Dynamic Modeling Approach Research Articles

Parametric global mode method for dynamical modeling and response analysis of a rotating and length-varying flexible manipulator

Rotating and Length-Varying Flexible Manipulators (RLVFMs) benefit from the ability to transform their length to adapt to complex and demanding workspaces but suffer from increased complexity in nonlinear dynamical characteristics and thus difficulties in modeling. To provide an in-depth understanding of the RLVFMs, this paper proposes a novel dynamical modeling approach for the RLVFMs, called the Parametric Global Modal Method (PGMM), and presents a framework to study their nonlinear responses. It is capable of addressing time-varying boundary conditions and describing the elastic deformation of all flexible components with only one set of modal coordinates. A low-dimensional dynamical model of a RLVFM is developed. The natural characteristic results obtained from the models developed by the PGMM and the finite element method (FEM) are compared for verifications of the PGMM. Via a convergence analysis of responses, the high precision of the model developed by the PGMM is verified to be achieved by using only the first two modes. On this basis, the dynamic responses and computational efficiency of the low-dimensional model are validated through experiments and finite element method (FEM) simulations. Moreover, the responses of the RLVFM under operations of rapid maneuvering are studied and a potential vibration control strategy for the RLVFM is preliminarily demonstrated. This work provides a new way of developing advanced dynamical modeling methods of reconfigurable and deformable multi-component mechanisms for their dynamical design, response analysis, and system control.

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

The modern synthesis and "Progress" in evolution: a view from the journal literature.

The concept of "progress" in evolutionary theory and its relationship to a putative notion of "Progress" in a global, normatively loaded sense of "change for the better" have been the subject of debate since Darwin admonished himself in a marginal note to avoid using the terms 'higher' and 'lower.' While an increase in some kind of complexity in the natural world might seem self-evident, efforts to explicate this trend meet notorious philosophical difficulties. Numerous historians pin the Modern Synthesis as a pivotal moment in this history; Michael Ruse even provocatively hypothesizes that Ernst Mayr and other "architects" of the Synthesis worked actively to eliminate Progress from evolutionary biology's scientific purview. I evaluate these claims here with a textual analysis of the journals Evolution and Proceedings of the Royal Society B (a corpus of 27,762 documents), using a dynamic topic modeling approach to track the fate of the term 'progress' across the Modern Synthesis. The claim that this term declines in importance for evolutionary theorizing over this period can, indeed, be supported; more tentative evidence is also provided that the discussion of 'progress' is largely absent from the British context, emphasizing the role of American paleontology in the rise and fall of 'progress' in 20th-century evolutionary biology.

Innovative solar-assisted direct contact membrane distillation system: Dynamic modeling and performance analysis

The study presents an innovative solar-assisted dual-tank direct contact membrane distillation (DCMD) system designed to enhance the operational stability and efficiency of solar-powered desalination. The proposed system integrates a dual thermal storage tank configuration, allowing for continuous operation by alternating between two tanks that store pre-heated water, thereby mitigating the impact of solar energy fluctuations. The dynamic modeling approach used in this study predicts the system's performance under varying solar conditions, focusing on key parameters such as permeate flux, evaporation efficiency, and specific thermal energy consumption. The simulation results show that the system achieves an average permeate flux of 14.4 L/h m² and a thermal efficiency of 53.3 % at a hot water temperature of 60 °C, with a corresponding average specific thermal energy consumption of 1567 kWh/m³. These findings highlight a substantial improvement in both thermal efficiency and water production compared to conventional single-tank systems.The dual-tank DCMD system is particularly suited for deployment in remote or arid regions where stable and efficient freshwater production is critical. This research provides a comprehensive analysis of a novel solar-assisted desalination technology, contributing to the advancement of sustainable water resources management by providing a reliable and scalable solution that can maintain high operational efficiency even in remote areas with variable solar conditions.

