Functional Decomposition Research Articles

무역과 ESG가 상호 미친 영향에 관한 연구

When infused with ESG principles, Trade transforms into a force for positive change, driving economies while safeguarding our planet's future. Trade and ESG form a powerful alliance, where economic growth is nurtured in the embrace of environmental stewardship, social responsibility, and ethical governance. So this article examine on the mutual influence between ESG index and trade index in South Korea. For this purpose, we employ the vector-autoregression of ESG index and trade index in Korea, impulse response function and variance decomposition of ESG index and trade index in South Korea, Granger causality test of ESG index and trade index in South Korea using weekly data on the amount of ESG index and trade index in South Korea. The sample period of ESG index and trade index in South Korea is covering from January 7, 2012 to Jun 10, 2023. This paper showed following main results. First, ESG index and trade index in South Korea have unit roots. Second, the first differential data of ESG index and trade index in South Korea have no unit roots. Third, there is no at least one cointegration between the level data of ESG index and trade index in South Korea and there is at least one cointegration between the first differential data of ESG index and trade index in South Korea. Fourth, ESG index and trade index in South Korea is –0.41573. In addition, we find that ESG index granger cause the trade index and the trade index granger cause the ESG index in South Korea.

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

Physics-Informed Neural Networks (PINNs) and extended PINNs (XPINNs) have emerged as a promising approach in computational science and engineering for solving partial differential equations (PDEs) by combining the power of artificial intelligence (AI) with the underlying physics to accurately model and predict the solutions to complex problems in science and engineering. In this work, we propose the augmented physics-informed neural network (APINN), which adopts soft and trainable domain decomposition and flexible parameter sharing to further improve the extended PINN (XPINN) as well as the vanilla PINN methods. Concretely, a trainable gate network is employed to mimic the hard decomposition of XPINN, which can be flexibly fine-tuned for discovering a potentially better partition. The gate network satisfying the partition-of-unity property, weighted averages several sub-networks as the final output. APINN does not require complex interface conditions, whose sub-nets can utilize all training samples rather than just part of the training data in their subdomains. Lastly, each sub-net shares part of the common parameters to capture the similar components in each decomposed function. Furthermore, following the PINN generalization theory (Hu et al., 2022), APINN is shown to improve generalization by proper gate network initialization and general domain & function decomposition. Extensive experiments on different partial differential equations (PDEs) demonstrate how APINN improves PINN and XPINN. Specifically, we present examples where XPINN performs similarly to or worse than PINN, so that APINN can significantly improve both. We also show cases where XPINN is already better than PINN, so APINN can still slightly improve XPINN. Furthermore, we visualize the optimized gating networks and their optimization trajectories, and connect them with their performance, which helps discover the possibly optimal decomposition. Interestingly, if initialized by different decomposition, the performances of corresponding APINNs can differ drastically. This, in turn, shows the potential to design an optimal domain decomposition for the PDE under consideration.

Dynamic analysis of damping structures considering support stiffness

An analysis method for the random seismic response of viscoelastic damper structures with support is proposed. This method considers the influence of support stiffness and analyzes the dynamic characteristics associated with support stiffness. It also combines the advantages of the complex modal method (CMM) and the pseudo-excitation method (PEM), improving the complex integration operation in calculating the spectral moments and variances of random seismic responses. The support and the viscoelastic damper constitute the equivalent damper. The damper's constitutive relationship and structural dynamic equations are decoupled to calculate conveniently the frequency domain solution of the responses. Second, the quadratic decomposition of the power spectral density function (QD-PSDF) is applied to simplify the power spectrum and obtain the closed-form solution of spectral moments and variances. Finally, the structural dynamic characteristics and the influence of support stiffness on the vibration control effect of the equivalent damper are investigated based on the closed-form solution. The structural dynamic reliability is also discussed according to the first excursion failure criterion. The results demonstrate that the damper's control effect and structural reactions are reduced, while the dynamic reliability is also enhanced when increasing support stiffness. The performance of the structure and damper tends to stabilize when the support coefficient reaches a specific level.

