Convergence Framework Research Articles

Community Convergence Framework for Solid Waste Management

The Community Convergence Framework for Solid Waste Management is a sound management plan to address the solid waste problem for the Tangkas Organic Fertilizer Facility in Bali, Indonesia and for private and government institutions worldwide. The framework’s components are based on the principle that solid waste management should be a collective effort between local governments, communities, and organic waste processing centres. The management plan is structured so that there is a convergence between these three components. In this way, communities can strive to create a cleaner and healthier environment for current and future generations. This research will guide understanding of waste management's importance and community convergence's role in achieving sustainable solutions. Contribution to Sustainable Development Goals (SDGs) SDG 3: Good Health and Well-being SDG 4: Quality Education SDG 6: Clean Water and Sanitation SDG 11: Sustainable Cities and Communities SDG 12: Responsible Consumption and Production SDG 13: Climate Action SDG 17: Partnerships for the Goals

The accounting professional’s competencies: does the supply fit with the demand? Evidence from Portugal

ABSTRACT Studies examining the alignment between supply and demand for accounting professional’s competencies have been conducted worldwide. However, these studies have predominantly relied on questionnaires and interviews, and have often been limited to similar contexts. Our study adopts a novel approach to diversify this body of literature. We applied content analysis to 306 job advertisements (demand perspective) and 32 accounting course programs (supply perspective) to assess the supply – demand alignment of competencies among entry-level accounting professionals in Portugal. This research offers practical contributions by supporting Higher Education Institutions (HEIs), students, and professional bodies in identifying gaps in the alignment of courses with the job market. Additionally, it makes a theoretical contribution by employing a segment of the university-market convergence framework for the accounting professional’s competencies. The findings revealed a strong alignment of the essential technical competencies. However, the data also highlight room for improvement in less specific (generic) competencies such as information technologies and foreign languages. Moreover, an analysis of the alignment of various courses with the respective areas of accounting expertise yielded interesting results, albeit not consistently positive. These findings underscore the importance of HEIs to remain vigilant regarding labor market changes and adjust their curricula accordingly.

Tail probability bounds for acceptable random variables of partial sums and their application in linear models

Exponential probability inequalities play an important role in many parts of probability and statistics, offering a fundamental tool for tail probability bounding and modeling stochastic processes. The present work proposes a new exponential inequality for acceptable random variables. This class covers dependencies that are not embraced by the standard independence assumptions. Our results extend the theoretical perspective by placing sharper bounds on the above-mentioned variables, which tends to increase the accuracy of probabilistic modeling. Moreover, we study complete convergence in first-order autoregressive processes with acceptable innovations. The complete convergence framework gives further insights into the long-term behavior of the wide class of processes commonly used in time series analysis, econometrics, and other areas of applied research. In turn, our results on the influence of acceptable innovations on the properties of complete convergence establish bridges between theoretical probability inequalities and statistical applications. In addition, these contributions provide new tools in investigating the stability and predictability of the autoregressive models. These considerations consequently set up significant perspectives and enrich a developing body of research related to stochastic processes by reinforcing the theoretical framework as well as the methodological apparatus of probability theory.

The centrality of English as one legacy of Lau: An interest convergence theory analysis of Massachusetts policy for English Learners

ABSTRACT Using the theoretical framework of interest convergence, this document analysis explores the legacy of Lau v. Nichols as a gateway to instructional programs for classified English learners in the state of Massachusetts that maintain the hegemony of English as the primary goal of schooling. Findings reveal that interest convergence is an organizing principle for how instruction for classified ELs has historically been organized and delivered throughout Massachusetts as a move both toward and away from English-only instructional policy.

Rapid affinity optimization of an anti-TREM2 clinical lead antibody by cross-lineage immune repertoire mining

We describe a process for rapid antibody affinity optimization by repertoire mining to identify clones across B cell clonal lineages based on convergent immune responses where antigen-specific clones with the same heavy (VH) and light chain germline segment pairs, or parallel lineages, bind a single epitope on the antigen. We use this convergence framework to mine unique and distinct VH lineages from rat anti-triggering receptor on myeloid cells 2 (TREM2) antibody repertoire datasets with high diversity in the third complementarity-determining loop region (CDR H3) to further affinity-optimize a high-affinity agonistic anti-TREM2 antibody while retaining critical functional properties. Structural analyses confirm a nearly identical binding mode of anti-TREM2 variants with subtle but significant structural differences in the binding interface. Parallel lineage repertoire mining is uniquely tailored to rationally explore the large CDR H3 sequence space in antibody repertoires and can be easily and generally applied to antibodies discovered in vivo.

