bluuebunny/crossref_metadata_2025 huggingface dataset:
Overview of Semantic Segmentation Algorithms Based on Threshold Method and Deep Learning
Authors: Bao Jucheng , Sun Lingxiao , Ye Hongling , Zhang Chenjia
Published: November 2024

With the development of hardware, the technology of artificial intelligence is more and more developed, and computer vision is once again a craze. Semantic segmentation is an important issue in computer recognition. Semantic segmentation makes the computer recognize the objects in a given image and classify and present them, which can save huge human time and cost, and the mature algorithms are more accurate than manual. The paper will focus on four sections: Firstly, different semantic segmentation algorithms were introduced. Then, the performance, advantages, and disadvantages of representative algorithms in each method were compared and analyzed, and the applications of semantic segmentation were listed. Finally, the optimization of semantic segmentation algorithms was introduced. The aim is to clarify the idea of the algorithms, the design intention, and the correlation between them, to understand the limitations of different algorithms, to enable the researcher to choose the most appropriate method, and to gain further new inspiration.
The Application of Different Surrogate Models in Engineering Predictions
Authors: Chen Wenqi
Published: November 2024

Surrogate models are widely used in engineering predictions to reduce computational costs and improve efficiency, especially in complex systems where direct simulations are time-consuming and expensive. This paper explores the application of four commonly used surrogate models: Response Surface Methodology (RSM), Radial Basis Function (RBF), Kriging, and Support Vector Machine (SVM). Each model's strengths, weaknesses, and suitability for various engineering scenarios are discussed. RSM is shown to be effective for process optimization in systems with moderately nonlinear responses. RBF excels in real-time predictions and nonlinear systems, while Kriging offers high accuracy in spatial data prediction along with uncertainty quantification. SVM demonstrates strong performance in high-dimensional classification tasks. Additionally, this paper addresses strategies for reducing computational costs when applying these models, including the use of efficient optimization techniques. The findings suggest that the selection of an appropriate surrogate model depends heavily on the specific application and the complexity of the system being modeled. Future research could focus on improving the computational efficiency of these models, especially for large datasets.
Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization
Authors: Song Zishuo
Published: November 2024

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.
Advancements of Exploiting Convolutional Neural Networks for Solving Differential Equations
Authors: Ren Zijie
Published: November 2024

Solving Partial Differential Equations (PDEs) is essential across various fields, including physics, mathematics, and engineering. This study explores innovative methods for solving PDEs using Convolutional Neural Networks (CNNs). Traditionally, solving PDEs through numerical methods like finite difference or spectral approaches is computationally intensive, particularly when addressing high-dimensional problems and complex boundary conditions. CNNs, with their ability to handle spatial data efficiently, offer a promising alternative. This research evaluates different neural network architectures, including data-driven, physics-driven, and hybrid approaches, and their role in enhancing the accuracy and scalability of PDE solutions. Data-driven methods rely on extensive datasets for training neural networks, while Physics-Informed Neural Networks (PINNs) integrate the underlying physical laws into the solution process. Hybrid approaches combine both strategies, leveraging data and physical principles. The paper emphasizes CNNs potential to transform traditional PDE-solving methods, making them more efficient and adaptable to complex, real-world applications. Future research directions include optimizing these methods for greater scalability and interdisciplinary applications.
Quantum Genetic Algorithm-Driven Optimization of Triple-Glazed Window Thickness for Enhanced Light Reduction
Authors: Jiang Chengke , Xiong Weijie , Zhao Zihan
Published: November 2024

Abstract. Glass, a common construction material, generally lacks significant thermal properties. As technology rapidly advances, the demand for glass materials with superior heat preservation and thermal insulation capabilities increases, driven by the need to reduce building energy consumption. Multi-objective optimization problems, particularly in complex multilayered systems, require advanced numerical optimization algorithms. The quantum-inspired genetic algorithm (QIGA) offers advantages such as fast solving speed and high precision, utilizing quantum coding, variation, rotation gates, and crossing techniques. This study focuses on designing the optimal thickness of triple-layer glass to minimize sunlight penetration through windows, using QIGA to achieve this goal. The results demonstrate that triple glazing effectively reduces solar radiation entering a room, though different layer combinations lead to significant variations in total energy reduction. The study also emphasizes the importance of concurrent research in glass layer splicing technology, coating film techniques, and new glass materials to enhance overall performance. These findings contribute to the development of energy-efficient building materials that meet modern standards for thermal insulation and light control.
Enhancing Salary Prediction Accuracy with Advanced Machine Learning Models
Authors: Bao Qingling
Published: November 2024

