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The search complexity for partial transmit sequence (PTS) and selective mapping (SLM) techniques increases exponentially with the number of sub-blocks, necessitating a comprehensive search over all possible combinations of phase-weighting variables. This paper proposes a novel complex system modeling approach for PTS and SLM in an Orthogonal Time Frequency Space (OTFS) system, utilizing phase factors and a sub-block partition scheme. We describe an OTFS system that achieves low computational complexity in identifying optimal phase-weighting factors and reducing the peak-to-average power ratio (PAPR) using sub-optimal PTS and SLM based on the particle swarm optimization (PSO) algorithm. Parameters such as PAPR, bit error rate (BER), and power spectral density (PSD) were analyzed for 64, 256, and 512 sub-carriers in Rayleigh and Rician channels. The experimental outcome reveals that the proposed approaches can effectively regulate the optimal phase-weighting factors, substantially lessening PAPR with modest complexity. Fractals enhance complex modeling by optimizing PAPR reduction in OTFS 6G waveforms using fractal-influenced PSO for sub-carrier efficiency. The proposed method incorporates fractal modeling to enhance the optimization process in complex environments. Fractals, known for their intricate patterns and self-similarity, provide a robust framework for exploring vast and complex search spaces, crucial in PSO. This approach improves the efficiency of the framework. [ABSTRACT FROM AUTHOR]

In some complex systems, because of the influence of internal and external factors, periodic changes occur among runtime stages, with each stage exhibiting distinct dynamics. When we employ data-driven parameterized methods to model and predict such systems, a unified model restricts the learning of the dynamics and transitions of multiple stages. To address the aforementioned challenges, inspired by the ordinary differential equations network (ODENet), this paper proposes a novel predictive simulation framework, referred to as the deterministic finite automaton ordinary differential equation net (DFA-ODENet). This framework is a continuous-time deep learning framework designed to model periodic multistage systems using irregularly–sampled historical system trajectories. The model includes two principal predictions for forecasting system dynamics and stage transition. In terms of learning the dynamics of the system, the model comprises several ODENets, whose number is determined from the number of stages of the modeled system. Each ODENet individually learns the continuous-time nonlinear dynamics within its respective stage. For learning the stage transitions, a stage transition predictor is employed to learn the duration of each stage from observational data. These stage transition predictors are prelabeled based on the prior knowledge of the system. During prediction, the stage transition predictor serves as a switcher for selecting the appropriate ODENet to predict the system outputs. Moreover, the framework incorporates a specific encoder–decoder structure, where the encoder solves the initial state based on historical system inputs and outputs, while the decoder predicts future system outputs using the inputs of the prediction window based on the solved initial state. To evaluate the feasibility and effectiveness of the proposed approach, the encoder–decoder framework is employed in a cooling system of a real data center to simulate specific dynamic variables during operation. After providing multivariate operational data, including server power and environmental temperature, the model successfully simulates the system behavior in the expected operational patterns and predicts the open-loop output variables, such as power consumption and inlet air temperature. Notably, when the prediction horizon extends beyond 30 min, the mean absolute percentage error of the predicted energy consumption remains

Human Digital Twins (HDTs) have emerged as a key technology in digitalizing human-centered applications, able to enhance decision-making, personalize healthcare, and optimize workplace environments. However, despite a growing research body, there exists a critical lack of guidance for designing sophisticated, user-centric HDTs. In addressing this gap, this paper reports a systematic literature review of 44 papers in the field of HDTs (published between 2022 and 2023) to distill key design process insights and identify gaps in the current understanding and approaches to HDT design. Based on insights from this review, we introduce ETHICA, a systematic methodology that advocates for an iterative development process, emphasizing stakeholder collaboration and focusing on ethical and user-centric principles throughout the design life cycle. The resulting ETHICA methodology offers a robust blueprint for constructing sophisticated HDTs that harmonizes technological advancements with human-centered design and ethical considerations, encouraging a shift towards more holistic and human-centric approaches in the emerging field of Digital Twin technologies.

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method. [ABSTRACT FROM AUTHOR]

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article. [ABSTRACT FROM AUTHOR]

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method.

The application of the game-based education technologies in a modified version of “the beer distribution game” on a number of disciplines devoted to the study of the system approach in economics has proven its effectiveness. This variant of the game has been updated by the professors of the Financial University. The gamification of education process basing on supply chain modeling made it possible to demonstrate clearly to students how the structure of the system determines its behavior and influences the behavior of individual components, as well as to focus their attention on the main system characteristics and features of socio-economic systems. Gaming technologies served as a starting point to analyze and summarize the data collected by students during the game, to acquire the skills of formalizing practical situations and building models of complex systems. Also they served to study the behavior of different systems by exploring their models, and for later learning of theoretical material based on the “drawing knowledge” principle and in-depth study of issues related to various aspects of system’s functioning. The gamification of the education process made it possible to increase the students’ involvement into subject being studied, to understand better the theory of system thinking and to develop practical research competences.

