Open-Source Tool for Transforming CityGML Levels of Detail

Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

Evaluation of HMDs by QFD for Augmented Reality Applications in the Maxillofacial Surgery Domain

Today, surgical operations are less invasive than they were a few decades ago and, in medicine, there is a growing trend towards precision surgery. Among many technological advancements, augmented reality (AR) can be a powerful tool for improving the surgery practice through its ability to superimpose the 3D geometrical information of the pre-planned operation over the surgical field as well as medical and instrumental information gathered from operating room equipment. AR is fundamental to reach new standards in maxillofacial surgery. The surgeons will be able to not shift their focus from the patients while looking to the monitors. Osteotomies will not require physical tools to be fixed on patient bones as guides to make resections. Handling grafts and 3D models directly in the operating room will permit a fine tuning of the procedure before harvesting the implant. This article aims to study the application of AR head-mounted displays (HMD) in three operative scenarios (oncological and reconstructive surgery, orthognathic surgery, and maxillofacial trauma surgery) by the means of quantitative logic using the Quality Function Deployment (QFD) tool to determine their requirements. The article provides an evaluation of the readiness degree of HMD currently on market and highlights the lacking features.

Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition

Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50–0.48 (DEAP) and 0.46–0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models.

Squashed-Slice Algorithm Based on STEP-NC for Multi-Material and Multi-Directional Additive Processes

Even though additive manufacturing is receiving increasing interest from aerospace, automotive, and shipbuilding, the legacy approach using tessellated form representation and cross-section slice algorithm still has the essential limitation of its inaccuracy of geometrical information and volumetric losses of final outputs. This paper introduces an innovative method to represent multi-material and multi-directional layers defined in boundary-representation standard model and to process complex sliced layers without missing volumes by using the proposed squashing operation. Applications of the proposed method to a bending part, an internal structure, and an industrial moulding product show the assurance of building original shape without missing volume during the comparison with the legacy method. The results show that using boundary representation and te squashing algorithm in the geometric process of additive manufacturing is expected to improve the inaccuracy that was the barrier of applying additive process to various metal industries.

High Efficiency Spam Filtering: A Manifold Learning-Based Approach

Spam filtering, which refers to detecting unsolicited, unwanted, and virus-infested emails, is a significant problem because spam emails lead to unnecessary costs of Internet resources, waste of people’s time, and even loss of property. Support vector machine (SVM) is the state-of-the-art method for high accuracy spam filtering. However, SVM incurs high time complexity because of the high dimensionality of the emails. In this study, we propose a manifold learning-based approach for time-efficient spam filtering. From the experiments that most of the features are not decisive, we can obtain the viewpoint that only a minor part of the spam emails can be detected using the nondecisive features. Based on the insight, we propose to employ the Laplace feature map algorithm to obtain the geometrical information from the email text datasets and extract the decisive features. Then, the extracted features are used as the input of SVM to spam filtering. We conduct extensive experiments on three datasets, and the evaluation results indicate the high accuracy time efficiency of our proposed algorithm.

A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation

Deidentifying MRIs constitutes an imperative challenge, as it aims at precluding the possibility of re-identification of a research subject or patient, but at the same time it should preserve as much geometrical information as possible, in order to maximize data reusability and to facilitate interoperability. Although several deidentification methods exist, no comprehensive and comparative evaluation of deidentification performance has been carried out across them. Moreover, the possible ways these methods can compromise subsequent analysis has not been exhaustively tested. To tackle these issues, we developed AnonyMI, a novel MRI deidentification method, implemented as a user-friendly 3D Slicer plugin-in, which aims at providing a balance between identity protection and geometrical preservation. To test these features, we performed two series of analyses on which we compared AnonyMI to other two state-of-the-art methods, to evaluate, at the same time, how efficient they are at deidentifying MRIs and how much they affect subsequent analyses, with particular emphasis on source localization procedures. Our results show that all three methods significantly reduce the re-identification risk but AnonyMI provides the best geometrical conservation. Notably, it also offers several technical advantages such as a user-friendly interface, multiple input-output capabilities, the possibility of being tailored to specific needs, batch processing and efficient visualization for quality assurance.

