Article
Mathematics, Applied
Yanyi Nie, Sheng Su, Tao Lin, Yanbing Liu, Wei Wang
Summary: This study proposes a model of vaccination on a hypergraph to explore the influence of individual and group factors on vaccination behavior. The experimental results show that vaccine cost determines vaccination in low adoption strength, while group-payoff is prioritized to promote vaccination in high adoption strength.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Lihong Sun, Qiang He, Yueyang Teng, Qi Zhao, Xin Yan, Xingwei Wang
Summary: In the event of an outbreak, it is important to stop the spread of infectious diseases, and vaccination plays a crucial role in achieving this. The research on infectious disease vaccination strategy aims to effectively control the spread of infectious diseases with limited vaccine resources. This study proposes a novel community-based targeted immunization framework (CTIF) that combines machine learning and evolutionary computation, and outperforms baseline strategies in terms of epidemic threshold optimization.
APPLIED SOFT COMPUTING
(2023)
Article
Multidisciplinary Sciences
Yung-Yu Tsai, Tzu-Ting Yang
Summary: During the COVID-19 pandemic, people voluntarily reduced their healthcare demand due to fears of contagion or COVID-related precautionary behaviors. The decline in healthcare utilization was greater and more persistent for Influenza-like illness compared to non-ILI, indicating the positive public health externality of prevention measures for COVID-19.
Article
Immunology
Fabrizio Bert, Antonino Russotto, Alex Pivi, Benedetta Mollero, Gianluca Voglino, Giancarlo Orofino, Roberta Siliquini
Summary: This study evaluated the KAP of PLWH regarding vaccines and trust in the Italian NHS. A survey involving 160 HIV-positive patients was conducted at a hospital in Turin, Italy. Results showed that some patients had hesitations or misconceptions about vaccination and there were concerns about trust in the Italian NHS and communication by healthcare workers.
Review
Green & Sustainable Science & Technology
Emmy Wassenius, Beatrice Crona
Summary: Human activities have caused erosion of the biosphere and introduced various risks. Risk assessments are a powerful tool for addressing sustainability challenges, but their effectiveness is hindered by the limited engagement with the multifaceted nature of risk and the lack of integration of social-ecological thinking into risk assessment. This Perspective reviews different risk definitions and identifies five challenges that need to be overcome to turn risk assessments into a comprehensive toolbox for dealing with the uncertainties of a complex future.
Article
Automation & Control Systems
Ying Cui, Luyang Yu, Yurong Liu, Wenbing Zhang, Fawaz E. Alsaadi
Summary: This paper investigates the non-fragile state estimation problem for a class of continuous-time delayed complex networks. A dynamic event-triggering mechanism is applied to improve resource utilization efficiency and gain matrices of the estimator are computed based on certain matrix inequalities to ensure robustly exponential boundedness for estimation error dynamics. An illustrative simulation is presented to demonstrate the validity of the proposed non-fragile estimator.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Editorial Material
Immunology
Rebecca Reece, Curt G. Beckwith
Summary: The results of the Infectious Diseases (ID) fellowship match over the past decade have raised concerns about the future of the specialty. Despite the high demand for ID expertise during the COVID-19 pandemic, the increase in ID fellowship applicants was short-lived, as indicated by the disappointing 2023 match results. Low interest in ID is attributed to various factors, including comparatively low salaries. Urgent solutions are needed to grow the ID workforce for the sake of public health.
JOURNAL OF INFECTIOUS DISEASES
(2023)
Article
Public, Environmental & Occupational Health
Nadine C. Lages, Luka J. Debbeler, Michael Blumenschein, Josianne Kollmann, Hermann Szymczak, Daniel A. Keim, Harald T. Schupp, Britta Renner
Summary: The study examined the accuracy of risk perceptions for three infectious diseases using three different standards, highlighting the dynamic changes in risk perceptions during epidemic/seasonal and nonepidemic/off-season times.
Article
Medicine, General & Internal
Joel G. Ray, Alison L. Park
Summary: This study compared outcomes between different blood types and different vaccine types for SARS-CoV-2 infection and severe COVID-19, showing that vaccination can reduce the risk of infection and disease. After receiving the first dose of mRNA or Ad-V vaccine, the risk of infection decreased, as did the risk of severe COVID-19.
