Construction Of 3D Model Research Articles

A Human Pose Estimation Method Based on the BlazePose Model

Objective: This study aims to evaluate the performance of the BlazePose model in human pose estimation for sports actions to improve the accuracy and computational efficiency of the model. Methods: The research employed a variety of datasets for training and evaluating the model, investigating the generalization capability of the model under different experimental conditions. Detection and tracking were carried out through the two-stage mechanism of the BlazePose model, identifying the human figure contours and initially predicting the key points, and then refining the positions of the key points through the tracking module. Different parameters such as model complexity and key point smoothing were set, and the steps including image preprocessing, feature extraction, key point detection, naming, and skeleton construction were conducted. Results: The research outcomes yielded the two-dimensional keypoints of single-frame human body images and the construction of 3D coordinate models, and obtained the visualization line charts of PCK experimental data. Through the comparison of PCK scores of different models, it was concluded that on the basketball Dataset, the performance of BlazePose was superior to that of OpenPose, particularly in terms of FPS performance. Although the PCK score of the lightweight version of BlazePose was slightly lower, its FPS performance was higher, making it suitable for scenarios with high requirements for speed. Furthermore, BlazePose effectively reduced the model complexity by offering models of different complexities and using occlusion simulation in training, without sacrificing too much accuracy. Conclusion: This research presents the efficacy of lightweight neural networks in real-time human pose estimation and, through further experimental analyses, investigates the possibilities of enhancing their performance advantages and application effects.

High-precision 3D Modeling of Construction Waste Pile Bodies by Integrating Multi-source Data

Abstract. The instability of construction waste pile bodies, as an increasingly concerning disaster, poses significant risks to the safety of people's lives and property. Currently, there is limited research on high-precision 3D modeling techniques for construction waste pile bodies, which significantly hinders the accuracy and reliability of early detection and risk assessment of pile body instability. Therefore, constructing high-precision 3D models of construction waste pile bodies using multi-source data plays a crucial role in improving the accuracy and timeliness of early warning systems for pile body instability, offering significant theoretical research and practical application value. Building 3D models of construction waste pile bodies solely based on unmanned aerial vehicle (UAV) oblique photography data faces challenges, such as various degrees of data voids and insufficient model completeness, and few methods currently address the construction of 3D models of construction waste pile bodies through the fusion of multi-source data. This paper attempts to use the Iterative Closest Point (ICP) algorithm, combining SLAM laser point cloud data with UAV oblique photography data to fill data voids, and utilizes the fused point cloud to reconstruct the triangulation network, achieving the transformation from 3D point cloud models to 3D surface models. On the basis of texture mapping, a high-precision 3D model of the construction waste pile body is constructed. The research results show that the 3D model of the construction waste pile body, integrated with multi-source data, has a planar error of 0.0187m and an elevation error of 0.0368m, meeting the corresponding model accuracy requirements. It can more realistically restore the fine 3D features of the construction waste pile body, effectively compensating for the shortcomings of single-source data in 3D modeling of construction waste pile bodies, providing a new method for the 3D model reconstruction of construction waste pile bodies, and offering effective data support for construction waste research.

Creation of self-aligning multisatellite planetary gears

In this study, we aim to identify shortcomings in the operation of multisatellite planetary gearboxes and to justify improved designs of planetary self-aligning mechanisms, which allow torque to be transmitted through all installed satellites. To that end, we studied a domestic planetary three-satellite MPZ gearbox installed on a magnetic separator, designed for wet separation of strongly magnetic ores and materials into magnetic and non-magnetic products, and a planetary gearbox of a special structure. 3D models of the classical three-satellite and single-sliding self-aligning gearboxes were constructed in the 3D module of the T-Flex CAD software. The performance of the mechanisms was evaluated by structural analysis and Chebyshev’s mobility formula. The constructed 3D models were used to study the engagement process of the classical three-satellite and single-sliding self-aligning gearbox. The latter is distinguished by the presence of two levers, which are introduced additionally to three satellites. During the research, the lateral clearance between the pairs of satellite teeth and center wheels was accepted according to GOST 1643–81. The conducted analysis of contacts at rotation of the central driving wheel by 30°, 110°, 200°, and 310° established further designing of the applied planetary gears to be inexpedient due to impossibility of realization of power transfer simultaneously through all satellites. The main disadvantage of the operation of multisatellite gearboxes was found to be the requirement for the side clearance selection, which should ensure the operability of the transmission at the moment of motion transfer. When designing multi-satellite single-slide selfaligning planetary mechanisms, additional levers should be introduced into the structure, the number of which should be equal to the number of satellites. Hence, the use of self-aligning planetary gears makes it possible to reduce the dimensions due to the uniform distribution of the transmitted torque, since the calculated value of the torque can be reduced by the number of satellites. This allows the gears to be adapted to the working loading conditions, which significantly improves the performance of the entire machine or unit.

