Article
Mathematics
Taehyeok Choi, Kyungeun Cho, Yunsick Sung
Summary: Research shows that the application of artificial intelligence in games is growing in importance. Most commercial games still use AI based on a finite state machine, but this approach decreases user satisfaction. A new AI method is needed that applies domain-specific expertise to existing reinforcement learning algorithms.
Article
Computer Science, Interdisciplinary Applications
Elias Fernandez Domingos, Jelena Grujic, Juan C. Burguillo, Francisco C. Santos, Tom Lenaerts
Summary: This study introduces a population-based learning model to investigate how individuals facing collective risks acquire strategies through reinforcement learning amidst uncertainties. The research shows that uncertainty about time frames can lead to more extreme reactions and polarization, reducing the number of agents contributing fairly in collective action problems.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Automation & Control Systems
Giacomo Como, Fabio Fagnani, Lorenzo Zino
Summary: This article investigates the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks, characterizing equilibrium points and proving global convergence. Results show that under specific conditions, convergence to a Nash equilibrium is possible from every fully supported initial state.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Biochemistry & Molecular Biology
Aleksandra E. Kornienko, Viktoria Nizhynska, Almudena Molla Morales, Rahul Pisupati, Magnus Nordborg
Summary: This study provides a comprehensive annotation of lncRNAs in Arabidopsis, revealing the natural variation in lncRNA transcription across different regions. The study finds that lncRNA loci are abundant but highly variable and largely repressed, with transposable elements enriching variability and silencing in intergenic lncRNAs.
Article
Physics, Multidisciplinary
Chaoqian Wang, Kangshuo Hui
Summary: A new type of group game called the involution game is proposed, where individuals compete for fixed resources by effort. Research shows that more abundant resources promote involution, while an increase in the relative utility of more effort aggravates involution. Increasing the cost of more effort may exacerbate involution in some cases, but ultimately leads to a disincentive to involution.
Article
Automation & Control Systems
Juan Martinez-Piazuelo, Nicanor Quijano, Carlos Ocampo-Martinez
Summary: This paper investigates the problem of seeking generalized Nash equilibrium in population games with general affine equality and convex inequality constraints. A novel payoff dynamics model is designed to guide decision-making agents to a generalized Nash equilibrium, where constraints are satisfied and no agent has incentives to deviate from their selected strategies. The paper provides sufficient conditions for the asymptotic stability of the equilibria set in stable population games using Lyapunov stability theory. Additional results characterizing the properties of the equilibria set are also presented for general continuous population games. The theoretical developments are numerically validated using a Cournot game with various market-related and production-related constraints.
Article
Sport Sciences
Sohei Takamori, Michael J. Hamlin, David C. Kieser, Doug King, Patria Hume, Tetsuya Yamazaki, Masashi Hachiya, Peter D. Olsen
Summary: This study investigated the movement patterns of 20 male amateur rugby players during 16 senior premier division one matches. The results showed that backs covered more distance in high-speed running, while forwards had higher collision loads. This information is valuable for coaches and support staff in managing the players' workloads.
JOURNAL OF STRENGTH AND CONDITIONING RESEARCH
(2022)
Article
Mathematics, Interdisciplinary Applications
Zhen-Wei Ding, Guo-Zhong Zheng, Chao-Ran Cai, Wei-Ran Cai, Li Chen, Ji-Qiang Zhang, Xu-Ming Wang
Summary: Cooperation is essential in ecosystems and human society, and reinforcement learning plays a crucial role in understanding its emergence. This study focuses on the individual level dynamics of cooperation in a two-agent system. It is found that strong memory and long-sighted expectation lead to the emergence of Coordinated Optimal Policies (COPs) which maintain high cooperation levels. However, when memory weakens and expectation decreases, cooperation becomes unstable, and the policy of defection prevails. The study also suggests that tolerance can be a precursor to a crisis in cooperation. The findings provide insights into the stability of cooperation and have implications for more complex scenarios.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Biology
Emil F. Frolich, Uffe H. Thygesen
Summary: Determining the spatial distribution and population dynamics of animals is important in ecology. This study proposes a model for determining the existence and uniqueness of habitat distributions and population dynamics of interacting populations, in both continuous and discrete habitats. The theoretical results are illustrated using a Rosenzweig-MacArthur model, and the emergent dynamics are analyzed using efficient numerical methods. The theoretical approach has the potential to study complex ecosystems.
JOURNAL OF MATHEMATICAL BIOLOGY
(2022)
Article
Biodiversity Conservation
Justin Merondun, Elizabeth M. Kierepka, Aaron B. A. Shafer, Dennis L. Murray
Summary: The study found that the eastern wolf in south-central Canada faces competition threats from gray wolves and coyote-like canids, with limited advantageous niche space, requiring conservation measures to maintain the survival of the population. The research also suggests that competitive disadvantage can limit species' recovery efforts, hence necessitating management measures to promote ecological differentiation between groups.
