Convergence Of Method Research Articles

Factor structure and validation of the electronic form of the Oxford Happiness Questionnaire in patients with COVID-19

Background and Objectives: Background and Aim: Validity Scale Validation can help identify coronavirus disease patients; therefore, the aim of this study was to evaluate the validation of the electronic form of the Oxford Happiness Questionnaire in patients with COVID-19. Methods: This study is instrumental psychometrics that the statistical population includes all patients in 19 cases in Tehran. The number of samples was determined based on psychometric criteria to be 100 for the predictive validity section and 251 for the convergence validity section. Samples were selected by cluster sampling. Research data were collected electronically through two questionnaires of happiness (Oxford) and happiness of positive psychological characteristics by sending the questionnaire link to the participants' mobile phones. To evaluate the validity of the scale, content validity, convergence, and factor analysis methods were used. The reliability of the scale was assessed by internal consistency methods and class reliability. Results: The results of exploratory factor analysis showed that the researcher-made questionnaire consists of three factors and has good validity and reliability. The second-order confirmatory factor analysis also confirmed the three-factor model. This questionnaire was administered along with the short form of the Oxford Happiness Scale, which had 13 items of good reliability and validity. Conclusion: The Oxford Happiness Scale in COVID-19 patients can be used to assess happiness in COVID-19 patients.

Homogenization of a Thermoelastic Bristly Structure Immersed in a Thermofluid

The article considers the mathematical model describing the joint motion of a viscous compressible heat-conducting fluid and a thermoelastic plate with a fine two-level thermoelastic bristly microstructure attached to it. The bristly microstructure consists of a great amount of taller and shorter bristles, which are periodically located on the surface of the plate, and the model under consideration incorporates a small parameter, which is the ratio of the characteristic lengths of the microstructure and the entire plate. Using classical methods in the theory of partial differential equations, we prove that the initial-boundary value problem for the considered model is well-posed. After this, we fulfill the homogenization procedure, i.e., we pass to the limit as the small parameter tends to zero, and, as a result, we derive the effective macroscopic model in which the dynamics of the interaction of the ‘liquid–bristly structure’ is described by equations of two homogeneous thermoviscoelastic layers with memory effects. The homogenization procedure is rigorously justified by means of the Allaire–Briane three-scale convergence method. The developed effective macroscopic model can potentially find application in further mathematical modeling in biotechnology and bionics taking account of heat transfer.

A Study of Algorithms for the p th Root of Matrix

Some results for Pth root of square matrix are revived. It shows that matrix sign function and Wiener- Hopf factorization plays important role in Pth root of matrix. Some new algorithms for computing P th root numerically can design by these results. We can analyze Stability properties of iterative methods for convergence.

European institutional quality and carbon emissions: Convergence club analysis

If the quality of institutions in the European Union (EU) converged, harmonized policies could achieve climate neutrality. This paper examines institutional convergence within the EU using log-t regression, allowing for the possibility of multiple equilibria. It also analyzes how overall institutional quality affects carbon dioxide (CO2) emissions in three identified convergence clubs over thirty years. Driscoll-Kraay and Prais-Winsten standard errors estimators are used, accounting for economic growth, renewable energy, industrialization, urbanization, innovation, and exogenous shocks. The results show that institutional quality affects CO2 emissions differently across clubs, emphasizing customized policy approaches. Addressing endogeneity, a two-stage least squares fixed effects estimator reveals that institutional quality has a negative, but statistically insignificant, effect on CO2 emissions in both the highest and lowest institutional quality clubs, while significantly increasing emissions in the mid-tier club. Economic growth increases CO2 emissions, whereas renewable energy consumption reduces them across all convergence clubs and methods. Other factors vary across clubs.

A computer tool for Aiken's coefficients calculation: Applications in clinical and research settings

AbstractRationaleIn clinical and research settings, the validity and reliability of measurement instruments are crucial for reliable results. Current methods for computing validation metrics like Aiken's coefficients are often complex and error‐prone, highlighting the need for a standardized computational tool to simplify and enhance this process.Aims and ObjectivesThe objective of this study was to develop AikenCalc, a tool created to automate the calculation of Aiken coefficients. This tool provides a quantitative method for examining the validity and reliability of consensus among expert judges.MethodAikenCalc was developed with Shiny app, an R library that allows an easy‐to‐use interface and improved computational efficiency. It was validated using a convergence method by comparing the manual calculations with those obtained using AikenCalc. Additionally, to examine usability and interface, six experts evaluated their experience using AikenCalc and provided improvement feedback.ResultsAikenCalc demonstrated good convergent validity, closely aligning with the results obtained manually, and the six experts gave favorable ratings regarding the usability and interface of AikenCalc. Finally, it was shown that AikenCalc significantly reduced the time required for calculations, thanks to its user‐friendly interface.ConclusionAikenCalc represents a significant improvement in the validation of measurement instruments by automating and simplifying the calculation of Aiken's coefficients through a Shiny web application. Additionally, it fills a knowledge gap, as there is currently no similar calculator available in the field of research.