Abstract. Accurate salary prediction is crucial for navigating the complexities of the job market and ensuring fair compensation practices. This research focuses on evaluating advanced machine learning models to improve salary prediction accuracy. The study integrates demographic, educational, and professional experience data to offer a comprehensive analysis of earning potential, aiming to foster equitable job markets and enhance strategic human resource planning. The methodology involves employing various models, including Linear Regression, Decision Tree, and Random Forest (RF). Key steps include preprocessing the dataset to address missing values, categorize data, and remove irrelevant features. The study finds that the RF model excels in predicting salaries, surpassing other models in performance metrics. This superior efficacy is attributed to RFs ability to handle complex, high-dimensional data and mitigate overfitting. The results of this study have significant implications for establishing fairer compensation practices and improving job market efficiency. By offering a reliable tool for understanding earning potential, this research contributes to better career decisions and strategic planning for both individuals and organizations. Future research will explore further refinements and applications of these models in real-world scenarios.
Prediction of Credit Default based on the XGBoost Model
Authors: Chen Yi , Dong Youzhong , Liu Wen
Published: November 2024

Abstract. Loans are an important source of revenue for most banks and lending institutions. Improving the accuracy of personal repayment capacity predictions is particularly crucial. It is of great significance for reducing credit risk, optimizing the credit assessment system, and ensuring market stability. This paper uses various models for the predictions. In terms of evaluation, this paper uses the ROC curve as a criterion to assess the practicality of the models. At the same time, to ensure that the most practical model does not suffer from overfitting or underfitting, this paper also uses learning curves to ensure the usability of the entire model. Experimental results have demonstrated that the XGBoost model outperformed other models in predicting credit defaults, achieving a high ROC score of 0.71. Although it cannot predict very well in actual situations, it can also demonstrate through various assessments that the XGBoost model is highly usable and has great potential. The XGBoost model can become a trend in future prediction models by continuously improving the dataset and conducting more tests. This can help governments or banks to roughly understand which customers will default in the next month and take corresponding actions accordingly.
A Review of Adversarial Attacks and Defense Techniques in Text Processing Models
Authors: Wang Ruihan
Published: November 2024

Abstract. With the rise of neural networks, the need for accuracy, robustness, and security has increased. Research has shown that small, carefully crafted perturbations, known as adversarial examples, can deceive models and lead to incorrect predictions. Current research focuses on the image domain, while there is a notable lack of exploration in the text domain, due to its discrete nature. This paper reviews adversarial attack techniques and defense strategies in text-based neural network models, aiming to improve the security and resilience of these models in practical applications. Adversarial examples, which can deceive models with small perturbations, expose vulnerabilities in their robustness and security. Techniques such as TextFooler focus on synonym replacement for generating adversarial examples, while Text Random Smooth (Text-RS) enhances defense through adaptive noise strategies. The research of search space aims to explore the feature of that, proposing search space for Imperceptibility (SSIP) and Search Space for Effectiveness (SSET) to estimate the different attack methods. Furthermore, the Chinese Variation Graph Integration (CHANGE) method improves the resilience of Chinese language models by leveraging variation graphs. These advancements highlight the importance of developing effective generation and defense mechanisms for adversarial examples in text processing models. Future research should enhance adversarial example techniques, explore efficient defense strategies, and investigate transferability to improve the security and robustness of text processing models.
A Closer Look at the Key Attributes of Breakfast Cereals: Comprehensive Data Analysis
Authors: Zeng Bojian
Published: November 2024

Abstract. This paper uses advanced techniques for data visualization in order to provide clear guidance for both consumers and manufacturers within the breakfast cereal industry. The research primarily focuses on analyzing key characteristics such as caloric content, fiber content, and sugar levels across various renowned brands of cereal including Larana Inc., General Mills, Kellogg's, and Nestl. By combining the powers of Python as a programming language along with the Pandas, Matplotlib, and Seaborn libraries, this analysis delves into consumer trends, general industry patterns, and nutritional concerns. Through a meticulous examination of these factors, this study aims to provide valuable insights that can aid stakeholders in making informed decisions towards promoting healthier product development. The findings obtained from this research act as a testament to the critical function that data-driven insights play in the formation of consumer decisions toward more nutritious options. These results not only contributes important knowledge for stakeholders but also pave the way for potential innovations in data analytics within the cereal market. Overall, by employing cutting-edge tools and methodologies in data visualization combined with an objective approach to examining relevant characteristics of breakfast cereals from prominent brands, this paper strives to enhance understanding within the industry while facilitating advancements in data analytics practices.
Research on the Construction of Music Recognition Model Based on Improved CNN Convolutional Neural Network
Authors: He Jing
Published: November 2024

