Decomposition Procedure Research Articles

Estimation of Transfer Time from Multimodal Transit Services in the Paris Region

A reliable public transport system is beneficial for people traveling in the metropolitan area. Transfer time in multimodal transit networks has been highlighted as one of the measures of public transport service quality. In this paper, we propose a novel method to estimate the passengers’ transfer time between the transit modes (i.e., train, metro, and bus) based on the 2018 Household Travel Survey in the Paris region, France. The transit trips with a single transit leg are primarily studied, wherein average wait time and mode speeds are estimated through an integrated linear regression model. Based on these inferences, transfer time is deduced within the trips of multiple transit legs. The decomposition procedure of journey time facilitates the estimation of the time components, and reveals the transfer variability in mode, time, and space. From the results, we find that the transfer to the railway modes, especially to the metro, costs less time on average than the transfer to the bus in the study area. The transfer patterns in the morning and evening peak hours are different regarding the transfer duration and locations. Lastly, the results’ reliability, method scalability, and potential applications are discussed in detail.

Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011-2020.

The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet's ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM2.5, and PM10). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month's PM2.5 and temperature (lag1) had a significant association with influenza incidence, while the previous month's temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM2.5 lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM10 and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.

Local characteristic decomposition based central-upwind scheme

We propose novel less diffusive schemes for conservative one- and two-dimensional hyperbolic systems of nonlinear partial differential equations (PDEs). The main challenges in the development of accurate and robust numerical methods for the studied systems come from the complicated wave structures, such as shocks, rarefactions and contact discontinuities, arising even for smooth initial conditions. In order to reduce the diffusion in the original central-upwind schemes, we use a local characteristic decomposition procedure to develop a new class of central-upwind schemes. We apply the developed schemes to the one- and two-dimensional Euler equations of gas dynamics to illustrate the performance on a variety of examples. The obtained numerical results clearly demonstrate that the proposed new schemes outperform the original central-upwind schemes.

A novel eigenvalue-based iterative simulation method for multi-dimensional homogeneous non-Gaussian stochastic vector fields

The generation of sample functions of stochastic fields or processes is a prerequisite to use the Monte Carlo method in stochastic mechanics and structural reliability analysis. When it comes to non-Gaussian characteristics such as the spatial variability of material or structural properties, the simulation of multi-dimensional non-Gaussian stochastic vector fields becomes necessary. However, the conventional Cholesky decomposition-based iterative simulation method requires three times of decomposition operations at each iteration and a complex procedure of random shuffle, which inevitably augment the model complexity. To this end, this study develops a novel eigenvalue-based iterative simulation method which only requires one decomposition operation in the whole simulation process. Central to this method is to represent the cross power spectral density matrix with its eigenvalue matrix during the iteration procedure, which avoids the cumbersome Cholesky decomposition and random shuffle procedure. Meanwhile, it can yield unique convergence result and present a powerful ability in addressing cases with the complex-valued spectral matrix. Numerical example demonstrates that the proposed method has a similar accuracy as that of the Cholesky decomposition-based iterative simulation method. Meanwhile, the estimated cumulative distribution function from one simulated sample and the estimated ensemble correlation function from 1000 simulated samples are all consistent with their respective targets. In addition, the sensitive analysis is performed to demonstrate that the proposed method has a relatively wide applicability. Therefore, this method may have a great potential to simulate multi-dimensional homogeneous non-Gaussian stochastic vector fields in practice.

Control and mitigation of microwave crosstalk effect with superconducting qubits

Improving gate performance is vital for scalable quantum computing. Universal quantum computing also requires gate fidelity to reach a high level. For a superconducting quantum processor, which operates in the microwave band, the single-qubit gates are usually realized with microwave driving. The crosstalk between microwave pulses is a non-negligible error source. In this article, we propose an error mitigation scheme to address this crosstalk issue for single-qubit gates. There are three steps in our method. First, by controlling the detuning between qubits, the microwave induced classical crosstalk error can be constrained within the computational subspace. Second, by applying the general decomposition procedure, the arbitrary single-qubit gate can be decomposed as a sequence of X and virtual Z gates. Finally, by optimizing the parameters in virtual Z gates, the error constrained in the computational space can be corrected. Using our method, no additional compensation signals are needed, arbitrary single-qubit gate time will not be prolonged, and the circuit depth containing simultaneous single-qubit gates will also not increase. The simulation results show that, in a specific regime of qubit–qubit detuning, the infidelities of simultaneous single-qubit gates can be as low as that without microwave crosstalk.

