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Urban flood risk management has been a hot issue worldwide due to the increased frequency and severity of floods occurring in cities. In this paper, an innovative modelling approach based on the Bayesian convolutional neural network (BCNN) was proposed to simulate the urban flood inundation, and to provide a reliable prediction of specific water depth. To develop the model, a series of historical rainfall data during the last 20 years were collected in Rushan China and the responding flood events were reproduced using physically based hydraulic model. The flood condition factors used in modeling include spacial factors and precipitation factors. The results showed that the BCNN model not only inherits the powerful ability of aggregating spacial information from CNNs to perform high level of accuracy and computational efficiency in predicting 2D urban flood inundation maps, but also offers a measure of uncertainty in the form of predictive variance, providing insights into the confidence and reliability of its predictions. The proposed BCNN method offered a new perspective for the analysis of surrogate model regarding real-time forecasting of flood inundation. [ABSTRACT FROM AUTHOR]

In computational homogenization approaches, data-driven methods entail advantages due to their ability to capture complex behavior without assuming a specific material model. Within this domain, constitutive model-based and model-free data-driven methods are distinguished. The former employ artificial neural networks as models to approximate a constitutive relation, whereas the latter directly incorporate stress–strain data in the analysis. Neural network-based constitutive descriptions are one of the most widely used data-driven approaches in computational mechanics. In contrast, distance-minimizing data-driven computational mechanics enables substituting the material modeling step entirely by iteratively obtaining a physically consistent solution close to the material behavior represented by the data. The maximum entropy data-driven solver is a generalization of this method, providing increased robustness concerning outliers in the underlying data set. Additionally, a tensor voting enhancement based on incorporating locally linear tangent spaces enables interpolating in regions of sparse sampling. In this contribution, a comparison of neural network-based constitutive models and data-driven computational mechanics is made. General differences between machine learning, distance minimizing, and entropy maximizing data-driven methods are explored. These include the pre-processing of data and the required computational effort for optimization as well as evaluation. Numerical examples with synthetically generated datasets obtained by numerical material tests are employed to demonstrate the capabilities of the investigated methods. An anisotropic nonlinear elastic constitutive law is chosen for the investigation. The resulting constitutive representations are then applied in structural simulations. Thereby, differences in the solution procedure as well as use-case accuracy of the methods are investigated. [ABSTRACT FROM AUTHOR]

With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand‐tuned signals. Here, a learning‐based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim‐to‐real transfer is presented. Developing a data‐driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback‐Leibler divergence‐based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost. [ABSTRACT FROM AUTHOR]

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010–2019. The burdens of five categories of disease causes – cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases – were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Industrial control systems (ICSs) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing their ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data. [ABSTRACT FROM AUTHOR]

Business process simulation is an established approach to estimate the potential impact of hypothetical changes on a process, particularly in terms of time and cost-related performance measures. To overcome the complexity associated with manually specifying and fine-tuning simulation models, data-driven simulation (DDS) methods enable users to discover accurate business process simulation models from event logs. However, in the pursuit of accuracy, DDS methods often generate overly complex models. This complexity can hinder analysts when attempting to manually adjust these models to represent what-if scenarios, especially those involving control-flow changes such as activity re-sequencing. This article addresses this limitation by proposing an approach that allows users to specify control-flow changes to a business process simulation model declaratively, and to automate the generation of what-if scenarios. The proposed approach employs a generative deep learning model to produce traces resembling those in the original log while implementing the user-specified control-flow changes. Subsequently, the technique generates a stochastic process model, and uses it as a basis to construct a modified simulation model for what-if analysis. Experiments show that the simulation models generated through this approach replicate the accuracy of models manually created by directly altering the original process model. [ABSTRACT FROM AUTHOR]

Abstract With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand‐tuned signals. Here, a learning‐based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim‐to‐real transfer is presented. Developing a data‐driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback‐Leibler divergence‐based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.

Abstract Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs $$\,\rightarrow \,$$ → Validate PDFs $$\,\rightarrow \,$$ → Design Image Masks $$\,\rightarrow \,$$ → Generate Intensities $$\,\rightarrow \,$$ → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks.

