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In this paper, the open-loop and closed-loop local and remote stochastic nonzero-sum game (LRSNG) problem is investigated. Different from previous works, the stochastic nonzero-sum game problem under consideration is essentially a special class of two-person nonzero-sum game problem, in which the information sets accessed by the two players are inconsistent. More specifically, both the local player and the remote player are involved in the system dynamics, and the information sets obtained by the two players are different, and each player is designed to minimise its own cost function. For the considered LRSNG problem, both the open-loop and closed-loop Nash equilibrium are derived. The contributions of this paper are given as follows. Firstly, the open-loop optimal Nash equilibrium is derived, which is determined in terms of the solution to the forward and backward stochastic difference equations (FBSDEs). Furthermore, by using the orthogonal decomposition method and the completing square method, the feedback representation of the optimal Nash equilibrium is derived for the first time. Finally, the effectiveness of our results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

In the paper, the modulation effect of rotor-stator interaction subjected to fluctuating rotation speed is investigated numerically. A quasi-bivariate variable mode decomposition method for analyzing non-stationary flow fields is proposed for the first time. Different from the previous studies, this paper emphasizes the local features of the transient flow field at fluctuating rotation speed. The results show that the impact of fluctuating rotation speed on the impeller force is limited, but it significantly impacts the pressure pulsation at fluctuating frequency. The correlation between amplitude and fluctuation ratio is positive. Meanwhile, the fluctuating rotation speed also induces sidebands centered on the blade passing frequency and its harmonics. The periodic changes in rotational speed lead to frequency and amplitude modulation of pressure pulsations. The magnitude of the instantaneous frequency and amplitude increases as the fluctuation ratio grows. The change in pressure field is well synchronized with the rotational speed, while the change in velocity field lags behind the rotational speed. The variation of pressure field with rotational speed is mainly attributed to two aspects. One is the flow structure at the fluctuating frequency, and the other is the instantaneous component of the flow structure at blade passing frequency. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 14(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper applies the Shapley-based decomposition approach to determine the impact of firm-, linkage- and location-specific factors to the survival probability of enterprises. The second paper applies Bayesian comparison methods to identify simultaneously the most likely spatial econometric model and spatial weight matrix explaining new business creation. The third paper compares the performance of continuous and discrete approaches to explain subjective well-being across space. The fourth paper applies a multiple imputation approach to determine regional purchasing power parities at the NUTS-3 level using data available at the NUTS-2 level. Finally, the last paper constructs a regional input-output table for Japan from its national counterpart using and comparing the performance of four non-survey techniques. [ABSTRACT FROM AUTHOR]

Glaucoma is a critical retinal disorder due to high intraocular pressure (IOP) within the eye. It causes irreversible damage of the optical nerve head (ONH). The available glaucoma detection methods using decomposition techniques with gray images or only green channel images are less accurate. In this paper, a more accurate method for glaucoma detection using image channels (ICs) and discrete wavelet transform (DWT) from fundus images is proposed. Firstly, input images are resized then red channel (RC), green channel (GC), blue channel (BC), and gray scale (Gs) images are extracted. Secondly, these four types of images are enhanced and decomposed separately into sub band images (SBIs) using second level (SL) DWT. Thirdly, most useful features are extracted from each of DWT SBIs. Fourthly, extracted features from each of RC, GC, BC, and Gs images are concatenated and normalized. Finally, robust features are selected and fed to the least square support vector machine (LS-SVM) classifier. The obtained glaucoma detection accuracy of the proposed method is 84.95% which is more than the existing methods using the same image database. The proposed method may become suitable for ophthalmologists to detect glaucoma with better accuracy. [ABSTRACT FROM AUTHOR]

In recent years, low-rank sparse representation (LRR) schemes have been applied to the field of hyperspectral image (HSI) anomaly detection (AD) and received attention. However, it is often accompanied by a small amount of noise in the AD, which leads to a high false alarm rate of detection results. In this paper, the HSI-AD algorithm is proposed to address this problem. First, the HSI is unmixed using the deep network to obtain the endmembers and abundance, and the abundance matrix is fused with the HSI to obtain the mixed data, which to a certain extent complements the spatial information of the HSI because the abundance matrix can reflect the subpixel-level information of the target image elements. Then, the mixed data are decomposed based on the model proposed in this paper. To overcome the situation that the sparse part of the traditional model is prone to mix with a small amount of noise, the sparse decomposition of the mixed data into anomaly and noise is used to remove the small amount of noise from the sparse part. A joint dictionary consisting of background and anomaly is used in the above model. In addition, to address the problem of information loss caused by the uniform accumulation of kernel norm over all singular values in the process of model decomposition, this paper designs the γ norm decomposition method to replace the general kernel norm decomposition for a more effective extraction of useful information. [ABSTRACT FROM AUTHOR]

