Structural Decomposition Method Research Articles

Determining the optimal bid direction of a generation company using the gradient vector of the profit function in the network constraints of the electricity market

AbstractOne of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Time-varying effects of structural oil price shocks on financial market uncertainty

This study employed the structural oil price decomposition method proposed by Ready (2018) to decompose oil price shocks into oil risk shocks, demand shocks, and supply shocks. By using the Diebold and Yilmaz (DY) spillover index and rolling window methods, the static and dynamic spillovers of structural oil price shocks on financial market uncertainty were examined. The findings suggest that oil risk shocks exhibited the strongest spillover effects on financial market uncertainty (except for South Africa), followed by oil demand shocks, while oil supply shocks had virtually no impact. Second, the effects of structural oil price shocks on financial market uncertainty were time-varying. Third, significant differences in the spillover effects of structural oil price shocks on financial market uncertainty were found across countries, and the spillover effect of oil risk shocks on the financial market uncertainty in the United States was the largest in all periods. Fourth, the spillover effects of oil demand shocks on financial market uncertainty were larger in the high than in the low oil price period (except for Japan). Finally, during the COVID-19 (coronavirus disease) pandemic compared with the pre-epidemic period, the spillover effects of oil risk shocks on the financial market uncertainty in China significantly decreased, while simultaneously, the spillover effects of oil supply shocks on the financial market uncertainty in Australia substantially increased.

System model of risks and trees of alternative solutions for improving the logistics chain at a manufacturing enterprise

The subject of this article is the decision-making processes for improving the elements of the supply chain of a manufacturing enterprise under conditions of uncertainty and risks. The purpose of the research is to reduce the time and cost of in supply and distribution of products of manufacturing enterprises by considering possible risks through making rational management decisions. The article addresses the following tasks: examination of the characteristics of supply chain elements; investigation of the main problems of supply and distribution management and identification of ways to solve them; development of a systemic model of logistic risks for a manufacturing enterprise; and formation of decision trees for alternative decisions in strategic supply chain management at manufacturing enterprises. The methods applied include a systems approach, structural decomposition methods, a risk-oriented approach. The following results were obtained: features and problems of supply chain elements such as supply, production, storage, and distribution were examined and identified; main supply chain management tasks were investigated and solutions identified; problems in warehouse management were pinpointed; internal and external supply risks were identified; a systemic model of logistic risks for a manufacturing enterprise was formed, which main components are logistic problems, partial risks, their consequences, and possible management decisions to mitigate risks; decision trees were built for problems of raw material supply instability and transportation bottlenecks and delays; an alternative decision diagram was constructed for an illustrative example. Conclusions: To overcome the problem of raw material supply instability, it is recommended to develop diversification of supply sources, stock reservation, and the use of alternative transport routes. Building alternative decision trees in strategic management is an effective tool for making rational decisions by manufacturing enterprises in complex conditions of uncertainty and risks. They help to analyze alternatives and their consequences to choose a path to optimize the supply chain.

Conformational and Substitution Effects on the Donor and Reducing Strength of Tin(II) Porphyrinogens.

Meso-octaalkylcalix[4]pyrrolates are a class of redox-active porphyrinogen ligands. They have been well established in d- and f-block chemistry for over three decades but have only recently been introduced as ligands for p-block elements. Here, we present a study on the influence of meso-substituents on the redox chemistry of calix[4]pyrrolato stannate(II) dianions [2R]2- (R=Me, Et). Expansion of the normal-mode structural decomposition (NSD) method, well known for porphyrin chemistry, provides insights into the ligand conformation of a calix[4]pyrrolato p-block complex. Combined with the results of spectroscopic donor scaling, electrochemical studies, and quantum mechanical bond analysis tools, subtle but significant substitution and conformational effects on the electronic structure are revealed. Exploiting this knowledge rationalizes the role of this class of tin(II) dianions to act as potent reducing agents, but can also be expanded for other central elements. Photoexcitation boosts this reactivity further, allowing for the reduction of even challenging chlorobenzene.

