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This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions. [ABSTRACT FROM AUTHOR]

In industrial production and engineering operations, the health state of complex systems is critical, and predicting it can ensure normal operation. Complex systems have many monitoring indicators, complex coupling structures, non-linear and time-varying characteristics, so it is a challenge to establish a reliable prediction model. The belief rule base (BRB) can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities. Since each indicator of the complex system can reflect the health state to some extent, the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction. A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper. Firstly, the LSTMis introduced to predict the trend of the indicators in the system. Secondly, theDensity PeakClustering (DPC) algorithmis used to determine referential values of indicators for BRB, which effectively offset the lack of expert knowledge. Then, the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference. Finally, the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump. [ABSTRACT FROM AUTHOR]

This paper presents a systemic view of human cognition that suggests complexityis an essential feature of such a system. It draws on the embodied, distributed, and extended cognition paradigms to outline the elements and the mechanisms that define cognition. In doing so, it uses an agent-based computational model (the TS 1.0.5Model) with a focus on learning mechanisms as they reflect on individual competence to gain insights on how cognition works. Results indicate that cognitive dynamics do not depend solely on macro structural elements, nor do they depend uniquely on individual characteristics. Instead, more insights and understanding are available through the consideration of all elements together as they co-evolve and interact over time. This perspective illustrates the essential role of how we define the meso domain and constitutes a clear indication that cognitive systems are indeed complex. [ABSTRACT FROM AUTHOR]

Liner shipping accounts for over 80 % of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed "Multi-Scale Higher-Order Dependencies (MSHOD)" that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network's flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development. [ABSTRACT FROM AUTHOR]

This paper explores the place of technological interventions in the conceptualisation of the Water–Energy–Food Nexus (WEF Nexus). The focus is on the just infrastructure interventions required to decarbonise and adapt to the challenges of the climate crisis for sustainable livelihoods. We explore the overlap between two bodies of work, the WEF Nexus and Socio-Technical Systems, grown from different disciplinary perspectives, to scrutinise the extent to which there is a coherent synthesis of work that can examine infrastructure impacts and trade-offs in a WEF system. Following a systematic literature review and analysis, a framework is proposed for water and energy infrastructure interventions to both support sustainable development and recognise infrastructure's role in a just and equitable society. This framework will support the creation of models that are less likely to miss vital components of a system or potential trade-offs and supports a multi-disciplinary approach to infrastructure interventions. [ABSTRACT FROM AUTHOR]

In the mathematical modeling of a far-from-equilibrium complex system an important target is the understanding of the large time behavioun This paper focuses on a continuous-homogeneousconservative mathematical framework coming from the thermostatted kinetic theory recently proposed for the modeling of complex living systems. Specifically by introducing a scaling parameter and letting this parameter going towards zero, the large time behavior of the system is reached, which consists in a nonequilibrium stationary state. The formal proof is obtained in the Lebesgue space L. [ABSTRACT FROM AUTHOR]

Challenges involving economic, environmental, and societal aspects necessitate organisations in business networks to collaborate. The scientific problem central to this paper is the difficulty of building sustainable collaborations. The research question is how to support organisations in building sustainable collaborations in their business relationships. This paper presents a new socio-technical approach to this end, i.e., PDPS (an acronym for Participatory Design of Participatory Systems) and explores its potential in a case study. PDPS is a value-based approach to the participatory design of participatory systems. Such socio-technical systems enable people working in different disciplines, departments, and organisational levels to create sustainable relationships supported by distributed information and communication technology. In a participatory system, participants gain trust, engagement, and empowerment to self-organise actions that produce results they could not have achieved alone. Following PDPS, participants collectively explore challenges in their relationship, define a joint value-based mission, and create a continuous process of self-organisation to fulfil this mission. In a case study, PDPS supported two Dutch business partners in solving recurring transport and logistics issues in retail store refurbishment projects. Turning their traditional business processes into participatory ones led to new solutions for sustainable transport and logistics, more joint business, and more profit. PDPS differs from other approaches in its involvement of all participants in a business relationship, its focus on shared values, and its capacity for creating a continuous process of self-organisation to fulfil a joint mission. This paper may support researchers, practitioners, and organisational policymakers interested in building sustainable collaborations in business networks. [ABSTRACT FROM AUTHOR]

