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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]

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]

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]

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 this paper, a generalized equal width wave equation involving space fractal derivatives and time Caputo fractional derivatives is studied and its approximate analytical solution is presented by the Adomian decomposition method. An example shows that the method is efficient to solve fractal non-linear partial differential equations. [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]

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The excessive growth of carbon emissions (CO2E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO2E. However, there are limited literatures on energy CO2E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO2E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO2E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO2E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO2E energy-related CO2E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO2E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO2E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced. [ABSTRACT FROM AUTHOR]

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

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of Q -learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, Q -learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the Q -learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain. [ABSTRACT FROM AUTHOR]

This paper proposes a novel path planning method that considers the natural frequencies of pipes. The approach begins by presenting an adaptive decomposition method to accurately define the routing space for aero engines. Compared with the traditional decomposition method, obstacle identification efficiency is improved by more than 50%. This paper improves the initial population of the genetic algorithm based on the rapidly exploring random tree. Subsequently, a numerical surrogate model is developed to predict the natural frequencies of pipes. An evaluation function is created incorporating the weighted values of the natural frequency and the tube length. Additionally, this paper introduces several new operators to mitigate the issue of illegal paths during algorithm iterations. Finally, the proposed algorithm is demonstrated through experiments on two well-designed examples and an application in an aero engine. [ABSTRACT FROM AUTHOR]

The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate. [ABSTRACT FROM AUTHOR]

The electronic monitoring of offenders is an increasingly popular technique in the criminal justice system. Worldwide, these systems are effectively utilized to monitor individuals on probation as they serve their sentence within the community. The use and significance of electronic monitoring systems are increasing day by day in Türkiye. This paper presents a complete ensemble empirical mode decomposition with adaptive noise and kernel-based meta-extreme learning machine hybrid forecasting model using data on active offenders convicted of different crimes between 2013 and 2021 in Türkiye. Thanks to the proposed model, it is aimed to plan the equipment that will be needed and to provide optimal system management by observing the development of electronic monitoring systems in Türkiye. To validate the proposed model, it is compared with some state-of-the-art models. The superiority of the proposed model is shown using some performance metrics. Moreover, the current status of electronic monitoring systems in Türkiye from the past to the present is shown statistically. While most studies on electronic monitoring focus on its financial or legal dimension, this paper uses a data-driven forecasting approach for optimal planning. [ABSTRACT FROM AUTHOR]

Purpose: Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method. Design/methodology/approach: The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition. Findings: Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces. Originality/value: The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods. [ABSTRACT FROM AUTHOR]

Bus passenger flow is one of the decisive factors for the development of public transportation. Therefore, accurate prediction of real-time passenger flow on bus routes not only helps bus companies to make reasonable scheduling plans to meet the travel needs of passengers but also promotes the sound development of urban public transportation and reduces pollution. In this paper, we propose a secondary decomposition integration method that combines empirical modal decomposition (EMD), sample entropy (SE), and kernel extreme learning machine (KELM) to achieve a short-time prediction of bus route passenger flow. The EMD decomposes the original passenger flow data into several intrinsic mode functions, measures the complexity of the decomposed intrinsic mode functions using SE, and performs a secondary decomposition of the intrinsic mode functions with the highest complexity using EMD, followed by the prediction of the two decomposition results using KELM. The final predicted result is the sum of the two results. The model is verified by the real cardswiping data of two bus lines per minute. Each group of data has 300 data, with 80% of the data as the training set and the remaining 20% as the test set, which can predict the passenger flow per minute. The experimental results show that the short-term bus passenger flow forecasting method proposed in this paper has high accuracy and good robustness. [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]

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]

The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety. [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]

