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In this paper, we investigate the global well-posedness of strong solution to a model recently derived by Constantin-Johnson \cite{CaJo} which describes the nonlinear wave propagation in the troposphere, especially for the morning glory cloud. Assuming that the initial velocity $v_0\in H^1$ and the thermodynamic forcing term $K\in L^2(0,T;L^2)$, we show that there exists a unique global strong solution to the initial boundary value problem of this system. Similar result was only known before for sufficiently small initial data in the existing literature.
We have measured the index of refraction for sodium de Broglie waves in gases of Ar, Kr, Xe, and nitrogen over a wide range of sodium velocities. We observe glory oscillations -- a velocity-dependent oscillation in the forward scattering amplitude. An atom interferometer was used to observe glory oscillations in the phase shift caused by the collision, which are larger than glory oscillations observed in the cross section. The glory oscillations depend sensitively on the shape of the interatomic potential, allowing us to discriminate among various predictions for these potentials, none of which completely agrees with our measurements.
Total (elastic + rotationally inelastic) integral cross sections are computed for O$_2(^3Σ_g^-)$-O$_2(^3Σ_g^-)$ using a recent ab initio potential energy surface. The sampled velocity range allows us a thorough comparison of the glory interference pattern observed in molecular beam experiments. The computed cross sections are about 10% smaller than the measured ones, however, a remarkable agreement in the velocity positions of the glory extrema is achieved. By comparing with models where the anisotropy of the interaction is reduced or removed, it is found that the glory pattern is very sensitive to the anisotropy, especially the positions of the glory extrema.
Context. Reflected light from a spatially unresolved planet yields unique insight into the overall optical properties of the planet cover. Glories are optical phenomena caused by light that is backscattered within spherical droplets following a narrow distribution of sizes; they are well known on Earth as localised features above liquid clouds. Aims. Here we report the first evidence for a glory in the disk-integrated photometry of Venus and, in turn, of any planet. Methods. We used previously published phase curves of the planet that were reproduced over the full range of phase angles with model predictions based on a realistic description of the Venus atmosphere. We assumed that the optical properties of the planet as a whole can be described by a uniform and stable cloud cover, an assumption that agrees well with observational evidence. Results. We specifically show that the measured phase curves mimic the scattering properties of the Venus upper-cloud micron-sized aerosols, also at the small phase angles at which the glory occurs, and that the glory contrast is consistent with what is expected after multiple scattering of photons. In the optical, the planet appears to be bright
Analytical explanation of the nuclear glory effect, which is similar to the known optical (atmospheric) glory phenomenon, is presented. It is based on the small phase space method for the multiple interaction processes probability estimates and leads to the characteristic angular dependence of the production cross section $dσ\sim 1/ \sqrt {π- θ}$ in the vicinity of the strictly backward direction, for any number of interactions $N\geq 3$, either elastic or inelastic. Rigorous proof of this effect is given for the case of the optimal kinematics, as well as for arbitrary polar scattering angles in the case of the light particle rescattering, but the arguments in favor of the backward azimuthal (axial) focusing are quite general and hold for any kind of the multiple interaction processes. Such behaviour of the cross section near the backward direction agrees qualitatively with available data. In the small interval of final angles including the value $θ=π$ the angular dependence of the cumulative particle production cross section can have the crater-like (or funnel-like) form. Further studies including, probably, certain numerical calculations, are necessary to clear up this point.
In this paper, we propose a new method to characterize the temporal structure of arbitrary optical laser pulses with low pulse energies. This approach is based on strong field photoelectron holography with the glory rescattering effect as the underlying mechanism in the near-forward direction. Utilizing the subfemtosecond glory rescattering process as a fast temporal gate to sample the unknown light pulse, the time-dependent vectorial electric field can be retrieved from the streaking photoelectron momentum spectra. Our method avoids the challenging task of generation or manipulation of attosecond pulses and signifies important progress in arbitrary optical waveform characterization.
