Approximate Model Research Articles

Extending the Tight‐Binding Model by Discrete Fractional Fourier Transform

An extension of the tight‐binding approximation model using the discrete fractional Fourier transform is proposed. The quantum state between localized and delocalized states is formulated, where the intermediate state is continuously parameterized. The mixed features of the localized molecular‐like state and delocalized wave‐like state are confirmed when the wavefunction and band diagram of the intermediate state are represented. The proposed model is expected to be used to represent the quantum state with localized/delocalized features in, for example, organic semiconductors.

CFD modeling and optimal design of louvered fins heat exchangers using radical basis function

Louvered fins heat exchanger is widely applied to chemical, energy, and refrigeration industries. The manuscript proposes a way for accurately and efficiently optimizing louvered fins heat exchangers. The louvered fin pitch, louvered fin height and louvered fin length are selected as main independent variable. The optimal structural parameters were extracted by CFD simulation, RBF and MOGA. The Colborn coefficient j and resistance coefficient f need to be optimized to be optimal. Using radical basis function (RBF) to find out approximation model, and the structure size is optimized by multi-objective genetic algorithm (MOGA). It shows that Colborn coefficient j reduces by 4 %, and resistance coefficient f reduces by 43 %. And the convection heat transfer is basically unchanged, when the flow resistent index is obviously reduced. Secondly, the effect of the optimized structure is analyzed in terms of pressure, velocity and temperature, and the optimization effect is further emphasized. Finally, the good performance of the optimization structure is further verified by the field synergy theory.

Dynamic Model Reduction for Viscously Damped Structures with Statically Determinate Interfaces

A novel model reduction method for viscously damped structures with statically determinate interfaces, such as spacecraft flexible appendages, is proposed. The paper presents a derivation of the complete complex modal expansion of the interface dynamic stiffness of these structures. Based on the identity relation for all complex modes, which is obtained during the derivation, it is found that the interface acceleration impedance can be expressed as a rational fraction with high accuracy using only low-order complex modes. Using this rational fraction as an approximation model, numerical results of the frequency response can be fitted. The fitted interface acceleration impedance can be applied to real-time control as a reduced model in the form of a transfer function. Furthermore, it can be transformed into the form of system matrices by introducing auxiliary variables, which then participate in the dynamic analysis of the assembly. The reduction process circumvents complex modal analysis and necessitates only the results of frequency responses. Thanks to the powerful ability of conventional finite element software to perform frequency response analysis, this reduction method can be used for large-scale complex models in actual engineering applications.

Impact of choice of fragility approaches on seismic risk quantification of nuclear power plants

Development and evaluation of seismic fragility of structures and components is crucial in seismic probabilistic risk assessment of nuclear power plants. Simulation-based fragility approaches are a prevailing trend in the literature, while industry-recommended methodologies rely heavily on engineering judgment and deterministic analysis. In this paper, four critical components are selected, modeled and analyzed as a case study. Their fragilities are evaluated using both state-of-the-art fragility methods and code-recommended methods with approximate models. The impact of the choice of fragility approaches on the fragilities of the components and conditional core damage probability of the plant are assessed. The findings reveal that the recommended approaches employed with approximate models have limitations in estimating the median capacity of complex equipment. While there is a notable variance in the treatment of uncertainty among fragility approaches, its influence on core damage probability remains limited unless the component is the primary contributor to core damage.

Multi-Objective Optimization Design of Dynamic Performance of Hydrofoil with Gurney Flap

The horizontal axis tidal turbine, as a crucial device for capturing tidal energy, has gained significant attention because it has better energy efficiency performance. Enhancing the performance of foils, a vital part of tidal turbine blades, can significantly improve tidal turbine performance. Among numerous methods to enhance the foil performance, the Gurney flap has gained significant attention due to its avoidance of complex structural design. Currently, there is limited research on optimizing the design of Gurney flaps while considering the dynamic performance of foils. In this study, the S809 foil with a blade cross-section was selected as the research subject, a multi-objective optimization design platform was created by integrating a multi-objective optimization algorithm with Computational Fluid Dy-namics (CFD) numerical simulation techniques. The objective of this platform is to enhance the dynamic performance of the hydrofoil by optimizing the geometric structure of the Gurney flap. The improvement of dynamic lift and the size of the dynamic stall hysteresis loop are used as objective variables in this study to evaluate the hydrofoil’s dynamic performance. The optimal Latin hypercube design method is used in the optimization process to choose sample locations, and the Kriging approximation model is used to determine the relationship between the design variables and the objective variables. Meanwhile, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used to create a multi-objective optimization platform for solving the optimization problem, and the optimized results are validated using CFD. Comparative validation results show that quantifying the dynamic performance during hydrofoil pitching oscillation and using the optimal Latin hypercube design method and Kriging approximation model for optimizing the Gurney flap structure is rational and accurate. This study explores the mechanism of the Gurney flap through in-depth CFD numerical simulations and finds that the Gurney flap affects the flow characteristics at the hydrofoil’s trailing edge, thereby influencing the performance. It increases the pressure difference between the pressure and suction surfaces, thus enhancing the hydrofoil’s lift. Finally, this article provides three recommended parameters to improve the dynamic performance of the hydrofoil. This research can serve as a reference for the application of Gurney flaps in tidal turbine blade design.

