An FBG staged monitoring method for carbon fiber reinforced plastics composite fracture status based on modulus/strain wave coupling property

From the sensitivity of the FBG center wavelength changing with the macro-elastic modulus and the instantaneous fracture strain wave on the surface of carbon fiber reinforced plastics (CFRP) composite, we investigate the correlation between the macro-elastic modulus (the changing rate of the FBG center wavelength during the stretching process) and the fracture status of CFRP specimen. An FBG staged monitoring method based on modulus/strain wave coupling properties designed to monitor tensile fracture state of composite has been proposed. By monitoring the change of macro-elastic modulus during the stretching process, the damage state of composite in a macro perspective is obtained; when the internal damage reaches a critical state, the fracture distribution status of CFRP specimen is captured by monitoring the strain wave response induced by stress relaxation in different locations. Simulated analysis and experimental results in this paper show that the proposed FBG staged monitoring method can achieve the identification of the damage state and the breakage position of CFRP composite effectively, with a good prospect

OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models

To enrich language models with domain knowledge is crucial but difficult. Based on the world's largest public academic graph Open Academic Graph (OAG), we pre-train an academic language model, namely OAG-BERT, which integrates massive heterogeneous entities including paper, author, concept, venue, and affiliation. To better endow OAG-BERT with the ability to capture entity information, we develop novel pre-training strategies including heterogeneous entity type embedding, entity-aware 2D positional encoding, and span-aware entity masking. For zero-shot inference, we design a special decoding strategy to allow OAG-BERT to generate entity names from scratch. We evaluate the OAG-BERT on various downstream academic tasks, including NLP benchmarks, zero-shot entity inference, heterogeneous graph link prediction, and author name disambiguation. Results demonstrate the effectiveness of the proposed pre-training approach to both comprehending academic texts and modeling knowledge from heterogeneous entities. OAG-BERT has been deployed to multiple real-world applications, such as reviewer recommendations and paper tagging in the AMiner system. It is also available to the public through the CogDL package

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions. By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better. Code and data are available at https://github.com/WadeYin9712/Dynosaur.Comment: EMNLP 2023. Code and data are available at https://github.com/WadeYin9712/Dynosau

Plasmoid ejection and secondary current sheet generation from magnetic reconnection in laser-plasma interaction

Reconnection of the self-generated magnetic fields in laser-plasma interaction was first investigated experimentally by Nilson {\it et al.} [Phys. Rev. Lett. 97, 255001 (2006)] by shining two laser pulses a distance apart on a solid target layer. An elongated current sheet (CS) was observed in the plasma between the two laser spots. In order to more closely model magnetotail reconnection, here two side-by-side thin target layers, instead of a single one, are used. It is found that at one end of the elongated CS a fan-like electron outflow region including three well-collimated electron jets appears. The ($>1$ MeV) tail of the jet energy distribution exhibits a power-law scaling. The enhanced electron acceleration is attributed to the intense inductive electric field in the narrow electron dominated reconnection region, as well as additional acceleration as they are trapped inside the rapidly moving plasmoid formed in and ejected from the CS. The ejection also induces a secondary CS

Anesthetic management of patients undergoing cardiac myxoma resection: a single-center retrospective analysis

