Functional Decomposition Research Articles

Analysis of the Combined Impact of Socio-Economic Factors on House Price Volatility in the Housing Market

As an important variable in China’s economic system, real estate price is a key point closely linked to the country’s economy and people’s livelihood. It is of great practical significance to study the impact of socio-economic factors in the housing market on China’s house prices and its transmission mechanism, so as to put forward corresponding policy recommendations for stabilizing house prices and preventing real estate market and financial risks. Taking China’s time series data from 2014 to 2023 as a sample, this paper empirically investigates the law of the relationship between socio-economic factors in the housing market and house price fluctuations by constructing a structural vector autoregressive model, and employs impulse response function and variance decomposition to analyze the relationship between the two dynamically. The study shows that the 15-period variance decomposition effect of itself and residents’ disposable income are the main influencing factors of house price fluctuation, and the degree of explanation of its fluctuation is 55.5% and 23.2% respectively. In a market economy, real estate prices fluctuate up and down around their value due to the law of value. Therefore, real estate prices cannot rise indefinitely, and the final result should be stabilized within a reasonable level.

Reinforcement Learning with Value Function Decomposition for Hierarchical Multi-Agent Consensus Control

A hierarchical consensus control algorithm based on value function decomposition is proposed for hierarchical multi-agent systems. To implement the consensus control algorithm, the reward function of the multi-agent systems can be decomposed, and two value functions can be obtained by analyzing the communication content and the corresponding control objective of each layer in the hierarchical multi-agent systems. Therefore, for each agent in the systems, a dual-critic network and a single-actor network structure are applied to realize the objective of each layer. In addition, the target network is introduced to prevent overfitting in the critic network and improve the stability of the online learning process. During the updating of network parameters, a soft updating mechanism and experience replay buffer are introduced to slow down the update rate of the network and improve the utilization rate of training data. The convergence and stability of the consensus control algorithm with the soft updating mechanism are analyzed theoretically. Finally, the correctness of the theoretical analysis and the effectiveness of the algorithm were verified by two experiments.

Determinants of the Developed Country Index and Indonesian Macroeconomic on the IDX Growth 30 and IDX Value 30

This study analyzes the determinants of the Developed Country Index (Hang Seng Index, Dow Jones Index) and Indonesian macroeconomic variables (10-Year Government Bond Yield, Foreign Ownership in Government Bonds, and the BI-7 Day Reverse Repo Rate, BI7DRR) on the movement of the IDX Growth 30 Index (IDXG30) and IDX Value 30 Index (IDXV30) from 2018–2022. Monthly time series data is used with a saturated sampling technique, processed via Eviews 12 using VECM analysis. The study conducts Stationarity, Optimal Lag, VAR Stability, and Cointegration Tests, along with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses Results show that the Hang Seng Index and Foreign Ownership in Government Bonds negatively affect IDXG30 and IDXV30, while BI7DRR positively influences both. The Dow Jones Index positively affects IDXG30 but negatively impacts IDXV30. Meanwhile, the 10-Year Government Bond Yield negatively affects IDXG30 and positively influences IDXV30. Additionally, the performance of IDXG30 and IDXV30 themselves and BI7DRR significantly contribute to both indices' movements.

Impact of Phase Angle Jump on a Doubly Fed Induction Generator under Low-Voltage Ride-Through Based on Transfer Function Decomposition

During the fault period, a phase angle jump may occur at the stator or the point of common coupling, which will deteriorate the low-voltage ride-through (LVRT) characteristics of a doubly fed induction generator (DFIG). The existing LVRT studies focus on the impact of a voltage drop on DFIGs but often ignore that of a phase angle jump. The time-domain simulation is accurate in describing the response of a DFIG during the LVRT process, but it is time-consuming for a DFIG with the full-order model. In this paper, by using the voltage magnitude and phase angle of the stator or the point of common coupling as the inputs, and the state variables as the outputs, the transfer function of a DFIG is derived to analyze its response and find the LVRT measures against the voltage drop and, especially, the phase angle jump. Firstly, the differential-algebraic equations of the DFIG are linearized to propose their transfer function model. Secondly, considering its high-order characteristic, a model reduction method for the transfer function of the DFIG using the Schur decomposition is proposed, and the analytical expression of the output variables of the DFIG with the phase angle jump is derived by the inverse Laplace transformation to judge the necessity of the LVRT measures. Finally, the simulation results of the DFIG are provided to verify the accuracy of the transfer function model and its reduced-order form and validate the feasibility of the LVRT against the phase angle jump with the proposed models.