Dynamic Modeling of a HeXe-Cooled Mobile Nuclear Reactor with Closed Brayton Cycle

Helium-xenon (HeXe)-cooled mobile nuclear reactors have promising potential in future low-carbon energy systems. However, there is currently a lack of fast and reliable tools for analyzing the complicated dynamic characteristics of such systems. In this study, we developed a comprehensive dynamic modeling approach for a HeXe-cooled nuclear power system coupled with a closed Brayton cycle (CBC). The system’s key components, including the reactor, printed circuit heat exchanger (PCHE), and turbomachinery, are lumped-modeled to capture their time-varying behavior. A step-solving algorithm that incorporates HeXe mass conservation iteration is designed. The verification results demonstrate that the dynamic program is robust and reliable, with each time step converging within 25 iterations and the HeXe mass remaining within the range of 3.755 ± 0.01 kg throughout the simulation meeting the law of mass conservation. Then, a 1500 s frozen start-up simulation for the coupled system is conducted, in which the CBC is started in the first 500 s by increasing the main shaft speed to 40% of the rated value, and then the reactor is started by inserting external reactivity between 500 and 800 s. Both the dynamic process and the steady-state performance after the start-up are analyzed. The results show that the system achieved a stable electrical output of 5.7 MWe with a thermal efficiency of 32.5%. This study lays a solid foundation for future work aimed at improving the overall efficiency and performance of HeXe-cooled nuclear power systems.

The Effectiveness of Asset Pricing Models Under Special Conditions: Evidence from the COVID-19 Pandemic, Emerging Markets, and Financial Crises

Asset pricing models are important tools in finance. However, special market conditions can make them highly ineffective. This paper evaluates the performance of classical asset pricing models (that is, CAPM and Fama-French models) under three specific scenarios: the COVID-19 pandemic, emerging markets, and financial crises. Recent research and market data are reviewed to conclude that these models have very low efficiency in times of extreme occurrences for capturing proper asset valuations. Our findings reveal that standard models lose a substantial part of their explanatory power in periods of crises while new context-specific risk factors come into existence in other exceptional conditions. This work argues for the inclusion of liquidity and tail risk considerations, especially during market stress, as we also observe a greater need for more dynamic and adaptive modeling approaches that capture time-varying risk premia and factor loadings. The findings drive home the point that standard asset pricing models cannot be applied across the board in all market conditions and stress the need for developing more flexible and situation-specific valuation tools. Therefore, this research lies in its dual contribution, both in academic terms and practical finance, by providing an understanding of the behavior of asset pricing models in extreme markets and giving indications for future model development.

Dynamics System Modeling of Declining Agricultural Land Area Potentially Causing Jakarta Flood Disaster

The change or evolution of land use in the DKI Jakarta area has caused various environmental problems in the city that have an impact on disasters that occur from time to time. The conversion of agricultural land that has a relatively high ability to absorb water into built-up land has reduced the water absorption capacity because the built-up area is increasingly impermeable which is at risk of causing flood disasters. This research focuses on a simple dynamic system modeling approach to understand the cause-and-effect relationship of the decline in agricultural land area in the DKI Jakarta area. The method used in this research is a qualitative-based dynamic system, dynamic system is a method that can be used to form structures and predict behavior and feedback. Dynamic system modeling using Powersim Studi 10 Express software uses time specifications ranging from 2020 to 2070. The results of the dynamic system model of agricultural land decline can be said to be valid, where the dominant structure formed is Negative Feedback (Balancing Loop) with Exponential Decay behavior and Average Means Error (AME) test results obtained a value of 29.6%

System Dynamics Model of CO2 Pollution Stocks Changes Due to Emissions and Absorbent in Indonesia

Air pollution, including CO2 pollution, is a serious issue worldwide, including in Indonesia. This CO2 pollution can impact public health and the environment. In the context of economic development, activities in Indonesia are increasing, leading to higher CO2 emissions. The highest CO2 emissions come from deforestation, large-scale peatland fires, and, to a lesser extent, burning fossil fuels for energy. This research focuses on a simple System Dynamics model approach to understand the cause-and-effect relationship of changes in CO2 pollution stocks concerning emissions and absorption in Indonesia. The method used in this study is qualitative based on system dynamics. System dynamics is a method that can be used to create structures, predict behavior, and provide feedback. The System Dynamics model uses Powersim Studio 10 Express, covering the research period from 2020 to 2040. The results of the system dynamics model show two structures related to CO2 pollution stocks: a positive feedback (reinforcing loop) from CO2 emissions and a negative feedback (balancing loop) from CO2 absorbent. The behavior of the model is exponential growth, it’s also considered valid due to simulation results based on real data processing and statistical analysis, yielding an Average Means Error (AME) of 0.56%.

