Systems, Volume 9, Issue 2 (June 2021) – 26 articles

In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given $SNR$ and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important ($SNR\ge 16$ dB). Full article

Since the migrant surge in 2015, social inclusion has become a crucial issue to be addressed effectively by the European Union, given that 39% of the population born outside of the EU member states faces the risk of poverty or social exclusion. Adding to that, the COVID-19 pandemic has severely affected migrant households worldwide, rendering migrant integration an urgent matter for national governments. Discrimination, racism, xenophobia, and radicalization are all societal threats emerging in periods of massive migrant flows and need appropriate policy measures to be employed in migrant host countries to tackle them. This paper suggests the integration of a multiple criteria decision analysis method, namely PROMETHEE, for policy making with regard to migrant social exclusion. In light of previous research findings and the recent release of the Migrant Integration Policy Index 2020, the authors argue that the method proposed could help policy makers to evaluate the effectiveness of the implemented policies, spot the discrepancies between policies and policy outcomes, and motivate knowledge sharing among the EU member states. The findings include a ten-year comparative list of the EU member states (2010–2019) driven by social inclusion indicators for the foreign-born (non-EU-born) population. The results are rather sensitive to changes in the data utilized but they provide an overall comparative picture of social inclusion policy effectiveness in the EU during the past decade. Full article

We discuss mathematical and physical arguments contrasting continuous and discrete, limitless discretization as arbitrary granularity. In this regard, we focus on Incomputable (lacking an algorithm that computes in finite time) Real Numbers (IRNs). We consider how, for measurements, the usual approach to dealing with IRNs is to approximate to avoid the need for more detailed, unrealistic surveys. In this regard, we contrast effective computation and emergent computation. Furthermore, we consider the alternative option of taking into account the properties of the decimal part of IRNs, such as the occurrence, distribution, combinations, quasi-periodicities, and other contextual properties, e.g., topological. For instance, in correspondence with chaotic behaviors, quasi-periodic solutions, quasi-systems, uniqueness, and singularities, non-computability represents and corresponds to theoretically incomplete properties of the processes of complexity, such as emergence and quantum-like properties. We elaborate upon cases of equivalences and symmetries, characterizing complexity and infiniteness as corresponding to the usage of multiple non-equivalent models that are constructively and theoretically incomplete due to the non-exhaustive nature of the multiplicity of complexity. Finally, we detail alternative computational approaches, such as hypercomputation, natural computing, quantum computing, and analog and hybrid computing. The reality of IRNs is considered to represent the theoretical incompleteness of complex phenomena taking place through collapse from equivalences and symmetries. A world of precise finite values, even if approximated, is assumed to have dynamics that are zippable in analytical formulae and to be computable and symbolically representable in the way it functions. A world of arbitrary precise infinite values with dynamics that are non-zippable in analytical formulae, non-computable, and, for instance, sub-symbolically representable, is assumed to be almost compatible with the coherence of emergence. The real world is assumed to be a continuous combination of the two—functioning and emergent—where the second dominates and is the norm, and the first is the locus of primarily epistemic extracts. Research on IRNs should focus on properties representing and corresponding to those that are detectable in real, even if extreme, phenomena, such as emergence and quantum phenomena. Full article

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference leads to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that they all can generate sustained exponential growth but differ in performance: even policies criticized in the literature for provoking overshoot and collapse can drive sustained growth. This leads to questions for further research regarding (1) the set of generic policies and its structure and (2) concerning the reasoning of human decision-makers when choosing between such policies and the salience of important but easily overlooked features of the decision situation. Full article

This article explores co-creative problem solving to support rapid learning of systems knowledge in the concept phase towards innovation. We introduce the term co-creative problem solving to describe the act of collective creation between systems engineers and stakeholders during problem solving. The context of this research is a mature Norwegian industry accustomed to efficiency and risk aversion, challenged by late validation of systems design due to poor utilization of systems knowledge. We have explored co-creation between systems engineers and stakeholders such as project managers, business developers, and subject-matter experts through a longitudinal in-depth industry case in the energy domain. The primary outcome is insights into how co-creative problem solving supports rapid learning of systems knowledge in the industry case. We propose a method building on the findings from the research results to support systems engineers in similar contexts facing similar challenges. Full article

