Digital Representation Research Articles

Prediction of fracture evolution in the TiN/Al thin films based on a full-field modelling approach

The classical approach to fracture simulation in a thin film for bioengineering applications neglects the underlying complex morphology of the deposited layer, leading to inaccurate numerical predictions. That may carry high risks during product development and its further exploitation, especially in biomedical applications. Therefore, here, we demonstrate the new approach to numerical investigation of the fracture evolution in pulsed laser-deposited (PLD) titanium nitride (TiN) thin films under loading conditions based on the full-field model of layer columnar morphology. We use the TiN film deposited on the aluminium (Al) substrate as a case study. Based on the nanoindentation test, we first determine the flow stress characteristics of the Al substrate and TiN/Al structure. An inverse analysis technique allowed us to precisely recalculate measured load–displacement values into the required stress–strain curve. Then, we developed a full-field model based on the digital material representation (DMR) concept for further investigation. The DMR was generated to replicate major morphological features of the deposited thin film identified under transmission electron microscop (TEM). Finally, we incorporated the TiN/Al full-field model into the finite element analysis with a cohesive zone approach for local analysis of fracture behaviour. Inverse analysis based on the developed direct model and experimental measurements allowed us to identify the parameters of the fracture model. As a result, we proved that the full-field model based on the digital material representation concept could be used for reliable predictions of local fracture development along the morphological features of the deposited thin film.

In Search of Disentanglement in Tandem Mass Spectrometry Datasets.

Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and more accurate queries of large MS/MS spectra databases for metabolite identification. In this study, we apply generative modeling and representation learning using variational autoencoders to understand the extent to which tandem mass spectra can be disentangled into their factors of generation (e.g., collision energy, ionization mode, instrument type, etc.) with minimal prior knowledge of the factors. We find that variational autoencoders can disentangle tandem mass spectra data with the proper choice of hyperparameters into meaningful latent representations aligned with known factors of variation. We develop a two-step approach to facilitate the selection of models that are disentangled, which could be applied to other complex and high-dimensional data sets.

Digital Twin for condition based maintenance within a railway infrastructure testing lab

This article presents a digital twin development in a railway case, in order to improve operations and maintenance decisions, aligned with an asset management strategy. A digital framework for the sustainable management of these assets is defined with the purpose of facilitating the implementation on a cloud platform, searching the generation and sharing of the produced models and the evaluation from different perspectives. The developed digital twin allows for the digital representation of railway lines and vehicles, the connection between different entities based on an ontology, the management of data ingestion and storage, and the administration of models for the detection, diagnosis, and prognosis, as well as the representation and control of the level of risk of the assets. When emulation of railway line degradation is searched, different types of data are combined, from on-board sensors in railway vehicles, and physical behaviours, up to machine learning algorithms for estimation. In this way, the degradation behaviour model for the railway line is shown and validated through intelligent models easily replicated in several areas of the railway network, showing risk levels for each one.

Magnifying Gwendolyn Brooks: Creating a digital collection at the University of Illinois Library at Urbana-Champaign

The University of Illinois Urbana-Champaign Library is describing and digitising the Gwendolyn Brooks collection as part of the Saving America’s Treasures grant. This project began in the autumn of 2021, with the goal of making Brooks’s work visible and accessible. This paper discusses the first two stages of the project — the Red Album and the Black Scrapbook — objects that are full of mementos from Brooks’s personal and professional life. The paper focuses on the workflow and digitisation practices that the project team employed to create the first digital representations. It also reviews the team’s decisions, which would set the procedural policies and standards for all the remaining materials in the collection. The Red Album and the Black Scrapbook contain a variety of physical formats and materials, resulting in display issues for the digital collection. Collaboration between stakeholders from disparate units allowed for a streamlined approach from the initial description of the material through the hosting of digitised materials on the institution’s Digital Collections platform. This paper discusses challenges, the curatorial process, metadata, the process of description, digitisation, technical aspects and lessons learned from this project. Due to delays from the COVID-19 pandemic, the project’s expected completion date is summer 2023. This paper reflects on the processes and lessons learned throughout the project. It also discusses the most valuable lesson from this project, which is that the digitisation of large and complex collections must be adaptable in order to successfully meet the challenges that such materials offer.

