eigenmodes (e-BAE) show excellent agreements with gyrokinetic particle-in-cell simulations, and the non-perturbative effects of EEs on e-BAE mode structure, growth rate and damping rate are demonstrated. With these efforts, the upgraded MAS greatly improves the computation efficiency for plasma problems related to deeply-trapped EEs, which is superior than initial-value simulations restricted by the stringent electron Courant condition regarding to the practical application of fast linear analysis.

We propose to use tightly focused lasers to generate high quality electron beams in laser wakefield accelerators. In this scheme, the expansion of the laser beam after the focal position enlarges the size of wakefield bubble, which reduces the effective phase velocity of the wake and triggers injection of plasma electrons. This scheme injects a relatively long beam with high charge. The energy spread of the injected beam can be minimized if an optimal acceleration distance is chosen so that the beam chirp is suppressed. Particle-in-cell simulations indicate that electron beams with the charge in the order of nanocoulomb, the energy spread of $\sim 1\%$, and the normalized emittance of $\rm \sim 0.1\ mm\cdot mrad$ can be generated in uniform plasma using $\sim 100\ \rm TW$ laser pulses. An empirical formula is also given for predicting the beam charge. This injection scheme, with a very simple setup, paves the way towards practical high-quality laser wakefield accelerators for table-top electron and radiation sources.

Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning computational framework, HL-nets, for computing the flow field of hydrodynamic lubrication involving cavitation effects. Two classical cavitation conditions, i.e., the Swift-Stieber (SS) condition and the Jakobsson-Floberg-Olsson (JFO) condition, are implemented in the PINNs to solve the Reynolds equation. For the non-negativity constraint of the SS cavitation condition, a penalizing scheme with a residual of the non-negativity and an imposing scheme with a continuous differentiable non-negative function are proposed. For the complementarity constraint of the JFO cavitation condition, the pressure and cavitation fraction are taken as the neural network outputs, and the residual of the Fischer-Burmeister (FB) equation constrains their complementary relationships. Multi-task learning (MTL) methods are applied to balance the newly introduced loss terms described above. To estimate the accuracy of HL-nets, we present a numerical solution of the Reynolds equation for oil-lubricated bearings involving cavitation. The results indicate that the proposed HL-nets can highly accurately simulate hydrodynamic lubrication involving cavitation phenomena. The imposing scheme can effectively improve the accuracy of the training results of PINNs, and it is expected to have great potential to be applied to different fields where the non-negativity constraint is needed.

We have developed a new global eigenvalue code, Multiscale Analysis for plasma Stabilities (MAS), for studying plasma problems with wave toroidal mode number n and frequency omega in a broad range of interest in general tokamak geometry, based on a five-field Landau-fluid description of thermal plasmas. Beyond keeping the necessary plasma fluid response, we further retain the important kinetic effects including diamagnetic drift, ion finite Larmor radius, finite parallel electric field, ion and electron Landau resonances in a self-consistent and non-perturbative manner without sacrificing the attractive efficiency in computation. The physical capabilities of the code are evaluated and examined in the aspects of both theory and simulation. In theory, the comprehensive Landau-fluid model implemented in MAS can be reduced to the well-known ideal MHD model, electrostatic ion-fluid model, and drift-kinetic model in various limits, which clearly delineates the physics validity regime. In simulation, MAS has been well benchmarked with theory and other gyrokinetic and kinetic-MHD hybrid codes in a manner of adopting the unified physical and numerical framework, which covers the kinetic Alfven wave, ion sound wave, low-n kink, high-n ion temperature gradient mode and kinetic ballooning mode. Moreover, MAS is successfully applied to model the Alfven eigenmode (AE) activities in DIII-D discharge #159243, which faithfully captures the frequency sweeping of RSAE, the tunneling damping of TAE, as well as the polarization characteristics of KBAE and BAAE being consistent with former gyrokinetic theory and simulation. With respect to the key progress contributed to the community, MAS has the advantage of combining rich physics ingredients, realistic global geometry and high computation efficiency together for plasma stability analysis in linear regime.