Productive interdependence of tourist activities in Brazil

This paper examines the production linkages of Tourism Characteristic Activities in the Brazilian economy and breaks down the output variation of these activities into technological change and final demand effects. To do so, we use the hypothetical extraction and structural decomposition methods based on the Brazilian input-output matrices of 2010 and 2015. The main results show that the absence of tourist activities would cause a 0.83% reduction in the total output in Brazilian economy, and the impacts caused by the absence of the purchasing and selling structures of these activities were relatively low, which can be partially explained by the fact that tourist activities are oriented towards final demand. Furthermore, variations in the output of tourist activities are predominantly explained by the behavior of final demand, with a reduced impact of technical progress. Household consumption and exports are the ones that contributed the most to the variations in output.

Exploring the drivers of agricultural wages growth in China: A comprehensive framework utilizing input-output and structural decomposition methods.

This study explores the factors driving agricultural wages growth in China from 1981 to 2020. We propose a comprehensive framework that combines input-output analysis and structural decomposition analysis to investigate the drivers of agricultural wages growth from four perspectives: supply, demand, industrial linkages, and agricultural support policies. The findings indicate that changes in consumer demand, investment demand, and labor mobility play significant roles in driving the growth of agricultural wages in China. Additionally, agricultural support policies have contributed to an increase in agricultural wages to some extent. However, changes in industrial linkages negatively affect agricultural wages growth. A notable strength of this study lies in the methodology employed, which ensures a comprehensive and systematic analysis encompassing diverse factors rather than a restricted perspective.

The impact of the Brexit referendum on UK’s value-added exports: A structural decomposition

This article uses for the first time inter-country input–output data and a structural decomposition method to assess the impact of the 2016 Brexit referendum on the United Kingdom’s value-added exports. We find that final demand explains less than 30% of the 6.9-point decline in the UK’s value-added exports in 2016 (with a market share loss that offset the positive impact of global demand), whereas more than 70% was due to extraordinary changes in input sourcing decisions of the rest of the world. This means that the UK’s value-added exports were structurally affected by the weakening of the linkage effects of providing inputs to the rest of the world, especially to the EU. This article paves the way for further analyses of the structural effects of Brexit and other trade and industrial policies that alter the international allocation of inputs.

Global transition of operational carbon in residential buildings since the millennium

The residential sector is the third-largest energy consumer and emitter globally and as such is at the forefront of the energy transition and net-zero emissions pathway. To accelerate the pace of decarbonization of residential buildings, this study is the first to present a bottom-up assessment framework integrated with the decomposing structural decomposition method to evaluate the emission patterns and decarbonization process of residential building operations in 56 countries spanning 12 regions worldwide from 2000 to 2020. The results show that (1) the operational carbon intensity of global residential buildings has maintained an annual decline of 1.2% over the past two decades, and energy intensity and average household size have been key to this decarbonization; (2) end uses have held an increasingly important role in decarbonizing global residential buildings (-46.3 kgs of carbon dioxide per household per year), with the largest contributors being appliances(38.3%), followed by space heating (21.2%) and lighting (12.6%); and (3) although the total decarbonization of global residential buildings was 7.1 gigatons of carbon dioxide and achieved a decarbonization efficiency of 9.4% per yr during this time period, regional decarbonization inequality and uneven distribution remained quite large, especially in emerging economy regions. Moreover, the uncertainty and robustness of the assessment framework are also tested, and adaptive high decarbonization strategies are further proposed for global residential buildings. Overall, this study reviews and compares global and regional performances and motivations for decarbonization to support national decarbonization efforts to reach net-zero emissions and advance the global residential building sector toward a carbon-free century.

Estimation of Cropland Nitrogen Runoff Loss Loads in the Yangtze River Basin Based on the Machine Learning Approaches

