CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most work has focused on cooperation and collaboration between agents, little work explores competition, another important mechanism that fosters the development of society and economy. In this paper, we seek to examine the competition behaviors in LLM-based agents. We first propose a general framework to study the competition between agents. Then, we implement a practical competitive environment using GPT-4 to simulate a virtual town with two types of agents, including restaurant agents and customer agents. Specifically, restaurant agents compete with each other to attract more customers, where the competition fosters them to transform, such as cultivating new operating strategies. The results of our experiments reveal several interesting findings ranging from social learning to Matthew Effect, which aligns well with existing sociological and economic theories. We believe that competition between agents deserves further investigation to help us understand society better. The code will be released soon.Comment: Technical report; 21 page

A MAC based excitation frequency increasing method for structural topology optimization under harmonic excitations

This work is focused on the topology optimization of structures that are subjected to harmonic force excitation with prescribed frequency and amplitude. As an important objective of such a design problem, the natural resonance frequency of the structure is driven far away from the prescribed excitation frequency for the purpose of avoiding resonance and reducing the vibration level. Therefore when the excitation frequency is higher than the natural resonance frequency of the structure, the natural resonance frequency will decrease, then the optimum topology configuration will be distorted with large amount of gray elements. A MAC (Modal Assurance Criteria) based excitation frequency increasing method is proposed to obtain a desired configuration. MAC is adopted here to track the natural resonance frequency which can provide the baseline reference for the current excitation frequency during the optimum iterative procedure. Then the excitation frequency increases progressively up to its originally prescribed value. By means of numerical examples, the proposed formulation can generate effective topology configurations which can avoid resonance

A MAC based excitation frequency increasing method for structural topology optimization under harmonic excitations

This work is focused on the topology optimization of structures that are subjected to harmonic force excitation with prescribed frequency and amplitude. As an important objective of such a design problem, the natural resonance frequency of the structure is driven far away from the prescribed excitation frequency for the purpose of avoiding resonance and reducing the vibration level. Therefore when the excitation frequency is higher than the natural resonance frequency of the structure, the natural resonance frequency will decrease, then the optimum topology configuration will be distorted with large amount of gray elements. A MAC (Modal Assurance Criteria) based excitation frequency increasing method is proposed to obtain a desired configuration. MAC is adopted here to track the natural resonance frequency which can provide the baseline reference for the current excitation frequency during the optimum iterative procedure. Then the excitation frequency increases progressively up to its originally prescribed value. By means of numerical examples, the proposed formulation can generate effective topology configurations which can avoid resonance

Advancements in electrochemical biosensing for respiratory virus detection: A review

International audienc

Characterization of complete chloroplast genome of Ulva torta (Mertens) Trevisan, 1841

Ulva torta (Mertens) Trevisan, 1841 was a global temperate widespread species. Green tide blooms caused by the green algae of the Ulva species occurred frequently in China. As a newly discovered species in the green tide bloom area, it was necessary to explore the relationship between U. torta and other green algae of the Ulva species. The complete chloroplast genome of U. torta was 105,423 bp in size. A total of 100 genes were annotated in the genome, containing 70 protein-coding genes, 27 transfer RNA (tRNA) genes, and three rRNA genes. The chloroplast genome had high AT content (74.76%). Phylogenetic analysis showed U. torta was clustered with Ulva meridionalis. This work could be useful for studying the evolution and genetic diversity of U. torta

Complete chloroplast genome of Ulva meridionalis (Ulvales: Ulvaceae): an extremely fast-growing green macroalgae

Ulva meridionalis is one of the causal green macroalgae for green tides in Japan and exists in coastal areas of China. In the present study, complete chloroplast (cp) genome sequence of Ulva meridionalis was reported, and the total length of this species was 88,653 bp (GenBank accession number MN889540). The overall base composition of cp genome was A (37.1%), T (39.0%), C (11.4%) and G (12.5%), similar to other Ulva macroalgae within cp genome, and the percentage of A + T (76.1%) was higher than C + G (23.9%). U. meridionalis cp genome encoded 113 genes, including 80 protein-coding genes, 27 transfer RNAs genes and 6 ribosomal RNAs genes. The maximum likelihood phylogenetic analysis shows that Ulva linza is the closest sister species of U. meridionalis

