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Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

Samples of papers artificially (2 to 60 days) and naturally (10, 45, and 56 years) aged were studied by the Py-GC/MS method to identify decomposition products. Possible reaction scenarios for cellulose degradation were developed. One of the degradation products is acetic acid, which can (auto)catalyze the cleavage of cellulose β(1→4)-glycosidic bonds of cellulose polymer chains. However, during 20 s of Py-GC/MS analysis, temperatures of up to 300 °C did not significantly increase or modify the formation of decomposition products of paper components. At 300 °C, the amount of several cellulose decomposition products increased regularly depending on the number of days of artificial aging and natural aging, demonstrated mainly by the generation of 2-furancarboxaldehyde, 5-hydroxymethylfurfural, and levoglucosan and its consecutive dehydration products. No correlation between the amount of lignin decomposition products and the time of aging was found when the pyrolysis was performed at 300 °C and 500 °C. Compounds present in the products of decomposition at 500 °C bear the imprint of the chemical composition of the sampled paper. Pyrograms taken at 300 °C using the Py-GC/MS method can give additional information on the changes in the chemical structure of paper during natural or artificial aging, mainly about the cleavage of β(1→4)-glycosidic bonds during aging. [ABSTRACT FROM AUTHOR]

Frequency-domain dielectric spectroscopy (FDS) is widely used in the insulation assessment of oil-immersed power equipment, and the moisture in insulating paper could be qualitatively predicted by mathematical approximation. However, due to the still lots unexplored underlying mechanisms, until now, there is still no convincing and physical method for the diagnosis of moisture content. With the aid of spectral decomposition methods, this article aims to explore the effects and mechanism of moisture on the independent dielectric response spectra. First, the measured dielectric response curve is unfolded and reconstructed by derivation of the real part curve. Second, the independent polarization and conductance spectra of damp samples are extracted, and then, the relationships between moisture content in insulating paper and spectral parameters are established quantitatively. The results show that moisture introduces a new polarization in the midfrequency region at room temperature, and both polarization and conductance increase with the moisture content. The multiple adsorption model and the proton exchange model of moisture are employed to illustrate the changes of the polarization and the conductance spectra, respectively. This research provides a methodological reference for the moisture assessment of oil–paper insulation. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

The Swedish pulp and paper industry accounts for half of industrial final energy use in Sweden and 2.3 % in EU-27. On the basis of a disaggregated set of physical production data, a Logarithmic Mean Divisia Index decomposition method is applied to disentangle the influence from activity, structure and energy efficiency improvement (EEI) on its fuel, electricity and primary energy use. An extended analysis tracks the fossil energy use and carbon dioxide (CO) emissions to discern past and present developments of industrial decarbonisation. In 1984-2011, the total production output increased by 49 %, whereas growth in primary energy use was limited to 26 %. Compared with an activity-based scenario, 50 PJ of primary energy use has been avoided through EEI and 6 PJ through structural change. The production has become oriented towards more electricity-intensive but less fuel-intensive segments. The electricity use EEI was negligible until year 2000 but sizeable thereafter as it started to outpace the counteracting impact from structural change. Results are consistent with previous bottom-up evaluations, and the policy context is further elaborated in a discussion about the role of relevant energy and climate policies in facilitating the enhanced EEI observed over the last decade. [ABSTRACT FROM AUTHOR]

The randomicity and fluctuation of the wind speed will influence the precision of the forecast. This paper presents a new method of combined wind speed forecast based on the two-level decomposition and weighted average, which can improve the accuracy of wind speed forecasting. First, the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the autoregressive integrated moving average (ARIMA) model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by backpropagation neural network (BPNN), variational mode decomposition (VMD) and predicted by nonlinear auto-regressive (NAR) and BP neural network with suitable weighting ratio for weighted average and particle swarm optimization- long short-term memory (PSO-LSTM) neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments are conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on two-level decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction. [ABSTRACT FROM AUTHOR]

