Digital Representation Research Articles

The system of headlights operation recognition using the digital twin method

Virtual digital representation of a physical object or system, created with precision through computer simulations, data analysis, and various digital technologies can be used as training set for real life situations. The principal aim behind creating a virtual representation is to furnish a dynamic, data-fueled, and digital doppelgänger of the physical asset. This digital counterpart serves multifaceted purposes, including the optimization of performance, the continuous monitoring of its well-being, and the augmentation of informed decision-making processes. Main advantage of employing a digital twin is its capacity to facilitate experimentation and assessment of diverse scenarios and conditions, all without impinging upon the actual physical entity. This capability translates into substantial cost savings and superior outcomes, as it allows for the early identification and mitigation of issues before they escalate into significant problems in the tangible world. Within our research endeavors, we've meticulously constructed a digital twin utilizing the Unity3D software. This digital replica faithfully mimics vehicles, complete with functioning headlamp toggles. Our lighting system employs polygons and normal vectors, strategically harnessed to generate an array of dispersed and reflected light effects. To ensure realism, we've meticulously prepared the scene to emulate authentic road conditions. For validation and testing, we integrated our model with the YOLO (You Only Look Once) neural network. A specifically trained compact YOLO model demonstrated impressive capabilities by accurately discerning the status of real vehicle headlamps. On average, it achieved an impressive recognition probability of 80%, affirming the robustness of our digital twin.

AI recommendations’ impact on individual and social practices of Generation Z on social media: a comparative analysis between Estonia, Italy, and the Netherlands

Abstract Social media (SM) influence young adults’ communication practices. Artificial Intelligence (AI) is increasingly used for making recommendations on SM. Yet, its effects on different generations of SM users are unknown. SM can use AI recommendations to sort texts and prioritize them, shaping users’ online and offline experiences. Current literature primarily addresses technological or human-user perspectives, overlooking cognitive perspectives. This research aims to propose methods for mapping users’ interactions with AI recommendations (AiRS) and analyzes how embodied interactions mediated by a digital agent can lead to changes in social and cultural practices. For this, this work proposes a comparative analysis of central practices evoked by AI recommendations-mediated communication on SM among users in Italy, Estonia, and the Netherlands in the age category 18–26 years old. The data used in the comparative analysis was collected via semi-structured interviews and elaborated based on cognitive psychology and semiotics. This research highlights the contextual significance of AI recommendations as a mediator in creating new communication practices. Findings confirm that young adults often choose practices that would enhance their digital representations according to AiRS’ dominant patterns and categories. AiRS impacts individual interpretations and practices and can further affect social and cultural levels.

Blockchain solutions with consensus algorithms and immediate finality: Toward Panopticon-style monitoring to enhance anti-money laundering

Banks can reduce resources spent on anti-money laundering (AML) compliance with blockchain-based transaction infrastructure. We consider AML compliance as a superset of know-your-customer (KYC) and transaction monitoring capabilities. We carried out this research with Danske Bank and Concordium, using internal documents and interviews that served as empirical data. We show how storing digital representations of verified IDs with a blockchain can automate tasks and reduce redundant verification in KYC onboarding. Blockchain transparency also improves identifying counterparties, determining funds sources, and creating alerts in transaction monitoring. These reduce time and labor costs for AML compliance, which may lead to smaller banks. When more banks commit to layer-1 blockchain technology, the benefits of blockchain-based AML will increase. We carried out this theory-based qualitative research and encourage ECRA readers to recognize that the emerging technology innovations we study in this article have not yet been widely adopted and implemented by financial services firms. We also include a theoretical model with study hypotheses to make the main constructs that we investigate easily understood by non-technical ECRA readers. The findings we have developed are consistent with early-stage exploration in our research context and are intended to encourage more well-developed empirical results as the passage of time permits such work to be undertaken.

