Test Examples Research Articles

An adaptive 6-dimensional floating-search multi-station seismic-event detector (A6-DFMSD) and its application to low-frequency earthquakes in the East Eifel Volcanic Field, Germany

We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There, both magmatic and tectonic earthquakes occur in a depth range between 3 km and 43 km. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

Numerical Solution of External Boundary Conditions Inverse Multilayer Diffusion Problems

The present study is concerned with the numerical solution of external boundary conditions in inverse problems for one-dimensional multilayer diffusion, using the difference method. First, we formulate multispecies parabolic problems with three types of Dirichlet–Neumann–Robin internal boundary conditions that apply at the interfaces between adjacent layers. Then, using the symmetry of the diffusion operator, we prove the well-posedness of the direct (forward) problem in which the coefficients, the right-hand side, and the initial and boundary conditions are given. In inverse problems, instead of external boundary conditions of the first and the last layers, point observations of the solution within the entire domain are posed. Rothe’s semi-discretization of differential problems combined with a symmetric exponential finite difference solution for elliptic problems on each time layer is proposed to develop an efficient semi-analytical approach. Next, using special solution decomposition techniques, we numerically solve the inverse problems for the identification of external boundary conditions. Numerical test examples are discussed.

Efficient Solutions for Stochastic Fractional Differential Equations with a Neutral Delay Using Jacobi Poly-Fractonomials

This paper introduces a novel numerical technique for solving fractional stochastic differential equations with neutral delays. The method employs a stepwise collocation scheme with Jacobi poly-fractonomials to consider unknown stochastic processes. For this purpose, the delay differential equations are transformed into augmented ones without delays. This transformation makes it possible to use a collocation scheme improved with Jacobi poly-fractonomials to solve the changed equations repeatedly. At each iteration, a system of nonlinear equations is generated. Next, the convergence properties of the proposed method are rigorously analyzed. Afterward, the practical utility of the proposed numerical technique is validated through a series of test examples. These examples illustrate the method’s capability to produce accurate and efficient solutions.

Construction and preliminary application of large language model for reservoir performance analysis

A large language model (LLM) is constructed to address the sophisticated demands of data retrieval and analysis, detailed well profiling, computation of key technical indicators, and the solutions to complex problems in reservoir performance analysis (RPA). The LLM is constructed for RPA scenarios with incremental pre-training, fine-tuning, and functional subsystems coupling. Functional subsystem and efficient coupling methods are proposed based on named entity recognition (NER), tool invocation, and Text-to-SQL construction, all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA. This study conducted a detailed accuracy test on feature extraction models, tool classification models, data retrieval models and analysis recommendation models. The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis. The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing. Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA. The research results provide a powerful support to the application of LLM in reservoir performance analysis.

A novel concurrent multiscale method based on the coupling of Direct FE2 and CPFEM

Performing concurrent simulations of macroscopic behaviors and microscopic structures using the crystal plasticity finite element method (CPFEM) presents a substantial difficulty with existing numerical techniques. To address this issue, a novel multi-scale method is proposed that couples CPFEM with a multiscale FEM, specifically Direct FE2. This facilitates the implementation of Direct CP-FE2 in this work. The micro representative volume elements (RVEs) equipped with a crystal plasticity constitutive model and the macro mesh are integrated into a monolithic solution scheme within the Direct FE2 framework. The proposed method integrates the multiscale simulation capability of Direct FE2 with the crystal plasticity model of CPFEM. Alpha titanium (α-Ti), which exhibits two distinct plastic mechanisms of slip and twinning, is chosen as the subject of investigation for conducting numerical experiments. The accuracy and efficiency of the Direct CP-FE2 model are evaluated through multiple plate tension and beam bending tests. The effective validation against the FEM model demonstrated the capability of Direct CP-FE2 to forecast macroscopic deformation behaviors. Meanwhile, the Direct CP-FE2 model can reveal the activation of slip/twinning systems and the evolution of crystal texture at a microscopic level. The influence of the grain orientation-dependent effect can be well considered into the macroscopic analysis with the help of Direct CP-FE2. Based on the testing examples, we demonstrate that the yield state of the macrostructure is enhanced when the crystal orientation is closer to the (0001) direction. Consequently, there exist very little crystal rotation behavior, hindering the evolution of the crystal texture.