Psychometric properties of the Questionnaire for the evaluation of digital learning in higher education in Peruvian university students

This research presented the results of the process of creation and arrangement of the psychometric characteristics of the Questionnaire for the Evaluation of Digital Learning in Higher Education (QEDLHE). With a quantitative, predominantly instrumental approach, 379 students from different universities in Peru were considered for the validity and reliability stage. The reliability of instruments was assessed using the method of split halves with Pearson and Cronbach's Alpha ratified by the Guttman coefficient; in the exploratory factor analysis, the KMO (Kaiser-Meyer-Olkin) contrast and Bartlett's test were used, the rotation method for convergence with Varimax with Kaiser normalization; in the confirmatory factor analysis, the AMOS23 tool was used. Two models were used to compare the best fit indicators as a result. The conclusions show that the instrument has been operationalized theoretically by five dimensions composed of twenty-four items.

Large deviation principle for pseudo-monotone evolutionary equation

. In this article, we explore Freidlin-Wentzell’s large deviation principle for a stochastic partial differential equation with the nonlinear diffusion-convection operator in divergence form satisfying p-type growth, involving coercivity assumptions, perturbed by small multiplicative Brownian noise. We use the weak convergence method to prove the Laplace principle, which is equivalent to the large deviation principle in our framework.

A meshless stochastic method for Poisson-Nernst-Planck equations.

A plethora of biological, physical, and chemical phenomena involve transport of charged particles (ions). Its continuum-scale description relies on the Poisson-Nernst-Planck (PNP) system, which encapsulates the conservation of mass and charge. The numerical solution of these coupled partial differential equations is challenging and suffers from both the curse of dimensionality and difficulty in efficiently parallelizing. We present a novel particle-based framework to solve the full PNP system by simulating a drift-diffusion process with time- and space-varying drift. We leverage Green's functions, kernel-independent fast multipole methods, and kernel density estimation to solve the PNP system in a meshless manner, capable of handling discontinuous initial states. The method is embarrassingly parallel, and the computational cost scales linearly with the number of particles and dimension. We use a series of numerical experiments to demonstrate both the method's convergence with respect to the number of particles and computational cost vis-à-vis a traditional partial differential equation solver.

Bipartite tracking for Euler–Lagrange systems via prescribed‐time hierarchical control based on the matrix‐weighted signed digraphs

AbstractThe goal of this article is to investigate the bipartite tracking control problem using a prescribed‐time convergence method for Euler–Lagrange systems (ELSs) with external disturbances. Agent interactions are represented by a matrix‐weighted signed directed graph. There are not only cooperative but also adversarial interactions. An essential component of the system, the prescribed‐time hierarchical control (PTHC) algorithm, including a more general time‐varying function, is newly developed to guarantee that all agents converge to the same leader state but with opposite signs within a prescribed time. The salient feature of the introduced method lies in the fact that the convergence time of the control objective can be prespecified by the user. The state transformation, the property of the matrix‐weighted Laplacian, and the generalized Lyapunov stability argument are employed to theoretically validate the proposed algorithms. Finally, the effectiveness of the algorithm is confirmed through the execution of numerical examples.

SIoT identity management with the ability to preserve owner privacy and built‐in resistance to Sybil attacks

SummaryThe Social Internet of Things (SIoT) allows for more efficient and intelligent communication between entities (humans and objects). Managing the digital identity of objects, protecting the owner's privacy, and resisting impersonation attacks are crucial challenges in SIoT. On the other hand, the classic centralized identity provider servers contain valuable information for hackers and malicious parties. Effective solutions can be presented using decentralized technologies such as blockchains. This paper proposes a decentralized identity management system for SIoT based on smart contract technology. This system drives object identity control; conversely, a zero‐knowledge‐based protocol and double‐spend prevention ideas are proposed for privacy preservation and Sybil attack resistance, respectively. The central idea is to implement self‐sovereign identity (SSI) so that even certifiers are not privy to object‐sensitive information. Additionally, it attempts to prevent the owner from creating Sybil objects using the web of trust method. The method's scalability and convergence can be proven by considering all acknowledgment nodes and different paths. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Convergence in economic complexity: global evidence

ABSTRACT This study explores the convergence pattern of economic complexity across a global sample of 129 countries from 1995 to 2020. The panel convergence method is employed, which endogenously identifies the membership of convergence clubs. The findings suggest that countries flock together in six distinct clubs. Upon further inspection, it becomes evident that factors such as population density, human capital, trade openness, foreign direct investment, and real GDP significantly influence the clubs’ membership of economic complexity. Therefore, government policies must be specifically tailored to each club’s determinants’ unique characteristics to enhance a country’s economic complexity.