Wijsman and Wijsman Randomly Triple Ideal Convergence Sequences of Sets in Probabilistic Metric Spaces

Many authors have extended the concept of convergence from number sequences to sequences of sets. In this paper, we focus on two notable adaptations: Wijsman convergence and randomly ideal convergence. We introduce and analyze several new types of convergence for sequences of sets: IψW3-convergence, I∗,ψW3-convergence, IψW3-Cauchy, I∗,ψW3-Cauchy, (IψW3, IψW)-convergence, and (I∗,ψW3, I∗,ψ)-convergence. These new concepts expand the framework of convergence and provide a deeper understanding of the behavior of set sequences under various conditions. Through rigorous analysis, we demonstrate the relationships between these new forms of convergence and their classical counterparts, highlighting their theoretical significance and potential applications in mathematical analysis and related fields. Our findings offer a comprehensive exploration of these advanced convergence concepts, paving the way for further research and development in this area.

Analisis Perbandingan Konsep Pendidikan Islam dan Barat

This paper presents a comparative analysis of Islamic and Western concepts, focusing on their philosophical, cultural, and social dimensions. The study examines the underlying principles and values that shape each conceptual framework, highlighting similarities, differences, and areas of convergence. Through a comprehensive review of literature and historical perspectives, the paper explores key themes such as ethics, governance, justice, and human rights within both Islamic and Western traditions. Additionally, the analysis delves into the influence of historical events, religious beliefs, and cultural norms on the development and interpretation of these concepts in each tradition. By critically examining the similarities and differences between Islamic and Western concepts, this study aims to contribute to a deeper understanding of the diverse philosophical and cultural perspectives that shape global discourse and facilitate dialogue between different civilizations.

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

International convergence towards a climate-neutral economy: modeling the agricultural sector

Purpose. This article aims to construct a comprehensive convergent model for assessing the global and EU’s progress, degree of consolidation and symmetry of agricultural economies towards climate neutrality in the context of key international green initiatives. Methodology / approach. This research employs both qualitative and quantitative analyses to assess convergence measures in the agricultural sector’s carbon emissions. The quantitative component relies on the sigma and beta convergence models to evaluate international convergence in carbon emissions CO2 dynamics. The dimensions of modeling are as follows: global sample within 194 countries with reliable emissions data; European Union, focusing on convergence within the EU27 member states, the United Kingdom, and Ukraine. The time period covers open data from 1972–2022. The dynamics of sigma and beta convergence is determined for the points, namely UN Stockholm Conference (1972), UN Rio Conference (1992), Kyoto Protocol (entry into force in 2005), Paris Agreement (2015). Additionally, statistical indicators of variation, skewness, Gini and Theil indices were calculated for delineating global smoothness and the concentration of agricultural emissions. Results. The results of the study reveal an inertial and divergent trend of the agricultural economy towards decarbonisation, which slows down the overall movement towards “net zero” due to the presence of clubs of lagging agricultural countries that increase emissions in violation of international agreements. The reduction in emissions skewness in recent years shows that more countries are “pulling” decarbonisation due to their high capacity to move towards net zero, but this is not enough. The EU is the most prominent example of accelerated climate convergence, but markers of its weakening in recent years are identified due to the inability of economies to maintain the pace of decarbonisation caused by economic constraints, technological barriers, policy and regulatory issues, and misunderstandings of climate neutrality goals. It is shown that the long-term decarbonisation capacity of the agricultural sector is the key trigger for a country to make a positive contribution to the global convergence towards climate neutrality. At the current stage, the pace of decarbonisation plays a much greater role for consolidating efforts in the agricultural economy and achieving climate neutrality than the initial level of emissions in the sector. Factors in this process include proactive compliance with global climate agreements, technology sharing and cooperation, digitalisation and smart agriculture, and green financing and investment. Its implementation requires a three-way integration of stakeholder actions, strategy selection and results evaluation. Originality / scientific novelty. The study’s originality lies in its large-scale analysis of over 50 years of emissions dynamics and the context of five key green agreements that provided support for the green transition. It allows studying international convergence in agricultural sectors globally and within the EU. The novelty implies the integrated use of sigma and beta convergence models that identifies predictors of convergent and divergent processes and separates countries into leaders and laggards of agricultural decarbonisation. This approach provides a comprehensive view of modern climate policy, the impact of international green initiatives, and the position of individual towards climate neutrality in agriculture. Practical value / implications. The practical value lies in the ability to adjust climate policies for the agricultural economy’s decarbonisation, facilitating the determination of prospective outcomes for achieving climate neutrality. The aforementioned factors facilitate the process of governmental decision-making. The assessment of international programmatic agreements' effectiveness is enhanced through these models. The study offers a framework for global convergence towards climate neutrality in agriculture, highlighting the importance of digital technologies and smart agriculture as significant factors in global convergence.

