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Measurement of the cervical range of motion (CROM) is significant for early diagnosis of cervical spondylosis and determination of the severity of the disease. Aiming at the problem of convenient and continuous measurement of CROM and the reduction of the impact of data fluctuations, this paper proposes a low‐cost method for testing and analyzing CROM. This paper analyzes the correspondence between smartphone orientation sensor angle and CROM, obtains smartphone orientation sensing data, and uses the extreme‐point symmetric mode decomposition method by calculating the energy of the difference value to extract the adaptive global mean curve of the CROM data, and calculates the cervical ROM. The statistical analysis of the CROM test results is carried out. Results show that the method proposed can obtain the CROM including flexion and extension, lateral flexion, and rotation in a single measurement, which has the advantages of continuous measurement and low cost. [ABSTRACT FROM AUTHOR]

This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio‐inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110‐kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on‐site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration‐based detection framework presents an innovative application of nature‐inspired computing for power infrastructure health monitoring. [ABSTRACT FROM AUTHOR]

This paper investigates the multi‐dimensional non‐linear exponential smoothing prediction method under different weight coefficients, so as to realize the short‐term prediction of the original wind power output. Based on the current research on wind power fluctuation prediction, this paper firstly proposes a new multi‐dimensional nonlinear exponential smoothing prediction method. Based on different weight coefficients, the corresponding short‐term forecast volatility is obtained. Then, the wavelet packet decomposition method is used to further realize the secondary smoothing of the prediction wind power fluctuations. Hybrid energy storage system (HESS) plays an important role in wind power fluctuation suppression from different frequency bands. However, the traditional fixed frequency power allocation method significantly degrades the performance of the accuracy of the algorithm. Therefore, this paper proposes a frequency conversion entropy strategy, which combines energy value with wavelet packet decomposition. By calculating the energy components of different frequency bands, the fluctuation can be divided effectively. Finally, simulation and experiment discussions show that the proposed algorithm in this paper can realize the power allocation effectively compared to the traditional one. And it further verifies the feasibility and reliability of the strategy proposed in this work. [ABSTRACT FROM AUTHOR]

Focuses on an experimental test of the Dantzig and Wolfe decomposition algorithm as a planning tool in a Swedish business firm in the pulp and paper industry. Convergence of the iterative planning process; Basis of iterative planning procedure; Superiority of quotas over orders in terms of the objective value of the final plans.

High energy consumption around the world and concerns over climate change and high greenhouse gas emissions have led to a significant increase in using renewable energy resources, particularly wind power plants. In spite of using small wind farms in distribution networks as distributed generation, the interest in employing large‐scale wind farms in transmission levels has grown significantly in recent years. The presence of large‐scale wind farms with a synchronous generator to supply the energy demand can affect the power swing parameters. These effects are in a way that can mislead power swing detection methods and cause undesirable tripping of transmission line due to mal‐operation of distance relay against power swing. Therefore, in this paper, a method has been presented based on modified empirical mode decomposition (EMD) method to differentiate fault from power swing in the networks connected to the large‐scale wind farms. The results prove the efficiency of the presented method in detecting the power swing in large‐scale wind farm networks. Finally, a comparison is conducted between the proposed method and similar methods which proves the efficiency and superiority of the proposed method in terms of noise tolerance, detection fault detection speed, and multi‐mode power swing detection compared to the current methods. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold‐based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short‐term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment. [ABSTRACT FROM AUTHOR]

The stabilization problem for uncertain discrete‐time singular Markovian jump systems (DSMJSs) with time‐varying delays is comprehensively covered in this paper. An updated Lyapunov–Krasovskii functional is presented via a discrete state decomposition method. With the help of this constructed Lyapunov–Krasovskii functional, some delay‐ and mode‐dependent sufficient conditions for the open‐loop DSMJSs are derived. Based on these circumstances, a memory mode‐dependent state feedback control is used to create a closed‐loop DSMJS with parameter uncertainties that is regular and causal. And then, the stochastically admissible conditions are attained. Through the exact calculation of each decomposition component for the designed memory state feedback controller, the intended memory state feedback controller settings are determined. It should be mentioned that the algorithm suggested in this article expands the controller design's feasibility and flexibility. The numerical results show how the approach is superior to previous ones, and the given results are less conservative. [ABSTRACT FROM AUTHOR]

Railway track defects must be detected and repaired to ensure the safe operation of the railway. Signals generated by wheelset contact with rail discontinuities contain information about railway track structure and health. Each signal has a characteristic pattern, which is modified in the presence of defects. Signal decomposition is commonly used to extract characteristics from impact response signals. It is necessary to select an appropriate decomposition method for the wheel/rail impact signal. This paper presents a study of the effectiveness of decomposition methods to identify the most appropriate technique for classification of rail conditions. Vibration signals have been generated in experiments and decomposed. The signal characteristics are presented and energy‐based methods are used in an evaluation of the different approaches. Standard deviation is used to assess the similarity of parameter value from different intrinsic mode functions (IMFs); maximum and minimum differences from the mean value are used to consider the effectiveness of each technique in identifying specific damage types. The evaluation shows that variational mode decomposition (VMD) is suitable for the rail application. Classifiers are applied to the outputs of the selected decomposition method. The results demonstrate the suitability of the approach for processing wheel/rail impact response signals to detect faults and improve safety. [ABSTRACT FROM AUTHOR]

