Graphical Modeling Research Articles

A framework for phenotyping rubber trees under intense wind stress using laser scanning and digital twin technology

Rubber trees in coastal habitats are exposed to a high degree of wind stress. An algorithm-hardware synergetic methodology was developed for investigating and predicting rubber tree phenotyping excited by strong winds. The framework includes (1) a custom-designed industrial fan that recreates a variable airflow field at wind speeds of 15, 30 and 45 m/s coupled with a terrestrial laser scanner and bundled motion sensors to acquire point clouds and vibration data; (2) a graphic model that approximates tree canopies based on foliage clumps with phenotypic traits that are derived from point clouds captured while trees are subjected to aerodynamic drag; and (3) the wind characteristic parameters of forest canopies were calculated by a developed forest-specialized k-ε turbulence model combining the constructed tree models and grid-scale subdivision of the wind fluid field. (4) A digital twin model that incorporates detailed tree phenotypic traits and considers plant mechanical characteristics was established, depicting the related wind-induced actions of target trees under various wind influences. The results show that tree crowns with spreading forms are prone to yield larger pendulum amplitudes than compact crowns, but trees directly exposed to wind exhibit greater crown volume reductions than trees in sheltered areas. Within tree canopies, a one-fold increase in inlet wind speed intensified crown compression (approximately 17 % decrease in crown volume), generated 2.1-fold pressure gradients and increased turbulence kinetic energy by approximately 60 %. Moreover, the entire scenario of the adaptation of experimental trees to wind perturbations was visually restored using digital twin techniques, serving as an integral behaviour dataset for further data-driven decision-making. In summary, this paper presents a comprehensive methodology that can decipher the phenotypic manifestations of trees' reactions to wind hazards, with potential applications in phenotyping or envirotyping studies designed to evaluate the wind resistance properties of rubber trees.

RESEARCH OF THE RELIABILITY OF THE GEODETIC NETWORK IN PROVIDING GEODETIC SUPPORT OF CONSTRUCTION

The safety and reliability of buildings and structures as a complex technical system depends not only on the correct consideration of data on the structural features of the building, but also on the behavior of the soil base and on the geodetic support of the construction. Geodetic works are an integral part of the technological process of construction production and belong to the main types of work. Ensuring reliable and safe, long-term operation of buildings and structures of residential, civil, industrial and agricultural purpose, located both in ordinary and in difficult engineering-geological, earthquake-hazardous, mining-geological conditions is an urgent scientific task in our time. Buildings located on artificial territories, on subsiding soils, landslide-prone slopes or in conditions of dense urban development need to determine the development of deformation processes based on the results of periodic geomonitoring at the stage of their operation. The purpose of the work is to create a Markov discrete-continuous stochastic model of the operation of the local geodetic network (GN) to provide geodetic support for construction and perform numerical calculations of the reliability and safety of the GN depending on the features of its restoration. Analyze regulatory and legal documents on the reliability and safety of construction objects. Perform an analysis of methods for assessing the reliability and safety of technical systems. Perform a numerical calculation of reliability, safety and efficiency indicators: availability ratio, limit probability states, mean time between failures, average duration of fault-free operation of the geodetic network (GN). Methodology. Analysis of regulatory documents on the reliability and safety of construction objects. Review of methods for assessing the reliability and safety of technical systems. Application of the state space method, construction of graphs of states and transitions of a renewable geodesic network. Construction of graphs of the readiness function, the probability of operation before the first failure and the frequency of getting into an emergency situation in the Mathcad software tool. Scientific novelty. The state space method allows simulating the behavior of the designed GN in order to identify the time spent in various states of the technical system depending on the intensity of failures of geodetic points and the intensity of their recovery. On the graph of states and transitions, in contrast to the “fault tree” method, it is possible to simultaneously see all possible situations separated by masks of emergency situations. Practical value. Due to the use of stable geodetic points selected based on the results of the performed reliability assessment, there is an opportunity to improve the quality of geodetic construction support. The most expedient scheme of GN restoration has been substantiated. The results of the analysis of the functioning models of the designed GN were used and the calculated values of: availability coefficient, frequency of GN failures and the probability of operation before the first failure. Results. A graphic model of the reliability behavior of GM in the form of a graph of states and transitions has been built. Geodetic network includes 8 points. A system of Kolmogorov-Chapman linear differential equations was compiled and solved. The distribution of probabilities of being in each state of GN has been obtained. The reasonable periodicity of GN restoration depending on the intensity of failures has been substantiated. It has been proven that periodic restoration of geodetic points allows maintaining a given level of GM reliability. This increases the accuracy of geodetic monitoring of construction and ensures functional safety.

