Graphical Modeling Research Articles

GRAPHICAL FACTORIZATION OF THE INFLUENCE THE TECHNOLOGICAL FACTORS ON CONTINUOUS ROLLING

Metal rolling on modern mills takes place continuously with band tension. If the interstand tensions are adjusted according to the results of scientifically based calculations, it is possible to provide more rational, from the point of view of energy saving, processing conditions. A generalized graphical factorization for the technological parameters of continuous rolling the grade was performed, which allows determining the cause-and-effect relationships of kinematic and power processes, as well as changes in the shape of workpeaces in processing.
 The construction of the graphic model begins with the use of the Ishikawa diagram with a clear definition the technological factors of continuous rolling with tension. Solving the problem of tension mode management research in the continuous rolling process is carried out for chosen 5M factors: (М2) Management; (М3) Measurement; (М4) Material; (М5) Machines; (М6) Manufacturing. Features of the model include a qualitative determination on a research basis of the influence the factors on energy saving, taking into account the longitudinal stability of rolling. When constructing the diagram using semantic communications, the results of a preliminary analysis the influence of technological factors during continuous rolling on modern mills with the inclusion of wire blocks are summarized. Visualization of factorization is achieved by the graphical modeling.
 The graphical model can be used as a basis in the following analytical modeling of the rolling process for operating conditions in manufacturing, ensuring its adequacy. It is actual the use of modern theoretical methods of calculating the stress state along the entire length of the workpeaces. For the future, it will be the use of the finite element method with the subsequent determination of the quantitative influence of technological parameters. For example, how the friction conditions in the neighboring zones of deformation will doing on the stressed state of the rolled metal.

Formation of literary knowledge and skills of schoolchildren with the help of frames

The educational process always requires the involvement of tools that enhance the developmental activities of students. These include the use of frames, which allows to make students' knowledge and skills systematic, structured, as well as to include children in the study of different types of literary material. There is a need to find "points of contact» between traditional in the methodology of studying literary activity mechanisms and additional didactic opportunities provided by frames. Therefore, the author solves the problem contained in the agreed procedural components of the literature lesson with frame technology, demonstrates the interaction of ways of analyzing works of art with educational models, which are carried out as a result of the application of this didactic technique. He describes the filling of the static and dynamic part of the frames, develops learning efficiency in performing various versions of literary analysis, establishes a connection with traditional graphic models. The article shows how, with the help of frames, research program of students at the level, chronotope of composition, images of characters, artistic language, themes and issues, worldview strategy of the writer, genre and style features of the text. A separate point is the work especially with the written and creative way, the study of which takes place on a chronological or thematic basis. In all cases, frames can give students the work of purposefulness, and if repeated research activities with the same type of educational material, they become a key point in the consolidation and application of a number of procedural skills.

Nomogram Model for Predicting the Overall Survival of Patients With Meningiomas: a Retrospective Cohort Study

To identify the significant prognostic factors of overall survival (OS) for patients living with meningiomas (MMs), and establish a novel graphical nomogram and an online dynamic nomogram. Patients diagnosed with MMs were identified retrospectively from the SEER database. The cohort was split into training (70%) and test (30%) groups randomly. Univariable and multivariable Cox models were successively used to screen the significant prognostic factors. Subsequently, the independent predictors were used as items to establish the graphic and dynamic nomogram model. To assess the accuracy of the model, a calibration curve was plotted. To assess the discrimination performance, C-index and time-dependent area under the receiver operator characteristic curve (AUC) were selected. Additionally, the decision curve was generated to evaluate the clinical net benefit of the model. A total of 899 patients were involved, of which 629 and 270 were split into training group and test group, respectively. Age, sex, radiotherapy, tumor size, and tumor histology were identified as the significant prognostic factors. Based on these factors, a graphical nomogram and online nomogram (Web site: https://helloshinyweb.shinyapps.io/dynamic_nomogram/) were developed. The calibration curve showed favorable consistence between predicted and actual survival rate. C-index and time-dependent AUC showed good discrimination ability, and the decision curve analysis showed positive net benefit of the model in clinical practice. Age of diagnosis, sex, tumor size, tumor histology, and radiotherapy were independent predictors for OS, while extent of resection had a borderline significant. A nomogram model was successfully developed and validated to dynamically predict the long-term OS for MM patients, expecting to help neurosurgeons optimize clinical management and treatment strategies.