Lyapunov stability of suspension bridges in turbulent flow

In the era of sleek, super slender suspension bridges, facing the issue of stability against dynamic wind actions represents an increasingly complex challenge. Despite the significant progress over the last decades, the impact of atmospheric turbulence on bridge stability remains partially not understood, evoking the need for innovative research approaches. This study aims to address a gap in current research by investigating the random flutter stability associated with variations in the angle of attack due to turbulence, which has not formally been addressed yet. The present investigation employs the 2D rational function approximation model to express self-excited forces in a turbulent flow. The application of this type of models to bridge dynamics yields a viscoelastic coupled dynamic system characterized by memory effects and driven by broad-band long-time-scale noise, described here by a linear homogeneous time-variant differential equation, which shows apparent nonlinear features, and which has rarely been matter of research. Utilizing a Monte Carlo methodology, this work innovates in applying the largest Lyapunov exponent (LE) and the moment Lyapunov exponents (MLE) to study bridge random flutter stability. The calculation of LE and MLE under diverse turbulent wind conditions uncovers lower flutter stability than without turbulence effects. In most cases, sample and low-order p-th moment stability thresholds closely align with the bridge dynamic response pattern; therefore, the flutter critical wind speed is unequivocal. However, under certain turbulence scenarios, it is necessary to resort to MLE for a complete description of stability, evoking some additional consideration of which statistical moments should be considered for the engineering assessment of the flutter limit. Finally, this work provides a qualitative insight into the instability mechanisms by approximating the random parametric excitation with a sinusoidal gust and evaluating the time-periodic system stability via Floquet theory.

Label‐Free Impedance Analysis of Induced Pluripotent Stem Cell‐Derived Spinal Cord Progenitor Cells for Rapid Safety and Efficacy Profiling

AbstractRegenerative therapies, including the transplantation of spinal cord progenitor cells (SCPCs) derived from induced pluripotent stem cells (iPSCs), are promising treatment strategies for spinal cord injuries. However, the risk of tumorigenicity from residual iPSCs advocates an unmet need for rapid SCPCs safety profiling. Herein, a rapid (≈3000 cells min‐1) electrical‐based microfluidic biophysical cytometer is reported to detect low‐abundance iPSCs from SCPCs at single‐cell resolution. Based on multifrequency impedance measurements (0.3 to 12 MHz), biophysical features including cell size, deformability, membrane, and nucleus dielectric properties are simultaneously quantified as a cell is hydrodynamically stretched at a cross junction under continuous flow. A supervised uniform manifold approximation and projection (UMAP) model is further developed for impedance‐based quantification of undifferentiated iPSCs with high sensitivity (≈1% spiked iPSCs) and shows good correlations with SCPCs differentiation outcomes using two iPSC lines. Cell membrane opacity (day 1) is also identified as a novel early intrinsic predictive biomarker that exhibits a strong correlation with SCPC differentiation efficiency (day 10). Overall, it is envisioned that this label‐free and optic‐free platform technology can be further developed as a versatile cost‐effective process analytical tool to monitor or assess stem cell quality and safety in regenerative medicine.

Selecting the number of factors in approximate factor models using group variable regularization

. We propose a novel method for the estimation of the number of factors in approximate factor models. The model is based on a penalized maximum likelihood approach incorporating an adaptive hierarchical lasso penalty function that enables setting entire columns of the factor loadings matrix to zero, which corresponds to removing uninformative factors. Additionally, the model is capable of estimating weak factors by allowing for sparsity in the non zero columns. We prove that the proposed estimator consistently estimates the true number of factors. Simulation experiments reveal superior selection accuracies for our method in finite samples over existing approaches, especially for data with cross-sectional and serial correlations. In an empirical application on a large macroeconomic dataset, we show that the mean squared forecast errors of an approximate factor model are lower if the number of included factors is selected by our method.

Fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems with dead-zone output

This paper investigates the fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems (MASs) with dead-zone output. First, by proposing a dead-zone output approximation model and utilizing Nussbaum-type functions, an adaptive compensation mechanism is developed to alleviate the effect of unknown dead-zone output. Meanwhile, the fuzzy logic systems are incorporated to approximate the unknown functions of the nonlinear MASs. Next, by adopting a time-varying scaling function, a consensus control method is recursively established to steer the consensus errors into a small range within the prescribed time. Furthermore, a self-triggered scheme is established to conserve communication resources while avoiding continuously monitoring trigger conditions. It is illustrated that boundedness of all signals and practical prescribed-time leader-following consensus can be achieved. Finally, the simulation results show the effectiveness of the proposed method.

Connecting palettes — An integrated spectroscopic analysis and UMAP visualisation of Wu Guanzhong's paint palette and his painting

A paint palette of the 20th-century Chinese modern artist Wu Guanzhong (1919–2010) was studied with an integrated spectroscopic approach, macro-scale non-invasive techniques hyperspectral imaging (HSI) and macro X-ray fluorescence (MA-XRF), complemented with Raman spectroscopy. The palette is revealed to be comprised of synthetic inorganic and organic pigments, typical of the 20th century modern palette that is dominated by azo and phthalocyanine pigments. HSI datasets of the paint palette and those of Wu's oil painting ‘Xidi Village’ (2001) were then processed within one unsupervised Uniform Manifold Approximation and Projection (UMAP) model, in an attempt to identify connections between the artefact and the artwork, so that known pigment results of the palette may provide preliminary pigment assignment for the painting. This study describes the steps taken to establish this automated assisted pigment analysis workflow, which involves processing multiple HSI datasets within one UMAP model, density clustering, non-negative least square (NNLS) fitting to extract endmembers (EM) spectra and to generate distribution maps. The approach to perform micro-sampling pigment analysis on the artist's palette (or artist's materials) is considered less invasive to the integrity than that from artworks, the results of which have demonstrated to serve as a critical resource to support further pigment analysis studies of the artist's paintings via non-invasive analytical techniques.

Bidirectional Probabilistic Subspaces Approximation for Multiview Clustering.

The existing multiview clustering models learn a consistent low-dimensional embedding either from multiple feature matrices or multiple similarity matrices, which ignores the interaction between the two procedures and limits the improvement of clustering performance on multiview data. To address this issue, a bidirectional probabilistic subspaces approximation (BPSA) model is developed in this article to learn a consistently orthogonal embedding from multiple feature matrices and multiple similarity matrices simultaneously via the disturbed probabilistic subspace modeling and approximation. A skillful bidirectional fusion strategy is designed to guarantee the parameter-free property of the BPSA model. Two adaptively weighted learning mechanisms are introduced to ensure the inconsistencies among multiple views and the inconsistencies between bidirectional learning processes. To solve the optimization problem involved in the BPSA model, an iterative solver is derived, and a rigorous convergence guarantee is provided. Extensive experimental results on both toy and real-world datasets demonstrate that our BPSA model achieves state-of-the-art performance even if it is parameter-free.

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood.

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.

Research on ride comfort optimization of the vehicle considering the subframe.

When the vehicle is in motion, the elastic deformation of the flexible subframe significantly influences ride comfort. Therefore, it is crucial to investigate the impact of flexible subframes on vehicle ride comfort. In order to enhance the reliability and optimization efficiency of our research, this paper incorporates the concept of elastic deformation in the flexible subframe into the investigation of vehicle ride comfort, and proposes a multi-objective optimization approach to enhance the overall vehicle ride comfort. The vibration mathematical model elucidates how flexible subframes affect vehicle ride comfort and establishes a rigid-flexible coupling model for a specific vehicle with a flexible subframe to analyze the impact of its elastic deformation on vehicle ride comfort through simulation experiments. Subsequently, a radial basis function approximation model is established, and the multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II algorithms are employed to conduct multi-objective optimization of the stiffness of the subframe bushing with the aim of enhancing vehicle ride comfort. The findings indicate that the flexible subframe has a significant impact on vehicle ride comfort. Specifically, on bump roads, peak values of vertical and longitudinal seat accelerations decrease while lateral seat acceleration increases. On random roads, peak values of longitudinal and lateral seat accelerations increase while vertical acceleration decreases. Furthermore, the stiffness of the subframe bushing optimized by the non-dominated sorting genetic algorithm II algorithm further enhances vehicle ride comfort and aligns more closely with the optimization requirements in this study.

