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1. Ultrabroadband, few-cycle pulses directly from a Mamyshev fiber oscillator

   https://doi.org/10.1364/PRJ.8.000065  

   Ma, Chunyang; Khanolkar, Ankita; Zang, Yimin; Chong, Andy (December 2019, Photonics Research)

   While the performance of mode-locked fiber lasers has been improved significantly, the limited gain bandwidth restricts them from generating ultrashort pulses approaching a few cycles or even shorter. Here we present a novel method to achieve few-cycle pulses ( $\sim 5\text{cycles}$) with an ultrabroad spectrum ( $\sim 400\mathrm{nm}$at $-20\mathrm{dB}$) from a Mamyshev oscillator configuration by inserting a highly nonlinear photonic crystal fiber and a dispersion delay line into the cavity. A dramatic intracavity spectral broadening can be stabilized by the unique nonlinear processes of a self-similar evolution as a nonlinear attractor in the gain fiber and a “perfect” saturable absorber action of the Mamyshev oscillator. To the best of our knowledge, this is the shortest pulse width and broadest spectrum directly generated from a fiber laser. 

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2. Extended metric validation of a semi-physical Space Weather Modeling Framework conductance model on field-aligned current estimations

   https://doi.org/10.3389/fspas.2024.1354615  

   Hathaway, Erika Y; Mukhopadhyay, Agnit; Liemohn, Michael W; Keebler, Timothy; Anderson, Brian J; Vines, Sarah K; Barnes, Robin J (August 2024, Frontiers in Astronomy and Space Sciences)

   In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by $\sim$12.7% and $\sim$6.24% before the storm, $\sim$4.52% and $\sim$2.13% better during the main phase, $\sim$1.98% and $\sim$1.27% worse during the second minimum, and better by $\sim$1.84% and $\sim$1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance. 

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3. Neutron skins: A perspective from dispersive optical models

   https://doi.org/10.3389/fphy.2024.1487314  

   Atkinson, M C; Dickhoff, W H (October 2024, Frontiers in Physics)

   Moreno, O (Ed.)

   An overview of neutron skin predictions obtained using an empirical nonlocal dispersive optical model (DOM) is presented. The DOM links both scattering and bound-state experimental data through a subtracted dispersion relation which allows for fully consistent, data-informed predictions for nuclei where such data exist. Large skins were predicted for both⁴⁸Ca ( $R_{\text{skin}}^{48}=0.25\pm 0.023$fm in 2017) and²⁰⁸Pb ( $R_{\text{skin}}^{208}=0.25\pm 0.05$fm in 2020). Whereas the DOM prediction in²⁰⁸Pb is within 1 $\sigma$of the subsequent PREX-2 measurement, the DOM prediction in⁴⁸Ca is over 2 $\sigma$larger than the thin neutron skin resulting from CREX. From the moment it was revealed, the thin skin in⁴⁸Ca has puzzled the nuclear-physics community as no adequate theories simultaneously predict both a large skin in²⁰⁸Pb and a small skin in⁴⁸Ca. The DOM is unique in its ability to treat both structure and reaction data on the same footing, providing a unique perspective on this $R_{\text{skin}}$puzzle. It appears vital that more neutron data be measured in both the scattering and bound-state domain for⁴⁸Ca to clarify the situation. 

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4. Enhancing material property prediction with ensemble deep graph convolutional networks

   https://doi.org/10.3389/fmats.2024.1474609  

   Rahman, Chowdhury_Mohammad Abid; Bhandari, Ghadendra; Nasrabadi, Nasser M; Romero, Aldo H; Gyawali, Prashnna K (October 2024, Frontiers in Materials)

   Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced technologies across fields such as energy, electronics, and biomedicine, potentially reducing the time and resources needed for new material exploration and promoting rapid innovation cycles. Recent efforts have focused on employing advanced ML algorithms, including deep learning-based graph neural networks, for property prediction. Additionally, ensemble models have proven to enhance the generalizability and robustness of ML and Deep Learning (DL). However, the use of such ensemble strategies in deep graph networks for material property prediction remains underexplored. Our research provides an in-depth evaluation of ensemble strategies in deep learning-based graph neural network, specifically targeting material property prediction tasks. By testing the Crystal Graph Convolutional Neural Network (CGCNN) and its multitask version, MT-CGCNN, we demonstrated that ensemble techniques, especially prediction averaging, substantially improve precision beyond traditional metrics for key properties like formation energy per atom $\left(\mathrm{\Delta }{E}^f\right)$, band gap $\left(E_g\right)$, density $\left(\rho \right)$, equivalent reaction energy per atom $\left(E_{\mathit{rxn,atom}}\right)$, energy per atom $\left(E_{\mathit{atom}}\right)$and atomic density $\left({\rho }_{\mathit{atom}}\right)$in 33,990 stable inorganic materials. These findings support the broader application of ensemble methods to enhance predictive accuracy in the field. 

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5. Morphology modification of LiNi0.5Co0.2Mn0.3O2 by incorporating cotton textiles in lithium-ion capacitors

   https://doi.org/10.3389/fbael.2024.1388494  

   Truong, Phat; Sun, Quanwen; Tang, Wei; He, Rong; Zhou, Meng; Luo, Hongmei (May 2024, Frontiers in Batteries and Electrochemistry)

   To address the alerting issue of energy demand, lithium-ion capacitors (LICs) have been widely studied as promising electrochemical energy storage devices, which can deliver higher energy density than supercapacitors (SCs), and have higher power density with longer cycling life than lithium-ion batteries (LIBs). In this work, the active material lithium nickel cobalt manganese oxide LiNi_0.5Co_0.2Mn_0.3O₂(NCM523) is grown on a cotton textile template and building a 3-dimensional (3D) integrity to improve capacitance and energy density of LICs by enhancing the interfacial ion-exchange process. With the 3D structure, the specific discharge capacitance is increased to 718.67 $F{g}^{-1}$at 0.1 $A{g}^{-1}$from that of non-textile NCM523 (265.97 $F{g}^{-1}$), and remains a high capacitance of 254.48 $F{g}^{-1}$at 10 $A{g}^{-1}$in the half-cell capacitors. In addition, the energy density can achieve up to 36.17 $Whk{g}^{-1}$at the power density of 1,200 $Wk{g}^{-1}$in the full-cell capacitor. The textile NCM can maintain an energy density of 28.26 $Whk{g}^{-1}$at the current density of 10 $A{g}^{-1}$and power density of 6,000 $Wk{g}^{-1}$. Our results present promising applications of electrodes with the 3D porous structure for high energy density LICs. 

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