Integrated high-precision real scene 3D modeling of karst cave landscape based on laser scanning and photogrammetry

In recent years, the application of real scene 3D technology has become widespread in urban planning and cultural heritage protection. However, there has been relatively little attention paid to the construction of real scene 3D models for special natural landscapes such as caves. Given the global distribution of karst topography and the large number of naturally developed caves with diverse types, unique landscape styles, and significant scientific value, this paper enriches the research in this field. By combining ground-based and aerial remote sensing techniques, and based on 3D laser scanning and photogrammetry, we have successfully constructed a real scene 3D model of the internal structure of a karst cave with a precision better than 4 cm. Utilizing Unmanned Aerial Vehicle (UAV) oblique photography, we established a real scene 3D model of the external karst landform with a precision better than 2 cm. We also integrated the internal and external 3D models of the cave, developing a new, complete, and high-precision method for constructing real scene 3D models of karst cave landscapes. Furthermore, we proposed a method for texture reproduction in the dark environment inside the caves, enhancing the reproduction and visual appeal of the real interior. The establishment of high-precision real scene 3D models can not only serve as an effective tool for scientific research on caves but also, as replicas of the real world, play a crucial role in public dissemination and education, thereby enhancing public understanding of cave geological landscapes.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

3D modelling method and application to a digital campus by fusing point cloud data and image data

ObjectiveThe use of single-source data for real-world 3D modelling currently faces problems such as deformation, pulling and fuzzy texture at the bottom of buildings in some feature models because of the lack of images. Moreover, LIDAR generates a huge amount of data, and the massive raw data processing and point cloud parsing puts high demands on the hardware arithmetic and algorithms. Aiming at the deficiencies and defects of the two data sources of inclined photogrammetry and airborne laser point cloud in the construction of high-quality and high-precision city-level 3D models. Methodsthis study uses a university library building as an example and proposes the main technical process and method of modelling after fusing the point cloud data acquired by inclined photogrammetry and 3D laser scanning technology. This is accomplished in the reconstruction stage of multi-source data fusion through data spatial alignment, coordinate system unification and data spatial integration. At the stage of multi-source data fusion and reconstruction, through data spatial alignment, coordinate system unification, point cloud coarse alignment and the iterative closest point (ICP) algorithm, a realistic 3D model of a building is constructed to verify the effectiveness of the modelling method. ResultsThe method can effectively improve the accuracy of the real-life 3D model, repair the deficiencies in the model and optimise the details of the model. It can also significantly improve the fineness of the tilt photography model and perfectly present the geometric and texture information of the building, making it a superior method for fine 3D reconstruction. ConclusionThis 3D reconstruction method of buildings, which integrates low-altitude inclined photogrammetry and airborne light detection and ranging (LiDAR), has high positional accuracy and can provide new methods and new ideas for the construction of digital campuses as well as for other engineering applications.

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation

Three-dimensional (3D) scenes of a maize canopy can be utilized to depict its vertical structure and serve as the foundation for a 3D radiative transfer model. Given the inherent heterogeneity of canopy structures, evaluating their spectral response is crucial for remote-sensing inversion algorithms. Yet existing methods to model 3D canopy scenes of crops lack the high efficiency required for large-scale breeding applications. Hence, this study aims to develop a method to rapidly construct 3D canopy scenes of maize based on key structural parameters—namely, leaf area, base angle, and inclination angle—across multiple cultivars and growth stages. The accuracy of this canopy scenes modeling method was validated by comparison with a fine-scale 3D model and a multi-growth stage reflectance dataset. The root mean square error (RMSE) and normalized root mean square error (NRMSE) between the fine-scale 3D model and the constructed 3D model were less than 0.021 and 6.4%, respectively. Moreover, the RMSE and NRMSE between the simulated reflectance and measured reflectance were less than 0.042 and 11.1%, respectively. Next, we integrated the 3D radiative transfer model with 3D scenes to analyze the spectral response of maize canopy structure. The findings revealed that the near-infrared band was affected more by leaf area than leaf base angle, while the opposite is observed for the red-edge band. Furthermore, the middle layer contributed more to the canopy reflectance than either the upper or lower layers. Notably, two maize scenes where two structural parameters simultaneously change could elicit the same canopy spectra. The proposed method thus offers the dual advantages of rapid modeling and high efficiency, making it highly applicable for maize and other similar crops.