For automobile manufacturers, reducing vehicle interior noise is essential for increasing customer satisfaction and vehicle quality. Windshield wipers are one of the major components that generate such noises, and faulty wipers could negatively affect passengers' psychological and physiological perceptions while driving. Thus, identifying faulty wipers during the manufacturing process would improve the driving experience and vehicle and road safety as well as reduce driver distraction. However, the existing windshield wiper noise-detection process is entirely manual, relies upon human subjectivity, and is time-consuming. Accordingly, this paper develops a novel automated windshield wiper fault-detection system. First, a novel binarization approach is used to effectively binarize the transformed spectrograms of sound signals from windshield wiper operation to segment nAoisy regions. Then, a new matrix-factorisation approach called orthogonal binary singular value decomposition is proposed to decompose binarized mel spectrograms into uncorrelated binary eigenimages to extract meaningful features and identify faulty wipers. Then, the $ k $ k -nearest neighbour classifier is utilised to classify the extracted features into normal or faulty windshield wipers. Finally, to demonstrate the effectiveness of the proposed system, it was validated on real-life windshield wiper reversal and squeal noise datasets, where it outperformed existing methods and achieved accuracies of 95% and 94%, respectively. [ABSTRACT FROM AUTHOR]

Statistically homogeneous pixel (SHP) selection and DS phase optimization are two critical steps in the distributed scatterer interferometric SAR (DS-InSAR) time series method. In this paper, a new algorithm named dynamic hypothesis test of confidence interval (D-HTCI) is proposed, which reduces the wrong selection rate of SHP and increases the number of SHP selections. Using adaptive spatial nonlocal filtering method for DS phase optimization, the phase standard deviation (PSD) and the sum of phase differences (SPD) show that compared with the traditional covariance matrix decomposition method, the optimization quality is improved by 2.1 and 1.8 times, respectively. Combining 24 scenes Sentinel-1A data from September 17, 2017 to July 14, 2018, the method is applied to monitor surface subsidence of the Daizhuang filling mining area (Jining, Shandong, China). The results show that the proposed method has mm-level accuracy for monitoring of surface subsidence in a filling mining area. [ABSTRACT FROM AUTHOR]

As an important component of China's social security system, the housing provident fund (HPF) scheme has seen its fairness called into question. Based on the data from the China Household Finance Survey in 2017, this paper uses the least squares method and the unconditional quantile regression and decomposition method to study the wage gap between the state and non-state sectors from the perspective of the HPF. Its findings suggest that the HPF is an important component of the state-sector wage premium. That is, whether wages include the HPF affects the direction and significance of the sectoral wage gap. A closer look reveals that in the state sector, low-income workers benefit less from the HPF scheme than middle- and high-income workers. The breakdown of the wage gap within the state sector indicates that the wage premium for the middle- and high-income bracket and for the high-income bracket are mainly credited to the differences in individual characteristics of state-sector workers, while the wage premium obtained by the middle- and low-income bracket takes the form of the sectoral wage level, which benefits from the HPF system to some extent. [ABSTRACT FROM AUTHOR]

In this paper we investigate how the Benders decomposition, Lagrangean relaxation, and Dantzig–Wolfe reformulation techniques can be accelerated when intertwined with the corridor method. We test the approaches on the capacitated lot sizing problem with setups. Due to the computational complexity of this lot sizing problem, one would expect to find a number of approaches based on decomposition techniques in the literature. While this is true for Lagrangean relaxation and Dantzig–Wolfe reformulation, we could not find any paper proposing the use of Benders decomposition for the problem at hand. Consequently, with this study, we pursue a two-fold goal: First, and foremost, we want to determine how effective the corridor method is as acceleration scheme for these decomposition techniques; second, we aim at gaining some insight into why Benders has not been proposed for this class of problems. Our results shed light on both issues. On the one hand, we show that all the decomposition methods benefit from the hybridisation with the corridor method. On the other hand, a thorough analysis on the behaviour and limitations of Benders algorithm is provided. We conclude the study with a statistical analysis to determine whether significant differences in performance among the different implementations arise. [ABSTRACT FROM AUTHOR]

This paper presents a new analytical approach to the analysis of transfer lines with unreliable machines and unreliable buffers running in a steady state. The buffers are assumed to be subject to time-dependent failure, i.e. they can fail in any condition, even when not loaded. As the most common representative of this type of buffer, accumulating conveyors are widely adopted in actual transfer lines. In this paper, a continuous model is established for such transfer lines in which the part flow is approximated by a continuous flow. Then, an efficient decomposition method based on a generalised exponential distribution is proposed to analyse the model. A new set of decomposition equations that take into account the mechanism of time-dependent failure are derived and then solved by an advanced algorithm. Extensive numerical experiments are performed. The results show that the proposed method is valid and efficient. [ABSTRACT FROM AUTHOR]

This paper examines serial production lines with unreliable non-identical parallel machines at each workstation and intermediate buffers with finite capacities. All machines are assumed to have exponential service times, times to failure and repair times. An efficient decomposition technique is introduced for the performance evaluation of such lines. Rather than replacing each parallel-machine workstation with an equivalent single-server workstation, the main contribution of this paper is the presentation of a direct approach to derive and apply decomposition equations directly for every parallel machine at each workstation. Experimental results indicate that such a method can provide a computationally efficient algorithm to analyse large serial unreliable multi-server production lines with a good accuracy compared against simulation and other available methods. [ABSTRACT FROM AUTHOR]