Development of a Cassava Grating Machine

Developing of a cassava grating machine is presented. This is a great boost in the development of local content and reduction of wastage in cassava produce in Africa. The use of internal combustion engine in powering the cassava grating machine makes the study unique as it goes a long way in eliminating the undue stress involved manual grating of cassava tubers. The developed machine is made up of components such as hopper, pulley belts, grating unit, internal combustion engine and shaft. Scientific formulae were employed to aid the design of the cassava grating machine. A detailed graphical modeling was done to serve as a guide for the fabrication of the machine. The developed grating machine had a volumetric capacity of hopper to be 50272000 mm3. A power capacity of 1.715 KW was delivered to the solid shaft of 27.05 mm diameter to grate the peeled cassava tubers at a designed torque of 10.23 Nm.

Assessment of the Ecological Safety of Honey with the Help of “Factor Area” Models

The man-made load on the environment and the decrease in biodiversity cause a direct negative environmental impact on the existence of honey bees and beekeeping products. The priority directions of the food industry are the use of high-quality environmentally friendly raw materials and the prevention of the ingress and formation of harmful substances in food products, including honey. This implies the need to develop methods for assessing the environmental safety of the studied raw materials and products. The purpose of this study was to implement a mathematical modeling method for studying the environmental safety of honey. Five types of honey were studied: Robinia, rapeseed, linden, buckwheat, and sunflower. Mathematical models were built according to the following parameters: total activity of β-emitting radionuclides; residues of levomycetin (chloramphenicol), nitrofuran (according to AOZ and AMOZ), metronidazole, and pesticides (according to hexachloran); and the content of water-insoluble substances (mechanical impurities) and heavy metals. On the basis of the obtained data and established quality criteria, calculation graphic models were built. Using algebraic methods, they derived new formulas for calculating quality coefficients. Multivariate analysis and programming methods were used to evaluate honey using mathematical modeling. The most and least ecologically dangerous contaminants and their share of influence for different types of honey were determined based on the complex of research on negative factors. The proposed mathematical models can be implemented for practical use in specialized laboratories as a tool for determining the environmental safety of honey of various botanical origins.

Highly realistic synthetic dataset for pixel-level DensePose estimation via diffusion model

Generating training data with pixel-level annotations for DensePose is a labor-intensive task, resulting in sparse labeling in real-world datasets. Prior solutions have relied on specialized data generation systems to synthesize datasets. However, these synthetic datasets often lack realism and rely on expensive resources such as human body models and texture mappings. In this paper, we address these challenges by introducing a novel data generation method based on the diffusion model, effectively producing highly realistic data without the need for expensive resources. Specifically, our method comprises annotation generation and image generation. Utilizing graphic renderers and SMPL models, we produce synthetic annotations solely based on human poses and shapes. Subsequently, guided by these annotations, we employ simple yet effective textual prompts to generate a wide range of realistic images using the diffusion model. Our experiments conducted on DensePose-COCO dataset demonstrate the superiority of our method compared to existing methods. Code and benchmarks will be released.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

Developing a Graphical Domain-Specific Modeling Language for Efficient Lightweight Block Cipher Schemas Configuration: LWBCLang

The lightweight block cipher is an encryption technique with negligible computational overhead. Despite its advantages, it faces a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this research, we suggest a new graphical domain-specific modeling language to make it easier for both non-technical users and domain specialists to implement lightweight block cipher schemes. The proposed language, called LWBCLang, is a modular and extensible language that offers graphical components for constructing three essential types of inner block cipher structures. Seven different methods of keystream generation and all the tests of the NIST suite with performance analysis are provided. In the context of its meta-model, LWBCLang's abstract and concrete syntaxes are specified. LWBCLang has been implemented as an internal DSML with Python as the host language. The evaluation of LWBCLang is based on qualitative analysis to demonstrate the language's effectiveness and efficiency. Further benefits of this proposed language are evaluated and discussed in depth in this research.