Featured Application: The roller bearing and the through shaft bearing are the rotating and load-bearing components of the annealing kiln roller system, which operate at different locations and under different working conditions. Their health status is significant for maintaining the stable operation of the glass production line. In order to improve the efficiency of bearing condition monitoring, this paper proposes a variable-scale attention mechanism guided time-frequency feature fusion transfer learning method, which is used for bearing fault diagnoses at different installation locations in the annealing kiln roller system. It effectively achieves the intelligent diagnosis of roller bearing and through shaft bearing faults in the annealing kiln roller system. Effective real-time health condition monitoring of the roller table and through shaft bearings in the annealing kiln roller system of glass production lines is crucial for maintaining their operational safety and stability for the quality and production efficiency of glass products. However, the collected vibration signal of the roller bearing system is affected by the low rotating frequency and strong mechanical background noise, which shows the width impact interval and non-stationary multi-component characteristics. Moreover, the distribution characteristics of monitoring data and probability of fault occurrence of the roller bearing and through shaft bearing improve the difficulty of the fault diagnosis and condition monitoring of the annealing kiln roller system, as well as the reliance on professional experience and prior knowledge. Therefore, this paper proposes a variable-scale attention mechanism guided time-frequency feature fusion transfer learning method for a bearing fault diagnosis at different installation positions in an annealing kiln roller system. Firstly, the instinct time decomposition method and the Gini–Kurtosis composed index are used to decompose and reconstruct the signal for noise reduction, wavelet transform with the Morlet basic function is used to extract the time-frequency features, and histogram equalization is introduced to reform the time-frequency map for the blur and implicit time-frequency features. Secondly, a variable-scale attention mechanism guided time-frequency feature fusion framework is established to extract multiscale time-dependency features from the time-frequency representation for the distinguished fault diagnosis of roller table bearings. Then, for through shaft bearings, the vibration signal of the roller table bearing is used as the source domain and the signal of the through shaft bearing is used as the target domain, based on the feature fusion framework and the multi-kernel maximum mean differences metric function, and the transfer diagnosis method is proposed to reduce the distribution differences and extract the across-domain invariant feature to diagnose the through shaft bearing fault speed under different working conditions, using a small sample. Finally, the effectiveness of the proposed method is verified based on the vibration signal from the experimental platform and the roller bearing system of the glass production line. Results show that the proposed method can effectively diagnose roller table and through shaft bearings' fault information in the annealing kiln roller system. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO2 emissions (ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during 2000e2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market. [ABSTRACT FROM AUTHOR]

Background: Presently, global aging has become increasingly serious, whereas the health concerns brought by aging have become a public issue that warrants an urgent solution from all countries across the world. Therefore, this research paper discusses the influence of neighborhood health on elderly individuals' health, and extending a realistic basis for the other economies to improve the neighborhood environment and promote the health of the elderly. Methods: Based on the data of CHARLS2018, this research paper adopts the samples that fulfill the study requirements (N = 7326). we constructed a comprehensive research framework integrating oprobit regression model, heterogeneity analysis, conditional mixed process(CMP)robustness testing, Furthermore, the KHB decomposition method is implemented to ascertain the influential mechanism of NMH and NPH on the mental- and physical health of elderly persons. Results: The oprobit regression model analysis indicates that NMH 0.434 and NPH 0.550 exert positive influences on the elderly's mental- and physical health. Meanwhile, the effects of conditional mixed process on NMH and NPH stand at 0.381 and4.372, which are different from the oprobit regression results; thereby, indicating the existence of endogeneity. Afterward, KHB mediating effect confirms that Internet use, gift reciprocity, and charity activity contribute 30.21% and 16.83% to mental- and physical health, respectively. Conclusions: Firstly, the NMH and NPH demonstrate a positive influence on the mental- and physical health of the elder population. However, there exist heterogeneous differences. Secondly, the conditional mixed process deals with the endogeneity of NMH and NPH. Thirdly, social integration, social interaction, and social engagement serve as significant transmission mechanisms for the influences of NMH and NPH on the health of elderly persons. [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]

As a high-rise structure, the tower of a suspension bridge has low stiffness due to the lack of cable system constraints when it is self-supporting. Wind-induced vibration is one of the key factors in design and construction. In this paper, the wind tunnel test of the bridge tower aeroelastic model is carried out in two different flow fields, and the wind vibration response under a self-supporting state is systematically studied. Meanwhile, numerical simulation of the unsteady flow field around the tower section is carried out, and the dynamic modal decomposition (DMD) method is used to study the modal frequency of the flow field, the surface pressure of the section, flow field reconstruction error, and the stability of the flow field around tower section is discussed. The results show that in the self-supporting state, the displacement of the tower top presents a quadratic curve relationship with the wind speed, and no galloping phenomenon occurs under the test wind speed. However, in the uniform flow, when the wind attack angle is 90°, the bridge tower has an obvious vortex-induced vibration (VIV) phenomenon. After decomposing the flow field by the DMD method, it is found that there is strong aerodynamic interference between the double rectangular column sections, the first seven orders of modal energy play a major role in the flow field, and when the main modal energy accounts for more than 90%, the reconstruction can realize the accurate restoration of the information such as the surface pressure. For the complex flow field structure, it shows that the method can more accurately identify the coherent structure and background part in the unsteady flow field. This paper provides an idea for future research on the characteristics of the flow field around complex sections and helps to further improve the understanding of the VIV mechanism. [ABSTRACT FROM AUTHOR]