Many regions or countries seek to transition to a low-carbon economy, but there is ambiguity about how carbon emissions change with economic growth. This paper captures characteristics of carbon emissions in China using the Tapio Method. To detect driving factors of carbon emissions in economic growth, we adopt the Log-Mean Divisia Index decomposition method. The results of the paper are as follows: (1) Economic growth in most regions of China has outpaced the growth rate of carbon emissions, showing the characteristics of decoupling. (2) Metropolitan cities have a greater impact on the level of decoupling in neighboring provinces and municipalities. (3) The decoupling of carbon emissions from economic growth is caused by declining energy intensity in China. (4) The energy mix and industrial structure have little impact on the growth rate of carbon emissions. In addition, some policy implications are summarized according to the findings of this paper. [ABSTRACT FROM AUTHOR]

Since 2016, the Bureau of Safety and Environmental Enforcement (BSEE) has collected decommissioning expenditure data from operators in the deepwater US Gulf of Mexico, defined as water depth greater than 122 m, to estimate the decommissioning liability of leases. BSEE uses both statistical tools and work decomposition methods to estimate the decommissioning cost of all active wells, structures, and pipelines on deepwater leases. The purpose of this paper is to evaluate BSEE cost estimates in the deepwater US Gulf of Mexico across four decommissioning stages and to infer unit cost statistics and cost functions in the region. Fixed platforms in 250–500 m water depth are expected to cost between $10 and $80 million to decommission, spars about $55 million, ship-shape floaters around $30 million, and tension leg platforms and their smaller (mini) versions between $18 and $25 million. The deepwater decommissioning market value is estimated at $24.3 billion circa 2022. [ABSTRACT FROM AUTHOR]

Wind power ramp event is a small probability and large hazard event, for effectively avoiding its impact on the grid, an offshore wind power ramp prediction method based on optimal combination model is proposed. First, on the basis of the output power data of existing offshore wind turbines, the data preprocessing of the wind power series is carried out by using the Variational Modal Decomposition method optimized by the novel Seagull Optimization Algorithm, which is decomposed into several Intrinsic Mode Function components and one residual component to extract the salient features of the power signal and reduce the data fluctuation. Second, the prediction models of Extreme Learning Machine and Bayesian optimized Long Short Term Memory network are built, respectively, while the optimal combined prediction method is adopted to combine the two single models for obtaining the optimal combined model. Individual components are treated as the inputs of the model, then the final offshore wind power is obtained by superimposing and integrating the prediction results of each component. Eventually, adopting the definition method to predict and characterize the offshore wind power ramp. The research shows that, compared with BP, RNN, LSTM and single model, the wind power prediction error of the combined model proposed in this paper is smaller, with RMSE of 71.10 kW, MAE of 50.26 kW and SMAPE of 0.01%, and it can completely predict 7 ramp events with a higher accuracy. Meanwhile, the applicability of the proposed method is verified by varying the time resolution. The final results show that the proposed method in this paper has a high accuracy in offshore wind power ramp prediction. [ABSTRACT FROM AUTHOR]

This paper deals with the Generalised Workforce Scheduling and Routing Problem (GWSRP) where 9 temporal constraints ensuring visit dependencies are all together taken into account and where customer and worker's quality of service are taken into consideration. A Constraint-Programming based Decomposition Method (CPDM) is proposed, firstly based on a relaxation of coordination constraints and a column generation, and secondly with an iterative insertion of coordination constraint by constraint programming solver. Numerical experiments are achieved on huge instances derived from WSRP benchmark instances with up to 177 customers, 59 vehicles and coordination constraints. The CPDM is able to find nearly optimal solution for medium-size instances and find high-quality solution for huge-size instances whereas CPLEX solver applied to a mixed integer linear model is not able to give a solution in this case. [ABSTRACT FROM AUTHOR]

Scheduled Caste (SC) and Scheduled Tribe (ST) women in India have relatively lower usage of period products than women of the General category. This paper attempts to understand and uncover the social factors explaining inequalities in the period product usage among these groups. This paper uses data from the National Family Health Survey (NFHS-5), conducted in 2019–21, for this study, with a sample of 49,136 SC, 44,392 ST, and 41,045 General caste women in the age group of 15 to 24 years. Using the Fairlie decomposition method, we explain the inequalities in the period product usage between SC, ST, and general categories. Differences in wealth index (SC-Gen: 49.54%; ST-Gen: 46.6%), respondent's education level (SC-Gen: 22.85%; ST-Gen: 17.3%), watching television (SC-Gen: 09.44%; ST-Gen: 10.1%), reading newspaper (SC-Gen: 08.71%; ST-Gen: 05.09%), and toilet facility (SC-Gen: 06.36%; ST-Gen: 03.05%) account for a large portion of the gap in the period product usage among these groups. Government, policymakers, and NGOs should focus on creating comprehensive menstrual health and hygiene (MHH) programmes, including educational programmes and mass media campaigns, while increasing focus on disadvantaged communities, viz. SCs and STs to make the necessary nudge for girls and women to adopt hygienic menstrual practices. [ABSTRACT FROM AUTHOR]

This paper focuses on finite-time robust adaptive simultaneous stabilisation for nonlinear time-delay systems, taking into account actuator saturation and unknown parameters. Firstly, by employing adaptive control strategies and finite-time stability theory, a finite-time adaptive simultaneous stabilisation scheme for the nonlinear time-delay systems is developed. Secondly, utilising the orthogonal decomposition method, a finite-time robust adaptive simultaneous stabilisation scheme for nonlinear time-delay systems with external disturbances is proposed. Finally, simulations demonstrate the effectiveness of the provided control scheme. [ABSTRACT FROM AUTHOR]