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, $R_0$, influence the simulated outbreaks, and find that our model behaves as expected: the higher the $R_0$, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases. Full article

This study utilized the application of authentic Virtual Reality (VR) to replicate the real-world complex system scenarios of a large retail supply chain. The proposed VR scenarios were developed based on an established systems thinking instrument that consists of seven dimensions: level of complexity, independence, interaction, change, uncertainty, systems’ worldview, and flexibility. However, in this study, we only developed the VR scenarios for the first dimension, level of complexity, to assess an individual’s Systems Thinking Skills (STS) when he or she engages in a turbulent virtual environment. The main objective of this study was to compare a student’s STS when using traditional ST instruments versus VR scenarios for the complexity dimension. The secondary aim was to investigate the efficacy of VR scenarios utilizing three measurements: Simulation Sickness Questionnaire (SSQ), System Usability Scale (SUS), and Presence Questionnaire (PQ). In addition to the three measures, NASA TLX assessment was also performed to assess the perceived workload with regards to performing the tasks in VR scenarios. The results show students’ preferences in the VR scenarios are not significantly different from their responses obtained using the traditional systems skills instrument. The efficacy measures confirmed that the developed VR scenarios are user friendly and lie in an acceptable region for users. Finally, the overall NASA TLX score suggests that users require 36% perceived work effort to perform the activities in VR scenarios. Full article

The implementation of information and communication technologies (ICT) in power systems increases the risks of cybersecurity threats, requiring protection measures that should reflect the multi-actor environment of the contemporary power systems. This paper provides a critical assessment of the cybersecurity practices of the transmission system operators (TSOs) from South East Europe (SEE) and the implementation of obligations for TSOs emerging from the complex set of cybersecurity and electricity legislation. The analyses of TSO cybersecurity practices are based on a survey conducted with the TSOs from SEE and show there is a lack of consistent cybersecurity policy at the TSO level. These analyses demonstrate that the differences between TSOs from the SEE region are not very significant with regards to implementation of technical protection and defense measures for critical infrastructures (CIs) and assets. The comprehensive analyses of electricity and cybersecurity legislation uncover the obligations of TSOs emerging from legislation and relate them to current TSO cybersecurity practices, confirming the necessity to boost existing practices. Considering the analyzed legislation and implemented practices, this paper presents a proposal for a cybersecurity framework for TSOs that should improve their organizational and operational response to the evolving cybersecurity challenges. Full article

This systematic review examines the importance of a systems/holistic approach in analyzing and addressing the footprints/impacts of business-as-usual activities regarding the development of a circular economy (CE). Recent works on why current CE approaches have to be examined in terms of reductionist vs. systems perspectives are reviewed to tackle questions pertaining to the right or the wrong way of CE implementation. ‘Doing the right thing right’ is essential for sustainability—the ultimate goal of a CE, which must be viewed as a system to begin with. The limited reductionist approach overlooks and thus cannot prognosticate on the formidable unintended consequences that emerge from ‘doing the right things wrong’, consequences that become too costly to undo. The systems approach, being holistic, is complicated and difficult to pursue but open to exciting opportunities to integrate innovations in CE analysis and implementation. Complexity is an inherent downside of the systems approach. However, both approaches are complementary, as reductionist models can be combined to create a system of comprehensive analysis to correct the approach towards implementation of current CE initiatives. This review reports that advancements in systems analytical frameworks and tools are highly important for creating general guidelines on CE analysis and implementation. Full article