Acetylated lignin sulfonate as a biodegradable coating for controlled-release urea fertilizer: A novel acetylation method and diffusion coefficient analysis

Plants require essential nutrients to grow, which soil alone cannot provide. Chemical fertilizers like urea supply the necessary nutrients, including nitrogen. They quickly dissolve in water and can contaminate it with nitrate and nitrite, which can cause diseases. Slow-release fertilizers are a better option to reduce environmental risks. Researchers are exploring cheap and biodegradable alternatives, such as lignin. A critical discussion in the coated urea fertilizer is modeling the nitrogen diffusion process in the coating, which predicts the system's behavior. This article uses lignin sulfonate to coat urea fertilizer, which should first be acetylated with decanoyl chloride. One of the critical parameters is the diffusion coefficient (D). D is determined using the mass transfer flux and the completion time of the effective substance, and with its help, the graph of the total mass transferred from the membrane in a specific time (Mt) is determined. D equals 6.298813 × 10−8 cm2/s using the time lag method. Also, with the fixed-point convergence method, 5.8849 × 10−8 cm2/s was obtained, which has about 0.80 % error with the D obtained by the time lag method. The Mt obtained from the analytical method and the experimental data coincides with a minimal error, which indicates high accuracy.

Novel Integer Division for Embedded Systems: Generic Algorithm Optimal for Large Divisors

The integer Constant Division (ICD) is the type of integer division in which the divisor is known in advance, enabling pre-computing operations to be included. Therefore, it can be more efficient regarding computing resources and time. However, most ICD techniques are restricted by a few values or narrow boundaries for the divisor. On the other hand, the main approaches of the division algorithms, where the divisor is variable, are digit-by-digit and convergence methods. The first techniques are simple and have less sophisticated conversion logic for the quotient but also have the problem of taking significantly long latency. On the contrary, the convergence techniques rely on multiplication rather than subtraction. They estimate the quotient of division providing the quotient with minimal latency at the expense of precision. This article suggests a precise, generic, and novel integer division algorithm based on sequential recursion with fewer iterations. The suggested methodology relies on extracting the division results for non-powers-of-two divisors from those for the closest power-of-two divisors, which are obtained simply using the right bit shifting. To the authors’ best knowledge of the state-of-the-art, the number of iterations in the recurrent variable division is half the divisor bit size, and the Sweeney, Robertson, and Tocher (SRT) division, which is named after its developers, involves <i>log</i><sub>2</sub>(n) iterations. The suggested algorithm has an [(m/(n-1))-1] number of recursive iterations, where m and n are the number of bits of the dividend and the divisor, respectively. The design is simulated in the Vivado tool for validation and implemented with a Zynq UltraScale FPGA. The technique performance depends on the number of nested divisions and the size of a LUT. The two factors change according to the value of the divisor. Nevertheless, the size of the LUT is proportional to the range and the number of bits of the divisor. Furthermore, the equation that controls the number of nested blocks is illustrated in the manuscript. The proposed technique applies to both constant and variable divisors with a compact hardware area in the case of constant division. The hardware implementation of constant division has unlimited values for dividends and divisors with a compact hardware area in the case of large divisors. However, using the design in the hardware implementation of variable division is up to 64-bit dividend and 12-bit divisor. The result analysis demonstrates that this algorithm is more efficient for constant division for large numbers.

Monte Carlo Source Convergence Diagnosis Method and Thermal-Physics Coupling Method Based on Functional Expansion Tallies

In iterative Monte Carlo calculations for nuclear reactors, the inactive cycles should be calculated first to ensure that the source distribution is converged, and then the tallies of various parameters in the active cycles can begin. In order to acquire the mesh-free distribution of the fission source, this research proposes the functional expansion tallies (FET) source convergence diagnosis method in the Reactor Monte Carlo code, which is a self-developed stochastic simulation code maintained by the Reactor Engineering Analysis Laboratory of Tsinghua University. Due to the randomness in Monte Carlo calculations and the difficulty in determining the precise source convergence, this paper proposes a diagnostic tool based on function curve similarity and moving average, and proposes an online real-time source convergence diagnosis method. The FET online source convergence method can terminate the calculation of the inactive cycle in real time according to the convergence diagnostic tool; thus it can greatly decrease the calculation time. The precise and effective transfer of data between different meshes is a difficult issue of thermal and physical coupling. Converting two separate meshes and transferring the data are exceptionally difficult and complex tasks within the conventional nuclear thermal-physics coupling approach. By applying the FET method to nuclear thermal-physics coupling, the mesh-free continuous-space fission source distribution can be obtained, which is suitable for more complex meshes. Additionally, computational memory can be minimized by replacing (transforming) the data from numerous mesh power distribution data points with the coefficients of the function.