The finite frequency domain H ∞ consensus control problem of neutral multi-agent systems with the input delay is investigated in this paper for the first time. According to the linear transformation, the problem of H ∞ consensus is converted into a set of H ∞ stability problems. Through the Lyapunov theory, combined with the time-delay interval decomposition method, a less conservative controller design method for the state feedback controller is presented. And the obtained controller can ensure closed-loop multi-agent systems to fulfil the consensus. Utilizing the generalised Kalman-Yakubovich-Popov (GKYP) lemma, the sufficient condition is derived for multi-agent systems achieving the H ∞ performance in the finite frequency domain (FFD). Then, via the orthogonal spatial information of the input matrix, the conservativeness of obtained design approach is further declined. Moreover, the design method with less conservativeness is proposed for the dynamic output feedback controller. Finally, a numerical case and a multi-pendulum system controlled by the multi-motor system are presented in this paper to demonstrate the availability and applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper addresses the aggregate production, procurement, and pricing planning problem of a supply chain network involving suppliers, manufacturer, and retailers in a multi-product and multi-period environment, with supply chain risk and visibility considerations. A supply chain product visibility index comprising multiple attributes is developed to capture the features of information sharing. We formulate the problem as a non-convex mixed integer programme and tailor a generalised Benders decomposition for solution. Computational experiments are conducted to validate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper mainly addresses the lack of labelled data and insufficient data utilization in PolSAR image classification. We propose a channel adaptive Complex-Valued Fully Convolutional Networks based on Transfer learning (TF-CVFCN). To solve the problem of less labelled data, we borrowed from the transfer learning method, which can use a large amount of easily obtained relevant or even irrelevant data for pre-training and then use a small amount of source data for network training fine-tuning. Since PolSAR data can obtain different data information by using different decomposition methods, the current research trend is to process PolSAR data in the complex-valued containing more information. In our work, to cater to the proposed network model and make the network design more liberal, a new transfer learning method is proposed, which uses a new method to realize partial layer parameter transfer, and achieves the purpose of optimizing the traditional Transfer learning method. On this basis, we optimize the network structure to improve the classification accuracy of PolSAR data, and evaluate its performance on three datasets. Furthermore, we lighten the TF-CVFCN and optimize the relevant parameters using Competitive Multi-Objective Particle Swarm Optimizer, emphasizing the importance of this parameter optimization in large networks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a full-Stokes image reconstruction (FIR) network to reconstruct polarization images from a snapshot of random modulated image. The Stokes-Mueller matrix theory demonstrates that a image captured by conventional polarization imaging system is a linear superposition of the four Stokes vectors, thus image decomposition methods can be used for full-Stokes image reconstruction. The network is constructed based on a cycled conditional adversarial network, which is trained utilizing the physical mapping relationships between the Stokes vectors and the modulated images. Simulations and experiments are conducted to demonstrate the network's wavelength independence and modulation independence, proving the effectiveness of proposed FIRnet. To some extent, our method alleviates the contradiction between measurement time and accuracy of polarization, promoting the application of compact snapshot full-Stokes imaging. [ABSTRACT FROM AUTHOR]

In robust parameter design, it is common to use computer models to simulate the relationships between input variables and output responses. However, for the contaminated experimental data, the model uncertainty between computer models and actual physical systems will seriously impair the robustness of the optimal input settings. In this paper, we propose a new weighted robust design approach concerning the model uncertainty from outliers based on the robust Gaussian process model with a Student-t likelihood (StGP). Firstly, to reduce the impact of outliers on the output means and variances, the StGP modelling technique is adopted to estimate the relationship models for contaminated data. Secondly, the Gibbs sampling technique is employed to estimate model parameters for better mixing and convergence. Finally, an optimisation scheme integrating the quality loss function and confidence interval analysis approach is built to find the feasible optimisation solution. Meanwhile, the hypersphere decomposition method and data-driven method are applied to determine the relative weights of objective functions. Two examples are used to demonstrate the effectiveness of the proposed approach. The comparison results show that the proposed approach can achieve better performance than other approaches by considering the model uncertainty from outliers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a multi criteria performance measurement model for newbuilding dry-docking purposes as a managerial tool. The dry-docking is presented as a process with multiple inputs and outputs, containing various technical and cost data related to the dry-docking project. The data envelopment analysis models with constant and variable returns to scale were used in this research along with the efficiency decomposition method to determine possible sources of inefficiency on the data sets available. The seven criteria to be used for dry-docking performance measurement. Research results determined the best practice/efficient vessels which create the efficient frontier. Projections to the efficient frontier, efficiency decomposition and sources of inefficiency recognition with improvement propositions were provided for the inefficient vessels. Due to the performance assessment carried out, the results represent benchmarks for dry-docking performance and a guideline to future dry-docking improvements on data set researched. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral target detection methods have been widely used in military or civilian fields. Many high-performance hyperspectral target detection methods have been proposed, and tensor decomposition-based methods have attracted the attention of researchers. However, the non-uniqueness of tensor rank and the interference of noise will reduce the effect of target detection. In order to solve these problems, a sparse and spectral smooth regularized low-rank tensor decomposition method for hyperspectral target detection is proposed in this paper. The proposed method adds the low-rank regularization to the factor matrix of the tensor decomposition framework, which can reduce the adverse effect of information redundancy on the detection effect of the algorithm. Furthermore, the spectral smooth regularization is added to the spectral factor matrix to suppress noise and the sparse regularization is added to the core tensor to solve the problems of non-uniqueness of the Tucker tensor decomposition, which can improve the effect of target detection. On this basis, it simplifies the calculation process and reduces the complexity of the algorithm by using the conjugate gradient algorithm and the tensor Kronecker product. In this paper, the algorithm is tested on four real hyperspectral data sets and compared with six state-of-the-art algorithms. The experimental results show that targets detected by the proposed method are more obvious and the background is more pure. [ABSTRACT FROM AUTHOR]

