Decomposition Analysis Method Research Articles

Calculation and Intensity Analysis of Logistics Industry Embodied CO2 Emissions in China

China’s logistics industry has been flourishing in recent years, with the high carbon emissions caused thereby receiving widespread attention. In this paper, the emissions and intensity of embodied carbon in the logistics industry in China are calculated for 2012, 2015 and 2017, using the multiregional input-output model and the changing trend. Additionally, influences of the intensity of embodied carbon emissions in the logistics industry across 30 provinces of China are assessed using the structural decomposition analysis method. The results show that from 2012 to 2017, the emissions of embodied carbon in the logistics industry from 30 provinces increased, while the intensity of embodied carbon emissions mainly decreased. The changes in the embodied carbon emissions intensity of the logistics industry are mainly affected by the direct carbon emission coefficient and added value coefficient. The intermediate input structure technology and the total scale of the final demand play a slight role in promoting the intensity of the embodied carbon emissions in the logistics industry. The direct carbon emission coefficient plays a major role in restraining provinces with negative intensity of embodied carbon emissions and promoting provinces with positive embodied carbon emissions intensity. The added value coefficient plays a major role in promoting the intensity of embodied carbon emissions. Finally, based on the analysis results, this paper presents suggestions for reducing the embodied carbon emissions in the logistics industry in 30 Chinese provinces, which include adjusting measures to local conditions, increasing the proportion of clean energy and clean technology in the logistics industry, increasing investment in green technology research and development, and improving the green technology innovation. Currently, researches on the implicit carbon emissions of the logistics industry focus mainly on the national, regional, and inter-provincial levels, with relatively few studies on the implicit carbon emissions of the logistics industry in each province. However, understanding the differences in the implicit carbon emissions of the logistics industry in each province and their influencing factors is crucial for identifying key emission reduction factors and developing carbon-neutral and carbon-reduction policies at the provincial level, which is the contribution that this paper makes.

Assessments of DFT-based energy decomposition analysis methods for intermolecular interactions.

In this work, the analysis results of three energy decomposition analysis (EDA) methods, namely, generalized Kohn-Sham (GKS) EDA, extended transition state EDA, and density functional theory symmetry-adapted perturbation theory (DFT-SAPT), were extensively assessed for various intermolecular interactions. According to the physical meanings of their definitions, the EDA terms in the three methods can be grouped into four categories: electrostatics, exchange-repulsion/Pauli/exchange, polarization/orbital/induction, and CD (correlation/dispersion/dispersion) terms. Test examples include 1092 non-covalent interaction complexes in the standard sets (S66, PNICO23, HAL59, IL16, S66 × 8, and X40 × 10). It is concluded that despite the different basis sets and different running platforms (programs), the results of the three EDA methods are comparable. In general, except the dispersion term, all the EDA terms in the three methods are in excellent agreement. The CD term in GKS-EDA is comparable with the dispersion term in the DFT-SAPT. GKS-EDA provides another way to explore the role of electronic correlations from DFT calculations.

Excimer Energies.

A multistate energy decomposition analysis (MS-EDA) method is introduced for excimers using density functional theory. Although EDA has been widely applied to intermolecular interactions in the ground state, few methods are currently available for excited-state complexes. Here, the total energy of an excimer state is separated into exciton excitation energy ΔEEx(|ΨX·ΨY⟩*), resulting from the state interaction between locally excited monomer states |ΨX*·ΨY⟩ and |ΨX·ΨY*⟩ , a superexchange stabilization energy ΔESE, originating from the mutual charge transfer between two monomers |ΨX+·ΨY⟩ and |ΨX-·ΨY+⟩ , and an orbital-and-configuration delocalization term ΔEOCD due to the expansion of configuration space and block-localized orbitals to the fully delocalized dimer system. Although there is no net charge transfer in symmetric excimer cases, the resonance of charge-transfer states is critical to stabilizing the excimer. The monomer localized excited and charge-transfer states are variationally optimized, forming a minimal active space for nonorthogonal state interaction (NOSI) calculations in multistate density functional theory to yield the intermediate states for energy analysis. The present MS-EDA method focuses on properties unique to excited states, providing insights into exciton coupling, superexchange and delocalization energies. MS-EDA is illustrated on the acetone and pentacene excimer systems; three configurations of the latter case are examined, including the optimized excimer, a stacked configuration of two pentacene molecules and the fishbone orientation. It is found that excited-state energy splitting is strongly dependent on the relative energies of the monomer excited states and the phase-matching of the monomer wave functions.

