Different Approach for the Structure Inclination Determination

Bacova, Dasa; Izvoltova, Jana; Sedivy, Stefan; Chromčák, Jakub

doi:10.3390/buildings13030637

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…Our open-access codes for both “checking” and “correcting” the stereochemistry are publicly accessible. 51 The above procedure might create a few overlaps between atoms; thus, we perform an additional energy minimization step followed by another short run, as illustrated in Figure 1 . Finally, the data for the analysis were collected from short MD simulations of a few (here 10) ns, labeled as runEQ, in which all monomers had the correct stereochemistry.…”

Section: Resultsmentioning

confidence: 99%

“…The code for the development of the Deep Learning model and the computation of the dihedral angle distributions reported in the manuscript is uploaded in GitHub (see ref ( 57 )). Moreover, the codes developed and used for both “checking” and “correcting” the stereochemistry are publicly accessible in GitHub, together with the scripts for the calculation of the inner distances and radial distribution functions (see ref ( 51 )). We note that due to the magnitude of the trajectory files, only a small sample of the Copo100 system (100 frames) is available in GitHub (see ref ( 57 )); nevertheless, the rest of the data that support the findings of this study are available from the corresponding author upon request.…”

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confidence: 99%

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Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Christofi,

Bačová,

Harmandaris

2024

J. Chem. Inf. Model.

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Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator (“one-to-one” problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones (“one-to-many” problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Christofi,

Bačová,

Harmandaris

2024

J. Chem. Inf. Model.

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“…LiDAR boasts an impressive accuracy, reaching up to 2-3 cm for horizontal coordinates (X, Y) and 3-5 cm for height (Z) when combined with GNSS data for georeferencing and IMU data for orientation [8]. It is important to note that such high-accuracy measurements are typically associated with large-scale surveys like topographic mapping [9].…”

Section: Introductionmentioning

confidence: 99%

Usage of a Conventional Device with LiDAR Implementation for Mesh Model Creation

Smrčková,

Chromčák,

Ižvoltová

et al. 2024

Buildings

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The trend of using conventional devices like mobile phones, tablets, and the other devices is gaining traction in improving customer service practices. This coincides with the growing popularity of building information modeling (BIM), which has led to increased exploration of various 3D object capture methods. Additionally, the technological boom has resulted in a surge of applications working with different 3D model formats including mesh models, point cloud, and TIN models. Among these, the usage of mesh models is experiencing particularly rapid growth. The main objective advantages of mesh models are their efficiency, scalability, flexibility, sense of detail, user-friendliness, and compatibility. The idea of this paper is to use a conventional device, specifically an iPad Pro equipped with light detection and ranging (LiDAR) technology, for creating mesh models. The different data capture methods employed by various applications will be compared to evaluate the final models´ precision. The accuracy of the 3D models generated by each application will be assessed by comparing the spatial coordinates of identical points distributed irregularly across the entire surface of the chosen object. Various available currently most-used applications were utilized in the process of data collection. In general, 3D representations of the object/area, etc., may be visualized, analyzed, and further processed in more formats such as TIN models, point cloud, or mesh models. Mesh models provide a visualization of the object mirroring the solid design of the real object, thus approximating reality in the closest way. This fact, along with automatized postprocessing after data acquisition, the ability to capture and visualize both convex and concave objects, and the possibility to use this type of 3D visualization for 3D printing, contribute to the decision to test and analyze mesh models. Consequently, the mesh models were created via the automatic post-processing, i.e., without external intervention. This fact leads to the problems of random coordinate systems being automatically pre-defined by every application. This research must deal with the resulting obstacles in order to provide a valid and credible comparative analysis. Various criteria may be applied to the mesh models’ comparisons, including objective qualitative and quantitative parameters and also the subjective ones. The idea of this research is not to analyze the data acquisition process in detail, but instead to assess the possibilities of the applications for the basic users.

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“…The application of these methods has its pros and cons. However, a number of recent studies have shown that any of these methods can be successfully applied to monitor historic buildings [14][15][16][17][18][19][20][21][22][23][24][25]. In general, geospatial monitoring of engineering structures contains well-known stages, namely design, target setup, geodetic network creation, observations, data processing, and analysis.…”

Section: Introductionmentioning

confidence: 99%

Historical Retaining Walls Monitoring: A Case Study of Debosquette Wall of Kyiv-Pechersk Lavra

Shults,

Bilous,

Khailak

2023

CEJ

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Geospatial monitoring of historic buildings has a valuable meaning for their restoration and preservation measures. The preparation and accomplishment of such monitoring have their features and cannot be standardized. Therefore, in each particular case, monitoring is carried out for specific requirements and conditions. The paper presents the results of geospatial monitoring for a part of the UNESCO object Kyiv-Pechersk Lavra. The primary subject of geospatial monitoring is a retaining wall known as the Debosquette Wall. The wall was built in the XVIII century and underwent restoration in 2014. A geospatial monitoring system has been established to prevent undesirable damage and displacements. Assigning the necessary observation accuracy for such a complex object is difficult. In the paper, the modern approach to observation accuracy calculation has been suggested and studied. The approach is based on the application of structural mechanics principles. The structural analysis of the Debosquette Wall has been accomplished. The output of the analysis was applied to calculate the required observation accuracy. The geospatial network and monitoring scheme were developed based on the calculated accuracy. The monitoring proceeded for half a year in 2012-2013, was interrupted for one year, and kept on in 2015. The primary stress was made on the horizontal displacements in that these displacements are the primary threats to the wall stability. The in-depth analysis of the monitoring results has been accomplished. It was found that the displacements have stayed within the allowable values. The developed monitoring approach is recommended for similar projects.

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Different Approach for the Structure Inclination Determination

Cited by 5 publications

References 25 publications

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers

Usage of a Conventional Device with LiDAR Implementation for Mesh Model Creation

Historical Retaining Walls Monitoring: A Case Study of Debosquette Wall of Kyiv-Pechersk Lavra

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