BackgroundMyxomas are the most common primary cardiac tumors. Intracardiac myxomas, although benign, could cause serious consequences such as tricuspid or mitral valve obstruction, hemodynamic collapse, and acute heart failure, which pose challenges during anesthetic management. The current study was designed to summarize the anesthetic management of patients undergoing cardiac myxoma resection.MethodsThis study was performed retrospectively from the perioperative period of patients who underwent myxoma resection. Patients were divided into two groups according to whether the myxoma prolapsed into the ventricle (group O) or not (group N) to evaluate the impact of tricuspid or mitral valve with obstruction.Results110 patients, aged 17–78 years, undergoing cardiac myxoma resection between January 2019 and December 2021 were collected, and their perioperative characteristics were recorded. In the preoperative evaluation, common clinical symptoms included dyspnea and palpitation, whereas embolic events occurred in 8 patients, including 5 (4.5%) cerebral thromboembolic events, 2 (1.8%) femoral artery, and 1 (0.9%) obstructive coronary artery. According to the echocardiography, left atrial myxoma was detected in 104 (94.5%) patients, the average dimension of myxoma was 4.03 cm ± 1.52 cm in the largest diameter, and 48 patients were divided into group O. During intraoperative anesthetic management, hemodynamic instability occurred in 38 (34.5%) patients after anesthesia induction. More patients in group O had hemodynamic instability (47.9% vs. 24.2%, p = 0.009) than in group N. The mean postoperative length of stay in the hospital was 10.64 ± 3.01 days, and most of the patients made an uneventful postoperative recovery.ConclusionsAnesthetic management for myxoma resection can be composed by assessing the myxoma, particularly the echocardiography evaluation and preventing cardiovascular instability. Typically, tricuspid or mitral valve with obstruction is a premier ingredient in anesthetic management

(Z)3,4,5,4'-trans-tetramethoxystilbene, a new analogue of resveratrol, inhibits gefitinb-resistant non-small cell lung cancer via selectively elevating intracellular calcium level.

Calcium is a second messenger which is required for regulation of many cellular processes. However, excessive elevation or prolonged activation of calcium signaling would lead to cell death. As such, selectively regulating calcium signaling could be an alternative approach for anti-cancer therapy. Recently, we have identified an effective analogue of resveratrol, (Z)3,4,5,4′-trans-tetramethoxystilbene (TMS) which selectively elevated the intracellular calcium level in gefitinib-resistant (G-R) non-small-cell lung cancer (NSCLC) cells. TMS exhibited significant inhibitory effect on G-R NSCLC cells, but not other NSCLC cells and normal lung epithelial cells. The phosphorylation and activation of EGFR were inhibited by TMS in G-R cells. TMS induced caspase-independent apoptosis and autophagy by directly binding to SERCA and causing endoplasmic reticulum (ER) stress and AMPK activation. Proteomics analysis also further confirmed that mTOR pathway, which is the downstream of AMPK, was significantly suppressed by TMS. JNK, the cross-linker of ER stress and mTOR pathway was significantly activated by TMS. In addition, the inhibition of JNK activation can partially block the effect of TMS. Taken together, TMS showed promising anti-cancer activity by mediating calcium signaling pathway and inducing apoptosis as well as autophagy in G-R NSCLC cells, providing strategy in designing multi-targeting drug for treating G-R patients

Functional building blocks for scalable multipartite entanglement in optical lattices

Featuring excellent coherence and operated parallelly, ultracold atoms in optical lattices form a competitive candidate for quantum computation. For this, a massive number of parallel entangled atom pairs have been realized in superlattices. However, the more formidable challenge is to scale-up and detect multipartite entanglement due to the lack of manipulations over local atomic spins in retro-reflected bichromatic superlattices. Here we developed a new architecture based on a cross-angle spin-dependent superlattice for implementing layers of quantum gates over moderately-separated atoms incorporated with a quantum gas microscope for single-atom manipulation. We created and verified functional building blocks for scalable multipartite entanglement by connecting Bell pairs to one-dimensional 10-atom chains and two-dimensional plaquettes of $2\times4$ atoms. This offers a new platform towards scalable quantum computation and simulation

Leakage current simulations of Low Gain Avalanche Diode with improved Radiation Damage Modeling

We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode (LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD, which agree with measurements' results before irradiation. And we propose an improved LGAD Radiation Damage Model (LRDM) which combines local acceptor removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1 LGAD and able to predict the leakage current well at -30 $^{\circ}$C after an irradiation fluence of $\Phi_{eq}=2.5 \times 10^{15} ~n_{eq}/cm^{2}$. The charge collection efficiency (CCE) is under development