Применение модального метода синтеза для дискретных систем

Works considering modal synthesis and using polynomial decomposition of transfer functions of object and controller models are considered mainly for continuous automatic control systems. At the same time, in some cases, it is necessary to consider systems of a discrete type. An example of the synthesis of an automatic control system for an accurate positioning of a table driven by a direct-drive motor is given. Such systems provide accurate positioning in the processes of manufacturing and processing of parts. They are used in various fields of industry: medical, aerospace, automotive and others. In particular, the above model of the precise positioning table is used for packaging semiconductor elements. A feature of this example is the representation of the transfer function of the object model in a discrete form and the presence of four-cycle delay links in it. In addition, this model is unstable due to the presence of poles lying on the stability boundary in it. As a synthesis method, a modal method is used, using a polynomial decomposition of the transfer function of the model of the control object and controller. An approach is also demonstrated that makes it possible to reduce the number of calculations in the synthesis of regulators by reducing the order of the transfer function of the considered object model and reducing the number of unknown parameters of the regulator. At the same time, a regulator is obtained that differs from the original one. Various options for choosing the desired poles of the transfer function of a closed system are analyzed. The choice of the desired poles is carried out in favor of reducing the amount of overshoot of the system relative to the transients of the system with the initial choice of poles.

Quantifying Carbon Active Sites Chemisorbing Hydrogen on Oxygen Containing Activated Carbons during Heat Treatment in Hydrogen Atmosphere.

Carbon edge sites have been widely studied because of their importance in surface reactivity and electronic properties. The surface chemistry of the carbon edge sites is relevant to various reactions, and carbon active sites are key topics in many applications. Temperature-programmed desorption (TPD) and temperature-programmed reaction (TPR) techniques are used to clarify the fate of oxygen atoms present as CO-yielding functional groups on the activated carbon during heat treatment in hydrogen with an argon balance atmosphere. It has been elucidated that CO is decomposed, H2O is released by a reduction reaction with atmospheric H2, and CO2 is evolved by secondary reactions from the CO-yielding functional groups during TPR. Atmospheric H2 consumption during TPR is observed and its rate is characterized. The amounts of carbon active sites are quantified by determining the amount of H2 chemisorbed onto the carbon surfaces. Finally, it is quantitatively determined that the active sites that chemisorb hydrogen are generated after the decomposition of CO and CO2 caused by secondary reactions between ca. 700 and 1100 K from the CO-yielding functional groups. The origin of these CO-yielding functional groups is generally attributed to phenol/ether groups. In addition to these oxygen-containing functional group decompositions, some free sites on the edge sites are activated for H2 chemisorption by heat treatment between ca. 700 and 1100 K.

The impact of government budget on national financial security: a case study in Vietnam

ABSTRACT This paper aims to examine the effect of the budget balance on national financial security in the linkage of macroeconomic variables by using the VAR model approach from 1995 to 2020 in Vietnam. Moreover, the methods of Impulse Response Function (IRF) and variance decomposition (FEDV) analysis show the impact of budget balance on the financial security situation in the short run. The results indicate that budget balance has a positive effect in the short term and explains about 2.04% of Vietnam’s financial security index. Several policy implications are suggested to enhance the financial stability in Vietnam during the Covid-19 pandemic.

Explicit formulae for surface gravities in stationary circular axi-symmetric spacetimes

Using minimalist assumptions we develop a natural functional decomposition for the spacetime metric, and explicit tractable formulae for the surface gravities, in arbitrary stationary circular (PT symmetric) axisymmetric spacetimes. We relate rigidity results, (the existence of a Killing horizon), and the zeroth law to the absence of curvature singularities at the would-be horizons. These observations are of interest to both observational astrophysicists (modelling the cold, dark, heavy objects at the center of most spiral galaxies), and to the analogue spacetime community, (wherein the presence of naked singularities is not necessarily deprecated, and the occurrence of non-Killing horizons is relatively common).

User Authorization in Microservice-Based Applications

Microservices have emerged as a prevalent architectural style in modern software development, replacing traditional monolithic architectures. The decomposition of business functionality into distributed microservices offers numerous benefits, but introduces increased complexity to the overall application. Consequently, the complexity of authorization in microservice-based applications necessitates a comprehensive approach that integrates authorization as an inherent component from the beginning. This paper presents a systematic approach for achieving fine-grained user authorization using Attribute-Based Access Control (ABAC). The proposed approach emphasizes structure preservation, facilitating traceability throughout the various phases of application development. As a result, authorization artifacts can be traced seamlessly from the initial analysis phase to the subsequent implementation phase. One significant contribution is the development of a language to formulate natural language authorization requirements and policies. These natural language authorization policies can subsequently be implemented using the policy language Rego. By leveraging the analysis of software artifacts, the proposed approach enables the creation of comprehensive and tailored authorization policies.