Specificities of Engineering of Training Information Systems With Virtual Electronic Tutoring Devices

In the study of engineering disciplines, special attention is paid to students’ participation in conducting scientific experiments. But not all experiments can be implemented in physical laboratories due to the high cost and great complexity. In such cases, information technologies provide an opportunity to create virtual learning systems, a kind of which is cognitive training information systems, including virtual tutoring devices. The methodological basis for researching the components of virtual tutoring devices and training informational system as a whole was the complex application of mutually complementary approaches and methods, the main of which were methods of decomposition, aggregation, structural and cognitive analysis. One of the most significant shortcomings of existing training information systems is their low didactic efficiency, therefore, the purpose of this article is to create virtual electronic training devices as part of cognitive learning systems which functioning algorithms take into account the education process participant’s personal qualities and thereby provide the opportunity of using a student-oriented approach in education. The article presents schematic solutions for the engineering of training information systems, which include virtual models of precision power systems and precision amplification systems. By building powerful power supply systems, it was possible to fully compensate the instability of the output voltage when the load current changes up to 50 A. Solutions were also proposed to create broadband amplifiers with a harmonic coefficient of 0.003%. The work proposes schematic solutions for virtual electronic training devices that can provide students with nodes of various complexities in engineering. The work presents methods for increasing the didactic efficacy of the use of training information systems.

What drives the decoupling progress of China's civil aviation transportation growth from carbon emissions? A new decomposition analysis.

Civil aviation carbon emission reduction is an inevitable requirement for achieving sustainable social development. Realizing the continuous expansion of air transportation scale while reducing the impact on the environment is particularly important. Therefore, it is necessary to accurately understand the relationship between civil aviation carbon emissions and the industry development. This study established a civil-aviation-pointed Tapio decoupling model to identify the decoupling state between transportation scale added and carbon dioxide emissions in China's civil aviation sector. The index decomposition analysis method is further applied to decompose the factors influencing the changes in decoupling states. The empirical study generated three important findings. Firstly, the overall carbon emissions in the civil aviation sector are still growing, while the energy intensity has a tendency to fluctuate and decrease. Secondly, the relationship between carbon emissions and transport turnover is dominated by the expansive coupling, that is, the development of the civil aviation sector is still at the cost of the growth of energy consumption. Nevertheless, the overall decoupling stability is unstable, and the decoupling state is likely to be changed by many external factors. Thirdly, the energy intensity decoupling effect and industry structure decoupling effect are the main reasons for civil aviation carbon decoupling. Meanwhile, the improvement of national economic level during the research period is the dominant negative factor that restrains the carbon decoupling of the civil aviation sector.

Drivers of China's carbon dioxide emissions: Based on the combination model of structural decomposition analysis and input-output subsystem method