Proportional Integral Derivative (PID) is the most popular controller used in automatic systems, because of its robustness, ability to adapt the behaviors of the system, making them converge toward its optimum. These advantages are valid only in the case of the linear systems, as they present poor robustness in nonlinear systems. For that reason, many solutions are adopted to improve the PID robustness of the nonlinear systems. The optimization algorithm presents an efficient solution to generate the optimums PID gains adapting to the system’s nonlinearity. The regulation speed in the Direct Torque Control (DTC) is carried out by the PID controller, which caused many inconveniences in terms of speed (overshoot and rejection time), fluxes, and torque ripples. For that, this work describes a new approach for DTC of the Doubly Fed Induction Motor (DFIM) powered by two voltage inverters, using a PID controller for the regulation speed, based on a Genetic Algorithm (GA), which has been proposed for adjustment and optimizing the parameters of the PID controller, using a weighted combination of objective functions. To overcome the disadvantages cited at the beginning, the new hybrid approach GA-DTC has the efficiency to adapt to the system’s nonlinearity. This proposed strategy has been validated and implemented on Matlab/Simulink, which is attributed to many improvements in DFIM performances, such as limiting speed overshoot, reducing response time and the rate of Total Harmonic Distortion (THD) of the stator and rotor currents, and minimizing the rejection time of speed and amplitude of the torque and flux ripples. Full article

Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee or candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study ($n=142$) and showed that a systems skills questionnaire can be enhanced. This research makes a significant contribution to improving the systems skills assessment process in particular, and lays the foundations for improving the evaluation of other skills or traits. Full article

Escalating climate crisis activism highlights the potential of self-organised approaches in sustainability to address the disconnect between corporate sustainability activities and globally declining ecological systems. This paper argues that corporate sustainability is a co-evolutionary process of emergence which may enable organisations to address this disconnect by creating a context supportive of emergence within the organisation rather than reacting to pressures from outside. An exploratory mixed-methods case study was used to explore how corporate sustainability emerged in two financial services institutions. This article develops the idea of corporate sustainability as a co-evolutionary process of emergence and presents a framework to assist organisations to cultivate sustainability. It adopts a complexity view and posits that reductionism associated with Newtonian thinking has contributed to the sustainability issues faced by humanity. This study suggests that the paradigmatic assumptions that have contributed to the sustainability crisis must be interrogated to create an environment which is conducive to the emergence of corporate sustainability. Through examining corporate sustainability as an emergent process, this paper sheds light on how businesses can foster conditions in which a self-organised response to sustainability challenges is distributed across the organisation whilst being embedded in the containing system. Full article

Metacybernetics refers to the higher cybernetic orders that arise in living system agencies. Agencies are complex, and for them to be viable and hence survive, they require both stability and uncertainty reduction. Metacybernetics is defined through a metasystem hierarchy, and is mostly known through 1st and 2nd order cybernetics. In this exploratory paper the purpose is to create a framework that can underpin metacybernetics and explain the relationship between different cybernetic orders. The framework is built on agency theory which has both substructural and superstructural dimensions. Substructure has an interest in stability, is concerned with the generation of higher cybernetic orders, and is serviced by horizontal recursion. Superstructure is concerned with uncertainty reduction by uncovering hidden material or regulatory relationships, and is serviced by vertical recursion. Philosophical aspects to the framework are discussed, making distinction between global rationality through critical realism, and local rationality that relates to different cybernetic orders that correspond to bounding paradigms like positivism and constructivism. Full article

Sustainability accounting is an emerging research area receiving growing awareness. This study examines the role of digital technology in manufacturing companies’ sustainability accounting. To guide the research, we use a triple layered business model canvas, which supports the accounting of a manufacturer’s performance for the economic, environmental, and social aspects of sustainability. We present an explorative case study of four Norwegian manufacturing companies representing different industries. The findings from the study indicate that while accounting for economic values is well taken care of, companies do not perform comprehensive environmental and social accounting. Furthermore, we observed a shift from a focus on sustainability issues related to the internal manufacturing process to a focus on sustainability issues for the life cycle of the product. Even though the manufacturers are at the forefront with regard to automation and control of production, with extensive use of robots giving a large amount of data, these data are not utilized towards sustainability accounting, showing that sustainability and digitalization are seen as two separate phenomena. This study sheds light on how digital data available from applied Industry 4.0 technologies could enhance sustainability accounting with limited efforts, linking sustainability and digitalization. The results provide insights for manufacturers and researchers in moving towards more sustainable operations and products. Full article