ESA’s Planetary Science Archive efforts to support the scientific community

<p class="p1"><strong>Introduction:</strong><span class="Apple-converted-space">  </span>With new missions being selected, missions moving to post-operations, and missions starting their journey to various targets in the Solar System, the European Space Agency’s Planetary Science Archive [1] (<span class="s1">http://psa.esa.int</span>) (PSA) is in constant evolution to support the needs of the projects and of the scientific community<span class="s2">.</span></p> <p class="p2"><strong>Geometry as a key input for users:</strong><span class="Apple-converted-space">  </span>While analysing the various services that the PSA already offers, geometrical information was lacking in many ways. Feedback received by the users and the PSA User Group pointed to improvements in this area.<span class="Apple-converted-space"> </span></p> <p class="p3">During the past years, the PSA structured its internal architecture to provide excellent services to the community. Through external partnership, we developed the GEOmetry GENerator that allows a consistent way of deriving geometrical information. This input provides a solid foundation to develop Geographical Information System (GIS) services into the PSA. At the end of 2020, the PSA released its 3D and 2D interfaces for Mars Express and Rosetta, providing a new generation of geometrical services. Although currently focused on Mars and comet 67P/C-G, our architecture enables a rapid growth to support in particular BepiColombo and JUICE.</p> <p class="p2"><strong>High level products through the Guest Storage Facility: </strong>One of the other new service provided to the scientific community recently is the Guest Storage Facility (GSF), which allows users to store derived products. Products such as geological maps, Digital Terrain Models, new calibrated files, and others can be stored in the GSF in the format most used by the users. The philosophy of the GSF service is to impose minimum requirements on the data producers, while delivering maximum usability to the end users. Various products related to Titan and Mars are available in the GSF. Products related to the Moon, comet 67P/C-G and other targets are in preparation. Contact us to preserve your science!<span class="Apple-converted-space"> </span></p> <p class="p2"><strong>Interact with the PSA and expect more in the years to come: </strong>The PSA aims to build on the previous development to further enrich its services. New GIS interfaces related to Phobos, the Moon and Mars are in development to facilitate the searching capabilities on those targets. In parallel to those major developments, new functionalities will be developed to support ESA missions, in particular ExoMars, Mars Express, and BepiColombo.</p> <p class="p3">At the PSA we constantly interact with our users to ensure that our services are in line with the expectations and needs of the community (despite massive disruptions in 2019 and 2020). We encourage feedback from community scientists through:</p> <ul class="ul1"> <li class="li3">PSA Users Group: A group of scientific experts advising the PSA on strategic development;</li> <li class="li3">Direct interactions: Scientists from the PSA are available and eager to receive your comments and suggestions;</li> <li class="li3">ESA missions: If you are part of a mission archiving its data at the PSA, tell us how your data should best be searched and used.</li> </ul> <p class="p2"><strong>Acknowledgments:</strong> The authors are very grateful to all the people who have contributed over the last 18 years to ESA's Planetary Science Archive. We are also thankful to ESA’s teams who are operating the missions and to the instrument science teams who are generating and delivering scientific calibrated products to the archive.</p> <p class="p6"><span class="s4"><strong>References:</strong> </span></p> <p class="p6">[1] Besse, S. et al. (2017) <em>Planetary and Space Science,</em> <span class="s5">10.1016/j.pss.2017.07.013</span>, ESA's Planetary Science Archive: Preserve and present reliable scientific data sets.<span class="Apple-converted-space"><br /></span></p>

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

N6-methyladenosine (m6A) is one of the most prevalent RNA post-transcriptional modifications and is involved in various vital biological processes such as mRNA splicing, exporting, stability, and so on. Identifying m6A sites contributes to understanding the functional mechanism and biological significance of m6A. The existing biological experimental methods for identifying m6A sites are time-consuming and costly. Thus, developing a high confidence computational method is significant to explore m6A intrinsic characters. In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features. To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction. Comparison results show that our proposed predictor achieved the best performance compared with other predictors on four public datasets across three species. On the A101 dataset, our predictor outperformed 1.34% (accuracy), 0.0227 (Matthew’s correlation coefficient), 5.63% (specificity), and 0.0081 (AUC) than comparing predictors, which indicates that m6AGE is a useful tool for m6A site prediction. The source code of m6AGE is available at https://github.com/bokunoBike/m6AGE.

Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

Determining Levels of Biological Geometric Information at Polygonal Shape Patterns

Based on a measuring system to determine the statistical heterogeneity of individual polygons we propose a method to use polygonal shape patterns as a source of data in order to determine the Shannon entropy of biological organizations. In this research, the term entropy is a particular amount of data related with levels of spatial heterogeneity in a series of different geometrical meshes and sets of random polygons. We propose that this notion of entropy is important to measure levels of information in units of bits, measuring quantities of heterogeneity in geometrical systems. In fact, one important result is that binarization of heterogeneity frequencies yields a supported metric to determine geometrical information from complex configurations. Thirty-five geometric aggregates are tested; biological and non-biological, in order to obtain experimental results of their spatial heterogeneity which is verified with the Shannon entropy parameter defining low particular levels of geometrical information in biological samples. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results show that a particular range (0.08 and 0.27) of information is intrinsically associated with low rates of heterogeneity. We conclude it as an intrinsic feature of geometrical organizations in multi-scaling biological systems.