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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.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

This study aims to fabricate the reduced graphene oxide (rGO)/ethylene vinyl acetate copolymer (EVA) composite films with electric-driven two-way shape memory properties for deployable structures application. The effect of dicumyl peroxide (DCP) and rGO on the structure and properties of the rGO/EVA composite films were systematically investigated.

The rGO/EVA composite films were fabricated by melting blend and swelling-ultrasonication method, DCP and rGO were used the crosslinking agent and conductive filler, respectively.

The research results indicate that the two-way shape memory properties of rGO/EVA composite films were significantly improved with the increase of DCP content. The rGO endowed rGO/EVA composite films with excellent electric-driven reversible two-way shape memory and anti-ultraviolet aging properties. The sample rGO/EVA-9 can be heated above Tm within 8 s at a voltage of 35 V and can be heated above the Tm temperature within 12 s under near-infrared light (NIR). Under a constant stress of 0.07 MPa, the reversible strain of the sample rGO/EVA-9 was 8.96% and its electric-driven shape memory behavior maintained great regularity and stability.

The rGO/EVA composite films have potential application value in the field of deployable structures.

With the increase of DCP content, the two-way shape memory properties of rGO/EVA composite films were significantly improved, which effectively solved the problem that the shape memory properties of EVA matrix decreased caused by swelling. The rGO endowed rGO/EVA composite films with excellent electric/NIR driven reversible two-way shape memory properties.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.

Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

Cloud manufacturing platform has a high degree of openness, with a large variety of users having different needs. Designers on such platforms exhibit great differences in their knowledge abilities and knowledge needs, necessitating the cloud platform to provide personalized knowledge recommendation. To satisfy the personalized knowledge needs of the designers in product design tasks and other manufacturing tasks on a cloud manufacturing platform and provide them with high-quality knowledge resources, a knowledge recommendation method based on designers’ knowledge ability is proposed. The proposed method, with appropriate adjustments, can also be used for personalized knowledge recommendation to other personnel or institutions in cloud manufacturing platforms.

A knowledge recommendation method model is developed. The method consists of three stages. First, a designer knowledge system is constructed based on customer reviews in historical tasks, and designer knowledge ability and knowledge demand degree are quantitatively evaluated by synthesizing customer reviews and expert evaluations. Subsequently, the design knowledge domain ontology is constructed, and knowledge resources and tasks are modeled based on the ontology. Finally, the semantic similarity between tasks and knowledge resources and the knowledge demand degree of designers are integrated to calculate the knowledge recommendation coefficient, which realizes the personalized knowledge recommendation of designers.

Two design tasks of a 3D printing cloud platform are taken as examples to verify the feasibility and effectiveness of the proposed method. Compared with other methods, it is proved that the method proposed in this paper can obtain more knowledge resources that meet the needs of designers and tasks.

The method proposed in this paper is important for the expansion of data applications of the cloud manufacturing platform and for enriching the knowledge recommendation method. The proposed method has two innovations. First, both designer needs and task needs are considered in knowledge recommendation. Compared with most of the existing methods, which only consider one factor, this method is more comprehensive. Second, the designer’s knowledge ability model is constructed by using customer reviews on the cloud manufacturing platform. This overcomes the defect of low accuracy of the interest model in existing methods and makes full use of the big data of the cloud manufacturing platform.

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

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.

This paper aims to explore the effect of different government subsidy decisions and the differences between the consequences of these decisions when supply chain members engage in cooperative green innovation through cost-sharing arrangements.

This paper investigates the optimal decisions for green supply chains under two types of subsidies, including subsidies for green innovation research and development (R&D) costs and subsidies for consumers, by integrating game theory with numerical simulation.

The optimal R&D cost-sharing ratio is found to be 2/3 for manufacturers and 1/3 for retailers. Under any subsidy policy, the supply chain can achieve maximum total profit. When the supply chain adopts the optimal R&D cost-sharing ratio, subsidies for green innovation R&D costs prove to be the most effective in increasing the supply chain’s profit. However, from the perspective of total social welfare, the analysis reveals that government subsidies to consumers are more beneficial for promoting overall social welfare.

Previous studies on green supply chain decisions have primarily focused on either government subsidies or corporate cost sharing in isolation. In contrast, this study combines both government subsidies and cost sharing within a unified framework for a more comprehensive analysis. Additionally, this paper examines the impact of government subsidies on supply chain cost-sharing decisions and their effect on overall social welfare while considering the presence of cost sharing and using the combination of theoretical modeling and simulation analysis.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.