Renewable energy, climate policy uncertainty, industrial production, domestic exports/re-exports, and CO2 emissions in the USA: A SVAR approach

For the first time, this study creates and introduces a new determinant to be used in empirical models of environmental pollution, especially in the USA. This new determinant, created as the domestic-exports/re-exports ratio, arises from the need due to the changing content and structure of exports in the increasingly complex world trade network. Because many countries, preeminently the USA, export imported products to other countries, they re-export. While re-export does not require a production-stage process in a country, domestic export does and can cause production-related environmental pollution. Therefore, this difference may require using re-export and domestic export separately instead of total export in models. Another determinant that we propose to use in the US models is the climate policy uncertainty index. The rationale for proposing this index is based on the decisive role of governments' uncertain climate policies on the environment, and the USA is the only country with this index. Using these two determinants together makes it possible to examine the impact of both government climate policy uncertainties and international trade on environmental pollution since both factors can contribute to pollution. The SVAR model, Impulse-Response Function, and Variance Decomposition were applied between February 2002 and November 2021. Empirical findings indicate that a rising share of re-exports (clean exports) in total exports (83 %), does lead to a decrease in CO2 emissions. In addition, this new determinant created in this study, which we found to be significantly explanatory of CO2, can be used in future environmental models. Finally, we also found that rising uncertainty in climate policy in the USA does not affect PCO2 emissions. Using the domestic-export/re-export ratio as a determinant in pollution models can provide policymakers with a valuable tool to monitor and analyze the environmental impact of trade patterns.

Regional effects of China's monetary policy during the economic transition period: Based on China's city classification system under the new normal.

We analyse the regional effects of China's monetary policy during the economic transition period based on the city classification system under the New Normal. Using panel vector autoregressive (PVAR) models, we examine the differences in the influence of a unified monetary policy on four types of Chinese cities: first-tier, quasi-first-tier, second-tier, and third-tier cities, from perspective of economic output. Based on a comparative analysis of the results of impulse response function and variance decomposition, we conclude that whether for quantitative monetary policy or price-based monetary policy, the degree to which economic growth responds to the unified monetary policy varies across the four types of cities in the short and long terms. With reference to China's economic transition under the New Normal, in the short term, this phenomenon probably relate to the varying degrees of financial marketization among the four city types; in the long run, this phenomenon may be attributed to the difference in the progress of industrial upgrading of these urban areas. Based on the analysis above, we suggest policy implications for Chinese cities based on the structural monetary policy, industrial upgrading, and market-oriented financial sector reforms.

Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.

Penalized wavelet nonparametric univariate logistic regression for irregular spaced data

This paper concerns the study of a non-smooth logistic regression function. The focus is on a high-dimensional binary response case by penalizing the decomposition of the unknown logit regression function on a wavelet basis of functions evaluated on the sampling design. Sample sizes are arbitrary (not necessarily dyadic) and we consider general designs. We study separable wavelet estimators, exploiting sparsity of wavelet decompositions for signals belonging to homogeneous Besov spaces, and using efficient iterative proximal gradient descent algorithms. We also discuss a level by level block wavelet penalization technique, leading to a type of regularization in multiple logistic regression with grouped predictors. Theoretical and numerical properties of the proposed estimators are investigated. A simulation study examines the empirical performance of the proposed procedures, and real data applications demonstrate their effectiveness.

