Analysis Of Complex Systems Research Articles

A Differentiable Physics-Informed Machine Learning Approach to Model Laser-based Micro-Manufacturing Process

Abstract Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new physics-informed machine learning (PIML) approach is presented to model the geometry of the cut produced by a Magnetically-assisted Laser Induced Plasma Micro Machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

A complex adaptive system analysis of emerging tourism work paradigms

ABSTRACT This conceptual paper offers a novel conceptual analysis of emerging work paradigms that have the potential to reshape the tourism professions, underscoring the need for a comprehensive perspective. It emphasises the necessity of examining the increasingly complex tourism professions through the lens of Complex Adaptive Systems (CAS). Additionally, we examine how a CAS analysis of tourism work paradigms can be effectively conducted. First, five fundamental tenets that guide a CAS exploration of work dynamics were identified. Subsequently, the conceptual foundation of the CAS approach was established through the development of a framework synthesised from existing literature on work paradigms and tourism work. This framework elucidates the mechanisms and implications of evolving tourism work dynamics, with the primary objective of addressing their inherent complexities while offering a precise structure for CAS-based simulation modelling. Moreover, we provide guidance on constructing a simulation model based on this framework to address 12 identified gaps in the current tourism work literature. The theoretical contributions, methodological advancements, and potential research directions arising from the CAS approach benefit in predicting and advancing the future of tourism professions. Finally, practical implications for organisational strategy development and policy-making toward fostering a sustainable tourism work ecosystem are discussed.

A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.

Electric vehicles in transition: Opportunities, challenges, and research agenda – A systematic literature review

This study conducts a systematic literature review on electric vehicle (EV) adoption, mapping critical themes and presenting an integrated framework to advance understanding of EVs' role in sustainable transportation. Drawing on 917 Scopus-indexed articles and 23 stakeholder interviews, it explores economic, environmental, energy, and social (EEES) dynamics through the Theory-Context-Methodology (TCM) framework and causal loop diagrams. Findings reveal dominant theories, such as the Theory of Planned Behavior and Value-Belief Norm Theory, and underscore the importance of methodological approaches like regression analysis and structural equation modeling. Key research themes include adoption intention, green infrastructure, and sustainable transport policy, all aligning with Sustainable Development Goals (SDGs) 7 (Clean Energy), 11 (Sustainable Cities), and 13 (Climate Action). By constructing a causal loop diagram, this study illustrates complex interrelations among EEES factors, highlighting the reinforcing and balancing influences on EV adoption behavior. The proposed integrated framework provides a roadmap for future research, identifying significant gaps and strategic directions for policymakers, industry stakeholders, and researchers. Practical implications include guidelines to foster EV integration into smart cities, addressing infrastructure and environmental challenges while encouraging policy incentives to enhance public adoption. The study's theoretical implications include the expansion of existing behavioral theories, the development of an integrated theoretical framework that combines TCM and CLDs, the introduction of CLDs as a tool for complex systems analysis, and the identification of thematic clusters within EV adoption research. This review supports informed decision-making for sustainable urban mobility, positioning EVs as transformative in the global shift toward eco-friendly transportation solutions.

Probabilistic Analysis of Different Redundant Complex System with Reper for the Unites

This paper presents the reliability and MTTF analysis of a two-state complex with repairable system, consisting of two sub-systems A and two sub-systems B arranged in series, incorporating the concept of hardware failures. Laplace transforms of the various state probabilities have been derived and then reliability of the complex system, at any time t, has been computed by inversion process. MTTF has also been evaluated; availability and steady-state availability for system are derived. The failure times of operating units and repair time of failed units are exponential distributed. Certain important results have been evaluated as special cases. Also, few graphical illustrations are also given at the end to high-light the important results.

Review of the Natural Time Analysis Method and Its Applications

A new concept of time, termed natural time, was introduced in 2001. This new concept reveals unique dynamic features hidden behind time-series originating from complex systems. In particular, it was shown that the analysis of natural time enables the study of the dynamical evolution of a complex system and identifies when the system enters a critical stage. Hence, natural time plays a key role in predicting impending catastrophic events in general. Several such examples were published in a monograph in 2011, while more recent applications were compiled in the chapters of a new monograph that appeared in 2023. Here, we summarize the application of natural time analysis in various complex systems, and we review the most recent findings of natural time analysis that were not included in the previously published monographs. Specifically, we present examples of data analysis in this new time domain across diverse fields, including condensed-matter physics, geophysics, earthquakes, volcanology, atmospheric sciences, cardiology, engineering, and economics.

