Identify Critical Nodes in Complex Network with Large Language Models

Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Language Models (LLMs), to generate a function called "score\_nodes" which can further be used to identify crucial nodes based on their assigned scores. Our model consists of three main components: Manual Initialization, Population Management, and LLMs-based Evolution. It evolves from initial populations with a set of designed node scoring functions created manually. LLMs leverage their strong contextual understanding and rich programming skills to perform crossover and mutation operations on the individuals, generating excellent new functions. These functions are then categorized, ranked, and eliminated to ensure the stable development of the populations while preserving diversity. Extensive experiments demonstrate the excellent performance of our method, showcasing its strong generalization ability compared to other state-of-the-art algorithms. It can consistently and orderly generate diverse and efficient node scoring functions. All source codes and models that can reproduce all results in this work are publicly available at this link: \url{https://anonymous.4open.science/r/LLM4CN-6520

S3: Social-network Simulation System with Large Language Model-Empowered Agents

Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering to the widely employed agent-based simulation paradigm, we employ prompt engineering and prompt tuning techniques to ensure that the agent's behavior closely emulates that of a genuine human within the social network. Specifically, we simulate three pivotal aspects: emotion, attitude, and interaction behaviors. By endowing the agent in the system with the ability to perceive the informational environment and emulate human actions, we observe the emergence of population-level phenomena, including the propagation of information, attitudes, and emotions. We conduct an evaluation encompassing two levels of simulation, employing real-world social network data. Encouragingly, the results demonstrate promising accuracy. This work represents an initial step in the realm of social network simulation empowered by LLM-based agents. We anticipate that our endeavors will serve as a source of inspiration for the development of simulation systems within, but not limited to, social science

Inverse design and demonstration of on-chip silicon high-order mode pass filter

We propose a concept of a high-order mode (HOM) pass filter based on the inverse-designed mode-routing, which enables an ultra-compact footprint and broad bandwidth. To validate the concept, we experimentally demonstrate two types of HOM pass filters using the direct-binary search topology optimization algorithm. In the first HOM pass filter, the mode-routing region is constructed using an inverse-designed adiabatic coupler, while the second filter utilizes a tapered asymmetric directional coupler. The subwavelength units based on the functional regions of both filters have an ultra-compact footprint of 4 µm × 800 nm. The experimental results indicate that the insertion losses of two HOM-pass filters are 3.13 and 1.94 dB, respectively, and their mode cross-talks are −15.8 and −27.36 dB at the center wavelength of 1550 nm. Both HOM pass filters exhibit high performance over a broad bandwidth of 130 nm

Study on the interaction mechanism in the Pr–Fe–As system

The influence of different heat preservation temperatures on the interaction in the Pr–Fe–As ternary system and the principle for generating the interaction products of the Pr–Fe–As ternary system were studied by metallographic microscopy, scanning electron microscopy, and x-ray diffraction. Results showed that the α -Fe with As (i.e., the compound formed when the solubility of As in Fe exceeds the maximum solubility), Fe _2 As, PrAs, and a small amount of Fe _17 Pr _2 were the main products when the atomic ratio of Pr: As is 1:3 and heat preservation for 20 h at 1173 K, 1223 K and 1273 K. PrAs decreased as the temperature increased, while the α -Fe with As decreased as the temperature decreased, and Fe _2 As increased gradually as α -Fe with As decreased. In the Pr–Fe–As ternary system, the diffusion of Pr is mainly short-range diffusion and double vacancies, and the PrAs develops in the margin of the penetration region, preventing the diffusion of As in Fe

Effects of Erbuzhuyu Decoction Combined with Acupuncture on Endometrial Receptivity Are Associated with the Expression of miR-494-3p

Background/Aim. Erbuzhuyu decoction (EBZYD) is a traditional Chinese medicine (TCM) formula and has been used in infertility treatment. Meanwhile, acupuncture is also used to treat female infertility. However, it is unclear whether EBZYD combined with acupuncture has better therapeutic effect. The aim of this study was to explore the effect of EBZYD combined with acupuncture and investigate its mechanism in superovulation mice. Methods. The mice received the treatment of EBZYD, acupuncture, EBZYD combined with acupuncture, or miR-494-3p agomir combined with EBZYD and acupuncture. The blastocysts’ number, endometrial microstructure, and endometrial thickness were observed, followed by the detection of endometrial receptivity-related factors, PI3K/Akt/mTOR pathway-related proteins, and miR-494-3p expression using quantitative real-time polymerase chain reaction (qRT-PCR) or western blot. Luciferase reporter assay was performed to confirm the targeting relationship between HOXA10 and miR-494-3p. Results. EBZYD combined with acupuncture treatment could increase the number of blastocysts, pinopodes, endometrial thickness, and the expression of endometrial receptivity-related factors, and the treatment effect of EBZYD combined with acupuncture was better than EBZYD or acupuncture alone. In addition, EBZYD combined with acupuncture treatment activated PI3K/Akt/mTOR pathway and inhibited the expression of miR-494-3p. HOXA10 is one of the target genes of miR-494-3p. Overexpression of miR-494-3p reversed the therapeutic effect of EBZYD combined with acupuncture and suppressed the expression of HOXA10 and the activity of PI3K/Akt/mTOR pathway. Conclusion. This study suggests that EBZYD combined with acupuncture could improve endometrial receptivity in superovulation mice via miR-494-3p/HOXA10 axis

东天山圪塔山口镁铁-超镁铁质岩体地球化学、锆石U-Pb年代学及其对Ni-Cu成矿的指示/Geochemistry and zircon U-Pb geochronology of Getashankou mafic-ultramafic intrusions, eastern Tianshan, and its implication for Ni-Cu mineralization[J]

新疆新近发现的圪塔山口镍铜硫化物矿床位于东天山康古尔-黄山镍铜硫化物成矿带的东端.矿区包含4个镁铁-超镁铁质岩体,其中Ⅰ、Ⅱ、Ⅲ号岩体均见镍铜硫化物矿化.本文利用SIMS锆石U-Pb法测得Ⅰ号矿化岩体辉长岩年龄为282.6±1.9Ma,不仅与东天山地区其它含Ni-Cu矿化的镁铁-超镁铁质岩体形成时代一致,而且与塔里木玄武岩、镁铁质岩墙及北山地区的镁铁-超镁铁质岩体形成时限相一致.其形成可能与造山后伸展背景下的地幔柱叠加作用有关.地球化学数据表明圪塔山口岩体具有高Mg特征,除2个辉长岩样品m/f值较低外,其余14个样品集中于2.73～ 5.05之间,属铁质超基性岩.岩石稀土元素配分模式为右倾式,轻、重稀土比2.64～3.39;含长角闪辉橄岩及部分含长角闪橄辉岩和含长橄辉岩δEu具正异常,可能与这3个岩相中存在斜长石的结晶有关.微量元素蛛网图表明岩石富集大离子亲石元素Cs、Rb、Ba、K、Sr,富集高场强元素U、Pb,亏损高场强元素Th、Nb等特征.主量元素SiO2-(Na2O +K2O)与(FeOT/MgO)-FeOT图解、微量元素相关图及微量元素比值相关图说明圪塔山口岩体成岩物质为来源于亏损地幔的钙碱性玄武质岩浆,成岩作用以岩浆结晶分异为主导,并受到地壳的混染作用,具有较好的镍铜硫化物矿床成矿潜力