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers. [ABSTRACT FROM AUTHOR]

Discrete-event simulations are widely used to research automated storage/retrieval systems (AS/RS). However, using commercial general-purpose simulators for this purpose has limitations such as lack of specific functionalities to capture the peculiarities of AS/RS, low model reusability, and lack of access to source code. Consequently, researchers have developed their own bespoke programs to meet their specific needs. These programs are specific to their research objective and are not meant for easy adoption, modification, or extension. As a result, there has been a lot of duplication of efforts across different studies. Motivated by this requirement for a customisable special-purpose simulator for AS/RS, this paper develops an open-source data-driven discrete-event simulator that allows its user to create and run simulation models of multi-aisle AS/RS without needing to write any code. The data-driven approach allows the quick creation of models of different multi-aisle AS/RS configurations and control policies. The simulator is developed in Python programming language, leveraging the functionalities of various libraries in its ecosystem. The simulator's architecture is kept modular to facilitate its management, modification, and extension. The simulator's features and ability to adapt to changes in input data are demonstrated through three example scenarios. [ABSTRACT FROM AUTHOR]

One of the most common and life-threatening cancers is that of the breast among women. The first step to successful treatment and better survival rates for breast cancer patients is prompt and precise assessment of cancer. Predicting cancer from biopsy images is challenging task. In the past few years, machine learning (ML), deep learning (DL), forecasting and response to the treatment therapy models were developed by several researchers to examine breast cancer histopathology images to boost the diagnostic precision. Current exemplar incorrectly classifies false positive pixels and does not extensively make use of existing clinical data. This paper proposes a novel mathematical model for breast cancer (BRCA) prediction. Additionally, the proposed model is integrated with ML approach to improve the accuracy of prediction. Mathematical models are fed with features of breast cancer cells and classify them into benign and malignant cases. We used publicly accessible dataset of BRCA histopathology images to test the proposed method. Different ML models were used to classify the extracted features. It was found that proposed mathematical model combined with different machine learning techniques achieved superior performance. This approach can be diversified to different cancer types and imaging modalities. In a nutshell, the proposed method has a lot of potential for enhancing the precision and efficacy of BRCA using histopathology images. [ABSTRACT FROM AUTHOR]

Business process simulation is an established approach to estimate the potential impact of hypothetical changes on a process, particularly in terms of time and cost-related performance measures. To overcome the complexity associated with manually specifying and fine-tuning simulation models, data-driven simulation (DDS) methods enable users to discover accurate business process simulation models from event logs. However, in the pursuit of accuracy, DDS methods often generate overly complex models. This complexity can hinder analysts when attempting to manually adjust these models to represent what-if scenarios, especially those involving control-flow changes such as activity re-sequencing. This article addresses this limitation by proposing an approach that allows users to specify control-flow changes to a business process simulation model declaratively, and to automate the generation of what-if scenarios. The proposed approach employs a generative deep learning model to produce traces resembling those in the original log while implementing the user-specified control-flow changes. Subsequently, the technique generates a stochastic process model, and uses it as a basis to construct a modified simulation model for what-if analysis. Experiments show that the simulation models generated through this approach replicate the accuracy of models manually created by directly altering the original process model.

Modal analysis of a vibrating plate can be effectively performed by dynamic mode decomposition (DMD). The advantages of DMD have been proven in various fields including image processing, modal analysis of dynamic systems, haptic localization of large display panel. Meanwhile, one of the disadvantages of DMD is that when the resolution of each sample in dataset is too high, the size of the matrix representing the dataset becomes too large. To overcome this difficulty, compressed DMD (cDMD) was recently developed. cDMD applies a compression operator before performing singular value decomposition for a matrix representing dataset, thereby lowering the resolution of the samples at once. By employing a low-resolution dataset, cDMD can reduce CPU time. In this work, we perform modal analysis of the Kirchhoff-Love vibrating plate using cDMD and propose an algorithm to predict the numerical solution. The performance of cDMD was almost similar to that of DMD in terms of accuracy. On the other hand, time cost of cDMD was reduced to one-tenth of that of DMD. [ABSTRACT FROM AUTHOR]

IntroductionData-driven simulation allows the discovery of process simulation models from event logs. The generated model can be used to simulate changes in the process configuration and to evaluate the expected performance of the processes before they are executed. Currently, these what-if scenarios are defined and assessed manually by the analysts. Besides the complexity of finding a suitable scenario for a desired performance, existing approaches simulate scenarios based on flow and data patterns leaving aside a resource-based analysis. Resources are critical on the process performance since they carry out costs, time, and quality.MethodsThis paper proposes a method to automate the discovery of optimal resource allocations to improve the performance of simulated what-if scenarios. We describe a model for individual resource allocation only to activities they fit. Then, we present how what-if scenarios are generated based on preference and collaboration allocation policies. The optimal resource allocations are discovered based on a user-defined multi-objective optimization function.Results and discussionThis method is integrated with a simulation environment to compare the trade-off in the performance of what-if scenarios when changing allocation policies. An experimental evaluation of multiple real-life and synthetic event logs shows that optimal resource allocations improve the simulation performance.