The interdependent nature of highly interconnected systems, such as supply chains, renders them incapable of quickly resuming operation following a disruptive event. In the context of supply chains, previous works have demonstrated the efficacy of inventory as a mitigation strategy to delay the impact of disruptive events. However, current methods tend to focus only on a demand-driven approach, which may be inappropriate for some systems where a value-added price perturbation represents an impact. Thus, this paper offers a methodology for understanding the role of inventory in the supply-side dynamic inoperability input–output model (DIIM). To demonstrate the applicability of the proposed model, an illustrative example is presented using data from the 2012 Philippines Input–Output (I-O) Table in five different scenarios. Given a predefined planning horizon, the results of this work can serve as guidelines for decision-makers for the provision of inventory that will keep the overall inoperability of an entire supply chain at a minimum. [ABSTRACT FROM AUTHOR]

Abstract Nonlinear complex system modeling has become the basis of many theoretical and practical problems, which requires balancing the correlations between the modeling accuracy and the modeling complexity. However, the two objectives may not be consistent with each other under many practical conditions, especially for complex systems with multiple influential factors. The belief rule base (BRB) has shown advantages in nonlinear complex system modeling under uncertainty. However, most of current works on BRB has focused only on the modeling accuracy. As such, an Akaike Information Criterion (AIC)-based objective, AIC BRB , is deduced to represent both the modeling accuracy (denoted by the Mean Square Error (MSE)) and the modeling complexity (denoted by the number of the parameters). Based on the proposed AIC BRB , a bi-level optimization model and a corresponding bi-level optimization algorithm are developed. Moreover, an empirical optimization path search strategy is proposed for upper-level optimization. The optimization path is comprised of multiple solutions with optimal performance. After the BRB learning process, both the structure and the parameters of BRB are optimized, which identifies the best decision structure of BRB. A numerical multi-extreme function case and a practical pipeline leak detection case are studied. The results show that an optimization path could be identified with a series of optimal solutions. With AIC BRB as the objective, the jointly optimized BRB in the best decision structure can be obtained with an improved modeling accuracy as well as reduced modeling complexity. [ABSTRACT FROM AUTHOR]

Electricity systems are undergoing unprecedented change, with growing capacity for low-carbon generation, and an increasingly distributed approach to network control. Furthermore, the severity of climate related threats is projected to increase. To improve our understanding of the risks from these changes, this paper presents a novel modeling approach to assess the resilience of future electricity networks to climate hazards. The approach involves consideration of the: 1) evolution of electricity networks in response to changes in demand, supply, and infrastructure development policies; 2) implication that these policies have on network configuration and resilience; and 3) impacts of potential changes in climate hazard on network resilience. We demonstrate the research on the National Electricity Transmission System of Great Britain and assess the resilience of this system to changes in the intensity of wind storms under alternative energy futures. The analysis shows that infrastructure policies strongly shape the long-term spatial configuration of electricity networks and consequently this has profound impacts on their resilience. Though the system is resilient to wind storms under the current climate, our analysis shows that the system fails to meet electricity demand after an increase of only 5–10% in the intensity and frequency of wind storms, and a 50% increase could lead to the loss of 85% of peak winter demand. The approach is useful for identifying and communicating potential network risks to wider stakeholders and policy makers seeking to design a transition toward a low-carbon, yet resilient, future electricity systems. [ABSTRACT FROM AUTHOR]

Nonlinear complex system modeling has drawn attention from diverse fields and many approaches have been developed. Among those approaches, the advantages of the belief rule base (BRB) expert system have been shown for managing multiple types of information under uncertainty and modeling the nonlinearity present in many theoretical and practical complex systems. However, two challenges still need to be addressed. First, BRB needs to be downsized to conserve modeling and computational effort. For this challenge, a new disjunctive assumption is applied, which can significantly downsize BRB while maintaining its completeness. Second, the structure and parameters of BRB need to be jointly optimized. For this challenge, a new Akaike information criterion (AIC)-based optimization objective is derived to represent both modeling accuracy and modeling complexity. Moreover, a joint bi-level optimization model with an AIC-based objective is constructed for the BRB structure and parameters, and a bi-level optimization algorithm is proposed. Three evolutionary algorithms, namely, the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, are tested in a comparative fashion to determine the best fit for the optimization engine. The results of two practical case studies show that the joint optimization approach can identify an optimal configuration for both its structure and parameters, which is referred to as the best decision structure in this paper. [ABSTRACT FROM AUTHOR]