Abstract. In the context of today's informationized society, music recognition technology within the field of intelligent audio processing has received extensive attention and become a research focus. However, traditional music recognition techniques are difficult to adapt to the complex changes of music signals due to the inherent defects in feature extraction and model construction. To address these difficulties, this study constructs an innovative music recognition algorithm, which is based on an optimized convolutional neural network (CNN). The efficacy of feature extraction is enhanced by fusing the hash convolutional neural network (Hash-CNN), and the temporal data is processed using the long short-term memory network (LSTM) to improve the accuracy of recognition. In the preprocessing stage of music signals, we applied various noise reduction and normalization means to enhance the data quality to a high standard. According to the experimental data, the model performs well in recognizing complex music segments, especially when analyzing multi-level and multi-dimensional music features, it shows strong robustness. Meanwhile, compared with traditional techniques, the model proposed in this study shows significant improvement in both recognition efficiency and accuracy, confirming its great potential and broad prospect in practical applications.
Deep Learning Research on Smart Home Compatibility
Authors: Zhang Bowen
Published: November 2024

Abstract. In recent years, with the rapid development of technology, the concept of Smart Home has gradually emerged into people's view., Because of its remarkable convenience, it has won the favor of many consumers in the present era. However, as the utilization of smart home devices becomes more widespread, several challenges have emerged that need to be addressed for future development. One such challenge is the compatibility problem, which has now transformed into a significant barrier hindering the development of Smart Home. Therefore, in order to bring back a truly intelligent smart home environment, it is of utmost urgency to address the compatibility problem. In this paper, the author mainly focuses on this issue. By leveraging the knowledge of IoT and transport protocols, and using Python as the research and test platform, the researcher strives to either work out or alleviate the compatibility problem within the Smart Home domain.
BERT and RoBERTa for Sarcasm Detection: Optimizing Performance through Advanced Fine-tuning
Authors: Shu Xin
Published: November 2024

Abstract. This paper presents an advanced approach to sarcasm detection in online discourse using state-of-the-art language models. The study systematically evolves Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) architectures from baseline to optimized versions, demonstrating significant improvements in sarcasm detection accuracy. Utilizing a balanced subset of 30,000 samples from a Reddit sarcasm dataset, the research implements gradual unfreezing, adaptive learning rates, and sophisticated regularization techniques. The final RoBERTa model achieves 76.80% accuracy, outperforming BERT and showing balanced precision and recall across sarcastic and non-sarcastic classes. The comparative analysis reveals interesting learning dynamics between BERT and RoBERTa, with RoBERTa demonstrating superior performance in later training stages. The study highlights the importance of architectural innovations and advanced training strategies in capturing the nuanced linguistic cues of sarcasm. While computational constraints limited the dataset size, the research provides valuable insights into model behavior and sets a foundation for future work. The paper concludes by discussing potential avenues for advancement, including scaling to larger datasets, exploring multi-modal approaches, and developing more interpretable models, ultimately contributing to the broader field of natural language understanding and affective computing.
Review on Application of Chi-square Statistic in Text Classification in Recent Five Years
Authors: Tang Chuanyu
Published: November 2024

Abstract. The swift expansion of online textual data has rendered text classification increasingly vital in information management. Despite the prevalent usage of the chi-square test in text classification, there has been a scarcity of thorough research regarding its specific uses in recent years. Therefore, it is vital to encapsulate the research about the use of the chi-square test in text classification throughout the last five years. This report reviews the application of the chi-square statistic in Arabic text classification, social media data analysis, and medical literature classification and analyses its effectiveness in feature selection and enhancing classification performance. By reviewing and analyzing the academic literature, this report summarizes the application of improved chi-square feature selection methods to different text data types. It explores the effectiveness of these methods in improving classification accuracy. The findings indicate that chi-square has significant advantages in text classification in different domains, especially when dealing with complex linguistic texts and user-generated content.
Deep Learning AI for DR Microaneurysm Screening Based on Fundus Images
Authors: Qi Yunfei
Published: November 2024