The design and simulation methods of composite components are vital to the performance and application research of composite materials. Research pertaining to composite component design and simulation systems begins late in China and confronts many risks，including inadequate theoretical levels，lack of independent standards，weak foundation of simulation software，and segregation between design and manufacture. Therefore，the large-scale application of composite components in major equipment can hardly be realized. Considering the problems and challenges，this study analyzes the macro demand for composite component design theory and simulation technique，summarizes the development status and main trends in China and abroad，and proposes the key directions in China：design theory of composite components under extreme and multi-field environment，dynamic analysis and design theory of composite components，data-driven simulation method of composite components，and simulation evaluation method of strength and life of composite components. We suggest that research should be conducted on multi-field and multi-scale design technology of composite components for equipment engineering applications，performance design technology of composite components under dynamic load，data-driven design and simulation technology of composite components，and strength and lifespan simulation software platform of composite components，thereby comprehensively improving the design and engineering application level of composite components in China.

Data-driven simulation is a promising approach to the development of heuristic models of complex physical systems. Within this approach the set of weights of an artificial neural network is optimized to predict directly the characteristics of the calculation blocks representing a system studied. The data-driven approach is applied for the first time to simulate the two-phase flow in a porous medium; specifically, to determine the saturations of two immiscible phases during their filtering in space at an arbitrary instant. A computational experiment is performed, in which a deep convolutional neural network is adversarially trained using statistical estimates of the deviation from reference numerical solutions, which serves the objective function. A network of original architecture and a training process including nontrivial weight updating sequence for subnetworks (two encoders and one decoder/generator) are considered. Within the methodology of adversarial training, a discriminator network is used, whose objective function is set to contradict the objective functions of the main subnetworks. The results of training of an artificial neural network of specified configuration proved the ability of the proposed architecture to successfully generalize the regularities learned from the set of training data. The developed technique, implying the existence of two main objective functions for optimizing the set weights for each subnetwork, allowed the heuristic model to obtain results comparable with those of reference imitative simulation of two-phase flow on the basis of numerical methods. The specificity of the tasks solved in the oil and gas industry is the inevitable occurrence of uncertainties in geological and hydrodynamic reservoir models, a circumstance making urgent research in the field of heuristic methods of hydrodynamic simulation. The data output rate for the developed model is 2 to 3 orders of magnitude higher than that of conventional solvers (with comparable accuracy values). Thus, the proposed synthetic simulation is applicable to the tasks of predicting hydrocarbon deposits and planning their development. [ABSTRACT FROM AUTHOR]

Kirchhoff-Love plate (KLP) equation is a well established theory for a description of a deformation of a thin plate under certain outer source. Meanwhile, analysis of a vibrating plate in a frequency domain is important in terms of obtaining the main frequency/eigenfunctions and predicting the vibration of plate. Among various modal analysis methods, dynamic mode decomposition (DMD) is one of the efficient data-driven methods. In this work, we carry out DMD based modal analysis for KLP where thin plate is under effects of sine-type outer force. We first construct discrete time series of KLP solutions based on a finite difference method (FDM). Over 720,000 number of FDM-generated solutions, we select only 500 number of solutions for the DMD implementation. We report the resulting DMD-modes for KLP. Also, we show how DMD can be used to predict KLP solutions in an efficient way. [ABSTRACT FROM AUTHOR]

Fourier neural operator (FNO) model is developed for large eddy simulation (LES) of three-dimensional (3D) turbulence. Velocity fields of isotropic turbulence generated by direct numerical simulation (DNS) are used for training the FNO model to predict the filtered velocity field at a given time. The input of the FNO model is the filtered velocity fields at the previous several time-nodes with large time lag. In the a posteriori study of LES, the FNO model performs better than the dynamic Smagorinsky model (DSM) and the dynamic mixed model (DMM) in the prediction of the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and the instantaneous flow structures. Moreover, the proposed model can significantly reduce the computational cost, and can be well generalized to LES of turbulence at higher Taylor-Reynolds numbers.

