Search Results (9)

To advance the design of novel clean coal-fired boilers, this study integrates artificial intelligence with numerical simulations to optimize a 130 MW conceptual boiler based on Moderate or Intense Low-oxygen Dilution (MILD) and oxy-coal combustion technologies. First, mathematical models for pulverized-coal MILD-oxy combustion are validated using experimental data from a 0.58 MW pilot-scale boiler and then applied to the full-scale 130 MW boiler. An orthogonal experimental design with four factors and five levels is employed to generate 25 simulation cases, evaluating the effects of burner nozzle configuration and furnace geometry on boiler performance. Based on the simulation dataset, mutual information analysis is conducted to identify key influencing features, guiding nine additional simulations to refine samples in critical design areas. Finally, using the complete 34 simulation data, an optimal boiler structure is identified using support vector machine and multi-objective optimization algorithms. The results indicate that both the burner circumferential diameter and the O₂/CO₂ inlet diameter are positively correlated with nitrogen oxide (NOx) emissions, whereas the former is negatively correlated with the wall thermal non-uniformity. After optimization, the average char burnout rate increased by 1.4%, NOx emissions decreased by 4%, and wall heat non-uniformity coefficient reduced by 1.1%, demonstrating the effectiveness of the proposed approach. Full article

Geological environment evaluation for urban underground space (UGEE) is a critical foundation for optimizing the utilization of urban underground space (UUS) and mitigating exploitation risks. With recent advancements in 3D geological modeling technology, 3D UGEE has emerged as a transformative approach, offering innovative perspectives and technical solutions for rational 3D spatial development and geological risk reduction in subsurface engineering. A core component of the 3D UGEE workflow is the integration of diverse 3D spatial analysis methods, which enable comprehensive extraction of evaluation indices from multidimensional datasets—forming the essential basis for scientifically informed development planning. Focusing on quantitative 3D UGEE, this study systematically investigates the implementation of 3D spatial analysis methods across four key stages: (1) geological condition analysis, (2) evaluation information extraction, (3) 3D comprehensive evaluation, and (4) result analysis. Specifically, five core methodologies are highlighted: (1) 3D spatial statistical analysis, (2) 3D mathematical morphological analysis, (3) 3D surface morphology analysis, (4) 3D spatial distance field analysis, and (5) 3D spatial interpolation analysis. To improve the reliability and objectivity of 3D comprehensive evaluation results, we integrate game theory-based combination weighting with an improved TOPSIS model, which balances the subjectivity of expert judgment and the objectivity of data characteristics while adapting to the 3D block unit data structure, effectively avoiding the bias of single weighting or evaluation models. To validate these techniques, a case study in Hangzhou, Zhejiang Province, is conducted, demonstrating their practical effectiveness in evaluating UUS resources. The findings underscore that advanced 3D spatial analysis methods significantly enhance decision-making precision in UUS planning and risk management, providing a replicable framework for sustainable subsurface development. Full article

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

Chinese Optical Character Recognition (OCR) technology is essential for digital transformation in Chinese regions, enabling automated document processing across various applications. However, Chinese OCR systems struggle with visually similar characters, where subtle stroke differences lead to systematic recognition errors that limit practical deployment accuracy. This study develops FLIP (Feedback Learning-based Intelligent Plugin), a lightweight post-processing plugin designed to improve Chinese OCR accuracy across different systems without external dependencies. The plugin operates through three core components as follows: UTF-8 encoding-based output parsing that converts OCR results into mathematical representations, error correction using information entropy and weighted similarity measures to identify and fix character-level errors, and adaptive feedback learning that optimizes parameters through user interactions. The approach functions entirely through mathematical calculations at the character encoding level, ensuring universal compatibility with existing OCR systems while effectively handling complex Chinese character similarities. The plugin’s modular design enables seamless integration without requiring modifications to existing OCR algorithms, while its feedback mechanism adapts to domain-specific terminology and user preferences. Experimental evaluation on 10,000 Chinese document images using four state-of-the-art OCR models demonstrates consistent improvements across all tested systems, with precision gains ranging from 1.17% to 10.37% and overall Chinese character recognition accuracy exceeding 98%. The best performing model achieved 99.42% precision, with ablation studies confirming that feedback learning contributes additional improvements from 0.45% to 4.66% across different OCR architectures. Full article