In many manufacturing contexts, performance modelling of an integrated production and material handling system is a complex problem. Existing research lacks an in-depth consideration of the integration of these two areas. A flexible flow shop with random and state-dependent batch transport, where the batch size depends on the number of jobs in the buffers and the capacity of automated guided vehicles, is considered and modelled as an open queueing network with blocking. A decomposition method of state space is proposed for computing system performance measures. The accuracy and efficiency of the proposed method are demonstrated by comparing the results with simulations from numerical experiments. Meanwhile, the properties of the system, especially for material handling processes, are investigated and analyzed according to the experiments. The results of this paper can be used as a basis for system design, analysis, and resource planning. [ABSTRACT FROM AUTHOR]

The analysis of hybrid-polarimetric synthetic aperture radar (hybrid-pol SAR) data can be carried out using two different types of analytical approaches. The first approach is by implementing direct-technique (DT), which directly takes hybrid-pol Stokes parameters as input. The second approach is reconstruction-based-technique (RBT), where the pseudo quad-polarimetric (quad-pol) data is reconstructed from the hybrid-pol measurements. Methods under DT and RBT categories have their own strengths and weaknesses, which are thoroughly investigated in this paper. Also, a comparative analysis of these methods is carried out on the basis of their ability for scattering type discrimination and land-cover classification using synthesized as well as true hybrid-pol data. Moreover, a new entropy (H)-based RBT approach is proposed in this paper which is being compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Solving the buffer allocation problem (BAP) in long production lines is difficult because it is an NP-hard problem. In this paper, we propose a new approach to solve the BAP for long series-parallel unbalanced production lines with the objective of minimising the average work-in-process subject to a throughput rate constraint. Instead of directly optimising a long line, this method decomposes the original long line into several small decoupled subsystems and adds relation condition variables between the subsystems. After several iterations between subsystem optimisation and condition updating, this method can obtain the optimal or near-optimal solution of the original system with a significantly improved computational efficiency. Extensive numerical experiments demonstrate the accuracy and efficiency of this approach. Finally, several examples and a multi-factorial experimental analysis are provided to show the influence of the decomposition strategy, initial values, and parameters of the target systems on the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Asynchronous Mixed-Model Assembly lines are common production layouts dedicated to large-scale manufacturing of similar products. Cyclically scheduling such products is an interesting strategy to obtain high and stable throughput. In order to best optimise these lines, it is necessary to combine line balancing, model sequencing, and buffer allocation. However, few works integrate these three degrees of freedom, and evaluating steady-state performance as a consequence of these decisions is challenging. This paper presents a mathematical model that allows an exact steady-state performance evaluation of these lines, and hence their optimisation. While the combination of degrees of freedom is advantageous, it is also computational costly. An iterative decomposition procedure based on alternation between two mathematical models and on optimality cuts is also presented. The decomposition is tested against the proposed mathematical model in a 700-instance dataset. The developed methods obtained 142 optimal answers. Results show that the decomposition outperforms the monolithic mathematical model, in particular for larger and harder instances in terms of solution quality. The optimality cuts are also shown to help the decomposition steps in terms of solution quality and time. Comparisons to a sequential procedure further demonstrate the importance of simultaneously optimising the three degrees of freedom, as both the proposed model and the decomposition outperformed such procedure. [ABSTRACT FROM AUTHOR]

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output. KEY POLICY HIGHLIGHTS: Spatially explicit uncertainty and sensitivity analysis extends the geodiversity assessment methods and comprises the next step in improving the accuracy of geodiversity assessments for land management decisions. An integrated uncertainty and sensitivity analysis helps focus on high-priority areas (high sensitivity and low uncertainty) to improve assessment reliability. The uncertainty and sensitivity analysis in models with spatially explicit output results depicted on maps can help reduce field research expenses by identifying a particular area or a specific natural component that warrants better-quality data. Bivariate maps employed to visualize the results of spatially explicit uncertainty and sensitivity analysis can potentially reduce the cognitive effort of visually switching between multiple single-variable (theme) maps. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease which has been one of the leading causes of loss of vision worldwide. It occurs when the fluid pressure in the optic nerve increases which causes the damage to the optic nerve. Detection of glaucoma is necessary for proper and timely treatment. In this paper, an automated system is developed for glaucoma diagnosis in which a two-dimensional empirical wavelet transform (2D-EWT)-based fundus image decomposition method is developed. A novel method is suggested for magnitude spectrum-based image segmentation. In 2D-EWT, the average magnitude spectrum along the horizontal or vertical axis is considered which discards the information along one axis. In the suggested method, the average magnitude spectrum along both axes is utilized and more decomposed images can be obtained. The decomposed fundus images are given to the ResNet-18-based deep learning model for glaucoma identification. The performance of the proposed method is computed in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPEC). The proposed method has achieved the ACC of 99.33% with 99.35% SEN, and 99.25% SPEC which is more than the compared methods. [ABSTRACT FROM AUTHOR]