Traffic at unsignalised intersection usually involves complex interactions. It is critical to explicitly model the two-dimensional driving behaviours and capture the interactions among vehicles. However, little effort has been made to develop microscopic traffic simulation models at unsignalised intersections, which can generate realistic vehicle trajectories. To fill this gap, this study aims to develop a two-dimensional data-driven simulation model at an unsignalised intersection based on multi-agent imitation learning for generating realistic trajectories of vehicles crossing the intersection and macroscopic traffic characteristics. We propose a multi-agent adversarial inverse reinforcement learning model with four policies (MA-AIRL-4) to separately learn the driving behaviours with different directions (East-bound, West-bound, South-bound, and North–bound) by capturing the interactions between the vehicles. Using the vehicle trajectories data extracted from an unsignalised intersection of the open-source Interaction Dataset, we evaluate the performance of the proposed model by comparing it with several bench-marking models, i.e., MA-AIRL-2 model with two policies (i.e., one policy for West-East and North-South direction, respectively), MA-AIRL-1 model with one policy for all vehicles, three corresponding multi-agent generative adversarial imitation learning (MA-GAIL) models with four policies, two policies and one policy, respectively, and long short-term memory (LSTM) model. The results demonstrate the superiority of the proposed MA-AIRL-4 model in generating accurate vehicle trajectories and reproducing realistic speed distributions. The interpretation of the recovered reward function also indicates the effective learning of the proposed model and provides insights into the learned behaviours. • Developed two-dimensional simulation model for unsignalised intersection with multi-agent AIRL. • Captured the interactions among four bounds of vehicles with heterogeneous policies. • Demonstrated the ability of the developed model in generating realistic trajectories. • Interpreted the driving behaviours with learned reward functions. [ABSTRACT FROM AUTHOR]

In high-energy physics, precise measurements rely on highly reliable detector simulations. Traditionally, these simulations involve incorporating experiment data to model detector responses and fine-tuning them. However, due to the complexity of the experiment data, tuning the simulation can be challenging. One crucial aspect for charged particle identification is the measurement of energy deposition per unit length (referred to as d E / d x). This paper proposes a data-driven d E / d x simulation method using the Normalizing Flow technique, which can learn the d E / d x distribution directly from experiment data. By employing this method, not only can the need for manual tuning of the d E / d x simulation be eliminated, but also high-precision simulation can be achieved. [ABSTRACT FROM AUTHOR]

Buildings contribute significantly to global energy consumption. Optimizing internal building space layout is an essential approach for reducing energy consumption. However, proactively improving energy efficiency by building space design is still challenging requiring comprehensive consideration of complex interactions between indoor environment and occupant behavior, which is less studied previously. Considering behavior-environment integration, this study proposes a data-driven approach based on multi-sensor fusion for energy-efficiency oriented occupancy space optimization in buildings. Firstly, time series data including indoor environment and occupant behavior were collected based on multi-sensor fusion. Then, a data-integrated Convolutional Neural Network (CNN) model was developed for occupancy state classification. Based on obtained occupant schedules, space occupancy patterns of users were extracted using hierarchical clustering, and space optimization was further conducted for energy efficiency improvement. Finally, energy consumption was predicted with random forest regression after space optimization, and the impact of occupancy space optimization on energy efficiency can be evaluated. The proposed method was successfully applied in an academic office building on a campus in Wuhan, China, which helped achieve energy consumption reduction by 23.5 %. This study presents a promising path towards sustainable energy goals in building design, which serves as advanced guidance in the management of building energy performance. • Capture environment and behavior information based on multi-sensor fusion. • Develop a data-integrated CNN model for occupancy state classification. • Optimize occupant distribution in building space for energy efficiency based on occupancy patterns. • Occupancy space optimization can help achieve 23.5 % reduction in building energy consumption. • Spatially and temporally granular occupancy states and space utilization were investigated. [ABSTRACT FROM AUTHOR]

This paper proposes 1) a new hybrid analysis technique by integrating a data-driven method with a physics-based technique to perform nonlinear analysis of steel structural systems under seismic loading, 2) two component-based data-driven models (PI-SINDy and DPI-SINDy) for predicting the nonlinear hysteretic response of steel seismic fuses with and without hysteretic degradation. The proposed hybrid data-driven and physics-based simulation (HyDPS) technique offers an efficient approach for seismic analysis of structures and is expected to address the challenges associated with computational cost and modeling uncertainties inherent in physics-based numerical simulations. In this technique, the well-understood components of the structure modeled numerically are combined with the critical components of the structure simulated using one of the data-driven models developed in this study. The proposed data-driven models were trained using experimental and numerical hysteresis data. The results show that these data-driven models can accurately and efficiently predict the nonlinear hysteretic response of steel structural components with and without degradation. Furthermore, the performance of the HyDPS technique powered by the PI-SINDy model is verified in the presence of noise using response history analyses performed on a steel buckling-restrained braced frame. • Proposed a hybrid seismic analysis technique for steel structures combining data-driven and physics-based models. • Developed two sparse regression-based data-driven models for simulating steel structures with/without degradation. • Utilized a dimensionality reduction algorithm to enhance the efficiency of the data-driven model with degradation. • Validated the data-driven models and hybrid technique with experimental and synthetic numerical data. [ABSTRACT FROM AUTHOR]

A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.