Method for Increasing the Energy Efficiency of the Gear Teeth Cutting Process by Smoothing the Cutting Force Variation

The energy efficiency in metal cutting is, sometimes, very low. Recent studies show that the energy consumed for detaching chips represents only about 15% of the total energy involved in material machining. The available solutions for energy optimization in cutting processes mentioned are the improvement of manufacturing equipment, the optimization of processes, and appropriate production scheduling. Already-performed research shows that to increase energy efficiency, the cutting force must be reduced, for example, by reducing the depth of the cut and by increasing the feed rate. However, this is applicable when the cutting force is quasi-constant during the process, which is not the case for gear teeth cutting when the cutting force significantly varies. The present paper proposes a new solution for energy efficiency increase in gear teeth cutting, namely, the smoothing of the cutting force variation during the machining process, by keeping the area of the detached chip section quasi-constant during the cutting process. This can be reached by replacing the constant rolling feed, currently used in gear teeth cutting, with a rolling feed that varies after an appropriate law. An algorithm dedicated to implementing this solution has been developed. It uses graphical modeling in CATIA to find the variation in the detached chip area. Original MatLab R2018a applications were developed (i) to identify the analytical form of the law that approximates this variation and (ii) to find the variation law of the feed during the rolling motion, if imposing a constant area of the detached chip. The algorithm was successfully applied in the cases of toothing with a rack tool (by slotting) and with a pinion cutter (by slotting and turning). A technical solution for method implementation in practice is also presented.

A preliminary exploration of quantitative models for evaluating binocular visual perception impairments among patients diagnosed with stroke.

The objective of this study is to formulate and implement graded biological models pertaining to binocular visual perception function with the use of computer algorithms. We aim to quantitatively assess the location, severity, and degree of impairment in binocular visual perception among patients who have suffered stroke, thereby providing valuable insights into the repercussions of cerebral tissue damage on the visual system. To overcome the shortcomings of previous instruments used to assess binocular function in terms of stereoscopic effects and the challenges posed by physiological and psychological interference during examinations, this study optimized its approach by integrating polarized stereovision technology with computer graphic modeling techniques. This study employed computer models to assess binocular visual perception function in stroke patients. Computer models refer to psychophysical testing methods used to measure binocular visual perception function, including various assessment tasks such as recognizing inverted letters and assessing stereopsis during high-speed movements. The cross-into-circle test was used as a means to quantify perceptual eye position. Subsequently, the collected data was analyzed to assess the magnitude of impairment in binocular visual perception. The results of the study revealed a spectrum of binocular visual perception impairment among patients diagnosed with stroke, demonstrating discernible variations in the recognition of inverted letters and stereopsis across different movement speeds. Importantly, perceptual eye position measurements offered valuable insights into ocular misalignment. The computational models effectively quantified both the spatial distribution and severity of these identified impairments. Damage to brain tissue resulting from a stroke can give rise to notable impairments in binocular visual perception function. Graded biological models, formulated through computer algorithms, provide a systematic framework for the comprehensive evaluation and quantification of these impairments. The comprehension of the nature and extent of visual impairments observed in patients with stroke establishes a basis for the development of personalized visual perception learning methodologies. Based on such tailored approaches, we aim to facilitate the recovery of impaired visual function, thereby contributing to the broader objective of neural system rehabilitation.

