Astronomical Knowledge Entity Extraction in Astrophysics Journal Articles via Large Language Models

Astronomical knowledge entities,such as celestial object identifiers,are crucial for literature retrieval and knowledge graph construction,and other research and applications in the field of astronomy.Traditional methods of extracting knowledge entities from texts face numerous challenging obstacles that are difficult to overcome.Consequently,there is a pressing need for improved methods to efficiently extract them.This study explores the potential of pre-trained Large Language Models(LLMs)to perform astronomical knowledge entity extraction(KEE)task from astrophysical journal articles using prompts.We propose a prompting strategy called PromptKEE,which includes five prompt elements,and design eight combination prompts based on them.We select four representative LLMs(Llama-2-70B,GPT-3.5,GPT-4,and Claude 2)and attempt to extract the most typical astronomical knowledge entities,celestial object identifiers and telescope names,from astronomical journal articles using these eight combination prompts.To accommodate their token limitations,we construct two data sets:the full texts and paragraph collections of 30 articles.Leveraging the eight prompts,we test on full texts with GPT-4and Claude 2,on paragraph collections with all LLMs.The experimental results demonstrate that pre-trained LLMs show significant potential in performing KEE tasks,but their performance varies on the two data sets.Furthermore,we analyze some important factors that influence the performance of LLMs in entity extraction and provide insights for future KEE tasks in astrophysical articles using LLMs.Finally,compared to other methods of KEE,LLMs exhibit strong competitiveness in multiple aspects.

An Intrusion Detection Algorithm Based on Feature Graph

With the development of Information technology and the popularization of Internet,whenever and wherever possible,people can connect to the Internet optionally.Meanwhile,the security of network traffic is threatened by various of online malicious behaviors.The aim of an intrusion detection system(IDS)is to detect the network behaviors which are diverse and malicious.Since a conventional firewall cannot detect most of the malicious behaviors,such as malicious network traffic or computer abuse,some advanced learning methods are introduced and integrated with intrusion detection approaches in order to improve the performance of detection approaches.However,there are very few related studies focusing on both the effective detection for attacks and the representation for malicious behaviors with graph.In this paper,a novel intrusion detection approach IDBFG(Intrusion Detection Based on Feature Graph)is proposed which first filters normal connections with grid partitions,and then records the patterns of various attacks with a novel graph structure,and the behaviors in accordance with the patterns in graph are detected as intrusion behaviors.The experimental results on KDD-Cup 99 dataset show that IDBFG performs better than SVM(Supprot Vector Machines)and Decision Tree which are trained and tested in original feature space in terms of detection rates,false alarm rates and run time.

Triboelectric nanogenerators:the beginning of blue dream

Wave energy is inexhaustible renewable energy.Making full use of the huge ocean wave energy resources is the dream of mankind for hundreds of years.Nowadays,the utilization of water wave energy is mainly absorbed and transformed by electromagnetic generators(EMGs)in the form of mechanical energy.However,waves usually have low frequency and uncertainty,which means low power generation efficiency for EMGs.Fortunately,in this slow current and random direction wave case,the triboelectric nanogenerator(TENG)has a relatively stable output power,which is suitable for collecting blue energy.This article summarizes the main research results of TENG in harvesting blue energy.Firstly,based on Maxwell’s displacement current,the basic principle of the nanogenerator is expounded.Then,four working modes and three applications of TENG are introduced,especially the application of TENG in blue energy.TENG currently used in blue energy harvesting is divided into four categories and discussed in detail.After TENG harvests water wave energy,it is meaningless if it cannot be used.Therefore,the modular storage of TENG energy is discussed.The output power of a single TENG unit is relatively low,which cannot meet the demand for high power.Thus,the networking strategy of large-scale TENG is further introduced.TENG’s energy comes from water waves,and each TENG’s output has great randomness,which is very unfavorable for the energy storage after large-scale TENG integration.On this basis,this paper discusses the power management methods of TENG.In addition,in order to further prove its economic and environmental advantages,the economic benefits of TENG are also evaluated.Finally,the development potential of TENG in the field of blue energy and some problems that need to be solved urgently are briefly summarized.