Level Of Detail Research Articles

Future of precision manufacturing: Integrating advanced metrology and intelligent monitoring for process optimization

Precision manufacturing is undergoing a transformative evolution fueled by the integration of advanced metrology techniques and intelligent monitoring systems. This abstract explores the future trajectory of precision manufacturing through the convergence of these technologies, focusing on their synergistic role in process optimization. Advanced metrology techniques, including high-resolution imaging, laser scanning, and non-contact surface measurement, provide unprecedented levels of accuracy and detail in capturing dimensional data. These techniques enable manufacturers to precisely analyze component geometry, surface finish, and tolerances, facilitating the production of parts with exceptional precision and quality. Moreover, the integration of metrology within the manufacturing process allows for real-time feedback, enabling rapid adjustments and corrections to ensure adherence to design specifications. Intelligent monitoring systems complement advanced metrology by continuously collecting data from various sensors embedded within manufacturing equipment. These systems utilize artificial intelligence (AI) and machine learning algorithms to analyze vast amounts of data in real-time, detecting anomalies, predicting equipment failures, and optimizing process parameters. By leveraging data-driven insights, manufacturers can enhance production efficiency, minimize downtime, and reduce scrap rates. The synergy between advanced metrology and intelligent monitoring extends beyond quality control to encompass holistic process optimization. Through the seamless integration of these technologies, manufacturers can achieve unparalleled levels of precision, efficiency, and flexibility in their operations. For instance, real-time metrology feedback combined with AI-driven monitoring enables adaptive manufacturing processes that dynamically adjust parameters based on changing environmental conditions or material properties. Furthermore, the future of precision manufacturing lies in the adoption of a digital twin approach, where virtual replicas of physical manufacturing systems are created and synchronized with real-time data. This enables predictive maintenance, virtual prototyping, and simulation-based optimization, leading to significant cost savings and accelerated innovation cycles. The future of precision manufacturing hinges on the integration of advanced metrology and intelligent monitoring technologies. By harnessing the synergies between these innovations, manufacturers can achieve unprecedented levels of precision, efficiency, and agility, driving forward the evolution of manufacturing in the digital era.

Digitalization and cross-border tax fraud: evidence from e-invoicing in Italy

AbstractThe digitalization of transaction processes through tools such as electronic invoicing (e-invoicing) aims to improve tax compliance and reduce administrative costs. Another important aspect of digitalization is its potential to reduce tax fraud. We exploit the comprehensive introduction of e-invoicing in Italy in 2019 and examine the effect of increased domestic tax enforcement capabilities on cross-border value-added tax (VAT) fraud. As a proxy for this fraud, we make use of the discrepancy in trade data that are double-reported in both the importing and exporting country (trade data gap, TDG). We calculate the TDG for imports to Italy from all other EU countries at the most detailed product level. Our results suggest a significant decline in cross-border fraud in response to the introduction of mandatory e-invoicing, providing an important rationale for the application of this measure by other countries. Furthermore, we estimate that e-invoicing decreased the Italian VAT loss in 2019 by about € 2.2 billion to € 2.6 billion compared to 2018. In this context, we underpin the suitability of the TDG as an approach for the study of anti-fraud measures.

NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networks

We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013 ], by leveraging recent advancements in machine learning. Our novel hybrid data structure can reduce the memory footprints of VDB volumes by orders of magnitude, while maintaining its flexibility and only incurring small (user-controlled) compression errors. Specifically, NeuralVDB replaces the lower nodes of a shallow and wide VDB tree structure with multiple hierarchical neural networks that separately encode topology and value information by means of neural classifiers and regressors respectively. This approach is proven to maximize the compression ratio while maintaining the spatial adaptivity offered by the higher-level VDB data structure. For sparse signed distance fields and density volumes, we have observed compression ratios on the order of 10× to more than 100× from already compressed VDB inputs, with little to no visual artifacts. Furthermore, NeuralVDB is shown to offer more effective compression performance compared to other neural representations such as Neural Geometric Level of Detail [Takikawa et al. 2021 ], Variable Bitrate Neural Fields [Takikawa et al. 2022a ], and Instant Neural Graphics Primitives [Müller et al. 2022 ]. Finally, we demonstrate how warm-starting from previous frames can accelerate training, i.e., compression, of animated volumes as well as improve temporal coherency of model inference, i.e., decompression.

Effects of operational parameters on bacterial communities in Hong Kong and global wastewater treatment plants.