3D-Bioprinting and AI-empowered Anatomical Structure Designing: A Review.

The recent advancements and detailed studies in the field of 3D bioprinting have made it a promising avenue in the field of organ shortage, where many patients die awaiting transplantation. The main challenges bioprinting faces are precision during printing, vascularization, and cell proliferation. Additionally, overcoming these shortcomings requires experts from engineering, medicine, physics, etc., and if accomplished, it will significantly benefit humankind. This paper covers the general roadmap of the bioprinting process, different kinds of bioinks, and available bioprinters. The paper also includes designing the anatomical structure, which is the first phase of the bioprinting process, and how AI has facilitated this entire process of 3D printing in healthcare and associated applications like medical modelling and disease modelling. The process of 3D bioprinting involves meticulous structure designing of the anatomical structure under study, which forms the base of the entire bioprinting process. One of the significant applications of 3D printing in healthcare is Medical Modelling and Disease Modelling, which requires the detection of disease in anatomy and its delineation from the rest of anatomy for meticulous creation of ROI using sophisticated segmentation software(s) for the construction of 3D models of diseased anatomy and healthy anatomical surroundings. The study concluded that bioprinting is the future of the worldwide organ transplantation crisis. Anatomical accuracy is an important aspect that must be considered while producing 3D models. The reproduction of patient-specific 3D models requires human rights and ethics approval under four principles of ethics in healthcare: autonomy, non-maleficence, beneficence, and justice.

Action-Aware Vision Language Navigation (AAVLN): AI vision system based on cross-modal transformer for understanding and navigating dynamic environments

Visually impaired individuals face great challenges with independently navigating dynamic environments because of their inability to fully comprehend the environment and actions of surrounding people. Conventional navigation approaches like Simultaneous Localization And Mapping (SLAM) rely on complete scanned maps to navigate static, fixed environments. With Vision Language Navigation (VLN), agents can understand semantic information to expand navigation to similar environments. However, both cannot accurately navigate dynamic environments containing human actions. To address this challenge, we propose a novel cross-modal transformer-based Action-Aware VLN system (AAVLN). Our AAVLN Agent Algorithm is trained using Reinforcement Learning in our Environment Simulator. AAVLNs novel cross-modal transformer structure allows the Agent Algorithm to understand natural language instructions and semantic information for navigating dynamic environments and recognizing human actions. For training, we use Reinforcement Learning in our action-based environment simulator. We created it by combining an existing simulator with our novel 3D human action generator. Our experimental results demonstrate the effectiveness of our approach, outperforming current methods on various metrics across challenging benchmarks. Our ablation studies also highlight that we increase dynamic navigation accuracy with our Vision Transformer based human action recognition module and cross-modal encoding. We are currently constructing 3D models of real-world environments, including hospitals and schools, for further training AAVLN. Our project will be combined with Chat-GPT to improve natural language interactions. AAVLN will have numerous applications in robotics, AR, and other computer vision fields.

MODELLING AND SIMULATION OF NONLINEAR DYNAMIC FLOW FIELD AND TEMPERATURE FIELD OF DEPYROGENATION TUNNEL

Nonlinear dynamics plays a crucial role particularly in equipment validation of preparation production, validation results of which will significantly influence the results and period of drug registration in drug production processes. In this research, the flow field and temperature field simulation, calculation and analysis are creatively carried out in terms of depyrogenation tunnel, a very popular preparation drying-sterilization equipment in pharmaceutical processes with strong dynamic characteristics. After construction of 3D model of this equipment using Catia and mesh generation applying ANSYS, the computational fluid dynamic (CFD) method and verification of irrelevance method are carried out regarding 6.05 million of meshes to identify the flow velocity model and heat transfer model inside the equipment, to further provide methodology for pharmaceutical process validation and to further optimize the design of control methods. After calculation and simulation, the low-velocity vortices of different sizes inside the hood and the drying chamber are identified, which could cause vials to fall down; meanwhile, vials that are farther away from the outlet receives less heat exchange effect, which would shrink the effective sterilization area, indicating an inadequate validation methodology in pharmaceutical processes.

Small and Micro-Water Quality Monitoring Based on the Integration of a Full-Space Real 3D Model and IoT

In order to address the challenges of small and micro-water pollution in parks and the low level of 3D visualization of water quality monitoring systems, this research paper proposes a novel wireless remote water quality monitoring system that combines the Internet of Things (IoT) and a 3D model of reality. To begin with, the construction of a comprehensive 3D model relies on various technologies, including unmanned aerial vehicle (UAV) tilt photography, 3D laser scanning, unmanned ship measurement, and close-range photogrammetry. These techniques are utilized to capture the park’s geographical terrain, natural resources, and ecological environment, which are then integrated into the three-dimensional model. Secondly, GNSS positioning, multi-source water quality sensors, NB-IoT wireless communication, and video surveillance are combined with IoT technologies to enable wireless remote real-time monitoring of small and micro-water bodies. Finally, a high-precision underwater, indoor, and outdoor full-space real-scene three-dimensional visual water quality monitoring system integrated with IoT is constructed. The integrated system significantly reduces water pollution in small and micro-water bodies and optimizes the water quality monitoring system.

Ray tracing-based construction of 3D background model for real-time stereoscopic rendering of live immersive video

Immersive video stored in multiview video-plus-depth format can provide viewers with vivid immersive experiences. However, rendering such video in real time in immersive environments remains a challenging task due to the high resolution and refresh rate demanded by recent extended reality displays. An essential issue in this immersive rendering is the disocclusion problem that inevitably occurs when virtual views are synthesized via the de facto standard 3D warping technique. In this paper, we present a novel virtual view synthesis framework that, from a live immersive video stream, renders stereoscopic images in real time for a freely moving virtual viewer. The main difference from previous approaches is that the surrounding background environment of the immersive video’s virtual scene is progressively reproduced on the fly directly in the 3D space while the input stream is being rendered. To allow this, we propose a new 3D background modeling scheme that, based on GPU-accelerated real-time ray tracing, efficiently and incrementally builds the background model in compact 3D triangular mesh. Then, we demonstrate that the 3D background environment can effectively alleviate the critical disocclusion problem in the immersive rendering, eventually reducing spatial and temporal aliasing artifacts. It is also suggested that the 3D representation of background environment enables extension of the virtual environment of immersive video by interactively adding 3D visual effects during rendering.

Construction and transformation method of 3D models based on the chain-type modular structure

This study proposes a method of constructing and transforming three-dimensional (3D) models that can convert a 3D model into a chain-type modular configuration and realize the mutual transformation between different configurations with a straight chain as the intermediate state through standard folding steps. A method for detailed representation of voxels is proposed. Based on detailed voxels, an accelerated generation algorithm for the connection forest, which can describe the possible chain configurations, is developed. The foldability verification of the configurations and the generation of the folding operations are realized according to the folding rules. A collision detection algorithm based on encoding and projection is also introduced to detect collisions in the process of folding sequence generation. In this work, an interactive platform is established for users to calculate the input model transformation through simple operations and obtain a simulation animation of the folding operations. The experimental cases prove the effectiveness of the method in constructing and transforming the chain-type modular configurations of the input 3D models.

Экспериментальная оценка рекомендаций метода по сопровождению оператора во время съемки фотографий для последующей 3D-реконструкции

The availability of 3D reconstruction technologies expands the possibilities of their application for analyzing the state of complex technical objects. However, solutions for 3D reconstruction still heavily depend on the quality of the input data and require significant time investments to build high-quality 3D models. The goal of this study is to expedite the process of constructing detailed and defect-free 3D models by creating an automated method to assist the operator during the capture of RGB images for subsequent 3D reconstruction. The method is used for preliminary analysis of the collected data and allows for the identification of problematic frames in advance, as well as determining the areas of the object to be captured that will be reconstructed with defects or low level of detail. The study describes algorithms for the two final stages of the method - global analysis and recommendations, based on the real-time construction of sparse point clouds and 3D models, as well as their properties assessment in conjunction with the operator's trajectory, reconstructed from accelerometer and gyroscope data. The effectiveness of the final stages of the method, based on the proposed algorithms, was evaluated through experiments. It was demonstrated that the use of global analysis and recommendation stages not only allows for predicting defective areas and low-detail zones in dense 3D models on mobile devices, but also accelerates the process of obtaining a high-quality 3D model by at least five times through automated analysis of the collected data.