BIOLOGICAL CONSERVATION
(2021)
Article
Computer Science, Artificial Intelligence
Yan Ngee Khaw, Ryszard Kowalczyk, Quoc Bao Vo, Nasrudin Abd Rahim, Hang Seng Che
Summary: Agent-based evolutionary game theory studies the dynamics of autonomous agents and introduces additional reward parameters to the learning algorithm. The replicator dynamics is extended to joint-action transition-state reward, showing that it can be changed to single-state reward and independent-action reward. Numerical simulations confirm the effectiveness of the approach and provide insights into coordination games.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Min-Bin Chen
Summary: Virtual reality technology in gaming provides immersive experience, but advanced computer graphics technology is needed to improve display effects under hardware constraints. Developing Cardboard games is challenging, but with specialized tools, students can easily create high-quality games while reducing programming pressure.
JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS
(2021)
Article
Computer Science, Artificial Intelligence
Liping Tang
Summary: This paper suggests that ambiguity is an inevitable feature of learning languages even without complexity costs, as ambiguous words occur more frequently and are easier to learn.
JOURNAL OF LOGIC LANGUAGE AND INFORMATION
(2022)
Article
Ecology
Gabriel R. Palma, Wesley A. C. Godoy, Eduardo Engel, Douglas Lau, Edgar Galvan, Oliver Mason, Charles Markham, Rafael A. Moral
Summary: The Pattern-Based Prediction (PBP) method is proposed for predicting population outbreaks, which shows a competitive performance compared to state-of-the-art machine learning methods and provides interpretability. The PBP method uses previous time series values as predictors and has been proven feasible in predicting outbreaks in simulated datasets and real aphid time series data. This method is especially valuable for non-specialists like ecologists who need a quantitative approach for pest monitoring.
ECOLOGICAL INFORMATICS
(2023)
Article
Economics
Craig A. Depken, John M. Gandar, Dmitry A. Shapiro
Summary: This study provides a theoretical and empirical analysis of strategic momentum and psychological momentum in best-of-three contests. The results show that both strategic momentum and psychological momentum play a role in determining the outcomes of these contests.
JOURNAL OF SPORTS ECONOMICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Arthur A. B. Pessa, Rafael S. Zola, Matjaz Perc, Haroldo Ribeiro
Summary: Machine learning methods are crucial for the development of materials science. Researchers have used image analysis to map optical textures into complex networks and investigate different physical properties of liquid crystals.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Biochemistry & Molecular Biology
Jenny Liu, Luis A. N. Amaral, Sinan Keten
Summary: A promising approach to study protein dynamics is to represent it using networks and take advantage of well-established methods from network science. Most studies construct protein dynamics networks using correlation measures, which are only applicable under specific conditions. In this study, the researchers applied an inverse approach to build networks based on protein dihedral angles, resulting in physically interpretable and robust networks. By using this method, dynamical differences were identified for proteins with structural similarity. The study demonstrates the importance of using the inverse approach to extract networks from protein dynamics.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Physics, Fluids & Plasmas
Ervin K. Lenzi, Aloisi Somer, Rafael S. Zola, Luciano R. da Silva, Marcelo K. Lenzi
Summary: In this study, the solutions of a generalized diffusion-like equation are investigated, taking into account spatial and time fractional derivatives as well as non-local terms. The Green function approach is used to obtain solutions and analyze the spreading of the system, revealing a diverse range of behaviors. The obtained results are also connected to anomalous diffusion processes.
Article
Thermodynamics
A. Somer, S. Galovic, E. K. Lenzi, A. Novatski, K. Djordjevic
Summary: We propose temperature distribution predictions for photothermal systems by extending the dual-phase lag approach. This extension incorporates fractional dual-phase lag from kinetic relaxation time into the GCE-II and GCE-III models. By solving a one-dimensional problem with planar and periodic excitation, we obtain the temperature distribution and Photoacoustic (PA) signal for transmission setups. Furthermore, we analyze the effects of fractional order derivatives and kinetic relaxation time. The derived models show promising results in explaining the experimentally observed behavior of PA signals measured on thin films with an inhomogeneous internal structure.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2023)
Article
Mathematics, Interdisciplinary Applications
Aloisi Somer, Andressa Novatski, Marcelo Kaminski Lenzi, Luciano Rodrigues da Silva, Ervin Kaminski Lenzi
Summary: We accurately predict the contribution of thermoelastic bending to the Photoacoustic signal by analyzing an extension of the dual-phase lag model of thermal diffusion theory. Incorporating the effects of fractional differential operators, we determine the temperature distribution and accurately assess the thermoelastic effects in solid samples. This study emphasizes the importance of considering fractional differential operators in the analysis of thermoelastic bending and contributes to understanding the mechanisms behind the PA signal.