Wastewater treatment plants (WWTPs) are an indispensable component of modern cities, as they can remove pollutants in wastewater to prevent anthropogenic activities. Activated sludge (AS) is a fundamental wastewater treatment process and it harbors a highly complex microbial community that forms the main components and contains functional groups. Unveiling "who is there" is a long-term goal of the research on AS microbiology. High-throughput sequencing provides insights into the inventory diversity of microbial communities to an unprecedented level of detail. At present, the analysis of communities in WWTPs usually comes from a specific WWTP and lacks comparisons and verification among different WWTPs. The wide-scale and long-term sampling project and research in this study could help us evaluate the AS community more accurately to find the similarities and different results for different WWTPs in Hong Kong and other regions of the world.

A biased random walk approach for modeling the collective chemotaxis of neural crest cells

Collective cell migration is a multicellular phenomenon that arises in various biological contexts, including cancer and embryo development. ‘Collectiveness’ can be promoted by cell-cell interactions such as co-attraction and contact inhibition of locomotion. These mechanisms act on cell polarity, pivotal for directed cell motility, through influencing the intracellular dynamics of small GTPases such as Rac1. To model these dynamics we introduce a biased random walk model, where the bias depends on the internal state of Rac1, and the Rac1 state is influenced by cell-cell interactions and chemoattractive cues. In an extensive simulation study we demonstrate and explain the scope and applicability of the introduced model in various scenarios. The use of a biased random walk model allows for the derivation of a corresponding partial differential equation for the cell density while still maintaining a certain level of intracellular detail from the individual based setting.

A multi-scale numerical approach to study monoclonal antibodies in solution.

Developing efficient and robust computational models is essential to improve our understanding of protein solution behavior. This becomes particularly important to tackle the high-concentration regime. In this context, the main challenge is to put forward coarse-grained descriptions able to reduce the level of detail, while retaining key features and relevant information. In this work, we develop an efficient strategy that can be used to investigate and gain insight into monoclonal antibody solutions under different conditions. We use a multi-scale numerical approach, which connects information obtained at all-atom and amino-acid levels to bead models. The latter has the advantage of reproducing the properties of interest while being computationally much faster. Indeed, these models allow us to perform many-protein simulations with a large number of molecules. We can, thus, explore conditions not easily accessible with more detailed descriptions, perform effective comparisons with experimental data up to very high protein concentrations, and efficiently investigate protein-protein interactions and their role in phase behavior and protein self-assembly. Here, a particular emphasis is given to the effects of charges at different ionic strengths.

Composition of public waste - a case study from Austria

Waste from public places like parks, pedestrian zones or sidewalks is a visible yet unexploited waste stream. Publications and information on the amounts and makeup of this waste flow are very scarce. To evaluate the resource potential and enable waste management planning, this study aims to assess the quality and quantity of public waste in a detailed waste characterisation campaign. For the first time, an analysis at this level of detail was conducted in a medium-sized town (25,000 inhabitants) in Austria. The sampling campaign included the whole town area. In total, almost 1000 kg of waste was sorted in up to 88 different fractions, which allows the detailed composition of, for example, packaging waste and single-use plastics to be delineated. We found that the amount of waste collected in public street bins is approximately 2.6 % of the total mixed municipal solid waste, resulting in 4.1 kg of public waste per inhabitant annually. The vast majority of this waste is currently collected as mixed waste. The results indicate a high share (52 %) of recyclable materials (glass, metal, paper and lightweight packaging) in mixed public waste, most of which is packaging (44 % of total waste). Other large shares include dog feces (18 % of total waste) and biogenic waste (17 % of total waste). The results lay the foundation for further investigations into, for example, exploiting recycling potential or evaluating possible improvements in separate waste collection in public spaces. Further, the collected data serve as an essential knowledge basis for policymakers and local authorities.

Technoeconomic characterisation of low-carbon liquid hydrocarbons production

With growing attention on low-carbon fuels and chemicals to meet climate targets, careful cost and performance evaluations become critical. These hydrocarbons can be derived from biomass or hydrogen with captured CO2 via established pathways such as Fischer-Tropsch and methanol synthesis. Techno-economic studies are influential in identifying the economic viability of promising technologies in the absence of reliable industry data. A considerable number of techno-economic studies that represent same technologies exhibit substantial variability in their estimations, posing challenges in decision-making. This work aims to characterise, based on available data, the investment cost and plant performance for the Nth (i.e. mature) low-carbon liquid hydrocarbon plants. The extent of data variabilities across technologies are presented and the factors contributing to differences in data interpretation and normalisation are determined. Through a full assessment of existing values, central tendencies for prospective techno-economic evaluations are identified. Processes utilising biomass have the greatest ranges in the investment cost, with higher costs from studies with the highest level of detail and proximity to real-world plant cost estimations. Energy efficiency variabilities arise when tied to plants with multiple outputs. Demonstration scale hydrogen-to-hydrocarbons in relation to economies of scale should be carefully evaluated.

