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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., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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., Competing Interests: The authors declare there are no competing interests., (©2024 Barón-Espitia et al.)

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.)

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 → Validate PDFs → Design Image Masks → Generate Intensities → 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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements., (© 2022. The Author(s).)

Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database., (Copyright © 2021. Published by Elsevier Ltd.)

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., Competing Interests: The authors declare that they have no competing interests., (© 2021 Camargo et al.)

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.