The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: "Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.". This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms. [ABSTRACT FROM AUTHOR]

The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot's operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal. [ABSTRACT FROM AUTHOR]

Background: This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion: This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion: Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society. [ABSTRACT FROM AUTHOR]

Model-based systems engineering (MBSE) is a leading paradigm for the analyses and development of complex systems. However, the development of modeling and simulation infrastructure supporting MBSE is lacking, which limits the application of MBSE. To address this problem, this paper proposes an SES-X methodology that integrates system modeling (following SES philosophy) with system simulation (supported by X language) to support the full lifecycle of MBSE modeling, including system analysis, architecture decomposition, physical modeling, and simulation. In the process, SES-X performs two levels of model pruning for model verification and simulation efficiency. This paper also conducts a case study on a car model to illustrate the effectiveness of the SES-X methodology. [ABSTRACT FROM AUTHOR]

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Contextappropriate decision making should enhance system resilience and mitigate extreme risks. [ABSTRACT FROM AUTHOR]

We present a routing model called adapted diffusion for ad hoc wireless sensor networks that is more energy efficient than directed diffusion. Adapted diffusion is modeled using NetLogo with agent-based modeling. In this agent-based NetLogo model, we set the distance from a random source and the distance from the sink to optimize the routing protocols. By using this routing technique significant energy savings were achieved. We consider a three-dimensional communication network that can be used in a building or a stack of shipping containers. Our model can be extended to a 3D model. [ABSTRACT FROM AUTHOR]

Disasters associated with natural hazards as well as climate change are happening within complex socio-economic systems and desired system states, including sustainable development and resource management, are formulated on the global as well as regional and national levels. However, complex system approaches are yet only rudimentarily incorporated in related applications, and we discuss modeling as well as policy challenges focusing on fiscal risk. As an intermediate step we suggest a gap approach which we relate to fiscal stress levels a complex system may experience due to natural hazard events. We argue that in case of no gaps one can assume a no stress situation and therefore modeling of disruptions including cascading effects is less necessary. However, at the same time we also acknowledge that there is an urgent need to address corresponding challenges with complex systemmethods. Policy-wise our paper responds to concerns for real-world applications and can provide insights to support current discussions within the UNFCCC and Paris Agreement around both adaptation finance and the new funding arrangements for loss and damage from climate impacts established at COP27. [ABSTRACT FROM AUTHOR]

The paper dates back to the period when the author became acquainted with Loet Leydesdorff and is mainly devoted to the research that the author had the opportunity to conduct together with Loet. Major research topics include the development of the Triple Helix model and the dynamic model of inter-social communication, which appear to be closely interrelated. The full consequences of his work are yet to be realized. I also present a vision of possible applications and future extensions of Loet's work. [ABSTRACT FROM AUTHOR]

Critical infrastructure systems—such as transportation—are the backbone of society. Infrastructure development can thus be acknowledged as a common societal concern in the field of governance, and its recognition as a policy problem is crucial to proper decision making. This study aims to build an in-depth understanding of the multi-level system concerned with transportation and applies a soft systems methodology to structure the investigation. The system analysis and conceptual modelling rests on publicly available documents and policies, group meetings and a workshop with stakeholders from the local, regional and national levels. The paper provides a thorough analysis of the Swedish transportation system through public policy and the perceptions of municipal officials. In addition, it proposes a novel conceptual framework of the transportation system, including a detailed discussion of stakeholders, activities and perspectives. Although transportation is vital for many stakeholders, improving only transport infrastructure does not guarantee regional growth. Therefore, the proposed framework constitutes a novel basis for constructive dialogue among concerned parties regarding improvements to transportation. Thereby, the paper provides an understanding of a society's transportation system that can be used to inform agenda setting for critical infrastructure governance. Further discussion in the scientific community and with officials entrusted with public administration could further validate and deepen the proposed understanding of the perspectives and constraints in the examined context and beyond. [ABSTRACT FROM AUTHOR]