Trend estimation of sub-national level daily smoking prevalence by age and sex in Australia.

Despite that the smoking prevalence has considerably declined in Australia after successful public health strategies over many decades, smoking is still the leading cause of preventable diseases and death in Australia. These declines have not occurred consistently across all geographical-demographic domains. In order to provide an evidence base for monitoring the trend towards the goal of reducing smoking across all domains in Australia, this study aims to estimate trends of smoking prevalence for small domains cross-classified by seven age groups (18-24, 25-29, 30-39, 40-49, 50-59, 60-69, and ≥70 years), two genders, and eight states and territories over twenty years (2001-2021). Direct estimates of smoking prevalence for the target small domains were calculated from the micro-data of the Australian National Health Surveys conducted in seven rounds during 2001-2021. The obtained direct estimates were then used as input for developing time-series models expressed in a hierarchical Bayesian structure as a form of small-area estimation. The developed models borrow cross-sectional, temporal, and spatial strength in such a way that they can interpolate smoking levels in the non-survey years for all detailed level small domains. Smoothed trends of smoking prevalence for higher aggregation levels are obtained by aggregation of the detailed level trend predictions. Model-based small area estimators provide consistent and reasonable smoothed trends at both detailed and higher aggregation levels. Results show that the national-level trend exhibits a steeper linear decline over the study period, from 24% in 2001 to 12% in 2021, with a considerable gender difference of around 5% over the period, with males reporting a higher prevalence. Improved model-based estimates at the state level and by age also show steady declines in trends except for the Northern Territory (still above 20%) and older age groups 60-69 and ≥70 years (declining trends remain stable after 2012). The findings of the study identify the geographical-demographic groups that had poor improvement over the period 2001-2021, and are still behind the target of achieving lower smoking prevalence. These, in turn, will help health researchers and policymakers deliver targeted programs to the most vulnerable, enabling the nation to meet its health goals in a timely way.

Analyzing land cover change dynamism through a GIS-based method: application to Gran Canaria (Canary Islands, Spain)

Landscapes are dynamic areas which can be studied from the perspective of different, but related, disciplines. The application of GIS methods to study landscape change is an interesting resource to help understand and explain landscape dynamics. Thus, the objective of this paper is to characterize land cover dynamism and identify land cover change ‘hotspots’. The method used here generates raster maps in which each pixel value represents the number of times each pixel has changed land cover between 1990-2018. Furthermore, it provides three of these maps, since it works with the 3 taxonomic levels of Corine Land Cover datasets, giving us different levels of detail in the analysis. On the other hand, the statistical treatment of the data has been done at the municipal level. The most important results reveal that Agüimes is the municipality with the most dynamic land cover change in the three levels, while Tejeda, in level 1, and Valsequillo, in levels 2 and 3, have the least changing land cover. These outputs are complemented with other statistical analyses which allow the integration of different data types such as those related to population, tourism and agriculture. Subsequently, some of the methodological issues and findings are discussed and put in context with the scientific literature.

A modern, rapid and simple investigation of Ampère’s law

Classical physics results are often taught purely from the theoretical side. Key results, especially in electromagnetism, are typically not explored experimentally, and in applications students are then expected to leap straight into more complex scenarios that make use of these principles in electronics, sensors and instrumentation. This is unfortunate because not all individuals are equally able to learn well purely from the mathematical angle, and even those who do are not exposed to exploring the magnitude of competing effects, for example isolating a particular magnetic field signal from the background of the Earth’s field. An experiment is presented here to test Ampère’s law with a setup that can be assembled out of everyday materials with minimal components—a smartphone, a DC power supply, wires—in a procedure that can be completed in just a few hours. The data from the three magnetic field sensors of the phones, together with the gyroscope sensors providing position, are recorded and numerically integrated. The experiment is also demonstrated using sensors collected by an Arduino board instead of a smartphone. The experiment allows to measure the net current carried by wires inside the closed path over which the magnetic field is integrated, i.e. Ampère’s law. This experimental approach to exploring Ampère’s Law can be adapted towards high school or university demonstrations, depending on the level of accuracy and detail that one aims to pursue.

