Full Convergence Research Articles

Navigating toward the promised land of digitalization and sustainability convergence

In recent years, companies have updated their strategic goals, adding the goal of digitalization to the already commonly accepted goal of sustainability. The convergence between these two strategic goals, digitalization, and sustainability (DS) convergence, is the complete and conjoint attention to both strategic goals. This paper analyzes this phenomenon in depth by studying the implementation of sustainable and digital measures in companies. In doing so, we categorize firms' strategies from less to more DS convergence, analyzing the factors that promote the full attention to both strategic goals, emphasizing those that enhance the mentioned DS convergence. The findings from the analysis of a sample of >16,000 small and medium-sized enterprises (SMEs) worldwide reveal that companies that devote more effort to business growth, innovation, exports and that perceive the environment as favorable are more likely to achieve full DS convergence.

Efficient energy management in smart grids with finite horizon Q-learning

Efficient energy distribution in smart grids is an important problem driven by the need to manage increasing power consumption across the globe. This problem has been studied in the past using different frameworks including Markov Decision Processes (MDP) and Reinforcement Learning. However, existing algorithms in Reinforcement Learning Theory largely deal with infinite horizon decision making. On the other hand, smart grid problems are inherently finite horizon in nature and so can best be addressed using Finite Horizon algorithms. We therefore analyze the smart grid setup using a finite horizon MDP model and develop, for the first time, a finite horizon version of the popular Q-learning algorithm. We observe that our algorithm shows good empirical performance on the smart grid setting. We also theoretically analyze the full convergence of the algorithm by proving both the stability and the convergence of the same. Our analysis of stability and convergence of finite horizon Q-learning is based entirely on the ordinary differential equations (O.D.E) method. Apart from smart grids, we additionally demonstrate the performance of our algorithm on a setting of random MDPs indicating that the algorithm is more generally applicable and can be studied on other settings in the future.

A reduced Jacobian method with full convergence property

A reduced Jacobian method with full convergence property

Colocação de clíticos em PE L2

This study investigates the acquisition of clitic placement in L2 EP, using an elicited oral production task and a speeded acceptability judgement task. Participants were 20 L1 EP speakers and 30 L1 Spanish-L2 EP adult learners at intermediate to near-native levels. Results show that enclisis stabilizes early (at least in production) and proclisis develops sequentially, following a route similar to that observed in L1 acquisition: Negation > Subjunctive complement clauses > Indicative complement clauses > Adverbial clauses, quantified subjects. The contexts that develop later are the less categorical ones in native grammars, which may give rise to greater input variability and add complexity to the acquisition task. As a result, learners need prolonged input exposure to discover the patterns of clitic placement in EP. Full convergence with the target language seems to be possible, but only at a near-native level.

Estimation of an ARDL (m, p, q, r, s) Model to Determine the Long-run Effects of World Income, Relative Price, Real Exchange Rate and Employment on Export Demand for the Garment Sector of Bangladesh

In this paper, an ARDL (m p, q, r, s, s) log-linear export demand function is estimated to find the long-run impacts of world income, relative price, real exchange rate, and employment on exports of the garment sector of Bangladesh using modern econometric techniques based on time series data from 1979-2020. Long-run significant relationships among exports, relative price, real exchange rate, world income and employment are found. The VEC model's estimated results indicate short-run bidirectional causalities between different pairs of variables. The long-run causalities are found while exports and employment are the endogenous variables. The estimated results indicate that both long-run and short-run exports are influenced positively and significantly by world income. Relative price, real exchange rate and employment carry negative signs for both long-run and short-run in which the effects of relative price and real exchange rate are statistically significant in the long run which implies that a 1% decrease in relative price, exports will increase by 1.256%. It is found that the coefficient of ECM(-1) is -0.9705 which is statistically significant at any significance level, suggesting that when the export value is above its equilibrium level, it will adjust about 97.05% within the first year. The full convergence process to reach the equilibrium level takes more than a year. Thus, this study will help the economists, policymakers and stakeholders of the garment sector of Bangladesh to predict the dynamics of export demand due to changes in world income level, relative price, real exchange rate and employment.

Research on airflow convergence patterns and air distribution characteristics under multi-column attachment ventilation

Multi-column attachment ventilation (M-CAV) is usually delivered to the occupied zone by floor-attached airflows in large spaces. However, after the floor-attached airflow converges, the original air distribution will change, which will have an important impact on the traditional air distribution design. In this paper, airflow convergence patterns and air distribution characteristics in the most common double-column and tri-column layouts are studied by experiments and computational fluid dynamics (CFD) simulations. A 1:3 scaled-down model was established for velocity experiments and flow field visualization, and the results were used to verify and optimize the CFD model. The results indicated a seagull-like convergence flow pattern under the symmetric double-column layout. Under the axisymmetric layout with different column spacings, the floor-attached airflow in the half-open air outlet mode exhibited full convergence with the merging point (point MP) and combined point (point CP), partial convergence with point MP, and non-convergence without point MP and point CP. Under the centrosymmetric layout, an M-shaped velocity distribution was formed, and no airflow converged. For equal-velocity conditions, the dimensionless convergence axial velocity distributions for different air supply velocities was approximately consistent. In the fully-open air outlet mode, the floor-attached airflow could be divided into the collision-dominated region, the combined collision and convergence-dominated region, and the convergence-dominated region based on the forms of airflow interaction. For unequal-velocity conditions, the high-velocity airflow restricted the diffusion of the low-velocity airflow.