With the continuous increase in car ownership, alleviating traffic congestion and reducing carbon emissions have become key challenges in urban traffic management. This study constructs a multi-agent model to evaluate the impact of various traffic optimization interventions on citizens’ travel behavior and traffic carbon emission levels. Different from previous mathematical models, this model integrates computer technology and geographic information systems, abstracting travelers as agents with self-control capabilities who can make independent decisions based on their own circumstances, thus reflecting individual differences in travel behavior. Using the real geographical and social environment of the high-density travel area in Xiaodian District, Taiyuan City as a case study, this research explores the overall improvement in the urban transportation system through the implementation of multiple traffic optimization interventions, such as a parking reservation system, the promotion of the park-and-ride mode, and the optimization of public transportation services. Studies have demonstrated that, compared to reducing bus fares, travelers exhibit a greater sensitivity to waiting times. Reducing bus departure intervals can increase the proportion of park-and-ride trips to 25.79%, surpassing the 19.19% increase observed with fare adjustments. A moderate increase in the proportion of reserved parking spaces can elevate the public transport load to 49.85%. The synergistic effect of a combined strategy can further boost the public transport share to 50.62%, while increasing the park-and-ride trip proportion to 33.6%, thereby highlighting the comprehensive benefits of implementing multiple strategies in tandem. When the parking reservation system is effectively implemented, carbon dioxide emissions can be reduced from over 800 kg to below 200 kg, and the proportion of vehicle cruising can decrease from over 20% to under 15%. These results underscore the critical role of the parking reservation strategy in optimizing traffic flow and advancing environmental sustainability. Full article

The Ordos Basin has abundant resources in its tight sandstone reservoirs, and the use of well logging technology stands out as a critical element in the exploration and development of these reservoirs. Unlike conventional reservoirs, the commonly used interpretation models are not ideal for evaluating tight sandstone reservoirs through logging. In order to accurately evaluate parameters and identify fluid properties in the tight sandstone reservoirs of the Ordos Basin, we propose the adaption of conventional logging curves. This involves establishing an interpretation model that integrates the response characteristics of logging curves to tight sandstone reservoirs in accordance with the principles of logging. In this approach, we create interpretation models specifically for shale content, porosity, permeability, and saturation within the tight sandstone reservoir. Using the characteristics of the logging curves and their responses, we apply a mathematical relationship to link these parameters and create a template for identifying fluid properties within tight sandstone reservoirs. The average absolute errors of the new multi-parameter weighting method shale content interpretation model and porosity classification saturation interpretation model for quantitative evaluation of reservoir shale content and oil saturation are small, and the accuracy meets the production requirements. In this paper, the four-step method is used to identify the fluid properties of tight sandstone reservoirs step by step, which is to use the interrelationship between curves, eliminate the useless information, enhance the useful information, and finally solve the problem of identifying the fluid properties of tight sandstone reservoirs, which is difficult to identify, and realize the linear discrimination of the interpretation standard, which improves the accuracy of interpretation. The proven multi-information, four-step, step-by-step fluid property identification template has an accuracy of more than 90%. The interpretation model has been applied to 20 wells on the block with a compliance rate of 95.23%, providing the basis for accurately establishing the tight sandstone interpretation standard. The newly introduced log evaluation approach for tight sandstone reservoirs effectively overcomes the technical hurdles that have previously hindered the evaluation of such reservoirs in the Ordos Basin. This method is suitable for wide application and can be used for quantitative evaluation of tight sandstone reservoirs in different regions. Full article