Extraction of bouton-like structures from neuropil calcium imaging data

The neuropil, the plexus of axons and dendrites, plays a critical role in operating the circuit processing of the nervous system. Revealing the spatiotemporal activity pattern within the neuropil would clarify how the information flows throughout the nervous system. However, calcium imaging to examine the circuit dynamics has mainly focused on the soma population due to their discrete distribution. The development of a methodology to analyze the calcium imaging data of a densely packed neuropil would provide us with new insights into the circuit dynamics. Here, we propose a new method to decompose calcium imaging data of the neuropil into populations of bouton-like synaptic structures with a standard desktop computer. To extract bouton-like structures from calcium imaging data, we introduced a new type of modularity, a widely used quality measure in graph theory, and optimized the clustering configuration by a simulated annealing algorithm, which is established in statistical physics. To assess this method’s performance, we conducted calcium imaging of the neuropil of Drosophila larvae. Based on the obtained data, we established artificial neuropil imaging datasets. We applied the decomposition procedure to the artificial and experimental calcium imaging data and extracted individual bouton-like structures successfully. Based on the extracted spatiotemporal data, we analyzed the network structure of the central nervous system of fly larvae and found it was scale-free. These results demonstrate that neuropil calcium imaging and its decomposition could provide new insight into our understanding of neural processing.

An empirical study on network conversion of stock time series based on STL method.

A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ( S T L) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the S T L method and the visibility graph further highlights the advantages of the S T L method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.

Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand

Service‐level requirements play a crucial role in eliminating stock‐outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and customer demand and causing the manufacturer to adapt the initial strategy in response to disruptions. Such deviations from optimality frequently result in unexpected (and potentially very high) costs and are complex to manage. On the one hand, the manufacturer can use a robust or distributionally robust approach to prepare for the worst‐case disruption, ensuring that the realized cost will be lower than the estimated cost with high probability. On the other hand, this solution may lead to overly conservative production schedules. In this article, we take a different approach and develop a bilevel stochastic optimization model with chance constraints, which allows us to make the production more predictable in the event of disruptions by driving costs and optimal schedules closer to the benchmark for each scenario considered. We introduce doubly probabilistic service‐level requirements to account for two interdependent layers of uncertainty, that is, production disruptions and distributional uncertainties in customer demand. This allows us to make high‐quality production decisions with only a limited understanding of the demand pattern. Approximating the problem for a numerical solution, we guarantee tight optimality gaps for high service levels and propose an efficient solution scheme, combining robust scenario reduction with a customized Benders decomposition procedure. In the managerial section, we use the Omega× Swatch MoonSwatches example to demonstrate that a desirable doubly probabilistic service level can be attained for disruptions with a drop in demand. In the case of disruptions followed by a peak in demand, one can tighten the optimality gaps if the service level is reduced.

Understanding the Drivers of the Gender Productivity Gap in the Economics Profession

In this paper, I construct a novel data set from individual curriculum vitae of economists at the top 100 US departments to study gender differences in the drivers of scholarly performance. Using a Shapley decomposition procedure to estimate the contribution of each driver in explaining the variation in mean and inequality of research performance, I find that for women, current placement is a more important driver of performance and inequality in performance compared to men. For men, co-authorship is the most important driver. Overall, my results suggest that networks are the key driver of performance and of inequality in performance. JEL codes: A14, J16, J24

MIMO Radar Design for Extended Target Detection in a Spectrally Crowded Environment

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness.

