Modeling Process Research Articles

Political communication from the standpoint of ideational turn: cognitive representation of the idea of Shanghai Cooperation Organization (in English)

The article examines the specifics of political cognition as a structural element of political communication. The main task of the study is to identify, in the conditions of an ideational turn, the characteristics of the Shanghai Cooperation Organization political and media axis, modeled in scientific and media publications and conditioned by different logic and political cognitions of the authors. The mediatization of modern political discourse makes it possible to consider ideology as a potential opportunity that creates grounds for organizing the institutional present through the symbolism recontextualization of the institutional past. The Argentine sociologist, anthropologist and semiotic, Professor of Communication Sciences at the University of San Andres, Elisio Verona’s position is recognized as a priority in understanding ideology. Critical linguistics is considered to be the most productive in terms of representing and describing the multiplicity of interaction between participants in political communication, balancing communication of political cognitions. In the conceptual modeling process, elements of political analysis are used, based on the appeal to ideas as causal variables. The research is carried out taking into account the importance of causal-constitutive logics, while causal logic is recognized as the priority logic of communication, with an orientation towards the idea as a focal point. The authors consider the idea of cooperation, which models the SCO’s institutional functions, through cognitive presentations in scientific and media sources, and ground its interpretation on the terminological and conceptual apparatus of Russian constructivism.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

A Survey of Data-Driven Construction Materials Price Forecasting

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.

A recursive algorithm for dynamics of planar multibody systems with frictional unilateral constraints

The contact impact problem in planar multibody systems can be efficiently solved by formulating it as the linear complementarity problem, which requires a complex modeling process. To simplify the process, a recursive algorithm for the dynamics of planar multibody systems with frictional unilateral constraints is proposed based on the reduced multibody system transfer matrix method. Firstly, the contact forces of frictional unilateral constraints are integrated into the recurrence relations of system components using Lagrange multipliers. Subsequently, the relative motion equations of the contact positions are discretized in time, which are then utilized to describe the linear complementarity problem for systems. The dynamics of the system is solved by the Moreau time-stepping method with the recursive method. Finally, the proposed algorithm was validated using the woodpecker toy and used to model a slider-crank mechanism with clearance, which shows its characteristics of facilitating modeling, universal, and highly programmable. This recursive algorithm provides an effective tool for solving non-smooth planar multibody systems while extending the application of the multibody system transfer matrix method.

Metacognitive strategies in mathematical modelling: a case study with engineering students

During the last years, mathematical modelling has been entering the field of engineering education because it is a bridge between mathematics and industry that also promotes the development of skills that require the engineer of the twenty-first century. Nevertheless, the modelling process is involved and requires the deployment of cognitive skills of superior level, particularly metacognitive skills. This contribution presents the results of a multiple qualitative case study on metacognitive strategies used by groups of engineering students associated with each transition of the mathematical modelling process. During the development of the course, observations and retrospective interviews were carried out, which evidenced the use of metacognitive planning strategies from simplification to interpretation; metacognitive monitoring strategies throughout the modelling process, metacognitive regulation strategies from mathematization to mathematical work and metacognitive evaluation strategies during simplification, mathematization and validation. The results expand the classification framework of metacognitive strategies for engineering students.

Simulation and optimization of cheese whey additive for value-added compost production: Hyperparameter tuning approach and genetic algorithm

Cheese whey is a difficult and costly wastewater to treat due to its high organic matter and mineral content. Although many management strategies are conducted for whey removal, its use in composting is limited. In this study, the effect of cheese whey in the composting of sewage sludge and poultry waste on compost quality and process efficiency was investigated. Also, valid and consistent simulations were developed with Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Neural Network Regression (NNR) Machine Learning (ML) algorithms. The results of all physicochemical parameters determined that 3% of cheese whey addition for both feedstocks improved the composting process's efficiency and the final product's quality. The best results obtained through hyperparameter tuning showed that Gaussian Process Regression (GPR) was the most effective modeling tool providing realistic simulations. The reliability of these simulations was verified by running the GPR process 50 times. MdAPE demonstrated the validity and consistency of the created process simulations. Moreover, a genetic algorithm was used to optimize these dependent simulations and achieved almost 100% desirability. Optimization studies showed that the effective cheese whey ratios were 3.2724% and 3.1543% for sewage sludge and poultry waste, respectively. Optimization results were compatible with the results of experimental studies. This study provides a new strategy for the recovery of cheese whey as well as a new perspective on the effect of cheese whey on both physicochemical parameters and composting phases and the modeling and optimization processes of the results.

Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model

Machine learning-based aerodynamic reduced-order models (ROMs) combine high accuracy with extremely low computational costs, making them highly effective in predicting nonlinear and unsteady bridge aerodynamic forces. Although several machine learning-based nonlinear aerodynamic models have been developed, the majority are built on a single wind speed parameter. However, in nonlinear aerodynamic prediction and aeroelastic analysis of bridges, the variability in incoming wind speed significantly influences the computed results. A ROM relying solely on a single wind speed lacks the ability to accurately forecast the intricate dynamic behaviors arising from changes in wind speed. When the incoming wind speed changes, the model's prediction accuracy significantly decreases. Usually, it is necessary to establish a new database and train a new model, which not only increases time and cost but also greatly reduces the convenience of the ROM. Addressing this challenge, this study proposes a multiple-wind-speed (MWS) nonlinear unsteady bridge aerodynamic model based on the LSTM deep neural network. Taking the Taohuayu Yellow River Bridge in the Henan Province of China as an example, the modeling process of the proposed MWS-ROM is demonstrated, along with non-linear aerodynamic predictions of the deck under various conditions and aerodynamic-elastic analysis of the deck under different wind speeds. The research results show that the trained LSTM network can accurately predict the nonlinear aerodynamic forces of bridges under single and double degrees of freedom vibration conditions. The MWS-ROM performed well in predicting convergent vibrations at low wind speeds and limits cycle oscillations at high wind speeds, aligning closely with results from the CFD full-order model. Compared to CFD, the aerodynamic ROM based on the LSTM network significantly enhances computational efficiency, consequently boosting the convenience and efficiency of bridge flutter analysis. Additionally, the methodology proposed herein can be extended for wind-induced vibration control and response prediction in other types of deck sections.

An investigation of students’ mathematical connection and metacognitive skills in the mathematical modelling process

The purpose of the study is to investigate the changing mathematical connection and metacognitive skills of students in the process of applying mathematical modelling. The data used for the study were collected from modelling activities, students’ written responses to modelling activities, audio recordings, semi-structured interviews, and observation notes. The data obtained were analysed using content analysis. As a result of the data analysis, it was found that metacognitive skills were used in each activity in the mathematical modelling process and were developed in interaction with each other. Although inter-concept and real-world connection skills are most commonly used in mathematical connection skills, modelling activities were found to enhance connection skills by allowing students to use connection skills with different disciplines and between different representations of the concept. It was found that metacognitive skills and mathematical connection skills develop interdependently, and it was found that mathematical modelling activities can be used as a tool for developing both of these skills.

Wind Power Curve Model Combining Smoothed Spline with First-Order Moments and Density-Adjusted Wind Speed Strategy

Given the variety of potential application scenarios, it is crucial to develop wind power curves that are more accurate, smoother, efficient, and applicable across a wider range of contexts. To this end, a wind power curve model combining smoothed spline with first-order moments (FOM) and density-adjusted wind speed (DAWS) strategy is proposed in this paper. First, the DAWS strategy is employed to consider the influence of meteorological factors on the wind power curve by adjusting wind speed. This strategy reduces the root mean square error (RMSE) by 5.81%-6.17% and the mean absolute error (MAE) by 5.84%-6.44% without adding complexity to the model. Secondly, FOM is proposed as a substitute for the original data during the modeling process. This approach reduces the number of operations on the similar data, resulting in a reduction of modeling time by 48.11%-99.89%. Furthermore, the impact on model accuracy is minimal. Finally, a the wind power curve model based on smooth spline is constructed, which exhibits superior smoothness, a broader range of generalizability, enhanced model accuracy, and reductions in RMSE by 5.67%-23.87% and MAE by 4.79%-22.35% in comparison to the control method.

Improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Geoinformation modeling of sand injectites

The results of application of geoinformation modeling technology to create a three-dimensional model of Paleogene sand injectites are presented. The mineralogenic significance of sand injectites as sources of glass and construction raw materials is described. The modeling process using the obtained data set and three-dimensional modeling environment Blender is revealed step by step. The compiled spatial models can be used in blocking and calculation of reserves and resources at the objects of subsoil use.

Study on whitecapping dissipation process for wave modelling during tropical cyclones

Study on whitecapping dissipation process for wave modelling during tropical cyclones

Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance.

Neural architecture search (NAS) has recently gained extensive interest in the deep learning community because of its great potential in automating the construction process of deep models. Among a variety of NAS approaches, evolutionary computation (EC) plays a pivotal role with its merit of gradient-free search ability. However, a massive number of the current EC-based NAS approaches evolve neural architectures in an absolutely discrete manner, which makes it tough to flexibly handle the number of filters for each layer, since they often reduce it to a limit set rather than searching for all possible values. Moreover, EC-based NAS methods are often criticized for their inefficiency in performance evaluation, which usually requires laborious full training for hundreds of candidate architectures generated. To address the inflexible search issue on the number of filters, this work proposes a split-level particle swarm optimization (PSO) approach. Each dimension of the particle is subdivided into an integer part and a fractional part, encoding the configurations of the corresponding layer, and the number of filters within a large range, respectively. In addition, the evaluation time is greatly saved by a novel elite weight inheritance method based on an online updating weight pool, and a customized fitness function considering multiple objectives is developed to well control the complexity of the searched candidate architectures. The proposed method, termed split-level evolutionary NAS (SLE-NAS), is computationally efficient, and outperforms many state-of-the-art peer competitors at much lower complexity across three popular image classification benchmark datasets.