Graphic Model of Virtual Teaching Supervision through Fuzzy Logic in Non-University Educational Centers

This research analyzes the supervision of non-university virtual training due to the unexpected non-face-to-face teaching scenario caused by COVID-19 with a graphic model using the SULODITOOL® instrument. It arises as a research line of the Chair of Education and Emerging Technologies, Gamification and Artificial Intelligence of the Pablo de Olavide University (Seville) and is developed under the auspices of other assessment instruments within the framework of the functions and attributions of the Education Inspectorate of Spain. The aforementioned instrument is made up of 10 weighted supervisory indicators using fuzzy logic. The aggregation of linguistic variables of 242 expert judges was performed using the probabilistic OR function and defuzzified using the area centroid method to calculate the aforementioned weights. Based on the innovative analytical and graphic methodology used to analyze the supervision of virtual teaching, both synchronous and asynchronous, it stands out from the results obtained that there are certain supervision indicators, such as the training design and the methodology used, which should be considered as factors key in all the scenarios studied (primary education, compulsory secondary education and post-compulsory education).

Can third-party market cooperation solve the dilemma of emissions reduction? A case study of energy investment project conflict analysis in the context of carbon neutrality

The conflict mechanism of transnational energy investment projects is complex. The current study uses the enhanced graphic model for conflict resolution to build a four-pole game model to discuss the conflict resolution mechanism of energy investment projects. The results depicted that the host country's dilemma of carbon emission reduction and power supply cannot be solved only by unilateral efforts. All parties adopt the game's best strategy (cooperation, cooperation, promotion, support). The project's main investor provides energy that meets the environmental protection requirements. Technology investor provides new technologies for energy conservation and emission reduction. Enterprises of the host country improve various basic measures for carbon emission reduction. Environmental protection organizations and the public support the project and participate in carbon emission supervision. The implementation of energy emission reduction requires the effective participation of social groups, and the participation and supervision of environmental protection organizations and the public are conducive to promoting various stakeholders to achieve a balanced strategy for energy emission reduction. The results provide valuable policy implications for investment decision-makers and an important reference value for third-party market cooperation to solve the dilemma of energy emission reduction.

Data augmentation: A comprehensive survey of modern approaches

To guarantee good performance, modern machine learning models typically require large amounts of quality annotated data. Meanwhile, the data collection and annotation processes are usually performed manually, and consume a lot of time and resources. The quality and representativeness of curated data for a given task is usually dictated by the natural availability of clean data in the particular domain as well as the level of expertise of developers involved. In many real-world application settings it is often not feasible to obtain sufficient training data. Currently, data augmentation is the most effective way for alleviating this problem. The main goal of data augmentation is to increase the volume, quality and diversity of training data. This paper presents an extensive and thorough review of data augmentation methods applicable in computer vision domains. The focus is on more recent and advanced data augmentation techniques. The surveyed methods include deeply learned augmentation strategies as well as feature-level and meta-learning-based data augmentation techniques. Data synthesis approaches based on realistic 3D graphics modeling, neural rendering, and generative adversarial networks are also covered. Different from previous surveys, we cover a more extensive array of modern techniques and applications. We also compare the performance of several state-of-the-art augmentation methods and present a rigorous discussion of the effectiveness of various techniques in different scenarios of use based on performance results on different datasets and tasks.

ФФОРМООБРАЗУЮЩАЯ РОЛЬ ЛИНИИ ПРИ ФОРМИРОВАНИИ ФАСАДНЫХ РЕШЕНИЙ ЗДАНИЙ

The facades of buildings and structures, which form the visual architectural and urban planning environment of a modern city, mainly consist of a variety of facade materials and decorative and (or) functional structural elements. The well-coordinated relationship between them, correlated according to certain compositional principles, creates the attractiveness of the visual perception of the architectural form inscribed in the space of the metropolis. Facades form the space of a modern city, which strives to create forms around itself, the direction of which is set by lines: urban planning lines that organize building and lines that create the architectural appearance of the city - formative. Thus, one way or another, the elements of the architectural and urban space are formed by means of a line. And the line acts as one of the main and primary compositional means of modeling, with the help of which an architectural form is developed. The article analyzes the architectural and compositional solutions of building facades in terms of using the line as a basic form-building tool when solving the facade plane. The general patterns of using the line in the development of facade solutions are revealed, regardless of the time period and stylistic direction. Its role is determined as a universal compositional means of graphic modeling of the external appearance of buildings, thanks to which architectural, structural and decorative elements are located along the facade plane, forming an expressive architectural image.