Computer Supported Accessible Dexterity-Based Board Games

Board game accessibility analyses conducted by the Meeple Centred Design project have identified a number of problem areas where there exist no accessible intersection of disability and game design paradigm. While there exist fun and innovative board games for most players, making use of most game mechanisms, there exists no identified dexterity game accessible to those with physical impairments. Indeed, it is hard to imagine what form a game of that nature might take. In this paper, we outline a technological solution making use of computer vision, digital representation, and accessible game design. In its proof of concept form, it serves as a way of making the game Crokinole playable by mixed-needs groups. Future work will generalise this solution to work for a wide array of flicking and pushing based dexterity games, along with investigating how existing digital accessibility support tools can be leveraged to expand the demographic that can benefit from this approach.

Concept for a digital twin of railway bridges on the example of the new Filstal bridges

AbstractThe preservation of infrastructure is becoming increasingly important. The current reactive maintenance management only initiates maintenance measures after damage has occurred. Short‐term measures can lead to unforeseen restrictions in infrastructure availability and thus cause high social and economic damage.High‐quality measurement technology, automated data analysis, structured data management and collaborative work are just a few technological developments that form the cornerstones for a future predictive maintenance strategy. The digital twin, a digital representation of the real object, combines these developments and is a promising method for a sustainable preservation of infrastructure.Using the example of the Filstal bridges, a concept for a digital bridge twin is introduced. The digital twin combines measurement data, bridge information, inspection results as well as structural safety verifications and aggregates them to continuously display the current bridge condition and thus supports the maintenance of bridges. For intuitive use, all information is located and presented in a way that is consumable by the user.

Probabilistic residual strength assessment of smart composite aircraft panels using guided waves

Typical prognosis methods in Structural Health Monitoring (SHM) link the degradation mechanisms to the extracted health indicators. Such approaches however do not provide estimations on the current strength loss when a new damage is detected. This study presents a framework that enables the SHM-informed asset integrity management of composites. First, the integrated guided wave based SHM system is used to inform on the existence, location and size of the damage and subsequently, the residual strength of the structure is predicted. The framework combines physics-based and data-driven models to mitigate the incompleteness of the former and address issues relating to the representativeness in the training datasets of the later. Detailed finite element models are used to create a digital representation of the structure while a building-block program is initiated to quantify and propagate the uncertainties observed experimentally. A 1.6 m composite panel with a skin-stringer delamination, equipped with a network of 24 piezoelectric transducers, is used as a case study to demonstrate the prognostic capabilities of the framework. The framework estimated the damage size with a mean absolute percent error (MAPE) of approximately 10% while the residual strength of a destructively tested damaged panel was predicted with a MAPE of 5%.

Digital Approaches for the Presentation of Tourist Sites with Historical Significance

This paper explores the digital approaches for the presentation of tourist sites with historical significance, focusing on the integration of cutting-edge technologies and innovative strategies. It investigates the use of virtual reality (VR), digital libraries and interactive web platforms to create immersive and interactive experiences for visitors. The paper highlights the benefits of these digital approaches in enhancing tourism, conservation efforts, and cultural understanding. Additionally, it discusses the role of digital representations in preserving both – the tangible and intangible heritage, while also promoting global accessibility, passing down the generations, volunteering and cultural exchange. Through case studies and examples, the paper showcases the transformative potential of digital approaches in presenting and safeguarding cultural heritage.

