SYSTEMS-BASED MODELING FOR PARTICIPATORY TOURISM PLANNING AND DESTINATION MANAGEMENT

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

PurposeScience of systems requires a specific and constructive mathematical model and language, which describe jointly such systemic categories as adaptation, self‐organization, complexity, evolution, and bring the applied tools for building a system model for each specific object of a diverse nature. This formalism should be connected directly with a world of information and computer applications of systemic model, developed for a particular object. The considered information systems theory (IST) is aimed at building a bridge between the mathematical systemic formalism and information technologies to develop a constructive systemic model of revealing information regularities and specific information code for each object.Design/methodology/approachTo fulfill this goal and the considered systems' definition, the IST joins two main concepts: unified information description of interacted flows, initiated by the sources of different nature, with common information language and systems modeling methodology, applied to distinct interdisciplinary objects; general system's information formalism for building the model, which allows expressing mathematically the system's regularities and main systemic mechanisms.FindingsThe formalism of informational macrodynamics (IMD), based of the minimax variational principle, reveals the system model's main layers: microlevel stochastics, macrolevel dynamics, hierarchical dynamic network (IN) of information structures, its minimal logic, and optimal code of communication language, generated by the IN hierarchy, dynamics, and geometry. The system's complex dynamics originate information geometry and evolution with the functional information mechanisms of ordering, cooperation, mutation, stability, diversity, adaptation, self‐organization, and the double helix's genetic code.Practical implicationsThe developed IMD's theoretical computer‐based methodology and the software has been applied to such areas as technology, communications, computer science, intelligent processes, biology, economy, management, and other nonphysical and physical subjects.Originality/valueThe IMD's macrodynamics of uncertainties connect randomness and regularities, stochastic and determinism, reversibility and irreversibility, symmetry and asymmetry, stability and instability, structurization and stochastization, order and disorder, as a result of micro‐macrolevel's interactions for an open system, when the external environment can change the model's structure.

Using Hardware-based Programming Experiences to Enhance Student Learning in a Junior-level Systems Modeling Course This paper describes hands-on activity enhancements in the re-design of a junior-level MechanicalEngineering Systems Modeling lecture course. The primary focus of this paper is the students’ ability toconstruct, understand and analyze linear time-invariant models for physical systems. This study buildsupon a model which uses hardware to integrate programming experiences throughout the curriculum; inthe model, the three learning principles deemed critical for success are student engagement, knowledgetransfer and self-directed learning. In traditional Systems Modeling lecture courses, core course conceptsare generally considered to be a bit abstract to a considerable percentage of students and there are oftendisconnects between theoretical course concepts, computational solution techniques and the behavior ofreal-world systems. Each of these challenges inhibit the three principles deemed critical for success and itis posited in this paper that the introduction of programming involving hardware will enhance the threeprinciples, resolve disconnects and improve overall student learning. The re-design introduces programming experiences performed on microcontroller hardwareintegrated with analytical techniques discussed during lectures to illustrate key topics and solutionmethods (e.g., experimental determination of a system transfer function). The instructor had previouslytaught the course numerous times, so a well-paced course schedule and solid foundation of course notesare already in place. Additionally, hybrid and problem-based learning (PBL) techniques wereincorporated to enhance student engagement and allow both the sufficient time to introduce programmingmodules and the ability of the instructor/research assistant (RA)/teaching assistant (TA) team to givenecessary assistance and feedback during the programming experiences. A hands-on programming toolkit has been developed by [xx]* for direct programming ofmicrocontroller units (MCUs) using MatLab as the programming environment. Using this toolkit, MCUsare used to teach initial programming skills to engineering students in a context that matches their notionsof engineering. In the Systems Modeling course, the model is used to enhance programming skills in in acontext that enhances students’ understanding of a somewhat abstract area of MechanicalEngineering. The overall goal is for students to be able to verify the analytical modeling and solution onreal-world hardware, without being hampered by significant obstacles or requirements forimplementation. The Matlab-to-MCU toolbox effectively addresses this challenge, allowing students toverify system models using Matlab, a language they concurrently use to simulate system response,“directly” on microcontrollers with little additional overhead requirements. In particular, curricular linkage “labs” developed for the Course are implemented using the MatLab-to-MCU toolbox via a Motorola processor implemented on a Dragon12 evaluation board. An evaluationof the early implementation of these labs is discussed and compared to the traditional (lecture-based)format. The paper concludes with a discussion of the efficacy of this model in upper-level courses,suggestions for improvement and plans for future work.*[xx] – Specific reference to developers of toolkit removed for double‐blind review of abstract