Assessing Organochlorine Pesticide Behavior in Agricultural Watershed Soil, Water and Sediment: A Dynamic Modeling Approach

ABSTRACT The utilization of pesticides has experienced a substantial rise in order to enhance both the quantity and caliber of the produced goods. Agricultural runoff, a major source of nonpoint pollution, adversely impacts surface water, groundwater, soil and sediment quality in the watershed. Especially, organochlorine pesticides have adverse effects on the environment because of their toxicity, persistence and bioaccumulation. In this study, the presence of organochlorine pesticides (DDTs- dichlorodiphenyltrichloroethane and HCHs- hexachlorocyclohexane) in the environment of the dryland agricultural watershed, was determined. For this purpose, soil, sediment and water samples from the landscape of pond were collected. The concentrations of organochlorine pesticides in water, sediment, and soil ranged from 0.0005 to 0.0138 μg/L, 0.03 to 1.02 μg/kg, and 0.54 to 52 μg/kg, respectively. The persistence of these pesticides in soil and sediment poses long-term risks to both terrestrial and aquatic ecosystems. Sediment, as a significant reservoir for these contaminants, can act as a secondary source of pollution, releasing pesticides back into the water column under certain environmental conditions. Similarly, soil contamination not only affects plant health and productivity but also leads to further leaching and runoff into water bodies. The behavior of a toxic substance in water, soil and sediment environment, which is organochlorine pesticide, in a well-mixed lake watershed, was modeled using mass balances in the lake. Statistical analysis, including a paired t-test and calculation of the Nash-Sutcliffe index, confirms the consistency and reliability of the model’s predictions compared to observed data. Simulated concentrations closely matched observed values, and the distribution patterns and trends remained consistent across different months. Moreover, the Risk Quotient (RQ) for DDTs and HCHs is less than 0.1, indicating low ecological risk.

The impact of counterfeit components and LRUs in the navy surface warfare supply chain: A systems dynamics approach

AbstractMicroelectronics integrity is a critical issue for many industries including the Department of Defense (DoD). The military systems the DoD operates are particularly vulnerable to counterfeiting, with potentially costly or even catastrophic consequences. Counterfeits, regardless of production intent (malign or ersatz), raise significant concerns for industry and the DoD because they often demonstrate operational performance shortcomings, have lower reliability, or make components or organizations more vulnerable to attack. This article uses a systems dynamics modeling approach to explore the economics of counterfeiting for a sample system, the interactions between counterfeiters and the US Navy supply chain, and the impacts of counterfeit surveillance and detection to address the question: Is it more effective to target detection efforts at the component level or at the Line Replaceable Units level? A case study of an engine control module for the LM2500 propulsion turbine used on US Navy Arleigh Burke class guided missile destroyer (DDG‐51) platforms is provided to demonstrate the approach.

Economic impact of screening on postdiagnosis work productivity in Japanese women with breast cancer: a life-table modeling approach.

In Japan, biennial mammography screening has been recommended for the early detection of breast cancer (BC) in women aged 40years or above since 2004 by the Ministry of Health, Labor and Welfare. The purpose of this study is to estimate the economic impact of BC screening on work productivity, using a new measure called the productivity-adjusted life-year (PALY). We used a dynamic life table modeling approach to estimate the work productivity of female patients aged 40-64years diagnosed with BC in 2019 over the year of diagnosis and the subsequent 5years. Changes in life-years, PALYs, and gross domestic product (GDP) were assessed by changing the screening detection rate from the current (34.2%) to an ideal (100%) percentage. Each input for modeling was obtained from the most recent public database available. BC patients were estimated to lose 1903 in life-years, 3596 in PALYs, and US$281 million in GDP at the current screening detection rate compared with the ideal detection rate. On the other hand, the following gains are expected when the current screening detection rate was increased to 40-80%; life-years gain; 168-1325, PALYs gain; 317-2503, GDP gain: US$25-196 million. This study has used modeling to show that detecting BC without screening is associated with a lower work productivity and an economic and life-years loss. Encouraging BC screening may be beneficial to maintaining work productivity after diagnosis.

