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In this paper, it is argued that it is necessary a conception of organizational knowledge which is pertinent for seizing the business opportunity created by three national problems of Mexico. Then, diverse disciplinary domains are integrated to conceive organizational knowledge as a complex system of actions in execution displaying the emergent properties of reiterated effectiveness and efficiency in the accomplishment of organizational objectives. Next, the MACOSC-IASC® model which implements this conception is presented as well as two results of using it. The first one, is in the field of open source software development; the second one, is in the Collaboration Fund's tripartite framework (CF) which is being designed to seize the business opportunity. To close this paper, the CF framework's approach to develop the Mexican Industry of Software Development is contrasted with a public policy project named PROSOFT 3.0.

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology–environment interactions. Investigating the limits of how morphology–environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms. [ABSTRACT FROM AUTHOR]

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

This paper is concerned with the modeling of complex living systems composed of active particles able to interact with each other and with the surrounding environment. Specifically the discrete thermostatted kinetic theory for active particles is generalized by extending the role of nonconservative interactions and by introducing the role of mutations. In particular the classical proliferation/destruction operator is generalized in order to take into consideration the role of nonlinear interactions, namely the dependence on the distribution functions and their moments. The natural birth and death events are recovered included the logistic growth rate. Finally the role of the outer environment is taken into consideration by introducing microscopic external agents. New thermostat operators are presented whose structures augment the degree of nonlinearity of the evolution equations. The different Cauchy problems are established and future research perspectives are discussed in the whole paper. [ABSTRACT FROM AUTHOR]

Since the advent of modern electronics technology, heat transfer science and engineering has served in the development of computer technology. The computer as an object of heat transfer research has a unique aspect; it undergoes morphological transitions and diversifications in step with the progress of microelectronics technology. Evolution of computer's hardware manifests itself in increasing packing density of electronic circuits, modularization of circuit assemblies, and increasing hierarchical levels of system internal structures. These features are produced by the confluence of various factors; the primary factors are the pursuit of ever higher processing performance, less spatial occupancy, and higher energy utilization efficiency. The cost constraint on manufacturing also plays a crucial role in the evolution of computer's hardware. Besides, the drive to make computers ubiquitous parts of our society generates diverse computational devices. Concomitant developments in heat generation density and heat transfer paths pose fresh challenges to thermal management. In an introductoiy part of the paper, I recollect out-experiences in the mainframe computers of the 1980s, where the system's morphological transition allowed the adoption of water cooling. Then, generic interpretations of the hardware evolution are attempted, which include recapturing the past experience. Projection of the evolutionary trend points to shrinking space for coolant flow, the process commonly in progress in all classes of computers today. The demand for compact packaging will rise to an extreme level in supercomputers, and present the need to refocus our research on microchannel cooling. Increasing complexity of coolant flow paths in small equipment poses a challenge to a user of computational fluid dynamics (CFD) simulation code. In highly integrated circuits the paths of electric current and heat become coupled, and coupled paths make the electrical/thermal codesign an extremely challenging task. These issues are illustrated using the examples of a consumer product, a printed circuit board (PCB), and a many-core processor chip. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) becomes increasingly integrated into our lives, the need for new norms is urgent. However, AI evolves at a much faster pace than the characteristic time of norm formation, posing an unprecedented challenge to our societies. This paper examines possible criticalities of the processes of norm formation surrounding AI. It focuses on how new norms can be established, rather than on what these norms should be. It distinguishes different scenarios based on the centralization or decentralization of the norm formation process, analysing the cases where new norms are shaped by formal authorities or informal institutions, or emerge spontaneously in a bottom-up fashion. On the latter point, the paper reports a conversation with ChatGPT in which the LLM discusses some of the emerging norms it has observed. Far from seeking exhaustiveness, this article aims to offer readers interpretive tools to frame society's response to the growing pervasiveness of AI. An outlook on how AI could influence the formation of future social norms emphasizes the importance for open societies to anchor their formal deliberation process in an open, inclusive and transparent public discourse. This article is part of the theme issue 'Social norm change: drivers and consequences'. [ABSTRACT FROM AUTHOR]

Reviewing complex interventions is challenging because they include many elements that can interact dynamically in a nonlinear manner. A systems perspective offers a way of thinking to help understand complex issues, but its application in evidence synthesis is not established. The aim of this project was to understand how and why systems perspectives have been applied in evidence synthesis. A methodological mapping review was conducted to identify papers using a systems perspective in evidence synthesis. A search was conducted in seven bibliographic databases and three search engines. A total of 101 papers (representing 98 reviews) met the eligibility criteria. Two categories of reviews were identified: (1) reviews using a "systems lens" to frame the topic, generate hypotheses, select studies, and guide the analysis and interpretation of findings (n = 76) and (2) reviews using systems methods to develop a systems model (n = 22). Several methods (e.g., systems dynamic modeling, soft systems approach) were identified, and they were used to identify, rank and select elements, analyze interactions, develop models, and forecast needs. The main reasons for using a systems perspective were to address complexity, view the problem as a whole, and understand the interrelationships between the elements. Several challenges for capturing the true nature and complexity of a problem were raised when performing these methods. This review is a useful starting point when designing evidence synthesis of complex interventions. It identifies different opportunities for applying a systems perspective in evidence synthesis, and highlights both commonplace and less familiar methods. [ABSTRACT FROM AUTHOR]

