System Dynamics Research Articles

Study of the factors influencing the life cycle cost of green buildings using SEM-SD method

ABSTRACT The recent surge in carbon emissions from the building sector has raised significant environmental concerns. While green building practices have proven effective, their widespread adoption has been hindered by high costs. This study aims to explore the factors impacting the life cycle costs of green buildings, with the aim of achieving “double carbon” reduction. Through an analysis of 42 articles, 24 factors were distilled based on expert opinion. After collecting 322 data through questionnaires, a model of the factors influencing the life cycle cost of green buildings was constructed using structural equation modeling, and a cost simulation study was carried out through the system dynamics method in the practical case to examine how the cost changed. The findings highlight the pivotal role of planning and design, implementation, and operation and maintenance phases. Notably, green building design programs, green construction programs, and the property management level stand out as critical factors emerged as critical factors. These insights can inform strategies to reduce the cost of green buildings, thereby fostering energy conservation and emission reduction in the construction industry.

Computational model of the spatiotemporal synergetic system dynamics of calcium, IP3 and dopamine in neuron cells

Computational model of the spatiotemporal synergetic system dynamics of calcium, IP3 and dopamine in neuron cells

Simulation of nonlinear system dynamics of calcium and dopamine signaling in neurons

Simulation of nonlinear system dynamics of calcium and dopamine signaling in neurons

Whistleblowing and interpretation, addressing organizational challenges: a conceptual reflection from Nigeria

Purpose This study aims to explore the effect of whistleblowing and interpretation among practitioners in the Nigerian economy. The research puts a premium on understanding the relevance and critical issues in its practices and developing an improved model for the effective practice of whistleblowing and interpretation in Nigeria. Design/methodology/approach This study adopted a conceptual approach, relying on extant literature to understand the management of whistleblowing incidents and identify the managers’ and other stakeholders’ responsibility in the whistleblowing process. It applied systems dynamics conceptual modelling and presented an improvement approach to addressing the complexities associated with whistleblowing and interpretation among Nigerian organizations. Findings This study contributed to the extant literature by developing a model for proper management of whistleblowing in the Nigerian context and enhancing the robust practice of whistleblowing and interpretation in Nigeria. The findings highlighted critical factors such as managers’ skills development, legal system support, institutional stakeholders’ function and ethical balance as key factors to effective whistleblowing management and interpretation. This implies that the act of identifying and developing responses to an emerging case of whistleblowing requires a process of developing underpinning assumptions, engagement and consideration of stakeholders’ interests while driving the sustenance of organizational focus. Originality/value This study emphasized the departure from absolute best practice to developing idea approaches that reflect stakeholders’ interests and the context of whistleblowing and interpretation. While the study acknowledges limitations in the sole focus on the Nigerian private sector and the Nigerian economic background, it recommends further exploration of whistleblowing and its interpretation on a comparative approach, to improving the current understanding of the topic.

A homotopy-based reinforcement learning scheme to optimal control for Markov switched interconnected systems

ABSTRACT In this paper, a homotopy-based reinforcement learning optimal control method is developed for Markov switched interconnected systems with unknown system dynamics. By utilising the subsystem decomposition method and parallel learning control method, the solution of the game coupled algebraic Riccati equations with jumping parameters is approximated. To dispense with the requirement of initial stability, a homotopy-based policy iteration is introduced, which can place unstable poles into a stable plane. In this regard, a model-free reinforcement learning method is presented to design the optimal controller for Markov switched interconnected systems. Finally, the effectiveness of the proposed method is verified by a numerical example and a practical example.

Non-Linear Dynamics of Simple Elastic Systems Undergoing Friction-Ruled Stick–Slip Motions

The stick–slip phenomenon is a jerking motion that can occur while two objects slide over each other with friction. There are several situations in which this phenomenon can be observed: between the slabs of the friction dampers used to mitigate vibrations in buildings, as well as between the components of the base isolation systems used for seismic protection. The systems of this kind are usually designed to work in a smooth and flawless manner, but under particular conditions undesired jerking motions may develop, yielding complex dynamic behavior even when only a few degrees of freedom are involved. A simplified approach to the problems of this kind leads to the mechanical model of a rigid block connected elastically to a rigid support and at the same time with friction to a second rigid support, both the supports having a prescribed motion. Despite the apparent simplicity of this model, it is very useful for studying important features of the non-linear dynamics of many physical systems. In this work, after a suitable formulation of the problem, the equations of motion are solved analytically in the sticking and sliding phases, and the influence of the main parameters of the system on its dynamics and limit cycles is investigated and discussed.