Based on a Fairlie decomposition method, this paper analyzes the drivers of gender gaps in financial inclusion in Cameroon. We use Finscope 2017 data for Cameroon and assess six distinct financial inclusion variables grouped into two dimensions mainly, access to and use of financial products and services. Our results show that there is a gap in all indicators of access to and use of financial products and services in favor of men. The results also show that the largest contributor to the gender gap in access to financial products and services is income, with more than 50% of the contribution, whereas the largest contributor to the gender gap in the use of financial products and services is education with an average contribution of more than 35%. Based on the above findings, decision‐makers in Cameroon must conduct economic policies toward facilitating equitable access to education, by providing incentives to attract women to gain higher education. [ABSTRACT FROM AUTHOR]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a flexible finite‐difference technique for analyzing the nonuniform guiding structures. Because the voltage and current variations along the nonuniform structure differ for each segment, this work considers the adaptable discretization steps. This technique increases the accuracy of the final response. Moreover, by applying the singular value decomposition and discarding the nonprincipal singular values, an optimal lower rank approximation of the discretization matrix is obtained. The computational cost of the introduced method is significantly reduced using the optimal discretization matrix. Also, the proposed method can be extended to the nonuniform waveguides. The technique is verified by analyzing several practical transmission lines and waveguides with nonuniform profiles. [ABSTRACT FROM AUTHOR]

With the development of the electric market, electric load forecasting has been increasingly pursued by many scholars. Because the electric load is affected by many factors, it is characterized by volatility and uncertainty, and it cannot be forecasted accurately only by a single model. In the research, a short‐term load forecasting integrated model is proposed to solve the problem of inaccurate forecasting of a single model. The key point of using the integrated model to forecast is to optimize the decomposed sequence to improve the accuracy of the forecast. empirical wavelet decomposition (EWT) is used to decompose the sequence into stationary sequences and avoid modal aliasing; the sparrow search algorithm (SSA) simulates the forecasting and anti‐forecasting behavior of the sparrow population, which is very similar to the electricity consumption behavior of various industries and has good optimization effect; generalized regression neural network (GRNN) is used for forecast and reconstruction; This is the EWT‐SSA‐GRNN model. This paper studies and analyzes the power load of a city in southern Australia. The results show that the integrated model reduces volatility through decomposition and optimization, and can improve forecast accuracy. [ABSTRACT FROM AUTHOR]

Crushing ratio and fracture conductivity are two key indicators for proppant evaluation. The most common way to evaluate these two indicators is physical testing, but physical testing is costly and time‐consuming, and the results are unstable. Therefore, a method for calculating proppant‐crushing ratio and fracture conductivity is proposed to replace repetitive physical experiments. However, in past attempts, only mathematical models using the same particle size have been established, while the particle‐size distribution in actual proppant arrangement is within a certain numerical range. Therefore, in order to establish a mathematical model more consistent with the actual situation of proppant arrangement, firstly, the Monte Carlo method was used to simulate proppant particles arranged with different particle sizes, and then a mathematical model was established describing the force of proppant particles. Secondly, according to the sparse, ill‐conditioned, and asymmetric characteristics of the coefficient matrix, an improved singular value decomposition method is proposed to improve the accuracy and efficiency of calculation. Finally, the crushing ratio and fracture conductivity of proppant were calculated through the new model in this paper. It was found that the results calculated by the new model in this paper are closer to actual test results. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to improve dynamic performance of a multi‐terminal DC (MTDC) grid in a wind‐integrated AC power system through DC voltage control. The proposed approach is based on design of robust H∞ controller and loop shaping along with mutual quality decomposition method (MQDM). For extracting transfer function of the plant, model of Master‐Slave control system is converted to standard feedback control system. Then, based on the obtained difference between singular‐value curves of the main open‐loop system and the proposed method, the weighting functions of robust H∞ controller are selected. In the next step, by using the MQDM technique, the transfer function ofthe system in state‐space form is evaluated to extract gain of the robust controller. For practicality of the conducted approach, singular‐value order reduction Hankel algorithm along with frequency response analysis have been used for designing the gain of the robust controller. The developed method has been implemented on the MTDC converter in the wind farm side to control the voltage of DC grid. The simulations are accomplished in MATLAB, where by considering different uncertainties in the DC grid, wind unit, and the AC grid, robustness of the proposed approach has been clearly demonstrated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how certain ideas in the so called decomposition theory of mathematical programming can be exploited for the computation of equilibrium in a system of linear capital markets. The fact that economic decision-making is made in a decentralized, i.e., independent, fashion by economic agents has a long been recognized and led economists to build models which, in one way or another, incorporated this feature. Two main departments of economic theory undertook the study of decentralization: on the one hand, the theory of decentralization of decisions inside the firm and the related decomposition theory of mathematical programming and, on the other hand, the study of general equilibrium systems and the decentralizing properties of competitive prices. The purpose of this paper is to show how certain ideas in the so called decomposition theory of mathematical programming can be exploited for the computation of equilibrium in a system of linear capital markets. The fact that economic decision-making is made in a decentralized, i.e., independent, fashion by economic agents has a long been recognized and led economists to build models which, in one way or another, incorporated this feature. Two main departments of economic theory undertook the study of decentralization: on the one hand, the theory of decentralization of decisions inside the firm and the related decomposition theory of mathematical programming and, on the other hand, the study of general equilibrium systems and the decentralizing properties of competitive prices. [ABSTRACT FROM AUTHOR]