Integrating Analytical Models with Descriptive System Models: Implementation of the OMG SyML Standard for the Tool-specific Case of MapleSim and MagicDraw

The National Health Service is being re-engineered. One of the components is that public health medicine needs to be delivered differently but effectively. It has been proposed that this should be done via managed public health networks (MPHNs). Underpinning this re-engineering there is an increasing role for ICT (information and communication technologies) and previous work suggests that most of the communication over the networks would be predominantly electronic. The development of MPHNs involves much complexity. This study aims to demonstrate the value of systems modelling when implementing and evaluating ICT in public health by applying it to their development. Our objectives are first to propose system models and methodologies, which can be used to understand the critical elements and their inter-relationships within the MPHN. Second, we wanted to apply the information derived from the models to propose a prototype model and to produce a system-based implementation and evaluation plan to realize the network. These objectives were achieved by combining soft system methodology with soft systems diagrams to propose relevant systems models. We conclude that systems modelling facilitates the understanding, implementation and evaluation of ICT in healthcare.

Scientists and decision-makers globally confront systemic challenges posed by the intertwining issues of water scarcity and climate change. These challenges give rise to cascading impacts across ecological and socioeconomic systems, often exacerbated by feedback loops and unforeseeable consequences (UNDRR, 2021). As non-linear changes loom, the reliance on consolidative modeling becomes dangerous, risking the activation of disastrous tipping points with severe implications for both nature and humans (Kreibich et al., 2022). The costs associated with neglecting uncertainties in modeling and policy spans diverse domains, including ecosystems, income, employment, capital value, insurance, etc. (UNDRR, 2022; Parrado et al., 2019; Adamson and Loch, 2021). This aligns with the concept of Knightian or deep uncertainty, where the external context, system dynamics, and conflicting outcomes are not fully known or agreed upon (Knight, 1921; Marchau et al., 2019; Lempert et al., 2006). A growing scientific and policy consensus emphasizes the need to move beyond traditional notions of optimality and deterministic prediction in conditions of deep uncertainty. Resilience and robustness emerge as crucial concepts, requiring the development of socio-ecological system (SES) models that explicitly quantify uncertainties (Adamson and Loch, 2021; Di Baldassarre et al., 2016; IPCC, 2021; UNDRR, 2021).Recent research in SES, including coupled human and natural systems and socio-hydrology science, offers innovative modeling techniques integrating human and natural components. These techniques account for feedbacks and heterogeneity between systems, improving insight and the ability to predict tipping points (Gain et al., 2021). Recent studies demonstrate the potential of linking coupled models of human-water systems with sensitivity analysis and multi-system ensembles for robust water management policies (Basheer et al., 2023; Smith et al., 2021).This review initially identified 2160 papers, filtering them to 198 studies that account quantitatively modelling in, both human and water systems. The geographical focus spans the USA, Europe, Australia, the Middle East, South America, China, and East Africa. The models range from piecewise equations to full-fledged representations. However, structural uncertainties are seldom explored, with only 3.5% of studies conducting multi-model ensemble experiments. This highlights a significant oversight in recognizing biases from simplifications. Conversely, parameter uncertainties are more frequently addressed (20.2%), focusing on hydrology, groundwater, behavioral, infrastructure, climatic, economic, and agronomic variables. Input uncertainties, notably contemporary (discharge data) and future (climate change) inputs, are extensively studied (148 out of 198), employing methods like expert judgment and Monte Carlo simulations. Despite this, the review highlights a limited exploration of structural uncertainties and the potential inadequacy of linear piecewise equations, emphasizing the need for more nuanced and robust approaches to enhance the accuracy and reliability of socio-environmental systems modeling.Based on this study, we make the following recommendations to mainstream uncertainty quantification into SES modeling: (i) Quantify parameter and structural uncertainties within systems, (ii) Quantify structural uncertainties between models, (iii) Input uncertainties must be more thoroughly assessed and model assumptions systematically revised, (iv) Deliver actionable science that mainstreams uncertainty quantification into decision making, (v) Establish balanced stakeholder engagement and clear and transparent science-policy engagement rules, (vi) Balance complexity and usefulness to keep the model relevant.