LCT-Based Framework for the Assessment of Sustainability: From the Perspective of Literature Review

AbstractIn the context of sustainable development, urban transformation should encourage the use of life cycle thinking tools for infrastructure development. By adopting sustainable economic, social, and environmental criteria, this study presents a review of sustainability assessment tools to build a framework for urban infrastructure. It incorporates a variety of methods that economists and industrial ecologists use for evaluating their projects, in addition to the LCT approach. To recognize the strategic sustainability indicators and criteria, the framework uses LCC, LCA, and SLCA in conjunction with stakeholder evaluation and multi-criteria decision analysis, which recognizes the foremost three pillars of sustainability, i.e., economy, environment, and society. Additionally, system dynamics and agent-based modelling are used to optimise the framework. The prominence of the framework is to understand the route an urban infrastructure development should follow to achieve key sustainability interests involving the interconnected environmental and socio-economic aspects. This sustainability assessment framework can be utilised by consultants or supporting agencies to help organisations carry out and monitor such projects.

Effect of Interstage Torsional Damping On Nonlinear Dynamic Characteristics of Two-Stage Planetary Gear System

Abstract This paper explores the influence of interstage torsional damping on the nonlinear dynamics of a two-stage planetary gear transmission system. A comprehensive nonlinear dynamic model is developed, incorporating interstage torsional damping, stiffness, time-varying meshing parameters, damping, and tooth side clearance. The nonlinear equations are derived and solved using the fourth-order Runge-Kutta method. The investigation reveals that the change of torsional damping between stages would significantly affect the nonlinear dynamic characteristics of the system, especially under load fluctuations. Bifurcation characteristics are analyzed using phase diagrams, Poincaré diagrams, time history diagrams, and frequency-domain spectrograms. The simulation results demonstrate that heightened torsional damping between stages distinctly affects motion states across excitation frequencies. Notably, it mitigates the unstable motion state caused by high-frequency excitation when tooth side clearance is minimal.

Analyzing and Enhancing You tube Ranking Algorithms for Video Recommendations

In the rapidly evolving digital space, staying ahead is pivotal for video platforms. The dynamics of recommendation systems, responsible for curating a tailored experience for millions of users daily, become paramount in this pursuit. This study embarks on a comprehensive journey to dissect, simulate, and optimize the algorithms underpinning these recommendations. The proposed segments delve deeper into the specific objectives of this research endeavor. Analyzing YouTube's existing recommendation algorithms and leveraging a proposed model to create a user-friendly interface for the simulation of these algorithms. Testing and evaluating the efficacy of the enhanced algorithms against a benchmark dataset. The future of digital video platforms is intertwined with the evolution of recommended algorithm By enhancing the way platforms like YouTube recommend videos, this study aspires to contribute significantly to improving user experience and platform efficiency. Keyword: ranking algorithms; video recommendation; Analysis, efficiency

An adaptive transition probability matrix with quality seeds for cellular automata models

ABSTRACT The cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, which encapsulate the local dynamics of complex systems and facilitate the manifestation of organized global patterns. While current studies largely concentrate on land use transition matrices as core elements of these rules, exclusive reliance on these matrices is insufficient for capturing the full spectrum of land use change potential. Addressing this gap, our research introduces the adaptive transition probability matrix with quality seeds (ATPMS) model, which incorporates both the Markov model and the genetic algorithm (GA) into the traditional CA framework. Furthermore, an artificial neural network (ANN) is utilized to determine land suitability. Implemented in Beijing, Wuhan, and the Pearl River Delta (PRD), our results indicate that the ATPMS-ANN-CA model surpasses the standard Markov-ANN-CA model in various validation metrics, displaying improvements in overall accuracy (OA) by 0.03% to 0.74% and figure of merit (FoM) by 3.67% to 63.14%. Additionally, the ATPMS-ANN-CA model excels in providing detailed landscape analysis.

Detecting ‘green shoots’ of agri-food systems transformation: a framework and insights from the spread of non-pesticide approaches in South India

ABSTRACT System innovation is a signature feature of agri-food system transformation. Such system innovation often occurs in niches. However, how the "green shoots" of transformation can be detected and appraised through time remains ambiguous. This paper proposes, applies and tests a framework that could be used as a ‘transformation assessment tool’ to evaluate the level of system innovation in a domain of change. The framework is tested against a case study of a Non-Pesticide Management initiative in South India. The framework helps to reveal how, over 20 years, the initiative triggered a number of system innovations that opened a new development pathway, more aligned to environmental sustainability, equity and social inclusion. A critical enabling factor identified for the expansionand "blossoming" of this green shoot was its capacity to flexibly respond and adapt to emergent and largely unknowable agri-food systems dynamics. In its conclusions, the paper sheds light on the ongoing tensions around the defining benchmarks or thresholds for assessing the ‘transformativeness’ of initiatives and change processes. Finding a way of combining qualitative assessments of system changes with quantitative measures of social, economic, and environmental impact could be a valuable vein of research to enhance our understanding of transformative processes and how to enable them.