Infrared (IR) and visible (VI) image fusion play an important role in improving the sea scene perception and ship target detection. Although there have been many studies on image fusion considering the sea scene characteristics, there has still been no adequate extraction method for detailed information of a ship target in a sea scene. To overcome this shortcoming, this paper proposes a fusion method based on a scale‐aware feature decomposition, which can accurately extract features of a ship target at different scales. First, a hybrid feature decomposition method based on the scale‐aware structure‐preserving filter and Gaussian filter is designed. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer‐scale division than traditional multiscale decomposition methods in the sea‐clutter background. Then, further decomposition of the texture layer was performed by the shearlet transform to obtain the directional texture feature. According to the characteristics of each layer, different strategies were adopted for the fusion, and the weighting factor was used to obtain the final fusion result. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state‐of‐the‐art methods. This method introduces a scale‐aware edge preserving filter to improve the ability of detail extraction and is suitable for image fusion of ship targets. [ABSTRACT FROM AUTHOR]

Park integrated energy system (PIES) can utilize multiple energy resources complementarily and promote comprehensive energy efficiency. However, the uncertainty of renewable energy generation poses significant challenges to the optimal operation of PIES. This paper proposes a data‐driven distributionally robust optimization (DDRO) model for the day‐ahead scheduling of PIESs with coordination of carbon capture and storage devices (CCS) and combined heat and power plants (CHP). First, a deterministic economic dispatch model of PIES was presented with the aim at minimizing the total operating costs of PIES and promoting the photovoltaic (PV) power accommodation. Then, a DDRO model was developed based on the historical data to yield the optimal solution in the worst PV output scenario, where the confidence set is established with comprehensive consideration of norm‐1 and norm‐inf constraints. Furthermore, an efficient solving framework was proposed for the DDRO based on the combination of the column‐and‐constraint generation (C&CG) algorithm and a duality‐free decomposition method. Finally, case studies are carried out to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Gaussianity, non‐Gaussianity, linearity, and nonlinearity generally coexist within industrial process variables, and should be taken into account simultaneously for process modelling with monitoring. This paper presents a comprehensive monitoring method of industrial processes using multivariable characteristics evaluation and subspace decomposition. First, a multivariable characteristics evaluation method is presented to divide the process variables into the Gaussian linear, Gaussian nonlinear, non‐Gaussian linear, and non‐Gaussian nonlinear subspaces. Second, the PCA‐ICA‐KPCA‐KICA‐based multivariable subspace decomposition is proposed for process modelling. Furthermore, comprehensive monitoring is developed and final results are combined using comprehensive statistics. By multivariable characteristics evaluation and subspace decomposition, the proposed method could evaluate and seek the multivariable characteristics and enhance the performance of process monitoring. The effectiveness and feasibility of the proposed comprehensive monitoring method are demonstrated by a numerical system and the benchmark Tennessee Eastman (TE) process. [ABSTRACT FROM AUTHOR]

Although it has been emphasized for years, there remains insufficient knowledge on how to decompose the driving forces of carbon emissions that fluctuate with time, especially when considering the major natural or socio‐economic events. To bridge the knowledge gap, we use the structural decomposition analysis method and select Japan for a case study. The results show that such events were followed by temporary emission increases due to economic recovery, but the general decreasing trend of carbon emissions has been unchanged in a long run. In the case of Japan, during the financial crisis period (2005–2011), emissions increased due to production structure (136.2 Mt‐CO2) and consumption structure (61.8 Mt‐CO2). However, the intensity change and per capita consumption effect were the main driving forces that offset this increasing trend, which reduced by 209.7 Mt‐CO2 in total. After the 2011 earthquake, a significant gain in upgraded production structure led to a 78.2 Mt‐CO2 reduction until 2015, partially offset the 126.4 Mt‐CO2 increase caused by per capita consumption and export volume. In conclusion, although post‐event economic recovery has led to an increasing trend of carbon emissions, continuously optimized emission intensity and production structure are the major driving forces for repressing carbon emission rebound brought by unexpected events. Plain Language Summary: When major natural or socio‐economic events occur, energy production and the economy are often affected, impeding the process of net zero emissions. This paper presents a case study on Japan using the structural decomposition analysis method to decompose the driving forces behind carbon emissions. Two major events (the global financial crisis of 2008–2009 and the Great East Japan Earthquake) that are responsible for the fluctuating emission trends in a time series are discussed. Although Japan's overall carbon emission trend has been decreasing since 2005, temporary emission increases due to economic recovery can be observed after these events. Their impact was incorporated into the discussion by dividing the research period into separate time spans. By quantifying the sectoral carbon emission data, we obtain direct and indirect carbon emissions by fuel type among 13 industrial sectors. Results show that "manufacturing" and "electric power, gas, and water supply" contribute the most to direct emissions among all sectors, while the indirect emission is mostly driven by "manufacturing" and "services" sector. Key Points: Sectoral carbon emission by fuel types in Japan is quantified from 2005 to 2015Economic recovery causes temporary CO2 emission rise after financial crisis and earthquakeThe ripple effects of these driving forces from 2005 to 2015 were analyzed [ABSTRACT FROM AUTHOR]