Stepwise decomposition-integration-prediction framework for runoff forecasting considering boundary correction

Predicting river runoff accurately is of substantial significance for flood control, water resource allocation, and basin ecological dispatching. To explore the reasonable and effective application of time series decomposition in runoff forecasting, this study proposed a novel stepwise decomposition-integration-prediction considering boundary correction (SDIPBC) framework by using the stepwise decomposition sampling method and multi-input neural network. On this basis, we implemented a hybrid forecasting model combining seasonal-trend decomposition procedures based on loess (STL) with the long short-term memory (LSTM) network called STL-LSTM (SDIPBC) to estimate mid-long term river runoff. The reliability of the method was assessed using the historical runoff series of the Lianghekou and Jinping I Reservoirs in the Yalong River Basin, China, and developed several single models and hybrid models for comparative experiments. The results show that the existing decomposition-based hybrid forecasting frameworks are not suitable for practical runoff forecasting. The proposed SDIPBC framework can avoid using future information and improve the prediction accuracy of the single prediction model. For the Nash-Sutcliffe efficiency coefficient (NSE), the ten-day runoff forecasting accuracy of STL-LSTM (SDIPBC) in Lianghekou reservoir and Jinping I Reservoirs reached 0.845 and 0.862 respectively, which improved 1.81 % and 2.38 % than the single LSTM model, indicating that this is a practical and reliable decomposition-based hybrid runoff forecasting method.

Simultaneous determination of Os, Ir, Pt and Au in sorbent phases by total reflection X-ray fluorescence

Simultaneous determination of trace and ultra-trace noble metal (NM) contents is a challenging analytical problem. Quantification of NMs in a solid sorbent phase by total reflection X-ray fluorescence (TXRF) was propounded to exclude the problematic stage of analytes desorption. In this study, line overlapping and spectra processing were discussed, theoretical upper limit of area-related mass of the sorbent on a reflector was evaluated, and different quantification procedures were compared. A procedure for the simultaneous determination of Os, Ir, Pt and Au in the solid sorbent phase was developed. This procedure includes sorption of NMs from a diluted solution of a digested sample, TXRF determination of analytes directly in the sorbent phase and data processing using empirical calibration or multivariate regression algorithms (principal component regression, projection on latent structures or partial least squares regression). The procedure allows determining Os, Ir, Pt, Au with a relative error of 15%. Calculated limits of quantification (LOQ) for rocks, considering our sample decomposition procedure, were equal to 0.43, 0.39, 0.43 and 0.31 μg g−1 for Os, Ir, Pt and Au, respectively. The procedure was verified using certificated reference materials and ICP-MS as a reference method.

Analysis of charged self-gravitational complex structures evolving quasi-homologously

This paper studies the complex mechanism of evolving charged self-gravitational (dissipating or non-dissipating) systems using a structure scalar [Formula: see text], resulting from the basic procedure of orthogonal decomposition of the Riemann–Christoffel curvature tensor. The influence of electrical charge on the complexity of the considered system is analyzed in detail. We find several analytical Einstein–Maxwell models fulfilling the quasi-homologous [Formula: see text] evolution plus the vanishing complexity factor ([Formula: see text]) condition. Few of the presented models fulfill the Darmois constraints and exhibit shells, however, others satisfy the Israel constraints on both the boundary surfaces. Finally, some possible applications of the presented solutions are mentioned, which are important from astrophysical stand points. It is expected that some of the provided evolving Einstein–Maxwell fluid configurations may be utilized as a toy model of general frameworks like supernova explosions. It is found that the [Formula: see text] implies the vanishing of the [Formula: see text] and gives rise to a unique and simplest configuration (Friedmann–Lemaître–Robertson–Walker model) fulfilling the condition [Formula: see text] and evolving in the [Formula: see text] regime.

Exploring the environmental efficiency of airlines through a parallel RAM approach.