We provide commentaries on the papers included in the Dynamics of Political Polarization Special Feature. Baldassarri reads the contribution of the papers in light of the theoretical distinction between ideological partisanship, which is generally rooted in sociodemographic and political cleavages, and affective partisanship, which is, instead, mostly fueled by emotional attachment and repulsion, rather than ideology and material interests. The latter, she argues, is likely to lead to a runaway process and threaten the pluralistic bases of contemporary democracy. Page sees the contribution of the many distinct models in the ensemble as potentially contributing more than the parts. Individual papers identify distinct causes of polarization as well as potential solutions. Viewed collectively, the papers suggest that the multiple causes of polarization may self-reinforce, which suggests that successful interventions would require a variety of efforts. Understanding how to construct such interventions may require larger models with greater realism. [ABSTRACT FROM AUTHOR]

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins. [ABSTRACT FROM AUTHOR]

Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach. [ABSTRACT FROM AUTHOR]

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with "black box" AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems' behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent "functions" (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering "models", and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site. [ABSTRACT FROM AUTHOR]

System dynamics approaches are increasingly addressing the complexity of public health problems such as childhood overweight and obesity. These approaches often use system mapping methods, such as the construction of causal loop diagrams, to gain an understanding of the system of interest. However, there is limited practical guidance on how such a system understanding can inform the development of an action programme that can facilitate systems changes. The Lifestyle Innovations Based on Youth Knowledge and Experience (LIKE) programme combines system dynamics and participatory action research to improve obesity-related behaviours, including diet, physical activity, sleep and sedentary behaviour, in 10–14-year-old adolescents in Amsterdam, the Netherlands. This paper illustrates how we used a previously obtained understanding of the system of obesity-related behaviours in adolescents to develop an action programme to facilitate systems changes. A team of evaluation researchers guided interdisciplinary action-groups throughout the process of identifying mechanisms, applying the Intervention Level Framework to identify leverage points and arriving at action ideas with aligning theories of change. The LIKE action programme consisted of 8 mechanisms, 9 leverage points and 14 action ideas which targeted the system's structure and function within multiple subsystems. This illustrates the feasibility of developing actions targeting higher system levels within the confines of a research project timeframe when sufficient and dedicated effort in this process is invested. Furthermore, the system dynamics action programme presented in this study contributes towards the development and implementation of public health programmes that aim to facilitate systems changes in practice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electrochemistry is the property of Journal of Electrochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Analytical thinking is dominated by binary ideas. From pairwise interactions to algebraic operations, to compositions of processes, to network models, binary structures are deeply ingrained in the fabric of most current scientific paradigms. In this paper, we introduce arity as the generic conceptualization of the order of an interaction between a discrete collection of entities and argue that there is a rich universe of higher arity ideas beyond binarity waiting to be explored. To illustrate this, we discuss several higher-order phenomena appearing in a wide range of research areas, paying special attention to instances of ternary interactions. From the point of view of formal sciences and mathematics, higher arity thinking opens up new paradigms of algebra, symbolic calculus and logic. In particular, we delve into the special case of ternary structures, as that itself reveals ample surprises: new notions of associativity (or lack thereof) in ternary operations of cubic matrices, ternary isomorphisms and ternary relations; the integration problem of 3-Lie algebras; and generalizations of adjacency in 3-uniform hypergraphs. All these are open problems that strongly suggest the need to develop new ternary mathematics. Finally, we comment on potential future research directions and remark on the transdisciplinary nature of higher arity science. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economía Institucional is the property of Universidad Externado de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape. [ABSTRACT FROM AUTHOR]