With the fast development of wind power generation, many countries actively encourage wind power to trade in the medium‐long term market. However, the traditional forward contracts from medium‐long term markets are only suitable to conventional power generation enterprises, instead of wind farms. To facilitate the accommodation of wind power in the medium‐long term market, this paper focuses on two problems: The framework of the block‐based forward contract trading method and the procurement strategy of retailers with block‐based contracts. First, a novel block‐based forward contract is proposed, where the time attributes, that is, starting and ending time, and power during each period are stipulated. Aggregating all the block‐based forward contracts will naturally form the power supply curve, which can provide boundary information for wind farms when they take part in other markets. Secondly, a chance‐constrained procurement strategy model is proposed for retailers, where the uncertainty of wind power generation, constraints of block‐based contracts, and quota obligations of retailers are considered. Furthermore, a bilinear Benders decomposition algorithm with a variant of Jensen's inequalities is applied to solve the proposed model. Finally, experimental results demonstrate the effectiveness of the proposed model and method, which is computationally efficient in solving the chance‐constrained procurement strategy problem. [ABSTRACT FROM AUTHOR]

This paper uses unconditional quantile regressions to decompose changes in the black–white wage gap for young men between 1990 and 2011. Introducing a new application of reweighting methods, we control for selection into employment which tends to widen the gap. We find no changes in the gap itself between 1990 and 2011, but reversals in the roles of ability and education across the distribution. Ability loses importance at the bottom and middle but gains importance at the top, while the opposite occurs for education. This results from heterogeneous changes in returns to ability and education across the distribution alongside a widening educational achievement gap. [ABSTRACT FROM AUTHOR]

This paper proposes a multiple‐input multiple‐output full‐duplex relay Internet of Things system under imperfect channel state information, where the relay uses time switching relaying protocol to harvest energy. Particularly, due to the uncertainty channel circumstance, the self‐interference in full‐duplex relay cannot be eliminated completely. For such a system, a joint source‐relay beamforming optimization problem which maximizes the system achievable rate subject to the transmit power and harvested energy constraints is studied. In light of the intractability of the problem, a singular value decomposition based geometric programming algorithm is investigated. The considered problem is reformulated to a diagonalized form by using singular value decomposition method. Then, the geometric programming algorithm is adopted to obtain the optimal solution for the reformulated problem. Numerical results validate the effectiveness and superiority of the proposed optimization scheme. [ABSTRACT FROM AUTHOR]

This paper provides recent evidence on the contribution of the spatial dimension to inequality and more specifically accounts for the impact of the changes in the territorial distribution of the population on the recent dynamics of income inequality. We use LIS harmonized microdata for a selected sample of OECD countries. We provide new evidence over a more varied group of countries and a more recent period than in previous studies. We perform different types of decompositions to isolate the contribution of the changes in the territorial distribution of the population. The results show a generalized increase in income inequality, with an interesting "reducing effect" on this trend due to inter–territorial population movements. [ABSTRACT FROM AUTHOR]

With the increasing proportion of wind power connected to grid, power system dispatching is facing more and more challenges from uncertainty. To cope with this uncertainty, robust optimization has been applied in unit commitment (UC) problem. In this paper, a multi-band uncertainty set considering the temporal correlation (MBUSCTC) of wind/load prediction error is proposed firstly, which has two characteristics: (1) The MBUSCTC rigorously and realistically reflect the distribution characteristics of uncertainties in uncertainty intervals, thereby effectively reducing the conservatism of the traditional singe-band uncertainty set; (2) the temporal correlation constraints of wind power/load prediction errors in MBUSCTC could limit the realization of uncertainties fluctuating frequently in uncertain intervals, thereby eliminating scenarios with lower probability in uncertainty sets. Then the proposed MBUSCTC is applied to UC problem, leading a robust UC model based on MBUSCTC, which is solved by Benders decomposition method and C&CG method. Finally, case studies based on the modified IEEE-118 bus system and an actual power system of China demonstrate that the proposed method can effectively reduce the conservativeness of the robust UC model and ensure the robustness of the unit commitment solution. [ABSTRACT FROM AUTHOR]

This paper investigates the main determinants of the welfare gap between rural–urban migrants and non‐migrants (both urban and rural) in Tunisia. The lack of data on the expenditure of migrant and non‐migrant households is overcome by applying the ELL (Elbers, Lanjouw and Lanjouw) methodology. The decomposition results show a positive welfare gains from rural‐to‐urban migration, especially among the younger generation born after the failure of the collectivism experiment in 1961. The education level of the household's head and the household's size appear as the main sources of the welfare gap. The unconditional quantile regression decomposition shows that the endowment effect contributes more to the welfare gap than the discrimination effect at the bottom part of the welfare distribution. [ABSTRACT FROM AUTHOR]

This paper explores new techniques that are based on a hidden‐layer linear transformation for fast speaker adaptation used in deep neural networks (DNNs). Conventional methods using affine transformations are ineffective because they require a relatively large number of parameters to perform. Meanwhile, methods that employ singular‐value decomposition (SVD) are utilized because they are effective at reducing adaptive parameters. However, a matrix decomposition is computationally expensive when using online services. We propose the use of an extended diagonal linear transformation method to minimize adaptation parameters without SVD to increase the performance level for tasks that require smaller degrees of adaptation. In Korean large vocabulary continuous speech recognition (LVCSR) tasks, the proposed method shows significant improvements with error‐reduction rates of 8.4% and 17.1% in five and 50 conversational sentence adaptations, respectively. Compared with the adaptation methods using SVD, there is an increased recognition performance with fewer parameters. [ABSTRACT FROM AUTHOR]

