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This paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones.

Initially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer.

Simulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation.

To the best of the authors’ knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method’s outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift.

Ciliated microelectromechanical system (MEMS) vector hydrophones pick up sound signals through Wheatstone bridge in cross beam-ciliated microstructures to achieve information transmission. This paper aims to overcome the complexity and variability of the marine environment and achieve accurate location of targets. In this paper, a new method for ocean noise denoising based on improved complete ensemble empirical mode decomposition with adaptive noise combined with wavelet threshold processing method (CEEMDAN-WT) is proposed.

Based on the CEEMDAN-WT method, the signal is decomposed into different intrinsic mode functions (IMFs), and relevant parameters are selected to obtain IMF denoised signals through WT method for the noisy mode components with low sample entropy. The final pure signal is obtained by reconstructing the unprocessed mode components and the denoising component, effectively separating the signal from the wave interference.

The three methods of empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and CEEMDAN are compared and analyzed by simulation. The simulation results show that the CEEMDAN method has higher signal-to-noise ratio and smaller reconstruction error than EMD and EEMD. The feasibility and practicability of the combined denoising method are verified by indoor and outdoor experiments, and the underwater acoustic experiment data after processing are combined beams. The problem of blurry left and right sides is solved, and the high precision orientation of the target is realized.

This algorithm provides a theoretical basis for MEMS hydrophones to achieve accurate target positioning in the ocean, and can be applied to the hardware design of sonobuoys, which is widely used in various underwater acoustic work.

The purpose of this paper is to analyze whether and how intergovernmental fiscal transfers (IFTs) affect technological innovation.

China’s provincial panel data from 2007 to 2019 are used in an empirical study to examine the effect of IFTs on technological innovation and the role of fiscal spending policy in the relationship between the two by using the spatial Durbin model.

Results show an evident spatial correlation for the effect of IFTs on technological innovation, indicating that IFTs have a significant negative influence on technological innovation in local and surrounding regions. IFTs also inhibit technological innovation by negatively affecting science and technology spending and education spending.

These findings can aid policymakers in advancing technological innovation by improving the system of fiscal transfers and optimizing the structure of fiscal spending.

Although the determinants of technological innovation have been analyzed, no studies have investigated the effect of IFTs on technological innovation. Thus, this paper aims to address this gap.

While supply chain scholars concur on the need to integrate supply chain finance (SCF) processes to meet ever-changing customer demands, it is unclear how SCF influences business performance in the presence of perceived opportunistic behavior. Therefore, the study aims to investigate the moderating role of perceived partner opportunism in the supply chain.

Drawing on the dynamic capability theory (DCT), this study investigates how perceived supply chain partner opportunism moderates the mediating role of supply- and demand-oriented performances on the link between SCF and business performance, from the retail industry perspective. Data was collected from Australian retailing firms. In all, 293 completed surveys were received. Moderated mediation analysis was conducted.

The results of this study indicate that supply- and demand-oriented performances serially mediate the relationship between SCF and business performance. The study also found that the effect of SCF on performance was higher when perceived partner opportunism was lower.

To respond to changes in consumer preferences and demand effectively, supply chain and marketing managers need to understand the complex interaction between supply- and demand-oriented performances and the key role of SCF in developing such capabilities.

The current study theorizes and demonstrates the effects of supply- and demand-oriented performances that can facilitate the effects of SCF on business performance. Also, the study reveals the effect of each dimension of SCF (accounts payable, accounts receivable and inventory finance) on supply- and demand-oriented performances. Additionally, the study shows the key role of perceived partner opportunism in supply chain management.

This paper aims to solve the problems of baseline drift, susceptibility to abnormal data interference during baseline drift processing, and phase inconsistency in underwater acoustic target detection and signal processing of single microelectromechanical systems (MEMS) vector hydrophone. To this end, this paper proposes a baseline drift removal algorithm based on Huber regression model with B-spline interpolation (H-BS) and a phase compensation algorithm based on the Hilbert transform.

First, the Huber regression model is innovatively introduced into the conventional B-spline interpolation (B-spline) to solve the control point vectors more accurately and to improve the anti-interference ability of the abnormal data when the B-spline interpolation fitting removes baseline drift and the baseline drift components in the signals are fitted accurately and removed by the above method. Next, the Hilbert transform is applied to the three-channel output signals of the MEMS vector hydrophone to calculate the instantaneous phase and the phase compensation is performed on the vector X/Y signals with the scalar P signal as the reference.

Through simulation experiments, it is found that H-BS proposed in this paper has smaller processing error and better robustness than variational modal decomposition and B-spline, but the H-BS algorithm takes slightly longer than the B-spline. In the actual lake test experiments, the H-BS algorithm can effectively remove the baseline drift component in the original signal and restore the signal waveform, and after the Hilbert transform phase compensation, the direction of arrival estimation accuracy of the signal is improved by 1°∼2°, which realizes high-precision orientation to the acoustic source target.

In this paper, the Huber regression model is introduced into B-spline interpolation fitting for the first time and applied in the specialized field of hydroacoustic signal baseline drift removal. Meanwhile, the Hilbert transform is applied to phase compensation of hydroacoustic signals. After simulation and practical experiments, these two methods are verified to be effective in processing hydroacoustic signals and perform better than similar methods. This study provides a new research direction for the signal processing of MEMS vector hydrophone, which has important practical engineering application value.