Review
Immunology
Piotr Rzymski, Halina Falfushynska, Andrzej Fal
Summary: The unprovoked aggression by Russian military forces on Ukraine in February 2022 led to a high influx of refugees, posing risks of infectious diseases like COVID-19, measles, pertussis, tetanus, and polio. Urgent measures, including vaccination and maintaining good immunization levels, are crucial to mitigate these risks.
CLINICAL INFECTIOUS DISEASES
(2022)
Article
Mathematics, Applied
Xinjie Fu, Jinrong Wang
Summary: In this paper, we establish an SAIQR epidemic network model and explore the global stability of the disease in both disease-free and endemic equilibria. We also consider the control of epidemic transmission through non-instantaneous impulsive vaccination and demonstrate the sustainability of the model. Finally, we validate the results through numerical simulations using a scale-free network.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos G. Vamvoudakis
Summary: A method is developed for obtaining safe initial policies for uncertain systems using ADP techniques and kernelized Lipschitz estimation. The multiplier matrices learned are used in semidefinite programming frameworks to compute admissible initial control policies with provably high probability, enabling safe initialization and constraint enforcement while ensuring exponential stability of the closed-loop system equilibrium.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Medicine, General & Internal
Cho Lee Wong, Alice W. Y. Leung, Oscar Man Hon Chung, Wai Tong Chien
Summary: This study explored factors influencing COVID-19 vaccination uptake among the general public in a developed country. The results showed that factors such as job affected by COVID-19, having an income source, perceived good/excellent physical health status, perceived COVID-19 exposure, good/excellent knowledge of COVID-19, learning about the vaccine from printed materials, and perceiving family members at risk of contracting COVID-19 were associated with vaccination uptake.
Article
Veterinary Sciences
Joel Fernando Soares Filipe, Stefania Lauzi, Lucrezia Pina, Paola Dall'Ara
Summary: This study in Italy found that the majority of cats (91%) were vaccinated, with 80% of cats vaccinated in the last 3 years. Veterinarians play a significant role in owners' decision-making process, with cat owners trusting their advice on vaccination.
BMC VETERINARY RESEARCH
(2021)
Article
Environmental Sciences
Rastko Jovanovic, Milos Davidovic, Ivan Lazovic, Maja Jovanovic, Milena Jovasevic-Stojanovic
Summary: This study proposes a novel statistical model based on a two-layer contact and information graph to investigate the impact of disease prevalence on voluntary general population vaccination during the COVID-19 outbreak. Results show that the type of disease information significantly influences individual vaccination decisions, with globally or locally broadcasted disease information having different impacts on vaccination. Additionally, prioritizing elderly population vaccination may lead to an increase in infected cases and a reduction in mortality.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Computer Science, Artificial Intelligence
Kaiwen Huang, Jie Gong, Ping Li, Jinsong Zhao
Summary: Recent years have seen significant improvements in collaborative filtering techniques with the help of graph convolutional networks (GCNs). However, there is a problem called oversmoothing that limits GCNs from benefiting from deep architecture - all nodes in the graph tend to converge to the same representation. This issue also exists in graph neural network based CF models. To address this, the authors propose a regularization technique named CateReg, which penalizes the distances between nodes in the embedding space based on the category of their relationships. The evaluation experiments show that this regularization method leads to improvements on recall and NDGC compared to state-of-the-art models.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Kezan Li, Mengchen Wang, Qi Yang, Yi Qin, Haifeng Zhang
Summary: In this article, a new nonlinear stochastic network model is proposed to achieve successive lag synchronization. Both constant and adaptive pinning control strategies are designed to regulate the synchronization. The theoretical results are validated through numerical simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Interdisciplinary Applications
Yaoshen Fan, Shaohua Zhang, Xiaokang Du, Guangzhou Wang, Shoubing Yu, Shentang Dou, Shenliang Chen, Hongyu Ji, Ping Li, Fucheng Liu
Summary: In this paper, an extended three-dimensional model of the Yellow River Estuary is established and calibrated using field data. The study explores the factors affecting the extension of the low salinity zone and the retardation time of salinity change in the estuary. The optimal time for reducing estuary salinity is suggested.