Visual Technology Development for Arabic Learning Based on ADDIE Development Model

This article aimed to determine the variety of visual technology and its development in learning Arabic. Visual technologies that could be used by Arabic language teachers were all kinds of media that could be used in general learning, both in the form of non-projected visual media and projected visual media, Non-projected visuals in the form of graphic media, realia (real objects), and models, while visual media that could be projected are LCDs, and digital cameras. In its development, Arabic learning could be developed with various models, one of which is the development of the ADDIE (analyze, design, development, implementation, and evaluation), ADDIE Development model was media development through the following steps: Analyse, Design, Develop, Implement, and Evaluate. Visual technology for learning Arabic was developed by first analyzing the needs of students, then designing and developing the right media (production process), then socializing it to students, and finally evaluating it. If it had been completed, visual technology could be implemented in the learning classroom

AI_deation: A Creative Knowledge Mining Method for Design Exploration

Ideation is a core activity in the design process which begins with a design brief and results in a range of design concepts. However, due to its exploratory nature it is challenging to formalise computationally. Here, we report a creative knowledge mining method that combines design theory with a machine learning approach. This study begins by introducing a graphic design style classification model that acts as a model for the aesthetic evaluation of images. A Grad-CAM technique is used to visualise where our model is looking at in order to detect and interpret visual syntax, such as geometric influences and color gradients, to determine the most influential visual semiotics. Our comparative analysis on two Nordic design referents suggests that our approach can be efficiently used to support and motivate design exploration. Based on these findings, we discuss the prospects of machine vision aided design systems to envisage concepts and possible design paths, but also to support educational objectives.

Regenerative Braking Optimization Using Particle Swarm Algorithm for Electric Vehicle

In recent years, the topic of reducing fuel consumption and greenhouse gas emission has become one of the major focuses on the automotive industry leading toward the development of electric vehicles to create awareness of environmental protection. Thus, the development of hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and fully electrical vehicle (EV) has started growing up to replace the gasoline car, which is fully depends on fuel to operate, to help fight against the world climate change issues. This research is mainly focused on solving the problem of charging period of traditional used batteries pack, energy storage system of EV, and the limitation on travel distance for EV with the use of batteries pack as an energy source. The proportional-integral (PI) controller based on particle swarm optimization (PSO) algorithm is implemented in this simulation to optimize the speed of BLDC motor by obtaining an optimized parameter of Kp and Ki. The MATLAB/Simulink software is used for graphical modelling, simulating, and analyzing the behavior of supercapacitor in various condition. The simulation results represent the proposed PSO-based energy management method can achieve greater energy efficiency as compared to the traditional method. All in all, moving forward in developing a fully electric buses or vehicles can bring society into a new generation of zero greenhouse gas emissions. In this paper, the optimization of the PI controller based on PSO algorithm is applied and the results show that there is an increment of 6% in total distance traveled by the EV. Besides, there is the 3.69% of improvement for maximum speed and peak to peak speed of the EV and 14.57% of improvement in terms of average speed of EV within the total travel duration of 1300 s.

Cost management as a key component of the organizational system of a developer implementing a unique nuclear industry infrastructure project abroad