Triad of violence: evolution and dynamics of conflict, terrorism, banditry and armed groups in northwest Nigeria

ABSTRACT This study is hinged on the theoretical framework of co-existence, convergence, cooperation and competition. It discusses the nature of interaction among armed groups, and their violent activities in northwest Nigeria. The determinants of the connections include geography, recruitment strategy, opportunities for mutual exchange of logistics and co-creation of violence. Bandits are outbidding ethno-religious militias, Ansaru, ISWAP, Boko Haram and herdsmen, and have committed 84% of recorded violence in the region from 2010 to 2022. This study highlights the implications of increasing footprints of armed groups and provides the imperatives for state response to protracted violence in northwest Nigeria. It concludes that the socio-economic problems that allow proliferation of armed groups and violent activities to fester must be addressed by the state. Even if ramped-up security responses turn northwest Nigeria into a police state, violence will continue until poverty, social inequality, injustice and gross failure of governance are tackled.

On the convergence of discrete dynamic unbalanced transport models

A generalized unbalanced optimal transport distance WBΛ on matrix-valued measures M(Ω, 𝕊n+) was defined in Li and Zou (arXiv:2011.05845) à la Benamou–Brenier, which extends the Kantorovich–Bures and the Wasserstein–Fisher–Rao distances. In this work, we investigate the convergence properties of the discrete transport problems associated with WBΛ. We first present a convergence framework for abstract discretization. Then, we propose a specific discretization scheme that aligns with this framework, whose convergence relies on the assumption that the initial and final distributions are absolutely continuous with respect to the Lebesgue measure. Further, in the case of the Wasserstein–Fisher–Rao distance, thanks to the static formulation, we show that such an assumption can be removed.

Public-private partnerships and land value capture: A convergent framework to improve the procurement of urban rail transit infrastructure

The use of Public-Private Partnership (PPP) and Land Value Capture (LVC) as a procurement model to deliver Urban Rail Transit (URT) systems and networks has received mixed reviews. This paper addresses the following research question: How can the public sector effectively realise the benefits of integrating PPP with LVC when procuring URTs? We use a structured-case method to build, test, and revise a series of conceptual frameworks from the experiences of cities (i.e., Guangzhou, Guiyang, and Wuhan). The contributions of this research are threefold as it: (1) provides a state-of-the-art examination of PPP and LVC practice; (2) develops a convergent framework that takes into consideration the context and sub-contexts of PPP and LVC used to procure URTs; and (3) presents the public sector with a frame of reference that can be drawn upon to realise the benefits of integrating PPPs with LVC.

CHL-DTI: A Novel High-Low Order Information Convergence Framework for Effective Drug-Target Interaction Prediction.

Recognizing drug-target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets. High-order information, as an essential component, exhibits complementarity with low-order information. Hence, the incorporation of higher-order associations between drugs and targets, while adequately integrating them with the existing lower-order information, could potentially yield substantial breakthroughs in predicting drug-target interactions. We propose a novel dual channels network-based learning model CHL-DTI that converges high-order information from hypergraphs and low-order information from ordinary graph for drug-target interaction prediction. The convergence of high-loworder information in CHL-DTI is manifested in two key aspects. First, during the feature extraction stage, the model integrates both high-level semantic information and low-level topological information by combining hypergraphs and ordinary graph. Second, CHL-DTI fully fuse the innovative introduced drug-protein pairs (DPP) hypergraph network structure with ordinary topological network structure information. Extensive experimentation conducted on three public datasets showcases the superior performance of CHL-DTI in DTI prediction tasks when compared to SOTA methods. The source code of CHL-DTI is available at https://github.com/UPCLyy/CHL-DTI .