Digital twin is a modern technology for many advanced applications. To construct a digital twin of a thermal system, it is required to make online estimations of unknown time-varying boundary conditions from sensor measured data, which needs to solve inverse heat transfer problems (IHTPs). However, a fast and accurate solution is challenging since the measured data is normally contaminated with noise and the traditional method to solve IHTP involves significant amount of calculations. Therefore, in this work, a rapid yet robust inversion algorithm called ANN-based extended Kalman smoothing algorithm is developed to realize the online prediction of desired parameter based on the measurements. The fast prediction is realized by replacing the conventional CFD-based state transfer models in extended Kalman smoothing algorithm with pre-trained ANN. Then, a two-dimensional internal convective heat transfer problem was employed as the case study to test the algorithm. The results have proved that the proposed algorithm is a computational-light and robust approach for solving IHTPs. The proposed algorithm can achieve estimation of unknown boundary conditions with a dimensionless average error of 0.0580 under noisy temperature measurement with a standard deviation of 10 K with a drastic reduction of computational cost compared to the conventional approach. Moreover, the effects of training data, location of sensor, future time step selection on the performance of prediction are investigated.

The Super $\tau$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $\tau$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies.

With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differential Equations (PDEs). Compared to traditional methods, PINNs can efficiently handle high-dimensional problems, but the accuracy is relatively low, especially for highly irregular problems. Inspired by the idea of adaptive finite element methods and incremental learning, we propose GAS, a Gaussian mixture distribution-based adaptive sampling method for PINNs. During the training procedure, GAS uses the current residual information to generate a Gaussian mixture distribution for the sampling of additional points, which are then trained together with historical data to speed up the convergence of the loss and achieve higher accuracy. Several numerical simulations on 2D and 10D problems show that GAS is a promising method that achieves state-of-the-art accuracy among deep solvers, while being comparable with traditional numerical solvers.

Niobium telluride (NbTe$_2$), an emerging transition metal dichalcogenide material, has been theoretically predicted to have nonlinear absorption properties and excellent optical response. However, only a few studies of the utilization of NbTe$_2$ in ultrafast photonics have been reported. In this work, a NbTe$_2$-based saturable absorber (SA) was applied in an erbium-doped fiber as a mode-locked device, and a vector soliton based on NbTe$_2$ was obtained for the first time. NbTe$_2$-PVA film SA was successfully prepared by the liquid-phase exfoliation and spin coating methods, with a modulation depth of up to 10.87%. The nonlinear absorption coefficient of NbTe$_2$-based SA film tested through the open-aperture Z-scan laser measurement is 0.62. A conventional soliton with a pulse duration of 858 fs was generated using NbTe$_2$-based SA, which was demonstrated to be a kind of polarization-locked vector soliton in further investigation. Our experimental results reveal the nonlinear optical properties of NbTe$_2$ and broaden its applications in ultrafast photonic devices.

Efficiency of Battery Energy Storage Systems (BESSs) is increasingly critical as renewable energy generation becomes more prevalent on the grid. Therefore, it is necessary to study the energy efficiency of lithium-ion batteries, which are typically used in BESSs. The purpose of this study is to propose the State of Efficiency (SOE) as a measure of how efficiently batteries transfer energy, and to analyze what factors affect the SOE of a battery throughout its lifetime. Using NASA's data set, we measure the SOE of Nickel-Cobalt-Aluminum (NCA) lithium-ion batteries by calculating the ratio of energy generated and consumed during discharge and charge phases. A linear trend was observed in the SOE trajectories, which is confirmed by the Mann-Kendall (MK) trend test. Following that, a linear SOE degradation model was presented. Further analysis shows that ambient temperature, discharge current, and cutoff voltage all affect SOE in different ways. Using the SOE and its behavior observed in this study, Battery Management Systems (BMS) can improve the energy efficiency of batteries by adjusting operating conditions or developing better management strategies.