Air pollution in the aviation industry is becoming increasingly severe worldwide, along with rapid economic development. Therefore, it is significant to pay close attention to airlines worldwide. Usually, the airlines contain passenger transportation and freight transportation on the operating move. This paper proposes a parallel range adjusted measure (PRAM) to comprehensively measure and evaluate the environmental efficiency of 18 airlines from 2014 to 2019. Different from existing models, the model can handle shared inputs, shared desirable outputs, and shared undesirable outputs simultaneously. We build a shared resource decomposition procedure to perform a comparative analysis of the highest subsystem efficiency, and the sensitivity analysis proves the validity of the results. The main findings are as follows: 1. The optimal efficiency can be achieved by most of the 18 airlines when sharing resources; 2. Operating costs in the freight system should be increased to achieve optimal efficiency; 3. Asian airlines show higher efficiency than the airlines in Europe.

Decomposing Variance in Co-Rumination Using Dyadic Daily Diary Data

It is unknown how co-rumination, or perseverating on problems or feelings with another person, unfolds in the daily lives of romantic couples. Using a variance decomposition procedure on data from a 14-day dyadic diary, we assessed how much variance in co-rumination was attributable to temporally stable and varying factors as well as whether co-rumination is better measured as a couple-level or individual-level process. Within-person, within-couple fluctuations in co-rumination contributed most (~33%) to the total variance, and summary scores based on these fluctuations were reliable. Stable between-couple differences accounted for ~14% of the total variance and could also be reliably assessed. However, within-couple agreement in co-rumination was low such that the reliability at the level of within-couple change was inadequate. Research is needed to understand these divergent perceptions of co-rumination and potential downstream consequences. We conclude by considering how these results inform theory and can be applied to similar dyadic constructs.

Temporal Variations and Spatial Clusters of Dengue in Thailand: Longitudinal Study before and during the Coronavirus Disease (COVID-19) Pandemic.

The efforts towards effective control of the COVID-19 pandemic may affect the incidence of dengue. This study aimed to investigate temporal variations and spatial clusters of dengue in Thailand before and during the COVID-19 pandemic. Reported dengue cases before (2011–2019) and during (2020–2021) the COVID-19 pandemic were obtained from the national disease surveillance datasets. The temporal variations were analyzed using graphics, a seasonal trend decomposition procedure based on Loess, and Poisson regression. A seasonal ARIMA model was used to forecast dengue cases. Spatial clusters were investigated using the local indicators of spatial associations (LISA). The cyclic pattern showed that the greatest peak of dengue cases likely changed from every other year to every two or three years. In terms of seasonality, a notable peak was observed in June before the pandemic, which was delayed by one month (July) during the pandemic. The trend for 2011–2021 was relatively stable but dengue incidence decreased dramatically by 7.05% and 157.80% on average in 2020 and 2021, respectively. The forecasted cases in 2020 were slightly lower than the reported cases (2.63% difference), whereas the forecasted cases in 2021 were much higher than the actual cases (163.19% difference). The LISA map indicated 5 to 13 risk areas or hotspots of dengue before the COVID-19 pandemic compared to only 1 risk area during the pandemic. During the COVID-19 pandemic, dengue incidence sharply decreased and was lower than forecasted, and the spatial clusters were much lower than before the pandemic.

The decomposition-based nonstationary flood frequency analysis

The nonstationary flood frequency analysis (NS-FFA) is conducted when the assumption of stationarity in hydrologic extremes is violated. The commonly used approach is the performance-based NS-FFA, which jointly determines the distribution and the nonstationary structure based upon the model performance. However, this approach is challenged from both theoretical and practical perspectives. An alternative is the herein named decomposition-based NS-FFA, which determines the two NS-FFA model components separately and explicitly uses the available knowledge of the nonstationarity. However, it has been barely implemented in practice. This paper proposed a novel decomposition procedure that strictly follows the theoretical decomposition of nonstationary stochastic processes to advance the decomposition-based NS-FFA. The proposed decomposition procedure was compared with a previously reported method in both an analytical deduction and a simulation study. The proposed decomposition-based NS-FFA was further compared to the performance-based NS-FFA using both synthetic and real datasets from North America, which exhibit different patterns of nonstationarity. The Particle Filter was adopted for uncertainty quantification and parameter estimation. The results revealed that the proposed decomposition-based approach was advantageous in preserving the moments of underlying stochastic component, particularly the higher-order moment (i.e., skewness). In addition, the comparison of the two NS-FFA approaches demonstrated the superiority of the proposed decomposition-based approach in capturing the underlying (known) nonstationary stochastic process and being competitive with the performance-based approach from the performance perspective in real applications. The results from the simulation study and the real application also revealed several caveats of the performance-based approach, including the potential overfitting and equifinality problems as well as the selection of distinct models when adopting different performance metrics. In addition, differing from the performance-based approach, the decomposition-based NS-FFA avoided/alleviated the ergodicity violation. All these results demonstrated the advancements of the proposed decomposition-based NS-FFA and advocated its application in the NS-FFA.