To study the key drivers of China's carbon dioxide emissions, this study combines the structural decomposition analysis method and the input-output subsystem analysis method to construct a decomposition model of the factors influencing the amount of change of the carbon dioxide emissions in China. From the perspective of final demand, this decomposition model (own internal component, feed-back component, spillover component, scale component) is used to analyze the amount of change of China's carbon dioxide emissions in the two periods of 2012–2017 and 2017–2018. Four scenarios are set up to predict the carbon dioxide emissions of China before 2035. The results show that in 2012, 2017 and 2018, the carbon dioxide emissions generated by final demand were respectively 9,018,810, 8,715,226, and 8,803,201 kt, of which the spillover component accounted for the largest share, namely 85.5%, 85.6% and 84.7% respectively. This indicates that the increase of carbon dioxide emissions in China is due to intersectoral pulling effects, and the activities of the Construction sector have had pulling effects on the carbon dioxide emissions of other sectors to a greater extent. The energy intensity effect is the significant influencing factor that inhibits carbon dioxide emissions and economic development is the main driver of the increase in carbon dioxide emissions; this implies that changing the current energy structure is necessary to reduce carbon dioxide emissions. The technical progress scenario (QN) and the energy structure optimization scenario (GN) are predicted to achieve the carbon peak in 2025 and 2030, respectively.

Understanding the enhanced reactivity of strained intramolecular Frustrated Lewis Pairs

AbstractThe poorly understood factors controlling the enhanced reactivity of strained intramolecular frustrated Lewis pairs (FLPs) having a rigid biphenylene tether have been quantitatively explored in detail by means of computational methods. With the help of the activation strain model of reactivity and the energy decomposition analysis methods, the challenging allene activation reaction has been selected and compared to the analogous process mediated by a related intramolecular FLP having a more flexible tether, which is significantly less reactive. In addition, the influence of the nature of the Lewis acid atom on the reactivity of the strained FLP has been considered as well showing that the reactivity steadily decreases when going down in group 13.

Understanding degradation of electroactive molecules in organic redox flow batteries: Decomposition analysis methods

Organic redox flow batteries have attracted a lot of interests both in academics and industries. Accordingly, many organic materials and chemistries have been studied, providing a solid foundation for development of low-cost organic flow batteries. However, capacity loss/fade as a result of organic molecule decomposition/degradation is a major hindrance towards further advancement of this promising, sustainable and large-scale energy storage technology. Understanding the causes of decomposition as well as its mechanism is thus necessary to unravel this major challenge. Therefore, this perspective/views focus on highlighting the different methods that can be employed for decomposition assessment of organic molecules in flow battery systems. This will help in engineering and designing stable electroactive organic molecule to enable development of durable and long cycle life redox flow battery.

Energy decomposition analysis methods for intermolecular interactions with excited states.

Intermolecular interactions with excited states play important roles in various photochemical and photophysical processes. In this work, an energy decomposition analysis (EDA) method of intermolecular interactions for systems in which one monomer is in a singly excited state while others are in their ground states, called GKS-EDA(TD), is proposed. Based on the computational results of time-dependent density functional theory (TD-DFT), GKS-EDA(TD) divides the total interaction energy with excited states into electrostatic, exchange-repulsion, polarization, correlation and dispersion. The nature of intermolecular interactions in test examples with their low-lying singly excited states is investigated, which shows that GKS-EDA(TD) can be used for various intermolecular interactions with different excitation modes. Furthermore, GKS-EDA(TD) is employed to explore the non-covalent interactions in a series of C60⋯ nucleic acid base complexes with the decomposition of excitation energy contribution.

Sulfur-arene interactions: the S⋯π and S-H⋯π interactions in the dimers of benzofuran⋯sulfur dioxide and benzofuran⋯hydrogen sulfide.

Non-covalent interactions between sulfur centers and aromatic rings play important roles in biological chemistry. We examined here the sulfur-arene interactions between the fused aromatic heterocycle benzofuran and two prototype sulfur divalent triatomics (sulfur dioxide and hydrogen sulfide). The weakly-bound adducts were generated in a supersonic jet expansion and characterized with broadband (chirped-pulsed) time-domain microwave spectroscopy. The rotational spectrum confirmed the detection of a single isomer for both heterodimers, consistent with the computational predictions for the global minima. The benzofuran⋯sulfur dioxide dimer exhibits a stacked structure with sulfur closer to benzofuran, while in benzofuran⋯hydrogen sulfide the two S-H bonds are oriented towards the bicycle. These binding topologies are similar to the corresponding benzene adducts, but offer increased interaction energies. The stabilizing interactions are described as S⋯π or S-H⋯π, respectively, using a combination of density-functional theory calculations (dispersion corrected B3LYP and B2PLYP), natural bond orbital theory, energy decomposition and electronic density analysis methods. The two heterodimers present a larger dispersion component, but nearly balanced by electrostatic contributions.