Detection of COVID-19 from chest X-rays using deep transfer learning

Objectives: As COVID-19 continues to wreak havoc around the world, effective screening methods for safeguarding public health and bringing the pandemic under control are urgently required. Accordingly, this study proposes a robust pipeline for training and evaluating deep learning predictive models for the detection of COVID-19 from chest X-rays. Methods: The pipeline incorporates multiple techniques to combat overfitting and optimize the prediction results, including data augmentation, Bayesian optimization, the selection of appropriate performance metrics, and the use of early stopping during model training. The proposed pipeline is applied to three common deep learning models: ResNet50, NASNet-A-Mobile, and Xception. Results: The experimental results obtained using the COVID-XRay-5K v3 dataset with 2084 training and 3100 test examples show that the three models achieve AU-PRC (area under precision-recall curve) scores of 0.977, 0.963 and 0.900, respectively; and AU-ROC (area under receiver operating characteristic) scores of 0.994, 0.996 and 0.981. Moreover, at a 98.00% recall (sensitivity), the models achieve high specificities of 97.53%, 97.60% and 86.00%, respectively. Conclusion: Overall, the results suggest that all three models provide a promising approach for the rapid and reliable detection of COVID-19.

Numerical solution of source identification multi-point problem of parabolic partial differential equation with Neumann type boundary condition

We study a source identification boundary value problem for a parabolic partial differential equation with multi-point Neumann type boundary condition. Stability estimates for the solution of the overdetermined mixed BVP for multi-dimensional parabolic equation were established. The first and second order of accuracy difference schemes for the approximate solution of this problem were proposed. Stability estimates for both difference schemes were obtained. The result of numerical illustration in test example was given.

Numerical implementation of solving a boundary value problem with parameter for Fredholm integro-differential equation

The boundary value problem with parameter for Fredholm integro-differential equation with degenerate kernel is investigated in this paper. The aim of the paper is to establish the solvability conditions, to construct analytical and numerical solutions of the considered problem. The basis for achieving the goal is the ideas of Dzhumabayev parameterization method, classical numerical methods of solving Cauchy problems and numerical integration techniques. A problem with parameters is obtained by introducing an additional parameter and a new unknown function. A system of equations with respect to parameters is compiled according to the initial data of the considered equation and boundary conditions. The unknown function is found as a solution of the Cauchy problem for the ordinary differential equation. The equivalence of the original problem and the problem with parameters, the conditions of unique solvability are established and the formula for finding an analytical solution is obtained. Test examples of finding analytical and approximate solutions of the original problem are given.

Multireference calculations on bond dissociation and biradical polycyclic aromatic hydrocarbons as guidance for fractional occupation number weighted density analysis in DFT calculations

This study explores open-shell biradical and polyradical molecular compounds based on extended multireference (MR) methods (MR-configuration interaction with singles and doubles (CISD) and MR-averaged quadratic coupled cluster (AQCC) approach) using the numbers of unpaired densities NU. These results were used to guide the analysis of the fractional occupation number weighted density (FOD) calculated within the finite temperature (FT) density functional theory (DFT) approach. As critical test examples, the dissociation of carbon–carbon (CC) single, double and triple bonds and a benchmark set of polycyclic aromatic hydrocarbons (PAHs) have been chosen. By examining single, double, and triple bond dissociations, we demonstrate the utility and accuracy but also limitations of the FOD analysis for describing these dissociation processes. In significant extension of previous work (Phys Chem Chem Phys 25: 27380–27393), the assessment of FOD applications for different classes of DFT functionals was performed examining the range-separated functionals ωB97XD, ωB97M-V, CAM-B3LYP, LC-ωPBE, and MN12-SX, the hybrid (M06-2X) functional and the double hybrid (B2P-LYP) functional. In all cases, strong correlations between NFOD and NU values are found. The major task was to develop a new linear regression formula for range-separated functionals allowing a convenient determination of the optimal electronic temperature Tel for the FT-DFT calculation. We also established an optimal temperature for the semiempirical extended tight-binding GFN2-xTB method. These findings significantly broaden the applicability of FOD analysis across various DFT functionals and semiempirical methods.

