239 research outputs found

    Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries

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    For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there are no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.Comment: 14 pages, 10 figure

    Mapping transient electric fields with picosecond electron bunches

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    Transient electric fields, which are an important but hardly explored parameter of laser plasmas, can now be diagnosed experimentally with combined ultrafast temporal resolution and field sensitivity, using femtosecond to picosecond electron or proton pulses as probes. However, poor spatial resolution poses great challenges to simultaneously recording both the global and local field features. Here, we present a direct 3D measurement of a transient electric field by time-resolved electron schlieren radiography with simultaneous 80-Êm spatial and 3.7-ps temporal resolutions, analyzed using an Abel inversion algorithm. The electric field here is built up at the front of an aluminum foil irradiated with a femtosecond laser pulse at 1.9 × 1012 W/cm2, where electrons are emitted at a speed of 4 × 106 m/s, resulting in a unique gpeak.valleyh transient electric field map with the field strength up to 105 V/m. Furthermore, time-resolved schlieren radiography with charged particle pulses should enable the mapping of various fast-evolving field structures including those found in plasma-based particle accelerators

    Graphene-analogues boron nitride nanosheets confining ionic liquids: a high-performance quasi-liquid solid electrolyte

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    Solid electrolytes are one of the most promising electrolyte systems for safe lithium batteries, but the low ionic conductivity of these electrolytes seriously hinders the development of efficient lithium batteries. Here, a novel class of graphene-analogues boron nitride (g-BN) nanosheets confining an ultrahigh concentration of ionic liquids (ILs) in an interlayer and out-of-layer chamber to give rise to a quasi-liquid solid electrolyte (QLSE) is reported. The electron-insulated g-BN nanosheet host with a large specific surface area can confine ILs as much as 10 times of the host's weight to afford high ionic conductivity (3.85 × 10−3 S cm−1 at 25 °C, even 2.32 × 10−4 S cm−1 at −20 °C), which is close to that of the corresponding bulk IL electrolytes. The high ionic conductivity of QLSE is attributed to the enormous absorption for ILs and the confining effect of g-BN to form the ordered lithium ion transport channels in an interlayer and out-of-layer of g-BN. Furthermore, the electrolyte displays outstanding electrochemical properties and battery performance. In principle, this work enables a wider tunability, further opening up a new field for the fabrication of the next-generation QLSE based on layered nanomaterials in energy conversion devices

    Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

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    Spatial organization of different epigenomic marks was used to infer functions of the epigenome. It remains unclear what can be learned from the temporal changes of the epigenome. Here, we developed a probabilistic model to cluster genomic sequences based on the similarity of temporal changes of multiple epigenomic marks during a cellular differentiation process. We differentiated mouse embryonic stem (ES) cells into mesendoderm cells. At three time points during this differentiation process, we used high-throughput sequencing to measure seven histone modifications and variants—H3K4me1/2/3, H3K27ac, H3K27me3, H3K36me3, and H2A.Z; two DNA modifications—5-mC and 5-hmC; and transcribed mRNAs and noncoding RNAs (ncRNAs). Genomic sequences were clustered based on the spatiotemporal epigenomic information. These clusters not only clearly distinguished gene bodies, promoters, and enhancers, but also were predictive of bidirectional promoters, miRNA promoters, and piRNAs. This suggests specific epigenomic patterns exist on piRNA genes much earlier than germ cell development. Temporal changes of H3K4me2, unmethylated CpG, and H2A.Z were predictive of 5-hmC changes, suggesting unmethylated CpG and H3K4me2 as potential upstream signals guiding TETs to specific sequences. Several rules on combinatorial epigenomic changes and their effects on mRNA expression and ncRNA expression were derived, including a simple rule governing the relationship between 5-hmC and gene expression levels. A Sox17 enhancer containing a FOXA2 binding site and a Foxa2 enhancer containing a SOX17 binding site were identified, suggesting a positive feedback loop between the two mesendoderm transcription factors. These data illustrate the power of using epigenome dynamics to investigate regulatory functions

    Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization

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    While large language models (LLMs) already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task settings is less studied. Therefore, we benchmark LLMs on instruction controllable text summarization, where the model input consists of both a source article and a natural language requirement for the desired summary characteristics. To this end, we curate an evaluation-only dataset for this task setting and conduct human evaluation on 5 LLM-based summarization systems. We then benchmark LLM-based automatic evaluation for this task with 4 different evaluation protocols and 11 LLMs, resulting in 40 evaluation methods in total. Our study reveals that instruction controllable text summarization remains a challenging task for LLMs, since (1) all LLMs evaluated still make factual and other types of errors in their summaries; (2) all LLM-based evaluation methods cannot achieve a strong alignment with human annotators when judging the quality of candidate summaries; (3) different LLMs show large performance gaps in summary generation and evaluation. We make our collected benchmark, InstruSum, publicly available to facilitate future research in this direction.Comment: GitHub Repo: https://github.com/yale-nlp/InstruSu

    Successful treatment of acrodermatitis continua of Hallopeau coexisting with generalized pustular psoriasis with spesolimab: a case report

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    Generalized pustular psoriasis (GPP) is a rare chronic inflammatory pustular dermatosis that presents as painful erythema with sterile pustules on nonacral skin. No unified standard and guideline for the treatment of GPP has been established. Several biologics have been tried for GPP, with varying success. Acrodermatitis continua of Hallopeau (ACH) is a very rare disabling variant of pustular psoriasis characterized by sterile pustules on the fingers and toes, including the nail bed. Comparatively, treating ACH is highly challenging due to its commonly therapy-resistant disease course. The pathogenic role of IL-36 signaling axis has been currently identified in GPP development. Spesolimab, the first anti-interleukin-36 receptor biologic, has been approved for treating GPP flares and shown promising results. In view of a shared pathogenesis between GPP and ACH, specolimab may be an effective treatment for ACH. Currently, there is no case and clinical trial data exist on this condition. Therefore, this case was aim to describe real-world experience of spesolimab use in ACH coexisting with GPP. We report an Asian patient with a 16-year-history of GPP and ACH with marked pustulosis on the nail bed and onychodystrophy. He received conventional systemic regimen acitretin, cyclosporine and biologics adalimumab and secukinumab, but experienced relapse for skin lesions and refractory for nail lesions. He was then treated with a single dose of spesolimab in combination with secukinumab, which resulted in skin clearance and nearly complete resolution of nail lesions over a 32-week period. Our observation suggests that spesolimab should be considered for the treatment of ACH, especially in the patients with intractable nail lesions and concomitant GPP
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