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5 µm3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250 µm3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. This work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

Tokenization: Overview and Financial Stability Implications

In this paper we outline tokenization, which is a new and rapidly growing financial innovation in crypto asset markets, and we discuss potential benefits and financial stability implications. Tokenization refers to the process of constructing digital representations (crypto tokens) for non-crypto assets (reference assets). As we discuss below, tokenizations create interconnections between the digital asset ecosystem and the traditional financial system. At sufficient scale, tokenized assets could transmit volatility from crypto asset markets to the markets for the crypto token’s reference assets.

Levels of Digitization, Digitalization, and Automation for Advanced Reactors

Much has been written about levels of automation (LOA), but comparatively little has been written about levels of digitization (LODi) and levels of digitalization (LODa). Digitization is a digital representation of analog information and is typical of migration from analog to digital control systems, digitalization involves enhancing the functionality of digital information, and automation changes control from humans to machines. Each of these technology implementations has its own scales, and each forms a viable type of functionality that should be considered not as a continuum toward automation but rather as separate categories of solutions that meet the needs of advanced reactors. In this paper we develop separate LODi, LODa, and LOA scales and demonstrate how conflation of these technologies, using the example of computer-based procedures, can lead to confusion in the design process. With the race to develop advanced reactors, the surest metric of success and safety is proper consideration of the right technology requirements for different control systems.

The Counter-Archive: Eluding the Erasures of Iraq's Successive Wars

ABSTRACT Institutional archives in Southwest Asia (or the “Middle East”) typically reflect the crises that have plagued the region since the advent of modernity. Documentation practices were scarce to begin with, and, if state-sponsored archives were ever established after independence, they usually remained inaccessible. This article ponders the fate of scholarship when the historical record is subjected to another degree of erasure, namely through the systematic destruction wrought by conflicts, as has been the case in Iraq over the past few decades. Using the challenge of accounting for the art-architectural movement that emerged in post–World War II Baghdad as a case study, this obliteration may provide an opportunity for redefining the role that scholars can play in writing histories of modernism in the region. Rather than dwelling on the incompleteness or loss of traditional archives, this article questions and decenters dominant archival practices, especially in places that have experienced, and continue to endure, organized violence. It adopts the notion of the “counter-archive” and demonstrates how alternative sources such as oral accounts, fieldwork, press coverage, memoirs, and private collections can shift the course of research and yield equally valuable alternative histories. Careful interpretation of these nonconventional and typically discredited sources, aided by novel digital representation methods, can not only produce more situated chronicles, defined by the agency of local protagonists, but can also demonstrate that crossing disciplinary boundaries can create richer, layered, and unexpected narratives.

Blockchain-based quality management for a digital additive manufacturing part record

Quality management (QM) of additive manufacturing (AM) processes is currently immature in terms of transparency, traceability and security. In particular, quality-relevant documents are not documented and communicated in a traceable and transparent manner, which often leads to quality deficiencies. However, combining AM with blockchain technology can enable a solution that maps the AM value chain digitally, transparently, traceably and securely in a part record. In this work, a quality assurance (QA) concept for the metal-based material extrusion (MEX) process is developed that enables a digital representation of the value chain in the form of an AM part record. The decentralized solution presented in this work uses an architecture consisting of a web application for data acquisition, a decentralized storage solution for storing larger amounts of data, a smart contract for capturing manufacturing events and the Ethereum blockchain for transparent, secure and traceable storage of blockchain data. The AM part record enables traceable and constantly available digital documentation of quality information. The cost-effectiveness of the solution is also shown in a demonstration study. The research results highlight the benefits of a blockchain-based AM part record for digital manufacturing documentation and represent an efficient alternative or extension to existing QM and QA solutions in AM.