To handle multimodal uncertainty and dynamics which are common in actual industrial processes, this paper proposes a multimode process monitoring method based on hierarchical mode division and subspace decomposition. First, hierarchical mode division is presented to achieve hierarchical mode information for multimode process modelling, which can improve the adaptability of industrial processes with multimodal uncertainty. Then, hierarchical subspace decomposition is presented to decompose multimode processes into multiple global subspaces and multiple local subspaces for monitoring. Lastly, the most similar mode with various hierarchical modes at the current sampling point is dynamically determined for monitoring, which considers multimodal dynamics. The simulation tests of the penicillin fermentation processes are developed to show feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A method for photovoltaic (PV) power interval prediction based on double sample entropy framework and integrated bidomain decomposition deep learning is proposed to address the volatility, randomness, intermittency and timeliness of PV power prediction. By using the integrated bidomain decomposition method under the framework of double sample entropy, the PV power time series is decomposed into several components with similar complexity and the deep learning network is used to learn and predict the results of each component reconstruction to obtain point predictions. The quantile regression method is then used based on the point prediction to obtain interval predictions. The actual calculation examples show that the RMSE and CPRS of the proposed method are reduced by an average of 45.56% and 31.42%, respectively, compared with other integrated bidomain decomposition deep learning methods that are not in the double sample entropy framework and single decomposition deep learning methods. [ABSTRACT FROM AUTHOR]

This study compares the ozone valleys over the Tibetan Plateau (TP) and the Rocky Mountains (RM) using the ERA5 reanalysis data set. The dynamical transport of the ozone over these two regions is analyzed using the Lorenz circulation decomposition method. The ozone content valley over TP is observed around 200–50 hPa (upper troposphere and lower stratosphere, or UTLS), and that over RM is around 300–100 hPa. It is shown that the TP ozone content is smaller than that over RM. By analyzing the spatiotemporal distribution of the ozone content and the general circulation, the anticyclone over Southern Asian (SAH) plays a significant role in existence of the TP ozone valley, and the ozone content flux reaches its maximum in July. Large‐scale terrain and related general circulation determine the ozone valley appearance. Further analysis suggests that stationary transport has a larger impact on the ozone valley formation than the transient transport. The transport by the zonal circulation nearly cancels out most of that by the meridional circulation, due to the fact that the zonal transport magnitude is nearly equal to the meridional transport. The transport center over the RM is much weaker than that over the TP. Furthermore, the contrasts between transient and stationary transports are less evident over RM than over TP. The eddy‐driven stationary ozone transport flux significantly impacts the development of the two low ozone centers across these large terrains. Plain Language Summary: Extremely low ozone content has been observed over the Arctic, Antarctic, and high‐terrain regions. Ozone absorbs most wavelengths of UV radiation, therefore understanding the formation of ozone valley plays an important role for climate research. We compared the climatic features of ozone valleys over two high‐terrain regions (i.e., Tibetan Plateau (TP) and Rocky Mountains (RM)). Dynamical transport is investigated using Lorenz circulation decomposition, and it shows the eddy stationary transport has a significant impact. The total dynamical transport of ozone over the RM is much weaker than that over the TP. This study advances our knowledge of ozone valley formation over these two regions. Key Points: The ozone valleys exist over both the Tibetan Plateau (TP) and the Rocky Mountains (RM)The TP ozone valley is stronger and locates higher than that over the RM, consistent with the terrain contrastThe stationary transport by the climatological eddies dominates the ozone budget for both high‐terrain regions [ABSTRACT FROM AUTHOR]

The rotor blades of an axial‐flow turbine are cantilever structures, and there is inevitably a gap between them and the casing. Due to factors such as rotor wear and unit vibration, the eccentricity of the impeller will change during the operation of the turbine, resulting in the impeller being affected by additional radial forces, which can even lead to rubbing or biting between the impeller and the casing. To monitor the eccentricity of the impeller and the additional radial forces in real time during the operation of the turbine, this study conducted numerical simulations of the internal flow of the turbine under different eccentricities of the impeller, and analyzed the characteristics of pressure pulsation and impeller radial force in the turbine using the variational mode decomposition method. The results showed that there was a good linear relationship between the eccentricity of the impeller and the amplitude of the frequency corresponding to the rotor in pressure pulsation at the monitoring point and the Alford force acting on the impeller. Based on this finding, we established mathematical formulas for the relationship between the pressure pulsation at the monitoring point and the eccentricity of the impeller, as well as the eccentricity of the impeller and the Alford force acting on it. According to these formulas, we only need to monitor the pressure pulsation during the operation of the turbine to realize the real‐time monitoring of the eccentricity of the impeller and the Alford force, which is of great significance for ensuring the safe and stable operation of the turbine. [ABSTRACT FROM AUTHOR]

This paper attempts, for the first time for the UK, to analyse the earnings of homosexuals and test for the possible existence of sexual orientation discrimination by using decomposition analysis. Homosexuals are identified as individuals living with‘same-sex partners’. Gay men and lesbians in our sample both earn more on average than non-gays. However, we find that the decomposition indicates that homosexual men may be subject to discrimination in pay, in the sense that they earn less than heterosexual men with similar characteristics would earn, and therefore they may be more likely to benefit from UK legislation. [ABSTRACT FROM AUTHOR]