Generalization of the Carothers equation for linear step growth polymers

In the synthesis of linear step-growth polymers molecular weight control is accomplished through the addition of a controlled amount of monomer carrying the functional group B in excess (B-B or H-B types). The Carothers equation is used to calculate average chain lengths. This paper extends the analysis of chain lengths to include the effect of monofunctional monomers carrying the limiting group A (H-A), allowing the systematic analysis of complex systems containing both bifunctional and monofunctional species of any type (A-A, B-B, H-B and H-A). This refinement is particularly relevant for the synthesis of oligomers from systems with complex feed composition.

Harmonic Analysis and Fractals in the Study of Dynamical Systems: Applied Asymptotic Methods

This research investigates the interplay between chaotic dynamics and fractal geometry in nonlinear dynamical systems, specifically through the analysis of the Logistic and Henon Maps. Even though chaos and fractals are important in many areas including ecology and economics, simultaneous investigation of these two concepts is rather scarce. It is our goal to fill this gap by using modern computational methods and mathematical tools such as harmonic analysis and fractal dimension calculation as well as asymptotic methods. The results show that both systems are chaotic and that the Henon Map is more sensitive to the initial conditions than the Logistic Map according to their Lyapunov exponent values equal to 0.89 and 0.92 respectively. In addition, the fractal dimensions obtained through the box-counting algorithm indicate that the system exhibits more complicated dynamics in the Henon Map (1.45) compared to the Logistic Map (1.35). These insights help in understanding dynamical systems and also bring out a framework to enhance the predictive modelling in real live applications like population growth and financial markets. This study highlights the mere linkage between chaos and fractals to open the ways for the subsequent analysis of complex systems, with the potential application in comprehending the risk and ecological conservation. The incorporation of highly formalized mathematical methods demonstrates the possibility of achieving substantial improvements in predictive performance in various fields of science.

Spatiotemporal variations of surface albedo in Central Asia and its influencing factors and confirmatory path analysis during the 21st century

Surface albedo (SA) is crucial for understanding land surface processes and climate simulation. This study analyzed SA changes and its influencing factors in Central Asia from 2001 to 2020, with projections 2025 to 2100. Factors analyzed included snow cover fraction, fractional vegetation cover, soil moisture, average state climate indices (temperature and precipitation), and extreme climate indices (heatwave indices and extreme precipitation indices). Pearson correlation coefficient, geographical convergent cross mapping, and geographical detector were used to quantify the correlation, causal relationship strength, and impact degree between SA and the influencing factors. To address multicollinearity, ridge regression (RR), geographically weighted ridge regression (GWRR), and piecewise structural equation modeling (pSEM) were combined to construct RR-pSEM and GWRR-pSEM models. Results indicated that SA in Central Asia increased from 2001 to 2010 and decreased from 2011 to 2020, with a projected future decline. There is a strong correlation and significant causality between SA and each factor. Snow cover fraction was identified as the most critical factor influencing SA. Average temperature and precipitation had a greater impact on SA than extreme climate indices, with a 1 °C temperature increase corresponding to a 0.004 decrease in SA. This study enhances understanding of SA changes under climate change, and provides a methodological framework for analyzing complex systems with multicollinearity. The proposed models offer valuable tools for studying interrelated factors in Earth system science.

Normal Approximation of Random Gaussian Neural Networks

In this paper, we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian neural network and (the law of) a random Gaussian vector. Our main results concern deep random Gaussian neural networks with a rather general activation function. The upper bounds show how the widths of the layers, the activation function, and other architecture parameters affect the Gaussian approximation of the output. Our techniques, relying on Stein’s method and integration by parts formulas for the Gaussian law, yield estimates on distances that are indeed integral probability metrics and include the convex distance. This latter metric is defined by testing against indicator functions of measurable convex sets and so allows for accurate estimates of the probability that the output is localized in some region of the space, which is an aspect of a significant interest both from a practitioner’s and a theorist’s perspective. We illustrated our results by some numerical examples. Funding: This research was supported by the European Union’s Horizon 2020 research project WARIFA under grant agreement no. 101017385, by the PRIN project 2022 “Variational Analysis of Complex Systems in Materials Science, Physics and Biology” (CUP B53D23009290006), and by the INdAM project “Modelli ed Algoritmi per dati ad elevata dimensionalità” (CUP E53C23001670001).