The calcareous coal gangue, fly ash, lime powder and a small amount of cement mixture, which are selected from the Hanxing area, are mixed into a calcareous coal gangue mixture. An experimental study was done on the cleavage strength of this mixture. According to the method of uniform design, 8 groups of test matching scheme of calcareous coal gangue mixture were designed. The experiments were conducted on calcareous coal gangue mixtures in various matching proportions. The experimental results are analyzed by regression analysis model. The regression equation between the calcareous coal gangue mixture indirect tensile strength (cleavage strength) and the admixture dosage is established. By using the regression analysis model, the influence of the dosage of fly ash, calces, and cement on the calcareous coal gangue mixture's indirect tensile strength is analyzed.

This study aims to explore the compressive behavior of hollow triply periodic minimal surface (HTPMS) cellular structures by selective laser melting (SLM).

This study presents a design method for gyroid hollow triply periodic minimal surfaces (G-HTPMS) and primitive hollow triply periodic minimal surfaces (P-HTPMS) cellular structures, and SLM technology was applied to manufacture these cellular structures. Compressive behaviors and energy absorption behaviors of hollow cellular structures were researched in this study.

Compared with normal gyroid triply periodic minimal surfaces (G-TPMS) and normal primitive triply periodic minimal surfaces (P-TPMS), the G-HTPMS and P-HTPMS have higher elastic modulus, plateau stress and effective energy absorption under uniaxial compression. The hollow design in HTPMS can enhance the mechanical properties and energy absorption of the cellular structure. Finite element analysis also demonstrates that the hollow design can reduce stress concentration, which improved the compressive curves from a severely fluctuating state to a relatively flat state and reduces fracture. According to compressive behaviors, G-TPMS and G-HTPMS are the bending-dominated cellular structures with strain hardening characteristics, and P-TPMS and P-HTPMS are the stretching-dominated cellular structures with strain softening characteristics.

This research provided a design method for HTPMS, and it was proved that the mechanical properties increased by hollow design inspired by bamboo.

The purpose of this paper is to study a fractional grey model FAGM(1,1,t^α) based on the GM(1,1,t^α) model and the fractional accumulated generating operation, and then predict the national health expenditure, the government health expenditure and the out-of-pocket health expenditure of China.

The presented univariate grey model is systematically studied by using the grey modelling technique, the fractional accumulated generating operation and the trapezoid approximation formula of definite integral. The optimal system parameters r and α are evaluated by the particle swarm optimisation algorithm.

The expressions of the time response function and the restored values of this model are derived. The GM(1,1), NGM(1,1,k,c) and GM(1,1,t^α) models are particular cases of the FAGM(1,1,t^α) model with deterministic r and α. Compared with other forecasting models, the results of the FAGM(1,1,t^α) model have higher precision.

The superiority of the new model has high potential to be used in the medicine and health fields and others. Results can provide a guideline for government decision making.

The univariate fractional grey model FAGM (1,1,t^α) successfully studies the China’s health expenditure.

Previous studies have shown that the application of information technology (IT) can help break through the innovation boundaries of firms and has undoubtedly become a key enabler of collaborative innovation. These studies, however, are mainly based on theoretical analysis and case studies, and little is empirically known about the relationship between IT investments and collaborative innovation. Therefore, the purpose of this study is to empirically explore how firms' IT investments affect the firms' collaborative innovation performance. The authors also examine the moderating roles of the top management team's (TMT's) educational background and absorptive capacity in this relationship.

The authors collected data on 2,097 listed Chinese manufacturing companies and used the ordinary least squares (OLS) method to perform regression analysis. In addition, the authors conducted robustness tests using the propensity score matching (PSM) method and the instrumental variable method.

The results show that the relationship between IT investments and collaborative innovation is inverted, U-shaped and curvilinear. In addition, the TMT's educational background and absorptive capacity positively moderate the inverted U-shaped relationship between IT investments and collaborative innovation.

The study's findings on the relationship between IT investments and collaborative innovation differ from previous mainstream findings that recognized a positive linear relationship. The authors' findings deepen the understanding of the dual role of IT investments. Moreover, this research helps expand the contingency perspective in IT investments and collaborative innovation research.

The purpose of this paper is to develop a novel multivariate fractional grey model termed GM(a, n) based on the classical GM(1, n) model. The new model can provide accurate prediction with more freedom, and enrich the content of grey theory.

The GM(α, n) model is systematically studied by using the grey modelling technique and the forward difference method. The optimal fractional order a is computed by the genetic algorithm. Meanwhile, a stochastic testing scheme is presented to verify the accuracy of the new GM(a, n) model.

The recursive expressions of the time response function and the restored values of the presented model are deduced. The GM(1, n), GM(a, 1) and GM(1, 1) models are special cases of the model. Computational results illustrate that the GM(a, n) model provides accurate prediction.

The GM(a, n) model is used to predict China’s total energy consumption with the raw data from 2006 to 2016. The superiority of the GM(a, n) model is more freedom and better modelling by fractional derivative, which implies its high potential to be used in energy field.

It is the first time to investigate the multivariate fractional grey GM(α, n) model, apply it to study the effects of China’s economic growth and urbanization on energy consumption.