Because distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2‐D) hybrid model is developed to investigate the second harmonic (SH) generation of electromagnetic ion cyclotron (EMIC) waves. Applying the singular value decomposition method to simulated fields, we show that the SH exhibits wave properties analogous to typical EMIC waves generated by ion cyclotron instabilities, that is, left‐hand polarization and small wave normal angle. However, the bicoherence index inferred from simulated fields reflects a strong phase coupling between the fundamental wave (FW) and the SH, illustrating the nonlinear generation of the SH by the FW. The necessary conditions, especially for the wave vector relation, are further verified from a 2‐D perspective. The simulated amplitude ratios well meet the theoretical results only in the SH saturation stage, while the necessary conditions remain satisfied almost throughout the simulation. This study provides a comprehensive analysis of the SH excitation in a 2‐D simulation domain, contributing to a deeper understanding of EMIC wave nonlinear generation. Plain Language Summary: Recent studies have unveiled the generation of nonlinear second harmonics (SH) of electromagnetic ion cyclotron (EMIC) waves. The SH is nonlinearly driven by the fundamental wave (FW) and satisfies the necessary conditions: ω2 = 2ω1 and k2 = 2k1, where ω1 (ω2) and k1 (k2) are the frequency and the wave vector of the FW (SH), respectively. Previous studies have relied on one‐dimensional (1‐D) hybrid simulations to investigate the SH. However, 1‐D simulations allow waves to propagate along a single dimension and lack spatial variation of the field orthogonal to this dimension, which impedes the complete verification of the relation k2//k1. Thus, in this study, the SH generation is modeled by two‐dimensional (2‐D) hybrid codes. The simulated SH exhibits characteristics similar to typical EMIC waves with left‐hand polarization and small wave normal angle. The bicoherence index is utilized to reveal the phase coupling between the SH and FW. The necessary conditions, especially for the wave vector relation, are verified from a 2‐D perspective for the first time. Additionally, the amplitude ratios of the SH to the FW and their phase velocities are compared with theoretical results. The comprehensive analyses of this study provide substantial evidence for the SH generation mechanism. Key Points: Two‐dimensional (2‐D) hybrid codes are developed to model the nonlinear second‐harmonic (SH) generation of electromagnetic ion cyclotron wavesThe necessary conditions required by the SH generation mechanism are verified from a 2‐D perspectiveThe theoretical amplitude ratios between harmonics are only satisfied in the SH saturation stage [ABSTRACT FROM AUTHOR]

With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long‐short‐term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high‐frequency component. A deep learning‐based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two‐stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2$R^2$ values up to 9.5% higher than those obtained using standard LSTM models. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources increases, the growing renewable power variability brings ramping issues to power systems. Meanwhile, the development of distributed energy resources (DERs) makes the distribution systems to provide both energy and ancillary services. To incentivise individual resources and customers to alleviate ramping issues on the demand side, a two-stage distribution locational marginal price (DLMP) calculation and decomposition method is developed to formulate the marginal power ramping price for DERs. In the first stage of the proposed method, a distribution system operator market scheduling model based on AC optimal power flow is designed to estimate the optimal operating point of the distribution system. Subsequently, the voltage and power flow constraints are linearised in stage two to calculate DLMP. Finally, based on the Lagrange function and sensitivity factors, DLMP is decomposed to the marginal costs for active/reactive power, voltage management, power loss and power variability. Case studies demonstrate that the proposed model can effectively smooth the power fluctuation and reduce the ramping flexibility requirements of distribution systems. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) investigations can identify and localise incipient failures in power transformers early, thus avoiding considerable financial losses. The feature extraction of PD signals is a fundamental step for the development of such location techniques since it directly influences the performance of a location method. This study presents a detailed comparative analysis of four traditional approaches for the obtaining of attributes towards a better set of signal features for the location of PDs. The approaches were critically compared regarding their ability to locate experimentally generated discharges between adjacent layers of a prototype winding. In order to perform such analysis, a localisation structure based on logistic regression models was elaborated, capable of determining both layers and sections of the winding affected by PDs and easily applicable in practice. The results show energy features of wavelet coefficients, obtained through the decomposition of high‐frequency current signals acquired at the winding endings, achieve better performance in the PD localisation, accurately indicating discharge occurrence points among layers and sections of the winding. [ABSTRACT FROM AUTHOR]

Suppressing the background noise of partial discharge (PD) is one of the key issues for accurately diagnosing the state of electrical equipment insulation. To solve this problem, this study proposes a new denoising method based on frequency spectrum clustering and local mean value decomposition. First, the K‐means clustering is employed on the frequency spectrum to pick out narrow‐band interference frequencies. Next, the PD signal with white noise is decomposed by local mean decomposition into different product function components, and the components contain more information about time–frequency than the intrinsic mode functions originated from empirical mode decomposition. Besides, the adaptive threshold is utilised to eliminate white noise in the components. Finally, the denoised PD signal is synthesised by these denoised components. The proposed method and three traditional methods are applied to simulated and field‐detected noisy PD signals, respectively. The results of the evaluation indicators confirm that the proposed method is better than the existing PD denoising methods. [ABSTRACT FROM AUTHOR]