• We propose an augmented crowd simulation system using automatic determination and reconstruction of navigable areas in static, surveillancealike videos. • We utilize pedestrian trajectory data and use deep learning-based semantic segmentation methods to identify navigable areas. • We simulate artificial agents over the reconstructed navigable area together with real agents in the video via collision avoidance. • We demonstrate the accuracy and applicability of the proposed navigable area reconstruction approach on various crowded outdoor scenarios. [Display omitted] Crowd simulations imitate the group dynamics of individuals in different environments. Applications in entertainment, security, and education require augmenting simulated crowds into videos of real people. In such cases, virtual agents should realistically interact with the environment and the people in the video. One component of this augmentation task is determining the navigable regions in the video. In this work, we utilize semantic segmentation and pedestrian detection to automatically locate and reconstruct the navigable regions of surveillance-like videos. We place the resulting flat mesh into our 3D crowd simulation environment to integrate virtual agents that navigate inside the video avoiding collision with real pedestrians and other virtual agents. We report the performance of our open-source system using real-life surveillance videos, based on the accuracy of the automatically determined navigable regions and camera configuration. We show that our system generates accurate navigable regions for realistic augmented crowd simulations. [ABSTRACT FROM AUTHOR]

In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this article, we investigate the design of an ambulance system in the presence of abandonment behavior, using a two-step approach. First, because the callers' actual willingness to wait for ambulances is censored, we adopt a Maximum Likelihood Estimator estimation approach suitable for interval censored data. Second, we employ a simulation-based optimization approach to explicitly incorporate customers' willingness to wait in: (i) tactical short-term decisions such as modification of dispatch policies and ambulance allocations at existing base locations; and (ii) strategic long-term network design decisions of increasing fleet size and re-designing base locations. We calibrate our models using data from a major metropolitan city in India where historically 81.3% of calls were successfully served without being abandoned. We find that modifying dispatch policies or reallocating ambulances provide relatively small gains in successfully served calls (around 1%). By contrast, increasing fleet size and network re-design can more significantly increase the fraction of successfully served calls with the latter being particularly more effective. Redesigning bases with the current fleet size is equivalent to increasing the fleet size by 8.6% at current base locations. Similarly, adding 29% more ambulances and redesigning the base locations is equivalent to doubling the fleet size at the current base locations and adding 34% more ambulances and redesigning base locations is equivalent to a three-fold increase. Our results indicate that in the absence of changes in behavioral factors, significant investment is required to modify operational factors by increasing fleet size, and to modify infrastructural factors by redesigning base locations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a machine learning-based simulation framework of general-purpose multibody dynamics (MBD). The aim of the framework is to construct a well-trained meta-model of MBD systems, based on a deep neural network (DNN). Since the main advantage of the meta-model is the enhancement of computational efficiency in returning solutions, the modeling would be beneficial for solving highly complex MBD problems in a short time. Furthermore, for dynamics problems, not only the accuracy but also the smoothness in time of motion solutions, such as displacement, velocity, and acceleration, are essential aspects to consider. We analyze and discuss the influence of training data structures on both aspects of solutions. As a result of the introduced approach, the meta-model provides motion estimation of system dynamics without solving an analytical equation of motion or a numerical solver. Numerical tests demonstrate the performance of the proposed meta-modeling for representing several MBD systems. [ABSTRACT FROM AUTHOR]

With advances in technology, researchers and test developers are developing new item types to measure complex skills like problem solving and critical thinking. Analyzing such items is often challenging because of their complicated response patterns, and thus it is important to develop psychometric methods for practitioners and researchers to analyze these new item types. In this study, we describe a generic approach that involves data‐driven analyses and expert feedback from different research areas so that the analysis results can provide valuable information to test developers and researchers on how complex item types contribute to score reliability and validity and on how to make the test more efficient and reliable in measuring complex skills. A real data example was used to illustrate how to identify nonfunctioning options that might be removed from the test and whether partial credit for certain response selections can be considered. [ABSTRACT FROM AUTHOR]