The COVID-19 pandemic and the global economic crisis did not lead to a collapse in the volume of construction work in the country and abroad, as well as a decrease in investment in infrastructure projects. However, the labor force was negatively affected (both quantitatively and qualitatively), industry prices, supply chains and the stability of companies. The low profi tability of the Russian sector of construction of infrastructure projects remains unattractive for capital investment by businesses and investment funds, and therefore, private companies have been selling their construction assets to state-owned companies or monopolies for 5 years (from 2018–2022). Also, for the participants in the implementation of infrastructure projects, the most serious problems remain imperfections in the public procurement system and the formation of project costs, an outdated pricing model, high cost and limited availability of loans, as well as the inability to plan their load for a long time. Almost all of these problems are somehow interconnected with each other, and they need to be addressed with the help of systemic transformations. Even if a large amount of funds is directed to infrastructure projects, it will only postpone the need for systemic changes in the industry in general and organizational changes at the level of the developer in particular. To solve the stated problem, the subject area of the study is defi ned: the subject of the research: the process of organizing the cost management of an infrastructure project of the nuclear industry abroad; the aim of the research: creation of a scheme of the developer’s organizational process for managing the cost of construction of an infrastructure project of the nuclear industry (IPAO) abroad; research objectives: develop a methodological approach to substantiate each stage of the implementation of a unique infrastructure project of the nuclear industry in terms of the processes of formation, calculation, approval, control and monitoring of cost; develop proposals for improving the cost management process by the customer when implementing a unique infrastructure project for the nuclear industry abroad. Research methods: analysis and synthesis, graphic modeling, comparative estimates; research results: the created scheme of the developer’s organizational process for managing the cost of construction of an infrastructure project of the nuclear industry (IPAO) abroad at the stages of project implementation, taking into account the process approach and refl ecting the responsibility of counterparties at each stage of the life cycle. Scope of application of the results: the introduction of the organizational process scheme into the activities of companies implementing infrastructure projects abroad will allow regulating and standardizing cash fl ow processes, as well as identifying deviation parameters of the established cost throughout the life cycle of the infrastructure project.

Physics‐data‐fusion based decoupling model for coupled faults of complex electromechanical systems

AbstractCoupled faults are formed by the nonlinear coupling of multiple lower‐level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics‐data‐fusion‐based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time‐varying fault characteristics were determined using real‐time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real‐time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.

Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm.

The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since it was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm-a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.

Systematization of the Factors Influencing the Activity of Private Investors in the Russian Securities Market, in the Context of Professional Mediation

The article outlines the problem of mobilizing the resource potential of private investors through the mechanisms of professional mediation in the securities market. The significance of the processes of transformation of private savings into investments for the purposes of ensuring economic growth and the stability of the economic system of the Russian Federation is beyond doubt. This aspect becomes especially relevant in the context of the sanctions load. In view of the limited nature of Russian institutional investors and the decrease in the share of non-residents in the turnover on the Russian securities market, the emphasis is shifting to private investors. Professional intermediaries providing investors with the necessary services, investment products and infrastructure solutions are able to contribute to the sustainable presence of private investors in the market. The authors present a detailed analysis of the processes of development of private investment using the mechanisms of professional mediation in the securities market, identifying cause-and-effect relationships of the current dynamics for the period 2017–2021. The study made it possible to identify a number of factors that determine the presence and activity of private investors in the market. The systematization of these factors has been carried out, taking into account the possibility of professional intermediaries influencing the processes of attracting private investors to the market. Using a graphical modeling method in the form of an Ishikawa relationship diagram, the factors were prioritized both by groups of factors and within each group. The results of the study can be used to modify the activities of professional intermediaries in the securities market in order to maintain the growth rate of the number of private investors and maximize their activity.

Deriving causal relationships in resting-state functional connectivity using SSFO-based optogenetic fMRI

Objective. The brain network has been extensively studied as a collection of brain regions that are functionally inter-connected. However, the study of the causal relationship in brain-wide functional connectivity, which is critical to the brain function, remains challenging. We aim to examine the feasibility of using (SSFO)-based optogenetic functional magnetic resonance imaging to infer the causal relationship (i.e. directional information) in the brain network. Approach. We combined SSFO-based optogenetics with fMRI in a resting-state rodent model to study how a local increase of excitability affects brain-wide neural activity and resting-state functional connectivity (RSFC). We incorporated Pearson’s correlation and partial correlation analyses in a graphic model to derive the directional information in connections exhibiting RSFC modulations. Main results. When the dentate gyrus (DG) was sensitized by SSFO activation, we found significantly changed activity and connectivity in several brain regions associated with the DG, particularly in the medial prefrontal cortex Our causal inference result shows an 84%–100% accuracy rate compared to the directional information based on anatomical tracing data. Significance. This study establishes a system to investigate the relationship between local region activity and RSFC modulation, and provides a way to analyze the underlying causal relationship between brain regions.

Intrinsic Image Decomposition Using Paradigms.