Project quality management in general and IT projects quality management in particular has been a research topic for several decades. However, project quality management is usually considered a subpart of project management per se, which includes such activities as project initiation, team formation, responsibilities allocation, and others. In this position paper, we abstract from the abovementioned activities and focus on only one perspective in project management, which, to the best of our knowledge, has not been intensively studied. We propose to focus on the perspective of Systems of Systems in project quality management paying more attention to the systems that are related or belong to a particular IT project. For this purpose, the questions for further research are defined regarding three aspects of IT project quality management, namely, product, process, and inputs of an IT project. The questions are derived based on common features of systems of systems and the enterprise architecture model represented in ArchiMate. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic accelerated and solidified the adoption of work-from-home systems (WFHSs) across all economic sectors, and finding ways to incorporate sustainability into these systems is a fundamental aspect of a sustainable future of work. Designing sustainable WFHSs requires an innovative approach capable of understanding complex interrelationships between multiple systems. This paper applies the sustainable system-of-systems (SSoSs) framework to find innovative design solutions for post-pandemic/endemic WFHSs. First, the SSoSs framework is depicted using examples of applications in systems to clarify its main ideas and concepts. Then, the literature on WFHSs is considered to discuss the impacts of COVID-19 on work from a systemic perspective. This provides inputs for the factors to be considered at each level of the SSoSs analysis. Finally, the SSoSs framework is applied to analyze the WFHSs' requirements in the context of COVID-19. The nature of the application of this framework is theoretical, facilitating the provision of a multi-layered perspective on sustainability in work systems that are increasingly prevalent across various economic sectors. This study contributes to research and practice by characterizing the various relevant systems and their impact on the design of sustainable WFHSs and identifies multiple intervention points to make predictions about how they might influence other systems. [ABSTRACT FROM AUTHOR]

The effect of physical activity (PA) on academic achievement has long been a hot research issue in physical education, but few studies have been conducted using machine learning methods for analyzing activity behavior. In this paper, we collected the data on both physical activity and academic performance from 2,219 undergraduate students (Mean = 19 years) over a continuous period of 12 weeks within one academic semester. Based on students' behavioral indicators transformed from a running APP interface and the average academic course scores, two models were constructed and processed by CHAID decision tree for regression analysis and significance detection. It was found that first, to attain higher academic performance, it is imperative for students to not only exhibit exceptional activity regularity, but also sustain a reduced average step frequency; second, the students completing running exercise with an average frequency of 1 time/week and the duration of 16-25 min excelled over approximately 88 percentage of other students on academic performance; third, the processing validity and reliability of physical observation data in complex systems can be improved by utilizing decision tree as a leveraging machine learning tool and statistical method. These findings provide insights for educational practitioners and policymakers who will seek to enhance college students' academic performance through physical education programs, combined with data mining methods. [ABSTRACT FROM AUTHOR]

Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors' own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs. [ABSTRACT FROM AUTHOR]

Although its uninterrupted supply is essential for everyday life, the electricity occasionally experiences disruptions and outages. The work presented in the current paper aims to initiate the research to design a strategy based on advanced approaches of algebraic topology to prevent such malfunctions in a power grid network. Simplicial complexes are constructed to identify higher-order structures embedded in a network and, alongside a new algorithm for identifying delegates of the simplicial complex, are intended to pinpoint each element of the power grid network to its natural layer. Results of this methodology for analysis of a power grid network can single out its elements that are at risk to cause cascade problems which can result in unintentional islanding and blackouts. Further development of the outcomes of research can find implementation in the algorithms of the energy informatics research applications. [ABSTRACT FROM AUTHOR]

The interest in large or extreme outliers in arrays of empirical information is caused by the wishes of users (with whom the author worked): specialists in medical and zoo geography, mining, the application of meteorology in fishing tasks, etc. The following motives are important for these specialists: the substantial significance of large emissions, the fear of errors in the study of large emissions by standard and previously used methods, the speed of information processing and the ease of interpretation of the results obtained. To meet these requirements, interval pattern recognition algorithms and the accompanying auxiliary computational procedures have been developed. These algorithms were designed for specific samples provided by the users (short samples, the presence of rare events in them or difficulties in the construction of interpretation scenarios). They have the common property that the original optimization procedures are built for them or well-known optimization procedures are used. This paper presents a series of results on processing observations by allocating large outliers as in a time series in planar and spatial observations. The algorithms presented in this paper differ in speed and sufficient validity in terms of the specially selected indicators. The proposed algorithms were previously tested on specific measurements and were accompanied by meaningful interpretations. According to the author, this paper is more applied than theoretical. However, to work with the proposed material, it is required to use a more diverse mathematical tool kit than the one that is traditionally used in the listed applications. [ABSTRACT FROM AUTHOR]