Interactions between sustainable development goals at the district level in Lao PDR

Monitoring the status and evolution of Sustainable Development Goals (SDGs) is typically carried out at the national level. However, significant variation can exist within countries, and this may not be captured by aggregate statistics. Here, we develop a unique dataset representing indicators for three SDGs at a district level for Lao PDR. The indicators comprise prevalence of stunting (SDG 2, Zero hunger), poverty headcount (SDG 1, No Poverty), and share of natural area (SDG 15, Life on land) for two moments in time: 2005 and 2015. In both years, we find considerable variation among district-level outcomes for stunting and poverty. We also find that higher stunting and poverty rates are significantly correlated with higher shares of natural land in both years. This is consistent with the common perception of a trade-off between environmental outcomes and socioeconomic wellbeing. The correlation vanishes, however, when we consider changes in poverty, stunting, and natural area over the ten-year study period. This holds as well when we focus on agricultural land instead of natural areas. We observe that most regions show improvements in both stunting and poverty, albeit not always in a statistically significant sense. This points to synergistic development. Similarly, improvements in both indicators are associated with losses in natural areas in all regions, indicating a trade-off. These results suggest that both trade-offs and synergies between SDGs can arise at the district level, but that context and local conditions likely moderate the strength of these interactions. Our results highlight the importance of quantifying and monitoring sustainable development at the detailed subnational level.

Forestry Scenario Modelling: Qualitative Analysis of User Needs in Lithuania

This paper aims to demonstrate the use of qualitative research methods, specifically in-depth interviews, to explore the intangible and often difficult-to-quantify needs for forestry scenario modelling in Lithuania, which are frequently not adequately perceived. The study involved informants representing key actors in forest policy, forest management, research, and education. A total of 21 informants from 11 different institutions, which hold significant power and expertise in forest decision making, were interviewed. The purpose of these interviews was to gather their perspectives on the potential forest decision support system in the country, aiming to address most of their needs. The interview questions explored various aspects, including the requirements for forestry scenario modelling, the desired level of detail and information content for decision making, and both functional and nonfunctional requirements for the scenario modelling system. It is worth noting that the expected functionality of the planned forest DSSs aligns with modern international standards. Nevertheless, the diversity of perspectives, wishes, visions, and intentions of key Lithuanian forestry actors regarding the aims, objectives, and essential functionality of forestry scenario modelling tools were identified. The understanding of the requirements for modern forest DSSs was greatly influenced by the current forestry paradigms in the country and the professional experiences of individual informants. In conclusion, our findings demonstrate that the utilization of qualitative research, particularly through in-depth interviews, has proven to be a highly effective tool for accurately specifying the requirements of a modern forest DSS. It helped mitigate preconceived notions and address gaps in the envisioned product, specifically by developing a framework of core solutions for the national forestry and land-use scenario modelling system.

Modeling the Geometry of Tree Trunks Using LiDAR Data

The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

‘A Prisoner on the Rack’: Marital Rape, Consent, and the Gothic in Late-Nineteenth-Century Colonial Women’s Writings

ABSTRACT This article explores how marital rape was represented in the fiction and non-fiction writings of four colonial female authors – Barbara Baynton, Ada Cambridge, Louisa Lawson, and Rosa Praed. It examines how these women conceptualised marital rape as a form of domestic violence or ‘cruelty’ in marriage, intrinsically linked to other forms of marital violence such as physical and mental abuse, and with marital rape representative of the unacceptability and immorality of a husband’s total control over his wife’s body. It also analyses how these literary representations differed from discussions about marital rape and domestic violence by suffrage agitators such as Rose Scott and Maybanke Anderson. The article argues that Baynton, Cambridge, and Lawson used the language and tropes of the Gothic in order to generate understanding for and empathy with wives and their inability to consent in the marital bed. This article therefore illuminates the intersecting conversations by both female authors and suffrage agitators in late-nineteenth-century Australia about married women’s right to consent, domestic violence, and acceptable marital masculine behaviour, highlighting how access to Gothic tropes allowed colonial female authors to foreground women’s bodily autonomy and depict marital rape in the level of graphic detail that suffrage agitators could not.

Estimating the Railway Network Capacity Utilization with Mixed Train Routes and Stopping Patterns: A Multiobjective Optimization Approach

Railway capacity estimation problem is typically defined as estimating the maximum number of trains that can be operated in a railway section within a given time interval. However, trains with different speeds, routes, and stopping patterns in a railway network will likely compete for the limited capacity of network nodes and sections. As these trains may provide different services, it is ambiguous to simply indicate the network capacity by a scalar number of trains. To comprehensively estimate and interpret the railway capacity considering the capacity competition between heterogeneous trains, we propose a multiobjective perspective for the capacity estimation problem to enrich the capacity theory while handling the competition among trains with different routes and stopping patterns. Based on a time-space network timetable saturation model, we extend the multiobjective capacity estimation approach to the detailed timetable level by optimizing the saturated timetable under capacity estimation objectives with respect to different routes and stopping patterns. With the ε-constraint method, we can obtain the Pareto front of saturated timetables, i.e., a set of nondominated optimized timetables that no more candidate train can be additionally scheduled. The result is a more comprehensive capacity representation than a single absolute scalar number. A case study is conducted on a combined high-speed and intercity network of Zhengzhou Railway group in China. An extensive set of Pareto-optimal saturated timetables describing the effects on the capacity of the railway network is obtained. The results can help infrastructure managers select saturated timetables as the capacity utilization reference by considering the trade-off between time indexes from passengers’ and operators’ perspectives.

