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

Early detection of cotton verticillium wilt based on root magnetic resonance images

Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton

Critical roles of edge turbulent transport in the formation of high-field-side high-density front and density limit disruption in J-TEXT tokamak

This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and the high-field-side high-density (HFSHD) front. These phenomena were extensively investigated by using Langmuir probe and Polarimeter-interferometer diagnostics

Health Consequences Among COVID-19 Convalescent Patients 30 Months Post-Infection in China

The health consequences among COVID-19 convalescent patients 30 months post-infection were described and the potential risk factors were determined. In August 2022 we recruited 217 COVID-19 convalescent patients who had been diagnosed with COVID-19 in February 2020. These convalescent patients were residents of multiple districts in Wuhan, China. All convalescent patients completed a detailed questionnaire, laboratory testing, a 6-min walk test, a Borg dyspnea scale assessment, lung function testing, and had a chest CT. The potential risk factors for health consequences among COVID-19 convalescent patients 30 months post-infection were identified using a multivariate logistic regression model. The majority of convalescent patients were in good overall health and returned to work 30 months post-infection; however, 62.2% of the convalescent patients had long COVID symptoms. The most common symptoms were chest pain, fatigue, and dizziness or headaches. The convalescent patients with severe symptoms had a significantly higher proportion of depression disorder ( P = 0.044) and lower health-related quality of life ( P = 0.034) compared to the convalescent patients with mild symptoms. Compared to convalescent patients who were not vaccinated, convalescent patients who received three vaccines had significantly less fatigue, lower anxiety and depression scores, and had a better health-related quality of life (all P < 0.05). Older age was associated with a higher risk of long COVID (OR = 1.52, 95% CI = 1.16–2.02) and chest CT abnormalities (OR = 1.75, 95% CI = 1.33–2.36). Female gender was associated with a higher risk of anxiety (OR = 3.20, 95% CI = 1.24–9.16) and depression disorders (OR = 2.49, 95% CI = 1.11–5.92). Exercise was associated with a lower risk of anxiety (OR = 0.41, 95% CI = 0.18–0.93). Notably, vaccination protected convalescent patients from developing long COVID symptoms (OR = 0.18, 95% CI = 0.06–0.50), anxiety disorders (OR = 0.22, 95% CI = 0.07–0.71), and depression disorders (OR = 0.33, 95% CI = 0.12–0.92). The majority of COVID-19 convalescent patients were in good overall health 30 months post-infection and returned to work. More attention should be paid to convalescent patients who are older, female, physically inactive, and not vaccinated

Simulation Analysis for Opening Performance of Medium Voltage Vacuum Circuit Breaker Based on ADAMS and Maxwell

The circuit breakers play a important role in control and protect the power systemand the vacuum circuit breaker has beenwidely used in the field of medium voltage with its excellent opening performance.Virtual prototyping technology is alsobecamemore and more popularin design and optimization of the vacuum circuit breaker. In this paper, the electromagnetic simulation software Ansoft Maxwell is used to analyze the electric repulsion of the circuit breaker in the case of open the rated short circuit breaking current. The 3D model that wasbuilt by CREOis imported into ADAMS. Thenconstraints, contact force, and the electric repulsion forcethat was analysezed in Ansoft Maxwell is added into the 3D model.Therefore, we can carry on the multi-body dynamics simulation to the 3D model. Then We can get the openingperformance of the vacuum circuit breakerin the condition of open circuit rated short circuit breaking current. The simulation results show that the circuit breaker can still meet the performance requirements in the condition of open circuit rated short circuit breaking current

Simulation Analysis for Opening Performance of Medium Voltage Vacuum Circuit Breaker Based on ADAMS and Maxwell