Number and dissimilarity of global change factors influences soil properties and functions

Soil biota and functions are impacted by various anthropogenic stressors, including climate change, chemical pollution or microplastics. These stressors do not occur in isolation, and soil properties and functions appear to be directionally driven by the number of global change factors acting simultaneously. Building on this insight, we here hypothesize that co-acting factors with more diverse effect mechanisms, or higher dissimilarity, have greater impacts on soil properties and functions. We created a factor pool of 12 factors and calculated dissimilarity indices of randomly-chosen co-acting factors based on the measured responses of soil properties and functions to the single factors. Results show that not only was the number of factors important, but factor dissimilarity was also key for predicting factor joint effects. By analyzing deviations of soil properties and functions from three null model predictions, we demonstrate that higher factor dissimilarity and a larger number of factors could drive larger deviations from null models and trigger more frequent occurrence of synergistic factor net interactions on soil functions (decomposition rate, cellulase, and β-glucosidase activity), which provides mechanistic insights for understanding high-dimensional effects of factors. Our work highlights the importance of considering factor similarity in future research on interacting factors.

Global effects of livestock grazing on ecosystem functions vary with grazing management and environment

Grasslands support multiple ecosystem functions and services, and diverse biota, and are critical for human well-being. Grazing is the most pervasive land use in grasslands, but can have damaging effects when poorly managed. How grazing management and the environment interact to affect ecosystem functions globally is less well understood. Addressing this knowledge gap is important if we are to evaluate where (climate region, soil texture, and grassland type), what (livestock type), and how (grazing intensity, grazing regime, and duration) grazing might minimize grassland degradation and sustain healthy grassland functions. We used a systematic meta-analysis to explore the effects of grazing on ecosystem functions (primary production, carbon sequestration, water conservation, nutrient cycle, and decomposition) based on 3917 paired data from 148 studies across the globe. We found that grazing substantially reduced plant productivity (-26 %), followed by water conservation (-18 %) and carbon sequestration (-19 %). The value of most ecosystem functions declined with increasing grazing intensity, and more pronounced negative effects of grazing with mixed-herbivore than single species grazing. Grazing impacts also varied with environmental conditions, with light grazing increasing carbon sequestration in arid regions, but reducing it in semi-arid regions. Further, increasing aridity indirectly weakened the positive impacts of light grazing on ecosystem functions by suppressing grazing effects. Our study suggests that the interactions between grazing management and environmental conditions are critical when assessing the effects of grazing on grassland functions, and this will likely be more important as climates become hotter and drier.

The past, present and future of multi-scale modelling applied to wave-structure interaction in ocean engineering.

Concepts and evolution of multi-scale modelling from the perspective of wave-structure interaction have been discussed. In this regard, both domain and functional decomposition approaches have come into being. In domain decomposition, the computational domain is spatially segregated to handle the far-field using potential flow models and the near field using Navier-Stokes equations. In functional decomposition, the velocity field is separated into irrotational and rotational parts to facilitate identification of the free surface. These two approaches have been implemented alongside partitioned or monolithic schemes for modelling the structure. The applicability of multi-scale modelling approaches has been established using both mesh-based and meshless schemes. Owing to said diversity in numerical techniques, massively collaborative research has emerged, wherein comparative numerical studies are being carried out to identify shortcomings of developed codes and establish best-practices in numerical modelling. Machine learning is also being applied to handle large-scale ocean engineering problems. This paper reports on the past, present and future research consolidating the contributions made over the past 20 years. Some of these past as well as future research contributions have and shall be actualized through funding from the Newton International Fellowship as the next generation of researchers inherits the present-day expertise in multi-scale modelling. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

On the Integration of Complex Systems Engineering and Industry 4.0 Technologies for the Conceptual Design of Robotic Systems