Energy spectra and asymptotic laws of the radial screened Coulomb potential

The energy spectra and corresponding asymptotic laws of the radial screened Coulomb potential V(r)=−exp⁡(−C/r)/r are investigated by employing the generalized pseudospectral method. Our numerical calculations manifest full convergence for all investigated bound states with the screening parameter C ranging from zero to infinity, but show significant discrepancies with previous predictions. Based on the Hellmann-Feynman theorem, we analyze the asymptotic behavior of the energy spectra for screening parameters close to zero. We further propose an empirical reciprocal law for energies when the screening parameter approaches infinity. We finally conclude that for the radial screened Coulomb potential there do not exist finite values of critical screening parameters at which the bound states merge into the continuum.

Impact of correlated observation errors on the conditioning of variational data assimilation problems

SummaryAn important class of nonlinear weighted least‐squares problems arises from the assimilation of observations in atmospheric and ocean models. In variational data assimilation, inverse error covariance matrices define the weighting matrices of the least‐squares problem. For observation errors, a diagonal matrix (i.e., uncorrelated errors) is often assumed for simplicity even when observation errors are suspected to be correlated. While accounting for observation‐error correlations should improve the quality of the solution, it also affects the convergence rate of the minimization algorithms used to iterate to the solution. If the minimization process is stopped before reaching full convergence, which is usually the case in operational applications, the solution may be degraded even if the observation‐error correlations are correctly accounted for. In this article, we explore the influence of the observation‐error correlation matrix () on the convergence rate of a preconditioned conjugate gradient (PCG) algorithm applied to a one‐dimensional variational data assimilation (1D‐Var) problem. We design the idealized 1D‐Var system to include two key features used in more complex systems: we use the background error covariance matrix () as a preconditioner (B‐PCG); and we use a diffusion operator to model spatial correlations in and . Analytical and numerical results with the 1D‐Var system show a strong sensitivity of the convergence rate of B‐PCG to the parameters of the diffusion‐based correlation models. Depending on the parameter choices, correlated observation errors can either speed up or slow down the convergence. In practice, a compromise may be required in the parameter specifications of and between staying close to the best available estimates on the one hand and ensuring an adequate convergence rate of the minimization algorithm on the other.

Electoral competition with costly policy changes: A dynamic perspective

We analyze two-party electoral competition with a one-dimensional policy space, costly policy changes, and random negative shocks to a party's viability over an infinite horizon. We show the existence and uniqueness of stationary Markov perfect equilibria in which parties use so-called simple strategies. Regardless of the initial policy, party choices converge in the long run to a stochastic alternation between two policies, with transitions occurring if and only if parties suffer a negative shock to their viability. Although costs of change have a moderating effect on policies, full convergence to the median voter position does not take place when parties are polarized.

Methods for measuring social and conceptual dimensions of convergence science

Abstract Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as ‘the coming together of insights and approaches from originally distinct fields’ to strategically address grand challenges. Despite its increasing relevance to science policy and institutional design, there is still no practical framework for measuring convergence. We address this gap by developing a measure of disciplinary distance based upon disciplinary boundaries delineated by hierarchical ontologies. We apply this approach using two widely used ontologies—the Classification of Instructional Programs and the Medical Subject Headings—each comprised of thousands of entities that facilitate classifying two distinct research dimensions, respectively. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equipment fundamental to a given target problem, which together may exceed the researchers’ core expertise. Considered in tandem, this decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems—a key aspect that eludes other approaches. We demonstrate the utility of this framework in a case study of the human brain science (HBS) ecosystem, a relevant convergence nexus that highlights several practical considerations for designing, evaluating, institutionalizing, and accelerating convergence. Econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the conceptual (topical) configuration of the research.