Early Design Phase Method for Characterizing and Comparing Wave Energy Converter Archetypes

The ocean is both a precious resource for coastal communities and a vast source of largely untapped renewable energy that can help combat climate change. This presents a unique opportunity for wave energy converters (WEC) to provide both utility-scale and small-scale electricity for resilient, energy-efficient communities that can better withstand the effects of climate change. Designing a WEC is not straightforward as the ocean supports a wide range of uses and the highly energetic environment can threaten survival. This requires WEC designers to deconflict their use area and select deployment locations that minimize risk. Subsurface WEC designs provide a potential solution for these challenges as their deployment depth can mitigate area-use conflicts with other ocean users. A fully submerged WEC also has increased survivability due to being in a less energetic environment and at a depth that they are less likely to collide with ocean vessels. However, a submerged WEC may also suffer from the less energetic environment as it decreases the amount of electricity the WEC can generate. These tradeoffs make it unclear when subsurface WECs are beneficial to deploy. There is little research to date that offers comprehensive understanding of this field of WECs in regard to their capabilities or constraints. This paper presents a method for characterizing and understanding the functions of WEC archetypes by leveraging functional decomposition to examine high level overlaps between archetypes. Functional decomposition is a well-established engineering design method for breaking down complex systems into constituent parts to track flows of energy, information, and materials. The method enables engineers understand how each component contributes to the overall functionality of the system and enables us to analyze functional performance overlaps between WEC archetypes. We apply this method to submerged WECs and present an analysis of the high level functions. The findings of this case study shed light on the functional uniqueness of submerged WEC archetypes and can be used to guide future development in subsurface WEC technology. Better identifying functional overlaps between WEC archetypes will help researchers and developers generate effective designs that build resilient coastal communities.

МЕТОДОЛОГІЯ ПОБУДОВИ ЗАХИЩЕНИХ ІНФОКОМУНІКАЦІЙНИХ МЕРЕЖ СПЕЦІАЛЬНОГО ПРИЗНАЧЕННЯ НА БАЗІ КАНАЛІВ ЗАГАЛЬНОГО ДОСТУПУ

This article is devoted to the solution of a new topical scientific task regarding the formation of a methodology for the construction of prospective secure special purpose information communication networks. The construction architecture was analyzed and a new classification of information communication networks by functional decomposition was presented for the first time. The content of the structural components of the network at the level of "network", "system", "information communication networks" has been determined. A new functional description of the image of a promising network and its quality has been developed. Based on the analysis of international and special standards, the first proposed decomposition of the scope of application in information communication networks of special purpose networks is presented. New scientific approaches to the design of special-purpose protected information communication networks are proposed, which include functional-structural and structural-functional approaches. According to the proposed approaches, the work presents a list and description of models for building special-purpose information communication networks. Their content, application limitations, advantages and disadvantages of individual models are revealed. A new scientific task of designing a promising special purpose secure information communication network based on public access channels was formed and its general description was given.

PP5GS—An Efficient Procedure-Based and Stateless Architecture for Next-Generation Core Networks

The introduction of the Service-Based Architecture (SBA) for the 5G Core Networks has drastically changed the way these networks are designed and operated. Aiming for higher flexibility and agility, the adoption of SBA is the first step towards cloud-native deployments of 5G Core. However, the high degree of functional decomposition in SBA has implications in terms of increased inter-NF signaling traffic during the execution of control plane procedures, as well as an increased complexity in orchestrating a system with tight inter-NF dependencies. In this work, we introduce PP5GS as a stateless 5G Core architecture that implements a procedure-based functional decomposition of the 5G Core NFs. We develop Per-Procedure NFs for four different control plane procedures and perform extensive evaluations in a private cloud environment orchestrated with Kubernetes. The results show that PP5GS requires up to 34% and 55% less computing resources compared to the baseline stateful and stateless systems, respectively, while generating at least 40% less signaling traffic. Moreover, complex control plane procedures can complete up to 50% faster. Lastly, the results show that PP5GS is a more feasible architecture in leveraging edge-offloading of 5G Core NFs.

Economic growth, energy consumption and CO2 emissions—An empirical study based on the Yangtze River economic belt of China

The Yangtze River Economic Belt plays a crucial role in the economic development of China from the perspective of economic growth, as well as from the perspective of the goal of the emission peak and carbon neutrality. In this study, the dynamic and causal relationships among economic growth, energy consumption and CO2 emissions of the provinces and municipalities along the Yangtze River Economic Belt were analyzed using the panel vector autoregression (PVAR) model. The results revealed that the economic growth and the changes in CO2 emissions in the current period are significantly influenced by the economic growth and CO2 emissions in the preceding period, while the energy consumptionin the current period is influenced by the economic growth, energy consumption and CO2 emissions in the preceding period. The results of the Granger causality test based on panel data suggested that among the three sets of causalities involving economic growth, energy consumption and CO2 emissions, only economic growth and CO2 emissions have one-way causality, while the other two sets of causalities are two-way. Further, the results of the impulse response function and variance decomposition showed significantly positive effects and a certain degree of path dependence among economic growth, energy consumption and CO2 emissions.