This paper presents a novel integration of Systems Engineering (SE) methodologies and Industry 4.0 (I4.0) technologies in the design of robotic systems, focusing on enhancing underwater robotic missions. Using the conceptual design of an underwater exploration vehicle as a case study, we demonstrate how SE can systematically incorporate I4.0 tools to improve mission performance and meet stakeholder expectations. The study begins with an overview of the SE approach, emphasizing the conceptual design stage and aligning it with the application and case study of design theories. We then explore various I4.0 technologies, highlighting their functional benefits rather than technical specifics and addressing design methods for I4.0. Remotely Operated Vehicles (ROVs) are examined in terms of classification, components, and tasks, showcasing their evolution driven by technological advancements, thus tackling the complexity and design of complex systems. The core of our study involves defining stakeholder expectations, using quality function deployment for requirements definition, and performing a functional and logical decomposition of the ROV system. To deal with design fixation within the design team, we developed a tool to help integrate new technologies by also empathizing with their functional capabilities rather than the technology itself. Our approach underscores the importance of understanding and incorporating new technologies functionally, aligning with the transition towards Industry/Society 5.0. This work not only illustrates the synergy between SE and I4.0, but also offers a structured methodology for advancing the design and functionality of complex systems, setting a blueprint for future developments in this field.

Revisiting democracy-growth nexus in Sub-Saharan Africa with macroeconomic variables modelling

PurposeA popular convention in the political-economy literature is that causality runs from democracy to economic growth. That thinking has continued to evoke significant debates among scholars. This study aims to propose a new research experiment by investigating whether macroeconomic variables cause democratisation.Design/methodology/approachThe study uses a panel data set of 48 carefully selected economies in Sub-Saharan Africa spanning 1991 to 2020 and uses the PVAR system generalised method of moment (GMM) approach.FindingsWith the application of three macroeconomic indicators: economic growth, full employment and balance of payment equilibrium, the study suggests that economic growth has a negative implication on the democratisation process and that the incentives that increase national income provide in the global South enable autocratic rulers to impede democratic growth. The Granger causality test demonstrates a unidirectional effect from economic growth to democracy. The eigenvalue stability condition, impulse response function and forecast-error variance decomposition all confirmed the validity of the findings with the PVAR system GMM. Finally, the study proposed policy and theoretical implications for the political stakeholders.Social implicationsRobust development of economic institutions (particularly the anti-corruption and rule of law) in the global South is required to tame the potential for the state actors to turn public resources into personal use to further their parochial political interest in the form of perpetuating themselves in office which negates the ideals of democracy and social norms. Strong institutions could prevent the misuse of national income and enhance a good quality of life for the citizenry, making them grow confidence in the democratisation process.Originality/valueThe research paper makes an insightful contributions from the methodological perspective and the sampling perspective. The author uses a new research method, the PVAR system GMM becoming the first attempt of such a method to be applied in determining the causal effects of macroeconomic variables on democracy in the literature. Another relevant contribution of the study relates to the sample technique of selecting economies from the Sub-Saharan Africa with notable weak or slow democratisation process.

Patterns and drivers in the functional diversity decomposition of invaded stream fish communities

AbstractAimThe assembly of real‐world ecological communities in human‐modified landscapes is influenced by a complex interplay of spatial, temporal, environmental and invasion gradients. However, understanding the relative importance of these drivers and their interactions in shaping functional assembly remains elusive. Our study aimed to investigate the relative influence of these drivers on the functional assembly of a stream fish metacommunity.LocationStreams of the Lake Balaton catchment, Hungary.MethodsWe analysed a long‐term (18‐year) dataset of the stream fish metacommunity, focusing on changes in functional diversity (Q), redundancy (R) and species dominance (D). Ternary diagrams were utilized to decompose functional diversity into Q, R and D components and to visualize diversity patterns. Linear mixed‐effect regression and separate structural equation models were employed to identify significant drivers of Q, R and D.ResultsNative fish communities exhibited low functional diversity (Q) but high redundancy (R) and dominance (D), indicating functional convergence and dominance. Stream habitat size, network position and associated spatial, physical and chemical gradients emerged as consistently significant drivers of D and R. Changes in Q were additionally linked to non‐native community properties and subtle shifts in land use and within‐stream habitat characteristics.Main ConclusionsOur findings suggest that both environmental filtering and interspecies interactions, particularly trait similarity between invaders and natives shape functional assembly of stream fish metacommunities. Despite minimal temporal directional changes, environmental drivers predominantly influence long‐term diversity patterns of native fish communities, overshadowing invasion effects. Our findings underscore the importance of considering both environmental filtering mechanisms and interspecies interactions in understanding functional assembly. Additionally, the joint application of diversity decomposition frameworks with predictive modelling provides comprehensive insight into patterns of functional diversity and assembly across ecological communities.