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

ABSTRACT High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. To elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

Design and assessment of sustainable spent automobile lithium-ion battery industries in Japan: A system dynamic business model approach

Design and assessment of sustainable spent automobile lithium-ion battery industries in Japan: A system dynamic business model approach

The cost of CO2 emissions abatement in a micro energy community in a Belgian context

This paper investigates and quantifies the benefits and the cost of CO2 emissions abatement of a Micro Energy Community (MEC) in a tiny residential cluster of three houses in a Belgian context. A simulation-based comparative analysis is performed of two individual and two MEC concepts, i.e. a reference scenario, an individual electrification scenario, an electricity sharing scenario, and an energy community scenario. A deterministic dynamic white-box modelling approach, including optimal control, is used, considering heat for space heating and domestic hot water, and electricity for electrical appliances. The energy system that achieves the greatest emission reduction at the lowest cost, has a collective heat pump and a photovoltaic installation sized based on the plug load demand. This system reduces the annual CO2 emissions by 11.48 tons at a cost of 179 EUR/ton CO2 considering 2021 dynamic retail electricity prices with a median of 251 EUR/MWh and a fixed retail gas price of 64 EUR/MWh. However, the results are strongly dependent on the gas-electricity price ratio. The highest value of the retail gas price in 2021 leads to a negative CO2 emissions abatement cost of 154 EUR/ton CO2, putting energy community concepts on the radar for a cost-effective energy transition.

Fiscal sustainability analysis in selected SADC region countries with emphasis on South Africa: dynamic modeling, nonlinear causality, and machine learning approaches

Fiscal sustainability analysis in selected SADC region countries with emphasis on South Africa: dynamic modeling, nonlinear causality, and machine learning approaches

Pore-to-Core Upscaling of Two-Phase Flow in Mixed-Wet Porous Media: Part II-A Dynamic Pore-Network Modeling Approach

Pore-to-Core Upscaling of Two-Phase Flow in Mixed-Wet Porous Media: Part II-A Dynamic Pore-Network Modeling Approach

CBDC and banking stability: Modeling cascading effects on reserves, lending, and liquidity

This research uses a dynamic modelling approach to design and simulate an equilibrium model of the interaction between CBDC issuance, deposits and bank reserves. With many central banks announcing their intention to issue central bank digital currencies (CBDCs), it has become imperative to rigorously analyse the potential impact of these issues on the banking system. This study aims to examine the implications of the introduction of CBDCs within a robust analytical framework, in order to inform policymakers and financial sector players about the possible consequences of this major monetary innovation. The study results show that overconfidence of economic agents towards CBDCs can drastically reduce bank reserves, thereby limiting the lending capacity of banks and creating liquidity problems. Similarly, increasing reserve requirements in fiat currency for each unit of CBDC issued can constrain bank reserves and restrict loans and deposits. Additionally, a rise in interest rates on CBDC-related loans can discourage borrowers, thereby reducing loan demand and affecting banking activity. An increase in interest rates leads to a decrease in the quantity of CBDC in circulation, an increase in bank reserves and deposits in CBDC, and a decrease in bank loans. Monitoring the level of confidence of economic agents towards CBDCs is crucial to avoid excessive speculation.

Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural–behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural–behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural–behavioral data.

Modeling fisheries and carbon sequestration ecosystem services under deep uncertainty in the ocean twilight zone

Mesopelagic fishes are a vital component of the biological carbon pump and are, to date, largely unexploited. In recent years, there has been an increased interest in harvesting the mesopelagic zone to produce fish feed for aquaculture. However, great uncertainties exist in how the mesopelagic zone interacts with the climate and food webs, presenting a dilemma for policy. Here, we investigate the consequences of potential policies relating to mesopelagic harvest quotas with a dynamic social-ecological modeling approach, combining system dynamics and global sensitivity analyses informed by participatory modeling. Our analyses reveal that, in simulations of mesopelagic fishing scenarios, uncertainties about mesopelagic fish population dynamics have the most pronounced influence on potential outcomes. The analysis also shows that prioritizing the development of the fishing industry over environmental protection would lead to a significantly higher social cost of climate change to society. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted.