Model Construction Method Research Articles

Multipath diffusion process and spatial accumulation simulation of Cd in lead-zinc mining areas

This study combined process simulation and actual measurement to construct a multipath diffusion and spatial accumulation model of Cd in a typical lead-zinc mining area through accuracy and root mean square error(RMSE) analysis. The results indicated that (1) the diffusion of Cd was in a quadratic inverse proportional relationship with the distance from the pollution source within watershed. The average annual atmospheric Cd sedimentation in study area was 0.71 * 10−6 g and the contribution of runoff diffusion to Cd exceeded 80%. (2) With the increase in the concentration range of Cd content (k) carried by unit runoff sediment, the model accuracy and RMSE showed decreasing trends. However, when the lower and upper limits of k were 10% and 90%, the model accuracy reached 75%. (3) Two sub-watersheds with same dominant wind direction but different runoff directions were selected to verify the model accuracy, indicating that the model construction method can precisely simulate the spatial accumulation of Cd in similar mining areas. The results provide a scientific basis for the prevention of heavy metal diffusion in lead-zinc mines. Future research should focus on the migration pathways of heavy metals through vertical infiltration caused by rainfall to further optimise the model structure and accuracy.

The LINC01176-miR-218-5p-IL-36G Network is Responsible for the Pathogenesis of Psoriasis by Promoting Inflammation.

Psoriasis is an incurable chronic inflammatory skin disease. The exact function and regulatory mechanism of non-coding RNA upregulation in psoriasis remains to be elucidated. The aim of this study was to analyse the role of the lncRNA-miRNA-mRNA network of psoriasis and LINC01176 in the pathogenesis of psoriasis. We performed miRNA, lncRNA, and mRNA sequencing analysis in pretreatment and treatment psoriatic tissues and normal tissues, constructed an lncRNA-miRNA-mRNA coexpression network and screened mRNA-associated pathways using bioinformatics analysis. We further validated the regulatory role of LINC01176-miR-218-5p on the proliferation and inflammation of the psoriatic model by dual-luciferase reporter assay, cell transfection, CCK-8 method, TUNEL staining and animal model construction method. An lncRNA-miRNA-mRNA coexpression network was successfully constructed by RNA-seq data analysis. We obtained the relationship between LINC01176, miR-218-5p and IL36-G. Analysis of the apoptotic and proliferative capacity of the transfected cells showed that miR-218-5p up-regulation significantly inhibited cell proliferation and promoted apoptosis. A mouse model of psoriasis was successfully established. Phenotypic observations revealed that keratin-forming cells in mice coated with LINC01176-shRNA emulsifier were significantly lower than those in the model group and close to those in the normal group. HE and immunohistochemical experiments were performed, and the results showed the role and mechanism of action of LINC01176-shRNA. Suppression of LINC01176 significantly inhibited the expression of IL-36G in psoriatic tissues. LINC01176 showed a targeting and positive correlation with IL36-G expression. Our study shows that LINC01176 promotes the proliferation and invasion of keratinocytes and inhibits apoptosis by targeting miR-218-5p, which acts as a repressor of the psoriasis-associated IL-36G. The shRNA-LINC01176 emulsion showed potential treatment capability in alleviating symptoms of psoriasis.

The Real-Time Local Surface Model Construction Method of Unknown-Model Workpieces for Robotic Polishing

The Real-Time Local Surface Model Construction Method of Unknown-Model Workpieces for Robotic Polishing

Chemical Structure Characteristics and Model Construction of Coal with Three Kinds of Coalification Degrees.

The coal structure is extensively used for studying the properties of coal, and the construction of accurate and reliable coal structure models can promote these researches. In this study, Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) were used to analyze the changes in the coal structure as a function of the coalification degree, and a semiquantitative model construction method based on FTIR, XRD, and X-ray photoelectron spectroscopy (XPS) analyses was proposed. With an increase in the coalification degree, the size of the aromatic cores in the coal increased. During the conversion from lignite to bituminous coal, the content of aliphatic structures increased, while the content of oxygen-containing functional groups (OFGs) significantly decreased. Conversely, during the conversion from bituminous coal to anthracite, the content of aliphatic structures decreased while the content of OFGs slightly increased. The calculated FTIR spectra of the coal structure models were consistent with the experimental FTIR spectra, which confirmed the accuracy of the models. Furthermore, the models successfully explained the microscopic mechanism underlying the differences in the wettability of the coal samples with varying coalification degrees. The construction method and coal structure models in this study will be more widely applied in future research.

The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop

The modeling process is resource-intensive, time-consuming, and expensive due to the large number and variety of production and auxiliary equipment in discrete manufacturing plants. The popular twin-model construction method based on a single type of equipment cannot meet the demand for rapid construction and high-quality model mapping in large discrete manufacturing plants. This paper proposes a strategy for developing a digital twin polymorphic model (DTPM) for discrete manufacturing workshops to meet complex manufacturing business scenarios, improve the richness of digital twin model varieties, construction efficiency and intelligence, and form an efficient equipment model construction method. The complete element information and real-time dynamic data of the minimal component unit (Functional Components, FC) of DTPM are clustered and analyzed. In addition, properties of the functional component information model are characterized from several dimensions, such as geometry, physics, and behavior. Furthermore, the FC with highly concentrated primary components is established based on the object-oriented derivation inheritance and attribute reuse hybrid drive technique. FC drastically reduces the duration of the digital twin model development process through encapsulation, inheritance, polymorphism, and other technologies. On this premise, the DTPM construction method that deeply decouples the physical structure and functional methods is proposed. DTPM integrates adaptive information interaction technology to accomplish intelligent data connection, dynamic data updating, and digitally accurate mapping of static-dynamic-virtual multidimensional models in discrete production workshops. Lastly, the method's validity is confirmed by creating a large discrete production workplace. The results indicate that the proposed technique may significantly enhance the model variety and building efficiency of large-scale digital twin workshop systems.

Adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure

In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field.

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.

Integrating vision and laser point cloud data for shield tunnel digital twin modeling

In shield tunnel digital twin modeling, constructing high-precision tunnel digital 3D models is essential but poses a challenging engineering problem that affects efficiency and accuracy. To address this, this paper presents a method based on 3D tunnel information models using image and point cloud data. A 3D Tunnel Information Model Construction Method (3DTIM) is proposed, based on vision and laser point cloud data, encompassing six core processing layers, and employing the YOLO-T tunnel inner wall image target recognition method alongside various point cloud data processing methods. Through this method, critical parameters can be rapidly and accurately extracted from images and point cloud data, enabling the direct creation of high-precision 3D tunnel models on the 3D modeling software with an accuracy better than 0.7 cm. The 3DTIM approach not only solves the problem of constructing high-precision tunnel 3D models, but also provides a valuable methodological reference for digital shield tunnel twin modeling, reducing the need for manual measurement and enhancing the efficiency of tunnel digital model construction. This research provides a new practical framework for future studies on tunnel 3D model construction and digital shield tunnel twin modeling based on image and point cloud data, forming a more valuable methodological reference.

Digital twin model construction of robot and multi-object under stacking environment for grasping planning

Object grasping is the basic function for robots to complete various tasks. However, it is still a hot and difficult problem to grasp object in multi-object stacking environment. A digital twin model construction method for robot grasping under multi-object stacking environment is proposed based on digital twin technology to realize multi-object grasping planning and virtual-real interaction. The robot digital twin model is constructed based on the requirements of the twin model, which includes the robot information model and information perception method. Then, the logical relationship between the virtual models of multi-object stacked objects is established based on the interactive relationship of stacked objects. The twin model of robot grasping under the multi-object stacking scene is built to realize the monitoring and visualization of the key elements of the physical entity of the robot based on ROS and Unity3D, and the interaction between the virtual model and the physical entity is realized to monitor the progress of robot grasping of multi-target objects. What’s more, the interaction relationship between objects is detected at the same time with an accuracy of 80 %. The experimental results verify the validity and reliability of the proposed method for establishing the digital twin model of robot grasping and the logical relation of the virtual model of objects.

Knowledge Guided Feature Aggregation for the Prediction of Chronic Obstructive Pulmonary Disease With Chinese EMRs.

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This article proposes a knowledge-guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.

An Accurate and Efficient BP Algorithm Based on Precise Slant Range Model and Rapid Range History Construction Method for GEO SAR

The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the Earth’s rotation, is influenced by the Earth’s curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the “Stop-and-Go” error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency.

Research on road damage recognition and classification based on improved VGG-19

In recent years, methods of road damage detection, recognition and classification have achieved remarkable results, but there are still problems of efficient and accurate damage detection, recognition and classification. In order to solve this problem, this paper proposes a road damage VGG-19 model construction method that can be used for road damage detection. The road damage image is processed by digital image processing technology (DIP), and then combined with the improved VGG-19 network model to study the method of improving the recognition speed and accuracy of VGG-19 road damage model. Based on the performance evaluation index of neural network model, the feasibility of the improved VGG-19 method is verified. The results show that compared with the traditional VGG-19 model, the road damage VGG-19 road damage recognition model proposed in this paper shortens the training time by 79 % and the average test time by 68 %. In the performance evaluation of the neural network model, the comprehensive performance index is improved by 2.4 % compared with the traditional VGG-19 network model. The research is helpful to improve the model performance of VGG-19 road damage identification network model and its fit to road damages.

A Novel Combined Model for Air Quality Index Forecasting in Changchun

With the rapid development of the economy and continuous improvement in people’s living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection.

Predicting the Risk of Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome in HIV-Infected Patients with Central Nervous System Tuberculosis

Objective — to identify risk factors associated with the development of Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome (TB-IRIS) in HIV-infected patients with central nervous system tuberculosis. The analysis assessed the relationship between the risk of develo­ping TB-IRIS with neurological manifestations and the following factors: 1) viral load before antiretroviral therapy (ART) initiation; 2) CD4+ lymphocyte of blood before ART initiation; 3) duration of antituberculosis therapy before starting ART; 4) presence of active tuberculosis at the time of ART initiation. Materials and methods. 112 cases of neurological TB-IRIS were analyzed. These patients were treated and observed during the 1 year in anti-tuberculosis facilities in Kyiv and Kyiv region in 2017—2021. All patients received ART in accordance with the HIV treatment protocols adopted in Ukraine. Treatment according to the standard of tuberculosis treatment adopted in Ukraine. The logistic regression model construction method was used to analyze. Statistical processing of the obtained data was carried out using free software — the EZR package (version 1.61; https://www.jichi.ac.jp/).The study was carried out as part of the applied research work 0121U107800 «Predicting the development of the incidence of tuberculosis in Ukraine in connection with the COVID-19 pandemic», funded by the Ministry of Health of Ukraine. Results and discussion. After calculations, two factor signs were identified, associated with the of deve­loping TB-IRIS with neurological manifestations: the baseline level of CD4+ lymphocytes in 1 μl of blood at the beginning of treatment and the presence of active tuberculosis at the time of ART initiation. Conclusions. It was found that the risk of developing TB-IRIS with neurological manifestations is significantly associated (p < 0.05) with the following factors: 1) the level of CD4+ lymphocytes in 1 μl at the beginning of treatment; 2) presence of active tuberculosis at the time of ART initiation.

Application of Data Visualization Interaction Technology in Aerospace Data Processing

A visualization and interactive network topology model are studied based on real-time features generated during spaceflight. Start by establishing a consistent set of data and logical interaction interfaces. This paper presents a method of scenario model construction and application programming based on virtual reality technology. The scene elements are extracted into two types of primitives, namely logical type and simulated object type. This provides a unified architecture for the editing and processing of graphic elements. This system can realize the automatic creation of the scene. Then the point cloud data obtained by sparse reconstruction of SFM is reconstructed to the Poisson surface. You get a dense, uniform grid. Experiments show that the proposed algorithm can realize the 3D reconstruction of non-cooperative objects. The spatial feature points obtained in the spatial positioning of non-cooperative objects can provide necessary technical support for its orbit positioning. The model can quickly generate new model scenario pages according to the characteristics of the task. This method changes the display mode, which can only be static or limited dynamic before. It has also improved the efficiency of space mission preparation.