As commonly stated, we cannot manage what we do not measure. Understanding the flow of energy and its end-uses within a building is critical for energy management. Therefore, the lack of high resolution energy submetering is a significant barrier to efficient energy management in buildings. Despite this, many buildings still lack adequate submetering for their major end-uses because of the cost and practical restrictions. Energy disaggregation techniques aim at breaking down the bulk meter energy data into primary end-uses to gain insight into consumption patterns. However, high resolution, trustworthy BAS trend data is essential to develop reliable disaggregation techniques and capture unmeasured energy flow accurately. This paper explores a time series decomposition based method to disaggregate the total energy use into three major end uses namely lighting and plug loads, cooling, and heating energy use without BAS trend data. The results were compared with actual submetered data from ten office buildings in Ottawa, Canada for validation purposes. Specific insights into lighting and thermal scheduling, as well as hourly, daily, and monthly operational variations based on the de-composition components were discussed. The promising performance of the proposed method suggests that it could be used for quick and low cost auditing of commercial buildings with access to only the building's total energy use data. [ABSTRACT FROM AUTHOR]

To comprehensively assess the economic impacts from China's tourism industry caused by COVID-19 in 2020, this article develops a new multiplier calculation and decomposition method based on the social accounting matrix (SAM). This method is suitable for situations in which multiple industries are simultaneously exposed to external shocks, especially comprehensive industries like tourism. By categorizing all industries as being in either the tourism sector or the nontourism sector, we calculate the output, value-added and employment impacts of COVID-19, then decompose them into four levels: direct, indirect, spillover and reverberation effects. There are some subindustries of both the tourism and nontourism sectors that were severely affected. Compared with the calculation results from the traditional SAM method, the method developed in this paper identifies quite different industrial structures, although there is almost no difference in the total impacts. [ABSTRACT FROM AUTHOR]

Here we deal with the stabilisation problem of non-diagonal systems by boundary control. In the studied setting, the boundary control input is subject to a constant delay. We use the spectral decomposition method and split the system into two components: an unstable and a stable one. To stabilise the unstable part of the system, we connect, for the first time in the literature, the famous backstepping control design technique with the direct-proportional control design. More precisely, we construct a proportional open-loop stabiliser, then, by means of the Artstein transformation we close the loop. At the end of the paper, an example is provided in order to illustrate the acquired results. [ABSTRACT FROM AUTHOR]

How to rapidly and accurately detect the financial crisis is one of the fundamental and challenging problems in the field of financial risk management. This paper aims to develop a novel network characteristic indicator to deal with this issue. Specifically, we select the daily closing price of stocks spanning from 2006 to 2020 in China's A-share market to establish a series of complex networks, and extract Laplacian energy measure as a new network indicator. By employing the method of seasonal-trend decomposition procedure based on loess, the proposed indicator successfully detects the global financial crisis, the Eurozone debt crisis, the Chinese stock market crash, the Sino-US trade friction and the COVID-19 pandemic. Furthermore, compared with the traditional topological indicators (e.g. global efficiency, average clustering coefficient, characteristic path length and network density), the proposed indicator demonstrates the outstanding characteristics of higher identification accuracy, wider application range and faster response speed. Lastly, the robustness of the Laplacian energy measure in the financial crisis detection is further confirmed in the US, UK, German, French and Spanish stock markets. [ABSTRACT FROM AUTHOR]

The article introduces series of topics discussed in the June 1996 issue of the "International Journal of General Systems." The first paper talks about multi-network evolutionary systems and automatic decomposition of complex problems. The other paper focuses on the integrated decision and control of human-engineered complex systems and the other is about observer design for complex systems in the presence of uncertainties, nonlinearities and distributed delays.

Damage in a pavement structure is usually initiated in the asphalt layers, making the Rayleigh wave ideally suited to the detection of shallow surface defects. However, the practical application of crack detection methods in asphalt is hampered by the dispersive behaviour of the road pavement. A new digital signal processing technique for the measurement of the amplitude and phase of the direct and reflected Rayleigh waves, scattered from the boundaries of a vertical crack in asphalt, is presented in this paper for the first time. The method uses multiple receivers and hence it finds an approximate solution with a least square optimisation. The resonant peak frequencies of the reflection coefficient and the cut-off frequencies of the transmission coefficient are used for assessing the depth of the crack. The method proved to be successful for the in-situ assessment of the depth of cracks both numerically and experimentally, since it can cope with the dispersive and heterogeneous nature of asphalt. This work supports a paradigm-shifting approach to the in-situ crack evaluation of roads, for which the road is holistically treated as a dispersive medium. [ABSTRACT FROM AUTHOR]