Efficient enhancement of low-rank tensor completion via thin QR decomposition.

Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues. The core tensor nuclear norm minimization (CTNM) method based on Tucker decomposition is one of common LRTC methods. However, the CTNM methods based on Tucker decomposition often have a large computing cost due to the fact that the general factor matrix solving technique involves multiple singular value decompositions (SVDs) in each loop. To address this problem, this article enhances the method and proposes an effective CTNM method based on thin QR decomposition (CTNM-QR) with lower computing complexity. The proposed method extends the CTNM by introducing tensor versions of the auxiliary variables instead of matrices, while using the thin QR decomposition to solve the factor matrix rather than the SVD, which can save the computational complexity and improve the tensor completion accuracy. In addition, the CTNM-QR method's convergence and complexity are analyzed further. Numerous experiments in synthetic data, real color images, and brain MRI data at different missing rates demonstrate that the proposed method not only outperforms in terms of completion accuracy and visualization, but also conducts more efficiently than most state-of-the-art LRTC methods.

Propagation reversal on trees in the large diffusion regime

In this work we study travelling wave solutions to bistable reaction–diffusion equations on bi-infinite k-ary trees in the continuum regime where the diffusion parameter is large. Adapting the spectral convergence method developed by Bates and his coworkers, we find an asymptotic prediction for the speed of travelling front solutions. In addition, we prove that the associated profiles converge to the solutions of a suitable limiting reaction–diffusion PDE. Finally, for the standard cubic nonlinearity we provide explicit formulas to bound the thin region in parameter space where the propagation direction undergoes a reversal.

Large deviations for stochastic predator–prey model with Lévy noise

This paper discusses the large deviations for stochastic predator–prey model driven by multiplicative Lévy noise. Using Galerkin approximation, we initially prove the existence and uniqueness of solution. Due to the equivalence between Laplace principle and large deviation principle under a Polish space, the method of weak convergence has been followed in order to establish our results for this coupled system of equations.

Approximation theorems via Pp -statistical convergence on weighted spaces

Abstract In this paper, we obtain some Korovkin type approximation theorems for double sequences of positive linear operators on two-dimensional weighted spaces via statistical type convergence method with respect to power series method. Additionally, we calculate the rate of convergence. As an application, we provide an approximation using the generalization of Gadjiev-Ibragimov operators for Pp -statistical convergence. Our results are meaningful and stronger than those previously given for two-dimensional weighted spaces.

A sufficient descent hybrid conjugate gradient method without line search consideration and application

PurposePrevious RMIL versions of the conjugate gradient method proposed in literature exhibit sufficient descent with Wolfe line search conditions, yet their global convergence depends on certain restrictions. To alleviate these assumptions, a hybrid conjugate gradient method is proposed based on the conjugacy condition.Design/methodology/approachThe conjugate gradient (CG) method strategically alternates between RMIL and KMD CG methods by using a convex combination of the two schemes, mitigating their respective weaknesses. The theoretical analysis of the hybrid method, conducted without line search consideration, demonstrates its sufficient descent property. This theoretical understanding of sufficient descent enables the removal of restrictions previously imposed on versions of the RMIL CG method for global convergence result.FindingsNumerical experiments conducted using a hybrid strategy that combines the RMIL and KMD CG methods demonstrate superior performance compared to each method used individually and even outperform some recent versions of the RMIL method. Furthermore, when applied to solve an image reconstruction model, the method exhibits reliable results.Originality/valueThe strategy used to demonstrate the sufficient descent property and convergence result of RMIL CG without line search consideration through hybrid techniques has not been previously explored in literature. Additionally, the two CG schemes involved in the combination exhibit similar sufficient descent structures based on the assumption regarding the norm of the search direction.

A corrector result for an electrostatic model of interdigitated combs

In this article, we consider two opposite and intergitated 2D-combs, each one with ε-periodically distributed fingers, each finger having cross-section of order ε and fixed height. Via an asymptotic analysis based on the two-scale convergence method, we study, as ε vanishes, the electrical potential in the vacuum between these two combs when one comb is grounded while the voltage in the other one is assumed of order ε, and we prove a corrector result.