Understanding the reactivity and selectivity of Diels-Alder reactions involving furans.

The reactivity and endo/exo selectivity of the Diels-Alder cycloaddition reactions involving furan and substituted furans as dienes have been computationally explored. In comparison to cyclopentadiene, it is found that furan is comparatively less reactive and also less endo-selective in the reaction with maleic anhydride as the dienophile. Despite that, both the reactivity and the selectivity can be successfully modified by the presence of substituents at either 2- or 3-positions of the heterocycle. In this sense, it is found that the presence of strong electron-donor groups significantly increases the reactivity of the system while the opposite is found in the presence of electron-withdrawing groups. The observed trends in both the reactivity and selectivity are analyzed quantitatively in detail by means of the activation strain model of reactivity in combination with the energy decomposition analysis methods.

Urban-rural inequalities and spatial arrangement of informed choice of family planning in Ethiopia: Further analysis of 2016 Ethiopian demographic health survey.

Ethiopia has made satisfactory progress in improving maternal and child health over the past two decades. The introduction of family planning through informed choice is one of the main strategies to improve maternal and child health. However, this positive progress may have masked the significant urban-rural disparities in informed choice for family planning. To identify factor contributing to observed urban-rural disparities and to determine the spatial distribution of informed family planning choices in Ethiopia. The study used information from 3,511 women currently using contraceptives (rural-2685 and urban-826) as per recent Ethiopian demographic health survey cross-sectional data. Spatial and descriptive, bivariable, and multivariable logit-based decomposition analysis methods were used. The spatial configuration of uninformed choice was clustered. The primary cluster (LLR = 34.8, p-value<0.001) was located at the southern portion of Amhara region that covers east & west Gojjam, south Gondar and south Wollo administrative zones. The magnitude of informed choice was 12 percent higher in urban residents compared to rural residents. Urban-rural gap was attributed to variations in characteristics (74%). Place of family planning offer i.e., private health facility, being aged between 35 and 49 years, and having visited to health facility in the last 1 year are found decrease the urban-rural gap of informed family planning choice by 15%, 9% and 5% respectively. Conversely, being aged between 25 and 34 years, being a listener to radio has increased the gap by 9% and 12% respectively. The variables being private health facility visitors, being aged between 35 and 49 years and having visited health facilities in the last one year are found to increase the gap of informed family planning choices between urban and rural residents Besides, the spatial distribution of uninformed family planning choices is non-random.

Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning.

The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%.

η6 -Metalated Aryl Iodides in Diels-Alder Cycloaddition Reactions: Mode of Activation and Catalysis.

The potential application of η6 -metalated aryl iodides as organocatalyst has been explored by means of computational methods. It is found that the enhanced halogen bonding donor ability of these species, in comparison with their demetalated counterparts, translates into a significant acceleration of the Diels-Alder cycloaddition reaction involving cyclohexadiene and methyl vinyl ketone. The factors behind this acceleration, the endo-exo selectivity of the process and the influence of the nature of the transition metal fragment in the activity of these species are quantitatively explored in detail by means of the combination of the Activation Strain Model of reaction and the Energy Decomposition Analysis methods.