Three-dimensional model construction of power distribution room and its digital twin physical structure research

Abstract Aiming at the problem of complex internal structure and difficult monitoring of the distribution room, a new monitoring method for the distribution room based on a digital twin is studied. Firstly, a 3D model design method for the distribution room is proposed by combining visualization technology and 3D Max software to complete the modeling. The 3D laser technology is used to scan the entity to get the texture photos of the 3D laser points and the raw data, then a complete model is obtained by modeling the details, and finally, the texture photos are mapped with the model to get the final model. To enhance the model’s accuracy, the optimal minimum spanning tree model optimization method is proposed, and the power distribution room’s three-dimensional model design is now finished. On the basis of the feature extraction of the sensor data of the distribution room, the digital twin technology is used to realize the matching of the information model architecture from the real entity of the distribution room to the virtual model, digital conversion, correlation of the operating data features and real-time monitoring, and then the monitoring method of the distribution room based on the digital twin is obtained. The experimental analysis yields a 3D Max modeling time of 9.98s and a fineness of 90%, which is more suitable for the construction of complex 3D models of distribution rooms than the other two methods. The accuracy of digital twin wireless communication is kept at 98% or above, and the system operation is disturbed to a small extent. It shows that digital twin technology is suitable for monitoring the complex structure inside the distribution room.

ИМПОРТОЗАМЕЩЕНИЕ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ В ПРОЕКТНОМ ОБРАЗОВАНИИ

In connection with the sanctions restrictions imposed on the Russian Federation, the issue of import substitution in all areas of the national economy is more acute than ever. Such a decision by the countries of the collective West had a particularly strong impact on the work of critical infrastructure enterprises, which include oil production and refining, petrochemicals, transport and storage of oil, gas and petroleum products. One of the priority areas of import substitution is the transition to domestic software. In the professional environment, ways of transitioning to domestic programs and related problems in the work of enterprises in the real sector of the economy, management and service organizations are intensively discussed. At the same time, the task of switching to domestic software is posed, among other things, to educational institutions of various levels and specializations, which are a source of personnel for industry. The presented article discusses the experience of solving the emerging problem of import substitution of software in design disciplines of the Department of Chemical Technology of Oil and Gas Processing of Kazan National Research Technological University. Taking into account the specifics of modern approaches used in the design of technological facilities of the petrochemical complex, the software used in the educational process must meet a number of criteria: development of intelligent technological schemes; three-dimensional layout of equipment and pipelines, preliminary design of metal structures (service platforms, flights of stairs, stepladders, cable and pipeline racks, etc.); preliminary design of architectural and construction elements (foundations, industrial buildings/structures, precast concrete structures, etc.); visualization of the constructed 3D model. Based on the stated software requirements, the family of software products from the domestic company “CSoft Development”, united under the Model Studio CS brand, was chosen as a non-alternative option.

Експертна система для підтримки побудови тривимірних моделей об’єктів методом фотограмметрії

The task of building high-quality three-dimensional (3D) models of objects is relevant, since such 3D models are widely used in various fields of science, technology and medicine. In this work, the construction of 3D models is performed by the photogrammetry method, which consists in the construction of a 3D model of an object based on a series of its photographs. The advantages of the photogrammetry method are low hardware requirements and relatively high accuracy. To build 3D models of objects by photogrammetry, the 3DF Zephyr program was used, which contains a set of tools for pre-processing images, reconstructing 3D models, editing and measuring the dimensions of 3D models, and exporting the obtained models. The principles of building three-dimensional models of objects by the method of photogrammetry based on initial images are considered. The main stages of building 3D models are described: calculation of sparse point cloud, key points, dense point cloud, polygon grid, texture grid. Model parameters are also edited and analyzed. An expert system was developed in the CLIPS environment to select the correct modes for building a 3D model. The knowledge base of the expert system contains production rules that allow you to establish the correct modes of building a 3D model based on the initial facts. 30 facts-conditions have been developed that describe the conditions for building a three-dimensional model. 20 facts-consequences and 15 facts-recommendations for building a 3D model have been developed. Using the developed rules, 36 production rules were built. Experimental verification of the developed system was carried out. Three-dimensional models of objects were built using the 3DF Zephyr program. After entering the facts that describe the process of obtaining the model into the expert system, a number of recommendations were obtained, in particular, to increase the area of textured surfaces and use uniform lighting of objects. After following these recommendations, the model was built with satisfactory accuracy.