JOURNAL OF HYDROINFORMATICS
(2023)
Article
Computer Science, Information Systems
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: We adopt reservoir computing, a machine learning technique, to study synchronization phenomena in complex networks. By constructing a coupled configuration, we demonstrate that coupled reservoir oscillators exhibit synchrony with the learned dynamical system. Through this synchronization scheme, we recover the observed system's bifurcation behavior solely based on its chaotic dynamics. Our work provides an alternative framework for studying synchronization phenomena in nature when only observed data are available.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Jia-Wei Wang, Hai-Feng Zhang, Xiao-Jing Ma, Jing Wang, Chuang Ma, Pei-Can Zhu
Summary: In this paper, a privacy-preserving framework named HE-ranking is proposed to identify influential nodes in networks based on homomorphic encryption protocol. The method collaboratively computes the nodes' importance and protects the sensitive information of each private network, effectively identifying the influential nodes while preserving privacy.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2023)
Article
Optics
He Huang, Yaoshuai Li, Chengzhi Qin, Wenwan Li, Lange Zhao, Chen Liu, Bing Wang, Chi Zhang, Peixiang Lu
Summary: In this study, the spectral self-imaging effect for a frequency comb is demonstrated using a four-wave mixing time lens. The time lens is created by applying a temporal quadratic phase modulation to the input signal pulses, which results in a frequency comb in the Fourier spectrum. The modulation is achieved by a Gaussian pump pulse in an external single-mode fiber. When both the signal and pump pulses are injected into a highly nonlinear fiber, four-wave mixing Bragg scattering occurs, leading to periodic revivals of the input frequency comb as the pump pulse propagates periodically. The study also reveals the impact of the envelope width of input pulses on the output spectrum width.
Article
Physics, Multidisciplinary
Siyang Jiang, Jin Zhou, Michael Small, Jun-an Lu, Yanqi Zhang
Summary: Searching for key nodes and edges in a network has been a longstanding problem. Recently, there has been increased attention on the cycle structure in networks. This study proposes a ranking algorithm for cycle importance by identifying key cycles that contribute significantly to the network's dynamics. The researchers provide a concrete definition of importance using the Fiedler value and present a neat index for ranking cycles based on the sensitivity of the Fiedler value to different cycles. Numerical examples demonstrate the effectiveness of this method.
PHYSICAL REVIEW LETTERS
(2023)
Article
Mathematics, Applied
Lixiang Liu, Shanshan Chen, Michael Small, Jack Murdoch Moore, Keke Shang
Summary: This paper presents a novel SIRS model on scale-free networks that considers behavioral memory and time delay to depict an adaptive behavioral feedback mechanism in the spread of epidemics. The study includes a rigorous analysis of the dynamics of the model, determines the basic reproduction number R0, uniform persistence, and global asymptotic stability of equilibria. The model exhibits a sharp threshold property, and optimal control strategies for effective vaccination and treatment are demonstrated. Stochastic network simulations validate the findings and indicate that time delay does not affect R0, but behavioral memory does.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Editorial Material
Biology
Shannon D. Algar, Jennifer Rodger, Michael Small
PHYSICS OF LIFE REVIEWS
(2023)
Article
Engineering, Industrial
Yucheng Hao, Limin Jia, Enrico Zio, Yanhui Wang, Michael Small, Man Li
Summary: The researchers studied the optimization repair strategy for high-speed trains by establishing an interdependent network and introducing a resilience metric based on network theory. They developed an interdependent machine-electricity-communication network and related cascading failure models and proposed comprehensive robustness metrics for the network and nodes. They solved the resilience optimization model of the network using a tabu search algorithm. They analyzed the optimal repair strategy for different numbers of failed nodes and analyzed the characteristics of the preferentially repaired node. The optimal repair strategy is not necessarily determined by topological metrics.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Energy & Fuels
Xiaoming Chen, Chuanping Wang, Rong Wu, Yingze Han, Rui Huang, Ping Li, Xueyan Zhong, Yuan Zhong
Summary: In this work, a graph neural network model is developed to automatically and timely diagnose ethane recovery quality based on temperature variations of the demethanizer's sensor array. By learning temperature vibration features, the classifier predicts the qualified/unqualified ethane recovery quality and locates abnormal sensors using attention scores on the edges.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
Article
Physics, Fluids & Plasmas
Jack Murdoch Moore, Haiying Wang, Michael Small, Gang Yan, Huijie Yang, Changgui Gu
Summary: The network correlation dimension controls the distribution of network distance in terms of a power-law model and has significant impacts on both structural properties and dynamical processes. We have developed new maximum likelihood methods that can robustly and objectively identify network correlation dimension as well as a bounded interval of distances where the model accurately represents the structure. We have also compared the traditional practice of estimating correlation dimension with a proposed alternative method using the fraction of nodes at a distance modeled as a power law.
