Real-world Dynamics Research Articles

Spatial Heterogeneity of Combined Factors Affecting Vegetation Greenness Change in the Yangtze River Economic Belt from 2000 to 2020

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect.

From dawn to dusk: daily fluctuations in pedestrian traffic in the city center

Pedestrian traffic in a city is subject to fluctuations throughout the day due to a variety of factors. The understanding of these variations can be achieved using properly calibrated agent-based simulation models that capture the dynamics of pedestrian movement. However, despite their significance, such models are currently underrepresented in scientific discussions. In addition, acquiring real-world pedestrian localization data for model calibration poses challenges. To address these issues, this paper presents an agent-based model specifically designed to examine pedestrian traffic fluctuations at a mesoscale level. The model uses popular times data from the Google Places service and population data from the Geographic Information System (GIS) for accurate calibration. As a result, it effectively captures the real-world dynamics of pedestrian movement in the city center. By harnessing the advantages of agent-based modeling (ABM), the model generates several valuable insights into daily pedestrian traffic. It estimates the capacity and speed of pedestrian flows and determines the daily load within the simulated area. Moreover, it enables the identification of bottlenecks and areas characterized by varying levels of pedestrian density. The model’s validation process involves comparing its output with empirical studies and pedestrian traffic data from selected points of interest (POIs). The model successfully captures key aspects associated with fundamental diagrams of pedestrian flow. Furthermore, the dynamics of pedestrians closely align with Google Places popular times data for the chosen POIs. Overall, this research contributes to advancing pedestrian traffic management and optimizing public transport organization by employing empirically calibrated agent-based simulation models.

Breaking Boundaries

Although the pandemic increased the adoption of non-education platforms for teaching, it brought challenges to online higher education. These challenges encompass fatigue from excessive use of video conferencing applications, frustration experienced by instructors when students keep their cameras off during online classes, low student engagement, and notable silence in response to questions or concerns. As digital education and online interaction continue to evolve, the concept of the metaverse has emerged as a potential solution to enhance engagement and collaboration within virtual spaces (Mistretta, 2022). The metaverse, as defined by the Acceleration Studies Foundation, represents the convergence of virtually enhanced physical reality and physical-persisted virtual space (Kye et al., 2021). It constitutes an interactive, three-dimensional virtual environment where individuals can interact with one another and digital entities, mirroring real-life interactions. The metaverse encompasses virtual worlds, online games, social networks, augmented and virtual reality experiences, and more. It blurs the line between the physical and digital realms, allowing for seamless and integrated experiences. Built upon the maturity of technologies such as virtual reality (VR), augmented reality (AR), big data, and blockchain, represents the future of education (Wu & Gao, 2022, cited in George, E. 2023). Hybrid and flexible environments facilitate the integration of these technologies, overcoming physical limitations and enabling students to immerse themselves in a completely virtual environment. Within this environment, students can engage in creative activities that foster learning, critical thinking, collaborative work, socialization, and positively impact student motivation (Almaguer et al., 2021; Burnett et al., 2021; Zhang et al., 2022). Virbela is a virtual reality platform that recreates real-world settings, dynamics, and human interactions in virtual environments. Unlike video conferencing, digital collaboration tools, and online forums, Virbela provides the ability to replicate physical offices, events, or learning spaces while maintaining a sense of community and culture (Virbela, 2023). Tec Virtual Campus is a Tecnologico de Monterrey virtual world created using Virbela. Accessible through a desktop application, this platform offers students a digital space where they can access using customizable avatars and enjoy freedom of movement as if they were on the physical campus (Villanueva, A. 2022). This environment enables the presentation of projects, complete class sessions, participation in forums, organization of educational events, and various other activities, all within an interactive 100% virtual space that utilizes three-dimensional animations and voice-enabled avatars. In this context, we will provide examples of two educational activities implemented within the Virtual Campus for high-enrollment online courses encompassing over 100 students from diverse regions in Mexico. These courses span distinct disciplines: Engineering and Sciences, and Social Sciences and Government. These examples aim to demonstrate that regardless of the subject being studied or course characteristics, this immersive resource can be utilized to add value and innovation to virtual classrooms.