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

Technique of functional simulation as a tool for providing the required quality of fortified bakery products

Relevance. The problem of irrational and unbalanced nutrition is relevant for almost all regions of Russia. There is a need to enrich everyday products with micronutrients that are of particular importance in the diet. Fortified bread and bakery products with specified consumer properties in the diet can be one of such valuable products.Methods. The methodology of structural analysis and design of systems (Structured Analysis and Design Technique, SADT) is often used in life cycle management of complex high-tech products and has not found wide application in the food industry. The authors substantiate the need to introduce tools for functional and graphical modeling of business processes that implement the SADT methodology in life cycle management to ensure the quality of finished bakery products.Results. An original generalized functional and logical model of the process “To produce enriched bread (bakery products) with regionally significant micronutrients” has been developed. The analysis of the technology of enriched bakery products, carried out at various levels of decomposition of the process, revealed that the quality of the finished product is significantly influenced by the choice of the technological stage of applying the components of the mixture proposed for the enriching composition. It has been established that preference should be given to organic forms of additives — components of the enriching composition. To preserve the nutritional properties of bakery products, an enriching composition is proposed to be applied to the surface of the finished product. The proposed method has been tested in experimental production conditions. Its manufacturability and controllability of the proposed technical solutions are proved. The approach to functional and graphical modeling of food technologies developed by the authors on the example of bakery products opens up prospects for improving the quality of finished products and can be widely used in food engineering.

Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Bayesian network modeling applied to food risks: Data from General Administration of Customs of China as an example

We introduce a multidimensional Bayesian Network (BN) modeling approach as an alternative to the classical multivariate regression approach commonly used for risk factor analysis. BN modeling is a rich and flexible analytical technique capable of elucidating complex food data. It is a data-driven graphical modeling technique and exploratory tool that visually presents dependencies and causal associations while retaining statistical rigor in holistic level inference. We used data on monthly unauthorized food imports to China for demonstrating a full Additive BN model in the open-source R language with all code necessary to reproduce our analyses. Compared to the classical regression model, the Additive BN model of this study described more relationships between variables, and overcome the problems of classical discretization of general BN. The analysis showed that food category and import weight were two actionable drivers directly related to risk and can be controlled to manage risk. The risk was also related to where the food is produced, but not where it is likely to be sold. The BN model also is able to systematically elaborate uncertainty and obtain information through probabilistic inference, which might be useful for giving additional new insights potentially not captured by classical methods. Finally, we discuss the potential and evolution of BN, such as dynamic BN.

Human Anthropometric Indicators for the Preparation of Simulation Models

Normal human anthropometric parameters are used in the development of medical rehabilitation techniques, in the performance of surgical techniques used for normal human functioning. In order to perform these operations successfully, it is necessary to model normal human physical characteristics and, based on these models, perform the operations correctly. Normal anatomical characteristics have a positive effect on the physiological state of the person. The relevance of the research stimulates the development of the science of valeology and increases the relevance and practical significance of the topic. Prosthetics of organs lost in wars and accidents are also performed on the basis of computer models. The science of digital anthropology is modeling human anthropometrics, and practically important predictions are being made based on this modeling. Digital anthropology is based on the research of modern technology to compile a database of the most diverse anthropological indicators of people. Anthropology occupies a special place in the system of scientific knowledge about the world, because it is a field of science that studies the origin, development and role of humans in nature and society. In modern medicine, mathematical and graphic modeling of human anatomical and anthropological indicators is required in all fields. Anthropometric indicators of humans are taken as the basis in surgery, military science, sports. The structure of shoulders, diaphragm, hips, face, and the age of a human being can also be defined by these anthropometric indicators.