During the ionization of atoms irradiated by linearly polarized intense laser fields, we find for the first time that the transverse momentum distribution of photoelectrons can be well fitted by a squared zeroth-order Bessel function because of the quantum interference effect of Glory rescattering. The characteristic of the Bessel function is determined by the common angular momentum of a bunch of semiclassical paths termed as Glory trajectories, which are launched with different nonzero initial transverse momenta distributed on a specific circle in the momentum plane and finally deflected to the same asymptotic momentum, which is along the polarization direction, through post-tunneling rescattering. Glory rescattering theory (GRT) based on the semiclassical path-integral formalism is developed to address this effect quantitatively. Our theory can resolve the long-standing discrepancies between existing theories and experiments on the fringe location, predict the sudden transition of the fringe structure in holographic patterns, and shed light on the quantum interference aspects of low-energy structures in strong-field atomic ionization.
A semi-classical approximation has been a powerful tool in understanding the dynamics of low-energy heavy-ion reactions. Here we discuss two topics in this regard, for which Mahir Hussein was a world leading pioneer. The first topic is heavy-ion fusion reactions of neutron-rich nuclei, in which the breakup process of the projectile nucleus plays a crucial role. The second is rainbow and glory scattering, for which characteristic oscillatory patterns in differential cross sections can be well understood in terms of intereferences among several semi-classical trajectories.
The angular dependence of the cumulative particles production off nuclei near the kinematical boundary for multistep process is defined by characteristic polynomials in angular variables $J_N^2(z_N^θ)$, where $θ$ is the polar angle defining the momentum of the final (cumulative) particle, $z_N^θ= cos (θ/N)$, the integer $N$ being the multiplicity of the process (the number of interactions). Physical argumentation, exploring the small phase space method, leads to the appearance of equations for these polynomials $J_N^2[cos(π/N)]=0$. The recurrent relations between polynomials with different $N$ are obtained, and their connection with known in mathematics Chebyshev polynomials of 2-d kind is established. As a result of this equality, differential cross section of the cumulative particle production has characteristic behaviour $dσ\sim 1/ \sqrt {π- θ}$ at $θ\sim π$ (the backward focusing effect). Such behaviour takes place for any multiplicity of the interaction, beginning with $n=3$, elastic or inelastic (with resonances excitations in intermediate states), and can be called the nuclear glory phenomenon, or 'Buddha's light' of cumulative particles.
We study the absorption and scattering of massless scalar waves by Frolov black holes, a regular deformation of the Reissner--Nordström geometry. A null-geodesic analysis provides the photon-sphere radius and the critical impact parameters governing capture and glory scattering. Using a partial-wave approach, we compute total/partial absorption cross sections and differential scattering cross sections over a broad frequency range. When the absorption spectrum is rescaled by photon-sphere scales, the high-frequency oscillations exhibit a pronounced data collapse in the variables $\hatσ=σ_{\rm abs}/σ_{\rm geo}$ and $x=ω/Ω_c$, highlighting photon-sphere control of the absorption fine structure. We clarify the parameter dependence under the horizon-radius normalization and relate the apparent trends to the variation of the dimensionless mass across parameter space. Finally, comparing Frolov, Reissner--Nordström, and Hayward black holes with matched critical or glory impact parameters, we find that their absorption and scattering patterns can become remarkably close, indicating that in the intermediate-to-high frequency regime the dominant signatures are largely governed by the unstable
Recursive reasoning models can solve complex structured tasks with only a few million parameters by repeatedly updating a latent state. Deploying these models on edge hardware requires significant compression, but unlike conventional sequence models, quantization errors compound across recursive reasoning cycles rather than across output tokens. As a result, standard intuitions about compression fail to apply. In this work, we ask what survives when recursive reasoners are compressed. Across a full precision sweep, three tasks, and two recursive architectures, we find that aggressive compression preserves local prediction but destroys global reasoning: cell accuracy holds while puzzle-exact accuracy collapses to zero under naive INT4 pruning, distillation, and linear attention alike. Token-level objectives, including quantization-aware training, cannot repair it. The collapse is architectural -- it strikes MLP-mixing recursion but not attention on the same task -- and we reverse it with per-channel calibrated INT4 without retraining. We also introduce carry-trajectory fidelity, the cosine similarity to the full-precision reasoning path, as a label-free signal that predicts this dam
Many networked systems require a central authority to enforce a global configuration against local peer influence. We study influence dynamics on finite weighted directed graphs with a distinguished hub node and binary vertex states ('Glory' or 'Gnash'). We give a sharp, local, and efficiently checkable criterion that guarantees global convergence to Glory in a single synchronous update from any initial state. At each non-hub vertex, the incoming weight from the hub must at least match the total incoming weight from all other nodes. Specialising in uniform hub broadcasts, the exact threshold equals the maximum non-hub incoming weight over all vertices, and we prove this threshold is tight. We extend the result to a tau-biased update rule and to asynchronous (Gauss-Seidel) schedules, where a single pass still suffices under the same domination hypothesis. Machine-checked proofs in Coq accompany all theorems.