Face-to-face live eye-tracking in toddlers with autism: Feasibility and impact of familiarity and face covering.

Studies utilizing eye-tracking methods have potential to promptly capture real-world dynamics of one of the core areas of vulnerability in autism spectrum disorders (ASD), selective social attention. So far, no studies have successfully reported utilizing the method to examine social attention in toddlers with neurodevelopmental vulnerabilities in real world and challenging settings such as an interactive face-to-face. This study examined the feasibility and validity of live eye-tracking method in response to live interaction occurring in several contexts in toddlers with and without ASD. Forty-seven toddlers with ASD, with atypical development (ATYP), or typically developing (TD), underwent a 30-s live eye-tracking procedure during a face-to-face interaction with a masked stranger using child-directed-speech (16 ASD, 14 ATYP, 17 TD; Mage = 23.44 months, SD = 6.02). Out of this group of toddlers, 29 (10 ASD, 8 ATYP, 11 TD, Mage = 21.97 months, SD = 5.76) underwent the same procedure with one of their maskless parent. Task completion rate, calibration accuracy, and affective response (feasibility measures) as well as attention to the task and the social partner (validity measures) were examined. Task completion rate and calibration accuracy were excellent. Despite the challenging context of face-to-face interaction, the toddlers exhibited a neutral affect, and high attention to the task and the speaker. As anticipated, toddlers with ASD looked less at the social partner compared with control groups. However, attention was comparable between the Stranger and Parent conditions, indicating that the effect was consistent regardless of presence of face covering or the familiarity of the interactive partner. The study demonstrates the high feasibility and validity of a live eye-tracking task involving face-to-face interaction in neurodiverse toddlers with social vulnerabilities. The effect of diminished attention to social partners in toddlers with autism is robust and present when interacting with an unfamiliar person and parent. The results suggest that a brief live eye-tracking method constitutes a promising ecologically valid candidate biomarker and potential intervention outcome in autism.

A table tennis serve versus rally hit elicits differential hemispheric electrocortical power fluctuations.

Human visuomotor control requires coordinated interhemispheric interactions to exploit the brain's functional lateralization. In right-handed individuals, the left hemisphere (right arm) is better for dynamic control and the right hemisphere (left arm) is better for impedance control. Table tennis is a game that requires precise movements of the paddle, whole body coordination, and cognitive engagement, providing an ecologically valid way to study visuomotor integration. The sport has many different types of strokes (e.g., serve, return, and rally shots), which should provide unique cortical dynamics given differences in the sensorimotor demands. The goal of this study was to determine the hemispheric specialization of table tennis serving - a sequential, self-paced, bimanual maneuver. We used time-frequency analysis, event-related potentials, and functional connectivity measures of source-localized electrocortical clusters and compared serves with other types of shots, which varied in the types of movement required, attentional focus, and other task demands. We found greater alpha (8-12 Hz) and beta (13-30 Hz) power in the right sensorimotor cortex than in the left sensorimotor cortex, and we found a greater magnitude of spectral power fluctuations in the right sensorimotor cortex for serve hits than return or rally hits, in all right-handed participants. Surprisingly, we did not find a difference in interhemispheric functional connectivity between a table tennis serve and return or rally hits, even though a serve could arguably be a more complex maneuver. Studying real-world brain dynamics of table tennis provides insight into bilateral sensorimotor integration.NEW & NOTEWORTHY We found different spectral power fluctuations in the left and right sensorimotor cortices during table tennis serves, returns, and rallies. Our findings contribute to the basic science understanding of hemispheric specialization in a real-world context.