A quantitative understanding of cropland nitrogen (N) runoff loss is critical for developing efficient N pollution control strategies. Using correlation analysis, a structural equation model, variance decomposition, and machine learning methods, this study identified the primary influencing factors of total N (TN) runoff loss from uplands (n=570) and paddy (n=434) fields in the Yangtze River Basin (YRB) and then developed a machine learning-based prediction model to quantify cropland N runoff loss load. The results indicated that runoff depth, soil N content, and fertilizer addition rate were the major influencing factors of TN runoff loss from uplands, whereas TN runoff loss rate from paddy fields was mainly regulated by runoff depth and fertilizer addition rate. Among the four used machine learning methods, the prediction models based on the random forest algorithm presented the highest accuracy (R2=0.65-0.94) for predicting upland and paddy field TN runoff loss rates. The random forest algorithm based model estimated a total cropland TN loss load in the YRB of 0.47 Tg·a-1 (upland:0.25 Tg·a-1; paddy field:0.22 Tg·a-1) in 2013, with 58% of TN runoff loss load derived from the midstream and downstream regions. The models predicted that TN runoff loss loads from croplands in YRB would decrease by 2.4%-9.3% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. To mitigate cropland N nonpoint source pollution in YRB, it is essential to integrate efficient water, fertilizer, and soil nutrient managements as well as to consider the midstream and downstream regions as the high priority area. The machine learning-based modeling method developed in this study overcame the difficulty of identifying the functional relationships between cropland TN loss rate and multiple influencing factors in developing relevant prediction models, providing a reliable method for estimating regional and watershed cropland TN loss load.

Low-light Image Enhancement via a Frequency-based Model with Structure and Texture Decomposition

This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website. 1

Optimal allocation of transmission fixed costs to electric vehicle charging stations and wind farms using an analytical method and structural decomposition

Renewable energies have found a suitable place in the electricity grid due to their non-pollution and cheapness. From the point of view of the grid operator, the allocation of costs should be appropriately allocated to producers and customers in competitive electricity markets. A fair allocation strategy can lead to more efficient use of current transmission lines while providing economic signals to guide future generation planning and load shedding. In this paper, first, using a powerful structural decomposition method, the optimal location of electric vehicle charging stations (EVCSs) and wind farms is determined from the flow aspect of grid lines. Then, the transmission fixed cost is assigned to each item placed in the network. The proposed method is implemented on the IEEE 24-bus network, and its results are analyzed. The fair allocation of transmission fixed costs prevents collusion, and the proposed method can be an effective tool for the network operator.

Pathway for decarbonizing residential building operations in the US and China beyond the mid-century

With global carbon budget targets looming, residential buildings in top economies must become carbon neutral as soon as possible to reserve more emission space for emerging carbon-emitting economies. This study is the first to compare the operational decarbonization process of China’s and the United States (US) residential buildings from 2000 to 2060 by combining the end-use emission model with the decomposing structural decomposition (DSD) method and Monte Carlo simulation. The results show that from 2001 to 2020 China decarbonized 1544 mega-tons of carbon dioxide (MtCO2) and the US decarbonized 1848 MtCO2. In the business-as-usual scenario, China will hit its emission peak in 2031 (±3) with 934 (±61) MtCO2, while the US will maintain a lock-in level of 736 (±133) MtCO2 since the 2030s. In the decarbonization scenario, operational carbon neutrality for residential buildings in 2060 is promoted by an increase in clean power generation proportion, building-integrated power generation level, building electrification level, and a reduction in end-use energy intensity, which will contribute 34.4 %, 21.4 %, 14.3 %, and 29.9 % in China and 32.9 %, 33.1 %, 8.2 %, and 25.8 % in the US, respectively. Especially, building-integrated power generation in China only costs about 40 % of what it costs in the US. Besides, high-decarbonization strategies for residential building operations are proposed as references for governments to formulate targeted climate policies. Overall, this study offers data benchmarks for buildings’ carbon neutrality of top economies to further promote synergistic carbon neutrality with the buildings of emerging economies in the age of Post COP27.

A structural decomposition analysis of “Latin American decade” for selected countries

The 2003-2014 period for Latin America refers to the recent boom period within the growth cycles of the region and is called as “Latin American decade”. Given that, the purpose of the paper is to analyze this specific period based on structural transformation dynamics. In this context, structural decomposition methods are evaluated to outline structural transformation patterns. For this analysis, the Groningen Growth and Development Centre (GGDC) Economic Transformation Database (ETD), which provides annual data on employment and real value added by 12 sub-sectors, is used for 8 LA countries (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Peru). The decomposition analysis is applied for three main periods (1990-2018, 1990-2003 and 2003-2014) within the growth cycles of the region. So it is predicted to define whether the “Latin American decade” differs from the previous period, regarding structural dynamics. During the “Latin American decade”, the region has gone commodity-led growth pattern and deindustrialization. Moreover, this pattern is not so homogenous. Specifically, the mining sector and non-tradeable sectors dominate the structural dynamics of some countries. Due to these patterns, the results underlie that productivity gains remained at low levels. Latin American countries need to design growth strategies that focus on escaping from productivity traps.