An important issue in analysing signals by the variational mode decomposition (VMD) algorithm is to confirm the number of modes and the balance parameter. In the applications of fault detection, most studies optimise parameters by the characteristics to extract fault information perfectly. Then, the results are used for subsequent operations such as fault analysis and classification. However, the optimal methods aiming at extracting fault information are not completely applicable to detect whether the fault occurs for different signal segments. To address this issue, this paper proposes a parameter optimised VMD method, and it is used to analyse acoustic emission (AE) signals from actual operating railway environment. Firstly, an optimised index is constructed based on a universally ideal decomposition result, which completely decomposes the signal without mode mixing and over-decomposition. Then, the VMD parameters are searched by the particle swarm optimisation (PSO) algorithm using the maximum index as the optimisation fitness function. Meanwhile, the permutation entropy feature of modes obtained by the optimised parameters is extracted to detect rail crack signals. After that, the proposed method is further analysed based on two different AE signals. Finally, the detection results are analysed and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of integrated task sequencing and path planning in Remote Laser Welding (RLW). It is shown that finding the appropriate order of welding tasks is crucial for exploiting the efficiency of this new joining technology, and this can be achieved only if the robot path is considered already at the time of sequencing. For modelling the problem, a novel extension of the well-know Travelling Salesman Problem with neighbourhoods and durative visits, denoted as TSP-ND, is introduced. Basic properties of this problem are formally proven, and a GRASP meta-heuristic algorithm is proposed for solving it. Extensive computational experiments demonstrate that the novel approach solves efficiently industrially relevant problems, and it achieves substantial improvement in cycle time compared to the single earlier approach in the literature dedicated to RLW, as well as compared to a decomposition approach to solving the TSP-ND model. [ABSTRACT FROM PUBLISHER]

In this paper, the spectral method is developed as a reduced-order model for the solution of parametric problems within the building refurbishment framework. We propose to use the spectral reduced-order method to solve parametric problems in an innovative way, integrating the unknown parameter as one of the coordinates of the decomposition. The residual is minimized combining the Tau–Galerkin method with the Collocation approach. The developed method is evaluated in terms of accuracy and reduction of the computational time in three different cases. The dynamic behaviour of unidimensional moisture diffusion is investigated. The cases focus on solving parametric problems in which the solution depends on space, time, diffusivity and material thickness. Results highlight that the parametric spectral reduced-order method provides accurate solutions and can reduce 10 times the degree of freedom of the solution. It allows efficient computation of the physical phenomena with a lower error when compared to traditional approaches. [ABSTRACT FROM AUTHOR]

This paper aims to obtain decompositions of higher dimensional functions into sums of non-tangential boundary limits of the corresponding Hardy space functions on tubes for the index range . In the one-dimensional case, Deng and Qian recently obtained such a Hardy space decomposition result: for any function , there exist functions and such that , where and are, respectively, the non-tangential boundary limits of some Hardy space functions in the upper-half and lower-half planes. In the present paper, we generalize the one-dimensional Hardy space decomposition result to the higher dimensions and discuss the uniqueness issue of such decomposition. [ABSTRACT FROM AUTHOR]

In this paper, we prove the existence and uniqueness of weak solutions for a singular evolutionary system, which is deduced from a model for image decomposition combining staircase reduction and texture extraction. The main method we used is p-Laplace regularization. The numerical experimental result shows the efficiency of this kind of model. [ABSTRACT FROM AUTHOR]

Given a decomposition of a portfolio P as a sum of K components, practitioners commonly decompose the risk of P as a corresponding sum of risk contributions. In this paper, we prove two theorems about risk contributions. The first theorem concerns a form of duality identified in Grinold [J. Portfolio Manage. 2011, 37(2), 15-30], which may be described as follows. When we view a portfolio decomposition as a coordinate representation of the portfolio with respect to a given vector-space basis, then there is a natural dual basis with respect to which there is an alternative decomposition, here referred to as the dual decomposition. The dual decomposition gives the same contributions to risk as the original decomposition. The first theorem gives necessary and sufficient conditions for a change of basis to preserve risk contributions, and shows that all such changes of basis can be explained in terms of dual decompositions. The second theorem explores sensitivity of portfolio risk to a risk regime change and indicates that large risk contributions or large risks of the components of a decomposition may be harbingers of high sensitivity. This provides a motivation for the practice of reporting both the risk contributions and the risks of the components in a decomposition. [ABSTRACT FROM AUTHOR]

Researchers and practitioners often separate logistics network planning into strategic, tactical, and operational decisions. Due to the interdependence among these levels of decisions, their integration can bring important cost reductions and better network responsiveness in scenarios where there is business change. However, integrated problems entail challenges, such as decision timing, and they are more difficult to model and solve. This article presents a literature review of integrated problems in logistics network planning. The objective is to identify the main integrated decisions, their scopes, integration approaches, and the solution methods used. Although this review addresses research with decisions at different hierarchical planning levels, we observed that integration of strategic and tactical decisions is more common and some of the integration approaches are single-level mono-period models, single-level multi-period models, multi-time scale models, and multi-level models. There is a predominance of aggregated data in these studies. Regarding the solution methods, there is a predominance of heuristic approaches over exact ones, including methods based on decomposition or sequential procedures. Based on the findings of this systematic review, we draw a conceptual framework presenting the main modelling assumptions, integration strategies, and solution methods to the integrated problems, and we also discuss some promising research opportunities. [ABSTRACT FROM AUTHOR]

