Article
Mathematics, Interdisciplinary Applications
Branislav M. Randjelovic, Vojislav V. Mitic, Srdjan Ribar, Dusan M. Milosevic, Goran Lazovic, Hans J. Fecht, Branislav Vlahovic
Summary: Several recently published research papers focus on the representation of nanostructures and biomimetic materials using mathematical methods, with fractal theory, artificial neural networks, and graph theory being the most commonly used methods. These methods are versatile, compatible, and complementary when applied in nanostructures, and the paper aims to provide an overview of existing research results in electrochemical and magnetic nanostructure parameter modeling using these three easy-to-use methods. New conclusions regarding their applicability, advantages, and disadvantages in various circumstances are also presented in the paper.
FRACTAL AND FRACTIONAL
(2022)
Review
Automation & Control Systems
John Meluso, Jesse Austin-Breneman, James P. Bagrow, Laurent Hebert-Dufresne
Summary: This article identifies the core elements of CES development processes through a literature review, and proposes the CESIUM model, which helps researchers compare how development process characteristics shape system outcomes.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Abdullah Al Maruf, Sandip Roy
Summary: This study investigates the design of feedback control systems to block observability in a network synchronization model. It presents a general design algorithm that applies state feedback controls to block observability at any group of nodes. The algorithm is based on eigenstructure assignment and allows surgical modification of eigenvectors. A sparser design is obtained by leveraging low-cardinality vertex cutsets, and a regional feedback design is introduced to reduce the required number of actuation nodes. Numerical examples demonstrate the effectiveness of these designs.
Article
Mathematics, Interdisciplinary Applications
BahaaAlDeen M. AboAlNaga, Lobna A. Said, Ahmed H. Madian, Ahmed G. Radwan
Summary: This paper explores the fractal-like behavior of the complex form of Gaussian chaotic map and the ability of digital architectures to mimic it, with the hope that a digital realization of fractals may also be achieved. The study involves analyzing the Gauss map in terms of its bifurcation behavior, time waveform plots, Lyapunov exponent, and attractor performance through parameter variation. Additionally, FPGA implementation of the fractal behavior is discussed, leading to an experimentally displayed fractal entity.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Physics, Multidisciplinary
J. Klinger, R. Voituriez, O. Benichou
Summary: We derive a universal and exact asymptotic form of the splitting probability for symmetric continuous jump processes, which highlights the importance of microscopic dynamics and provides explicit predictions for characterizing the effective random process in light scattering.
PHYSICAL REVIEW LETTERS
(2022)
Article
Neurosciences
Taiane Coelho Ramos, Janaina Mourao-Miranda, Andre Fujita
Summary: Clustering networks with similar connectivity structures is the main goal of this research, instead of clustering the vertices of the graph. We applied this approach to functional brain networks to identify subgroups based on their functional connectivity. Real-world networks exhibit natural fluctuations, which should be taken into consideration. We introduced two clustering methods: k-means for graphs with the same size, and gCEM for graphs with different sizes. Their performance was evaluated using toy models and real-world data. The results demonstrate the effectiveness of our methods in clustering graphs with different connectivity structures, even with the same number of edges, vertices, and degree of centrality.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Psychology, Multidisciplinary
Patrick Smith, Steven C. Hayes
Summary: Relational models of cognition offer actionable models of generative behavior and guidance for computational analogs. However, the black box nature limits scientific and applied progress. This paper presents an attempt to model relational processes using logical derivation scripts and network graph visualizations, providing tools for exploring complex relational models.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Keping Yu, Jerry Chun-wei Lin
Summary: This paper proposes a graph embedding-based intelligent industrial decision system for modeling complex sewage treatment processes. Experimental results show that the efficiency of this system exceeds traditional methods by 6%-12%, and it is not susceptible to parameter changes.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Andrei A. Klishin, Dani S. Bassett
Summary: Random walks are commonly used as a model for exploring and discovering complex networks. Exposure theory, a statistical mechanics framework, is introduced to predict the learning of nodes and edges in various types of networks and demonstrates a universal trajectory for edge learning.