Breaking the silence: Student affairs’ role in Black male faculty well‐being at predominantly White institutions

AbstractIn this article, we investigate the nuanced challenges facing Black male faculty members in predominantly White institutions (PWIs) regarding their mental health. Through an exploration of the multifaceted roles and responsibilities of student affairs professionals, we uncover actionable strategies that can foster a supportive and inclusive environment for this affinity group in PWIs. Drawing from historical context, contemporary experiences, and future considerations, our analysis highlights the systemic barriers and cultural dynamics contributing to this crisis. Through an intersectional lens, we explore the unique stressors faced by Black male faculty, including racial discrimination, microaggressions, isolation, and the pressure to perform “diversity work” without adequate institutional support. By shedding light on these issues, we aim to spark critical conversations and advocate for systemic changes that prioritize the mental health and well‐being of Black male faculty in PWIs, fostering inclusive and supportive academic environments for all faculty members.

Long-eccentricity pacing of alluvial stratigraphic architecture in the Eocene Bighorn Basin, Wyoming, USA

Alluvial stratigraphy builds up over geologic time under the complex interplay of external climatic and tectonic forces and internal stochastic processes. This complexity makes it challenging to attribute alluvial stratigraphic changes to specific factors. Geological records indicate pronounced and persistent climatic changes during the Phanerozoic, while the effects of these changes on alluvial stratigraphy remain insufficiently documented. We provide evidence for 405 k.y. long-eccentricity climate forcing of alluvial stratigraphy in the lower Eocene Willwood Formation of the Bighorn Basin, Wyoming (USA). Two ∼90-m-thick intervals, characterized by a relative paucity of sand, dominance of sinuous-river channels, and floodplain sediments with better-developed paleosols, coincide with eccentricity maxima as determined through integrated stratigraphic methods. These intervals are interspersed with three contrasting intervals, marked by relatively high sand content, prevalent braided-river channels, and less-developed paleosols, corresponding to eccentricity minima. A comprehensive genetic model that integrates climate, source-to-sink system, and alluvial dynamics to explain these findings remains to be elucidated. Given the consistent presence of the 405 k.y. eccentricity cycle throughout Earth’s history, it is plausible to infer that its influence may be discernible across a wide array of alluvial stratigraphic records.

Examining the performance of Laguerre-based and nonlinear predictive control models for wheeled mobile robots when facing obstacles

This focuses on the problem of controlling the formation of a team of rotating robots that are not homonomic and are not able to interact in a barrier environment. The virtual structure formation control strategy is obtained here to calculate each path of the robot separately while the reference path of the virtual center of the formation is generated by artificial potential fields. In this presentation, we will introduce a new control algorithm that utilizes Model predictive control and nonlinear system dynamics. Three traditional model prediction control controllers (MPC): logger-based MPC, nonlinear MPC, and traditional MPC are used to apply the control algorithm for the nonlinear system. Model predictive control is used to calculate the torques required to track the path by using the dynamic model of the Rotary moving robot. A proposed control rule is the most effective approach to solving the formation and tracking problems. Model Predictive Control will be introduced through the introduction of basic ideas and terms. Traditional MPC, logger-based MPC, and non-linear MPC are among the three MPC methods to be offered. The discrete time display of the power plant under control is taken into account by developing MPC methods for linear or nonlinear systems for practical implementation. The model predictor controller optimizes a cost function to calculate an optimal control sequence at any sampling point. While ignoring the rest, the system only achieves the first control action of this sequence. During subsequent sampling, the optimization problem is resolvable with the use of updated process measurements and a different horizon. The cost function formula is determined by the controller’s purpose. An error function often plays a role in determining the objective by revealing the difference between the desired and actual response. The shape of the formation can be preserved by the law of control by utilizing information from other robots, which can be assured through a new cost function. In the current conditions of disruption, the proposed controllers can also achieve the goal of tracking the path and maintaining formation, according to the results.

Experimental investigation of geometric quantum speed limits in an open quantum system

The quantum speed limit (QSL) is a fundamental lower bound on the evolution time for quantum systems, and its tightness has been observed to be dependent on the properties of the physical process. However, experimental studies exploring the QSL in open quantum systems are still missing. Here, we studied geometric quantum speed limits of a qubit subject to decoherence in an ensemble of chloroform molecules in a Nuclear Magnetic Resonance experiment. We controlled the system-reservoir interaction and the spin relaxation rates by adding a paramagnetic salt, allowing the observation of both Markovian and non-Markovian open system dynamics for the qubit. We used two distinguishability measures of quantum states to assess the speed of the qubit evolution: the quantum Fisher information (QFI) and Wigner-Yanase skew information (WY). For non-Markovianity and low salt concentrations, we found crossovers between QSLs related to those metrics. The WY metric sets the tighter QSL for high concentrations and Markovian dynamics. We also show that QSLs are sensitive even to small fluctuations in spin magnetization.

