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Povzetek: Cell rearrangements are fundamental mechanisms driving large-scale deformations of living tissues. In three-dimensional (3D) space-filling cell aggregates, cells rearrange through local topological transitions of the network of cell-cell interfaces, which is most conveniently described by the vertex model. Since these transitions are not yet mathematically properly formulated, the 3D vertex model is generally difficult to implement. The few existing implementations rely on highly customized and complex software-engineering solutions, which cannot be transparently delineated and are thus mostly non-reproducible. To solve this outstanding problem, we propose a reformulation of the vertex model. Our approach, called Graph Vertex Model (GVM), is based on storing the topology of the cell network into a knowledge graph with a particular data structure that allows performing cell-rearrangement events by simple graph transformations. Importantly, when these same transformations are applied to a two-dimensional (2D) polygonal cell aggregate, they reduce to a well-known T1 transition, thereby generalizing cell-rearrangements in 2D and 3D space-filling packings. This result suggests that the GVM’s graph data structure may be the most natural representation of cell aggregates and tissues. We also develop a Python package that implements GVM, relying on a graph-database-management framework Neo4j. We use this package to characterize an order-disorder transition in 3D cell aggregates, driven by active noise and we find aggregates undergoing efficient ordering close to the transition point. In all, our work showcases knowledge graphs as particularly suitable data models for structured storage, analysis, and manipulation of tissue data.
Ključne besede: 3D vertex models, cell, software-engineering
Objavljeno v DiRROS: 03.09.2024; Ogledov: 97; Prenosov: 1363
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Povzetek: In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project.
Ključne besede: machine learning, artificial intelligence, data-driven models
Objavljeno v DiRROS: 23.07.2024; Ogledov: 137; Prenosov: 57
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Povzetek: Abstract Although small in size, the Gulf of Trieste (GoT), a marginal coastal basin in the northern Adriatic Sea, is characterized by very complex dynamics and strong variability of its oceanographic conditions. In April–May 2012, a persistent, large-scale anticyclonic eddy was observed in the GoT. This event was captured by both High Frequency Radar (HFR) and Lagrangian drifter observations collected within the European MED TOSCA (Tracking Oil Spill and Coastal Awareness) project. The complexity of the system and the variety of forcing factors constitute major challenges from a numerical modeling perspective when it comes to simulating the observed features. In this study, we implemented a high-resolution hydrodynamic model in an attempt to reproduce and analyze the observed basin-wide eddy structure and determine its drivers. We adopted the Massachusetts Institute of Technology General Circulation Model (MITgcm), tailored for the GoT, nested into a large-scale simulation of the Adriatic Sea and driven by a tidal model, measured river freshwater discharge data and surface atmospheric forcing. Numerical results were qualitatively and quantitatively evaluated against HFR surface current maps, Lagrangian drifter trajectories and thermohaline data, showing good skills in reproducing the general circulation, but failing in accurately tracking the drifters. Model sensitivity to different forcing factors (wind, river and tides) was also assessed.
Ključne besede: Adriatic Sea, surface circulation, HF coastal radars, Lagrangian drifters, wind-driven currents, ocean circulation models
Objavljeno v DiRROS: 19.07.2024; Ogledov: 213; Prenosov: 164
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Ključne besede: brain tumors, tumor microenvironment, animal models, xenotransplantation
Objavljeno v DiRROS: 09.05.2024; Ogledov: 297; Prenosov: 516
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Povzetek: Innovation in the circular economy (CE) and the deployment of effective circular business models (CBM) have attracted significant attention in times of growing natural resource scarcity. Despite this widespread interest, significant challenges remain between theoretical innovations and effective CBM implementation in any industrial sector where companies pursue cost-saving opportunities through waste valorisation strategies. Since current methods mislead in terms of the real limitations to designing feasible novel products and services under a circular economy, this study proposes exploring determinants underpinning the organisational resilience of CBMs under a resource efficiency strategy through three case studies. As a result of a co-creation process, the implementation of a CBM framework was built upon empirical data and, thence, a phase-based implementation guide was laid out to assist companies in designing and implementing innovative CBM dealing with the complexity of innovative waste valorisation strategies between the PPI and construction sectors. Relevant findings on managerial and policy recommendations encountered along the demo stage are provided in this paper favouring an effective implementation of CE strategies: the role of technological and non-technological aspects within the CBM, the perspective of the ecosystem and its value proposition, and specific guidelines for the different phases of CBM life cycle.
Ključne besede: circular business models (CBMs), resource recovery, waste valorisation, strategic management, pulp and paper industry (PPI), construction sector
Objavljeno v DiRROS: 13.12.2023; Ogledov: 458; Prenosov: 221
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