Feasibility of direct analysis of algae contamination with chromium and copper on the filter with laser-induced breakdown spectroscopy and laser ablation inductively coupled plasma mass spectrometry

The chromium and copper contamination of algae on a filter was directly monitored with laser-induced breakdown spectroscopy (LIBS) as a demonstration of LIBS capabilities. Liquid samples of the green alga Desmodesmus subspicatus were intentionally contaminated with chromium, incubated, filtered, and the filters with the algae were analysed. Inductively coupled plasma mass spectrometry (ICP-MS) was used as a reference method. One half of the filter with the algae was dissolved and analysed as a solution with ICP-MS and the second half was subjected to the LIBS and laser ablation-ICP-MS analysis. Chromium and copper were attributable to the algae contamination and detectable with LIBS. The results from the LIBS provided almost equivalent information about the relative changes of the Cr and Cu concentration in the algae. The sensitivity of LIBS with a Czerny-Turner spectrometer was fully comparable with the ICP-MS procedure. The trends from LIBS were more similar to the LA-ICP-MS ones. It means that the algae growth inhibition in dependence on the chromium or copper content can be alternatively monitored with LIBS instead of the complicated decomposition procedure in ICP-MS analysis.

The cosine-sine decomposition with different-orbitals-for-different-spins determinants

The spin decomposition of a spin-contaminated different-orbitals-for-different-spins (DODS) wave function is formulated in terms of the Krylov space of the operator. The number of determinants that contribute to the various projected spin eigenfunctions depends on the sparsity of the orbital overlap matrix X between the α and β spatial orbitals. The cosine-sine decomposition (CSD) procedure may be applied to this overlap matrix, and the resulting redundant orbital transformations may be applied to the α and β spatial orbitals. This produces a sparse X′ matrix in the transformed basis in which each row or column has either one or two nonzero elements. This sparse X′ matrix simplifies the spin-decomposition procedure in three ways: 1) it reduces the number of contributing determinants within the Krylov basis functions and within the projected spin functions, 2) it reduces the effective orbital dimension through elimination of the frozen core and frozen virtual orbitals, and 3) it simplifies the spin-decomposition procedure in both the original and transformed bases by limiting the Krylov space dimension. These simplifications all reduce the computational effort for the spin-decomposition process. This procedure is implemented within a string-based DODS determinant formulation and applied to spin projection of unrestricted Hartree–Fock determinants.

Origin of the Microsolvation Effect on the Central Barriers of SN2 Reactions.

We have quantitatively analyzed the microsolvation effect on the central barriers of microsolvated bimolecular nucleophilic substitution (SN2) reactions by means of a two-step energy decomposition procedure. According to the first energy decompositions, an obvious increase in the central barrier for a microsolvated SN2 reaction against its unsolvated counterpart can be mainly ascribed to the fact that the interaction between the solute and the conjunct solvent becomes less attractive from the reactant complex to the transition state. On the basis of the second energy decompositions with symmetry-adapted perturbation theory, this less attractive interaction in the transition state is primarily due to the interplay of the changes in the electrostatic, exchange, and induction components. However, the contribution of the change for the dispersion component is relatively small. A distinct linear correlation has also been observed between the changes of the total interaction energies and those of the corresponding electrostatic components for the microsolvated SN2 reactions studied in this work. Moreover, the two-step energy decomposition procedure employed in this work is expected to be extensively applied to the gas phase reactions mediated by molecules or clusters.