Dynamics of the Fractional-Order Lorenz System Based on Adomian Decomposition Method and Its DSP Implementation

Dynamics and digital circuit implementation of the fractional-order Lorenz system are investigated by employing Adomian decomposition method (ADM). Dynamics of the fractional-order Lorenz system with derivative order and parameter varying is analyzed by means of Lyapunov exponents (LEs), bifurcation diagram, chaos diagram and phase diagram. Results show that the fractional-order Lorenz system has rich dynamical behavior and it is a potential model for application. It is also found that the minimum order is affected by numerical algorithm and time step size. Finally, the fractional-order system is implemented on DSP digital circuit. Phase diagrams generated by the DSP are consistent with that generated by simulation.

Balancing supply diversification and environmental impacts: A system dynamics approach to de-risk rare earths supply chain

We introduce a System Dynamics (SD) model to examine policy measures aimed at diversifying the global supply chain of rare earth elements (REEs) and reducing reliance on China. Through meticulous simulation experiments, the paper evaluates the effectiveness of individual policies and their combinations (strategies) on enhancing the Rest of the World’s (RoW) market share, minimizing environmental impacts, and meeting the burgeoning demand for Neodymium—essential for green technologies such as electric vehicles and wind turbines.Our findings reveal that direct governmental interventions, particularly those focused on expanding mining and recycling capacities, significantly bolster the RoW’s market share and decrease unmet Neodymium demand. The study also underscores the importance of environmental regulations in promoting sustainable mining practices, which, while slightly constraining market share growth, result in considerable environmental benefits. Among the evaluated strategies, one combining capacity expansion, recycling, and international cooperation emerges as the most effective, marking a substantial increase in RoW market share and a notable reduction in unmet demand.Furthermore, the paper discusses the complex trade-offs between economic development, environmental stewardship, and supply security in policy-making. It calls for a comprehensive approach that considers economic, environmental, and social factors, including public perception and regulation, in REEs mining.In conclusion, our research provides vital insights for policymakers, illustrating how simulated policy interventions can facilitate informed decision-making in the REEs market. By advocating for a balanced consideration of multiple factors, this study contributes to the discourse on sustainable supply chain management for critical minerals.

Modeling the Complex Interplay: Dynamics of Job Displacement and Evolution of Artificial Intelligence in a Socio-Economic Landscape

AbstractThis research explores the intricate dynamics of job displacement resulting from artificial intelligence (AI) using a sophisticated non-linear dynamical system modeled through the Lotka-Volterra equations, commonly employed in ecology to elucidate predator–prey dynamics. In this study, we interpret human jobs as the “prey” and AI technology as the “predator,” identifying two equilibrium points: E1 ≡ (0, 0) signifies a state with no jobs and no AI technology, while E2 ≡ (s/β, r/α) denotes a balanced coexistence where job growth and AI development are in equilibrium. Qualitative analysis reveals four regions characterized by different trends in job expansion and AI development, with Region IV indicating a co-evolutionary phase marked by positive feedback. Stability analysis demonstrates that while E1 remains consistently unstable, E2 remains stable, providing valuable insights into system dynamics. Scenarios presented suggest a promising future where balanced growth fosters sustainable coexistence between human workers and AI, although challenges arise when AI outpaces human job growth, emphasizing the necessity of effective policy responses to mitigate adverse effects and maximize the benefits of technological advancement. Understanding these dynamics is crucial for policymakers to navigate the complexities of AI-induced job displacement and ensure equitable societal outcomes.

Stabilization with unknown-input estimation for time-delayed systems using functional-observer-based feedback control design

This paper focuses on the stabilization and unknown-input estimation (UIE) problem of continuous-time delayed systems by virtue of functional-observer (FO)-based feedback controller. It allows us to estimate the unknown external variable that degrades the system performance with respect to the partially known input of the system. In particular, the unknown inputs are considered in system dynamics’s state as well as in output, which makes the addressed problem to be more applicable. In addition to that, the aim is then to design minimal order functional observer. Notably, the FO-based feedback control law is introduced for the first time and it is used to ensure the asymptotic stabilization with UIE of the proposed system. Whilst, the design and analysis of UIE and FO is based on Lyapunov stability theory and applying the inequality analysis technique, the sufficient stability conditions are established in the forms of matrix inequality. Eventually, the numerical examples with simulation results are provided to validate the practicability and efficacy of the developed control strategy.

Modeling Oil Spill Disasters Using System Dynamics: A Case Study on the MT Princess Empress Oil Spill in Oriental Mindoro, Philippines

Modeling Oil Spill Disasters Using System Dynamics: A Case Study on the MT Princess Empress Oil Spill in Oriental Mindoro, Philippines