Resilient digital twin modeling: A transferable approach

High-fidelity digital twin models include not only concrete geometric models but also intrinsic models such as mechanism models and data models, which require high information dimensionality. Consequently, modeling, validation, and implementation cycles become challenging and time-consuming. The reuse of existing digital twin models to achieve rapid transference and utilization of models is becoming one of the hot topics in digital twin modeling. To this effect, aiming at how to reuse the multi-dimensional information of existing models to improve modeling efficiency and resilience, this paper proposes a rapid transferable modeling approach for digital twin models. The approach proposes a unified mapping and digital representation of physical entities based on information metamodels, which can rapidly instantiate digital twin assets. Then, the transferable approaches from the geometry dimension, behavior dimension, and algorithm dimension are given respectively, which include the information model reuse, action feature query and rule generation, as well as fine-tuning of data-driven mechanisms and domain adaptive of digital twins. Finally, through a practical case study, the result shows that the efficiency of modeling improves by 60.39%, and the effectiveness of the model after domain adaptation improves by 6.94%.

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.

Robust image hashing based on multi-view dimension reduction

In the field of multimedia security, image hashing is an effective technology for solving image content identification. Image hashing algorithm aims to map the high dimensional input features onto perceptual hashing values in terms of their perceptual content, in which visually identical images are mapped to similar digital representations. In this paper, we focus on multi-view dimensionality reduction based hashing generation, and propose a novel method, called Multi-view Dimensionality Reduction based Robust Image Hashing (MDRIH). Our MDRIH scheme maps the multi-view features into a compact binary space by considering the complementarity and similarity between image features across these different views. For preprocessing, bilinear interpolation is applied on the host image to adjust to a standard size, and ensure the different images with same hash size. Specifically, in order to improve the robustness of the algorithm against content-preserving operations, multi-view features, including structural features, edge features, and color features are extracted. After that, multi-view dimension reduction is adopted to obtain meaningful low-dimensional information from multi-feature fusion. Finally, the data in low-dimensional subspace is encoded into a compact binary sequence. The experimental results indicate that the proposed hashing method not only preserves the correlation between multiple views and the similarity among different data points within each view, but also achieves a better balance between robustness and discriminability compared to existing hashing methods.

Intrinsic Shape Analysis in Archaeology: A Case Study on Ancient Sundials

The fact that the physical shapes of man-made objects are subject to overlapping influences—such as technological, economic, geographic, and stylistic progressions—holds great information potential. On the other hand, it is also a major analytical challenge to uncover these overlapping trends and to disentagle them in an unbiased way. This article explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all shape information in a find collection , it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefined archaeological types and analyzing the associated distinguishing characteristics. Recently, a series of high-resolution digital representations of artifacts have become available. Such datasets enable the application of extremely sensitive and flexible methods of shape analysis. We explore this potential on a set of 3D models of ancient Greek and Roman sundials, with the aim of providing alternatives to the traditional archaeological method of “trend extraction by ordination” (typology). In the proposed approach, each 3D shape is represented as a point in a shape space —a high-dimensional, curved, non-Euclidean space. Proper consideration of its mathematical properties reduces bias in data analysis and thus improves analytical power. By performing regression in shape space, we find that for Roman sundials, the bend of the shadow-receiving surface of the sundials changes with the latitude of the location. This suggests that, apart from the inscribed hour lines, also a sundial’s shape was adjusted to the place of installation. As an example of more advanced inference, we use the identified trend to infer the latitude at which a sundial, whose location of installation is unknown, was placed. We also derive a novel method for differentiated morphological trend assertion, building upon and extending the theory of geometric statistics and shape analysis. Specifically, we present a regression-based method for statistical normalization of shapes that serves as a means of disentangling parameter-dependent effects (trends) and unexplained variability . In addition, we show that this approach is robust to noise in the digital reconstructions of the artifact shapes.