The oil‐immersed transformer is a crucial power equipment in power system, which is prone to abnormality or failure under long running. In order to improve the level of operation and maintenance and ensure the safe and reliable operation of the power grid, it is necessary to diagnose the fault of the oil‐immersed transformer in time. Most of the traditional fault vibration methods are off‐line detection, and are easily disturbed by mechanical deterioration. In order to solve the above problems, this paper established a 3D electromagnetic and stress coupling simulation model of oil‐immersed transformer by finite element simulation software. The vibration characteristics of oil‐immersed transformer core winding are obtained, and at the same time, this paper obtained the vibration signals at different positions on transformer core winding and oil tank. According to the vibration signal of the transformer, the best measuring point position of the tank wall is proposed to accurately reflect the vibration characteristics of the transformer. The signal data is preprocessed by CEEMDAN decomposition method, and the signal data is classified by GWO‐BP composite classification algorithm. This paper proposed an online transformer fault diagnosis method based on vibration signal, which achieves the purpose of accurately diagnosing oil‐immersed transformer faults. The test results show that in the case of oil‐immersed transformers the accuracy of the proposed transformer fault diagnosis method is more than 93%, which provides important guiding significance for the safe and stable operation of the transformer. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Data Sources Results Conclusions Impact Reporting Method Patient or Public Contribution What Does This Paper Contribute to the Wider Global Clinical Community To identify the evidence of gender disparities in frailty and explore the factors contributing to male–female differences.A longitudinal study.A total of 24,429 older adults (60+) were enrolled. Frailty was assessed by frailty index and frailty risk based on Rockwood's cumulative deficit frailty index. OLS and logistic regression models were conducted, with Oaxaca–Blinder and Fairlie decomposition methods to further analyse the factors contributing to gender disparities in frailty.The paper used four waves of the China Health and Retirement Longitudinal Study from 2011 to 2018, a nationwide survey organised by the National Development Institute of Peking University.Women had higher frailty status than men, with more pronounced increases. Among the factors, education has the most significant association with frailty gender disparities. The contribution of individual characteristics, particularly education, to gender disparities in frailty appeared to diminish over time, while the contribution of family and regional factors remained relatively stable, and the contribution of institutions was deemed inadequate.The frailty in Chinese older adults is generally on the rise with continuously expanded gender disparities. It is crucial to consider the contributing factors to frailty in older adults for effective prevention and intervention strategies. Additional gender‐specific geriatric care policies are needed in order to address gender inequality in health.The findings of this study highlight the prevalence of increasing gender disparities in frailty and identify that the level of education, per capita annual household income as well as marital status are the most significant factors contributing to the gender gap. Those findings provide policy implications for healthcare nursing service from a gender‐specific perspective in order to achieve health equity.This study has adhered to the STROBE guideline.No Patient or Public Contribution. This study provided implications on gender‐specific geriatric care nursing services. The study highlighted the importance of focusing on frailty and its gender disparities in geriatric clinical nursing. [ABSTRACT FROM AUTHOR]

The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In the context of achieving the goals of carbon peaking and carbon neutrality, the development of clean resources has become an essential strategic support for the low-carbon energy transition. This paper presents a method for the modal decomposition and reconstruction of time series to enhance the prediction accuracy and performance regarding the 70 m wind speed. The experimental results indicate that the STL-VMD-BiLSTM hybrid algorithm proposed in this paper outperforms the STL-BiLSTM and VMD-BiLSTM models in forecasting accuracy, particularly in extracting nonlinearity characteristics and effectively capturing wind speed extremes. Compared with other machine learning algorithms, including the STL-VMD-LGBM, STL-VMD-SVR and STL-VMD-RF models, the STL-VMD-BiLSTM model demonstrates superior performance. The average evaluation criteria, including the RMSE, MAE and R2, for the proposed model, from t + 15 to t + 120 show improvements to 0.582–0.753 m/s, 0.437–0.573 m/s and 0.915–0.951, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods. [ABSTRACT FROM AUTHOR]

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]

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]

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]

[Display omitted] The present paper reports the fabrication of novel types of hybrid fibrous photocatalysts by combining block copolymer (BCP) templating, sol–gel processing, and coaxial electrospinning techniques. Coaxial electrospinning produces core–shell nanofibers (NFs), which are converted into hollow porous TiO 2 NFs using an oxidative calcination step. Hybrid BCP micelles comprising a single plasmonic nanoparticle (NP) in their core and thereof derived silica-coated core–shell particles are utilized as precursors to generate yolk-shell type particulate inclusions in photocatalytically active NFs. The catalytic and photocatalytic activity of calcined NFs comprising different types of yolk-shell particles is systematically investigated and compared. Interestingly, calcined NFs comprising silica-coated yolk-shells demonstrate enhanced catalytic and photocatalytic performance despite the presence of silica shell separating plasmonic NP from the TiO 2 matrix. Electromagnetic simulations indicate that this enhancement is caused by a localized surface plasmon resonance and a confinement effect in silica-coated yolk-shells embedded in porous TiO 2 NFs. Utilization of the coaxially electrospun TiO 2 NFs in combination with yolk-shells comprising plasmonic NPs reveals to be a potent method for the photocatalytic decomposition of numerous pollutants. It is worth noting that this study stands as the first occurrence of combining yolk-shells (Au@void@SiO 2) with porous electrospun NFs (TiO 2) for photocatalytic purposes and gaining an understanding of plasmon and confinement effects for photocatalytic performance. This approach represents a promising route for fabricating highly active and up-scalable fibrous photocatalytic systems. [ABSTRACT FROM AUTHOR]