OPTIMIZATION OF VR APPLICATION IN TEXTURING CULTURAL HERITAGE

Abstract. The research question underlying this short essay refers to the possibility of realizing through a well-established workflow a high level of immersivity in the VR representation of Cultural Heritage to be used as a working tool by architects and renovators as well as heritage scholars. At present active or passive 3D scanning techniques are a known reality where the choice of data acquisition system depends both on the final purpose and characteristics of the object to be surveyed. With the maturity achieved in point cloud acquisition, post processing phases and the development of BIM-based systems, the model has given the possibility to become a repository of information related to the existing, to be used for maintenance or renovation processes. If the model can represent the existing with a certain level of detail, it can be assumed that a differentiated Level of Immersivity can be organized depending on the specific needs it intends to fulfil. In the context of renovation or preservation of Cultural Heritage, the potential offered by VR becomes more interesting when it can provide a realistic portrait not only of the geometry, but also of the materiality and state of preservation of the buildings. This research opens new possibilities to develop tools to aid designers and renovators in degradation analysis, intervention projects, and scheduled maintenance. The research question starts analyzing the possibility of creating photorealistic immersive environments that are easy to access, organized based on surveys of existing heritage buildings using specific datasets.

NEURAL RADIANCE FIELDS (NERF) FOR MULTI-SCALE 3D MODELING OF CULTURAL HERITAGE ARTIFACTS

Abstract. This research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts.

A COMPARISON BETWEEN TERRESTRIAL LASER SCANNING AND HAND-HELD MOBILE MAPPING FOR THE DOCUMENTATION OF BUILT HERITAGE

Abstract. The increasing demand for 3D digital models has spurred the rapid evolution of technologies to acquire, process, and disseminate 3D data of physical artifacts. Current scanners meet building surveying needs, prompting the exploration of closed systems with multiple sensors, custom accessories, control systems, and varied data processing approaches. While efficient in specific conditions, these systems require careful consideration of factors like technical requirements, environmental conditions, and the intended use of the final model. This article compares a static terrestrial laser scanner (TLS) and a portable mobile mapping system (PMMS) for documenting built heritage, focusing on the City Hall of Montecatini Terme in Italy. The Leica Geosystems RTC360 scanner provides quick scanning times and pre-alignment during fieldwork, reducing post-processing efforts. The Leica BLK2GO, belonging to the PMMS family, utilizes laser SLAM, visual SLAM, and inertial IMU measurements for rapid large-area documentation. The comparison assesses accuracy, completeness, and detail recognition, revealing that both systems are suitable for heritage documentation requiring centimetre-level accuracy. However, differences in point density and roughness indicate that the RTC360 may be better for intricate details. The BLK2GO excels in efficiency but is more oriented toward general representations at the architectural scale. This study emphasizes the importance of evaluating different methodologies based on project objectives and desired levels of detail, providing insights into the strengths and limitations of each system for diverse applications in heritage documentation.

Multi-scale simulations of pulmonary airflow based on a coupled 3D-1D-0D model

Pulmonary airflow simulation is a valuable tool for studying respiratory function and disease. However, the respiratory system is a complex multiscale system that involves various physical and biological processes across different spatial and temporal scales. In this study, we propose a 3D-1D-0D multiscale method for simulating pulmonary airflow, which integrates different levels of detail and complexity of the respiratory system. The method consists of three components: a 3D computational fluid dynamics model for the airflow in the trachea and bronchus, a 1D pipe model for the airflow in the terminal bronchioles, and a 0D biphasic mixture model for the airflow in the respiratory bronchioles and alveoli coupled with the lung deformation. The coupling between the different components is achieved by satisfying the mass and momentum conservation law and the pressure continuity condition at the interfaces. We demonstrate the validity and applicability of our method by comparing the results with data of previous models. We also investigate the reduction in inhaled air volume due to the pulmonary fibrosis using the developed multiscale model. Our method provides a comprehensive and realistic framework for simulating pulmonary airflow and can potentially facilitate the diagnosis and treatment of respiratory diseases.