Discrete Event Simulation-Based Risk Analysis for Efficient, Sustainable and Resilient Transportation


 
 
 In a resource-constrained world the avoidance of transport-related emissions is crucial to fulfill the sustainable development goals, the Paris agreement on climate change and the Aichi biodiversity targets. Transport simulation enables the development of transport fleet strategies in risk scenarios to further improve efficientcy, sustainability and resilience of supply chains. Discrete event simulation is a valuable methodology for transport simulation, because it focuses on business processes for a digital representation of supply chains and provides an intuitive approach for facilitating stakeholder participation. Consequently, discrete event simulation models for unimodal, multimodal and multi-echelon unimodal wood transport are presented, which enable multicriteria-based strategy development, optimal fleet configuration as well as risk management and wood quality preservation. Furthermore, the verificated and validated discrete event simulation models were used in game-based stakeholder workshops to establish credibility, provide hands- on decision support and facilitate knowledge transfer between science, industry and education. A previous version of this revised and extended journal article was published as conference paper in the Proceedings of the XIVth Junior Forensic Science Conference (JuFoS), 18–19 May 2023 in Brno.
 
 

Risk Topics Discovery and Trend Analysis in Air Traffic Control Operations—Air Traffic Control Incident Reports from 2000 to 2022

The safety of air traffic control (ATC) operations is an important cornerstone for the sustainable development of the civil aviation industry. In order to clarify the risk factors in the control operation process and to achieve digital representation of the safety risks of civil aviation control operations, starting from the ATC incident reports, we fully mine the safety risk information and unspoken rules of ATC operations. A risk perception model for air traffic control operations safety based on the Latent Dirichlet Allocation (LDA) topic model and the Semantic Network Based on BERT (BSN) model is suggested. First, 17 risk topics and keywords were found in the incident reports collected using the LDA topic model. These topics included those pertaining to the stage of aircraft operation, human factors in control operation, and the sector or airspace operation status and structure. The findings indicate that while most risk subjects have not changed significantly, they do show an upward tendency. Human factors and operational rules and procedures account for the highest share of all key causes, and they also have a significant impact on how risk topics evolve over time. Finally, the BSN model in the air traffic control field was built based on the keywords of each risk issue in order to highlight any potential correlations between distinct risk topics. The results show that some risk topics have interrelated risk characteristics, and there are regularities of mutual evolution between these risk topics. The relevant research results can better mine air traffic control unsafe information and lay a foundation for accurately perceiving air traffic control operations risks.

The Anatomage Table: A Promising Alternative in Anatomy Education.

Anatomy is one of medical and nursing education's most prominent and crucial keynotes. For ages, conventional lectures and the analysis of actual human corpses were employed as predominant teaching techniques. However, the sphere of healthcare pedagogy has been greatly altered by the developing passion for technology over the past few years. Anatomage offers a life-size digital representation of the human body, allowing the visualization, manipulation, and virtual dissection of complex anatomical structures, using detailed 3D (three-dimensional) models. Academic institutions utilize Anatomage as a means to enhance and contemporize the acquisition of anatomy knowledge. This systematic review aims to present the educational role of Anatomage in anatomy and whether it can replace the use of cadaveric material in medical education entirely in the future. A detailed search on PubMed, SCOPUS, Wiley Online Library, and Google Scholar databases was performed. The criteria for the selection were the English language and the year of publication between 2018 and 2023. We rejected publications that were irrelevant to the topic. Before applying the filters, we found 198 publications, from which 24 were finally chosen for the purpose of this review. The results of this systematic review indicate that most students agree on the beneficial role of Anatomage in the thorough comprehension of anatomical knowledge, and they prefer it over traditional learning methods, such as the use of cadaveric material. Anatomage not only offers a deeper insight into the relations between inner formations, since it is a particularly easy-to-use and pleasant teaching tool, but also contributes to the improvement of learning outcomes in the classroom, which is proved by higher grades in the anatomy course. However, it can be an effective teaching method if it is used in addition to the classic method of cadaver training, rather than being the only educational practice. Integrating the Anatomage Table (AT) into undergraduate courses is paramount to the comprehensive learning and application of human anatomy in students' future health careers. Learners who have utilized the table note it to be a beneficial and effective tool in preparing them to enter into the healthcare profession.