On function-on-function linear quantile regression

We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated using a basis expansion approach. Consequently, we approximate the infinite-dimensional function-on-function linear quantile regression model using a multivariate quantile regression model constructed from these partial quantile regression components. To evaluate the efficacy of our proposed techniques, we conduct a series of Monte Carlo experiments and analyze an empirical dataset, demonstrating superior performance compared to existing methods in finite-sample scenarios. Our techniques have been implemented in the ffpqr package in .

Bio-inspired distributed load frequency control in Islanded Microgrids: A multi-agent deep reinforcement learning approach

To prevent errors in load frequency control (LFC) decision-making in an islanded microgrid and reduce the waste of frequency regulation resources, a fully distributed “starfish” load frequency control method is proposed. For this method, a novel deep reinforcement learning algorithm called the distributed decomposed multirole multiagent deep deterministic policy gradient (DDMR-MADDPG) algorithm is introduced. This algorithm treats each unit as an agent, enabling centralized training of all the agents. Through the coordination and control of multiple agents, a global optimal strategy can be obtained. During online application, the algorithm employs a distributed implementation strategy, enabling each unit to make decisions autonomously. This decision-making strategy is akin to the distributed neural network of a starfish, and it can fundamentally improve the efficiency and correctness of decision-making. In addition, it employs multiple techniques, including value function decomposition, multirole learning, imitation learning, and curriculum learning approaches. The performance of the proposed method compared with 24 existing methods is demonstrated on a simulated LFC model of the Zhuzhou Island microgrid in China.

Analysis of the Factors Influencing House Prices Based on the Var Model

Var model is established to study the relationship between house price, inflation rate and GDP in this article. By using Granger causality test, cointegration test, impulse response function and variance decomposition to analyze, we conclude that the value of inflation rate and GDP can be predicted by the past data of house price, and GDP has a significant impact on house price.

Exploring the Interrelationship between Scientific Knowledge and Economic Growth in Serbia: Empirical Insights

The primary objective of this study is to examine the nature of the relationship between the production of scientific knowledge and economic growth in Serbia during the period 1996-2022. For this purpose, the Vector Autoregression approach, along with the impulse response function and forecast error variance decomposition, was employed. Results suggest an impact of economic growth on the production of scientific knowledge, while no impact in the opposite direction is detected. The Serbian government should persist in investing in science and work closely with the scientific community to overcome barriers to scientific knowledge's full contribution to economic prosperity.

The hydraulically smooth limit of flow over surfaces with spanwise heterogeneity

Wall slip is conventionally obtained through riblets or grooves. As riblets are macroscopic objects, it is usually not practical to discuss slip patterns at the hydraulically smooth limit. Per Nikuradse, the smooth limit is when the characteristic length of the surface pattern is comparable to the viscous length scale. Recent studies show the possibility of slip at the microscopic scale, making slip patterns at the hydraulically smooth limit relevant. In this study, we leverage a high-fidelity pseudo-spectral code and study flow over surfaces featuring alternating strips of slip and no-slip wall conditions. The wavelength of the surface pattern, denoted as l, varies from 1.5 times the half channel height to 2 viscous units (plus units), eventually approaching the anticipated hydraulically smooth limit. The presence of surface slip gives rise to a slip velocity at the wall, denoted as Us, which contributes to drag reduction. The surface spanwise heterogeneity leads to secondary flows and intensifies turbulent mixing, consequently leading to drag increase. This drag increase effect can be parameterized using the “roughness function” ΔU+. The sum of Us+ and −ΔU+ determines whether the surface increases or reduces drag. Here, the superscript ＋ denotes normalization by the wall units. In most cases, the slip velocity at the wall Us+ predominates over −ΔU+, resulting in drag reduction. However, when l is a few viscous units, the roughness function ΔU+ does not vanish and overwhelms the slip velocity, giving rise to a net drag increase. Considering that the wall is a mixture of slip and no-slip conditions, this drag increase at the anticipated hydraulically smooth limit is unexpected. To gain an insight into the mechanism responsible for this drag increase, we derive a Navier–Stokes-based decomposition of the roughness function. Here, we generalize the definition of the roughness function such that it is a function of the wall-normal coordinate, thereby overcoming the difficulty of measuring the roughness function when the log region is narrow and hard to define. Analysis shows that when l is a few plus units, secondary flows contribute to a slightly positive ΔU+, while turbulent and viscous contributions, by and large, cancel out, ultimately leading to an overall drag increase at the anticipated hydraulically smooth limit. The evidence in the paper suggests that the hydraulically smooth limit does not exist for certain surfaces.