Short-Wave Infrared Upconverting Nanoparticles.

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

EP.08E.08 Local Advanced Lung Cancer: Artificial Intelligence, Complex System Analysis, Simulation of Alive Supersysems for Optimal Management

EP.08E.08 Local Advanced Lung Cancer: Artificial Intelligence, Complex System Analysis, Simulation of Alive Supersysems for Optimal Management

EP.03A.01 Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystems

EP.03A.01 Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, Statistics and Simulation of Alive Supersystems

Managing and editing UML documents: structure, integration, automation

Unified Modeling Language (UML) plays an important role in the modern software development process by offering standardized methods for visual modeling, analysis, and design of complex systems. This paper discusses the main aspects of storing, editing, and integrating UML documents with integrated development environments (IDEs). The main focus is on comparing the two main formats for storing UML models – XML Metadata Interchange (XMI) and JavaScript Object Notation (JSON). XMI, being an official standard developed by Object Management Group, provides a high degree of detail and compatibility with various UML tools, making it preferable for large and complex projects that require support for the full UML specification. At the same time, JSON is simple and flexible, which makes it suitable for projects where speed of development and ease of integration are important, although it is inferior to XMI in describing complex aspects of UML models. The paper analyzes in detail the methods of editing UML documents, including manual editing and automated approaches, using APIs and scripts. Manual editing is useful when you need to make small changes to the structure of a UML document, but it can be time-consuming and error-prone when working with large projects. Automation, on the other hand, provides effective tools for mass editing and generating UML elements, which significantly speeds up development and minimizes the likelihood of errors, especially since modern tools such as StarUML offer the necessary tools for programmatically modifying UML documents by integrating them into development processes. Combining of UML documents with integrated development environments also plays a key role in increasing development efficiency, as this allows automating code generation based on UML models, maintaining synchronization between code and diagrams, and facilitating visualization and documentation of architectural solutions. Despite the significant advantages, there are challenges associated with limited support for all UML features and potential conflicts between models and code. This only emphasizes the need for further research in this area. Prospects for further research include the development of new methods and tools for working with UML documents, improving their integration with development environments, and using artificial intelligence to automate the analysis and design of UML models. Attention should also be paid to the using of UML with continuous integration processes, which can significantly increase the flexibility and adaptability of software development. Thus, the study of the structure of storing, editing, and integrating UML documents is an important area for optimizing the design and development of modern software systems.

Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments

Computer simulations have become essential for analyzing complex systems, but high-fidelity simulations often come with significant computational costs. To tackle this challenge, multi-fidelity computer experiments have emerged as a promising approach that leverages both low-fidelity and high-fidelity simulations, enhancing both the accuracy and efficiency of the analysis. In this article, we introduce a new and flexible statistical model, the Recursive Non-Additive (RNA) emulator, that integrates the data from multi-fidelity computer experiments. Unlike conventional multi-fidelity emulation approaches that rely on an additive auto-regressive structure, the proposed RNA emulator recursively captures the relationships between multi-fidelity data using Gaussian process priors without making the additive assumption, allowing the model to accommodate more complex data patterns. Importantly, we derive the posterior predictive mean and variance of the emulator, which can be efficiently computed in a closed-form manner, leading to significant improvements in computational efficiency. Additionally, based on this emulator, we introduce four active learning strategies that optimize the balance between accuracy and simulation costs to guide the selection of the fidelity level and input locations for the next simulation run. We demonstrate the effectiveness of the proposed approach in a suite of synthetic examples and a real-world problem. An R package RNAmf for the proposed methodology is provided on CRAN.

Detection of diffusion anisotropy from an individual short particle trajectory

In parallel with advances in microscale imaging techniques, the fields of biology and materials science have focused on precisely extracting particle properties based on their diffusion behavior. Although the majority of real-world particles exhibit anisotropy, their behavior has been studied less than that of isotropic particles. In this study, we introduce a method for estimating the diffusion coefficients of individual anisotropic particles using short-trajectory data on the basis of a maximum likelihood framework. Traditional estimation techniques often use mean-squared displacement (MSD) values or other statistical measures that inherently remove angular information. Instead, we treated the angle as a latent variable and used belief propagation to estimate it while maximizing the likelihood using the expectation-maximization algorithm. Compared to conventional methods, this approach facilitates better estimation of shorter trajectories and faster rotations, as confirmed by numerical simulations and experimental data involving bacteria and quantum rods. Additionally, we performed an analytical investigation of the limits of detectability of anisotropy and provided guidelines for the experimental design. In addition to serving as a powerful tool for analyzing complex systems, the proposed method will pave the way for applying maximum likelihood methods to more complex diffusion phenomena. Published by the American Physical Society 2024

Exploring Limit Cycles of Differential Equations through Information Geometry Unveils the Solution to Hilbert's 16th Problem.