On Lower Bounding Minimal Model Count

AbstractMinimal models of a Boolean formula play a pivotal role in various reasoning tasks. While previous research has primarily focused on qualitative analysis over minimal models; our study concentrates on the quantitative aspect, specifically counting of minimal models. Exact counting of minimal models is strictly harder than $\#\mathsf{P}$ , prompting our investigation into establishing a lower bound for their quantity, which is often useful in related applications. In this paper, we introduce two novel techniques for counting minimal models, leveraging the expressive power of answer set programming: the first technique employs methods from knowledge compilation, while the second one draws on recent advancements in hashing-based approximate model counting. Through empirical evaluations, we demonstrate that our methods significantly improve the lower bound estimates of the number of minimal models, surpassing the performance of existing minimal model reasoning systems in terms of runtime.

TIGA-Based English Learning Platform for Language Learner Competency Development in a Digital Environment

This study aimed to develop and implement a digital learning platform (DLP) for English language learners at the junior high school level in Thailand. The study emphasizes the necessity of customizing digital tools to individual needs and behaviors for effective implementation. The successive approximation model (SAM) (Herrholtz, 2020) was used for instructional design, focusing on rapid prototyping and continuous feedback. The DLP was meticulously designed to align with the Basic Education Core Curriculum 2008, the Common European Framework of Reference for Languages (CEFR), and reading literacy under the Programme for International Student Assessment (PISA). The architecture of the platform prioritized open standards and web services to ensure seamless integration and data exchange, although integration challenges were noted. Content and activities were acquired from the TIGA English teaching model, fostering various language skills through tasks (T), language input (I), genre (G), and authentic assessments (A). The TIGA-based DLP was implemented in 8 schools involving 7 teachers and 212 students. Questionnaires and group discussions were used to obtain participant feedback. The questionnaire data were analyzed quantitatively using means and standard deviations. The discussion data were analyzed qualitatively to identify themes. Positive feedback and challenges were revealed. Identified limitations include the need for additional learning activities, clearer teacher guidelines, and enhancements to the graphical user interface (GUI). These findings highlight the potential of the DLP to enrich English language learning and guide future enhancements in digital learning environments.

Removal and Recovery of Europium with a New Functionalized Mesoporous Silica-Based Adsorbent

The discharge of acid mine drainage (AMD), characterized by a high concentration of rare earth elements (REEs), poses a significant threat to the health of ecosystems surrounding water sources. The global market demand for REEs has experienced a notable surge in the past decade. Consequently, recovering REEs from waste streams like AMD not only benefits the environment but also offers financial advantages. Europium (Eu), the rarest among REEs, constitutes only 0.1% w/w in monazite and bastnaesite ores. Eu is extensively used in the production of phosphors, alloys, and additives, and is a critical raw material for developing smart devices, ranging from high-resolution color screens to circuitry. Traditional adsorbents typically exhibit limited selectivity towards REE recovery. Mesoporous silica materials, such as SBA15 (Santa Barbara Amorphous-15), provide excellent tunability and modification capabilities, making them an attractive and cost-effective alternative. This research focused on two key aspects: (i) evaluating the dynamic adsorption column performance of granulated SBA15–NH–PMIDA to preferentially recover Eu, and (ii) employing mathematical modeling to optimize the dynamic adsorption column’s operating conditions for real-world applications with a minimal number of experimental runs. Granulated SBA15–NH–PMIDA was chosen as the adsorbent due to its high adsorptive capacity and selectivity in capturing Eu. The study revealed that granulated SBA15–NH–PMIDA exhibited 57.47 mg/g adsorption capacity and an 81% selectivity towards Eu. Furthermore, SBA15–NH–PMIDA demonstrated preferential adsorption toward Eu in complex multi-component solutions, such as AMD. The linear driven force approximation model (LDFAM) provided an acceptable simulation (R2 > 0.91) under varying operational conditions. This validates the use of the model as a tool to effectively simulate and optimize column experiments that used granulated SBA15–NH–PMIDA to recover Eu.

Innovative Combination of Dodgeball and Soccer Skills in Developing a Recreational Game

Today, traditional games are important because they hold cultural value, foster social interaction, require physical movement, and impart critical thinking skills needed in a multifaceted, interconnected world. This research focuses on creating a modified recreational game called Likyan-Bola, blending elements of dodgeball and soccer kicking skills to suit the needs and abilities of different groups. The study involved 200 participants from Mindanao State University- General Santos City, including varsity athletes, physical education students, and Bachelor of Physical Education students. The Input-Process-Output (IPO) Model was used for qualitative research design, employing the Successive Approximation Model (SAM). Feedback from participants highlighted the game's positive reception, with themes emerging around the modified game's development, gender inclusivity, and skill competence. Overall, Likyan-Bola was found to be playable, valid, and well-received, suggesting its potential for widespread adoption in physical education classes.

