21 papers · ranked by Valyu relevance
L. M. Clemon
Indoor spread of infectious diseases is well-studied as a common transmission route. For highly infectious diseases, like Sars-CoV-2, considering poorly or semi ventilated areas outdoors is increasingly important. This is important in communities with high proportions of infected people, highly infectious variants, or…
Mei Tessum, Susan Anenberg, Zoe Chafe, Daven Henze + 4 more
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside…
Kate E. Dray, Joseph J. Muldoon, Niall M. Mangan, Neda Bagheri + 1 more
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier…
Keith D. Harris, Guy Hadari, Gili Greenbaum
Modeling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a significant challenge to the dissemination of model-derived results. With the existing requirements of scientific publishing, most often only a small…
Daniel R. Weilandt, Pierre Salvy, Maria Masid, Georgios Fengos + 3 more
Large-scale kinetic models are an invaluable tool to understand the dynamic and adaptive responses of biological systems. The development and application of these models have been limited by the availability of computational tools to build and analyze large-scale models efficiently. The toolbox presented here provides…
Authors not listed
The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high…
Mo Li, Wesley W. Ingwersen, Ben Young, Jorge Vendries + 1 more
'Catherine Birney'] useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input-output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their…
Alex Gu, Tamara Mitrovska, Daniela Velez, Jacob Andreas + 1 more
'Armando Solar-Lezama'] We introduce ObSynth, an interactive system leveraging the domain knowledge embedded in large language models (LLMs) to help users design object models from high level natural language prompts. This is an example of specification reification, the process of taking a high-level, potentially vague…
Antonio Bucchiarone, Juri Di Rocco, Damiano Di Vincenzo, Alfonso Pierantonio
Engineering Authors: ['Antonio Bucchiarone' 'Juri Di Rocco' 'Damiano Di Vincenzo' 'Alfonso Pierantonio'] Abstract Jjodel is a cloud-based reflective platform designed to address the challenges of Model-Driven Engineering (MDE), particularly the cognitive complexity and usability barriers often encountered in existing…
Marcello Pompa, Simona Panunzi, Alessandro Borri, Laura D’Orsi + 2 more
Large volumes of data are these days collected and archived from patients in a variety of clinical settings. This phenomenon is the product of the evolution of analysis systems and the appearance of new diagnostic techniques . The physician has thus in many cases observations of the clinical status of the patient at…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Kyungdahm Yun, Soo-Hyung Kim
Crop models mirror our knowledge on crops in silico. Therefore, crop modeling inherits common issues of software engineering and often suffers from technical debts. We introduce a new crop modeling framework: Cropbox as a declarative domain-specific language. Recognizing that a crop model is fundamentally an integrated…
Jonathan Karr, Rahuman S. Malik-Sheriff, James Osborne, Gilberto Gonzalez-Parra + 14 more
'Gilberto Gonzalez-Parra' 'Eric Forgoston' 'Ruth Bowness' 'Yaling Liu' 'Robin Thompson' 'Winston Garira' 'Jacob Barhak' 'John Rice' 'Marcella Torres' 'Hana M. Dobrovolny' 'Tingting Tang' 'William Waites' 'James A. Glazier' 'James R. Faeder' 'Alexander Kulesza'] During the COVID-19 pandemic, mathematical modeling of…
Thomas Wöhling, Alvaro Oliver Crespo Delgadillo, Moritz Kraft, Anneli Guthke
'Anneli Guthke'] Title: Abstract Groundwater level observations are used as decision variables for aquifer management, often in conjunction with models to provide predictions for operational forecasting. In this study, we compare different model classes for this task: a spatially explicit 3D groundwater flow model…
Reiner Jedermann, Walter Lang, Leopoldo Angrisani, Domenico Accardo
Analog sensors often require complex mathematical models for data analysis. Digital twins (DTs) provide platforms to display sensor data in real time but still lack generic solutions regarding how mathematical models and algorithms can be integrated. Based on previous tests for monitoring and predicting banana fruit…
Philippe J. Giabbanelli, John Beverley, István Dávid, Andreas Tolk
Large Language Models (LLMs) offer transformative potential for Modeling & Simulation (M&S) through natural language interfaces that simplify workflows. However, over-reliance risks compromising quality due to ambiguities, logical shortcuts, and hallucinations. This paper advocates integrating LLMs as middleware or…
Kara Bocan, Nataša Miškov-Živanov
—Computational modeling of a complex system is limited by the parts of the system with the least information. While detailed models and high-resolution data may be available for parts of a system, abstract relationships are often necessary to connect the parts and model the full system. For example, modeling food…
Klaus‐Dieter Sommer, P M Harris, Sascha Eichstädt, Roland Füßl + 9 more
'Tanja Dorst' 'Andreas Schütze' 'Michael Heizmann' 'Nadine Schiering' 'Andreas Maier' 'Yuhui Luo' 'Christos Tachtatzis' 'Ivan Andonović' 'Gordon Gourlay'] - 1 Technische Universitaet Ilmenau, Germany - 2 National Physical Laboratory, Teddington, United Kingdom - 3 Physikalisch-Technische Bundesanstalt, Braunschweig and…
Authors not listed
Kinetic modeling is essential for predicting changes in food quality during processing and storage. This study evaluates the application of physics-informed neural networks (PINN) for food kinetic modeling, integrating kinetic insights into neural network frameworks. Based on three case studies, namely seed drying…
Chao Xu, Emily Mazeau, Richard West
Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and…