DEVELOPMENT PROCESS OF THREE-DIMENSIONAL CHARACTER DESIGN AND MODELING FOR MOBILE GAMES

This research focuses on the development process of three-dimensional character design and modeling in mobile games with the rapid development of the game market in the world and in Turkey. The rapid development of the game market has led to an increase in the number of these companies and the necessity to create, develop and improve the infrastructure of the games by programming mobile games and adding new design elements with the increasing number of companies invested or acquired by important national or international companies. Undoubtedly, the visuality used in games is very important in terms of increasing the demand for them and the pleasure they will give to the player. Especially three-dimensional character design ranks first among the criteria that increase the appreciation of the game. Characters should be well designed and original in order to be adopted and preferred by users. This study aims to reveal the development process of three-dimensional character design for mobile games. With this research, it is aimed to draw attention to the design and modeling stages of three-dimensional characters produced for mobile games. The data obtained as a result of the research on the subject show that character design, which is one of the design steps that determine the success of a game, has important contributions from the development stage of the game to the marketing stage. The effectiveness of the character reinforces the bond that the users establish with him/her and thus enables them to internalise and adopt the game they play. For this reason, it has been determined that while character design is critical to the success of a game, it should also be considered that it includes various other elements that also affect the overall game experience.

AUTOLYCUS: Exploiting Explainable Artificial Intelligence (XAI) for Model Extraction Attacks against Interpretable Models

Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models. However, the data used for such explanations can pose security and privacy risks. Existing literature identifies attacks on machine learning models, including membership inference, model inversion, and model extraction attacks. These attacks target either the model or the training data, depending on the settings and parties involved. XAI tools can increase the vulnerability of model extraction attacks, which is a concern when model owners prefer black-box access, thereby keeping model parameters and architecture private. To exploit this risk, we propose AUTOLYCUS, a novel retraining (learning) based model extraction attack framework against interpretable models under black-box settings. As XAI tools, we exploit Local Interpretable Model-Agnostic Explanations (LIME) and Shapley values (SHAP) to infer decision boundaries and create surrogate models that replicate the functionality of the target model. LIME and SHAP are mainly chosen for their realistic yet information-rich explanations, coupled with their extensive adoption, simplicity, and usability. We evaluate AUTOLYCUS on six machine learning datasets, measuring the accuracy and similarity of the surrogate model to the target model. The results show that AUTOLYCUS is highly effective, requiring significantly fewer queries compared to state-of-the-art attacks, while maintaining comparable accuracy and similarity. We validate its performance and transferability on multiple interpretable ML models, including decision trees, logistic regression, naive bayes, and k-nearest neighbor. Additionally, we show the resilience of AUTOLYCUS against proposed countermeasures.

Retrieval In Decoder benefits generative models for explainable complex question answering

Large-scale Language Models (LLMs) utilizing the Chain-of-Thought prompting demonstrate exceptional performance in a variety of tasks. However, the persistence of factual hallucinations remains a significant challenge in practical applications. Prevailing retrieval-augmented methods treat the retriever and generator as separate components, which inadvertently restricts the generator’s capabilities to those of the retriever through intensive supervised training. In this work, we propose an unsupervised Retrieval In Decoder framework for multi-granularity decoding called RID, which integrates retrieval directly into the decoding process of generative models. It dynamically adjusts decoding granularity based on retrieval outcomes, and duly corrects the decoding direction through its direct impact on the next token. Moreover, we introduce a reinforcement learning-driven knowledge distillation method for adaptive explanation generation to better apply to Small-scale Language Models (SLMs). The experimental results across six public benchmarks surpass popular LLMs and existing retrieval-augmented methods, which demonstrates the effectiveness of RID in models of different scales and verifies its applicability and scalability.