Abstract. Since 2014, Artificial Intelligence (AI) image detection has developed rapidly in the medical field, while the global incidence of diabetes has increased rapidly. As an early sign of diabetic retinopathy (DR), the detection of diabetic microaneurysms is particularly important for the prevention and treatment of diabetes. Therefore, the artificial intelligence exhibition of DR microaneurysm screening based on fundus images shows great potential in this regard. This paper reviews the research progress of artificial intelligence technology for DR microangioma screening based on fundus images in recent years, and focuses on the improvement measures taken for different depth learning methods. These improvement measures aim to optimize model performance, improve training efficiency and enhance the generalization ability of the model. Some model experimental results are compared and analyzed in order to get the best model in a specific dataset or result. Finally, the current challenges are discussed and suggestions for the development of this field are put forward.
THE BIBLICAL CREATION ACCOUNT IN THE LIGHT OF ANCIENT WEST ASIAN CREATION NARRATIVES
Authors: Moudekar Vinod K.
Published: November 2024

Creation narratives can be found in every religion and in every territory. In Ancient West Asia, every nation had its own god(s) to govern them. The authority of such god(s) is attached to the story of the origin of the world and the formation of humankind. The biblical creation narrative has an important place in the history of Israelite people. It narrates the story of God’s act of creation, which then leads to narrate the salvation history of the Israelites. Though there were so many creation stories already present in the surrounding nations, God gave the biblical creation account to Israelites with a specific purpose. This article elucidates the motif behind giving the biblical creation narrative amidst Ancient West Asian creation stories by bringing out the convergence and divergence.
Semi-autonomous sensor fusion-based strategy for unmanned aerial atmospheric surveillance system in open field environments
Authors: Gorbounov Yassen , Dinchev Zahari , Peychinov Petar
Published: November 2024

This paper explores the convergence of semi-autonomous systems and sensor fusion for monitoring hazardous atmospheric substances in open and semi-confined environments such as open-pit mines. As the heart of this system, a universal and flexible device leveraging wireless technologies to collect and analyze large volumes of data, designed by the authors, is proposed. The article outlines the key components of the platform, emphasizing its potential for increasing personnel safety levels as stipulated by the international public exposure guidelines. In modern mines, it becomes crucial to monitor elevated concentrations of nitrogen and carbon oxides, as well as other pollutants after blasting and the toxic gas emissions produced by the heavy transportation equipment such as mining trucks and excavators. In this paper, industrial-grade electrochemical sensors are compared with price-affordable but less precise microelectrochemical ones. The performed experiments for lowering computational requirements show promising results. The integration of an ultrasonic anemometer enhances the system’s capabilities, contributing to a comprehensive understanding of atmospheric conditions. In parallel, the research discusses the role of computer vision in autonomous control systems, with a focus on the architecture and processing pipeline of the state-of-the-art system-on-module Kria by AMD. Advocating the potential of adaptive computing for enhanced efficiency in dynamic environments, the paper underlines the importance of the integrated approach in developing a semi-autonomous atmospheric surveillance system and highlights the impact of adaptive computing in dynamic scenarios. The main part of the measuring equipment and methods has been experimented with in real conditions in open-pit mines in Bulgaria. This is an initial phase of ongoing research aimed at serving as a foundation for future improvements and the elaboration of a fully autonomous prototype for hazardous substances evaluation in the atmosphere of open-pit mines.
Analyzing the performance of secondary users in satellite-terrestrial systems with cognitive radio assistance
Authors: Tran Huu Q.
Published: November 2024

The paper investigates the outage probability (OP) of a cognitive radio-based satellite-ground transmission system. In this configuration, both direct and relay links are activated to facilitate transmission from the primary satellite source to terrestrial users. The primary metric under scrutiny is the outage probability for both the primary and secondary networks. Utilizing the Shadowed-Rician fading model, commonly applied to satellite channels, for the satellite segment, and Nakagami-m fading models for terrestrial channels, we assess the OP by analyzing the expressions for both primary and secondary users. Additionally, we explore the impact of key system parameters on the OP’s performance. Indeed, the signal-to-noise ratio (SNR) and target rate are the main factors affecting the outage behavior of users on the ground. We identify certain conditions necessary to achieve improved performance by controlling key system parameters. Furthermore, this paper provides guidelines for designing cognitive radio (CR) systems in satellite configurations to meet the quality requirements of received signals on the ground. The analysis results are validated through Monte Carlo simulations implemented using MATLAB.
ÓRTESE DE BAIXO CUSTO PARA TRATAMENTO DE CRIANÇAS COM PÉS TORTOS
Authors: Lajarin Sérgio Fernando , Molina Mateus , Almeida Sofia Tortelli , Saccol Ana Clara , Araujo Ana Cristina de Souza , Silva Ellian dos Anjos , Xie Lamoncito , Okimoto Maria Lúcia Leite Ribeiro
Published: November 2024