The electrochemical chlorine evolution reaction (CER) is an important electrochemical reaction for industrial applications, but it has serious problems, such as the short service life and low electrolytic efficiency in the extremely dilute chlorine-containing solution. In this paper, a RuO2–TiO2/IrO2–Ta2O5 (RuTi/IrTa) electrode was prepared via a thermal decomposition method involving alternate coating with precursor solutions with two active components, which significantly improved the service lifetime and CER selectivity of electrode materials with low Ir content. The experimental results indicate that the accelerated lifetime (AL) of the RuTi/HC–IrTa electrode with an Ir loading of 374 μg cm−2 can reach 602 h. Although the Ir loading of the HC–IrTa electrode (680 μg cm−2) is about twice that of the RuTi/HC–IrTa electrode, the AL of the HC–IrTa electrode is only 245 h, and the AL of the RuTi electrode (Ru loading: 180 μg cm−2) is only 5 h. If the precursor solutions of HC–IrTa and RuTi electrodes are simply mixed, the AL of the obtained RuTi–HC–IrTa electrode is only 41 h. This suggests that the increase in AL does in fact originate from changing the coating method, rather than simply the use of multiple active components. In addition, the SEM results indicate that the increase of the AL of RuTi/HC–IrTa is due to the change of the corrosion mechanism from pitting corrosion to surface corrosion of active components. Moreover, the CER selectivity of the RuTi/IrTa electrode has also been improved. This may be due to the change in the electronic structure, which is demonstrated through XPS characterization. The RuTi/IrTa electrode has a high electrolysis efficiency in the electrolysis of low-chlorine-concentration solutions. In the preparation of electrochemical disinfection technologies (EDTs), such as acidic electrolyzed water (AEW), more available chlorine content (ACC) can be obtained. [ABSTRACT FROM AUTHOR]

In this paper, a formulation of reduced-order finite element (FE) model is presented for geometrically nonlinear dynamic analysis of viscoelastic structures based on the fractional-order derivative constitutive relation and harmonic balance method. The main focus is to formulate the nonlinear reduced-order models (ROMs) in the time and frequency domain without involving the corresponding full-order FE models, and it is carried out by means of a special factorization of the nonlinear strain–displacement matrix. Furthermore, a methodology for the enrichment of reduction basis (RB) over that obtained from conventional approaches is presented where the proper orthogonal decomposition method is utilized by comprising the correlation matrix as the union of stiffness-normalized reduction basis vectors and the corresponding static derivatives. The results reveal a significantly reduced computational time due to the formulation of the nonlinear ROMs without involving the full-order FE model. A good accuracy of the nonlinear ROMs of viscoelastic structures is also achieved through the present method of enrichment of RB. [ABSTRACT FROM AUTHOR]

Due to the increasing applications of dual quaternion and their matrices in recent years, as well as the significance of LU decomposition as a matrix decomposition technique, in this paper, we propose dual quaternion Gaussian transformation and obtain dual quaternion LU decomposition by using Gaussian transformation. We also use the total order of dual numbers to obtain the partial pivoting dual quaternion LU decomposition. Based on the real structure-preserving algorithm of quaternion matrix, we propose the real structure-preserving algorithms of LU decomposition and partial pivoting LU decomposition for dual quaternion matrix. Numerical experiments have verified the effectiveness of the new real structure-preserving approaches. [ABSTRACT FROM AUTHOR]

The Boltzmann equation is a typical example of partially dissipative equations, where the linearized collision operator is positive definite with respect to the microscopic part and the dissipation of the hydrodynamic part is discovered from the coupling structure between the transport operator and the linearized collision operator. Guo and Wang (Comm. Partial Differential Equations, 37, 2012) developed a general energy method for proving the optimal time decay rates of the solution to such type of equations in the whole space; however, the decay rate of the highest order spatial derivatives of the solution is not optimal. In this paper, by incorporating the high‐low frequency decomposition in the energy estimates, both linearly and nonlinearly, we prove the optimal decay rates of any high order spatial derivatives of the low frequency part of the solution to the Boltzmann equation and the almost exponential decay rate of the high frequency part, which imply in particular the optimal decay rate of the highest order spatial derivatives of the solution. Moreover, the velocity‐weighted assumption of the initial data required in Guo and Wang (Comm. Partial Differential Equations, 37, 2012) is removed by capturing the time‐weighted dissipation estimates via the time‐weighted energy method. The method can be applied to the compressible Navier–Stokes equations and many partially dissipative equations in kinetic theory and fluid dynamics. [ABSTRACT FROM AUTHOR]

Spherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics. [ABSTRACT FROM AUTHOR]

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation. [ABSTRACT FROM AUTHOR]

A reasonable crack driving force is the key to preventing compression cracks and eliminating unrealistic crack propagation in phase field fracture analysis. Thus, examining the roles of volumetric strain energy (VSE) and deviatoric strain energy (DSE) in influencing crack propagation is necessary. This paper presents a comprehensive analysis of the following four different approaches to energy decomposition‐driven crack propagation: the positive volumetric strain energy method (VSEM), deviatoric strain energy method (DSEM), volumetric‐deviatoric strain energy method (VDSEM), and no decomposition method (NM). All of the strain energy is involved in the development of the crack propagation. The following four examples are investigated: two benchmark single‐side notched examples, a complex crack containing a hole example, and a crack coalescence in an asymmetric double notch example. The VSEM's numerical findings align with literature and experimental observations, confirming that positive VSE rather than DSE promotes crack propagation. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the ergonomics of industrial workers. It addresses the critical implications of poor ergonomics, which can lead to musculoskeletal disorders over time. A novel methodology for a path-planning algorithm designed for human–robot collaboration was introduced to tackle this challenge. The algorithm's essential contribution lies in determining the most ergonomic path for a robot to guide a human's hand during task execution, facilitating a transition toward an optimized body configuration. The algorithm effectively charts the ergonomic path by adopting a Cartesian path-planning approach and employing the cell decomposition method. The methodology was implemented on a dataset of ten individuals, representing a diverse group of male and female subjects aged between 20 and 35, with one participant being left-handed. The algorithm was applied to three different activities: "stacking an item," "taking an object from a shelf," and "assembling an object by sitting over a table." The results demonstrated a significant improvement in the REBA score (as a measure of ergonomics condition) of the individuals after applying the algorithm. This outcome reinforces the efficacy of the methodology in enhancing the ergonomics of industrial workers. Furthermore, the study compared the performance of A* with three heuristic functions against Dijkstra's algorithm, aiming to identify the most effective approach for achieving optimal ergonomic paths in human–robot collaboration. The findings revealed that A* with a specific heuristic function surpassed Dijkstra's algorithm, underscoring its superiority in this context. The findings highlight the potential for optimizing human–robot collaboration and offer practical implications for designing more efficient industrial work environments. [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]

The Boltzmann equation with multiple-relaxation-time (MRT) collision operators has been widely employed in kinetic theory to describe the behavior of gases and liquids at the macro-level. Given the successful development of deep learning and the availability of data analytic tools, it is a feasible idea to try to solve the Boltzmann-MRT equation using a neural network-based method. Based on the canonical polyadic decomposition, a new physics-informed neural network describing the Boltzmann-MRT equation, named the network for MRT collision (NMRT), is proposed in this paper for solving the Boltzmann-MRT equation. The method of tensor decomposition in the Boltzmann-MRT equation is utilized to combine the collision matrix with discrete distribution functions within the moment space. Multiscale modeling is adopted to accelerate the convergence of high frequencies for the equations. The micro–macro decomposition method is applied to improve learning efficiency. The problem-dependent loss function is proposed to balance the weight of the function for different conditions at different velocities. These strategies will greatly improve the accuracy of the network. The numerical experiments are tested, including the advection–diffusion problem and the wave propagation problem. The results of the numerical simulation show that the network-based method can obtain a measure of accuracy at O 10 − 3 . [ABSTRACT FROM AUTHOR]

In order to improve the safety of wind turbine, this paper takes the high-speed spur gear on output shaft in the transmission system of the wind turbine as the study object. The original signals of the gear in different fault states which obtained from the experimental platform decompose by the empirical mode decomposition method (EMD) and the variational mode decomposition method (VMD), respectively. Then the different gear fault types' eigenvectors were built. Fuzzy neural network (FNN) is adopted to learn the fault types and eigenvector samples of gears, and then the fault types of gears are identified. It is found that the fault features decomposed by VMD method have reached a high accurate recognition rate point to the fact that VMD has good applicability in the fault recognition of gear in wind turbine. [ABSTRACT FROM AUTHOR]

Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array. Using basis function decomposition, conventional SFR methods are straightforward and computationally efficient but may require more microphones than needed to measure the sound field. Recent studies show that pure data-driven and learning-based methods are promising in some SFR tasks, but they are usually computationally heavy and may fail to reconstruct a physically valid sound field. This paper proposes a compact acoustics-informed neural network (AINN) method for SFR, whereby the Helmholtz equation is exploited to regularize the neural network. As opposed to pure data-driven approaches that solely rely on measured sound pressures, the integration of the Helmholtz equation improves robustness of the neural network against variations during the measurement processes and prompts the generation of physically valid reconstructions. The AINN is designed to be compact and able to predict not only the sound pressures but also sound pressure gradients within a spatial region of interest based on measured sound pressures along the boundary. Experiments with acoustic transfer functions measured in different environments demonstrate the superiority of the AINN method over the traditional cylindrical harmonics and singular value decomposition methods. [ABSTRACT FROM AUTHOR]

In this paper we carry out an error analysis for a reduced order method for the Allen-Cahn equation. First, an ensemble of snapshots is formed from the numerical solutions at some time instances of the full order model, which is a time-space discretisation of the Allen-Cahn equation. The reduced order model is essentially a new spatial discretisation method by using low dimensional approximations to the original approximation space. The low dimensional approximation space is generated from the ensemble of snapshots by applying a proper orthogonal decomposition method. To determine the error between the exact solution and the solution of the reduced order model. We consider a time-space discretisation for which an error estimate of the full model solution is available. Specifically, the full discretisation is based on a stabilized auxiliary variable approach for the time stepping and a spectral Galerkin method for the spatial discretisation. The advantages of this full discretisation are its unconditional stability, the availability of error estimates and its ease of implementation. An estimate of the errors in the H 1 seminorm is rigorously derived for both the full order model and the reduced order model, which is then verified by some numerical examples. [ABSTRACT FROM AUTHOR]

In order to identify the greater risks in the implementation of the new construction technology of 'Embedded positioning construction technology of rotary digging pile hole casing'. And further explore the construction technical risk factors and the relationship between them. On the basis of the existing research and related standards, this paper uses the work decomposition method (WBS-RBS) to construct the construction technical safety risk index system from four dimensions: natural environment, material and equipment, construction quality and working personnel. And use the decision laboratory method (DEMATEL) to establish the internal relationship between the risk evaluation indicators, and then use the network analytic hierarchy process (ANP) method to determine the weight of the risk evaluation index, establish a risk assessment model based on DEMATEL-ANP and carry on the example operation. The results show that the burying stability, fixing mode, type, upper position and installation stability of the reamer are the key work to ensure the normal implementation of the technology. [ABSTRACT FROM AUTHOR]

This paper studies a distributionally robust multi-item newsvendor problem, where the demand distribution is unknown but specified with a general event-wise ambiguity set. Using the event-wise affine decision rules, we can obtain a conservative approximation formulation of the problem, which can typically be further reformulated as a linear program. In order to efficiently solve the resulting large-scale linear program, we develop a column generation-based decomposition scheme and speed up the computational efficiency by exploiting a special column selection strategy and stopping early based on a Karush-Kuhn-Tucker condition test. Focusing on the Wasserstein ambiguity set and the event-wise mean absolute deviation set, a computational study demonstrates both the computational efficiency of the proposed algorithm, which significantly outperforms a commercial solver and a Benders decomposition method, and the out-of-sample superiority of distributionally robust solutions relative to their sample average approximation counterparts. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [492997-2016, RGPIN-2016-05208], the National Natural Science Foundation of China [71972012], Alliance de recherche numérique du Canada, and Canada Research Chairs [CRC-2018-00105]. It was also supported by Groupe d'études et de recherche en analyse des décisions (GERAD). Finally, this research was enabled in part by support provided by Digital Research Alliance of Canada (https://alliancecan.ca/en). Supplemental Material: The software that supports the findings of this study is available within the paper and its supplemental information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0010) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0010). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Purpose: Fractional Fokker-Planck equation (FFPE) and time fractional coupled Boussinesq-Burger equations (TFCBBEs) play important roles in the fields of solute transport, fluid dynamics, respectively. Although there are many methods for solving the approximate solution, simple and effective methods are more preferred. This paper aims to utilize Laplace Adomian decomposition method (LADM) to construct approximate solutions for these two types of equations and gives some examples of numerical calculations, which can prove the validity of LADM by comparing the error between the calculated results and the exact solution. Design/methodology/approach: This paper analyzes and investigates the time-space fractional partial differential equations based on the LADM method in the sense of Caputo fractional derivative, which is a combination of the Laplace transform and the Adomian decomposition method. LADM method was first proposed by Khuri in 2001. Many partial differential equations which can describe the physical phenomena are solved by applying LADM and it has been used extensively to solve approximate solutions of partial differential and fractional partial differential equations. Findings: This paper obtained an approximate solution to the FFPE and TFCBBEs by using the LADM. A number of numerical examples and graphs are used to compare the errors between the results and the exact solutions. The results show that LADM is a simple and effective mathematical technique to construct the approximate solutions of nonlinear time-space fractional equations in this work. Originality/value: This paper verifies the effectiveness of this method by using the LADM to solve the FFPE and TFCBBEs. In addition, these two equations are very meaningful, and this paper will be helpful in the study of atmospheric diffusion, shallow water waves and other areas. And this paper also generalizes the drift and diffusion terms of the FFPE equation to the general form, which provides a great convenience for our future studies. [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]