In order to make a sensible prediction on the air traffic flow management with conditions of wave-off and bolter, a system dynamic model for the night recovery operations of embarked aircrafts is built to ensure the adaption of air traffic flow with the capacity of air control at each phase of the recovery operations. The model aims at the characteristics of multiple feedbacks, delays and complex time varying, builds a stock flow diagram and operation model with impact factors of the night recovery system, and is simulated in Vensim® Personal Learning Edition 5.9. The simulation shows a reasonable prediction result for the night recovery of embarked aircrafts with conditions of bolter and wave-off and can provide a theoretical basis for scheduling the air traffic flow management of embarked aircrafts formation recovery. [ABSTRACT FROM AUTHOR]

• Randomness and interpretability are major challenges which motivates this study. • The cloud model is adopted for modeling the randomness from an interval perspective. • The belief rule base (BRB) is adopted to provide interpretability. • The new Cloud-BRB approach is proposed and applied in a practical risk assessment case. • Case study results are more informative and show consistency with previous approaches. Many complex system modeling problems have to face two major challenges, randomness and interpretability, which are the motives and major contributions of this study. For the randomness challenge, the cloud model is adopted which reveals randomness by assuming the points within an interval following a certain distribution. For the interpretability challenge, the Belief Rule Base (BRB) is adopted which is essentially a white-box with direct access to experts and decision-makers. Therefore, a new approach is proposed to meet the two challenges by combining the cloud model and BRB, namely Cloud-BRB. In the new Cloud-BRB approach, new rule activation and matching degree calculation procedures have been proposed to better accommodate the cloud model into BRB. Moreover, new weight calculation procedures have also been proposed based on the multi-dimensional cloud model which connects the multiple attributes in practical problems and multiple attributes in BRB. A practical case of risk assessment on tunnel-induced damages to pipes is studied. Case study results show the consistency with previous approaches, which validate the efficiency of the proposed Cloud-BRB approach. As the belief distribution and randomness have been included in the Cloud-BRB approach, the results are more informative for decision-makers than previous approaches. Furthermore, Pipe No. 10 has been used to demonstrate the key influential factors identification process by the Cloud-BRB approach. [ABSTRACT FROM AUTHOR]

When it comes to pandemics, such as the one caused by the Coronavirus disease COVID-19, various issues and problems have arisen for the healthcare infrastructure and institutions. With increasing number of patients in need of urgent medical care and hospitalizations, the healthcare systems and regional hospitals may approach their maximum service capacity and may face shortage of various parameters, such as supplies including PPE, medications, therapeutic devices, ventilators, beds, and many more. The article at hand describes the development and framework of a simulation model that enables the modeling and evaluation of the COVID-19 pandemic progress. To achieve this, the model dynamically mimics and simulates the developments and time-dependent behavior of various crucial parameters of the pandemic, among others, the daily infection numbers and death rate. In addition, the model enables the simulation of single events and scenarios that occur outside of the regular pandemic developments as anomalies, such as holidays. Unlike traditional models, the proposed framework is based on factors and parameters closely derived from reality, such as the contact rate of individuals, which allows for a much more realistic representation. In addition, the real connection enables the assessment of effects of various influences regarding the development and progress of the pandemic, such as hospitalization numbers over time. All the aforementioned points are possible within the simulation framework and do not require awaiting the unfolding of the effects in reality. Thus, the model is capable of dynamically predicting how different scenarios turn out. The abilities of the model are demonstrated, illustrated, and proven in a specific case study that shows the impact of holidays, such as Passover and Easter in New York City when quarantine measures might have been ignored, and an increase in extended family gatherings temporarily occurred. As a result, the simulation showed significant impacts and disproportionate number of patients in need of medical care that could be potentially detrimental in reality. For example, compared to the previous trajectory of the pandemic, for a temporary increase of 50% in the contact rate of individuals, the model showed that the total number of cases would increase by 461 090, the maximum number of required hospitalizations would rise to 79 733, and the total number of fatalities would climb by 19 125 over 90 days. In addition to its function and proven capabilities, the model can and is furthermore planned to be adapted to other areas, not necessarily only metropolitan regions in order to expand the utilization of its predictive power. Such predictions could be used to derive regulatory measures and to test various policies for COVID-19 containment., (© IEEE 2021.)