Article
Energy & Fuels
Yuan Zhong, Jing Zhou, Tao Zhang, Jianxin Yang, Ping Li, Dan Deng
Summary: In this study, a Transformer network model named NTformer is proposed for predicting the wellhead pressure changes during the construction of oil and gas well fracturing. By applying data cleaning, temporal encoding, feature conversion, and Transformer structure, accurate prediction of future multi-step pressure is achieved.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
Article
Physics, Multidisciplinary
Eugene Tan, Shannon D. Algar, Debora Correa, Thomas Stemler, Michael Small
Summary: A method of constructing a discretised network representation of a system's attractor is proposed and its applicability in identifying dynamical change points in different systems is demonstrated.
COMMUNICATIONS PHYSICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Tongfeng Weng, Xiaolu Chen, Zhuoming Ren, Huijie Yang, Jie Zhang, Michael Small
Summary: This study investigates the collective behavior of multiply moving reservoir computing oscillators. These oscillators gradually exhibit coherent rhythmic behavior when their number is large enough, showing excellent agreement with their learned dynamical system. Furthermore, the oscillators can exhibit significantly distinct collective behaviors resembling bifurcation phenomenon when changing a critical reservoir parameter. Intermittent synchronization emerges among the oscillators when studying a continuous chaotic system.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Applied
Melanie Kobras, Valerio Lucarini, Maarten H. P. Ambaum
Summary: In this study, a minimal dynamical system derived from the classical Phillips two-level model is introduced to investigate the interaction between eddies and mean flow. The study finds that the horizontal shape of the eddies can lead to three distinct dynamical regimes, and these regimes undergo transitions depending on the intensity of external baroclinic forcing. Additionally, the study provides insights into the continuous or discontinuous transitions of atmospheric properties between different regimes.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Shu-hong Xue, Yun-yun Yang, Biao Feng, Hai-long Yu, Li Wang
Summary: This research focuses on the robustness of multiplex networks and proposes a new index to measure their stability under malicious attacks. The effectiveness of this method is verified in real multiplex networks.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Julien Nespoulous, Guillaume Perrin, Christine Funfschilling, Christian Soize
Summary: This paper focuses on optimizing driver commands to limit energy consumption of trains under punctuality and security constraints. A four-step approach is proposed, involving simplified modeling, parameter identification, reformulation of the optimization problem, and using evolutionary algorithms. The challenge lies in integrating uncertainties into the optimization problem.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Alain Bourdier, Jean-Claude Diels, Hassen Ghalila, Olivier Delage
Summary: In this article, the influence of a turbulent atmosphere on the growth of modulational instability, which is the cause of multiple filamentation, is studied. It is found that considering the stochastic behavior of the refractive index leads to a decrease in the growth rate of this instability. Good qualitative agreement between analytical and numerical results is obtained.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Ling An, Liming Ling, Xiaoen Zhang
Summary: In this paper, an integrable fractional derivative nonlinear Schrodinger equation is proposed and a reconstruction formula of the solution is obtained by constructing an appropriate Riemann-Hilbert problem. The explicit fractional N-soliton solution and the rigorous verification of the fractional one-soliton solution are presented.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Marzia Bisi, Nadia Loy
Summary: This paper proposes and investigates general kinetic models with transition probabilities that can describe the simultaneous change of multiple microscopic states of the interacting agents. The mathematical properties of the kinetic model are proved, and the quasi-invariant asymptotic regime is studied and compared with other models. Numerical tests are performed to demonstrate the time evolution of distribution functions and macroscopic fields.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Carlos A. Pires, David Docquier, Stephane Vannitsem
