Self-similar planar graphs as models for complex networks

In this paper we introduce a family of planar, modular and self-similar graphs which have small-world and scale-free properties. The main parameters of this family are comparable to those of networks associated to complex systems, and therefore the graphs are of interest as mathematical models for these systems. As the clustering coefficient of the graphs is zero, this family is an explicit construction that does not match the usual characterization of hierarchical modular networks, namely that vertices have clustering values inversely proportional to their degrees.Comment: 10 pages, submitted to 19th International Workshop on Combinatorial Algorithms (IWOCA 2008

Planar unclustered scale-free graphs as models for technological and biological networks

Many real life networks present an average path length logarithmic with the number of nodes and a degree distribution which follows a power law. Often these networks have also a modular and self-similar structure and, in some cases - usually associated with topological restrictions- their clustering is low and they are almost planar. In this paper we introduce a family of graphs which share all these properties and are defined by two parameters. As their construction is deterministic, we obtain exact analytic expressions for relevant properties of the graphs including the degree distribution, degree correlation, diameter, and average distance, as a function of the two defining parameters. Thus, the graphs are useful to model some complex networks, in particular several families of technological and biological networks, and in the design of new practical communication algorithms in relation to their dynamical processes. They can also help understanding the underlying mechanisms that have produced their particular structure.Comment: Accepted for publication in Physica

MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents

Significant advancements have occurred in the application of Large Language Models (LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate within task-oriented social contexts are under-explored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behavior and produce meaningful results. To bridge this gap, we introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities. We situate these agents in a simulated job fair environment as a case study to scrutinize their coordination skills. We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills. Our evaluation demonstrates that these agents show promising performance. However, we also uncover limitations that hinder their effectiveness in more complex coordination tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations

Analysis on Influencing Factors of Services Satisfaction with Family Doctors and Contract Signing----Take Hangzhou as an Example

This research is financed by the 2019 Zhejiang University of Science and Technology extracurricular Science and Technology Innovation and Practice project; Science and Technology Innovation Activity Plan for Zhejiang province University Students in 2020 (Grant No.2020R415022);Innovation and Entrepreneurship Training Program for University Students of Zhejiang University of Science and Technology in 2020(Grant No.2020-CXCY033); National Innovation and Entrepreneurship Training Plan for College Students in 2020(Grant No.202011057033). Abstract Family doctors, as an important part of primary medical care, are the cornerstone for the full implementation of the Healthy China policy and the realization of health for all in 2030. In order to understand the current situation of family doctor service and contract signing in Hangzhou, on the basis of a questionnaire survey with residents in various districts of Hangzhou, the importance matrix was adopted to analyze the satisfaction of contracted residents, principal component analysis and binary logistic regression were used to explore the influencing factors of signing the contract, it has been found that the service level, medical guidance, derivative services and future development significantly influenced the residents' intention to sign contracts. According to the research and analysis, some suggestions have been proposed to further improve the family doctor policy. Keywords: Family doctor; Medical services; Satisfaction; Contract situation DOI: 10.7176/JESD/11-20-01 Publication date:October 31st 202

Exact analytical solution of average path length for Apollonian networks

The exact formula for the average path length of Apollonian networks is found. With the help of recursion relations derived from the self-similar structure, we obtain the exact solution of average path length, $\bar{d}_t$, for Apollonian networks. In contrast to the well-known numerical result $\bar{d}_t \propto (\ln N_t)^{3/4}$ [Phys. Rev. Lett. \textbf{94}, 018702 (2005)], our rigorous solution shows that the average path length grows logarithmically as $\bar{d}_t \propto \ln N_t$ in the infinite limit of network size $N_t$. The extensive numerical calculations completely agree with our closed-form solution.Comment: 8 pages, 4 figure

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Exact solution of mean geodesic distance for Vicsek fractals

The Vicsek fractals are one of the most interesting classes of fractals and the study of their structural properties is important. In this paper, the exact formula for the mean geodesic distance of Vicsek fractals is found. The quantity is computed precisely through the recurrence relations derived from the self-similar structure of the fractals considered. The obtained exact solution exhibits that the mean geodesic distance approximately increases as an exponential function of the number of nodes, with the exponent equal to the reciprocal of the fractal dimension. The closed-form solution is confirmed by extensive numerical calculations.Comment: 4 pages, 3 figure

Planar unclustered graphs to model technological and biological networks

Many real life networks present an average path length logarithmic with the number of nodes and a degree distribution which follows a power law. Often these networks have also a modular and self-similar structure and, in some cases - usually associated with topological restrictions- their clustering is low and they are almost planar. In this paper we introduce a family of graphs which share all these properties and are defined by two parameters. As their construction is deterministic, we obtain exact analytic expressions for relevant properties of the graphs including the degree distribution, degree correlation, diameter, and average distance, as a function of the two defining parameters. Thus, the graphs are useful to model some complex networks, in particular technological and biological networks

Parameter sensitivity and economic analyses of an interchange-fracture enhanced geothermal system

Previous research has shown that interchange-fracture enhanced geothermal systems show desirable heat extraction performance. However, their parameter sensitivity has not been systematically investigated. In this study, a three-dimensional, unsteady flow and heat transfer model for an enhanced geothermal system with an interchange-fracture structure was established. The influences of pivotal parameters, including stimulated reservoir volume permeability, fracture spacing, fracture aperture, and injection flow rate on the thermal extraction performance of the interchange-fracture enhanced geothermal system were systematically researched. In addition, the economics of this system were evaluated. The results show that the heat extraction performance of the interchange-fracture system is significantly affected by a change of stimulated reservoir volume permeability and injection flow rate. Increasing permeability reduces electricity costs and improves economic income, while increasing the injection flow rate increases output power but hinders the long-term running stability of the system. Our research provides guidance for the optimal design of an interchange-fracture enhanced geothermal system.Cited as: Yu, G., Liu, C., Zhang, L., Fang, L. Parameter sensitivity and economic analyses of an interchange-fracture enhanced geothermal system. Advances in Geo-Energy Research, 2021, 5(2): 166-180, doi: 10.46690/ager.2021.02.0