472 research outputs found
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, ,
for Apollonian networks. In contrast to the well-known numerical result
[Phys. Rev. Lett. \textbf{94}, 018702
(2005)], our rigorous solution shows that the average path length grows
logarithmically as in the infinite limit of network
size . 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
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