A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data

Functional dependencies in complex technical infrastructures can cause unexpected cascades of failures. Given the complexity and continuous evolution in time of the infrastructure architecture, the identification of functional dependencies using the classical methods of system decomposition and logic analysis is not feasible. Then, we consider the availability of databases of alarm messages and frame the identification of functional dependencies in complex technical infrastructures as an optimization problem whose objective is the maximization of a metric measuring the level of dependence among alarms. A niching-based evolutionary algorithm has been developed to sequentially evolve a population of candidate solutions (group of alarms), maintaining diversity among them thanks to the use of a mechanism of population augmentation. The proposed algorithm is applied to a synthetic database of alarms generated by a complex technical infrastructure simulation model and to a real large-scale database of alarms collected from the particle accelerator of European Organization for Nuclear Research. The proposed algorithm is shown able to identify functional dependencies and to overperform other approaches based on the use of association rule mining algorithms, in terms of capability of extracting rare rules and computational efficiency.

Influence of boundary conditions on acoustic emission propagation characteristics of Zelkova schneideriana

To study the propagation characteristics of acoustic emission signals in Zelkova schneideriana under different boundary conditions, three types of boundary conditions were generated by applying aluminum plates and sound-absorbing cotton on the surface of Zelkova schneideriana specimens. Firstly, the sudden and continuous acoustic emission (AE) sources were simulated by PLB (pencil–lead break) tests and signal generator on the specimen surface, and the AE signals were collected by 5 sensors equally spaced on the surface of the specimen, and the sampling frequency was set to 500 kHz. Then, the detailed signals of different frequency bands were obtained by wavelet decomposition, and TDOA (the time difference of arrival) and correlation analysis method were used to calculate the time difference of longitudinal wave and surface transverse wave and the corresponding propagation velocity, respectively. Finally, the pulse trains with different energy levels generated by the signal generator were used as AE sources to study the attenuation law of AE signal energy with distance under different boundary conditions. The results show that the boundary changes can lead to a significant increase in the surface transverse wave velocity, and have no significant effect on the longitudinal wave velocity. At the same time, the energy attenuation of surface and longitudinal waves is faster after the aluminum plate and sound-absorbing cotton are affixed, and the distance of longitudinal waves attenuation to 90% is reduced from 186 to 139 mm, and the distance of surface transverse waves propagation is reduced from 312 to 226 mm.

How Does Energy Consumption and Economic Development Affect Carbon Emissions? A Multi-Process Decomposition Framework

Against the background that climate warming has become a global challenge, exploring the factors that drive carbon emissions change is important to achieve emission reduction targets. Because of the differences in economic development, resource endowment, and historical accumulation, different countries generally have significant technological heterogeneity in the carbon generation process. Therefore, the heterogeneity-related factors should also be understood, which can help policy making and responsibility attribution more accurate. As such, this study developed a meta-frontier-based production–theoretical decomposition analysis method to track the progress of carbon emission change in 42 countries during 2012–2019 with production heterogeneity between the countries taken into account. The empirical study draws the following three meaningful conclusions: firstly, the carbon emission process of different countries has clear technological heterogeneity, mainly reflected in aspects of their energy-use efficiency and energy-use technology. Secondly, the decomposition analysis results showed that the potential energy intensity effect and the economic activity effect played the dominant role in driving and reducing carbon emissions, respectively. Additionally, this conclusion is right for all types of countries. Thirdly, the attribution analysis showed that different types of countries have significantly different contributions to the influencing factors of carbon emission changes, among which countries with large energy consumption and large economies need to take more responsibility for emission reduction.

Assessing the embodied CO2 emissions of ICT industry and its mitigation pathways under sustainable development: A global case