Accuracy Evaluation for Plan-Reliefs and Historical Maps Created during WWI in Northern Italy

The availability of digital copies of historical artifacts modeling the territory through the so-called “plan-reliefs” is important for many reasons: the preservation of the artifact if the physical object is damaged or destroyed, the possibility of creating virtual showrooms and providing researchers a tool to study the object combining information from different sources. For these reasons, a set of plan-reliefs created during World War I on the Italian front and kept by the Italian Historical War Museum of Rovereto (Italy) was surveyed to create digital models of the surfaces, which were georeferenced in the ETRS89 datum. A set of historical military maps of the same period was georeferenced to overlay the sets to the surface in the digital representation and to try to infer clues about the cartographic sources used in the historical artifact creation. The best transformation for georeferencing the maps is different depending on the map scale, map origin, conservation status and number of Ground Control Points. The georeferencing process precision and accuracy were evaluated. The digital models created in this study were compared to the official Digital Terrain Model (DTM) provided by the Regions or the autonomous provinces. The results demonstrate the feasibility of the approach, and the combination of the models with the georeferenced maps is used by historians to describe the process used in the creation of plan-reliefs.

Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.

Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion

Artistic image completion assumes a significant role in the preservation and restoration of invaluable art paintings, marking notable advancements through the adoption of deep learning methodologies. Despite progress, challenges persist, particularly in achieving optimal results for high-resolution paintings. The intricacies of complex structures and textures in art paintings pose difficulties for sophisticated approaches like Generative Adversarial Networks (GANs), leading to issues such as small-scale texture synthesis and the inference of missing information, resulting in distortions in lines and unnatural colors. Simultaneously, patch-based image synthesis, augmented with global optimization on the image pyramid, has evolved to enhance structural coherence and details. However, gradient-based synthesis methods face obstacles related to directionality, inconsistency, and the computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a pioneering approach, integrating Weighted Similarity-Confidence Laplacian Synthesis to comprehensively address these challenges and advance the field of artistic image completion. Experimental results affirm the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with intricate details and irregular missing regions. The integration of weighted Laplacian synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. A comparative analysis underscores our method’s superiority in artifact reduction and minimizing blurriness, particularly addressing challenges related to color discrepancies in texture areas. Additionally, the incorporation of pyramid blending proves advantageous, ensuring smoother transitions and preventing noticeable seams or artifacts in blended results. Based on empirical results, our method consistently outperforms previous methods across both high and low resolutions. Responding to these insights, our approach emerges as an invaluable guide for both curators and artists. The algorithm’s performance yields insights that underscore the central role of thoughtful decision making in the creation of art paintings. This guidance extends to informing choices related to color selection, brushstrokes, and various other elements integral to the artistic process. During the creation phase, employing these insights enables artists and curators to optimize not only the digitization but also the subsequent restoration process. This proves especially vital when dealing with the intricacies involved in physically restoring damaged original art paintings. Importantly, our approach not only streamlines the restoration process but also contributes significantly to the preservation and enhancement of the digital representations of these distinctive and often intricate works of art.

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0

Abstract. Up-to-date digital soil resource information and its comprehensive understanding are crucial to supporting crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, and is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small scaled (1 : 2 M), which limit its practical applicability. Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. Thus, a group of researchers formed a Coalition of the Willing for soil and agronomy data-sharing and collated about 20 000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and 14 681 profile data were prepared for modeling. Random forest was used to develop a continuous quantitative digital map of 18 World Reference Base (WRB) soil groups at 250 m resolution by integrating environmental covariates representing major soil-forming factors. The map was validated by experts through a rigorous process involving senior soil specialists or pedologists checking the map based on purposely selected district-level geographic windows across Ethiopia. The map is expected to be of tremendous value for soil management and other land-based development planning, given its improved spatial resolution and quantitative digital representation.

Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives

Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions: Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review & editing. Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review & editing. Kristi Holmes: Funding acquisition, Supervision, Writing – review & editing. Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review & editing. Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration.

Daylight Diagram – A Method to Map and Analyse the Temporal Conditions of Daylight Intensity

This study proposes and examines a new method and diagram for revealing and analysing temporal and transitional daylight conditions based on computational modelling and simulations. For a human to experience and decipher the world, daylight is a predominant resource to engage with the surroundings. With humans spending the prevalent time inside buildings, it is important to understand and design the daylight environments in the early phases of design processes. The aim is to retain and reveal the information of high-resolution simulations and to explore, test and verify the capacities of the method in comparison to Illuminance, Daylight Factor and Daylight Autonomy values. The research design uses digital modelling and representation techniques combined with computational simulation methods for daylight analysis, evaluation and communication. Eight digital test cases show that the proposed Daylight Diagram enables the observation of temporal daylight dynamics. The proposed method and diagram employ existing simulation systems, allowing a fast integration and use in early-phase design processes as a key instrument to design advanced daylight phenomena and conditions focusing on transitional and temporal daylight intensities.