Design and Evaluation of Control Algorithms for MEMS Devices in MATLAB/Simulink

The field of Micro-Electro-Mechanical Systems (MEMS) Has witnessed significant advancements in recent years, enabling the development of miniaturized devices with diverse applications. Efficient control algorithms play a crucial role in optimizing the performance of MEMS devices, ensuring accurate sensing and actuation capabilities. This paper presents the design and evaluation of control algorithms for MEMS devices using MATLAB/Simulink, a versatile simulation environment. The proposed research focuses on the development of control algorithms tailored specifically for MEMS devices. The design process involves modeling the MEMS system in MATLAB/Simulink and incorporating real-world constraints and dynamics. To evaluate the performance of the control algorithms, extensive simulations are conducted. The outcomes of this research provide valuable insights into the design and evaluation of control algorithms for MEMS devices in MATLAB/Simulink. The results demonstrate the effectiveness and efficiency of the developed algorithms in enhancing the performance of MEMS systems. Furthermore, the research contributes to expanding the knowledge base of MEMS control design, paving the way for advanced applications and innovations in this rapidly evolving field.

Improving model robustness of traffic crash risk evaluation via adversarial mix-up under traffic flow fundamental diagram

Recent state-of-art crash risk evaluation studies have exploited deep learning (DL) techniques to improve performance in identifying high-risk traffic operation statuses. However, it is doubtful if such DL-based models would remain robust to real-world traffic dynamics (e.g., random traffic fluctuations.) as DL models are sensitive to input changes, where small perturbations could lead to wrong predictions. This study raises the critical robustness issue for crash risk evaluation models and investigates countermeasures to enhance it. By mixing up crash and non-crash samples under the traffic flow fundamental diagram, traffic flow adversarial examples (TF-AEs) were generated to simulate real-world traffic fluctuations. With the developed TF-AEs, model accuracy decreased by 8% and sensitivity dropped by 18%, indicating weak robustness of the baseline model (a convolutional neural network, CNN-based crash risk evaluation model). Then, a coverage-oriented adversarial training method was proposed to improve model robustness in highly imbalanced crash and non-crash situations and various crash risk transition patterns. Experiments showed that the proposed method was effective to improve model robustness as it could prevent 76.5% accuracy drops and 98.9% sensitivity drops against TF-AEs. Finally, the evaluation model outputs’ stability and limitations of the current study are discussed.

Empirical study of the effects of physics-guided machine learning on freeway traffic flow modelling: model comparisons using field data

Recent studies have shown the successful implementation of classical model-based approaches (e.g. macroscopic traffic flow modelling) and data-driven approaches (e.g. machine learning – ML) to model freeway traffic patterns, while both have their limitations. Even though model-based approaches could depict real-world traffic dynamics, they could potentially lead to inaccurate estimations due to traffic fluctuations and uncertainties. In data-driven models, the acquisition of sufficient high-quality data is required to ensure the model performance. However, many transportation applications often suffer from data shortage and noises. To overcome those limitations, this study aims to introduce and evaluate a new model, named as physics-guided machine learning (PGML), that integrates the classical traffic flow model (TFM) with the machine learning technique. This PGML model leverages the output of a traffic flow model along with observational features to generate estimations using a neural network framework. More specifically, it applies physics-guided loss functions in the learning objective of neural networks to ensure that the model not only consists with the training set but also shows lower errors on the known physics of the unlabelled set. To illustrate the effectiveness of the PGML, this study implements empirical studies with a real-world dataset collected from a stretch of I-15 freeway in Utah. Experimental study results show that the proposed PGML model could outperform the other compatible methods, including calibrated traffic flow models, pure machine learning methods, and physics unguided machine learning (PUML).