Study on Dynamic Model Construction and Analysis Method of Bacteria-algae Co-culture

To investigate the dynamic changes in growth and product synthesis of a mixed culture of Rhodotorula glutinis and Chlorella vulgaris, this study utilized fermentation data to analyze the rules and preliminary results of the microbial-algal symbiotic system. Employing Matlab, the author fitted a mathematical model proposed by predecessors, seeking parameters to establish a dynamic model for microbial fermentation. This model not only characterized parameter significance but also shed light on interactions, energy transfer mechanisms, and natural regulation balance during symbiotic fermentation. Results revealed that, under the influence of algae's photosynthetic carbon fixation, bacteria exhibited secondary growth, coupled with lipid synthesis and cell growth. The mixed culture model demonstrated a significant increase of 25.9% in biomass and 42.1% in oil content (reaching 30.694 g/L and 14.54 g/L, respectively). These findings suggest the model's potential as a theoretical guide for optimizing mixed culture fermentation processes.

The 3D model construction method of overhead transmission line based on GIM

With the continuous progress of power grid digitization, Building Information Model (building information modeling, BIM) technology can no longer meet the application display requirements of power grid engineering. Power grid information model (grid information model, GIM) technology uses parameterization to build a 3D model, which reduces redundancy and is conducive to the digital transfer of the project. This paper briefly analyzes the structure of the GIM data model. It carries out research on 3D parametric modeling of overhead transmission line ground wire, tower, foundation, and insulator string based on GIM. It uses a 3D graphics engine to realize the visual rendering of ontology 3D models. On this basis, a 3D scene construction method of overhead transmission line engineering based on GIM is proposed, which provides reliable technical support for digital power grid applications.

A Data-Driven Method for Online Constructing Linear Power Flow Model

To adapt the data-driven linear power flow (DD-LPF) model for online dispatch in power systems, especially when the size of historical samples or system scale are relatively large, this paper proposes an online LPF model construction method. First, the raw measurements are filtered to suppress the adverse impact of measurement noise and outliers. Then, an adaptive moment estimation-based distributed stochastic gradient descent (Adam-DSGD) method is utilized to improve solution efficiency of DD-LPF parameters. Considering the features of online dispatch in power systems, a recursive forgetting factor (RFF) method is further introduced to adapt the LPF model for the present operating condition. Subsequently, the proposed method is applied to an online dispatch-command-tracking problem to demonstrate its practicability. Case studies confirm that the proposed method can achieve 2- to 100-fold less computing time and maintain satisfactory linearization accuracy simultaneously. Moreover, the tracking rate for the optimization objective is also enhanced with applying the proposed method to the real-time optimization problem.

Research on Cache Coherence Protocol Verification Method Based on Model Checking

This paper analyzes the underlying logic of the processor’s behavior level code. It proposes an automatic model construction and formal verification method for the cache consistency protocol with the aim of ensuring data consistency in the processor and the correctness of the cache function. The main idea of this method is to analyze the register transfer level (RTL) code directly at the module level and variable level, and extract the key modules and key variables according to the code information. Then, based on key variables, conditional behavior statements are retrieved from the code, and unnecessary statements are deleted. The model construction and simplification of related core states are completed automatically, while also simultaneously generating the attribute library to be verified, using “white list” as the construction strategy. Finally, complete cache consistency protocol verification is implemented in the model detector UPPAAL. Ultimately, this mechanism reduces the 142 state-transition path-guided global states of the cache module to be verified into 4 core functional states driven by consistency protocol implementation, effectively reducing the complexity of the formal model, and extracting 32 verification attributes into 6 verification attributes, reducing the verification time cost by 76.19%.

Neuro-safety science: an emerging discipline to reveal the neural mechanisms of safety problems.

At present, the research of safety science discipline is limited to the level of describing psychology and behaviors, because the cognitive neural mechanisms behind them are unknown. This paper introduces an emerging interdiscipline, namely neuro-safety science, which uses the neuroscientific methods to investigate the neural systems behind safely relevant behaviors. Qualitative methods such as literature review method and theoretical model construction method were adopted for this study. Based on the background of neuro-safety science, the definition of neuro-safety science was defined, its connotation was analyzed, and the research contents from two aspects of theoretical research and practical application research were proposed. Methodology system including research principles, research routes, research procedure and research methods, and the paradigm system of neuro-safety science were put forward. At last, the application research on neuro-safety science was forecasted. This paper opens up a new research perspective for the research of safety science, and provide guidance and reference to develop neuro-safety science.