Kırılgan Piyasa Ekonomileri Arasında Döviz Kurları Yakınsıyor mu? LM ve RALS-LM Birim Kök Testlerinden Yeni Kanıtlar

Global financial integration causes the economic consequences of economic crises, wars, or pandemics to be felt more in developing countries and triggers high exchange rate volatilities in these economies. In such an integrated financial environment, it is an interesting research domain how and why the exchange rate volatilities of countries are not affected similarly but tend to diverge from each other. This study investigates whether the exchange rate volatilities of fragile market economies converge in the stochastic convergence framework. To answer this question, we analyzed the stochastic behavior of the series using the traditional and structural break unit root tests besides RALS unit root tests, which consider the information of non-normal errors. The discussions regarding the size and power properties of test procedures in the unit root testing literature have formed a crucial part of the implications of the test results. In light of these discussions, we conclude that the stochastic convergence assumption is valid for Brazil, South Africa, India, and Hungary, whereas it is not valid for Argentina, Mexico, and Türkiye. The policy implications of our findings are that fragile market economies have different fragility levels among themselves and countries with high fragility levels show higher volatility than others.

Homogenization and corrector result of optimal control problem for Stokes system

This article introduces the boundary optimal control problem in an N-dimensional domain governed by the stationary Stokes equations. Controls are applied to the states through Neumann data on the boundary. The aim of this paper is to study the asymptotic behavior of optimal control and states and identify the limit optimal control problem in the framework of the two-scale convergence. Finally, strong convergence of L 2 -cost functional and a corrector result for velocities is proved.

Optimal implicit single-step time integration methods with equivalence to the second-order-type linear multistep methods for structural dynamics: Accuracy analysis based on an analytical framework

This paper presents a comprehensive study of the second-order-type time integration methods family encompassing the linear multistep (LMS) methods, the single-step methods, and the LMS equivalent single-step methods for structural dynamics. An analytical accuracy framework for algorithm design, convergence, and optimization is developed, where the obstacles encountered in algorithms construction, convergence accuracy, algorithms equivalence, accuracy measurement, and accuracy optimization in traditional accuracy frameworks are well addressed. Rigorous accuracy analysis successively to the second-order-type LMS (LMS2) methods, the single-step methods, and the LMS2 equivalent single-step methods aims to reveal optimal algorithms with desirable numerical properties. First, independent implicit algorithms including the velocity type and the acceleration type LMS2 methods except the displacement type ones are newly revealed in the LMS2 methods family with second-order convergence and A-stability, and three error constants that can play essential roles in the accuracy measurement and algorithm optimization are developed. Second, a single-step methods family where the displacement, velocity, and acceleration can achieve the same order of accuracy simultaneously is revealed in a generalized 16-parameters methods family, which contains numerous conventional single-step single-solve methods as subfamily methods. It is found that the unit consistent and non-consistent representations show a significant difference in the local truncation error, stability, and convergence performances. Third, a family of accuracy-improved single-step methods with algorithmic equivalence, which is stricter than the traditional spectral equivalence, to the LMS2 methods is revealed. It is clarified that the difference between the methods with equivalence can significantly influence their convergence accuracy, and the error constants concept of the LMS2 methods can be extended to the single-step method with algorithmic equivalence. Last, an algorithm optimization strategy is developed based on the error constants and the ultimate spectral radius, and an optimal single-step method with a single parameter to control the accuracy and numerical dissipation is revealed analytically without empirical assumptions. The optimal method is second-order accurate, unconditionally stable, overshooting acceptable, and equivalent to an optimal route between the Houbolt method and the trapezoidal rule, and its numerical performance is verified by the spectral accuracy and two numerical examples.