JOURNAL OF COMPLEX NETWORKS
(2022)
Article
Mechanics
Zu-Yu Qian, Cheng Yuan, Jie Zhou, Shi-Ming Chen, Sen Nie
Summary: This study explores the incorporation of conformity behavior into network control and finds that controlling undirected networked systems with conformity becomes easier after the network connectivity reaches a critical point. The research also identifies key nodal structural characteristics and proposes an optimal control strategy to reduce energy consumption. These findings are validated in synthetic and real networks, highlighting their significance in describing control energy in networked systems.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2022)
Article
Neurosciences
Scott Trinkle, Sean Foxley, Gregg Wildenberg, Narayanan Kasthuri, Patrick La Riviere
Summary: Diffusion MRI tractography is a noninvasive method for measuring the structural connectome in humans, but recent studies have shown limitations due to local uncertainties in fiber orientations. Geometry plays a larger role in determining the topology of graphs produced by tractography compared to neural tracers, underestimating weights at long distances and affecting the placement of network hubs. The role of spatial embedding in modular structure and network efficiency is explored in both modalities, with geometric biases inherent in tractography quantified for future validation efforts.
Article
Mathematics, Interdisciplinary Applications
Pablo Medina, Sebastian C. Carrasco, Maria Sara Jofre, Jose Rogan, Juan Alejandro Valdivia
Summary: This study investigates the possibility of characterizing city transportation using the analogy of diffusing particles. By using a cellular automata model in a road network, the dynamics of vehicles in a city are recreated. The mean velocity and diffusion coefficient are calculated through statistical analysis of the parametric curves generated by car movements. The study also discusses the potential use of the diffusion coefficient to characterize a city, similar to the traditional use of mean speed and flux rate, and explores methods to calculate this quantity in a smart city.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Mathematical & Computational Biology
Manuel Miranda, Ernesto Estrada
Summary: This paper proposes a degree-biased advection process for undirected networks and provides computational evidence of its utility by studying artificial graphs and a real-life landscape network.
MATHEMATICAL MODELLING OF NATURAL PHENOMENA
(2022)
Article
Neurosciences
Shun Yao, Hong-Ying Zhang, Ren Wang, Ding-Sheng Cheng, Jing Ye
Summary: This study found topological abnormalities in the white matter structural network of asymptomatic patients with early-stage LA, potentially providing a connectome-based measure for detecting LA before clinical symptoms appear.
Article
Geosciences, Multidisciplinary
Aurelie Charles, Guillaume Bouleux, Chantal Cherifi
Summary: Information management is crucial for success in various fields, including disaster relief operations. Despite being overlooked in the past, collecting and using data has become more important in the last decade, with the advent of data pooling. This article proposes using complex networks to improve understanding of relief responses, considering various elements such as existing infrastructures, local resources, and relief activities. The approach can be used to analyze weaknesses in a country's situation before disasters occur, and to capture the dynamics of the system during operations.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2022)
Article
Engineering, Environmental
Junmin Ji, Wenlei Xie
JOURNAL OF HAZARDOUS MATERIALS
(2020)
Article
Green & Sustainable Science & Technology
Wenlei Xie, Hao Wang
Article
Automation & Control Systems
Yuhao Yi, Zhongzhi Zhang, Stacy Patterson
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Chemistry, Multidisciplinary
Wenlei Xie, Hao Wang
JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY
(2020)
Article
Green & Sustainable Science & Technology
Wenlei Xie, Mengyun Huang
Article
Chemistry, Physical
Wenlei Xie, Chunli Gao, Hongyan Wang
Article
Energy & Fuels
Wenlei Xie, Hao Wang
Summary: The study aims to prepare an efficient and magnetically separable Bronsted acid catalyst for converting low-quality oils to biodiesel. By coating silica shells on magnetic Fe3O4 nanoparticles and grafting copolymerization of dual acidic ionic liquids, a solid catalyst with strong catalytic activity was obtained. This catalyst shows good catalytic activities towards transesterification of soybean oil and esterification of free fatty acids, making it a robust solid acid catalyst for one-pot biodiesel production from low-quality acidic oils.