Blast furnace gas (BFG) is an important secondary energy in the iron and steel industries, and its efficient and reasonable utilization is the key to improving the economic efficiency of enterprises and the level of energy conservation and emission reduction. Aiming at the problems of difficult accurate modeling on the generation process and difficult prediction of real-time flow, this paper proposes a generation prediction model based on spectrum decomposition. Firstly, the major chemical reactions, production process, and data characteristics of blast furnace are analyzed, and the input variables for the prediction model are reasonably selected based on the correlation analysis results. Then, according to the spectrum characteristics, the BFG data is decomposed into low-frequency and medium-frequency parts by two finite impulse response filters. Next, for the low- and middle-frequency components of data, a low-frequency component prediction model based on the support vector regression, and a middle-frequency component prediction model based on the Elman neural network (ENN) are designed respectively. Finally, we decompose the spectrum of the actual industrial production data and find that the spectrum of the decomposed data basically meets the expected target, which verifies the effectiveness of the finite impulse response filters. In addition, we compare the prediction effect of the designed combined model with other models, such as the support vector regression, the back-propagation neural network, and the ENN. The final experimental results show the correctness, effectiveness, and superiority of the combined model and the spectral decomposition method proposed in this paper. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s. [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]

Laboratory studies were conducted to evaluate effect of soil water content, organic soil amendment, and mineral nutrient application on decomposition of three types of biodegradable containers: recycled paper, wood pulp fiber, and coconut coir over 182 days. Soil respiration was assessed through alkaline trap and titration method to determine decomposition of biodegradable containers. Percentage of carbon content remaining in the container material after 182 days was also quantified. Relative to soil water content, recycled paper containers under 40% water holding capacity (WHC) had higher carbon dioxide released than 60%WHC. Carbon analysis indicated that less carbon remained for all container types under 60% than 40%WHC. For recycled paper and coconut coir pots, treatments receiving container and soil amendment had significantly increased soil respiration. Post-experiment carbon analysis revealed less carbon remained in the recycled paper, wood pulp fiber, and coconut coir containers in the absence of organic soil amendment. For coconut coir, the interaction of container × mineral nutrients appeared to increase soil respiration with higher carbon dioxide released under biodegradable container × low mineral nutrient. This study suggests that cultural practices (i.e., irrigation and organic matter amendment) could facilitate degradation for certain biodegradable container types; however, to provide extensive cultural practice recommendations regarding biodegradable containers, more research is needed. [ABSTRACT FROM AUTHOR]

In this article, we present as a comparative result of Adomian Decomposition Method (ADM), Modified Adomian Decomposition Method (MADM) and Variational Iteration Method (VIM). These methods used for developed to find the analytical approximate solution of linear Fredholm integro-differential equations. The main purpose of this paper was to show a better method for Numerical equations which does not give easily analytical solution. So, in this paper, we find approximate solutions of linear Fredholm integro-differential equations. We explain the convergence of ADM, MADM and VIM by using examples of a deterministic model by graphs and tables. All the calculations performed by the help of MATLAB (2018) Version 9.4. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of an efficient technique, namely, the fractional natural decomposition method (FNDM). The numerical solutions of the model containing the water transport in unsaturated porous media, called Richards equation, are extracted. This model is used to describe the non-locality behaviors which cannot be modeled under the framework of classical calculus. To demonstrate the effectiveness and efficiency of the scheme used, two cases with time-fractional problems are considered in detail. The numerical stimulation is presented with results accessible in the literature, and corresponding consequences are captured with different values of parameters of fractional order. The attained consequences confirm that the projected algorithm is easy to implement and very effective to examine the behavior of nonlinear models. The reliable algorithm applied in this paper can be used to generate easily computable solutions for the considered problems in the form of rapidly convergent series. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines India's socio-economic attributes and different financial dimensions of financial inclusion (FI). Design/methodology/approach: The paper uses a principal component analysis (PCA) to build indexes related to financial dimensions. It applies the logistics regression model and the Fairlie decomposition method to determine India's socio-economic and financial characteristics of FI. Findings: Based on the logistic regression, socio-economic factors like age, gender, marital status, level of education and religion have an impact on FI. The use of financial institutions has positively contributed to the probability of FI, while the low proximity of financial service providers retards the process of FI. Fairlie decomposition concludes regional disparity and gender disparity in FI; however, the rural–urban gap in FI is not captured by the variables included in the study. The main reasons for the discrepancy are lack of education, financial literacy, the proximity of financial service providers and lack of financial institutions. Originality/value: This paper makes two important contributions: first, it presents a micro-level analysis of FI across the socio-demographic strata of India, and second, it demonstrates the regional, rural–urban and gender disparity in FI in India. [ABSTRACT FROM AUTHOR]

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg–Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves. [ABSTRACT FROM AUTHOR]