This paper describes a cardinality constrained network flow structure whose special characteristics are used to analyse different risk aspects under an environment of uncertainty. The network structure developed is a suitable alternative to support financial planning and many other decision-making problems with limited resources. By setting a diversification level, we can manage systematic and non-systematic risks under a stochastic mixed integer linear programming framework. A dual decomposition method, Progressive Hedging (PH), is applied to more efficiently accommodate instances with large numbers of scenarios. We studied the impact of the level of the diversification on transaction costs and considered different factors that influence the performance of the algorithm. In particular, a Lagrangian bound is embedded to enhance the capacity of the method. Numerical results show the effectiveness of the proposed decision support approach. [ABSTRACT FROM AUTHOR]

The paper proposes a novel hybrid method that extends previous work incorporating the fractionally cointegrated vector autoregressive and the permanent-transitory decomposition model. Using the hybrid method, we investigate whether the rapid rise in China's housing prices is a trend or cyclical fluctuation as well as whether there exists a housing price bubble. The findings indicate there exists a fractional cointegration relationship between housing prices and macroeconomic fundamentals in China, and that the fundamental value of housing prices is determined by macroeconomic factors. In particular, the upward trend in China's housing prices has shifted and decelerated since 2012, in line with GDP and money supply. Moreover, we provide evidence of the existence of a housing price bubble in China since 2012, particularly during the 2015–2018 period. We demonstrate that the hybrid model has a superior forecasting performance and decomposes the trend and cycle components more accurately than the conventional methods when estimating a system of possibly fractional cointegration relationship. [ABSTRACT FROM AUTHOR]

This paper studies mathematics learning gaps within Indian children at two points in time. Dividing them into two groups, better performing and the rest, we investigate the causes of the difference in the average learning gap between them at those two points. We explore this question using the threefold Blinder-Oaxaca decomposition at these survey points (collected over a gap of four years). We find that when the children were younger the private schooling effect was the core contributor towards this learning gap. When these children got older, the effect vanished and the gap in average years of schooling, which has magnified during this time between these groups of children, contributes most to this learning gap. [ABSTRACT FROM AUTHOR]

This paper aims to examine the difference in role of public governance on the performance of each business ownership type in Vietnam. Using FGLS regression and Blinder – Oaxaca decomposition methods, the study finds out gaps of enterprises performance among state-owned enterprises, non-state enterprises and FDI enterprises in Vietnam in the period 2016–2019. The variation is due to business characteristics and distinction between ownership types. Besides, the decomposition results show that public governance is a factor of the enterprises performance's differentiation in Vietnam, and state-owned enterprises are often more favoured than FDI enterprises and non-state enterprises. Incentives for state-owned enterprises are mainly presented in policies, such as information public and transparency, reducing time-cost, reducing informal charges and local fair competition promotion. Meanwhile, incentives for FDI enterprises are mainly presented in policies, such as land policy, information public and transparency, reducing time-cost, fair competition promotion, business support services, local legal frame and security. Specifically, the study implies that improving public governance in Vietnam in the period 2016–2019 has resulted in positively effect on state-owned enterprises' performance and fairly low effect on those of FDI enterprises and non-state enterprises. [ABSTRACT FROM AUTHOR]

Hermitian tensors are natural generalizations of Hermitian matrices, while possessing rather different properties. A Hermitian tensor is separable if it has a Hermitian decomposition with only positive coefficients, i.e. it is a sum of rank-1 psd Hermitian tensors. This paper studies how to detect the separability of Hermitian tensors. It is equivalent to the long-standing quantum separability problem in quantum physics, which asks to tell if a given quantum state is entangled or not. We formulate this as a truncated moment problem and then provide a semidefinite relaxation algorithm to solve it. Moreover, we study psd decompositions of separable Hermitian tensors. When the psd rank is low, we first flatten them into cubic order tensors and then apply tensor decomposition methods to compute psd decompositions. We prove that this method works well if the psd rank is low. In computation, this flattening approach can detect the separability for much larger-sized Hermitian tensors. This method is a good starting point to determine psd ranks of separable Hermitian tensors. [ABSTRACT FROM AUTHOR]

This paper presents an l 1 -gain control design for two-dimensional (2D) delayed positive systems (PSs) in the Fornasini-Marchesini local state-space model. First, for a stable 2D delayed PS, the necessary and sufficient conditions are derived for the calculation of the exact value of the l 1 -gain. Then, the controller design is given with the aid of the singular value decomposition method, which ensures that the closed-loop system is asymptotically stable at the specified l 1 -gain level. In the end, two examples are provided to identify the validity and correctness of the results of the theory. [ABSTRACT FROM AUTHOR]

This paper is concerned with the exponential admissibility problem of singular systems with interval time-varying delay. A parameter-dependent reciprocally convex inequality (PDRCI) is developed, which covers some existing ones as its special case. By using the system decomposition method, the considered system is decomposed into differential equations and algebraic ones. Unlike some existing works, the components of state vectors of the subsystems are applied to construct a new augmented Lyapunov–Krasovkii functional (LKF) with fewer decision variables. Based on the new integral inequality and augmented LKF, two exponential admissibility criteria are obtained in terms of linear matrix inequalities (LMIs). It should be mentioned that the derivative of the time-varying delay does not need to be smaller than one in this paper. The effectiveness of the proposed methods is demonstrated by two examples. [ABSTRACT FROM AUTHOR]