The complete mitochondrial genome of a Green macroalgae species: Ulva meridionalis (Ulvales: Ulvaceae)

Ulva meridionalis, a green macroalgae, is one of the causal species for green tides in Japan and spread into the coast of China. During this research, we sequenced the complete mitochondrial genome of U. meridionalis. The mitogenome is 62,887 bp in length, including 28 encoding genes and 29 tRNA genes. Compared with the Ulva species from mitogenome, the gene order and organization of this mitogenome are similar to most of other determined Ulva mitogenomes, with the nucleotide base composition of A 33.6%, T 32.2%, C 16.2%, and G 18.0%. Phylogenetic analysis shows U. meridionalis is closely related to Ulva flexuosa

Non-structural carbohydrates in maize with different nitrogen tolerance are affected by nitrogen addition.

Non-structural carbohydrates (NSCs) are an important energy source for plant growth and metabolism. Analysis of NSC changes can provide important clues to reveal the adaptation mechanisms of plants to a specific environment. Although considerable differences have been reported in NSCs in response to nitrogen (N) application among crop species and cultivars, previous studies have mostly focused on the differences in leaves and stems. However, the effects of N on the characteristics of accumulation and translocation of NSC in maize with different levels of N tolerance remain unclear. To determine differences in the N levels, two cultivars (N-efficient ZH311 and N-inefficient XY508) were grown in field pots (Experiment I) and as hydroponic cultures (Experiment II) and were supplemented with different concentrations of N fertilizer. In both experiments, low-N stress significantly increased the accumulation of NSCs in maize vegetative organs and increased the translocation rate of NSCs in the stems and their apparent contribution to yield, thereby reducing the yield loss caused by low-N stress. N application had a greater effect on starch content in the vegetative organs of ZH311, but had less effect on soluble sugar (SS) and NSC content in the whole plant and starch content in the ears. ZH311 could convert more starch into SS under low N conditions to adapt to low N environments than XY508, while ensuring that grain yield and starch quantity were not affected. This is evidently an important physiological mechanism involved in this cultivar's tolerance to low N conditions

Effect of Chemical Fertilizer Application on Maize Production in China over the Past 15 Years: A Meta-Analysis

Although there are many new types of environmentally friendly fertilizers that can improve maize yield, chemical fertilizers are the most widespread type of fertilizer used in the agricultural sector of China due to their low cost and ease of application. However, the misuse of chemical fertilizers could lead to environmental problems, such as the massive emission of greenhouse gases (GHG). Therefore, it is important to determine how fertilizer-use efficiency (FUE) could be improved to stabilize or increase maize yield while reducing GHG emissions. In this study, we collected 6618 date records which include three datasets (for N, P, and K) from five maize-growing regions in China from 2005 to 2018, and performed a meta-analysis on the effects of N, K, and P fertilization levels on maize yield, partial factor productivity (PFP), agronomic efficiency (AE), and the carbon footprint of maize production. Additionally, scenario analyses were performed to estimate optimal fertilizer application rates for stabilizing or increasing maize yield while reducing GHG emissions. It was shown that FUE and maize yield responses to fertilization level varied in different regions. Compared to the past, the maize production of China has improved significantly in terms of FUE and its carbon footprint in recent years. Because of improvements in maize cultivars and cultivation technologies, it is possible to decrease N, P, and K application rates and reduce per unit area carbon footprint of maize, without compromising yield. In the future, N fertilization should be reduced by 10% from current levels, and the application of P and K fertilizers should be increased or decreased depending on the conditions of each maize-growing region. Thus, it should be possible to stabilize or even increase yields and reduce GHG emissions of maize production, thereby achieving green and efficient development