Fractional-order modelling and analysis of diabetes mellitus: Utilizing the Atangana-Baleanu Caputo (ABC) operator

This article aims to introduce and analyze a diabetes mellitus model of fractional order, utilizing the ABC derivative. Diabetes mellitus is a prevalent and significant disease worldwide, ranking among the top causes of mortality. It is characterized by chronic metabolic dysfunction, leading to elevated blood glucose levels and subsequent damage to vital organs including the nerves, kidneys, eyes, blood vessels, and heart. The fractional ABC derivative is used in this study to describe and analyze diabetes mellitus mathematically while removing hereditary influences. The investigation begins by exploring the initial points of the diabetes mellitus model. Under the fractional ABC operator, Picard's theorem is used to prove the existence and uniqueness of solutions. For the numerical approximation of solutions in the fractional-order diabetes mellitus model, this study used a specialized technique that combines the principles of fractional calculus and a two-step Lagrange polynomial interpolation. Finally, the obtained results are visually presented through graphical representations, serving as empirical evidence to support our theoretical findings. The numerical experiments showed that the proportion of patients with diabetes mellitus increased as the fractional dimension (θ) reduced. The combination of mathematical modelling, analysis, and numerical simulations provides insights into the dynamics of diabetes mellitus, offering valuable contributions to the understanding and management of this prevalent disease. Additionally, the proposed scheme can be enhanced by incorporating the ABC operator, allowing for the simulation of real-world dynamics and behavior in the coexistence of diabetes mellitus and tuberculosis.

Iterative residual policy: For goal-conditioned dynamic manipulation of deformable objects

This paper tackles the task of goal-conditioned dynamic manipulation of deformable objects. This task is highly challenging due to its complex dynamics (introduced by object deformation and high-speed action) and strict task requirements (defined by a precise goal specification). To address these challenges, we present Iterative Residual Policy (IRP), a general learning framework applicable to repeatable tasks with complex dynamics. IRP learns an implicit policy via delta dynamics—instead of modeling the entire dynamical system and inferring actions from that model, IRP learns delta dynamics that predict the effects of delta action on the previously observed trajectory. When combined with adaptive action sampling, the system can quickly optimize its actions online to reach a specified goal. We demonstrate the effectiveness of IRP on two tasks: whipping a rope to hit a target point and swinging a cloth to reach a target pose. Despite being trained only in simulation on a fixed robot setup, IRP is able to efficiently generalize to noisy real-world dynamics, new objects with unseen physical properties, and even different robot hardware embodiments, demonstrating its excellent generalization capability relative to alternative approaches.

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

In this study, the Giant Trevally Optimizer (GTO) is employed to solve the probabilistic optimum power flow (P-OPF) issue, considering Renewable Energy Source (RES) uncertainties, achieving notable cost reduction. The objective function is established to minimize the overall generation cost, including the RES cost, which significantly surpassing existing solutions. The uncertain nature of the RES is represented through the employment of a Monte Carlo Simulation (MCS), strengthened by the K-means Clustering approach and the Elbow technique. Various cases are investigated, including various combinations of PV systems, WE systems, and both fixed and fluctuating loads. The study demonstrates that while considering the costs of solar, wind, or both might slightly increase the total generation cost, the cumulative generation cost remains significantly less than the scenario that does not consider the cost of RESs. The superior outcomes presented in this research underline the importance of considering RES costs, providing a more accurate representation of real-world system dynamics and enabling more effective decision making.

Multivariate stochastic modeling of plugin electric vehicles charging profile and grid impact analysis