O pé torto congênito (PTC) é uma deformidade ortopédica de grande incidência. O tratamento precisa ser feito logo na primeira infância e o método mais empregado consiste em alongar e imobilizar com gesso o membro deformado, fazendo trocas semanais dessa imobilização até que o membro chegue na posição adequada. Posteriormente, inicia-se uma fase de tratamento por meio de órtese para continuar a correção. Diante do alto custo dessas órteses o objetivo deste trabalho é propor um método alternativo, de baixo custo para sua fabricação. Ele consiste em utilizar o processo de termoformagem de chapas de Poliestireno, aliado a impressão 3D. O resultado é uma redução significativa no custo de fabricação, indicando que o emprego de conceitos de Design e Engenharia podem beneficiar e inovar áreas da Tecnologia Assistiva e Saúde.
IMPACTO COMPORTAMENTAL DO USO DE TELAS POR CRIANÇAS DE 0 A 2 ANOS: UMA REVISÃO SISTEMÁTICA
Authors: Oliveira Isabella Nicole Magno , Miyake Júlia Franco , Almeida Lara Mamede , Abreu Melissa Carvalho Martins de , Miguel Camila Botelho , Rodrigues Wellington Francisco
Published: November 2024

Justificativa/Problema: A exposição precoce de crianças de 0 a 2 anos ao uso de telas tem sido uma crescente preocupação, dada a possível associação com alterações comportamentais significativas durante uma fase crítica de desenvolvimento. Objetivos:
Este estudo visa analisar de forma sistemática os impactos comportamentais do uso de telas em crianças de 0 a 2 anos, compreendendo as correlações entre o tempo de exposição, o tipo de conteúdo consumido e as potenciais mudanças comportamentais. Metodologia: Foi realizada uma revisão sistemática nas bases de dados PubMed e MEDLINE, sem restrições de idioma ou ano de publicação. Foram utilizadas as palavras-chave “telas”, “tecnologias”, “impactos comportamentais” e “crianças”. Os critérios de inclusão abrangeram estudos que avaliaram o comportamento de crianças na faixa etária de 0 a 2 anos. Resultados: Os resultados indicam que a maioria dos estudos revisados reporta uma associação entre o uso de telas e alterações comportamentais em crianças dessa faixa etária. Contudo, as variações nas intervenções e nas metodologias de avaliação utilizadas limitam a possibilidade de se estabelecer conclusões definitivas. Conclusões: Conclui-se que, apesar das evidências sugerirem uma relação entre o uso de telas e alterações comportamentais, há uma necessidade premente de estudos mais robustos e consistentes que possam oferecer maior clareza sobre o impacto comportamental das telas em crianças pequenas.
Septic shock due to cytomegalovirus colitis associated with rituximab use: A case report
Authors: Patel Siddharth , Jay Jordan , Pathak Prutha , Antony Mc Anto , Thiriveedi Mrudula
Published: November 2024

BACKGROUND

Cytomegalovirus (CMV) infections can cause significant morbidity and mortality in immunocompromised individuals. CMV targets dysfunctional lymphocytes. Chronic rituximab (RTX) therapy can cause B-lymphocyte dysfunction, increasing CMV risk. Rarely, CMV infections present with critical illness such as septic shock.

CASE SUMMARY

A 64-year-old African American woman presented with generalized weakness and non-bloody watery diarrhea of 4-6 weeks duration. She did not have nausea, vomiting or, abdominal pain. She had been on monthly RTX infusions for neuromyelitis optica. She was admitted for septic shock due to pancolitis. Blood investigations suggested pancytopenia and serology detected significantly elevated CMV DNA. Valganciclovir treatment led to disease resolution.

CONCLUSION

This case illustrates an extremely rare case of CMV colitis associated with RTX use presenting with septic shock. High suspicion for rare opportunistic infections is imperative in individuals with long-term RTX use.
Total Quality Management, Market Orientation, and Performance: Empirical Insights from the Moroccan Hospitality Industry
Authors: HADDIOUI Ikram BEL , JAHIDI Rachid
Published: November 2024

The intensity of competition in the hospitality industry has long since highlighted the importance of addressing the intellectual aspect through management practices that focus on the environment, which have become essential in responding to changing market demands. Market Orientation (MO) is widely recognized as a critical growth strategy. It enables organizations to use real-time market intelligence to capitalize on business opportunities and make strategic and operational decisions that align with changing market dynamics. This study takes MO as the research object and investigates the factors influencing its implementation focusing on Total Quality Management (TQM) practices. The research also examines the impact of MO on hotel performance (HP) and assesses how interfunctional coordination (IC) moderates the MO/HP relationship. The study formulates a comprehensive theoretical model, presenting 3 hypotheses. Subsequently, the questionnaire was distributed to hotels based in Morocco, and a total of 93 usable responses were collected. PLS-SEM via Smart PLS 4.0 is used to test the model. Results showed that TQM practices are internal drivers of MO and that MO fosters HP. Moreover, IC moderates the MO/HP relationship, reducing the direct effect. This research offers an empirical contribution by examining how hotels embrace two managerial approaches to enhance performance. Furthermore, it provides a deeper explanation of the current challenges within the Moroccan hospitality industry that justify the focus of the study.
DESIGN OF EDUCATIONAL SOFTWARE IN THE TOOL SYSTEM DOCENS
Authors:   Kazan National Research Technological University, DMITRIEV М.Е.
Published: November 2024