ACTION OF A SOLID BODY ON THE INNER SURFACE OF A THICK-WALLED BIMETAL CYLINDER

Purpose. The goal of the work is to obtain an exact solution to the problem of the stress-strain state of a long thick-walled bimetallic cylinder within the framework of the classical theory of elastic materials. Then, using the results obtained with this formulation, it is necessary to propose simpler engineering approaches and explore the possibilities of using asymptotic formulas for express analyzes at the design stage of such structural elements. Research methods. For the main body of the cylinder, the classical equations of the theory of elasticity in displacements are used. For the outer coating (sputtering), the shell theory equations are written based on the Kirchhoff-Love hypotheses. The complex integral Fourier transform and the Failon method are used to approximately find the original stresses and displacements. Asymptotic representations of cylindrical Bessel functions for large values of the argument and representations of improper integrals in the form of combinations of elementary and special tabulated functions are also used. Results. A mathematical model has been constructed to analyze the stress-strain state of a bimetallic cylinder with a thin outer layer of a material different from that of the inner layer. Various boundary conditions are recorded on the inner surface of the cylinder, describing the transmission from a rigid body of either specified forces or specified displacements. For all considered options, using the method of integral transformations, the results were obtained in the form of improper integrals, for the calculation of which a special method was used, aimed at calculating integrals with highly oscillating functions. Examples of specific graphs of changes in the components of the stress-strain state in the cylinder material are given. Depending on the conditions on the inner surface of the cylinder, simpler models are proposed to describe the main body, which are based, depending on the nature of the description of the interaction of the rigid body and the cylinder, on one equation of the theory of elasticity. With this approach, in some important cases it was possible to obtain improper inversion integrals using the asymptotic approach in closed form as a combination of elementary and special tabulated functions. Comparison with the exact approach allowed us to prove the possibility of using approximate models. Scientific novelty. A model of the behavior of a bimetallic cylinder as a body is constructed, the main layer of which is described by the equations of the theory of elasticity, and the theory of shells is used for the outer coating. Various methods of describing the transfer of forces and displacements from a rigid body to the inner surface of the cylinder are considered. The possibility of using the asymptotic approach to obtain relatively simple formulas for carrying out preliminary calculations at the design stage of such structural elements is shown.

Deceptive Path Planning via Count-Based Reinforcement Learning under Specific Time Constraint

Deceptive path planning (DPP) aims to find a path that minimizes the probability of the observer identifying the real goal of the observed before it reaches. It is important for addressing issues such as public safety, strategic path planning, and logistics route privacy protection. Existing traditional methods often rely on “dissimulation”—hiding the truth—to obscure paths while ignoring the time constraints. Building upon the theory of probabilistic goal recognition based on cost difference, we proposed a DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints. Furthermore, to extend this method to continuous domains, we proposed a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) and verified its feasibility in discrete path-planning domains. Finally, we also proposed a DPP method based on proximal policy optimization for continuous path-planning domains under specific time constraints called DPP_PPO. DPP methods like DPP_Q and DPP_PPO are types of research that have not yet been explored in the field of path planning. Experimental results show that, in discrete domains, compared to traditional methods, DPP_Q exhibits better effectiveness in enhancing the average deceptiveness of paths. (Improved on average by 12.53% compared to traditional methods). In continuous domains, DPP_PPO shows significant advantages over random walk methods. Both DPP_Q and DPP_PPO demonstrate good applicability in path-planning domains with uncomplicated obstacles.

Measurement and modeling of the electrical conductivity of organic electrolyte solutions and their mixtures with compressed CO2

The electrochemical carbon dioxide reduction reaction (CO2RR) is of increasing importance for the development of a closed carbon cycle. Here, electrolytes are often used to increase the electrical conductivity and thus the current density. In addition, the use of compressed CO2 and electrolyte-containing organic solvents suppresses hydrogen evolution. However, the electrical conductivity of CO2-saturated electrolyte solutions is mostly unknown. Therefore, their electrical conductivity was investigated in this work at different electrolyte concentrations, pressures (up to 150 bar) and temperatures (298.15 K and 343.15 K). In addition, the simplified Mean Spherical Approximation (MSA-simple) model, supported by ePC-SAFT for density modeling, was used to successfully model the conductivity. The results show that CO2-saturated electrolyte solutions generally exhibit lower conductivities than CO2-free electrolyte solutions. However, at very low electrolyte and CO2 concentrations, the conductivities were higher in CO2-saturated electrolyte solutions than in CO2-free electrolyte solutions, which is a new finding in the literature.