Prompt-NER: Zero-shot Named Entity Recognition in Astronomy Literature via Large Language Models

This study delves into the application of Large Language Models (LLMs) for Named Entity Recognition (NER) tasks in the field of astronomy literature. To enhance the zero-shot recognition capabilities of LLMs for astronomical named entities, we propose a strategy called Prompt-NER. Prompt-NER includes five prompt elements: Task Descriptions, Entity Definitions, Task Emphasis, Task Examples, and Second Conversation. To assess the effectiveness of the Prompt-NER strategy, we utilize three representative LLMs (Claude-2, GPT-3.5, and LLaMA-2-70b) to identify telescope and celestial object named entities in astronomical literature. Our experiments are conducted based on two distinct datasets. The first dataset comprises 30 original PDF documents, which we split into paragraphs in sequential order, resulting in a second dataset consisting of 30 paragraph collections. Additionally, we incorporate 30 astronomical telegrams to diversify our experiments and assess the performance of LLMs based on Prompt-NER on concise, complete texts. Our experimental results indicate that the Prompt-NER strategy enables LLMs to effectively accomplish NER tasks in the field of astronomy, even without prior astronomical knowledge during training. We carefully analyze the experimental results, including the mechanism of different prompt elements and the influence of different features of long and short texts on their respective experimental results. This research provides experience for zero-shot NER tasks in astronomical literature and suggests future work in this area

3D printing of hybrid MoS2-graphene aerogels as highly porous electrode materials for sodium ion battery anodes

This study reports a 3D freeze-printing method that integrates inkjet printing and freeze casting to control both the microstructure and macroporosity via formation of ice microcrystals during printing. A viscous aqueous ink consisting of a molecular MoS2 precursor (ammonium thiomolybdate) mixed with graphene oxide (GO) nanosheets is used in the printing process. Post-treatments by freeze-drying and reductive thermal annealing convert the printed intermediate mixture into a hybrid structure consisting of MoS2 nanoparticles anchored on the surface of 2D rGO nanosheets in a macroporous framework, which is fully characterized with FESEM, TEM, XRD, Raman spectroscopy and TGA. The resulting hybrid MoS2-rGO aerogels are studied as anodes for sodiumion batteries. They present a high initial specific capacity over 429 mAh/g at C/3.3 rate in the potential range of 2.5–0.10 V (vs Na+/Na). The process involves both reversible 2 Na+ insertion and slow irreversible conversion of MoS2 to metallic Mo. At higher rates, the conversion reaction is suppressed and the electrode is dominated by fast Na+ intercalation with good stability. This demonstrates that the 3D printing technology can be used as a processing technique to control the materials properties for energy storage

Plant buffering against the high-light stress-induced accumulation of CsGA2ox8 transcripts via alternative splicing to finely tune gibberellin levels and maintain hypocotyl elongation

Ajuts: this study was supported by The National Key Research and Development Program of China (2019YFD1000300), the International Postdoctoral Exchange Fellowship Program from the China Postdoctoral Council (20170053), the Technology System Construction of Modern Agricultural Industry of Shanghai (19Z113040008), and the Presidential Foundation of Guangdong Academy of Agricultural Sciences (BZ201901).In plants, alternative splicing (AS) is markedly induced in response to environmental stresses, but it is unclear why plants generate multiple transcripts under stress conditions. In this study, RNA-seq was performed to identify AS events in cucumber seedlings grown under different light intensities. We identified a novel transcript of the gibberellin (GA)-deactivating enzyme Gibberellin 2-beta-dioxygenase 8 (CsGA2ox8). Compared with canonical CsGA2ox8.1, the CsGA2ox8.2 isoform presented intron retention between the second and third exons. Functional analysis proved that the transcript of CsGA2ox8.1 but not CsGA2ox8.2 played a role in the deactivation of bioactive GAs. Moreover, expression analysis demonstrated that both transcripts were upregulated by increased light intensity, but the expression level of CsGA2ox8.1 increased slowly when the light intensity was >400 µmol·m −2 ·s −1 PPFD (photosynthetic photon flux density), while the CsGA2ox8.2 transcript levels increased rapidly when the light intensity was >200 µmol·m −2 ·s −1 PPFD. Our findings provide evidence that plants might finely tune their GA levels by buffering against the normal transcripts of CsGA2ox8 through AS

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

3D printing of hybrid MoS2-graphene aerogels as highly porous electrode materials for sodium ion battery anodes

This study reports a 3D freeze-printing method that integrates inkjet printing and freeze casting to control both the microstructure and macroporosity via formation of ice microcrystals during printing. A viscous aqueous ink consisting of a molecular MoS2 precursor (ammonium thiomolybdate) mixed with graphene oxide (GO) nanosheets is used in the printing process. Post-treatments by freeze-drying and reductive thermal annealing convert the printed intermediate mixture into a hybrid structure consisting of MoS2 nanoparticles anchored on the surface of 2D rGO nanosheets in a macroporous framework, which is fully characterized with FESEM, TEM, XRD, Raman spectroscopy and TGA. The resulting hybrid MoS2-rGO aerogels are studied as anodes for sodiumion batteries. They present a high initial specific capacity over 429 mAh/g at C/3.3 rate in the potential range of 2.5–0.10 V (vs Na+/Na). The process involves both reversible 2 Na+ insertion and slow irreversible conversion of MoS2 to metallic Mo. At higher rates, the conversion reaction is suppressed and the electrode is dominated by fast Na+ intercalation with good stability. This demonstrates that the 3D printing technology can be used as a processing technique to control the materials properties for energy storage

Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Prognostics and health management (PHM) applications can prevent engines from potential serious accidents by predicting the remaining useful life (RUL). Recently, data-driven methods have been widely used to solve RUL problems. The network architecture has a crucial impact on the experiential performance. However, most of the network architectures are designed manually based on human experience with a large cost of time. To address these challenges, we propose a neural architecture search (NAS) method based on gradient descent. In this study, we construct the search space with a directed acyclic graph (DAG), where a subgraph represents a network architecture. By using softmax relaxation, the search space becomes continuous and differentiable, then the gradient descent can be used for optimization. Moreover, a partial channel connection method is introduced to accelerate the searching efficiency. The experiment is conducted on C-MAPSS dataset. In the data processing step, a fault detection method is proposed based on the k-means algorithm, which drops large valueless data and promotes the estimation performance. The experimental result shows that our method achieves superior performance with the highest estimation accuracy compared with other popular studies

Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search

Prognostics and health management (PHM) applications can prevent engines from potential serious accidents by predicting the remaining useful life (RUL). Recently, data-driven methods have been widely used to solve RUL problems. The network architecture has a crucial impact on the experiential performance. However, most of the network architectures are designed manually based on human experience with a large cost of time. To address these challenges, we propose a neural architecture search (NAS) method based on gradient descent. In this study, we construct the search space with a directed acyclic graph (DAG), where a subgraph represents a network architecture. By using softmax relaxation, the search space becomes continuous and differentiable, then the gradient descent can be used for optimization. Moreover, a partial channel connection method is introduced to accelerate the searching efficiency. The experiment is conducted on C-MAPSS dataset. In the data processing step, a fault detection method is proposed based on the k-means algorithm, which drops large valueless data and promotes the estimation performance. The experimental result shows that our method achieves superior performance with the highest estimation accuracy compared with other popular studies

Proteome-wide mendelian randomization investigates potential associations in heart failure and its etiology: emphasis on PCSK9

Summary Background Heart failure (HF) is a prevalent clinical syndrome with diverse etiologies. It is crucial to identify novel therapeutic targets based on underlying causes. Here, we aimed to use proteome-wide Mendelian randomization (MR) analyses to identify the associations between genetically predicted elevated levels of circulating proteins and distinct HF outcomes, along with specific HF etiologies. Methods Protein quantitative trait loci (pQTL) data for circulating proteins were sourced from the Atherosclerosis Risk in Communities (ARIC) study, encompassing 7,213 individuals and profiling 4,657 circulating proteins. Genetic associations for outcomes were obtained from the HERMES Consortium and the FinnGen Consortium. Colocalization analysis was employed to assess the impact of linkage disequilibrium on discovered relationships. For replication, two-sample MR was conducted utilizing independent pQTL data from the deCODE study. Multivariable MR (MVMR) and two-step MR were further conducted to investigate potential mediators. Results Two proteins (PCSK9 and AIDA) exhibited associations with HF in patients with coronary heart disease (CHD), and four proteins (PCSK9, SWAP70, NCF1, and RELT) were related with HF in patients receiving antihypertensive medication. Among these associations, strong evidence from subsequent analyses supported the positive relationship between genetically predicted PCSK9 levels and the risk of HF in the context of CHD. Notably, MVMR analysis revealed that CHD and LDL-C did not exert a complete mediating effect in this relationship. Moreover, two-step MR results yielded valuable insights into the potential mediating proportions of CHD or LDL-C in this relationship. Conclusions Our findings provide robust evidence supporting the association between PCSK9 and concomitant HF and CHD. This association is partly elucidated by the influence of CHD or LDL-C, underscoring the imperative for additional validation of this connection and a thorough exploration of the mechanisms through which PCSK9 directly impacts ischemic HF

Research on evaluation method of energy supply reliability of regional energy Internet

This paper studies the reliability evaluation technology of regional energy Internet and represents a reliability evaluation method considering the optimal load reduction strategy. The application scenario of regional Energy Internet with power system as the core is built. The specific form is that multiple Integrated Energy systems are connected to the superior distribution network through the tie line. The reliability evaluation index system of power system and regional energy Internet is introduced. Based on the modelling of components in the regional energy Internet application scenario in this paper, the operation model and two-state Markov model are mainly established for the renewable energy power generation devices, energy coupling devices and energy storage devices in IES. The optimal load reduction models based on load classification are established for the faults of distribution network lines and the faults of IES internal components connected to the distribution network in regional energy Internet respectively. Through the construction of different application scenarios, the load reduction situation under different faults and different component parameters is analysed, and the corresponding reliability index calculation and result analysis are carried out

Synthesis of Peptide-Based Hybrid Nanobelts with Enhanced Color Emission by Heat Treatment or Water Induction

We demonstrate that an inorganic lanthanide ion (Tb3+) or organic dye molecules were encapsulated in situ into diphenylalanine (FF) organogels by a general, simple, and efficient co-assembly process, which generated peptide-based hybrid nanobelts with a range of colored emissions. In the presence of a photosensitizer (salicylic acid), the organogel can serve as an excellent molecular-donor scaffold to investigate FRET to Tb3+. More importantly, heat treatment or water induction instigated a morphology transition from nanofibers to nanobelts, after which the participation of guest molecules in the FF assembly was promoted and the stability and photoluminescence emission of the composite organogels were enhanced.</p