Timings of Dance Techniques as a Separate Lexical Component of Papping

The purpose of the work is to identify, analyse, and structure one of the understudied lexical components of papping and illusory styles. Research methodology. In the process of research, the principle of induction was used, as well as the following methods: analysis and synthesis – in the process of working with information sources; comparison – to identify identical facts and patterns; formalisation – for recording the detected timings in a single format. Scientific novelty. For the first time, in the process of analysing the dance vocabulary of popping and other illusory styles, their little-studied lexical component – timings – was revealed. A clear definition of this term is given, that is, the main characteristics of this component. Most of the used (basic) timings are identified and described, and their graphic record is also given. Conclusions. The analysis of the dance vocabulary of popping and other illusory styles made it possible to highlight their little-studied lexical component – timings. A clear meaning was also given to the term "timing", which is rhythmic after the use of dance technique (muscle control), which the dancer can impose on their own dance (dance movements, position). Research into this lexical constituent process has identified several "baseline" timings for popping and illusory styles. They are based on two basic dance techniques – pap and dime stop. Some of them correspond to individual dance movements or a specific illusory style. For a simple analysis and understanding of the timings, the article used a graphic model of their recording, which allows you to visually display the timings in accordance with the musical score. Thus, timings can be used as a separate lexical component of papping and its adjacent styles with their own characteristics and specificity. This component is closely related to music and, for the most part, a conflict for the dancer to diversify their performance by adding to its various rhythms.

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore

Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detection method called PatchCore is customized for 3D applications for this purpose. First, Fast Point Feature Histograms (FPFH) are used to extract geometric features. Then PatchCore is applied on synthetic point clouds with crack labels, generated using 3D finite element modelling (FEM) and graphical modelling. Results show that the proposed method can capture surface and internal cracks in arches. Analyses show that the method is robust against measurement noise, initial damage and masonry surface roughness, and can be applied to other bridge components. Limitations of the method in detecting small changes in curvature and in-plane geometric distortions are highlighted for further improvements.

Bayesian continuous wavelet transform for time-varying damping identification of cables using full-field measurement

Cables serve as the primary load-bearing element in cable-stayed bridges, making their damping level critical for structural safety evaluation. Traditional operational modal analysis (OMA) faces challenges in damping identification due to result discreteness, and limited sensor deployment often leads to the loss of crucial modal information. This paper proposes a Bayesian continuous wavelet transform with Gabor wavelet (BCWT-G) method for time-varying damping identification using full-field measurement data. A computer vision technique combining the pyramid grafting network (PGNet) with neighboring frame pixel fitting (NFPF) is used to accurately capture full-field vibration data. The time-frequency domain properties of these data are then extracted and incorporated into a Bayesian probabilistic estimation framework for modal updating. The proposed method was validated through numerical simulations using a physics-based graphics model (PBGM), and actual cable testing under complex environments, demonstrating its effectiveness and robustness in identifying the time-varying dynamic characteristics of cables.

Maximum permissible levels of laser radiation as a tool for ensuring the sanitary and epidemiological well-being of the population

Introduction. Laser radiation is the youngest production factor, but not the most harmless. The rapid development of modern laser technologies and their introduction into various fields of science and technology has allowed lasers entering widely into everyday human life, as well as into the sphere of culture, fine arts, and the show industry. Thus, the development of a unified algorithm for calculating the maximum permissible levels (MPL) of laser radiation is becoming particularly relevant. The purpose of this work is to assess the subjective feelings of persons exposed to laser radiation at levels not exceeding the maximum permissible levels and clarify the methodology for calculating acceptable levels of laser radiation. Materials and methods. Graphical modelling was used to develop an algorithm for calculating the MPL. To assess the subjective perception of laser radiation, a survey of 20–25 years five hundred eighteen people was used according to a specially designed questionnaire. Results. Exposure to laser radiation at levels not exceeding the MPL caused unpleasant sensations in the eyes of the participants of the show, temporary violation of colour perception, temporary blindness, headache. An algorithm for calculating the MPL of laser radiation has been developed, consisting of six stages, which allows optimizing the computational component of laser safety assessment in the implementation of State sanitary and Epidemiological supervision and control. Limitations. The age range of the study participants is 20–25 years old. Conclusion. It is proposed to revise the standards of MPL for the population based on the temporary effects of radiation. This will minimize the negative impact on the organ of vision in terms of reversible changes and qualitatively improve the hygienic assessment of laser radiation. The developed algorithm for calculating the permissible levels of laser radiation will help minimize errors and reduce the time spent by specialists when assessing the safety of exposure to laser radiation on the body.