Considering the rapid development of the global digital industry and the need for sustainable development and climate change, this paper explores the global ICT-embodied CO2 emissions changes and intensities from 2000 to 2014, and the driving factors behind ICT-embodied CO2 emissions changes in 15 countries by using the multi-regional input–output (MRIO) method and structural decomposition analysis (SDA) method. We also use Monte Carlo simulations to forecast the ICT-embodied CO2 emissions from 2020 to 2050. The results indicate that: (1) After 2009, global ICT’s embodied CO2 emissions first decline and then increase, and the sector that manufactures computer, electronic, and optical products account for 82.83% of the global ICT’s embodied CO2 emissions in 2014; (2) judging from the emission-reduction trends of various countries, CO2 emissions intensity factors are the main drivers restricting the growth of embodied CO2 emissions in the ICT sector, while final demand composition factors play a significant role in promoting the increase of ICT’s embodied CO2 emissions; (3) Australia, Germany, and Indonesia contribute to an increase in ICT’s embodied CO2 emissions due to sectoral heterogeneity, while Canada, China, Germany, and South Korea have contributed to an increase in ICT’s embodied CO2 emissions in terms of regional heterogeneity; (4) the drivers have also changed significantly with the impact of the financial crisis; and (5) with the current pattern of development, it is difficult to reach the peak of ICT-embodied CO2 emissions in most countries before 2050. This paper also provides feasible implementation plans for carbon mitigation in the global ICT industry.

Uncovering the key mechanisms of how deep decarbonization benefits air pollution alleviation in China

Addressing climate change and air pollution goals in conjunction would be efficient and cost-effective. Dealing with these two challenges is a common issue for urban clusters pursuing sustainable development. Expected to become the fourth international first-class bay area, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) takes the lead in exploring a green and low-carbon transition path as a key element of being a pioneering economic reform demonstration zone. Based on an integrated modeling framework that couples an energy-economy model (IMED|CGE), decomposition analysis, and regression methods, the potential contribution of achieving the climate mitigation target to air pollutant reduction in the GBA by 2050 was quantified. The results showed that the transport sector has the most significant potential for carbon reduction. Energy intensity and structural transformations are the main contributors to reducing carbon emissions, with the latter becoming increasingly important over time. Climate policy can effectively reduce air pollutant emissions; however, this effect varies for different pollutants and sectors. Based on the assessment of the synergy index and cost of abatement, sectors with priority for synergic governance were identified. The regression results indicated that the carbon shadow price would be significantly more effective in reducing air pollutant emissions in the post-2030 period than before 2030, except for SO2 and NH3, partially because of the existing actions that cause the synergistic effects to decline. In addition, end-of-pipe removal measures still play a relatively significant role in reducing air pollutants, particularly VOC, NH3, and primary PM2.5. Thus, the findings suggest that priority should be given to sectors with huge synergistic benefits, such as transportation and power generation while paying attention to possible trade-offs.

An operating condition information-guided iterative variational mode decomposition method based on Mahalanobis distance criterion for surge characteristic frequency extraction of the centrifugal compressor

As a significant high-speed rotating machinery being widely used in modern industry, the centrifugal compressor is subject to a potentially damaging phenomenon called surge, which may lead to property damage, casualties and even catastrophic accidents. Therefore, it is necessary to identify the early surge state of centrifugal compressors to prevent such accidents. In this paper, an operating condition information-guided iterative variational mode decomposition (OCIGIVMD) analysis method is proposed to extract the surge characteristic frequencies (SCFs) from the vibratory signals of centrifugal compressors under early surge state. The variational mode decomposition (VMD) is an effective methodology to decompose a multi-component signal into a series of amplitude-modulated-frequency-modulated (AM-FM) sub-signals with limited bandwidth, from which the characteristic frequencies can be extracted. However, when implementing VMD, the optimal determination of the decomposition mode number is still a challenging task, especially in face of complex signals containing a lot of noise and unknown interference components. Coping with the complex vibration signals of the centrifugal compressor, the VMD in an iterative form based on operating condition information is developed to extract the SCFs, while the decomposition process is controlled by Mahalanobis distance criterion to avoid under-decomposition or over-decomposition and it is needless to select the sensitive intrinsic mode function (IMF) from the multiple IMFs generated by OCIGIVMD. Finally, comparisons with the conventional VMD, empirical mode decomposition (EMD), spectral kurtosis (SK), local mean decomposition (LMD) and two improved VMD methods are conducted using both simulation and experimental data, which demonstrates that the proposed OCIGIVMD is a superior approach in extracting SCFs of the centrifugal compressor under different operating conditions.