This paper introduces a novel process for decomposing N2O through interstage cooling utilizing a heat pipe heat exchanger. The reactor design involves segmenting the fixed bed reactor into multiple layers and integrating heat pipe heat exchangers between these layers to efficiently dissipate the high heat generated by the upper fixed bed reactor. This innovative approach facilitates the direct decomposition of N2O feedgas with high concentrations, obviating the need for gas dilution. The study conducted in this paper employed Fluent and ASPEN PLUS to investigate N2O decomposition with interstage cooling using heat pipe heat exchangers, as well as decomposition after dilution. A comparison between the two methods was made based on catalyst dosage, temperature uniformity, and reactor energy consumption. The results demonstrate that the proposed method for N2O decomposition via interstage cooling with a heat pipe heat exchanger is a viable option, offering the desired temperature control and enhanced efficiency. Furthermore, this reactor design effectively reduces both catalyst usage and energy consumption, providing substantial advantages over traditional approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient variant of the multiplayer battle game optimizer (MBGO) named gene-targeting MBGO (GTMBGO). We simplify the original MBGO and introduce a well-performed gene-targeting search operator to strengthen its optimization performance. Comprehensive numerical experiments on CEC2017 and CEC2022 benchmark functions confirm the efficiency and effectiveness of GTMBGO compared with state-of-the-art optimizers, and the ablation experiments are also implemented to investigate the contribution of proposed strategies independently. Additionally, we extend the proposed GTMBGO to solve large-scale optimization problems (LSOPs). Since the existence of the curse of dimensionality, LSGO challenges the performance of optimizers severely. Inspired by the divide-and-conquer, the cooperative coevolution (CC) framework decomposes the LSOP and optimizes them alternatively, which provides a potential avenue to solve LSOPs. Based on the efficient recursive differential grouping (ERDG) decomposition method, we propose an enhanced version named ERDG with maximum volume (ERDG k ) and incorporate it with GTMBGO to deal with LSOPs. The experimental results and statistical analyses on CEC2013 LSGO benchmark functions verify the competitiveness of GTMBGO-ERDG k when compared with state-of-the-art methodologies specifically designed for LSOPs. [ABSTRACT FROM AUTHOR]

Bound states in the continuum (BICs), manifesting themselves as the collapse of Fano resonance, are observed in many photonic and plasmonic systems. The BICs have been studied systematically through various methods such as the topological photonics analysis, temporal coupled mode theory, multipole decomposition method, and the cavity quantum electrodynamics (CQED) method. Since CQED can give a macroscopic and intrinsic description of light–matter interaction, it is expected to study BIC that participates in strong coupling. What is more, the relation between coupling strength, the Fano parameter, and the asymmetry property of BICs needs to be clarified. In this paper, we investigated the strong coupling between the cavity mode and Bloch-surface plasmon polariton (Bloch-SPP) mode induced by BICs within the plasmonic lattices of the metal-dielectric-metal (MDM) layer. The properties of strong coupling and BIC were revealed theoretically by the quantum model based on the CQED. The increase in the Fano parameters of BICs was proved to facilitate the coupling strength, which was indicated by the monotonically increasing relation between the Fano parameter and the coupling strength. This work may pave the way for flexible modulation and application of BIC in the fields of high-quality plasmonic nanocavity, low-threshold nano-lasers, and quantum information. [ABSTRACT FROM AUTHOR]

With the continuous increase in air conditioning installations, the proportion of air conditioning electricity consumption to total electricity consumption is growing. Research on the decomposition of air conditioning electricity consumption is of great significance for assessing electricity usage, formulating electricity scheduling plans, and ensuring the stable operation of the power grid. Currently, typical day selection strategies for the decomposition of air conditioning electricity consumption often overlook the corresponding relationship between typical daily electricity consumption and temperature. Therefore, this paper proposes an air conditioning electricity consumption decomposition method based on the dependence between temperature and electricity consumption. This method filters typical days based on Copula-based dependence indicators and equiprobable ellipses, determines the baseline electricity consumption curve through model selection voting, and ultimately calculates the air conditioning electricity consumption. The effectiveness of the proposed method is validated by applying it to electricity consumption data in Fuzhou from 2019 to 2022. [ABSTRACT FROM AUTHOR]