Summary: This study presents a general theory for computing information transfers in nonlinear stochastic systems driven by deterministic forcings and additive and/or multiplicative noises. It extends the Liang-Kleeman framework of causality inference to nonlinear cases based on information transfer across system variables. The study introduces an effective method called the 'Causal Sensitivity Method' (CSM) for computing the rates of Shannon entropy transfer between selected causal and consequential variables. The CSM method is robust, cheaper, and less data-demanding than traditional methods, and it opens new perspectives on real-world applications.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Feiting Fan, Minzhi Wei
Summary: This paper focuses on the existence of periodic and solitary waves for a quintic Benjamin-Bona-Mahony (BBM) equation with distributed delay and diffused perturbation. By transforming the corresponding traveling wave equation into a three-dimensional dynamical system and applying geometric singular perturbation theory, the existence of periodic and solitary waves are established. The uniqueness of periodic waves and the monotonicity of wave speed are also analyzed.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Wangbo Luo, Yanxiang Zhao
Summary: We propose a generalized Ohta-Kawasaki model to study the nonlocal effect on pattern formation in binary systems with long-range interactions. In the 1D case, the model displays similar bubble patterns as the standard model, but Fourier analysis reveals that the optimal number of bubbles for the generalized model may have an upper bound.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Corentin Correia, Ana Cristina Moreira Freitas, Jorge Milhazes Freitas
Summary: The emergence of clustering of rare events is due to periodicity, where fast returns to target sets lead to a bulk of high observations. In this research, we explore the potential of a new mechanism to create clustering of rare events by linking observable functions to a finite number of points belonging to the same orbit. We show that with the right choice of system and observable, any given cluster size distribution can be obtained.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Enyu Fan, Changpin Li
Summary: This paper numerically studies the Allen-Cahn equations with different kinds of time fractional derivatives and investigates the influences of time derivatives on the solutions of the considered models.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Yuhang Zhu, Yinghao Zhao, Chaolin Song, Zeyu Wang
Summary: In this study, a novel approach called Time-Variant Reliability Updating (TVRU) is proposed, which integrates Kriging-based time-dependent reliability with parallel learning. This method enhances risk assessment in complex systems, showcasing exceptional efficiency and accuracy.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Chiara Cecilia Maiocchi, Valerio Lucarini, Andrey Gritsun, Yuzuru Sato
Summary: The predictability of weather and climate is influenced by the state-dependent nature of atmospheric systems. The presence of special atmospheric states, such as blockings, is associated with anomalous instability. Chaotic systems, like the attractor of the Lorenz '96 model, exhibit heterogeneity in their dynamical properties, including the number of unstable dimensions. The variability of unstable dimensions is linked to the presence of finite-time Lyapunov exponents that fluctuate around zero. These findings have implications for understanding the structural stability and behavior modeling of high-dimensional chaotic systems.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Christian Klein, Goksu Oruc
Summary: A numerical study on the fractional Camassa-Holm equations is conducted to construct smooth solitary waves and investigate their stability. The long-time behavior of solutions for general localized initial data from the Schwartz class of rapidly decreasing functions is also studied. Additionally, the appearance of dispersive shock waves is explored.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
Article
Mathematics, Applied
Vasily E. Tarasov
Summary: This paper extends the standard action principle and the first Noether theorem to consider the general form of nonlocality in time and describes dissipative and non-Lagrangian nonlinear systems. The general fractional calculus is used to handle a wide class of nonlocalities in time compared to the usual fractional calculus. The nonlocality is described by a pair of operator kernels belonging to the Luchko set. The non-holonomic variation equations of the Sedov type are used to describe the motion equations of a wide class of dissipative and non-Lagrangian systems. Additionally, the equations of motion are considered not only with general fractional derivatives but also with general fractional integrals. An application example is presented.
PHYSICA D-NONLINEAR PHENOMENA
(2024)