Uncertainty guided test-time training for face forgery detection

The rapid development of generative image modeling poses security risks of spreading unreal visual information, even though those techniques make a lot of applications possible in positive aspects. To provide alerts and maintain a secure social environment, forgery detection has been an urgent and crucial solution to deal with this situation and try to avoid any negative effects, especially for human faces, owing to potential severe results when malicious creators spread disinformation widely. In spite of the success of recent works w.r.t. model design and feature engineering, detecting face forgery from novel image creation methods or data distributions remains unresolved, because well-trained models are typically not robust to the distribution shift during test-time. In this work, we aim to alleviate the sensitivity of an existing face forgery detector to new domains, and then boost real-world detection under unknown test situations. In specific, we leverage test examples, selected by uncertainty values, to fine-tune the model before making a final prediction. Therefore, it leads to a test-time training based approach for face forgery detection, that our framework incorporates an uncertainty-driven test sample selection with self-training to adapt a classifier onto target domains. To demonstrate the effectiveness of our framework and compare with previous methods, we conduct extensive experiments on public datasets, including FaceForensics++, Celeb-DF-v2, ForgeryNet and DFDC. Our results clearly show that the proposed framework successfully improves many state-of-the-art methods in terms of better overall performance as well as stronger robustness to novel data distributions.

Building the Bridge: How System Dynamics Models Operationalise Energy Transitions and Contribute towards Creating an Energy Policy Toolbox

The complexity and multi-dimensionality of energy transitions are broadly recognised, and insights from transition research increasingly support policy decision making. Sustainability transition scholars have been developing mostly qualitative socio-technical transition (STT) frameworks, and modelling has been argued to be complementary to these frameworks, for example for policy testing. We systematically evaluate five system dynamics (SD) energy models on their representation of key STT characteristics. Our results demonstrate that (i) the evaluated models incorporate most of the core characteristics of STT, and (ii) the policies tested in the models address different levels and aspects of the multi-level perspective (MLP) framework. In light of the increasing emergence of energy (transition) models, we recommend to systematically map models and their tested policy interventions into the MLP framework or other sustainability transition frameworks, creating an overview of tested policies (a “policy navigator”). This navigator supports policy makers and modellers alike, facilitating them to find previously tested policy options and related models for particular policy objectives.

Reference alternatives based knockout-tournament procedure for ranking and selection

The knockout-tournament (KT) procedure is an efficient parallel procedure recently developed to solve large-scale ranking and selection (R&S) problems. The procedure adopts a selection structure which is commonly used in many sports tournaments, and eliminates alternatives by conducting “matches” between paired alternatives round-by-round. In this paper, to further improve the procedure’s performance in solving large-scale problems, we propose a major modification of the procedure. Specifically, in each round of the selection, before pairing the surviving alternatives and conducting the matches, we first choose an alternative as the reference alternative and then add the reference alternative to each match. We call the new procedure Procedure i-KT, where i-KT stands for “improved knockout-tournament”. We show that by carefully choosing the reference alternative and designing the pairing scheme for the remaining surviving alternatives in each round of the selection, Procedure i-KT can achieve significant improvements on both the average sample size required in each match and the total number of matches required during the entire selection process. In the meantime, we demonstrate that after the modifications, Procedure i-KT still fits parallel computing environments well. We compare Procedure i-KT with various procedures on different test examples and numerically justify our theoretical analysis.