The electrification of the transportation sector presents a significant challenge to the power grid. The accurate modeling of the plug-in electric vehicle (PEV) charging load is essential since it can guide policymakers and grid operators to make informed decisions regarding grid reinforcement to facilitate the additional charging demand of PEVs. This research paper introduces a hybrid technique for estimating the aggregated PEV charging load based on a stochastic model and real-world dynamics. The proposed methodology includes multivariate stochastic modeling of PEV driving behaviors which is combined with the powertrain simulations performed on multiple models of PEVs operating under two different driving cycles to determine critical parameters such as energy consumption, the initial state of charge (SOC), and grid charging energy requirements. These parameters are used to estimate daily PEV charging demand based on real-world dynamics. The grid impact analysis is performed on the model of an actual distribution network, located at the National University of Sciences and Technology (NUST), through load flow analysis to identify the weakest nodes in the grid and the percentage of transformer loading due to additional PEV charging load. In addition, the research paper proposes a techno-economic grid reinforcement solution, performed on the Homer Pro, to minimize the detrimental impact of the additional PEV charging load on grid voltage stability. The proposed solution includes measures such as optimal placement of distributed generations (DGs), including photovoltaic (PV) panels, storage systems, and diesel generators, to ensure reliable and efficient charging infrastructure for PEVs.

Learning high-level robotic manipulation actions with visual predictive model

Learning visual predictive models has great potential for real-world robot manipulations. Visual predictive models serve as a model of real-world dynamics to comprehend the interactions between the robot and objects. However, prior works in the literature have focused mainly on low-level elementary robot actions, which typically result in lengthy, inefficient, and highly complex robot manipulation. In contrast, humans usually employ top–down thinking of high-level actions rather than bottom–up stacking of low-level ones. To address this limitation, we present a novel formulation for robot manipulation that can be accomplished by pick-and-place, a commonly applied high-level robot action, through grasping. We propose a novel visual predictive model that combines an action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. Experiments show that our model can accurately predict the object dynamics (i.e., the object movements under robot manipulation) while trained directly on observations of high-level pick-and-place actions. We also demonstrate that, together with a sampling-based planner, our model achieves a higher success rate using high-level actions on a variety of real robot manipulation tasks.

Influence and influenceability: global directionality in directed complex networks.

Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many traditional measures of importance focus on node level information without considering the global network architecture. We use the method of trophic analysis to study directed networks and show that both 'influence' and 'influenceability' in directed networks depend on the hierarchical structure and the global directionality, as measured by the trophic levels and trophic coherence, respectively. We show that in directed networks trophic hierarchy can explain: the nodes that can reach the most others; where the eigenvector centrality localizes; which nodes shape the behaviour in opinion or oscillator dynamics; and which strategies will be successful in generalized rock-paper-scissors games. We show, moreover, that these phenomena are mediated by the global directionality. We also highlight other structural properties of real networks related to influenceability, such as the pseudospectra, which depend on trophic coherence. These results apply to any directed network and the principles highlighted-that node hierarchy is essential for understanding network influence, mediated by global directionality-are applicable to many real-world dynamics.

Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data.

Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.

Predictive control of linear discrete-time Markovian jump systems by learning recurrent patterns

Incorporating pattern-learning for prediction (PLP) in many discrete-time or discrete-event systems allows for computation-efficient controller design by memorizing patterns to schedule control policies based on their future occurrences. In this paper, we demonstrate the effect of PLP by designing a controller architecture for a class of linear Markovian jump systems (MJSs) where the aforementioned “patterns” correspond to finite-length sequences of modes. In our analysis of recurrent patterns, we use martingale theory to derive closed-form solutions to quantities pertaining to the occurrence of patterns: (1) the expected minimum occurrence time of any pattern from some predefined collection, (2) the probability of a pattern being the first to occur among the collection. To make our method applicable to real-world dynamics, we make two extensions to common assumptions in prior pattern-occurrence literature. First, the distribution of the mode process is unknown, and second, the true realization of the mode process is not observable. As demonstration, we consider fault-tolerant control of a dynamic topology-switching network, and empirically compare PLP to two controllers without PLP: a baseline based on the novel System Level Synthesis (SLS) approach and a topology-robust extension of the SLS baseline. We show that PLP is able to reject disturbances just as effectively as the topology-robust controller at reduced computation time and control effort. We discuss several important tradeoffs, such as the size of the pattern collection and the system scale versus the accuracy of the mode predictions, which show how different PLP implementations affect stabilization and runtime performance.