The work is devoted to the technology of designing algorithms for interactive educational software using the DOCENS tool system. This tool system was created at the Department of Methodology of Engineering Activities of the Kazan National Research Technological University. With its help, teachers who do not have programming skills can independently create and autonomously use interactive software products for various purposes of the educational process of a technological university. Along with the possibility of creating frames with ergonomic feedback understandable to the student, including graphic elements with various functional purposes, this tool system allows you to design teaching and test programs with algorithms of different structures. The elements of the algorithm structure of the designed pedagogical software are briefly described, which are modules for displaying structured educational information on the monitor in the form of a frame (hereinafter we call them frames), receiving a response from the student, if one is required, evaluating the response and generating a control signal. Particular attention is paid frames with the function of managing the learning process at the request of the student. A basic control scheme is presented, shown in the form of a digraph, as well as an algorithm in the form of a table reflecting the algorithmization process. Examples of digraphs and linear records of algorithms of various structures (cyclic, branching), as well as a digraph for the case of using one frame in different positions of the algorithm are given. The recording of an algorithm that allows organizing an automated survey of students on tickets is considered. The recording is carried out in the form of a sequence of lines in which the positions of frames are recorded to be displayed on the monitor in the form of a matrix.
CONVOLUTIONAL NEURAL NETWORK MODEL FOR HANDWRITTEN MATHEMATICAL EXPRESSIONS RECOGNITION
Authors:   Kazan National Research Technical University named after A.N. Tupolev, KATASEV A.S. , KATASEVA D.V. ,   Kazan National Research Technical University named after A.N. Tupolev, SMIRNOV YU.N. ,   Kazan State Power Engineering University, LITINSKY M.S. ,   Kazan National Research Technical University named after A.N. Tupolev
Published: November 2024

This article is devoted to solving the problem of developing a convolutional neural network model for recognizing handwritten mathematical expressions consisting of Arabic numerals and basic symbols of mathematical operations. Its solution required the formation of training and test samples, development of the structure of the neural network and its training, selection of a method for segmenting the image into individual characters, as well as testing and evaluating the results of the convolutional neural network model. In the work, image processing and classification was carried out using a multilayer convolutional neural network. To build a convolutional neural network model, the deep learning library Keras was used in this work. To train the model, a set of handwritten mathematical symbols was used, obtained using the publicly available machine learning library FastAI and consisting of 10,379 images of handwritten mathematical expressions. To test the equation classifier, a random expression generator was created based on the initial data of the handwritten training sample. Examples of generated mathematical expressions are shown. To simplify the learning process, all images were converted to binary format, saving it in black and white. Thus, image processing required the steps of generating data, receiving input images, scaling them, building an architecture and training a convolutional neural network, as well as its practical use. The result of image processing is to obtain a table of probabilities that the image belongs to a particular class of numbers and expressions of mathematical operations. To divide the images into different areas, a segmentation process was carried out using contour analysis methods. The OpenCV library was used to detect the outlines of numbers and mathematical symbols. Image segmentation required the steps of converting the gray image to binary format, finding contours using the findContours function, and checking for contour intersections. After detecting objects in the image, elements are extracted from the mathematical expression, analyzed using a trained neural network, and the string is digitized. The constructed neural network model showed high accuracy of image recognition of mathematical expressions on training and test data samples. The recognition accuracy was no less than 98%.
MODEL OF A UNIFIED INFORMATION SPACE IN PROJECT-BASED EDUCATION
Authors:   Kazan National Research Technological University, MURZIN V.M. , GARIFULLINA E.V. , OKRUZHNOV A.V. ,   Kazan National Research Technological University
Published: November 2024