Water flow in porous media is one of many phenomena in nature that can demonstrate both simple and complex behaviors. A soil–water retention curve (SWRC) is needed to characterize this flow properly. This curve relates the soil water content and the matric potential (or porepressure), being fundamental for simulating unsaturated soil behaviors. This article proposes a new model based on simple assumptions regarding the saturated and unsaturated branches of soil–water retention curves. Despite its simplicity, the modeling capability of the proposed SWRC is shown for two types of soil. This new SWRC is obtained as a logistic function after solving an ordinary differential equation (ODE). This ODE can also be solved numerically using the Finite Difference Method (FDM), which indicates that the discrete version of the SWRC can be represented as the logistic map for specific parameters. On the other hand, this discrete representation is known to encompass chaotic and fractal behaviors. This link is used to investigate the stability and convergence of the FDM scheme, indicating that for values pre-bifurcation, both the FDM and the analytical solution of the ODE represent the new SWRC. This way, the present paper is the first step to better understating how a chaotic framework could be related to SWRCs and geotechnics in general. [ABSTRACT FROM AUTHOR]

Purpose -- The world is facing unprecedented opportunities to improve welfare and reduce poverty, so every day more and more public spending is becoming important in every country. The purpose of the research was prompted by the questions of whether there were development reforms and what is the complexity that has evolved in each variable (WS, GS, EU, ST, CE, and TE) for the time interval 2007--2020. How and are governing bodies able to continuously drive growth for decades by being more efficient users of government spending planning in complex financial and economic systems? Therefore, this paper aims to understand and advance by bringing a new approach to unstoppable and navigating reforms to government spending in complex financial and economic systems. Research methodology -- The research was conducted through secondary data from annual financial reports and statements for both central and local levels. The time interval for 14 years was analyzed through two analyses and one matrix such as descriptive analysis (9 tests), correlation analysis (3 tests), and Proximity Matrix (Euclidean Distance between years and variables, Z stress test) as in the Tables (1-12), in the Figures (1-11) using SPSS version 23.0 for Windows. Findings -- The findings showed that: a) the data had a normal distribution, b) there was an increase in expenditures for each year, especially in times of pandemic COVID-19, c) the data were obtained from financial reports and statements as well as different institutions over different years, d) there is a strong and positive relationship between the variables for government spending in complex systems, e) Public expenses have increased due to COVID-19 and the damage caused is continuing, affecting the decline in the well-being of the residents. Research limitations -- The limitations of this paper are that only a considerable number of variables are studied and only in the state of Kosovo for 14 years (2007-2020). In this case, for other analyses by other researchers' other variables can be analyzed, more extended periods or comparability with other states. Practical implications -- based on the above questions, it was confirmed that there were reforms in the complex financial and economic systems for government expenditures each year. Originality/Value -- Such research has not been analyzed before and the findings of this research can help budget experts to accurately plan expenses based on the three periods studied (past, present or the period of the Covid-19 pandemic and the future or the post-COVID-19 pandemic period). It is strongly recommended that governing bodies develop and improve the category of public investment expenditures. [ABSTRACT FROM AUTHOR]

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective. [ABSTRACT FROM AUTHOR]

We propose and examine a model expressed by stochastic differential equations for the evolution of a complex system. We refer in particular to a market society, in which the state of each individual is identified by the amount of money at his/her disposal. The evolution of such a system over time is described by suitable equations that link the instantaneous changes in the probability of each state with the probable outcomes of pairwise interactions between elements of the system. In the context at hand, these pairwise interactions simply represent money exchanges, due to the sales and purchases of goods and services. In this paper, unlike the usual method in the literature, the interaction frequencies and the consequent probabilities of passing from one state to another are not considered as assigned once and for all but are supposed to be randomly variable. This choice, as also shown by several numerical simulations, seems likely to have fruitful consequences, especially for a more realistic representation of economic issues and phenomena. [ABSTRACT FROM AUTHOR]

In present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively. [ABSTRACT FROM AUTHOR]