New covering techniques and applications utilizing multigranulation fuzzy rough sets

In order to conduct an in-depth study of Zhan’s methodology pertaining to the covering of multigranulation fuzzy rough sets (CMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {C}_{{MG}}$$\\end{document}FRSs), we build two families: the family of fuzzy β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-minimum descriptions and the family of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}-maximum descriptions. Subsequently, utilizing these notions, we proceed to develop two variations of covering via optimistic (pessimistic) multigranuation rough set samples (CO(P)MG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO(P)}_{{MG}}$$\\end{document}FRS). The axiomatic properties are examined. In this study, we examine four models of covering using variable precision multigranulation fuzzy rough sets (CVPMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CVP}_{{MG}}$$\\end{document}FRSs). We proceed with analyzing the features of these models. Interconnections between these planned plans are also elucidated. This study explores algorithms that aim to identify innovative strategies for addressing multiattribute group decision-making problems (MAGDM) and multicriteria group decision-making problems (MCGDM). The test examples have been elucidated to provide an inclusive grasp of the efficacy of the offered samples. Ultimately, the distinctions between our methodologies and the preexisting research have been demonstrated.

A reliability-based design optimization strategy using quantile surrogates by improved PC-kriging

In recent years, the surrogate-assisted reliability-based design optimization (RBDO) methods have been continuously developed, and numerous advanced optimization strategies have boosted efficiency and accuracy. However, ensuring sufficient accuracy and feasibility at the optimal is still a challenge. In order to achieve a well-balanced between efficiency, accuracy, and optimal feasibility, in this work, a RBDO strategy using quantile surrogates by improved PC-Kriging model is proposed. The novelty of the proposed method lies in the following main aspects: Firstly, an improved learning function has been developed to significantly enhance the convergence efficiency during the construction of the PC-Kriging model. Secondly, in the RBDO analysis process, a novel "MP+EI" combination point addition strategy is adopted to enhance the approximation of the surrogate model to the optimum of the objective function. It can further improve optimization efficiency and accuracy. On the basis of the rough probability constrained surrogate model established by the global enrichment strategy, a local refinement strategy is introduced to guarantee the accuracy of the quantile evaluation of the probability constrained surrogate model for each iteration solution during the optimization process. Finally, the proposed method is validated by three typical RBDO test examples and one engineering application example.

Numerical Algorithms for Identification of Convection Coefficient and Source in a Magnetohydrodynamics Flow

We consider three integral observations inverse problems (IP1, IP2, IP3) for reconstruction of a time-dependent convection coefficient and a source in magnetohydrodynamics (MHD) model. On the first stage, using the integral observations, we reduce the inverse problems to nonclassical direct (forward) ones. The equivalence between the inverse and direct problems is established. Then, the well-posedness of nonclassical problems is proved. Further, to overcome the difficulties arising from the nonlinear nonlocal parabolic operators, we construct a linarization algorithm in time after their difference space discretizatons. Next, on each iteration, to solve the corresponding linear systems of algebraic equations, we propose adequate fast elimination algorithms. Computational results of test examples data are discussed.

Inverse problem of reconstructing source term for a class of non‐divergence parabolic equations

This paper explores an inverse problem pertaining to the determination of a source function in non‐divergence parabolic equations, where the solution is known at a discrete set of points. Being different from other ordinary inverse source problems, which are often dependent on only one variable, the unknown coefficient in this paper not only depends on the space variable but also depends on the time . On the basis of the optimal control framework, the existence of the optimal solution of the control function is proved. The necessary conditions to be satisfied by the optimal solution are given. The convergence of the optimal solution when the mesh parameters tend to zero is obtained. The conjugate gradient method is applied to the inverse problem and some numerical results are presented for various typical test examples.