A graph decomposition-based approach for the graph-fused lasso

We propose a new algorithm for solving the graph-fused lasso (GFL), a regularized model that operates under the assumption that the signal tends to be locally constant over a predefined graph structure. The proposed method applies a novel decomposition of the objective function for the alternating direction method of multipliers (ADMM) algorithm. While ADMM has been widely used in fused lasso problems, existing works such as the network lasso decompose the objective function into the loss function component and the total variation penalty component. In contrast, based on the graph matching technique in graph theory, we propose a new method of decomposition that separates the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty. We develop an exact convergence rate of the proposed algorithm by developing a general theory on the local convergence of ADMM. Compared with the network lasso algorithm, our algorithm has a faster exact linear convergence rate (although in the same order as for the network lasso). It also enjoys a smaller computational cost per iteration, thus converges overall faster in most numerical examples.

Duality origami: Emergent ensemble symmetries in holography and Swampland

We discuss the interrelations between several ideas in quantum gravity – holography, the Swampland, and the concept of ensemble averaging. To do so, we study ensemble averages of Narain-type theories associated with general even quadratic forms and their holographic duals. We establish the emergence of global symmetries and discuss their consistency with conjectures forbidding such symmetries. We also discuss how the spectral decomposition of Narain partition functions suggests a natural embedding of ensemble averaging within the low-energy limit of certain string compactifications, which in turn allows a connection with the Swampland program.

Optimisation design of water-fertiliser infiltration device based on TRIZ theory

Abstract This paper is based on functional flow and modular design to optimize the design of water-fertilizer integrated percolation irrigation device. Functional decomposition and the construction of functional structure model are completed, and the detection module and the delivery and irrigation module are identified as the modules to be optimized. Apply TRIZ theory to solve the power supply problem of the detection module in harsh environment and design the energy conversion structure. For the clogging problem of the irrigator, an anti-clogging design, labyrinth flow channel and unclogging pipe are proposed. For the labyrinth flow channel built-in elastic small pieces, can be compensated for the pressure of the irrigator outlet, labyrinth flow channel to form turbulence is not easy to form a blockage. When the pipe is clogged, through the inflatable unclogging pipe, to realize the clogging position of the air pressure impact, to complete the clogging unclogging.

Functional Decomposition of Symmetric Multiple-Valued Functions and Their Compact Representation in Decision Diagrams

Functional Decomposition of Symmetric Multiple-Valued Functions and Their Compact Representation in Decision Diagrams

Analysing the drivers of stunting reduction in twelve sub-saharan African countries using the RIF decomposition approach

BackgroundThis study examines how significant is the changes in child stunting in Sub-Saharan African countries (SSA). Then, it investigates factors that contributed to the reduction in child stunting in those countries. For each country, we distinguish the contribution of compositional effects and structural effect.MethodsThis paper uses data from Demographic and Health Surveys of 12 sub-Saharan African countries conducted between 2000 and 2020. The z-test to compare two independent proportions was used to assess changes in child stunting and explanatory variables over the period. Recentred influence function (RIF) decomposition method was used to decompose changes in stunting over the year in each country, and to determine the contribution of each variable to the changes.ResultsThe prevalence of child stunting declines significantly in 11 countries over the year. The decline varies from 6.8% in Cameroun to 19% in Mali. The average year of education of the child’s mother and father, and the proportion of households with access to an improved drinking water source have contributed to the reduction in child stunting. This result was found in all the countries. Improvements in living standards, child vaccination, antenatal care attendance, delivery to health care centres, maternal education, improved drinking water sources, and improved sanitation make the largest contribution to the composition component, hence reducing child stunting.ConclusionsThis study sheds light on what has contributed to the achieved improvement in child nutritional status and suggests how to possibly accelerate the reduction in undernutrition in countries that lag.