In this article, we investigate the existence and uniqueness of solutions to a generalized nonlinear functional integral equation (G‐NLFIE) associated with certain epidemic models of infectious diseases, defined within the Banach space C[0,1]$$ C\left[0,1\right] $$. Our existence results include several specific cases of nonlinear functional integral equations that commonly occur in nonlinear sciences. We then introduce an iterative algorithm that combines Adomian's decomposition method (ADM) with the modified homotopy perturbation method (mHPM) to approximate solutions to the G‐NLFIE. The paper addresses the convergence properties and error analysis of this method. Finally, we present numerical examples to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input–multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time–frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation. [ABSTRACT FROM AUTHOR]

Nonlinear time-fractional partial differential equations (NTFPDEs) play a great role in the mathematical modeling of real-world phenomena like traffic models, the design of earthquakes, fractional stochastic systems, diffusion processes, and control processing. Solving such problems is reasonably challenging, and the nonlinear part and fractional operator make them more problematic. Thus, developing suitable numerical methods is an active area of research. In this paper, we develop a new numerical method called Yang transform Adomian decomposition method (YTADM) by mixing the Yang transform and the Adomian decomposition method for solving NTFPDEs. The derivative of the problem is considered in sense of Caputo fractional order. The stability and convergence of the developed method are discussed in the Banach space sense. The effectiveness, validity, and practicability of the method are demonstrated by solving four examples of NTFPEs. The findings suggest that the proposed method gives a better solution than other compared numerical methods. Additionally, the proposed scheme achieves an accurate solution with a few numbers of iteration, and thus the method is suitable for handling a wide class of NTFPDEs arising in the application of nonlinear phenomena. [ABSTRACT FROM AUTHOR]

Bound states in the continuum (BICs) are a special kind of resonant states that remain localized even though they coexist with a continuous spectrum of radiating waves. They have already emerged as an important design principle for creating systems with high-quality (Q) factor states to enhance light–matter interaction. Many approaches have been proposed to achieve BICs, but it is still of great challenge to design multiple BICs simultaneously, and especially working at terahertz (THz) frequencies. In this paper, we propose an all-dielectric metamaterial, consisting of four hollow cylinders in each unit cell. We show dual BICs exist in such a simple structure, and as breaking the symmetry via varying the inner radius of two diagonal cylinders, they will turn to quasi-BICs with high yet finite Q-factor. These quasi-BICs are manifested themselves as two new dips in the transmission spectrum, of which the resonance shapes can be well described by the Fano formula with a few fitting parameters. We find the evolution of their Q-factors still follows the simple linear relationship with respect to the inverse square of the asymmetry parameter. Based on the multipole decomposition method, two BICs are further investigated to show different multipole components for them. Interestingly, the higher frequency BIC is closely related to the excitation of toroidal dipole (TD), which will split into two TDs as breaking the symmetry. The proposed metamaterial provides an alternative platform to merge the physics of BICs, Fano, and TD, and to pave the way for potential device applications (since the states of extremely high-Q factor). [ABSTRACT FROM AUTHOR]

This paper presents a method called MCADFusion, a feature decomposition technique specifically designed for the fusion of infrared and visible images, incorporating target radiance and detailed texture. MCADFusion employs an innovative two-branch architecture that effectively extracts and decomposes both local and global features from different source images, thereby enhancing the processing of image feature information. The method begins with a multi-scale feature extraction module and a reconstructor module to obtain local and global feature information from rich source images. Subsequently, the local and global features of different source images are decomposed using the the channel attention module (CAM) and the spatial attention module (SAM). Feature fusion is then performed through a two-channel attention merging method. Finally, image reconstruction is achieved using the restormer module. During the training phase, MCADFusion employs a two-stage strategy to optimize the network parameters, resulting in high-quality fused images. Experimental results demonstrate that MCADFusion surpasses existing techniques in both subjective visual evaluation and objective assessment on publicly available TNO and MSRS datasets, underscoring its superiority. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach to inventory management modeling, specifically tailored for growing items. We extend traditional economic growth quantity (EGQ) models by introducing the fractional economic growing quantity (FEGQ) model. This new approach improves the model's symmetry and dynamic responsiveness, providing a more precise representation of the changing nature of inventory items. Additionally, the use of fractional derivatives allows our model to incorporate the memory effect, introducing a new dynamic concept in inventory management. This advancement enables us to select the optimal business policy to maximize profit. We adopt the fractional derivative in terms of Caputo derivative sense to model the inventory level associated with the items. To analytically solve the (FEGQ) model, we use the Laplacian transform to obtain an algebraic equation. As for the logistic function, known for its symmetrical S-shaped curve, it closely mirrors real-life growth patterns and is defined using fractional calculus. We apply an iterative approximation method, specifically the Adomian decomposition method, to solve the fractional logistic function. Through a sensitivity analysis, we delve for the first time into the discussion of the initial weights, which have a massive impact on the total profit level. The provided numerical data indicate that the firm began with a favorable policy. In the following years, several misguided practices were implemented that led to a decrease in profitability. The healing process began once again by selecting more effective strategies. [ABSTRACT FROM AUTHOR]