An experimental analysis of mechanical properties for a dissimilar pattern structure in the 3-D printing of a PLA5F filament using the Taguchi method

Abstract Many automobile components and machine parts can be fabricated using the Fused Deposition Modeling (FDM) process with materials such as Polylactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PET-G), and polymeric composite materials (e.g., PLA with carbon fiber, PLA with glass fiber). In this study, a new polymeric composite material was fabricated using Polylactic Acid and natural flax fiber was analysed for tensile stress, elongation, and impact load resistance using Taguchi Analysis. This analysis optimized the printing parameters, including layer thickness (0.15, 0.25, 0.35 mm), nozzle movement speed (80, 100, 120 mm s−1), filling structure (Lines - a, Triangular - b, and Octet - c), and occupancy rate (20%, 40%, 60%). The American Society for Testing and Materials (ASTM) standards for tensile strength (ASTM D638) and impact strength (ASTM D256) were used for evaluation. As a result, layer thickness was found to be the most effective variable for improving tensile characteristics, compared to extruder temperature, occupancy rate, or filling structure pattern. Mechanical properties including a layer thickness of 0.25 mm, an occupancy ratio of 20% for the bottom of the 2nd layer and 40% for the top of the 4th layer, triangular and octet filling structures, a nozzle speed of 100 mm s−1, and an extruder temperature of 200 °C are considered the most appropriate parameters for producing automotive parts in Three Dimensional (3D) Printing. Due to its tensile properties and impact strength resistance, these settings can be utilized in potential application in a wide variety of machine parts and vehicle components.

Automatic modeling of the current state components of ancient buildings based on 3D deformation algorithm

AbstractAncient buildings possess significant artistic and cultural value. The digitization of these structures is a crucial aspect of their restoration, providing foundational data for subsequent stress and health analyses. However, creating accurate models of ancient buildings is a challenging endeavor, even for experienced researchers, especially when dealing with a large number of structures. A pressing issue that needs addressing is how to quickly obtain accurate models of ancient buildings while ensuring both precision and efficiency. Currently, one of the more precise methods for reconstructing models of ancient buildings involves the use of scanned point clouds and manual reconstruction through modeling software. However, this method suffers from poor accuracy and low efficiency, making the modeling process complex and time-consuming. In this article, we will refer to the model generated from the point cloud of real components as the "current state model", and the unforced mesh model of the ancient building as the "standard model". An algorithm is proposed to construct the current state model of ancient buildings by guiding the deformation of the unforced standard model using the scanned point cloud model. First, this paper designs an automatic modeling method for ancient building components as the foundational data before deformation, addressing the issue of low modeling efficiency for ancient buildings. Second, it proposes a method for automatically calculating deformation control point pairs based on the characteristics of ancient buildings, solving the problem of manually locating control points. Finally, the proposed adaptive weight Laplacian deformation algorithm is applied to deform the standard model into the current state model.

Extended matrix modeling of integrated energy systems considering network dynamic characteristics and source–load uncertainty

A refined and efficient modeling approach is indispensable for enhancing the timeliness of scheduling strategies for the integrated energy system (IES). Non-flowing variables such as pressure and temperature introduced by energy devices, however, worsen the complexity while improving the completeness for models. Nonlinear and uncertain variables associated with network dynamic characteristics and source–load uncertainty in the systems seriously postpone the modeling processes. An extended matrix modeling approach is proposed in this paper to tackle such issues. New nodes, branches, definition rules and topological matrices are first presented to create an extended matrix model that is compatible with both flowing and non-flowing variables. To boost the modeling efficiency, adaptive piecewise linearization (APL) with extended matrix form and modified Taylor expansion are applied to approximate various nonlinear characteristics. The convex approximation based on Kullback–Leibler (KL) divergence and the strong duality theory characterized by Wasserstein distance are adopted to deal with uncertain variables, which adapt to combine with the above matrix model. A linearized matrix modeling framework applicable to highly automated by computers is thus established. The distributionally robust optimization (DRO) scheduling strategy is verified in a standard test system with the proposed modeling. The effect of dynamic characteristics and uncertain factors on the strategy is discussed. The results show that the extended matrix model simulates the response for real physical systems more effectively, which exhibits the better performances in modeling, objectives and scheduling strategies.

A robust data-driven model predictive thermal control for rack-based data center

Optimizing and controlling of air-cooled data centers cooling systems are essential for reducing energy consumption. However, traditional cooling system control strategies have been proven inadequate due to the thermodynamic processess complexity and the challenge for accuratly modeling the thermal behavior of data center (DC). Therefore, a data-driven control method whithout explicit modeling process is urgently needed. In this paper, a robust data-driven model predictive control (MPC) method for air-cooled rack-based DCs, based on Willems fundamental lemma, is proposed. The methodology is executed in three phases: Firstly, we show that the system is approximately linear time-invariant (LTI) by zonal model method and experiment. Secondly, we propose a data-driven MPC framework for air-cooled rack-based DC. Thirdly, considering the uncertainties caused by disturbances and the nonlinear dynamics, the data-driven MPC approach is tailored as a robust one. Finally, the proposed approach is verified by computational fluid dynamics simulation experiments. This approach ensures the rack-based DC cold aisle temperature to the reference temperature as well as within acceptable limits. The experiment shows that the steady-state error is guaranteed to be less than 2%, with a control accuracy increased by 2.4% compared with the regular MPC. Thus, this method provides an alternative solution for maintaining the reference temperature in air-cooled rack-based DCs by utilizing only input–output data.