Misalignment between the different levels of complex societal systems (e.g. socio-technical, socio-ecological) are often a barrier to transitioning these systems toward more sustainable behavior. One way to understand alignment is through the Multilevel Perspective Theory of sustainable transitions in sociotechnical systems (MLP) which posits that when a regime (the current "ways of doing" something) exhibits behavior that negatively impacts its surrounding economic, social and environmental landscape, that landscape will exert pressure on the regime to change. This in turn opens the window of opportunity for niche developments such as novel technology adoption or policy changes. Therefore, understanding the relationships between the landscape, the regime and emerging niches can help align these different levels of perspective to facilitate a successful transition toward more sustainable practices. This paper describes a methodology for mapping complex systems such as agricultural systems based on MLP theory, and incorporates a fourth "user" level (e.g. the farm) into this multilevel framework (MLP+ F). The methodology is illustrated with a case study example of North Carolina's lagoon and sprayfield swine waste management regime. By integrating well-established techniques for data collection and analysis, the case study engages a diverse panel of local experts through a modified Delphi approach to identify the relevant concepts and their cause-effect relationships for the current regime. These concepts and relationships are then categorized and organized into a multilevel, multi-perspective conceptual model using fuzzy cognitive maps. The conceptual model identifies how stakeholder groups align in their perception of the system, and provides a foundation for future qualitative, semi-quantitative, and visual analyses of the system and how it can be changed through alternative scenarios. Although this methodology was initially developed for agricultural applications, it has potential for application to other complex societal issues where understanding stakeholder alignment and the potential for sustainability transitions through change are needed. [ABSTRACT FROM AUTHOR]

A new experimental setup combining X-ray photon correlation spectroscopy (XPCS) in the hard X-ray regime and a high-pressure sample environment has been developed to monitor the pressure dependence of the internal motion of complex systems down to the atomic scale in the multi-gigapascal range, from room temperature to 600 K. The high flux of coherent high-energy X-rays at fourth-generation synchrotron sources solves the problems caused by the absorption of diamond anvil cells used to generate high pressure, enabling the measurement of the intermediate scattering function over six orders of magnitude in time, from 10-3 s to 10³ s. The constraints posed by the high-pressure generation such as the preservation of X-ray coherence, as well as the sample, pressure and temperature stability, are discussed, and the feasibility of high-pressure XPCS is demonstrated through results obtained on metallic glasses. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of complex systems defined in the sense of Caputo, such as fractional differences, is crucial for predicting their behavior and improving their functionality. In this paper, the emergence of chaos in complex dynamical networks with indirect coupling and discrete systems, both utilizing fractional order, is presented. The study employs indirect coupling to produce complex dynamics in the network, where the connection between the nodes occurs through intermediate fractional order nodes. The temporal series, phase planes, bifurcation diagrams, and Lyapunov exponent are considered to analyze the inherent dynamics of the network. Analyzing the spectral entropy of the chaotic series generated, the complexity of the network is quantified. As a final step, we demonstrate the feasibility of implementing the complex network. It is implemented on a field-programmable gate array (FPGA), which confirms its hardware realizability. [ABSTRACT FROM AUTHOR]

An important challenge in the study of complex systems is to identify appropriate effective variables at different times. In this paper, we explain why structures that are persistent with respect to changes in length and time scales are proper effective variables, and illustrate how persistent structures can be identified from the spectra and Fiedler vector of the graph Laplacian at different stages of the topological data analysis (TDA) filtration process for twelve toy models. We then investigated four market crashes, three of which were related to the COVID-19 pandemic. In all four crashes, a persistent gap opens up in the Laplacian spectra when we go from a normal phase to a crash phase. In the crash phase, the persistent structure associated with the gap remains distinguishable up to a characteristic length scale ϵ * where the first non-zero Laplacian eigenvalue changes most rapidly. Before ϵ * , the distribution of components in the Fiedler vector is predominantly bi-modal, and this distribution becomes uni-modal after ϵ *. Our findings hint at the possibility of understanding market crashs in terms of both continuous and discontinuous changes. Beyond the graph Laplacian, we can also employ Hodge Laplacians of higher order for future research. [ABSTRACT FROM AUTHOR]