FRACTAL AND FRACTIONAL
(2023)
Article
Mathematics, Interdisciplinary Applications
Haroldo Ribeiro, Diego D. Lopes, Arthur A. B. Pessa, Alvaro F. Martins, Bruno R. da Cunha, Sebastian Goncalves, Ervin K. Lenzi, Quentin S. Hanley, Matjaz Perc
Summary: Recent advances in deep learning have allowed researchers to develop algorithms for analyzing and modeling complex networks. This study explores the potential of graph convolutional networks in predicting various properties of criminal networks, and shows impressive accuracy in recovering missing partnerships, distinguishing types of associations, predicting monetary exchanges, and anticipating partnerships and recidivism in corruption networks. The deep learning models outperform shallow learning approaches and provide high-quality embeddings for node and edge properties. Additionally, the models inherit the advantages of the GraphSAGE framework, including generalization to unseen nodes and scalability for large graph structures.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Luiz R. Evangelista, Ervin K. Lenzi
Summary: We investigate the dynamics of a system composed of two different subsystems when subjected to different nonlinear Fokker-Planck equations by considering the H-theorem. We use the H-theorem to obtain the conditions required to establish a suitable dependence for the system's interaction that agrees with the thermodynamics law when the nonlinearity in these equations is the same. In this framework, we also consider different dynamical aspects of each subsystem and investigate a possible expression for the entropy of the composite system.
Article
Physics, Multidisciplinary
Diego B. Sanchez-Espinosa, Eric Hernandez-Ramirez, Marcelo del Castillo-Mussot
Summary: We constructed 42 independent weighted directed networks from surveys in classrooms, which showed general trends of students' friendships and animosities based on age and grade level. Friendship entropy was higher than enmity entropy in all classrooms, indicating fewer students experienced enmity links. Popular students had more reciprocal nominations among themselves and opposite-sex friendships increased with age.
Article
Multidisciplinary Sciences
Andre S. S. Sunahara, Arthur A. B. Pessa, Matjaz Perc, Haroldo V. V. Ribeiro
Summary: This study investigates the COVID-19 pandemic in the city of Maringa, Brazil, and finds that despite prompt and robust interventions, cases increased exponentially during the early spread of the disease. Non-pharmaceutical interventions had a significant impact on controlling the pandemic, but the city's measures were primarily reactive. Maringa faced six waves of cases, with the third and fourth waves being the deadliest and overwhelming the local healthcare system. The study highlights the heterogeneities in the spread and impact of the disease compared to the national context and other similarly sized cities. Importance rating: 8 out of 10.
SCIENTIFIC REPORTS
(2023)
Article
Mathematics
Ervin Kaminski Lenzi, Luiz Roberto Evangelista, Luciano Rodrigues da Silva
Summary: We investigated two different approaches, fractional calculus and the extension of entropy concept, in order to extend standard quantum statistical mechanics. By using the thermal Green function formalism, we analyzed the dynamics and thermodynamics aspects of each case, examining how the extensions affect the behavior of system-related quantities, particularly fluctuations.
Article
Mathematics, Interdisciplinary Applications
Enrique C. Gabrick, Paulo R. Protachevicz, Ervin K. Lenzi, Elaheh Sayari, Jose Trobia, Marcelo K. Lenzi, Fernando S. Borges, Ibere L. Caldas, Antonio M. Batista
Summary: This paper proposed a numerical method to solve the fractional reaction-diffusion equation under different kernels and obtained general inequalities for stability conditions. The effectiveness of the method was validated through numerical examples.
FRACTAL AND FRACTIONAL
(2023)
Article
Engineering, Chemical
Wesley P. do Carmo, Alexandre F. Santos, Marcelo Kaminski Lenzi, Montserrat Fortuny, Ervin K. Lenzi
Summary: The objective of this study was to investigate and model the viscoelastic creep behavior of two Brazilian crude oils and their emulsions. The fractional model was able to accurately describe the experimental data.
DIGITAL CHEMICAL ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Diego D. Lopes, Bruno R. da Cunha, Alvaro F. Martins, Sebastian Goncalves, Ervin K. Lenzi, Quentin S. Hanley, Matjaz Perc, Haroldo Ribeiro
Summary: Recent research has shown that structural properties of criminal networks can be used to recover missing criminal partnerships, distinguish between different types of criminal and legal associations, and predict the total amount of money exchanged among criminal agents with outstanding accuracy. Additionally, this approach can anticipate future criminal associations in corruption networks with significant accuracy.
SCIENTIFIC REPORTS
(2022)
Article
Physics, Fluids & Plasmas
Luciano Zunino, Felipe Olivares, Haroldo V. Ribeiro, Osvaldo A. Rosso
Summary: This paper introduces the permutation Jensen-Shannon distance as a symbolic tool for quantifying the similarity between arbitrary time series. The distance measure combines the Jensen-Shannon divergence with an encoding scheme based on the sequential ordering of the data elements. Numerical and experimental applications demonstrate the versatility and robustness of this ordinal symbolic distance in characterizing and discriminating different dynamics. The simplicity, low computational cost, wide applicability, and ability to handle large amounts of data effectively make this method valuable in addressing current big data challenges.