A dynamic capillarity equation with stochastic forcing on manifolds: A singular limit problem

We consider a dynamic capillarity equation with stochastic forcing on a compact Riemannian manifold ( M , g ) (M,g) : d ( u ε , δ − δ Δ u ε , δ ) + d i v f ε ( x , u ε , δ ) d t = ε Δ u ε , δ d t + Φ ( x , u ε , δ ) d W t , \begin{equation*} d \left (u_{\varepsilon ,\delta } -\delta \Delta u_{\varepsilon ,\delta }\right ) +div\mathfrak {f}_{\varepsilon }(\mathbf {x}, u_{\varepsilon ,\delta })\, dt =\varepsilon \Delta u_{\varepsilon ,\delta }\, dt + \Phi (\mathbf {x}, u_{\varepsilon ,\delta })\, dW_t, \end{equation*} where f ε \mathfrak {f}_{\varepsilon } is a sequence of smooth vector fields converging in L p ( M × R ) L^p(M\times \mathbb {R}) ( p > 2 p>2 ) as ε ↓ 0 \varepsilon \downarrow 0 towards a vector field f ∈ L p ( M ; C 1 ( R ) ) \mathfrak {f}\in L^p(M;C^1(\mathbb {R})) , and W t W_t is a Wiener process defined on a filtered probability space. First, for fixed values of ε \varepsilon and δ \delta , we establish the existence and uniqueness of weak solutions to the Cauchy problem for the above-stated equation. Assuming that f \mathfrak {f} is non-degenerate and that ε \varepsilon and δ \delta tend to zero with δ / ε 2 \delta /\varepsilon ^2 bounded, we show that there exists a subsequence of solutions that strongly converges in L ω , t , x 1 L^1_{\omega ,t,\mathbf {x}} to a martingale solution of the following stochastic conservation law with discontinuous flux: d u + d i v f ( x , u ) d t = Φ ( u ) d W t . \begin{equation*} d u +div\mathfrak {f}(\mathbf {x}, u)\,dt =\Phi (u)\, dW_t. \end{equation*} The proofs make use of Galerkin approximations, kinetic formulations as well as H H -measures and new velocity averaging results for stochastic continuity equations. The analysis relies on the use of a.s. representations of random variables in some particular quasi-Polish spaces. The convergence framework developed here can be applied to other singular limit problems for stochastic conservation laws.

Effective weak and vague convergence of measures on the real line

We expand our effective framework for weak convergence of measures on the real line by showing that effective convergence in the Prokhorov metric is equivalent to effective weak convergence. In addition, we establish a framework for the study of the effective theory of vague convergence of measures. We introduce a uniform notion and a non-uniform notion of vague convergence, and we show that both these notions are equivalent. However, limits under effective vague convergence may not be computable even when they are finite. We give an example of a finite incomputable effective vague limit measure, and we provide a necessary and sufficient condition so that effective vague convergence produces a computable limit. Finally, we determine a sufficient condition for which effective weak and vague convergence of measures coincide. As a corollary, we obtain an effective version of the equivalence between classical weak and vague convergence of sequences of probability measures.

Strong convergence of an explicit numerical approximation for [formula omitted]-dimensional superlinear SDEs with positive solutions

For a stochastic differential equation (SDE) with a unique positive solution, a rational numerical method is expected to be structure preserving. However, most existing methods are not, as far as we know. Some characteristics of the SDE models including the multi-dimension and super-linearity make it even more challenging. In this work, we fill the gap by proposing an explicit numerical method which is not only structure preserving but also cost effective. The strong convergence framework is set up by moment convergence analysis. We use the Lotka–Volterra system to elaborate our theory, nevertheless, the method works for a wide range of multi-dimensional superlinear SDE models.

Green productivity divergence and factor endowments: Evidence from the Yellow River Basin, China

Using panel data for the Yellow River Basin (YRB) of China from 2006 to 2017, we investigate cross-city conventional total factor productivity (TFP) and green TFP convergence, and the moderating effects of relative factor endowments on TFP growth. Allowing for cross-city and cross-time variation in the production function, we estimate TFP across cities using a nonlinear varying coefficient model, and decompose it into various input-embedded and input-free productivity components based on the new growth accounting, covering all growth-driving channels. This paper then employs a conditional convergence framework to examine whether convergence occurs, through which channels, and the effects of relative factor endowments on them. Empirical results show that lagging cities fail to achieve TFP catch-up, and that the divergence of capital-embedded and labor-embedded productivity instead triggers a widening of the cross-city TFP gap. Part of the cause of this increase in these gaps is that cities with relatively high capital deepening and capital-to-energy ratio are experiencing rapid TFP growth by driving the quality of capital and labor. Nor have these effects been altered in examining environmentally constrained or green TFP convergence.