Until recently, in the territory of the Russian Federation, foreign software was predominantly used in the educational process for disciplines related to the design of objects in the oil and gas chemical complex. Currently, some universities, including the Federal State Budgetary Educational Institution of Higher Education Kazan National Research Technological University (KNRTU), have already transitioned to domestic computer-aided design systems. Along with the process of import substitution, the main direction of reforming the educational process is comprehensive integration of knowledge from various fields, allowing a transition from the formation of digital competencies in the field of 'Design' to structurally and logically connected digital competencies, implementing the concept of a 'Unified Information Space' throughout the entire lifecycle of the modeling object. It should be noted that the implementation of software in an educational institution differs from that in a project or engineering company, since higher education institutions, in the vast majority of cases, lack the ability to establish educational support structures functionally similar to support and customization departments of software in commercial structures. However, in the case of using software products from the 'CSoft Development' company, this factor is compensated by the completeness of delivery and the functionality of the software, which significantly expands the volume of executed term papers and diploma works while simultaneously increasing their quality within the same timeframe. However, despite the noticeably expanded capabilities for competence formation following the implementation of the Model Studio CS software family, a key change in the process of project-based education is the implementation of the CADLib Model and Archive program into the educational process. The result of implementing the CADLib Model and Archive program is the ability to form a unified database from previously unconnected information blocks at the software level. Given the limited time allocated for student training, an extremely important consequence of implementing the CADLib Model and Archive program for the university is the ability to use the cumulative effect and create, based on local digital models developed by individual students, complex models of technological installations of large production structures. On the other hand, the implementation of the CADLib Model and Archive program has provided the opportunity to begin forming a fundamentally new educational track, which can be denoted by the term 'Digital Twin of an Oil Extraction, Oil Refining, and Petrochemical Plant.' The basis for planning the development and implementation of new educational programs is the announced possibility of integrating CADLib Model and Archive with other software united under the common brand IndustriCS.
USE OF HYDROCRACKING IN THE PROCESSING OF EXTRA-VISCOUS OIL FROM THE ASHALCHINSKOYE FIELD
Authors:   Kazan National Research Technological University, AGLIULLINA I.D. , ANTONOVA M.YU. , ZAITSEVA E.G. , PETROV S.M. ,   Kazan National Research Technological University, KAYUKOVA G.P. ,   Kazan National Research Technological University,   Kazan Federal University,   Institute of Organic and Physical Chemistry A.E. Arbuzova, KSC RAS, BASHKIRTSEVA N.YU. ,   Kazan National Research Technological University
Published: November 2024

The high necessity of development of innovative technologies for heavy oil processing is caused by decreasing reserves and increasing volumes of conditioned oil and gas fields in the world. One of the alternatives to primary processing of heavy oils, implemented at the fields, can be technologies based on extraction processes to obtain deasphalted oil. The present study is aimed at studying the products of various stages of hydrotreating of heavy high-sulfur deasphalted oil from the Ashalchinskoye field (Tatarstan, Russia) in the presence of aluminocobaltmolybdenum and aluminoplatinum catalysts. Conversion of deasphalted heavy oil on aluminocobaltmolybdenum catalyst in the hydrocracking process led to changes in the properties and composition of distillate fractions. Decrease in the content of bicyclic aromatic hydrocarbons, increase in the amount of monocycloaromatic and paraffin-naphthenic hydrocarbons, decrease in refractive indices, density and viscosity of distillate fractions were found. The revealed regularities in the composition of hydrotreating products testify to the hydrogenation reactions of polycyclic aromatic compounds, naphthene rings splitting, decomposition of long paraffin chains and their isomerization. It was shown that sulfur compounds contained in different fractions of deasphalted heavy oil differ in their stability to transformation reactions under conditions of catalytic hydrogenation on aluminocobalt-molybdenum catalyst. The second stage of hydrogenation on aluminoplatinum catalyst allowed to reduce the sulphur content in the final product to 0.035 % wt. The obtained data on the composition and technological properties of products of catalytic hydrotreating of heavy deasphalted oil from Ashalchinskoye field indicate the possibility of using widely available aluminocobaltmolybdenum catalysts in processing to obtain high quality hydrocarbon feedstock for petrochemical processes.
INVESTIGATION OF THE EFFECT OF PHYSICO-CHEMICAL MODIFICATION OF WOOD FILLER ON THE STRENGTH CHARACTERISTICS OF WOOD-POLYMER COMPOSITES
Authors:   Kazan National Research Technological University, ANIKEEVA K.G. , SAFIN R.R. ,   Kazan National Research Technological University
Published: November 2024