Digital representation of continuous observables in quantum mechanics

Digital representation of continuous observables in quantum mechanics

Gas turbine aero engine fault detection using Geo-TLSVM and digital twin with multimodal data analysis

DT is the digital representation of physical systems to simulate real situations in a digital version. Here, the DT of the GaT-AE is created for the Fault Detection (FD) process. The GaT is a type of internal combustion engine that uses the air as the working fluid to propel the flight. However, the incorporation of big data analysis in the DT model is challenging in previous works. So, a multimodal GaT-AE fault identification system is proposed. First, the 3D print of the GaT-AE is created. Then, the data is collected from both built-in sensors and DT. Afterward, the multimodal data is pre-processed and balanced by using HT-QNN and QADASYN, respectively. Next, the features are processed by GT-PFS, and finally, the faults are detected by Geo-TLSVM. The analysis proved that the developed model outperformed the other state-of-the-art model with its effective FD rate of 97.24%.

Firebrands Generated During WUI Fires: A Novel Framework for 3D Morphology Characterization

The goal of the present work is to establish a framework for firebrand morphology characterization. Central to this framework is the development of a simple firebrand shape classification model using multi-dimensional particle shape descriptors. This classification model is built upon a series of synthetically generated 3D particles whose shapes and sizes are chosen to be representative of actual firebrands typically encountered during vegetative and structural fuel burns. Principal Component Analysis (PCA) is applied to the synthetic dataset and used to structure the classification model. The model is then verified using 3D digital representations of real-world particles (firebrands collected during tree burns and unburned bark pieces from oak trees). The classification model, which will allow meaningful comparisons of firebrand morphological features by shape class, is expected to be gradually refined as more datasets are made available throughout the Wildland–Urban Interface (WUI) fire research community.

Photo-able Urban Green-Blue Spaces

Urban green–blue spaces (UGBSs) are increasingly integrated into the urban fabric transformation, playing a pivotal role in shaping everyday interactions and experiences with nature in the Anthropocene era. Crowdsourcing, facilitated by digital technologies, has emerged as a novel methodological advance for accessing subjective place-based information, allowing the public to produce and share photographs at an unprecedented scale. As people can now readily capture and disseminate images on social media from virtually any location, these photographs contribute to the digital representation of places. Many studies have leveraged social media imagery to explore mental maps of cities, place perceptions, and ecosystem services. This raises fundamental questions: What are the UGBSs that promote photography across cities? What are the similarities and differences among cities’ photogenic UGBSs? And how do cities’ photogenic UGBSs relate to the happiness of their inhabitants? This study investigates these questions through an analysis of 203,020 photographs, taken from social media, of sites across 186 cities over a five-year period (2014 to 2018). Employing Google Cloud Vision and topic modelling with a state-of-the-art neural network model, photographs are clustered and used to identify cross-city features. The correlation between prevalence of photogenic UGBSs and levels of happiness is investigated using linear regression analysis. The results show that UGBSs eliciting photography practice are predominantly characterised by water-related subjects, confirming a widespread aesthetic appreciation for urban blue spaces. Perceptions of UGBSs exhibit significant variability among cities, offering a metric to assess environmental policy efficacy. Lastly, a positive association is found in high-income contexts between number of social media photographs of UGBSs and subjective well-being, while this correlation is not significant in middle- and low-income contexts. This sheds light on human–nature relationships by providing global evidence of how aesthetic appreciation of urban nature influences human well-being.