The mismatch between experimental and computational fluid dynamics analyses for magnetic surface microrollers

Magnetically actuated Janus surface microrollers are promising microrobotic platform with numerous potential biomedical engineering applications. While the locomotion models based on a "rotating sphere on a nearby wall" can be adapted to surface microrollers, real-world dynamics may differ from the proposed theories/simulations. In this study, we examine the locomotion efficiency of surface microrollers with diameters of 5, 10, 25, and 50 µm and demonstrate that computational fluid dynamics simulations cannot accurately capture locomotion characteristics for different sizes of microrollers. Specifically, we observe a significant mismatch between lift forces predicted by simulations and opposite balancing forces, particularly for smaller microrollers. We propose the existence of an unaccounted force component in the direction of lift, which is not included in the computational fluid dynamics simulations. Overall, our findings provide a deeper understanding of the physical mechanisms underlying surface microroller locomotion and have important implications for future applications in biomedical engineering.

Fuzzy Random Option Pricing in Continuous Time: A Systematic Review and an Extension of Vasicek’s Equilibrium Model of the Term Structure

Fuzzy random option pricing in continuous time (FROPCT) has emerged as an active research field over the past two decades; thus, there is a need for a comprehensive review that provides a broad perspective on the literature and identifies research gaps. In this regard, we conducted a structure review of the literature by using the WoS and SCOPUS databases while following the PRISMA criteria. With this review, we outline the primary research streams, publication outlets, and notable authors in this domain. Furthermore, the literature review revealed a lack of advancements for the equilibrium models of the yield curve. This finding serves as a primary motivation for the second contribution of this paper, which involves an extension of Vasicek’s yield curve equilibrium model. Specifically, we introduce the existence of fuzzy uncertainty in the parameters governing interest rate movements, including the speed of reversion, equilibrium short-term interest rate, and volatility. By incorporating fuzzy uncertainty, we enhance the model’s ability to capture the complexities of real-world interest rate dynamics. Moreover, this paper presents an empirical application of the proposed extension to the term structure of fixed-income public bonds in European Union. The empirical analysis suggests the suitability of the proposed extension of Vasicek’s model for practical applications.

Scholarly Discourse on the Visuality of Ethics in Gaming: An Opening Conversation

Abstract This article offers perspectives into some of the key intersections between ethics in representation and game design by showcasing an extended ten-minute version of the presenters’ original video about the provocation of visual ethics in gaming, first shown at the summit ‘Re/Sponse-able Visual Ethics.’ This version particularly divulges further detail into the ludic and congruent real-world dynamics that come into play in coding and facilitating both the game’s system of rules and mechanics as well as the player’s immersion in its game world. Topics principally addressed included the relationship and function of violence in procedural-based game activity, and game developer intentions or responses to their chosen subjects and themes embedded into their game. While the video and its topics primarily aligned to the presenters’ respective disciplines, it is still a valuable platform to instigate new or expand existing avenues of research into the relationship of ethics and gaming.

Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning

Bottom-up, technology-rich electricity system models are commonly used to generate scenarios for policy support at a national or global level. In literature, previous hindcasting studies (also called retrospective modeling or ex-post modeling) evaluated existing models rather than sought to inform model development from the beginning. In this study, we present a hindcasting exercise with D-EXPANSE model for national electricity systems in 31 European countries over the 1990–2019 period. We develop several model versions with or without elastic electricity demand and with or without endogenous technology learning, and use hindcasting to choose the most accurate configuration of the bottom-up model. The hindcasting results show that a model with endogenous elastic demand can capture well the real-world evolution of electricity demand, if elasticity factor is chosen well and if the countries did not undergo severe structural changes. Endogenous technology learning, however, increases the uptake of new emerging technologies in cost-optimal scenarios, but still cannot fully capture the real-world dynamics and at times even introduces further inaccuracies.