Cybersecurity awareness training has historically adopted a one-size-fits-all approach, despite established individual differences in how users process and retain security information. Personality has been proposed as one axis along which training content might be tailored; yet no prior study has implemented and empirically evaluated a complete personality-conditional system end-to-end. This paper reports the design, implementation, and quasi-experimental evaluation of \emph{TailoredSec}, a mobile cybersecurity awareness application that routes training content based on a user's dominant Five-Factor Model (FFM) personality trait, as measured by the ten-item Big Five Inventory (BFI-10). Seventy-four UK-based adults were allocated to a traditional video-training condition ($n = 40$) or a personality-conditional condition ($n = 34$). Both groups completed a four-item scenario-based pre-assessment (scored 0--40), a single training session, and an equivalent post-assessment. The personality-conditional group additionally completed the BFI-10 (Big Five Inventory-10) and was routed to one of four training modules covering five FFM traits (Conscientiousness and Neuroticism share a module).
DNA nanotechnology is a unique field, where physics, chemistry, biology, mathematics, engineering, and materials science can elegantly converge. Since the original proposal of Nadrian Seeman, significant advances have been achieved in the past four decades. During this glory time, the DNA origami technique developed by Paul Rothemund further pushed the field forward with a vigorous momentum, fostering a plethora of concepts, models, methodologies, and applications that were not thought of before. This review focuses on the recent progress in DNA origami-engineered nanomaterials in the past five years, outlining the exciting achievements as well as the unexplored research avenues. We believe that the spirits and asset that Seeman left for scientists will continue to bring inter-disciplinary innovations and useful applications to this field in the next decade.
This paper presents a systematic review of recent advancements in V2G cybersecurity, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for detailed searches across three journal databases and included only peer-reviewed studies published between 2020 and 2024 (June). We identified and reviewed 133 V2G cybersecurity studies and found five important insights on existing V2G cybersecurity research. First, most studies (103 of 133) focused on protecting V2G systems against cyber threats, while only seven studies addressed the recovery aspect of the CRML (Cybersecurity Risk Management Lifecycle) function. Second, existing studies have adequately addressed the security of EVs and EVCS (EV charging stations) in V2G systems (112 and 81 of 133 studies, respectively). However, none have focused on the linkage between the behaviour of EV users and the cybersecurity of V2G systems. Third, physical access, control-related vulnerabilities, and user behaviour-related attacks in V2G systems are not addressed significantly. Furthermore, existing studies overlook vulnerabilities and attacks specific to AI and blockchain technologies. Fourth, blockcha
Retrieval-Augmented Generation (RAG) represents a major advancement in natural language processing (NLP), combining large language models (LLMs) with information retrieval systems to enhance factual grounding, accuracy, and contextual relevance. This paper presents a comprehensive systematic review of RAG, tracing its evolution from early developments in open domain question answering to recent state-of-the-art implementations across diverse applications. The review begins by outlining the motivations behind RAG, particularly its ability to mitigate hallucinations and outdated knowledge in parametric models. Core technical components-retrieval mechanisms, sequence-to-sequence generation models, and fusion strategies are examined in detail. A year-by-year analysis highlights key milestones and research trends, providing insight into RAG's rapid growth. The paper further explores the deployment of RAG in enterprise systems, addressing practical challenges related to retrieval of proprietary data, security, and scalability. A comparative evaluation of RAG implementations is conducted, benchmarking performance on retrieval accuracy, generation fluency, latency, and computational effici
Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each with distinct strengths and limitations. We present a hybrid method that integrates template fitting with deep learning using physics-guided neural networks. By embedding spectral energy distribution templates into the network architecture, our model encodes physical priors into the training process. The system employs a multimodal design, incorporating cross-attention mechanisms to fuse photometric and image data, along with Bayesian layers for uncertainty estimation. We evaluate our model on the publicly available PREML dataset, which includes approximately 400,000 galaxies from the Hyper Suprime-Cam PDR3 release, with 5-band photometry, multi-band imaging, and spectroscopic redshifts. Our approach achieves an RMS error of 0.0507, a 3-sigma catastrophic outlier rate of 0.13%, and a bias of 0.0028. The model satisfies two of the three LSST photometric redshift requirements for redshifts below 3. These results highlight the potential of combinin
Tear film break-up (TFBU) analysis is critical for diagnosing dry eye syndrome, but automated TFBU segmentation remains challenging due to the lack of annotated datasets and integrated solutions. This paper introduces the Tear Film Multi-task (TFM) Dataset, the first comprehensive dataset for multi-task tear film analysis, comprising 15 high-resolution videos (totaling 6,247 frames) annotated with three vision tasks: frame-level classification ('clear', 'closed', 'broken', 'blur'), Placido Ring detection, and pixel-wise TFBU area segmentation. Leveraging this dataset, we first propose TF-Net, a novel and efficient baseline segmentation model. TF-Net incorporates a MobileOne-mini backbone with re-parameterization techniques and an enhanced feature pyramid network to achieve a favorable balance between accuracy and computational efficiency for real-time clinical applications. We further establish benchmark performance on the TFM segmentation subset by comparing TF-Net against several state-of-the-art medical image segmentation models. Furthermore, we design TF-Collab, a novel integrated real-time pipeline that synergistically leverages models trained on all three tasks of the TFM dat
In this work, we will explore the precession of particle spins in spherical spacetimes. We first argue that the geometrical optics (WKB) approximation is insufficient, due to the absence of a glory spot in the backward scattering of massless particles, making an analysis of spin precession necessary. We then derive the precession equation assuming the spin is parallel transported, which is supported by the sub-leading order of the WKB approximation. The precession equation applies to both massless and massive particles. For particles moving at the speed of light, we show that spin is always reversed after backward scattering in any spherically symmetric spacetime, confirming the absence of a glory spot for massless particles. Finally, we solve the precession equation for Schwarzschild and Reissner-Nordström spacetimes and discuss the spin precession of massive particles, particularly in the non-relativistic limit. We find that, in Schwarzschild spacetime, the spin precession for particles moving with very small velocities compared to the speed of light depends only on the deflection angle, while in Reissner-Nordström spacetime, it also depends on the black hole charge, as revealed
Cognitive workload is a topic of increasing interest across various fields such as health, psychology, and defense applications. In this research, we focus on classifying cognitive workload using the COLET dataset, employing a window-based approach for feature generation and machine/deep learning techniques for classification. We apply window-based temporal partitioning to enhance features used in existing research, followed by machine learning and deep learning models to classify different levels of cognitive workload. The results demonstrate that deep learning models, particularly tabular architectures, outperformed traditional machine learning methods in precision, F1-score, accuracy, and classification precision. This study highlights the effectiveness of window-based temporal feature extraction and the potential of deep learning techniques for real-time cognitive workload assessment in complex and dynamic tasks.