Bioenergy is one of the potential solutions to satisfy the extensive demand for energy and reduce fossil fuel dependency. For biomass to be an efficient source of bioenergy, it must be converted to a usable form, one of which is pellets. This study compares three commonly used methods to produce pellets in a biomass depot and presents a framework to select the most effective and economic pelleting processes. The comparison is performed using a data driven decision-making method called the Preference Index Selection Method (PSI). We considered three main pelletization technologies and compared four of their most critical attributes. The three popular biomass pellet processing methods used for this study are the conventional pelleting process (CPP), the high moisture pelleting process (HMPP), and the ammonia fiber expansion (AFEX). These processes were evaluated from both economic and environmental perspectives. We used the state of Mississippi as a testing ground for our analyses. The results obtained through the PSI method were validated with the Grey relational analysis (GRA) method. The results revealed that of the three available pelleting processes, the conventional pelleting process and the high moisture pelleting process were the most economic and environmentally friendly. Full article

The dual goal of this Special Issue is to highlight the implementation of computational systems modeling tools for K12 science teachers and students and to address equity and access for student groups who have historically been left out of mainstream research on computational systems modeling [...] Full article

System dynamics, as a methodology for analyzing and understanding various types of systems, has been applied in research for several decades. We undertook a review to identify the latest application domains and map the realm of system dynamics. The systematic review was conducted according to the PRISMA methodology. We analyzed and categorized 212 articles and found that the vast majority of studies belong to the fields of business administration, health, and environmental research. Altogether, 20 groups of modeling and simulation topics can be recognized. System dynamics is occasionally supported by other modeling methodologies such as the agent-based modeling approach. There are issues related to published studies mostly associated with testing of validity and reasonability of models, leading to the development of predictions that are not grounded in verified models. This study contributes to the development of system dynamics as a methodology that can offer new ideas, highlight limitations, or provide analogies for further research in various research disciplines. Full article

The primary objective of this study is to reveal macro-level knowledge to aid the optimization, evaluation, and strategic planning of technological innovation abandonment. This research uses an exploratory data analysis (EDA) approach to extract directional and associative patterns (macro-level knowledge) to assess technological innovation abandonment optimization. Deterministic and stochastic simulations are employed to reveal the impact of three factors on abandonment optimization, namely, a technological innovation’s diffusion rate, a technological innovation’s probability of achieving a given diffusion rate, and the point of abandonment. The patterns and insights revealed through the graphical examination of the simulation provide associative and directional knowledge to assess the abandonment optimization of technological innovation. These revealed patterns and insights enable decision-makers to develop an abandonment assessment framework for optimizing, evaluating, and proactively planning abandonment at the macro level. Full article

The conventional approach of policy interventions in water management that focus on the portions of the system that directly relate to water often lead to unintended consequences that potentially exacerbate water scarcity issues and present challenges to the future viability of many rural agricultural communities. This paper deploys a system dynamics model to illustrate how expanding the policy space of hydrology models to include socioeconomic feedbacks could address these challenges. In this regard, policies that can potentially mitigate general water scarcity in a region of the American Southwest in southern New Mexico are examined. We selected and tested policies with the potential to diminish water scarcity without compromising the system’s economic performance. These policies included supporting choices that reduce or limit the expansion of water-intensive crops, promoting workforce participation, encouraging investment in capital, and regulating land use change processes. The simulation results, after the proposed boundary expansion, unveiled intervention options not commonly exercised by water decision-makers, bolstering the argument that integrated approaches to water research that include socioeconomic feedbacks are crucial for the study of agricultural community resilience. Full article

The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties. Full article

This theoretical article provides a brief description of the model of living systems’ functioning by defining them as self-reproducing information or as self-reproduction of resource flows patterns. It reviews the living systems growth limitation between their development cycles by the Fibonacci sequence. Besides, there are presented systems resource base criteria, necessary for accumulating the resources and their investment. The article also considers the conditions for the formation of various systems strategies. Then we reviewed the principles of elemental analysis of information by a person as a living system according to the considered model. The study also shows the possibility of forming priorities in analyzing information for 16 combinations as maximum. At that, it remains crucial to divide a human’s information analysis between the two hemispheres of the brain. The described combinations of priorities in a person’s information analysis are compared with the existing differential personality models, such as the big five personality traits, the Myers–Briggs type indicator, temperaments model and Honey and Mumford Learning styles. Full article