In this paper, a neural network-based optimal control synthesis is presented for distributed optimal control problems. We deal with solutions of systems controlled by parabolic differential equations with control and state constraints and discrete time delays. The given optimal control problem is transformed into a discrete nonlinear problem and then implemented into a feed-forward adaptive critic neural network. We propose a new algorithm to reach optimal control and an optimal trajectory using a feed-forward neural network. We present a concrete application of this simulation method on the SEIR (Susceptible—Exposed—Infectious—Recovered) optimal control problem of a distributed system for disease control. The results show that the adaptive-critic-based neural network approach is suitable for the solution of optimal distributed control problems with delay in state and control variables subject to control-state constraints and simulates the spread of disease in the SEIR system. [ABSTRACT FROM AUTHOR]

The phenomenon of intermittency has remained a theoretical concept without any attempts to approach it geometrically with the use of a simple visualization. In this paper, a particular geometric model of point clustering approaching the Cantor shape in 2D, with a symmetry scale θ being an intermittency parameter, is proposed. To verify its ability to describe intermittency, to this model, we applied the entropic skin theory concept. This allowed us to obtain a conceptual validation. We observed that the intermittency phenomenon in our model was adequately described with the multiscale dynamics proposed by the entropic skin theory, coupling the fluctuation levels that extended between two extremes: the bulk and the crest. We calculated the reversibility efficiency γ with two different methods: statistical and geometrical analyses. Both efficiency values, γ s t a t and γ g e o , showed equality with a low relative error margin, which actually validated our suggested fractal model for intermittency. In addition, we applied the extended self-similarity (E.S.S.) to the model. This highlighted the intermittency phenomenon as a deviation from the homogeneity assumed by Kolmogorov in turbulence. [ABSTRACT FROM AUTHOR]

The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in computational power, the effectiveness and efficiency of such algorithms, i.e., their ability to identify as many Pareto-optimal solutions as possible with as few simulation samples as possible, plays a decisive role. However, the question of which class of MOO algorithms is most effective or efficient with respect to which class of problems has not yet been resolved. To tackle this performance problem, hybrid optimization algorithms that combine multiple elementary search strategies have been proposed. Despite their potential, no systematic approach for selecting and combining elementary Pareto search strategies has yet been suggested. In this paper, we propose an approach for designing hybrid MOO algorithms that uses reinforcement learning (RL) techniques to train an intelligent agent for dynamically selecting and combining elementary MOO search strategies. We present both the fundamental RL-Based Hybrid MOO (RLhybMOO) methodology and an exemplary implementation applied to mathematical test functions. The results indicate a significant performance gain of intelligent agents over elementary and static hybrid search strategies, highlighting their ability to effectively and efficiently select algorithms. [ABSTRACT FROM AUTHOR]

Changeability analysis methods primarily assist with formulating a response to uncertain and new requirements from various system stakeholders and include asset management issues such as modelling lifecycle path dependency. Epoch-era networks proved to be an effective tool for managing the evolving requirements of a capability system, ensuring sustained value through life. Over the life of a system, stakeholders are faced with countless options to change their capability systems to sustain value, which is path dependent and can greatly impact the scope of decisions available later in life. This paper introduces and demonstrates the application of a revised epoch-era network approach to explore many potential lifecycle paths, along with utility vs. expense strategies, demonstrated through an example of a military frigate subject to evolving requirements. Results indicated the future limitations to sustaining value if the largest and most capable technology upgrades are selected too early in life. The two best lifecycle paths from different strategies were compared to understand the utility/expense trade-offs for the most optimal frigate upgrade trajectory. [ABSTRACT FROM AUTHOR]

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels. [ABSTRACT FROM AUTHOR]

Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and removing restrictions on a system with a given latency as represented by an epidemic outbreak (and formally comparable with mechanical inertia), may create critical instabilities, overshoots, and strong oscillations in the number of infected people around the desirable set-point, defined in a practical way as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to any change of the input control variable can be reasonably achieved using a proportional-integral-derivative controller (PID), which is a widely used technique in various physics and technological applications. In this paper, this control theory to is proposed to be applied epidemiology, to understand the reaction of COVID-19 propagation to social restrictions and to reduce epidemic damages through the correct tuning of the containment policy. Regarding the synthesis of this interdisciplinary approach, the extended to the susceptible-infectious-recovered (SIR) model name "SIR-PID" is suggested. [ABSTRACT FROM AUTHOR]