The circuit breakers play a important role in control and protect the power systemand the vacuum circuit breaker has beenwidely used in the field of medium voltage with its excellent opening performance.Virtual prototyping technology is alsobecamemore and more popularin design and optimization of the vacuum circuit breaker. In this paper, the electromagnetic simulation software Ansoft Maxwell is used to analyze the electric repulsion of the circuit breaker in the case of open the rated short circuit breaking current. The 3D model that wasbuilt by CREOis imported into ADAMS. Thenconstraints, contact force, and the electric repulsion forcethat was analysezed in Ansoft Maxwell is added into the 3D model.Therefore, we can carry on the multi-body dynamics simulation to the 3D model. Then We can get the openingperformance of the vacuum circuit breakerin the condition of open circuit rated short circuit breaking current. The simulation results show that the circuit breaker can still meet the performance requirements in the condition of open circuit rated short circuit breaking current

Damage detection of long-span bridges using stress influence lines incorporated control charts

Numerous long-span bridges have been built throughout the world in recent years. These bridges are progressively damaged by continuous usage throughout their long service life. The failure of local structural components is detrimental to the performance of the entire bridge, furthermore, detecting the local abnormality at an early stage is difficult. This paper explores a novel damage detection method for long-span bridges by incorporating stress influence lines (SILs) in control charts, and validates the efficacy of the method through a case study of the Tsing Ma Suspension Bridge. Damage indices based on SILs are subsequently proposed and applied to hypothetical damage scenarios in which one or two critical bridge components are subjected to severe damage. The comparison study suggests that the first-order difference of SIL change is an accurate indicator for location of the damage. To some extent, different levels of damage can be quantified by using SILs incorporating with X-bar control chart. Results of this study indicate that the proposed SIL-based method offers a promising technique for damage detection in long-span bridges. ? 2014 Science China Press and Springer-Verlag Berlin Heidelberg

Locate damage in long-span bridges based on stress influence lines and information fusion technique

To ensure bridge safety and functionality under in-service conditions, detecting local abnormalities of a long-span bridge at the early stage is always a desirable but challenging task. Stress influence lines (SIL) or its derivatives are recognized as very promising indices for damage detection. Compared with bridge global responses (such as displacement and acceleration), stress/strain can be more conveniently measured and is often more sensitive to local damages. This paper explores a novel damage localization approach by synthesizing SIL measurements from multiple locations, in which Dempster-Shafer data fusion technique is utilized. Compared with the measurement from a single sensor, more reliable damage-related information with the improved sensitivity and capability in damage localization can be obtained by synthesizing the measured SILs from a number of sensors. The effectiveness of the proposed method is validated through a numerical case study of the Tsing Ma Suspension Bridge. Different hypothetical scenarios, including single-damage case, double-damage, and no-damage cases, are considered in the validation. The comparison with the damage detection results using single-sensor data clearly indicates that the data fusion technique effectively enhance the consistency in the information (e.g., damage-induced structural change) and minimize non-consistent information (e.g. "noise" effect) from multiple sensors installed close to damage. The increasing number of sensors benefits the damage detection results. Excellent damage detection accuracy can be achieved, if different types of bridge components are properly selected for the monitoring. Therefore, it is promising to use the proposed approach in this study in the damage localization of real-world long-span bridges. Parametric studies are conducted to examine the effects of parameter selections and noise levels in this approach

The complete chloroplast genome of Amana baohuaensis (Liliaceae)

Amana baohuaensis is a new species that was just named in 2019. Here, we obtained the complete chloroplast (cp) genome of A. baohuaensis using the Illumina paired-end sequencing technology. The cp genome has a typical quadripartite structure with 150,757 bp in length, containing a large single-copy (LSC) region of 81,757 bp, a small single-copy (SSC) region of 16,962 bp, and two inverted repeat (IR) regions of 26,019 bp. The total GC content is 36.73%, of which, the GC content of LSC, SSC and IR regions are 34.63%, 30.11% and 42.20%, respectively. The cp genome of A. baohuaensis contains 111 unique genes, including 78 protein-coding genes, 29 tRNA genes, and four rRNA genes. The Maximum Parsimony (MP) phylogenetic analysis suggested that A. baohuaensis had the closest relationship with A. wanzhensis, and all Amana species grouped together with high bootstrap support