The detection of limit cycles of differential equations poses a challenge due to the type of the nonlinear system, the regime of interest, and the broader context of applicable models. Consequently, attempts to solve Hilbert's sixteenth problem on the maximum number of limit cycles of polynomial differential equations have been uniformly unsuccessful due to failing results and their lack of consistency. Here, the answer to this problem is finally obtained through information geometry, in which the Riemannian metrical structure of the parameter space of differential equations is investigated with the aid of the Fisher information metric and its scalar curvature R. We find that the total number of divergences of |R| to infinity provides the maximum number of limit cycles of differential equations. Additionally, we demonstrate that real polynomial systems of degree n≥2 have the maximum number of 2(n-1)(4(n-1)-2) limit cycles. The research findings highlight the effectiveness of geometric methods in analyzing complex systems and offer valuable insights across information theory, applied mathematics, and nonlinear dynamics. These insights may pave the way for advancements in differential equations, presenting exciting opportunities for future developments.

Photoinduced Electron-Transfer-Mediated Differential Recognition of Proteins on Plasmonic Surfaces.

Photoinduced enhanced Raman spectroscopy (PIERS) has emerged as an efficient technique for enhancing the vibrational modes of analyte molecules adsorbed on a plasmonic nanoparticle-semiconductor hybrid material through chemical enhancement governed by electron transfer from the semiconductor to the plasmonic nanoparticles under an additional ultraviolet (UV) preirradiation step. The increase in chemical enhancement is imperative in analyzing and detecting pharmaceutically important moieties, such as amino acids and proteins, with a low Raman scattering cross section, even in complex biological environments. Herein, we demonstrate that UV preirradiation induced the creation of additional oxygen vacancies by introducing a low concentration (≈1%) of Ni as a dopant in the 2D platelike morphology of the BiOCl semiconductor; i.e., defect states in the semiconductor can induce charge transfer from the semiconductor to the plasmonic nanoparticles. This phenomenon facilitates electron transfer to the adsorbed analyte on the plasmonic surface. Additionally, we have shown the usefulness of this method in protein immobilization on the substrate surface, followed by the identification of a specific protein in the mixture of proteins. Proteins containing cysteine residues capture these electrons to form a surface-bound thiol group via a transient disulfide electron adduct radical. This allows differential binding of the protein molecules to the semiconductor plasmonic hybrid depending on the concentration of surface cysteine residues in proteins. Through PIERS and principal component analysis, we demonstrate the possibility of probing and distinguishing biomolecules based on their surface composition and secondary structure components even in their mixtures, thus paving the way for efficient analysis of complex biological systems.

Novel approach for precise identification of vibration frequencies and damping ratios from free vibration decay time histories data of nonlinear single degree of freedom models

The significance of Single Degree of Freedom (SDOF) systems lies in their ability to serve as foundational elements for modeling more complex dynamic problems. By capturing essential dynamic behavior with simplicity, SDOF models enable efficient analysis and comprehension of complex systems, justifying the investigation of these simplified models. In nonlinear scenarios, SDOF models result in time series data wherein vibration frequencies vary over time. Classically, time–frequency or Hilbert transforms applied to temporal responses are frequently used to identify the evolution of frequencies and damping ratio over time. These techniques provide results that reflect the spectrum composition achieved for the analyzed time window and present difficulties in precisely determining the magnitude and the exact instant of an effective frequency or damping ratio variation. In this sense, this work introduces a new methodology capable of accurately identifying the vibration frequency as a function of time, i.e., the instantaneous frequency, along with the instantaneous damping ratio. At this initial stage, the focus is on validating the methodology by comparing its performance with the classical approach based on time–frequency transforms. The initial results obtained from synthetic free vibration decay responses of SDOF nonlinear models highlight the accuracy of our findings compared to those obtained from time–frequency transforms. The presented methodology holds promise for further advancement, with potential impacts including structural damage identification, modal identification and nonlinear dynamic analysis.