One approach to tackle the complexity in the development of cyber-physical systems is system decomposition and modelling in a central system model with model-based system engineering (MBSE). However, MBSE also requires increased development effort due to the high degree of formalisation of the development results using the Systems Modelling Language (SysML). One solution is reusing system elements from model libraries, which requires formalization that allows them to be networked into consistent models. Due to the abstract nature of SysML, the options for formalizing system elements are extremely diverse, meaning that even identical technical system elements are modelled with a different structure by engineers. As a consequence, no standardized structure is maintained and the reuse of system elements from model libraries is very unlikely. This leads to high, redundant modelling efforts as well as a considerable error potential and significantly hinders the economic application of MBSE in mechanical engineering. A standardized structure of the system elements can be ensured through MBSE methodology specific modelling guidelines. Current modelling guidelines for MBSE are based on the SysMLv1 language standard and cannot be used unchanged in SysML v2. Nevertheless, SysML v2 offers great potential for modelling reusable system elements, especially with its synonymous textual and graphical modelling capabilities, enabling the easy linkage of knowledge from various domain-specific tools. Consequently, modelling guidelines based on SysML v2, which guarantee a standardized structure, are required to fully exploit the potential of model libraries in MBSE. To solve this deficit, this paper presents modelling guidelines based on SysML v2 and their application for building reusable system elements.

In today's expansive world of social media and collaborative systems [1], firms that invest immensely in Technology to enable their Businesses achieve goals effectively, competitively, and globally [2] expect their Technology Centers to deliver solutions and services at shortest turnaround time, lowest cost [3], with yet very rich functionality and sophistication. Additionally, such firms wish to be able to determine the relationship between the Technology investment [3] they make and the quantities of Technology Services generated as a result. To address such wishes, in this Paper empirical Artificial Neural Network - Expert System (ANN-ES) models are being introduced that utilize previous fiscal periods' technology related investments, channeled through Information Technology's (IT) classical services, for developing patterns, fine tuning and training the models [4]. The approach, illustrated via examples, is based on the distribution of Technology investment across IT service channels and converting them into more tangible services, called IT Generated Consumable Services in this Paper, that are directly utilized by Business Users in a firm. A limited number of Services have been chosen to demonstrate the Proof Of Concept, for each one of which the construction of the model and training method [4] follow a common methodology with different Inputs, Patterns and Outputs. Post convergence, it would be possible to feed a certain period's relevant data as Input in to the model to determine quantities (Output) of a specific type service and its associated costs in relevance to the total cost of IT investment for a specific period in an organization [5]. The output of each model, which is the quantity of generated service of a particular type along with its associative cost is illustrated in one of the Tables 1 through 6 with summaries in Tables 7 & 8. In the following sections of this Paper details for developing ANN-ES models [6] relevant to quantification of each service category are provided.

It is a modern trend to develop a CASE tool for system modeling with an ability to transform models defined in different notations and also to generate a program code. Such a system modeling tool tries to bridge the gap between the system specification and the software components. A tool called BrainTool has been developed for generation of the UML diagrams from the initial presentation of problem domain by the two-hemisphere model. The paper presents the main components of BrainTool and compares it to other system modeling tools.

The Business Process Management & Enterprise Architecture (BPMEA) track aims at gathering researchers and practitioners around the broad topics of business processes and enterprise architecture with a special interest in modeling. These disciplines are quickly evolving and intertwining with each other, and are often referred to with the broad term of business modeling. While several researches are addressing these aspects, there is still a strong need of exploring new paths of improvement and consolidation, especially in light of the recent trends in the business, which put a lot of emphasis on large-scale system engineering, through modeling techniques that are applied at global scale, and data analysis approaches that combine cloud-based big data analytics with enterprise and system models. Topics of relevance for the BPMEA track include enterprise and systems architecture and modeling, multilevel models tracing and alignment, models transformation, IT & business alignment (both in terms of modeling and goals) and big data analysis applied to enterprise-wide data, tackling both technical (languages, systems, patterns, tools) and social (collaboration, human-in-the-loop) issues.

The development of future food systems will depend on normative decisions taken at different levels by policymakers and stakeholders. Scenario modeling is an adequate tool for assessing the implications of such decisions, but for an enlightened debate, it is important to make explicit and transparent how such value-based decisions affect modeling results. In a participatory approach working with five NGOs, we developed a future food vision for the Nordic countries (Denmark, Finland, Norway and Sweden) through an iterative process of defining the scenario, modeling, and revising the scenario, until a final future food vision was reached. The impacts on food production, land use, and greenhouse gas emissions, and the resulting diets in the food vision, were modeled using a mass flow model of the food system. The food vision formulated was an organic farming system where food is produced locally and livestock production is limited to “leftover streams,” i.e., by-products from food production and forage from pastures and perennial grass/clover mixtures, thus limiting food-feed competition. Consumption of meat, especially non-ruminant meat, was substantially reduced compared with current consumption in the Nordic countries (− 81%). An estimated population of 37 million people could be supplied with the scenario diet, which uses 0.21 ha of arable land and causes greenhouse gas emissions of 0.48 tCO2e per diet and year. The novelty of this paper includes advancing modeling of sustainable food systems by using an iterative process for designing future food visions based on stakeholder values, which enables results from multidisciplinary modeling (including agronomy, environmental system analysis, animal and human nutrition) to be fed back into the decision-making process, providing an empirical basis for normative decisions and a science-based future vision of sustainable food systems.

