24 papers · ranked by Valyu relevance
Pedro-Aarón Hernández-Ávalos, Luciano García-Bañuelos
The advent of Large Language Models (LLMs) has significantly transformed tasks across Software Engineering. In the context of Business Process Management, LLMs are now being explored as tools to derive process models directly from textual descriptions. Existing approaches range from chatbot-driven systems that assist…
Ishaan Kaushal, Amaresh Chakrabarti
This paper presents the Unified Smart Factory Model (USFM), a comprehensive framework designed to translate high-level sustainability goals into measurable factory-level indicators with a systematic information map of manufacturing activities. The manufacturing activities were modelled as set of manufacturing, assembly…
Carlos Martínez, Facundo Rocha Calvette, Marielle Péré, Mauricio Barrientos + 1 more
A hybrid model combines a mechanistic model, described by ordinary differential equations, with a data-driven component, such as neural networks. This strategy leverages both the physical knowledge of the system and the predictive power of machine learning methods, and it has been applied to a variety of bioprocesses.…
Viki Peeva, Wil M. P. van der Aalst
Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analysis and improvement of operational processes. More often than not, surprising findings…
Alberto Ronzoni, Antony, Anina, M.P. Anjana + 7 more
The academic evolution of process mining is moving toward object centric process mining, marking a significant shift in how processes are modeled and analyzed. IBM has developed its own distinctive approach called Multilevel Process Mining. This paper provides a description of the two approaches and presents a…
Dan Vasilescu, James C. Schaff, Ion I. Moraru, Michael L. Blinov
Mechanistic modeling in biology aims to describe biological processes based on details on molecular mechanisms and interactions. Rule-based mechanistic modeling enables the simulation of biological systems while explicitly accounting for molecular details, such as protein domains and their specific interactions.…
Daniel Amyot
Given the increasing amount of data available in organizational systems, there is an opportunity for early requirements engineering (RE) activities to be better based on evidence than ever before. Process mining (PM) has been used for over two decades to discover and analyze as-is process models from event logs…
Jean-Baptiste Gartner, Paolo Landa, Matthew T Haren, Célia Lemaire + 6 more
Background Health care systems are increasingly confronted with the challenge of managing complex clinical processes. One proposed solution is a patient-centered management intervention called a care pathway that needs process mapping to support process improvement. Although the adoption and use of Business Process…
Milliam Maxime Zekeng Ndadji
Business Process Management (BPM) is concerned with the systematic design, execution, monitoring, and improvement of business processes. Formal grammars have emerged as a particularly fruitful formalism for BPM, offering generative, declarative, and analytical capabilities that are uniquely well-suited to…
Mengjia Zhu, Oliver Pennington, Tararag Pincam, Mohammadamin Zarei + 4 more
Bioprocesses are critical for sustainable industrial development but face challenges from their inherent uncertainties that affect efficiency and scalability. This study in-troduces a worst-case operational space design framework, integrating symbolic optimization with scenario-based validation, to preemptively…
Steffen Zschaler, Navonil Mustafee, Alison Harper, Thomas Monks + 3 more
Simulation remains a promising technology in healthcare operations research and process optimisation. However, while there have been many research projects applying simulation in this context, the level of sustained uptake in healthcare practice has been lower. We conjecture that an important reason for this is the…
Nataliia Klievtsova, Juergen Mangler, Stefanie Rinderle-Ma
The artefact at the intersection of knowledge and process management is the process, which describes how enterprises are generating value. In knowledge management literature the relation of knowledge and processes is discussed, often leading to the definition of knowledge intensive processes, which entail a high level…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Authors not listed
Process chemistry creates scalable routes for new lead molecules and is a crucial but laborious stage in pharmaceutical and agrochemical development cycles. We have built an automated process chemistry platform that tackles late-stage process development. The modular workflow integrates both industry-standard tools and…
Simon Mählkvist, Pontus Netzell, Thomas Helander, Konstantinos Kyprianidis + 1 more
In industrial machine learning, predictive performance alone is insufficient to ensure reliable deployment, as model behaviour may vary across different regions of the input space under limited data and evolving process conditions. This work investigates whether such variation can be systematically analysed through…
Authors not listed
Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
Philip Greulich
Mathematical and computational modelling can do far more than reproduce experimental data or make predictions. When used with intent, models become instruments of discovery: they translate qualitative biological ideas into quantitative, testable hypotheses; they connect microscopic questions to macroscopic data; and…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
Authors not listed
Large Language Models and Agentic Artificial Intelligence stand poised to completely overhaul our collective approach to science. Within chemical property prediction in particular, natural language-based workflows for modeling are already making serious inroads. To further this aim, in this white paper we introduce…
Luna Xingyu Li, Carissa Bleker, Sylvain Soliman, Laurence Calzone + 13 more
Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce…
Sreenivas Bhattiprolu, Manita Toor, Sebastian Soyer
Modern biological imaging generates large, complex datasets that require scalable and reproducible image analysis methods. Deep learning has demonstrated strong performance on bioimage segmentation tasks, but training custom models has remained inaccessible to many researchers due to requirements for GPU…
Authors not listed
Metal–organic frameworks (MOFs) represent a versatile class of porous materials, yet efficiently exploring their vast chemical space for target gas adsorption properties remains a major challenge. MOFid, a text-based encoding of MOF structures, has enabled large-scale data mining using natural language processing (NLP)…
Marie Corradi, Ivo Djidrovski, Luiz Ladeira, Bernard Staumont + 30 more
As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and…
Madeline Jarvis-Cross, Andrew W. Bateman, Cole B. Brookson, Nicole Mideo + 1 more
Despite the impacts of within-host disease dynamics on disease outcomes in individual hosts and disease spread among-hosts, generic models of within-host population dynamics have received far less attention than their among-host counterparts. While a number of models have been proposed to explore theoretical…