Convergence continuum of the mass media Riau Pos

Background: The flow of globalization and the rise of the Internet have forced the mass media to come to terms with these changes and go hand in hand to survive. Riau Pos, the oldest mass media in Riau Province has become the first print media in Riau to practice convergence by establishing an online news portal now named RiauPos.co. Purpose: This study aims to analyze the convergence continuum of the mass media Riau Pos on the five Continuum Convergence processes. Methods: This study uses a qualitative method with the Continuum Convergence theory published by Daily, Demo, Spillman, and the concept of Management. Results: The result revealed that Riau Pos carried out all stages of Continuum Convergence such as Cross Promotion, Cloning, Coopetition, and Content which are done simultaneously with media within the Riau Pos Group. The basis of the implementation of this convergence is a collaboration that emphasizes the characteristics of each platform. Conclusions: Full convergence has finally been finally implemented, although not yet perfect, in the implementation of 3M, namely Multichannel, Multimedia, and Multiplatform. Implications: Implementing this convergence is indeed a way for Riau Pos to survive as a print media in the form of newspapers and cyber media through its website, RiauPos.co.

Adaptive FEM with quasi-optimal overall cost for nonsymmetric linear elliptic PDEs

Abstract We consider a general nonsymmetric second-order linear elliptic partial differential equation in the framework of the Lax–Milgram lemma. We formulate and analyze an adaptive finite element algorithm with arbitrary polynomial degree that steers the adaptive meshrefinement and the inexact iterative solution of the arising linear systems. More precisely, the iterative solver employs, as an outer loop, the so-called Zarantonello iteration to symmetrize the system and, as an inner loop, a uniformly contractive algebraic solver, for example, an optimally preconditioned conjugate gradient method or an optimal geometric multigrid algorithm. We prove that the proposed inexact adaptive iteratively symmetrized finite element method leads to full linear convergence and, for sufficiently small adaptivity parameters, to optimal convergence rates with respect to the overall computational cost, i.e., the total computational time. Numerical experiments underline the theory.

Health expenditure convergence and the roles of trade and governance in Africa

This study investigates the convergence hypothesis in health expenditures in 40 African countries over the 2000–2019 period. The new non-linear dynamic factor model is used on panel data extracted from the World Development Indicator and the World Governance Indicator. We consider two categories of health expenditures: the domestic general government health expenditure as a percentage of government expenditure and the domestic general government health expenditure per capita. The results show the absence of full panel convergence for the two indicators used. However, there is evidence of convergence clubs. The overall marginal effect of the control variables is consistent with the existing literature. The results further show a strong influence of trade in attaining convergence among the clubs for both models. However, governance quality only affects the probability of converging in a club for the general government health expenditure as a percentage of government expenditure model. The findings suggest that policies on promoting health expenditure convergence should pursue initiatives that encourage trade. Efforts should also be targeted to improve the quality of governance.

The virtual element method for the 3D resistive magnetohydrodynamic model

We present a four-field virtual element discretization for the time-dependent resistive magnetohydrodynamics equations in three space dimensions, focusing on the semi-discrete formulation. The proposed method employs general polyhedral meshes and guarantees velocity and magnetic fields that are divergence free up to machine precision. We provide a full convergence analysis under suitable regularity assumptions, which is validated by some numerical tests.

A case study on the deformation and failure mechanism of a soft rock mining roadway in the Xin'Shang'Hai No. 1 coal mine, China

Mining roadways in soft rock are seriously deformed and damaged, which affects the normal production of the mine. Based on the Xin'Shang'Hai No. 1 Coal Mine project, a true 3D physical model test was carried out to obtain the deformation evolution and failure features of the mining roadway. The results show that the strata movement of the panel drives the surrounding rock of the mining roadway to deform toward the center; the net size of the roadway near the longwall face decreases by 48.3%. The convergence of soft rock mining roadways can be divided into four phases, and the average convergence rate of a fast phase is 3.1 times that of a slow phase. The evolution of roadway failure is deformation accumulation → sidewall cracking → lining spalling → coal sidewall breaking → floor heave → bolt (cable) failure → full section deformation. Coal sidewall failure is an important turning point of mining stress-induced roadway failure. The extension of the broken area into the rock leads to the failure of the coal seam between the roof and floor and then induces the overall instability of the roadway. The radial deformation and stress evolution of the mining roadway are analyzed by numerical simulation, and the model test results are verified. The mining roadway in soft rock shows full section convergence characteristics under the stress of mining. Finally, countermeasures are proposed according to the failure mechanism to provide a reference for similar roadways.