New Forms of alphag - continuous Maps in Topology

Abstract: In this paper, we describe a novel class of continuous maps called as strongly alpha generalised continuous maps in topological spaces. Furthermore, we investigate their properties with existing continuous maps, and we also study their decomposition of continuous functions.

Not coped with by a machine: on Frege's conception of logic as science

Following logicism, Frege famously held that logic is a science on its own. Particularly, he held the informativity thesis, viz., that logic is a science because it is deductively informative. This paper aims to understand Frege’s informativity thesis and its connection with the conception of logic as science. For such, it focuses on some features of Frege’s philosophy that are key for understanding this connection, particularly his conception of analyticity, the role of judgments in inferential reasoning, and the use of decomposition of functions as a means for concept-formation, which Frege names as fruitful definitions.
 Keywords: Gottlob Frege; Informativity of Logic; Judgements; Fruitful Definitions.

Analysis of the Impact of Remittance Inflows on Economic Growth: Evidence from Nigeria

This study investigated the impact of remittances on economic growth of Nigeria using time series data from 1986 to 2018 by examining the long -run and short-run equilibrium relationship between remittances and economic growth. The study used unrestricted Vector Auto regression (VAR), granger causality, Auto regressive distributive lag (ARDL), impulse response function and variance decomposition. The result of ARDL bound test, indicates the existence of long-run equilibrium relationship between remittance inflows and economic growth in Nigeria. The impulse response function, as well as variance decomposition result shows the mixture of both positive and negative shocks from GDP per capita to remittance, household consumption expenditure, foreign direct investment and official development assistance based on the past and current values. The study found the existence of unidirectional causality running from GDP per capita to remittances and foreign direct investment. The study recommends that, government should expand and improve the financial sector and make the process of transfer of remittance much easier and less expensive. This will enable the economy to capture remittance inflows that comes in through informal channels which are usually difficult to capture officially, and also remittances inflows need to be invested into productive sector. This is because without such investment the inflows cannot play any significant role in the economy particularly households that do receive remittance.

Structuring Complex or Wicked Problems: A Multimethod Approach in Practice

Complex or wicked problems necessarily require multidisciplinary approaches to shape the issue(s) into a structure that can lead to viable solutions. This paper presents a case in which a systematic approach was used to combine structuring methods to support a research design that intended to quantitatively relate variables which have been insufficiently defined. Misconstrued definitions of variables and the dearth or missing data are just some of the characteristics that make this problem “complex.” The scientist’s primary work in the early stages of this type of research effort is to apply a series of techniques that meaningfully define the variables, specify relevant data to support development of quantitative models, and generate and\or collect that data. The sequence of discussion in this paper is a multimethod outline for addressing complex problems. This paper begins by defining complex or wicked problems. Next, an illustrative military problem that exhibits complex characteristics is presented. Sequentially, various structuring techniques from several fields of study such as systems engineering, operations research, and computer science are discussed and applied to the problem. Activities began with a workshop to learn about the problem and provide context. This venue allowed primary stakeholders to describe the variables from their stance. During the workshop, participants conceptualized relationships among the variables. An in-depth literature review before and after the workshop informed development of theories and hypotheses about the variables and plausible connections among them. Using functional decomposition, the team broke down the variables into their fundamental components. Relational matrices and causal loop diagrams formed initial ideas for how the components may be linked. Functional decomposition syntax also led to relevant, quantifiable measures that afforded an opportunity to mathematically formulate relationships among components, thereby achieving the primary objective of the problem. Blending techniques from different disciplines and their respective structuring methods is a powerful approach for creating conditions to solve complex or wicked problems. The utility of this study is in identifying the critical properties of a complex or wicked problem and mapping suitable methods to “tame” them. The overall approach is applicable to complex problems found in industry, government, and scientific research.