This paper focuses on the reversibility of multidimensional linear cellular automata with an intermediate boundary condition. We begin by addressing the matrix representation of these automata, and the question of reversibility boils down to the invertibility of this matrix representation. We introduce a decomposition method that factorizes the matrix representation into a Kronecker sum of significantly smaller matrices. The invertibility of the matrix hinges on determining whether zero can be expressed as the sum of eigenvalues of these smaller matrices, which happen to be tridiagonal Toeplitz matrices. Notably, each of these smaller matrices represents a one-dimensional cellular automaton. Leveraging the rich body of research on the eigenvalue problem of Toeplitz matrices, our result provides an efficient algorithm for addressing the reversibility problem. As an application, we show that there is no reversible nontrivial linear cellular automaton over Z 2 . This paper focuses on the reversibility of multidimensional linear cellular automata with an intermediate boundary condition. We begin by addressing the matrix representation of these automata, and the question of reversibility boils down to the invertibility of this matrix representation. We introduce a decomposition method that factorizes the matrix representation into a Kronecker sum of significantly smaller matrices. The invertibility of the matrix hinges on determining whether zero can be expressed as the sum of eigenvalues of these smaller matrices, which happen to be tridiagonal Toeplitz matrices. Notably, each of these smaller matrices represents a one-dimensional cellular automaton. Leveraging the rich body of research on the eigenvalue problem of Toeplitz matrices, our result provides an efficient algorithm for addressing the reversibility problem. As an application, we show that there is no reversible nontrivial linear cellular automaton over . [ABSTRACT FROM AUTHOR]

In the present paper, we discuss the application of the Laplace decomposition method (LDM) to find approximate solutions for some classes of time and space-conformable fractional evolution equations. Some illustrative examples are presented showing that the LDM is an efficient method for finding solutions of nonlinear conformable fractional problems. [ABSTRACT FROM AUTHOR]

Breakable semihypergroups, defined by a simple property: every non-empty subset of them is a subsemihypergroup. In this paper, we introduce a class of semihypergroups, in which every hyperproduct of n elements is equal to a subset of the factors, called πn-semihypergroups. Then, we prove that every semihypergroup of type π2k, (k ≥ 2) is breakable and every semihypergroup of type π2k+1 is of type π3. Furthermore, we obtain a decomposition of a semihypergroup of type πn into the cyclic group of order 2 and a breakable semihypergroup. Finally, we give a characterization of semi-symmetric semihypergroups of type πn. [ABSTRACT FROM AUTHOR]

In this paper, we state the problem of the network flow interdiction in a set of initial and destination nodes so that each initial is capable of only delivering products to certain pre-determined destinations. The network user's purpose is to deliver the highest value of flow from the sources to the sinks and the network interdictor's purpose is to reduce the highest value of flow being used. In this paper, the networks flow interdiction in multi-source and multi-sink conditions is addressed in a way that the parameters of arc capacity are trapezoidal fuzzy sets. [ABSTRACT FROM AUTHOR]

In this paper, we solved a model of a well-known infectious disease called dengue fever via fractional natural decomposition and modified Predictor–Corrector (PC) methods. A study of the dengue epidemic in the Cape Verde Islands off the coast of West Africa in 2009 has been resumed here for a better understanding of the results. The results are obtained using Liouville–Caputo and new generalized Caputo-type fractional derivatives. The numerical simulations are presented for various orders of given derivatives. Existence and uniqueness analysis of the given problem are also performed in the new generalized Caputo sense. The explored results are verified using figures. The main target of this paper is to explore the different dynamics of the given dengue fever model via two types of fractional numerical algorithms. [ABSTRACT FROM AUTHOR]

Vortex shedding at the tail of a high-speed train changes the aerodynamic characteristics of the train, which affects the safety and stability of train operation. This paper takes CR400AF as the research object, and uses dynamic monitoring points to realize the whole process monitoring of the flow field at the tail of the train running in open air and in tunnel for the first time. The wake of the train in different infrastructure scenarios is analyzed by the proper orthogonal decomposition method. The study found that the wake vortex structure is quite different when the train runs in different scenarios, and the turbulent kinetic energy intensity of the wake in tunnel is higher than that of the open air running. Modal decomposition method can identify flow structures that have a large impact on train aerodynamics. Through frequency analysis, it is found that the modal frequency obtained from the decomposition is higher when running in open air than when running in tunnel. With the increase of train speed, the modal strouhal number increases when the train is running in open air, and decreases when the train runs in tunnel. After the train enters the tunnel, the reverse movement of the air around the train body suppresses the development and separation of the boundary layer, which is the main reason for the low frequency of wake vortex shedding in tunnel. The stability of the train running in tunnel is worse than that when running in open air, which is closely related to the more complex flow structure around the car body and the drastic change of aerodynamic force when running in the tunnel. [ABSTRACT FROM AUTHOR]