In the current digital transformation to Industry 4.0, the demands on the ability of countries to react responsibly and effectively to threats in the field of cyber security (CS) are increasing. Cyber safety is one of the pillars and concepts of Industry 4.0, as digitization brings convergence and integration of information technologies (IT) and operational technologies (OT), IT/OT systems, and data. Collecting and connecting a large amount of data in smart factories and cities poses risks, in a broader context for the entire state. The authors focus attention on the issue of CS, where, despite all digitization, the human factor plays a key role—an actor of risk as well as strengthening the sustainability and resilience of CS. It is obvious that in accordance with how the individuals (decision-makers) perceive the risk, thus they subsequently evaluate the situation and countermeasures. Perceiving cyber threats/risks in their complexity as a part of hybrid threats (HT) helps decision-makers prevent and manage them. Due to the growing trend of HT, the need for research focused on the perception of threats by individuals and companies is increasing. Moreover, the literature review points out a lack of methodology and evaluation strategy. This study presents the results of the research aimed at the mathematical modelling of risk perception of threats to the state and industry through the disruption of CS. The authors provide the developed factor model of cyber security (FMCS), i.e., the model of CS threat risk perception. When creating the FMCS, the researchers applied SEM (structural equation modelling) and confirmatory factor analysis to the data obtained by the implementation of the research tool (a questionnaire designed by the authors). The pillars and sub-pillars of CS defined within the questionnaire enable quantification in the perception of the level of risk of CS as well as differentiation and comparison between the analyzed groups of respondents (students of considered universities in SK and CZ). The convergent and discriminant validity of the research instrument is verified, and its reliability is confirmed (Cronbach’s alpha = 0.95047). The influence of the individual pillars is demonstrated as significant at the significance level of α = 5%. For the entire research set N = 964, the highest share of risk perception of CS threats is achieved by the DISRIT pillar (disruption or reduction of the resistance of IT infrastructure). Full article

At the present time, an effective methodological toolkit has not been developed to manage economic processes in modern conditions in order to ensure the sustainable economic development of enterprises. On the basis of mathematical modeling, it has been proposed that such a tool could be created to provide a quantitative assessment of the main parameters in the creation of innovative products and activities of enterprises. Additionally, on the basis of this tool, decision makers could regulate these processes by introducing additional resources and buying risks. For this purpose, it is necessary to modernize the means of production, which requires a reassessment of the technical level of enterprises. It is possible to achieve these parameters through a certain technical policy of production modernization. In this aspect, the technical policy of enterprises should consider the fact that, depending on the complexity of innovative products put for development and production, the time lag between the start of production of such products and the moment when they enter the market with competitive advantage changes. This competitive advantage is determined by the design methods at a given cost. At the same time, the service models of enterprises are used as one of the most important tools to achieve the profitability of the enterprise, leading to its sustainable economic development. As a result of the proposed toolkit, a strategy for managing the processes of the sustainable economic development of enterprises could be developed based on the creation of an intelligent system using artificial intelligence and the use of large databases that create information support for decision makers to regulate these processes. This set of tools makes it possible to achieve the organization of sustainable economic development. To assess the technical, economic, and technological level of the enterprise, a complex integral indicator was obtained on the basis of mathematical modeling. It was proposed to confirm the obtained integral assessment by comparing the obtained calculated data with an expert assessment. The method of expert evaluation is used in the article. Expert assessments were obtained in science-intensive organizations in which the authors held managerial positions and were engaged in scientific projects. The obtained inputs were formulated based on the analysis of the reporting documentation provided by the organizations. The conducted research indicates the importance of the proposed practical recommendations and materials for solving the tasks set in this study. Full article

The article is dedicated to the development of a mathematical model and methodology for evaluating the effectiveness of integrating information technology solutions into digital platforms using virtual simulation infrastructures. The task of selecting a stack of technologies is formulated as the task of selecting elements from sets of possible solutions. This allows us to develop a mathematically unified approach to evaluating the effectiveness of different solutions, such as choosing programming languages, choosing Database Management System (DBMS), choosing operating systems and data technologies, and choosing the frameworks used. Introduced technology compatibility operation and decomposition of the evaluation of the efficiency of the technology stack at the stages of the life cycle of the digital platform development allowed us to reduce the computational complexity of the formation of the technology stack. A methodology based on performance assessments for experimental research in a virtual software-configurable simulation environment has been proposed. The developed solution allows the evaluation of the performance of the digital platform before its final implementation, while reducing the cost of conducting an experiment to assess the characteristics of the digital platform. It is proposed to compare the characteristics of digital platform efficiency based on the use of fuzzy logic, providing the software developer with an intuitive tool to support decision-making on the inclusion of the solution in the technology stack. Full article