The system that shapes a problem can be represented using a map, in which relevant constructs are listed as nodes, and salient interrelationships are provided as directed edges which track the direction of causation. Such representations are particularly useful to address complex problems which are multi-factorial and may involve structures such as loops, in contrast with simple problems which may have a clear root cause and a short chain of causes-and-effects. Although students are often evaluated based on either simple problems or simplified situations (e.g., true/false, multiple choice), they need systems thinking skills to eventually deal with complex, open-ended problems in their professional lives. A starting point is thus to construct a representation of the problem space, such as a causal map, and then to identify and contrast solutions by navigating this map. The initial step of abstracting a system into a map is challenging for students: unlike seasoned experts, they lack a detailed understanding of the application domain, and hence struggle in capturing its key concepts and interrelationships. Case libraries can remedy this disadvantage, as they can transfer the knowledge of experts to novices. However, the content of the cases can impact the perspectives of students. For example, their understanding of a system (as reflected in a map) may differ when they are exposed to case studies depicting successful or failed interventions in a system. Previous studies have abundantly documented that cases can support students, using a variety of metrics such as test scores. In the present study, we examine the ways in which the representation of a system (captured as a causal map) changes as a function of exposure to certain types of evidence. Our experiments across three cohorts at two institutions show that providing students with cases tends to broaden their coverage of the problem space, but the knowledge afforded by the cases is integrated in the students’ maps differently depending on the type of case, as well as the cohort of students. Full article

Associative knowledge networks are often explored by using the so-called spreading activation model to find their key items and their rankings. The spreading activation model is based on the idea of diffusion- or random walk -like spreading of activation in the network. Here, we propose a generalisation, which relaxes an assumption of simple Brownian-like random walk (or equally, ordinary diffusion process) and takes into account nonlocal jump processes, typical for superdiffusive processes, by using fractional graph Laplacian. In addition, the model allows a nonlinearity of the diffusion process. These generalizations provide a dynamic equation that is analogous to fractional porous medium diffusion equation in a continuum case. A solution of the generalized equation is obtained in the form of a recently proposed q-generalized matrix transformation, the so-called q-adjacency kernel, which can be adopted as a systemic state describing spreading activation. Based on the systemic state, a new centrality measure called activity centrality is introduced for ranking the importance of items (nodes) in spreading activation. To demonstrate the viability of analysis based on systemic states, we use empirical data from a recently reported case of a university students’ associative knowledge network about the history of science. It is shown that, while a choice of model does not alter rankings of the items with the highest rank, rankings of nodes with lower ranks depend essentially on the diffusion model. Full article

Contemporary manufacturing systems are undergoing a major change promoted by emerging technologies such as Cyber-physical Systems (CPS) or the Internet of Things (IoT). This trend, nowadays widely known by the term “Industry 4.0”, leads to a new kind of automated production. However, the rising number of dynamically interconnected elements in industrial production lines results in such a system being transformed into a complex System of Systems (SoS). Due to the increasing complexity and the challenges accompanied by this change, conventional engineering methods using generic principles reach their limits when developing this type of systems. With varying approaches only trying to find a solution for small-scaled areas of this problem statement, the need for a holistic methodology becomes more and more obvious. Having recognized this issue, one of the most promising approaches has been introduced with the Reference Architecture Model Industry 4.0 (RAMI 4.0). However, in the current point of view, this domain-specific architecture framework is missing specifications to address all aspects of such a critical infrastructure. Thus, this paper introduces a comprehensive modeling approach utilizing methods applied in Model-Based Systems Engineering (MBSE) and including domain-specific particularities as well as architectural concepts with the goal to enable mutual engineering of current and future industrial systems. The resulting artifacts, a domain-specific language (DSL), an architecture definition and a development process, are thereby consolidated in a ready to use software framework, whose applicability was evaluated by a real-world case study. Full article