Improving the Spatial Characteristics of Three-Level LUT-Based Mealy FSM Circuits

The main purpose of the method proposed in this article is to reduce the number of look-up-table (LUT) elements in logic circuits of sequential devices. The devices are represented by models of Mealy finite state machines (FSMs). Thesee are so-called MPY FSMs based on two methods of structural decomposition (the replacement of inputs and encoding of output collections). The main idea is to use two types of state codes for implementing systems of partial Boolean functions. Some functions are based on maximum binary codes; other functions depend on extended state codes. The reduction in LUT counts is based on using the method of twofold state assignment. The proposed method makes it possible to obtain FPGA-based FSM circuits with four logic levels. Only one LUT is required to implement the circuit corresponding to any partial function. An example of FSM synthesis using the proposed method is shown. The results of the conducted experiments show that the proposed approach produces LUT-based FSM circuits with better area-temporal characteristics than for circuits produced using such methods as Auto and One-hot of Vivado, JEDI, and MPY FSMs. Compared to MPY FSMs, the values of LUT counts are improved. On average, this improvement is 8.98%, but the gain reaches 13.65% for fairly complex FSMs. The maximum operating frequency is slightly improved as compared with the circuits of MPY FSMs (up to 0.64%). For both LUT counts and frequency, the gain increases together with the growth for the numbers of FSM inputs, outputs and states.

Decomposition analysis of China's chemical sector energy-related CO2 emissions: From an extended SDA approach perspective

The excessive growth of carbon emissions (CO2E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO2E. However, there are limited literatures on energy CO2E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO2E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO2E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO2E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO2E energy-related CO2E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO2E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO2E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced.

Morphological responses of Bombax ceiba to habitat heterogeneity in Southwest China

In order to cope with environmental changes, plants constantly adjust their morphological characteristics in order to adapt to changing environment. In the present study, populations of Bombax ceiba from Mengla area and Yuanjiang area in Yunnan Province were selected as the research objects. Six tree structure factors, such as tree height and crown width, eight leaf trait factors, such as leaf area and leaf length, and several habitat factors, such as area topography, meteorology and soil nutrients, were measured. Structural equation model and variation decomposition method were applied to analyze the effects of various habitat factors on tree structure and leaf traits of B. ceiba, and to reveal its morphological responses to habitat heterogeneity. The results showed that there was a significant negative correlation between tree structure and leaf traits in the two study habitats (Mengla area and Yuanjiang area), and the correlation coefficient was −0.47 in Mengla area and −0.22 in Yuanjiang area. Both topographic and soil factors had positive effects on tree structure of the two habitats, and the topographic factors had a greater impact on tree structure than leaf traits. The main difference was that meteorological factors had a positive effect on tree structure of Mengla, but a negative effect on leaf traits, while Yuanjiang showed the opposite patterns. The variation analysis showed that the superposition of three environmental factors in Mengla area had a greater explanation power of tree structure and leaf traits than that in Yuanjiang area, and the topographic factors had the largest explanation power of tree structure in both areas, which reflected that fact that the characteristics of Mengla habitat imposed a greater influence on B. ceiba. The soil factors in Mengla area accounted for 20.1% of the leaf traits, while the meteorological factors in Yuanjiang area accounted for 11.6%. The results showed that leaf traits were sensitive to environmental differences. In general, the responses of B. ceiba to heterogeneous habitats is based on the specific performance of its resource utilization capacity. The research results can provide references for exploring the morphological responses of plants to heterogeneous habitats.