Multi-level system modelling of the resource-food-bioenergy nexus in the global south

Systems engineering is defined as a “full life cycle” discipline and provides methodologies and processes to support the design, development, verification, sustainment, and disposal of systems. While this cradle-to-grave concept is well documented throughout literature, there has been recent emphasis on evolving and digitally transforming systems engineering methodologies, practices, and tools for the latter phases of system life cycles. This article adapts principles from the software engineering domain DevOps concept (a collaborative merger of system development and operations) into a Systems Engineering DevOps (SEDevOps) life cycle model. This facilitates a merger of systems engineering processes, tools, and products into a surrogate operational environment in which the sustainment of a system is tied closely to the curation of a system model expanded to include the enabling system elements necessary for operations and sustainment (procedures, scripts, etc.). This progression of the systems engineering mindset, focused on digitally transforming and enhancing system operations and sustainment, improves agility in later life cycle phases. A framework for applying SEDevOps is introduced as a new systems modeling language profile. A use-case leveraging this “model-based system operations” framework, shows how merging support elements into a spacecraft system model improves adaptability during operations, exemplifying elements of a DevOps approach to cyber-physical system sustainment.

This paper examines the potential integration of Large Language Models (LLMs) with the Systems Modeling Language version 2 (SysML v2), proposing a novel methodology for systems engineering by capitalizing on the enhanced readability and human‐friendly syntax of SysML v2. Given the emergent sophistication of LLMs and the coincidental development of SysML v2—‐an endeavor that presents a pivot toward naturally articulated model interaction—‐we explore the possibilities and implications of such an intersection. Our investigation posits that LLMs can serve not only as an interpretive layer, allowing for the syntactically simplified manipulation of system models, but also as a catalyst for a knowledge‐driven design approach.We highlight the efficiencies gained by deploying LLMs for SysML v2 interactions, which reduce the dependency on technical expertise traditionally needed for API navigation and model management. Through case studies and analysis, we demonstrate that the conversational engagement with system models facilitated by LLMs can lead to a democratized and accelerated design process. However, this advent is tempered by a critical awareness of potential pitfalls, such as automation bias and overreliance on automated systems—‐underscoring the need for continued human oversight and the examination of ethical considerations.Emphasizing the chance of SysML v2 being inherently English‐based and the parallel maturation of LLMs, this paper suggests that the collaborative utilization of these concurrent advancements may offer an opportune fusion, potentially revolutionizing the way systems are modeled and managed. Future work involves the empirical validation of these approaches and a deeper investigation into interoperability with existing and future systems engineering ecosystems. The ultimate goal is to ensure that this fusion not only complements human expertise but also propels systems engineering into a new era of innovation and holistic design.

System models are constructed to design, study, and understand complex systems. According to the systems modeling language (SysML) that is a standard for model-based system engineering, all engineering activities should be performed using a common model. To validate complex system models defined in SysML, simulation is usually employed. There are numerous efforts to simulate SysML models using different simulation methods and tools. However, the efficient support of automated generation of executable simulation code is still an issue tangled by the research community. This paper introduces DEVSys, an integrated framework for utilizing existing SysML models and automatically producing executable discrete event simulation code, according to model driven architecture (MDA) concepts. Although this approach is not simulation-specific, discrete event system specification (DEVS) was employed, due to the similarities between SysML and DEVS, mainly in system structure description, and the mature, yet ongoing research on expressing executable DEVS models in a simulator-neutral manner. DEVSys framework elements include (a) a SysML profile for DEVS, enabling integration of simulation capabilities into SysML models, (b) a meta-model for DEVS, allowing the utilization of MDA concepts and tools, (c) a transformation of SysML models to DEVS models, using a standard model transformation language as query/view/transform (QVT), and (d) the generation of DEVS executable code for a DEVS simulation environment with an extensible markup language (XML) interface. The definition and implementation of DEVSys elements, as well as the process for its application are demonstrated and discussed, with the aid of a simple working example.