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Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse.Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse.Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools.Findings value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

\"The Final Volume of the Groundbreaking Trilogy on Agent-Based ModelingIn this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent Zero. This software individual, or \"agent,\" is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics.Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his \"computational parables.\" Agent Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, Agent Zero presents a groundbreaking vision and the tools to realize it\"-- Provided by publisher.

Primary care systems are a cornerstone of universally accessible health care. The planning, analysis, and adaptation of primary care systems is a highly non-trivial problem due to the systems’ inherent complexity, unforeseen future events, and scarcity of data. To support the search for solutions, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models and tracks the micro-interactions of patients and primary care physicians on an individual level. At the same time, it models the progression of time via the discrete-event paradigm. Thereby, it enables modelers to analyze multiple key indicators such as patient waiting times and physician utilization to assess and compare primary care systems. Moreover, SiM-Care can evaluate changes in the infrastructure, patient behavior, and service design. To showcase SiM-Care and its validation through expert input and empirical data, we present a case study for a primary care system in Germany. Specifically, we study the immanent implications of demographic change on rural primary care and investigate the effects of an aging population and a decrease in the number of physicians, as well as their combined effects.

Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical pro- cess monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning- assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly opera- tions in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing envi- ronments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber–physical manufacturing sys- tems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi- sensory extended reality and simulation modeling technologies, and machine and deep learn- ing algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings value added: Modal actuators and sensors, robot trajectory planning and compu- tational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelli- gence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms im- prove IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber– physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic per- formance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufac- turing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.

This paper focuses on the issue of payments for ecosystem services (PES) mechanism design when the activity incentivised through the scheme benefits multiple groups, each of whom might be prepared to contribute to payments made through the scheme. In particular, we examine spatial coordination on the demand side of the market; that is to say, the question of which beneficiary of the PES scheme buys land-management changes on which land parcels. We show through spatial simulation modelling that it is possible for negotiation to lead to Pareto improvements when compared to solutions reached through non-cooperative strategic solutions; however, we also show that this result is not universal and only holds under certain conditions. In particular, the spatial correlation and spatial interdependence of the ecosystem service benefits are key in determining whether negotiation between beneficiaries is optimal and therefore if policy makers and designers of PES schemes should be prioritising bringing together multiple beneficiaries of ecosystem services.

This paper addresses the background and current state of the field of agent-based modelling and simulation (ABMS). It revisits the issue of ABMS represents as a new development, considering the extremes of being an overhyped fad, doomed to disappear, or a revolutionary development, shifting fundamental paradigms of how research is conducted. This paper identifies key ABMS resources, publications, and communities. It also proposes several complementary definitions for ABMS, based on practice, intended to establish a common vocabulary for understanding ABMS, which seems to be lacking. It concludes by suggesting research challenges for ABMS to advance and realize its potential in the coming years.

Structures with artificially engineered mechanical properties, often called mechanical metamaterials, are interesting for their tunable functionality. Various types of mechanical metamaterials have been proposed in the literature, designed to harness light or magnetic interactions, structural instabilities in slender or hollow structures, and contact friction. However, most of the designs are ideally engineered without any imperfections, in order to perform deterministically as programmed. Here, we study the mechanical performance of randomly stacked cylindrical shells, which act as a disordered mechanical metamaterial. Combining experiments and simulations, we demonstrate that the stacked shells can absorb and store mechanical energy upon compression by exploiting large deformation and relocation of shells, snap-fits, and friction. Although shells are oriented randomly, the system exhibits statistically robust mechanical performance controlled by friction and geometry. Our results demonstrate that the rearrangement of flexible components could yield versatile and predictive mechanical responses. Mechanical metamaterials are artificially designed structures with tunable behavior, typically obeying precisely programmed dynamics. Here, a metamaterial based on randomly stacked flexible cylindrical shells provides a disordered yet statistically robust and controllable structure for mechanical energy dissipation and storage.

One of the most important environmental problems of our era is Global Warming (GW), which derives its roots mainly from anthropogenic activities and is expected to cause far-reaching and long-lasting impacts to the natural environment, ecosystems and human societies. The purpose of this paper is twofold: (a) to develop a model of the causal relationships that exist in the field of GW, using the well-established Artificial Intelligence technique of Fuzzy Cognitive Maps (FCMs) and (b) to develop a Semantic Web simulation software tool, that visually simulates the FCM dynamic behavior and studies the equilibrium that the FCM dynamic system reaches. Using this generic tool, various scenarios can be imposed to the FCM model and predictions can be made on these, in a “what-if” manner. The features of the web simulation tool are exhibited using the FCM that was created and concerns “Global Warming”. By applying Semantic Web technologies, the tool makes the results and the various FCM models, that can be implemented in it, easily accessible to various users or systems, through the Internet. In this way, policy makers can use this technique and tool to make predictions by viewing dynamically the consequences that the system predicts to their imposed scenarios and share them through the world wide web.

Abstract The global research and development (R&D) location strategy of multinational enterprises (MNEs) is examined using agent-based simulation (ABS) modeling. This study focuses on the positioning strategy of MNEs to understand the impact of their R&D location strategy. In ABS modeling, agents search for knowledge owners or universities in the global host market using Hotelling’s location model algorithm. We measure the result of increasing the number of entry agents from 2 to 121. Three types of equilibria are found in our agent-based simulation model: pure equilibrium, saturated equilibrium, and quasi-saturated equilibrium. Core locations attract MNEs, while peripheral countries in the global market are the least preferred. As a result, peripheral countries experience a paucity of foreign R&D investments. Even though emerging economies absorb foreign direct investment (FDI) inflows from MNEs, least-less developed countries (LLDC) may experience a dearth in FDI. Thus, the optimal location strategy of MNEs leads to economic disparities between the core and peripheral countries. This study suggests the need for developing official assistance to attract FDI inflows to LLDCs so that peripheral countries emerge as attractive global market destinations for MNEs.