Mathematical Camera Array Optimization for Face 3D Modeling Application.

Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability needed for image-based 3D face models. This is caused among many factors by the hardware limitations and imperfection of the cameras and the lack of proficiency in designing the ideal camera-system configuration. Accordingly, for precise measurements, we still need engineering-based techniques to ascertain the specific level of deliverables quality. In this paper, an optimal geometric design methodology of the camera network is presented by investigating different multi-camera system configurations composed of four up to eight cameras. A mathematical nonlinear constrained optimization technique is applied to solve the problem and each camera system configuration is tested for a facial 3D model where a quality assessment is applied to conclude the best configuration. The optimal configuration is found to be a 7-camera array, comprising a pentagon shape enclosing two additional cameras, offering high accuracy. For those who prioritize point density, a 9-camera array with a pentagon and quadrilateral arrangement in the X-Z plane is a viable choice. However, a 5-camera array offers a balance between accuracy and the number of cameras.

Discovery of high‐affinity amyloid ligands using a ligand‐based virtual screening pipeline

AbstractBackgroundA ligand‐based virtual screening pipeline was developed to discover structurally novel ligands for Aβ(1‐42) fibrils.MethodA database of ligands that bind to Aβ(1‐42) fibrils was used to develop a two‐step ligand‐based virtual screening pipeline. The first step involved developing machine learnings models that use relatively simple chemical descriptors to predict ligand dissociation constants (Kd ). The second step involved constructing 3D models based on field points that describe the surface, shape, and electronic properties of ligands. The combined pipeline was then used to screen 63 million compounds from the ZINC15 database.1 The most promising compounds were experimentally evaluated using binding assays.ResultFor the first chemical descriptor model, a support vector machine model was found to predict the ‐log(Kd /M) of Aβ(1‐42) fibril ligands with a mean absolute error of only 0.41. An ensemble of four 3D models were then developed that each predicted ‐log(Kd /M) with a cross‐validation regression coefficient of 0.48–0.56. The ZINC15 database compounds were screened through the first chemical descriptor model, and the 10,000 compounds with the highest predicted affinities were then screened through the 3D models. The 46 highest‐ranked ligands were then evaluated using Thioflavin T competition assays, resulting in five new Aβ(1‐42) ligands with novel structures that exhibited nanomolar binding.ConclusionA ligand‐based virtual screening pipeline has been developed to discover new fibril‐binding ligands. Using Aβ(1‐42) fibrils as a target, five new structurally diverse ligands were found that exhibit nanomolar binding affinities.1. Sterling and J. J. Irwin, J. Chem. Inf. Model. 2015, 55, 2324‐2337.

Exploration on 3D imaging model construction of clothing fitting based on virtual reality technology

AbstractWith the continuous development of virtual reality technology (VRt), the clothing industry has begun to use VRt to build three‐dimensional clothing fitting models, which provide consumers with a comprehensive range of fitting effects. However, the traditional 3D (three‐dimensional) imaging model construction method for clothing has certain shortcomings. For example, the construction of fitting models is a process from planar to three‐dimensional, while the three‐dimensional fitting model is a process from two‐dimensional to three‐dimensional, which makes it impossible for users to obtain a more intuitive, visual, and comprehensive fitting effect during fitting. On the basis of summarizing the existing methods for building 3D imaging models of clothing, this paper proposed a method for building 3D clothing fitting models based on VRt and applied it to actual clothing fitting. This provided consumers with a comprehensive method for fitting clothing, thereby improving the shopping efficiency and quality of consumers when purchasing clothing. The research results show that the proportion of positive evaluations using VRt systems was 92%, while the proportion of positive evaluations using conventional technology systems was only 10%, indicating a positive relationship between VRt and the construction of 3D imaging models for clothing fitting. The VRt‐based clothing fitting system has strong practical significance, but it is still in the preliminary exploration stage. The system proposed in this paper does not include face modeling function. Therefore, in the future, we can consider adding a face reconstruction module to build a more personalized human model through photo reconstruction and other ways.