In this paper, a method for detecting surface roughness in machining processes is proposed to solve the problem of low detection accuracy caused by a small sample size in machine vision detection. The proposed method combines QR decomposition with the support vector machine (SVM) classifier to accurately assess surface roughness. First, a contact roughness detector is used to measure the surface roughness value, and a CCD camera is used to capture the processed surface image to obtain the sample. Subsequently, an improved QR decomposition method is employed to generate virtual samples and expand the sample size. Texture feature values of the image are then extracted using the grayscale level co-occurrence matrix, and the correlation between roughness and texture features is determined. Finally, SVM is employed to classify the surface roughness of machined components. Experimental results demonstrated that the accuracy of the machine vision-based surface roughness detection method increased from 80.6 to 96.5%, thus validating the feasibility of the proposed method and providing a theoretical basis for on-site detection of small-sample surface roughness. This method has potential for practical engineering applications. [ABSTRACT FROM AUTHOR]

The unsteady flow field in the aerostatic bearing always induces micro-vibrations, which are severely detrimental to the stability and precision of the bearing. Extensive research has been conducted on the mechanism of micro-vibration, but a consensus has not yet been reached. To this end, the large eddy simulation (LES) and proper orthogonal decomposition methods were employed to analyze the flow field of an annular aerostatic bearing in this paper. A mechanism for inducing micro-vibration and the identification of a novel flow behavior were ultimately revealed. First, the accuracy of our LES method has been validated through quantitative comparison with experimental data. Then, the mode decomposition has been conducted to analyze the flow field under various gas supply pressures. The results demonstrate that when the supply pressure Ps = 0.4 MPa, the micro-vibration is dominated by a pair of adjacent large-scale vortices with low frequencies in the recess. However, when Ps = 0.5 and 0.6 MPa, the convection and shearing processes near the orifice outlet and the rectangular recess inlet become intense, resulting in the displacement of large-scale vortices. Eventually, the small-scale high-frequency pressure fluctuation structures have been also observed, which are closely related to the convection process within recess. With the increase in gas supply pressure, the high-frequency pressure fluctuations at the circular recess outlet gradually diminish, while those at the orifice outlet emerge and gradually enlarge. Meanwhile, the mode dominant frequency is transferred from around 200 kHz to around 1000 kHz. The energy fraction of the high-frequency pressure fluctuations is also greatly increased. [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]

ABSTRACT Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR‐ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR‐ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR‐ALS, offers the flexibility to be applied component‐wise or data block‐wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher‐order dataset with whatever pattern of missing values. [ABSTRACT FROM AUTHOR]

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness. [ABSTRACT FROM AUTHOR]

In this work, we use two unique methodologies, the homotopy perturbation transform method and Yang transform decomposition method, to solve the fractional nonlinear seventh-order Kaup–Kupershmidt (KK) problem. The physical phenomena that arise in chemistry, physics, and engineering are mathematically explained in this equation, in particular, nonlinear optics, quantum mechanics, plasma physics, fluid dynamics, and so on. The provided methods are used to solve the fractional nonlinear seventh-order KK problem along with the Yang transform and fractional Caputo derivative. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approaches and the fractional operator to develop satisfactory approximations to the offered problem. To solve the fractional KK equation, we first use the Yang transform and fractional Caputo derivative. He's and Adomian polynomials are useful to manage nonlinear terms. It is shown that the suggested approximate solution converges to the exact one. In these approaches, the results are calculated as convergent series. The key advantage of the recommended approaches is that they provide highly precise results with little computational work. The suggested approach results are compared to the precise solution. By comparing the outcomes with the precise solution using graphs and tables we can verify the efficacy of the offered strategies. Also, the outcomes of the suggested methods at various fractional orders are examined, demonstrating that the findings get more accurate as the value moves from fractional order to integer order. Moreover, the offered methods are innovative, simple, and quite accurate, demonstrating that they are effective for resolving differential equations. [ABSTRACT FROM AUTHOR]