Digital representation of continuous observables in quantum mechanics

Для симуляции квантовых систем на классических или квантовых компьютерах необходимо дискретизировать непрерывные квантовые наблюдаемые (например, координату и импульс или время и энергию). Рассмотрено представление непрерывных наблюдаемых в позиционных системах счисления как рядов по степеням основания системы счисления, умноженных на "цифры", каждая из которых по отдельности оказывается эрмитовым оператором с дискретным спектром. В работе исследуются полученные таким образом операторы цифр, коммутационные соотношения между ними и эффекты, оказываемые на решетки и представления выбором той или иной позиционной системы счисления. Естественным образом возникают перенормировки расходящихся сумм и интегралов.

A Digital Twin for a Chiral Sensing Platform

AbstractNanophotonic concepts can improve measurement techniques by enhancing and tailoring the light–matter interaction. However, the optical response of devices that implement such techniques can be intricate, depending on the sample under investigation. Nanophotonics is therefore a ripe field for applying the concept of a digital twin: a digital representation of an entire real‐world device. In this work, the concept of a digital twin is detailed with the example of a nanophotonically enhanced chiral sensing platform. In that platform, helicity‐preserving cavities enhance the interaction between chiral molecules and light, allowing faster measurement of the circular dichroism of the molecules. The sheer presence of the molecules affects the cavity's functionality, demanding a holistic treatment to understand the device's performance. In the digital twin, optical and quantum chemistry simulations are fused to provide a comprehensive description of the system and predict the circular dichroism spectrum. Performing simulations in lockstep with the experiment will allow a clear interpretation of measurement results. This work also demonstrates how to design a cavity‐enhanced circular dichroism spectrometer by utilizing the digital twin. The digital twin can be used to guide experiments and analyze results, and its underlying concept can be translated to many other optical experiments.

Use of Geoinformatics for the Digitization and Visualization of Sensitive Space in the Urban Landscape: A Case Study of the Gross-Rosen Sub-Camps Systems

Geospatial technologies enable accurate and detailed documentation of cultural heritage sites. They allow for the creation of digital representations of these sites that can be shared with interested audiences. Given the above, this paper presents the possibility of using archival data to reconstruct the topography of two German labor camps, Dyhernfurth I and II, which operated during World War II. To perform this task, multi-source archival data was obtained and interpreted. These data came from various sources: archives, historical institutes, and museums. The results of the study were presented to the staff of the Gross-Rosen Museum, who anticipated the possibility of using the proposed tools in the management of other labor camps. The proposed methodology can be replicated at other locations and easily implemented by other martyrdom museums involved in the preservation of cultural heritage.

Engineering design and the impact of digital technology from computer-aided engineering to industrial metaverses: A perspective

Digital technology for engineering design is evolving from computer-aided engineering tools via digital twins to industrial metaverses. These advances are resolving many of the challenges associated with implementing concurrent design and enabling larger design domains to be considered and designs to be developed, reviewed and demonstrated more rapidly and with a wider range of stakeholders. However, the new technology is also creating new challenges associated with confidence in digital representations and with methods of working with a rapidly evolving design. These challenges need to be addressed so that radical new designs of complex systems can be produced quickly to support society in addressing the evolving crisis of climate change.

Integrated multivariate digital representation model for rock microstructure

Unravelling the intricate relationships between mineralogy, defects, and other microstructural features during the rock fracturing process is challenging due to the inherent invisibility of rocks. In this study, we propose a novel integrated multivariate digital representation model that discretizes the rock into differential elements and applies the Matter-Element Theory to represent the microstructure. This model successfully captures the complexity and diversity of rock microstructural properties, enabling transparency of the internal rock structure. CT scanning, mechanical loading, and rock-mineral identification experiments were performed on rock samples to validate the model. Microstructural data, including pores, fractures, and minerals at different stages of failure, were obtained and integrated into the model. The results show that the integrated digital model is an accurate representation of the distribution states of internal microstructural features within rocks. The influence of microstructural features on rock fracture was investigated by extracting microstructural data corresponding to specific fracture evolution by comparing the model output with actual rock fracture results. This research provides a theoretical basis for the extraction of universal laws and characteristics, facilitating the comprehensive investigation of diverse microstructural features. In addition, it will enable the dynamic assessment of rock fracture and the early warning of rock hazards in engineering applications.

A Framework for Communicating and Building a Digital Twin Model of the Electric Car

The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car’s speed and current in the electric motor. The achieved results are presented and then discussed.

An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture

A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.