MAIN STAGES OF THE LANGUAGE TEST DEVELOPMENT PROCESS AND VALIDATION ANALYSIS METHODS: THE CASE OF GAZI TÖMER

Testing is the most widely used assessment tool in measuring the development process of students' languagecompetence. Therefore, the existence of valid and reliable language tests appropriate to student achievements in education is of great importance. Research on language test development in foreign language teaching in Kazakhstan is insufficient. The aim ofthis study is to practically demonstrate the basic stages of the test development process and how to conduct content validity analysis with the example of the proficiency test of foreign language (Turkish) prepared by Gazi TÖMER. The research method was based on a quantitative research design. The sample of the research for validation was consisted of 7 experts with experience in Gazi TÖMER. For reliability, there were a total of 66 students studying at Abai KazNPU. The first part of the study was examinedfor the content validity of the relevant test. For this purpose, based on the criteria determined by Webb for the evaluation of learning outcomes, the opinions received from field experts were evaluated using the Lawshe technique. The CVR value of the 45-question test was .96, and the CVI was “significant”. The second part of the study was about reliability. For this purpose, the difficulty level of each item was examined based on the data obtained from the pilot application of the test. Data were analyzedusing Excel and SPSS programs. Additionally, the KR-20 value was calculated and found to be .80, and the test was considered reliable. It is believed that the steps and results included in the study will guide the process of creating valid and reliable tests in various fields.

A general consonance principle for closure tests based on -values.

The closure principle is a powerful approach to constructing efficient testing procedures controlling the familywise error rate in the strong sense. For small numbers of hypotheses and the setting of independent elementary -values we consider closed tests where each intersection hypothesis is tested with a -value combination test. Examples of such combination tests are the Fisher combination test, the Stouffer test, the Omnibus test, the truncated test, or the Wilson test. Some of these tests, such as the Fisher combination, the Stouffer, or the Omnibus test, are not consonant and rejection of the global null hypothesis does not always lead to rejection of at least one elementary null hypothesis. We develop a general principle to uniformly improve closed tests based on -value combination tests by modifying the rejection regions such that the new procedure becomes consonant. For the Fisher combination test and the Stouffer test, we show by simulations that this improvement can lead to a substantial increase in power.

A semi-analytical solutions of the multi-dimensional time-fractional Klein-Gordon equations using residual power series method

This study presents a novel approach to getting a semi-analytical solution to the multi-dimensional time-fractional linear and nonlinear Klein–Gordon equations with appropriate initial conditions using the residual power series method. The time-fractional derivative (β) is used in the context of the Caputo approach. Some test examples of KGEs are considered to illustrate the validity and efficiency of the employed RPS method. The RPS solutions are compared with the exact solutions for β = 2 to ensure the method’s reliability and precision. The error bound and convergence analysis of the proposed method are also examined. The effects of the distinct values of fractional order β ∈ (1, 2] on the behavior of the proposed equations are also discussed.

A new filled function method for solving constrained global optimization problems

ABSTRACT Filled function methods have been considered as effective algorithms for solving global optimization problems. However, their effectiveness is greatly affected by the selection of parameters, the noncontinuous or non-differentiable properties of the constructed filled function. In addition, many of the constructed filled functions are only for unconstrained optimization problems, and they are unable to solve constrained optimization problems. In this paper, a new filled function is constructed for solving constrained global optimization problems. The new filled function has only one parameter which needs to be adjusted, and, when the objective functions and constrained functions are all continuously differentiable functions, the constructed filled function is also a continuously differentiable function. Then, the classical local optimization methods can be used to find a better minimum of the proposed filled function and a few parameter adjustments are needed. At last, a new filled function algorithm for constrained global optimization is developed based on the proposed filled function. The new algorithm is applied to several test examples. The results of the numerical experiments show that the new filled function algorithm is effective and efficient.