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]

The debate on whether the peer-review system is in crisis has been heated recently. A variety of alternative systems have been proposed to improve the system and make it sustainable. However, we lack sufficient evidence and data related to these issues. Here we used a previously developed agent-based model of the scientific publication and peer-review system calibrated with empirical data to compare the efficiency of five alternative peer-review systems with the conventional system. We modelled two systems of immediate publication, with and without online reviews (crowdsourcing), a system with only one round of reviews and revisions allowed (re-review opt-out) and two review-sharing systems in which rejected manuscripts are resubmitted along with their past reviews to any other journal (portable) or to only those of the same publisher but of lower impact factor (cascade). The review-sharing systems outperformed or matched the performance of the conventional one in all peer-review efficiency, reviewer effort and scientific dissemination metrics we used. The systems especially showed a large decrease in total time of the peer-review process and total time devoted by reviewers to complete all reports in a year. The two systems with immediate publication released more scientific information than the conventional one but provided almost no other benefit. Re-review opt-out decreased the time reviewers devoted to peer review but had lower performance on screening papers that should not be published and relative increase in intrinsic quality of papers due to peer review than the conventional system. Sensitivity analyses showed consistent findings to those from our main simulations. We recommend prioritizing a system of review-sharing to create a sustainable scientific publication and peer-review system. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of self-organized criticality (SOC) to research on, and debates about, preventing the occurrence of human-mediated failures in aviation security. It aims to persuade readers (policymakers, security professionals, and academics) from different fields that this is a good topic for further research and one that is worth pursuing. This paper views aviation security as a complex system that produces risky behaviors because of chance interactions between many forms of human error. By rethinking the Bak-Tang-Wiesenfeld (BTW) sandpile model, the paper uses the explanatory nature of SOC to explore whether criticality may be viewed as a crucial component of aviation security. It elaborates on the effects of arbitrary interactions between various types of error using the BTW sandpile model’s illustrative characteristics. The paper concludes by speculating that SOC can be used to generate new ways of thinking about interventions to mitigate failure and that academics and security professionals from various fields could collaborate to develop fresh ideas and integrated concepts to further our understanding of a crucial subject for the aviation industry. [ABSTRACT FROM AUTHOR]

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy Systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems. [ABSTRACT FROM AUTHOR]

The proliferation of new and more diverse number of actors in the space industry in recent decades has created a more complex, decentralized, and unpredictable global governance of the space system. In the contemporary era, space activities are conducted by a diverse multitude of actors, engaging in a multiplicity of interactions across a variety of levels and arenas. Although there is a substantial body of literature addressing the global governance of space, few of these works have analyzed the multilevel governance of the current space system. The primary hypothesis of this research is that the governance of the space system has become more fragmented, decentralized, and distributed across multiple levels or arenas. This research emphasizes the necessity of utilizing systems approaches and an International Relations (IR) perspective to analyze the global governance of the space system. Therefore, the primary objective of this research is to examine the key characteristics of the 21st-century space system using a multilevel governance framework (MLG) and to assess the relevance and implications of each level. This paper contributes to the emerging field of global space governance by applying a MLG framework to the space system. The result is a comprehensive, multilevel analysis of the contemporary space system, identifying, describing, and explaining the role of the space actors in various levels of analysis (international, regional, national, and subnational). [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 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]

A complex system is made up of multiple related and coupled subsystems. Each subsystem has its own set of multiple constraints and objectives, and is commonly found in real-world applications. Biogeography-based complex system optimization (BBO/Complex) is a population-based evolutionary intelligence paradigm that has been developed to solve complex system problems. The paper first presents the framework of the proposed method that consists of within-subsystem migration, cross-subsystem migration, and mutation operators. Then a theoretical model of the proposed method is derived using Markov chain and the probability transition matrix of each operator, along with the generalized multinomial theorem. The model provides exact mathematical formulas that predict the limiting probabilities of optimization performance for each possible population in the proposed method. Additionally, simulation results of two sample problems, which include multiple subsystems and multiple objectives, confirm the Markov model derived. Finally, the performance of the proposed method is investigated on an application of a battery charging system, and numerical simulation shows that the proposed method is a promising optimization method for the studied complex system. [ABSTRACT FROM AUTHOR]