Qubit dynamics driven by dipole field in thermal noise environment

Quantum computing is a new way to process quantum information by using superposition and entanglement of the quantum system. Quantum state’s vast Hilbert space allows it to perform operations that classical computers cannot. The quantum computing has unique advantages in dealing with some complex problems, so it has attracted wide attention. Computing a single qubit is the first of seven fundamental stages needed to achieve a large-scale quantum computer that is universal, scalable and fault-tolerant. In other words, the primary task of quantum computing is the careful preparation and precise regulation of qubits. At present, the physical systems that can be used as qubits include superconducting qubits, semiconductor qubits, ion trap systems and nitrogen-vacancy (NV) color centers. These physical systems have made great progress of decoherence time and scalability. Owing to the vulnerability of qubits, ambient thermal noise can cause quantum decoherence, which greatly affects the fidelity of qubits. Improving the fidelity of qubits is therefore a key step towards large-scale quantum computing. Based on the dipole field driven qubit, the stochastic dynamic structure decomposition method is adopted and the Kubo-Einstein fluctuation-dissipation theorem is used to study the qubit control in a thermal noise environment. The dipole field has components in three directions, not just in one plane, which allows more flexible control of quantum states. Without considering the noise, the quantum state can reach the target state 100%. In the noisy environment, thermal noise will cause the deviation between the actual final state and the target final state caused by thermal fluctuation, which becomes the main factor affecting the quantum fidelity. The influence of thermal noise is related to temperature and the evolution trajectory of quantum state. Therefore, this paper proposes an optimal scheme to improve the qubit fidelity in the thermal noise environment. The feasibility of this method is verified by numerical calculation, which can provide a new solution for further guiding and evaluating the experiment. The scheme is suitable for qubit systems of various physical control fields, such as semiconductor qubits and nitrogen vacancy center qubits. This work may have more applications in the development of quantum manipulation technology and can also be extended to multi-qubit systems, the details of which will appear in the future work.

Incremental Updates of Generalized Hypertree Decompositions

Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition, even if the scopes change only slightly. In this article, we make the first steps toward solving the problem of updating the decomposition of a CSP P so that it becomes a valid decomposition of a new CSP P ' produced by some modification of P . Even though the problem is hard in theory, we propose and implement a framework for effectively updating generalized hypertree decompositions. The experimental evaluation of our algorithm strongly suggests practical applicability.

Identifying the key factors to China's unsustainable external circulation through the accounting of the flow of embodied energy and virtual water

In 2020, China launched the Dual Circulation Strategy to tackle difficult international trade situations and COVID-19. To examine the environmental pressure induced by China's domestic and international trade from the perspective of dual circulation, this study quantifies the flows of embodied energy and virtual water in China's domestic and international trade and analyzes the driving factors to the net outflow, using the multi-regional input-output model and spatial structural decomposition method. The main findings of this study are: (1) Domestic trade of China alleviated the pressures of both energy and water shortage through the flow of embodied energy from Northern to Southern and Eastern regions and the flow of virtual water from Southern to Northern regions. In contrast, international trade yet increased China's environmental burden and tends to be unsustainable. (2) Production structure effect was the key factor to the net outflow of embodied energy (contribution degree: 58%) and virtual water (contribution degree: 46%) to major economies. For China, the export scale effect and export structure effect played an important role in increasing the net outflow of China's embodied energy, with contribution degrees of 17% and 23%, respectively, while the export structure effect and water efficiency effect reduced the net outflow of virtual water, with contribution degrees of −23% and −19%, respectively. Multidimensional Policies, including industrial structure transition, renewable energy deployment, resource efficiencies improvement, trade pattern upgrade, and regional free trade promotion, are suggested for implementing the Dual Circulation Strategy, considering environmental sustainability and industrial competition advantages.

The main transmission paths of price fluctuations for tungsten products along the industry chain

Tungsten is deemed a critical raw material by many nations, given its irreplaceable use in industrial and military applications. In particular, China has high share of global tungsten supply. In this research, we study the tungsten product price fluctuation transmission of trade activities between China domestic market and international trade market from the industry chain perspective. Specifically, we embed the Chinese domestic market into the international trade market to construct price fluctuation transmission network (network-in-network) in tungsten industry chain. Drawing on the analysis ideas of the input-output price impact model, the total transmission impact of the tungsten product price fluctuation is revealed. We then combine the structural path decomposition method and the network cascade theory to identify the main transmission path of tungsten product price fluctuation. The results show that the total transmission impact of price fluctuation between the international trade relationships of cemented carbide in the tungsten industry chain is large. The cumulative transmission of the 3-step price fluctuation transmission paths of tungsten products between the Chinese domestic market and the international trade market accounts for more than 80% of the total transmission. The main transmission path of price fluctuations in the tungsten industry chain exists in the same product. Our research will provide effective information for governments to develop measures to control price fluctuation, thereby minimizing the losses from price fluctuation and transmission.