Ten thousand children are admitted to emergency rooms due to accidents every year in Tokyo. The most frequent accident is a fall accident. Fall accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. Predicting children's behavior is necessary in order to improve the environment. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating cameras, a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. Thirty five children participated in an experiment to collect climbing posture data. A simulation is performed based on the posture database and allows us to visually understand how children climb up in daily living space. This makes it possible to improve to achieve a safe environment for children without the need for specialized knowledge, which is useful for parents, nursery teachers, nurses, etc. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function. [ABSTRACT FROM AUTHOR]

Comprehensive knowledge of built-in batteries in waste electrical and electronic equipment (WEEE) is required for sound and save WEEE management. However, representative sampling is challenging due to the constantly changing composition of WEEE flows and battery systems. Necessary knowledge, such as methodologically uniform procedures and recommendations for the determination of minimum sample sizes (MSS) for representative results, is missing. The direct consequences are increased sampling efforts, lack of quality-assured data, gaps in the monitoring of battery losses in complementary flows, and impeded quality control of depollution during WEEE treatment. In this study, we provide detailed data sets on built-in batteries in WEEE and propose a non-parametric approach (NPA) to determine MSS. For the pilot dataset, more than 23 Mg WEEE (6500 devices) were sampled, examined for built-in batteries, and classified according to product-specific keys (UNUkeys and BATTkeys). The results show that 21% of the devices had battery compartments, distributed over almost all UNUkeys considered and that only about every third battery was removed prior to treatment. Moreover, the characterization of battery masses (BM) and battery mass shares (BMS) using descriptive statistical analysis showed that neither productnor battery-specific characteristics are given and that the assumption of (log-)normally distributed data is not generally applicable. Consequently, parametric approaches (PA) to determine the MSS for representative sampling are prone to be biased. The presented NPA for MSS using data-driven simulation (bootstrapping) shows its applicability despite small sample sizes and inconclusive data distribution. If consistently applied, the method presented can be used to optimize future sampling and thus reduce sampling costs and efforts while increasing data quality. [ABSTRACT FROM AUTHOR]

Aiming at objectively measuring the realism of virtual traffic flows and evaluating the effectiveness of different traffic simulation techniques, this paper introduces a general, dictionary-based learning method to evaluate the fidelity of any traffic trajectory data. First, a traffic pattern dictionary that characterizes common patterns of real-world traffic behavior is built offline from pre-collected ground truth traffic data. The corresponding learning error is set as the benchmark of the dictionary-based traffic representation. With the aid of the constructed dictionary, the realism of input simulated traffic flow data can be evaluated by comparing its dictionary-based reconstruction error with the dictionary error benchmark. This evaluation metric can be robustly applied to any simulated traffic flow data; in other words, it is independent of how the traffic data are generated. We demonstrated the effectiveness and robustness of this metric through many experiments on real-world traffic data and various simulated traffic data, comparisons with the state-of-the-art entropy-based similarity metric for aggregate crowd motions, and perceptual evaluation studies. [ABSTRACT FROM AUTHOR]

Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which does not have to deal with a huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian - one obstacle problem. Experimental data were collected in a motion capture laboratory providing high-precision trajectories. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction. Together with the methodology specifications, we provide the data set needed for performing the simulations of this kind of pedestrian dynamic system. [ABSTRACT FROM AUTHOR]

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming., Competing Interests: Xiao-Nong Zhou is on the editorial board of the journal Infectious Diseases Modeling. He was not involved in the peer-review or handling of the manuscript. The authors have no other competing interests to disclose., (© 2024 The Authors.)

Summary: There exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering. [ABSTRACT FROM AUTHOR]

In physically-based simulation, it is essential to choose appropriate material parameters to generate desirable simulation results. In many cases, however, choosing appropriate material parameters is very challenging, and often tedious trial-and-error parameter tuning steps are inevitable. In this paper, we propose a real-to-virtual parameter transfer framework that identifies material parameters of viscous fluids with example video data captured from real-world phenomena. Our method first extracts positional data of fluids and then uses the extracted data as a reference to identify the viscosity parameters, combining forward viscous fluid simulations and parameter optimization in an iterative process. We evaluate our method with a range of synthetic and real-world example data, and demonstrate that our method can identify the hidden physical variables and viscosity parameters. This set of recovered physical variables and parameters can then be effectively used in novel scenarios to generate viscous fluid behaviors visually consistent with the example videos. [ABSTRACT FROM AUTHOR]