Rational use of wood in the manufacture of new materials promotes the use of new technologies and combinations of wood with other materials. Of greatest interest are technologies for manufacturing products using modern polymeric materials with the inclusion of modified wood filler in their composition, which leads to increased strength and stiffness, as well as reduces the influence of properties and defects of wood on the material. Recently, there has been an increased interest in the development and study of wood-polymer composite materials (WPCMs) that combine the advantages of both, polymers and wood. The main objective of this study was to evaluate the effect of modified wood flour on the strength characteristics of composites based on polyethylene, polyhydroxybutyrate and thermoplastic starch. In the experiments, composites with different content and type of wood fillers were prepared and then their tensile strength was investigated. The results showed that the addition of modified wood flour to polyethylene and polyhydroxybutyrate based composites leads to an improvement in their strength properties. However, when the modified filler is added to thermoplastic starch based composite, an unexpected result is observed that there is no improvement in strength. Analysis of the obtained data indicates that the role and effect of wood filler modification significantly depend on the type of polymer matrix of the composite. Based on the study, it was concluded that the application of wood filler modification technology is justified to improve the strength characteristics for polyethylene and polyhydroxybutyrate based WPCMs, but is not appropriate for thermoplastic starch based composites. Thus, these results emphasise the importance of selecting the appropriate type of filler modification based on the characteristics of the polymer matrix to achieve optimum performance properties of the composite material.
TECHNOLOGY OF RECYCLING USED PHARMACEUTICAL PLUGS MADE OF MIXED THERMOPLASTIC ELASTOMER
Authors:   Kazan National Research Technological University, KHUSAINOV A.D. , VOLFSON S.I. , ROMANOVA S.M. ,   Kazan National Research Technological University
Published: November 2024

In the course of the activities of medical institutions, waste of various degrees of danger is generated, both for people and the environment. Collection and processing of such waste is currently carried out in accordance with SanPiN 2.1.3684-21. Elastomeric materials are mainly used as primary packaging material for the production of pharmaceutical closures. They are a complex multicomponent polymer elastic material, including an elastomeric component and various additives. In order to reduce the negative impact on the environment in terms of reducing the amount of medical polymer waste, a waste-free technology for processing used pharmaceutical plugs made of mixed thermoplastic elastomer, which belong to Class A waste, has been developed. According to SanPiN 2.1.3684-21, medical waste of class A is the only one that can be sent for recycling as recyclable materials without additional actions. They are safe because they did not interact with infectious patients and biological fluids, which means there are no toxic and radiation components. The starting raw materials for processing are pharmaceutical stoppers intended for sealing injectable forms of antibiotics, bacteriological and biological pharmaceuticals. The plugs are made of a medical-grade thermoplastic elastomer based on polyisobutylene and low-melting thermoplastic. The product has wear resistance, shock resistance, and elasticity at low temperatures. In addition, the material has good fluidity and is excellent for injection molding. The introduction of this technology will make it possible to recycle polymer medical packaging waste to obtain safe secondary material resources without having a negative impact on environmental components.
LITHIOPHILIZED BY COPPER OXIDE THREE-DIMENSIONAL MATRIX BASED ON CARBON NANOTUBES AS A CURRENT COLLECTOR FOR LITHIUM METAL BATTERIES
Authors:   Mendeleev University of Chemical Technology of Russia, KOVAL K.A. , KRYUKOV A.YU. ,   Mendeleev University of Chemical Technology of Russia, EMETS V.V. ,   Frumkin Institute of Physical Chemistry and Electrochemistry of Russian Academy of Sciences, DESYATOV A.V. ,   Mendeleev University of Chemical Technology of Russia
Published: November 2024

Lithium metal batteries, using lithium metal as anode, demonstrate a higher potential for improving energy density and power compared with traditional lithium-ion batteries. But dendritic growth is impeding the application of this technology. The uniformity of lithium deposition is considered as a key to solve this problem. Among the possible ways, creation of 3D structures on the surface of the current collector, especially based on carbon materials, stands out, but at the same time these materials have a low lithium wettability, which makes further modification of the carbon material necessary to achieve uniformity of deposition. In this research the modification of carbon nanotubes obtained by chemical vapor deposition method with the deposition of nanodispersed copper (I) oxide on its surface by using a «copper mirror» reaction is described. The cell modified by this method demonstrates a higher efficiency, as well as having a significantly longer lifetime. However, this method is not enough to completely solve the problem of lithium dendrites, because with increasing uniformity of deposition the amount of metallic lithium losses at the first charge/discharge cycles increases due to the interaction of pure metallic lithium with electrolyte and formation of a larger quantity of solid electrolyte, which in turn reduces the overall performance of the cell. Nevertheless, the obtained modification, although limited in application, can also be deemed as an efficient current collector modification method for designing lithium metal batteries in combination with other techniques of improving the cyclic stability of the investigated promising energy storage systems with increased energy and power density.