The vanishing discount problem for monotone systems of Hamilton-Jacobi equations: a counterexample to the full convergence

<abstract><p>In recent years there has been intense interest in the vanishing discount problem for Hamilton-Jacobi equations. In the case of the scalar equation, B. Ziliotto has recently given an example of the Hamilton-Jacobi equation having non-convex Hamiltonian in the gradient variable, for which the full convergence of the solutions does not hold as the discount factor tends to zero. We give here an explicit example of nonlinear monotone systems of Hamilton-Jacobi equations having convex Hamiltonians in the gradient variable, for which the full convergence of the solutions fails as the discount factor goes to zero.</p></abstract>

Spectral and cross-spectral densities expressions’ refinement for rail vehicle oscillations

In this paper, we derive analytical expressions describing spectral and mutual spectral densities of random processes by performing analytical Fourier transform of the corresponding correlation functions. On the basis of accepted analytical expressions for correlation functions of differentiable random processes, analytical expressions for spectral density and mutual spectral density components, which is a complex frequency function, are derived. The influence of the accepted analytical expressions on the correlation functions, spectral densities, and mutual spectral density components (real, imaginary, amplitude, and phase) on the parameters is presented. Plots obtained using obtained analytical expressions have showed full convergence with those obtained by direct integration of the analytical expression of the correlation function. The analytical expressions given in this paper may be used for investigating various random processes. In particular, spectral and reciprocal spectral densities can be used for approximation of experimentally received spectral densities, including equivalent geometrical irregularities of a rail track and random fluctuations of a rail vehicle. The parameters of analytical expression obtained by such an approximation can be used for generation of analogous experimental multidimensional random processes in the tasks of mathematical modelling.

An abstract convergence framework with application to inertial inexact forward–backward methods

In this paper we introduce a novel abstract descent scheme suited for the minimization of proper and lower semicontinuous functions. The proposed abstract scheme generalizes a set of properties that are crucial for the convergence of several first-order methods designed for nonsmooth nonconvex optimization problems. Such properties guarantee the convergence of the full sequence of iterates to a stationary point, if the objective function satisfies the Kurdyka-Lojasiewicz property. The abstract framework allows for the design of new algorithms. We propose two inertial-type algorithms with implementable inexactness criteria for the main iteration update step. The first algorithm, i$^2$Piano, exploits large steps by adjusting a local Lipschitz constant. The second algorithm, iPila, overcomes the main drawback of line-search based methods by enforcing a descent only on a merit function instead of the objective function. Both algorithms have the potential to escape local minimizers (or stationary points) by leveraging the inertial feature. Moreover, they are proved to enjoy the full convergence guarantees of the abstract descent scheme, which is the best we can expect in such a general nonsmooth nonconvex optimization setup using first-order methods. The efficiency of the proposed algorithms is demonstrated on two exemplary image deblurring problems, where we can appreciate the benefits of performing a linesearch along the descent direction inside an inertial scheme.

Disturbance region update method with preconditioning for steady compressible and incompressible flows

The disturbance region update method (DRUM) was an acceleration methodology devoted to steady compressible flows. In this work, we develop DRUM for steady incompressible flows, named the disturbance region update method with preconditioning (DRUM-P). Compared with the conventional global update method (GUM), DRUM-P adopts the advective and the viscous dynamic computational domains (DCDs) updated with the time-marching process to achieve a computational speedup for compressible and incompressible flows. When DRUM is extended to incompressible flows, the stiffness of the governing equations would slow down the convergence to the steady state and thus delay the contraction of the DCDs. By employing the local preconditioning technique, DRUM-P overcomes those difficulties and exploits the full convergence potential of DRUM for incompressible flows. Besides, the operation of contracting the advective DCD is modified in subsonic flows, where the advective DCD is allowed to contract from the downstream before the upstream. A new viscous-dominated criterion of the viscous DCD based on the total pressure loss and the total enthalpy loss is proposed to improve accuracy in practical cases with complex geometric shapes and flow features. Eight test cases demonstrate the efficiency and accuracy of DRUM-P. Firstly, DRUM-P achieves remarkable convergence efficiency for solving compressible and incompressible flows, especially the compressible and incompressible mixed flows, e.g., the flow over high-lift configuration. The acceleration ability benefits from the preconditioning technique, the two high-flexible DCDs, and the modified operation of the advective DCD. Secondly, the numerical results obtained by DRUM-P are in excellent agreement with those obtained by GUM. Moreover, the numerical results verify that the proposed viscous-dominated criterion is grid-independent and more accurate in practical cases.

Club convergence analysis of city-level electricity consumption in Turkey

This study examines the presence of convergence clubs in per capita electricity consumption across cities in Turkey over the period 2000–2020. Using the Phillips-Sul club convergence test and the clustering algorithm, we explore whether total, industrial, and residential electricity consumption across regions converge to the same long-run equilibrium or diverge. We also analyze the drivers of the club formation after identifying convergence clubs using ordered logit regressions. The findings support the full convergence in residential consumption while separate clubs with different convergence dynamics and steady-state levels are identified in industrial and total electricity consumption. Results indicate that there are two convergence clubs in total electricity consumption which may be clustered as high and low consumption regions. Our analysis also identifies two clusters in industrial consumption with a dominating club. Results from the ordered logit analysis suggest that income, industrial activities, and international trade are positively associated with club formation in total electricity consumption. Overall, our findings suggest that energy policies need to be tailored and implemented on a regional and sectoral basis since energy user groups have heterogeneous convergence behavior.