Aiming at the problem that fault signals of rolling bearing are easily submerged by the strong background noise, which makes it difficult to extract fault information, so a method based on kurtosis criterion variational mode decomposition (VMD) and modulo square threshold is proposed and applied to fault diagnosis of rolling bearing. First, the vibration signals of rolling bearing are processed by VMD. Second, the signals are reconstructed by selecting the intrinsic mode function (IMF) components with the largest kurtosis and the second largest kurtosis. Finally, the reconstruction signals are de-noised by the modulo square threshold. Through the test of the inner ring and outer ring of the rolling bearing, the feasibility and effectiveness of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Robust optimisation has been applied to the dispatching problem considering fault outage of power systems. The traditional fault outage uncertainty set are too conservative. To solve this problem, a robust contingency constrained unit commitment (CCUC) model considering the outage probability of units and transmission lines is proposed. The proposed model has the following characteristics: (i) the outage probability of units is determined by normal operation time and fault repair time, whereas the outage probability of transmission lines is determined by length and load rate; (ii) based on the probability criterion of unit outage contingencies, the outage probabilities of units and transmission lines are both incorporated into the modelling of uncertainty sets; (iii) the robust CCUC model obtains UC decision of base case and the UC decision under uncertainty scenarios can adjust the output of units by spinning reserve capacity to cope with stochastic fault outage scenarios. The proposed robust CCUC model is solved with Benders-column and constraint generation (C&CG) method combined with Benders decomposition method and C&CG method. Simulations on the modified IEEE-RTS-79 system and IEEE-RTS-96 system demonstrate the effectiveness and reliability of the proposed robust CCUC model. [ABSTRACT FROM AUTHOR]

Transient overvoltage represents a high magnitude voltage surge of a short duration which has to be considered when performing proper insulation coordination. Overhead line energisation is found to be a fundamental cause of transients. Both normal and faulty conditions are possible during line energisation. The latter implies the existence of an asymmetrical phase-to-ground fault and facing with two asymmetrical processes which take place concurrently. This type of energisation results in severe overvoltage, as well as in a voltage rise at the line receiving end. However, in practice, measuring devices are usually placed at the line sending end, which points to difficulties in determination of phase-voltages at the opposite end of the line. This study offers a solution for the aforementioned issue by proposing the frequency-dependent voltage transfer functions (TFs). By using the functions any voltage value at the sending line end will obtain the corresponding value at the receiving end. The functions are derived through complex mathematical modelling and by signal processing based on the empirical mode decomposition method. The TFs validity is confirmed through performed simulation in MATLAB Simulink and real-site measurements. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyse the impact of psychological traits on earnings differentials between second-generation immigrants and individuals with native-born parents. The study is based on a cohort of men born in 1973 and residing in Sweden in 1990. In this paper, we use an indicator of psychological ability measured in connection with the military enlistment test in Sweden. The results show that the measure of psychological traits is an important determinant of earnings at the age of 30. Using an Oaxaca-Blinder decomposition, we find that earnings differentials between second-generation immigrants and individuals with native-born parents are explained, to a large degree, by differences in endowments of psychological traits. [ABSTRACT FROM AUTHOR]

This paper examines how productivity changes and price changes have contributed to short-run profit change in the railroad industry. Using an unbalanced panel of US Class I railroads for the period 1996–2003, a short-run profit change decomposition model is used to attribute intertemporal profit change to its causal factors. We find that productivity improvements and an increased scale of production contributed to increases in profit, and that variation in operating efficiency had a mixed impact on profit. We also find that relative changes in rail rates and variable input prices exerted downward pressure on profit. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Although the contribution of community members to functional diversity is a key question of conservation ecology, its measurement and interpretation are rather problematic. In this paper, we suggest a novel method for decomposing functional diversity. To do this we consider functional units (i.e. species or a group of species with identical traits) as the functional building blocks of communities. Then we propose the use of a recently developed measure of functional diversity (called modified functional attribute diversity or MFAD) and suggest additive decomposition of MFAD into functional values contributed by the functional units. We point out that functional values are related to changes in MFAD if the functional unit is removed from the community. This property of decomposition allows the quantification of the contribution of community members to functional diversity. By studying artificial and actual communities we compare the performance of our new method with other recently developed contribution measures, which are based on dendrograms and ordinations. Both theoretical considerations and analyses of artificial and actual data sets suggest that the proposed method of calculating functional values expresses more explicitly the contribution of community members to functional diversity and hereby can be used as a simple, yet efficient method for searching for functional keystones in ecological communities or for quantifying the contribution of community members to functional diversity. [ABSTRACT FROM AUTHOR]

Shunt reactor with auxiliary windings (SRAW) can compensate inductive reactive power for long‐distance transmission lines and supply steady power for low‐voltage devices on offshore platforms. The operation characteristics of SRAW can be commonly described by the air‐core transformer equivalent model. However, this model can only make an approximate simulation of the external characteristics of SRAW, and cannot reflect the actual electrical features inside the SRAW. To solve this problem, the authors propose a three‐segment magnetic circuit equivalent model (TMCEM) of SRAW based on the magnetic circuit decomposition method. This model can accurately describe the coupling relationship inside SRAW and can be used for the analysis of internal fault characteristics. Furthermore, through the fault analysis utilizing TMCEM, a novel protection scheme of SRAW based on virtual magnetizing differential current is proposed for the detection of the inner‐turn fault in SRAW. This protection scheme has high sensitivity and can effectively identify the inner‐turn faults without measuring voltages. By testing the physical model of SRAW, the effectiveness of the TMCEM and the proposed protection scheme against inner‐turn fault has been confirmed. [ABSTRACT FROM AUTHOR]