Intrinsic image decomposition is the task of mapping image to albedo and shading. Classical approaches derive methods from spatial models. The modern literature stresses evaluation, by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods train a map from image to albedo using images rendered from computer graphics models and example human judgements. This approach yields practical methods, but obtaining rendered images can be inconvenient. Furthermore, the approach cannot explain how a one could learn to recover intrinsic images without geometric, surface and illumination models, as people and animals appear to do. This paper describes a method that learns intrinsic image decomposition without seeing human annotations, rendered data, or ground truth data. Instead, the method relies on paradigms - spatial models of albedo and of shading. Rather than finding the "best" albedo and shading for an image via optimization, our approach trains a neural network on synthetic images. The synthetic images are constructed by multiplying albedos and shading fields sampled from our models. The network is subject to a novel smoothing procedure that ensures good behavior at short scales on real images. An averaging procedure ensures that reported albedo and shading are largely equivariant - different crops and scalings of an image will report the same albedo and shading at shared points. This averaging procedure controls long scale error. The standard evaluation for an intrinsic image method is a WHDR score. Our method achieves WHDR scores competitive with those of strong recent methods allowed to see training WHDR annotations, rendered data, and ground truth data. Our method produces albedo and shading maps with attractive qualitative properties - for example, albedo fields do not suppress wood grain and represent narrow grooves in surfaces well. Because our method is unsupervised, we can compute estimates of the test/train variance of WHDR scores; these are quite large, and suggest is unsafe to rely small differences in reported WHDR.

A mission reliability modelling method for intuitively displaying time elements

In view of the problems that the commonly used reliability block diagram modelling methods can not directly reflect the operation time elements of units and that whether an unit can be repaired or not during mission period, this paper proposes a graphical reliability modelling method that can uniformly represent the two key elements of equipment unit reliability logic relationships and its operation times, so as to improve the comprehensiveness of the display elements of reliability model and the accuracy of the logical relationship of each unit. Finally, a comprehensive example shows the feasibility of this method.

Graphical modeling of stochastic processes driven by correlated noise

We study a class of graphs that represent local independence structures in stochastic processes allowing for correlated noise processes. Several graphs may encode the same local independencies and we characterize such equivalence classes of graphs. In the worst case, the number of conditions in our characterizations grows superpolynomially as a function of the size of the node set in the graph. We show that deciding Markov equivalence of graphs from this class is coNP-complete which suggests that our characterizations cannot be improved upon substantially. We prove a global Markov property in the case of a multivariate Ornstein-Uhlenbeck process which is driven by correlated Brownian motions.

Assessing Suitability of Applying Big Data Analytics within Small to Medium-Sized Businesses via an ROI-Based Graphic Model

Big data has presented itself as a term, phenomena, and paradigm with the potential for many opportunities and challenges. The new potentials of big data seemingly continue to expand both in possibility and complexity. With efforts to exploit these new potentials requiring investments from businesses and organizations it becomes necessary to understand what value is gained from such efforts. Through the methodology of design science this study develops an artifact which incorporates the return on investment model which can be utilized by SMEs. The artifact provides an abstract process model for the use of value assessment of big data efforts by SMEs. This study finds through using test cases that the ROI model can be applied to a generalized artifact which guides the assessment of big data efforts. Further, it is found that through a graphical design the development of a simple and intuitive artifact can be accomplished.

Bayesian graphical modeling for heterogeneous causal effects.

There is a growing interest in current medical research to develop personalized treatments using a molecular-based approach. The broad goal is to implement a more precise and targeted decision-making process, relative to traditional treatments based primarily on clinical diagnoses. Specifically, we consider patients affected by Acute Myeloid Leukemia (AML), an hematological cancer characterized by uncontrolled proliferation of hematopoietic stem cells in the bone marrow. Because AML responds poorly to chemotherapeutic treatments, the development of targeted therapies is essential to improve patients' prospects. In particular, the dataset we analyze contains the levels of proteins involved in cell cycle regulation and linked to the progression of the disease. We evaluate treatment effects within a causal framework represented by a Directed Acyclic Graph (DAG) model, whose vertices are the protein levels in the network. A major obstacle in implementing the above program is represented by individual heterogeneity. We address this issue through a Dirichlet Process (DP) mixture of Gaussian DAG-models where both the graphical structure as well as the allied model parameters are regarded as uncertain. Our procedure determines a clustering structure of the units reflecting the underlying heterogeneity, and produces subject-specific estimates of causal effects based on Bayesian Model Averaging (BMA). With reference to the AML dataset, we identify different effects of protein regulation among individuals; moreover, our method clusters patients into groups that exhibit only mild similarities with traditional categories based on morphological features.