Article
Chemistry, Applied
Junmin Ji, Wenlei Xie
Summary: The efficient magnetic adsorbent Fe3O4@ATP, prepared by dispersing Fe3O4 nanoparticles on attapulgite, showed a high removal efficiency of 86.82% for AFB(1) from contaminated oils. With paramagnetic properties and good adsorption kinetics, it exhibited potential for practical applications in AFB(1) elimination from oils.
Article
Green & Sustainable Science & Technology
Wenlei Xie, Chunli Gao, Jiangbo Li
Summary: Efficient and reusable catalysts are desired for improved biodiesel production, and a solid catalyst was successfully developed by incorporating ZIF-8 MOF into Fe3O4 nanoparticles and encapsulating a vanadium-substituted heteropolyacid. The catalyst showed good magnetic responsiveness and achieved simultaneous transesterification of soybean oil and esterification of free fatty acids, with high conversion rates and good reusability.
Article
Green & Sustainable Science & Technology
Wenlei Xie, Yunfei Xiong, Hongyan Wang
Summary: This study developed an efficient and reusable solid base catalyst for biodiesel production, showing excellent catalytic activity and magnetic responsiveness. The catalyst has potential for sustainable and clean biodiesel production, with successful transesterification of soybean oil to biodiesel.
Article
Energy & Fuels
Lihong Guo, Wenlei Xie, Chunli Gao
Summary: The heterogenization of Keggin-structure heteropoly acids (HPAs) on activated carbon modified with metal ions (Cs+, K+ and Ag+) resulted in efficient solid catalysts for biodiesel production. The 35%H6PV3MoW8O40/AC-Ag hybrid catalyst showed high catalytic performance in transesterification of soybean oil, with good recyclability and resistance to impurities. This novel solid catalyst offers an efficient and sustainable approach for biodiesel production, particularly from low-quality oils.
Article
Green & Sustainable Science & Technology
Quan Wang, Wenlei Xie, Lihong Guo
Summary: The development of feasible and ecofriendly processes for biodiesel production is of great importance for green chemistry and sustainable development. In this study, a one-pot heterogeneous catalytic system using MoO3/ZrO2/KIT-6 catalyst was developed for the synthesis of high-performance biodiesel. The optimized catalyst showed excellent tolerance and reusability.
Article
Green & Sustainable Science & Technology
Gaoqiang Zhang, Wenlei Xie
Summary: Ameliorating reactant mass transfer and fabricating multiple active sites are effective strategies for synthesizing efficient biodiesel catalysts using acidic oils as feedstocks. In this study, hierarchical porous solid acids with Zr and Mo oxides as active sites were prepared and showed improved catalytic performances due to mass transfer enhancement and more accessible active sites. The solid catalyst displayed high water and FFA resistance and could be easily recycled, making it suitable for cost-effective biodiesel production from low-grade acidic oils.
JOURNAL OF CLEANER PRODUCTION
(2023)
Review
Green & Sustainable Science & Technology
Wenlei Xie, Jiangbo Li
Summary: Biodiesel has gained attention as a potential alternative energy source. The use of heterogeneous catalysts for biodiesel production offers a more eco-friendly and attractive process. Magnetic solid catalysts have become a recent research focus due to their easy separation and minimal mass loss. This review discusses various magnetic solid catalysts, their preparation, catalytic performances, and future development trends, with a focus on their active species, surface properties, and synergisms. The review also presents possible reaction mechanisms and influential parameters for improved catalytic performances.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Green & Sustainable Science & Technology
Qiaofei Zhang, Wenlei Xie, Jiangbo Li, Lihong Guo
Summary: Bimetallic silica mesoporous composites with different Zr/Al molar ratios were synthesized via a one-step hydrothermal method and sulfated with sulfuric acid to obtain a series of sulfated Zrx-Aly-KIT-6 catalysts. The catalysts exhibited a large amount of acidic sites derived from Zr and Al incorporation and further sulfation, and the acidic strength was greatly influenced by the inductive effect of SO42- species. The interconnected mesoporous structure and enhanced mass transfer facilitated efficient transesterification reactions.