We examine differential impacts of COVID‐19 on minority unemployment in the U.S. beyond the initial recession. The Black‐White unemployment gap did not immediately rise as much as for other groups, but subsequently increased and was stubbornly higher through 2021 Q3. The Latinx‐White gap generally decreased after peaking in May 2020 to pre‐pandemic levels by 2021 Q4, but experienced a unique "hiccup" in early 2021. The Asian‐White gap peaked later (in June 2020) and was high in Q3 of 2020 (the "stall"), and remained positive through 2021 Q4. Decomposition methods are used to examine factors that contribute to these patterns. [ABSTRACT FROM AUTHOR]

This study presents a probabilistic model to determine the optimal expansion of transmission network under uncertainties of demand, wind power generation, and energy price. The Benders decomposition approach and the point estimate method (PEM) are used to solve the developed mixed-integer optimisation problem and tackle the uncertainties, respectively. The proposed market-based expansion problem is a two-stage model in which the master problem optimises the investment cost, and the sub-problem (SP) includes the cost of energy not served, congestion cost and congestion rent. A new objective function is proposed to consider the congestion rent in the Benders algorithm. In the SP, a two-step optimisation process is then proposed to generate proper Benders cuts. The proposed model has been applied to the Garver's, and modified IEEE 24-bus and IEEE 118-bus test systems. Furthermore, to validate the accuracy of the proposed PEM, a Monte Carlo simulation method (MCSM) has been used and the results of PEM have been compared to the MCSM. [ABSTRACT FROM AUTHOR]

This study investigates the sampled‐data containment control problem for linear multi‐agent systems with multiple leaders under undirected or directed network topology. First, by means of the decomposition method, the containment control problem is transformed into the simultaneous stability analysis issue of certain subsystems. Then, the solution of a class of differential equations, which plays a key role for the addressed problem, is exploited by using the contradiction method. Together with a novel Lyapunov function, sufficient conditions are established to guarantee that all the followers move into the convex hull spanned by the leaders. The proposed results are dependent on the information of the system dynamics, the coupling strength as well as the eigenvalues of topology matrix. Furthermore, the established results are specialised to both the leader–following case and the traditional consensus case. Finally, simulations are given to illustrate the theoretical results derived in this study. [ABSTRACT FROM AUTHOR]

Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging. Besides, most dehazing models are trained on synthetic data, resulting in poor generalization ability to real‐world images. To address the aforementioned problems, a semi‐supervised learning dehazing method based on the decomposition model of Osher, Solé, and Vese(The OSV model) is presented. Specifically, the OSV model is first applied to decompose the hazy image into the structure layer and texture layer, save the texture layer and dehaze for the structure layer to restore images with sharper texture and edge. Furthermore, the network adopts a semi‐supervised learning algorithm based on generative adversarial networks (GAN) to generalize better to real‐world images, which includes two branches: supervised learning and unsupervised learning. Extensive experiments indicate that the proposed method preserves the texture and edge information of images more accurately while dehazing better, and performs favourably against the advanced dehazing algorithms on both synthetic outdoor datasets and real‐world hazy images. [ABSTRACT FROM AUTHOR]

Compared with electrons, photons have the potential to realise ultra‐high speed operations because of its unique high speed and high parallelism. In recent years, there have been many researches on neural networks using optical hardware. The Mach–Zehnder interferometer (MZI) and micro‐ring resonator (MRR) are commonly used as optical devices to realise linear operation units in optical neural networks (ONN). MZI has the advantages of simple fabrication, high sensitivity, and easy integration, which has attracted the attention of researchers. We summarise the implementation methods of ONN matrix multiplication based on MZI, the implementation methods of non‐linear activation, and the on‐chip training methods. We first summarise the researches on matrix multiplication of ONN based on MZI. Three kinds of MZI grid decomposition methods, Fast Fourier Transform (FFT) grid structures, and the corresponding derivation processes are introduced, respectively. Then, several experimental implementations of ONN based on MZI are summarised, and the characteristics of optical processors fabricated in these references are analysed. Finally, the realisation methods of non‐linear activation and on‐chip training of silicon ONN are introduced, respectively. [ABSTRACT FROM AUTHOR]

Seven microporous MgO–Al2O3 ceramics with an Al2O3 content of 15–90 wt% were prepared using Al(OH)3 and calcined magnesite as raw materials. A wet mixing process was employed during sample preparation to transform the calcined magnesite with a larger particle size to smaller Mg(OH)2 particles. The in situ decomposition synthesis method and the Kirkendall effect were utilized to produce and control the pore structure of the microporous MgO–Al2O3 ceramics. There were two kinds of pores in the microporous MgO–Al2O3 ceramics. The first one resulted from the in situ decomposition of Al(OH)3 and Mg(OH)2 particles, which were small and equally distributed. Another one originated from the position of the Mg(OH)2 particles due to the Kirkendall effect caused by MgO diffusion. They were similar in size to the Mg(OH)2 pseudomorph particles. Simultaneously, the Al2O3 content affected the packing behavior and the spinel formation, which changed the characteristics of the pores and necks among the particles. These mechanisms also affected the strengths of the microporous MgO–Al2O3 ceramics. Thus, when the Al2O3 content was 45–90 wt%, the microporous MgO–Al2O3 ceramics had a high compressive strength (10.0–18.3 MPa) and apparent porosity (52.2%–58.4%). [ABSTRACT FROM AUTHOR]