Search results

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.

China’s overall grain production efficiency is currently relatively low, and how to improve it is key to high-quality grain development. High-standard farmland construction fills the gaps in grain production, providing a path for improving comprehensive grain production capacity.

This paper analyzes grain production data from 281 Chinese cities and uses the meta-Malmquist index to calculate total factor productivity (TFP) of grain and the spatial autoregressive model with spatial error (SARAR) model to analyze the impact of high-standard farmland construction on TFP of grain. Finally, it tests the model for robustness and heterogeneity.

TFP of grain has room for improvement, and technological efficiency has a greater impact on it than technological progress. TFP of grain in the eastern region is significantly higher than that in central and western regions. High-standard farmland construction can significantly improve TFP and technology efficiency of grain, and its economic benefits are directly affected by the differences in regional grain economic development. High-standard farmland construction improves TFP of grain through agricultural mechanization, and its impact is heterogeneous; TFP of grain in the (poor, plain) eastern region is more likely to share its benefits.

This paper evaluates whether high-standard farmland construction has achieved its purpose of enhancing comprehensive grain production capacity. It offers novel insights into elevating element quality and comprehensive grain production capacity, provides a theoretical reference from the perspective of element quality and draws corresponding policy implications for high-standard farmland.

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.

In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

Mountain metro vehicles have unique wheel wear characteristics due to the complex flat and longitudinal lines. With a combination of flat and longitudinal curved tracks, the traction and braking conditions are more frequent in mountain metro vehicles. This paper aims to analyze the wheel wear characteristics of mountain metro vehicles in complex flat and longitudinal lines.

A dynamic model of the mountain metro vehicle and a wear model are established to analyze the dynamic and wheel wear characteristics of mountain metro vehicles. The wheel wear law of mountain metro vehicles under complex track conditions is analyzed, and the suppression measure based on variable stiffness rotary arm nodes of mountain metro vehicles is proposed.

The results showed that the maximum wheel wear depth without considering the ramp track and considering the ramp track are 3.283 mm and 3.717 mm, respectively; the maximum wheel wear depth increases by 13.2%. Wheel wear can be effectively suppressed by the variable stiffness rotary arm model, and the maximum wear depth of the wheel profile is 3.316 mm, which is reduced by 10.79% compared with the constant stiffness model.

A dynamic model of a mountain metro vehicle is established, and the metro vehicle wheel wear under the large ramps under the traction and braking conditions is analyzed, and the metro vehicle wheel wear suppression measure based on variable stiffness rotary arm nodes is proposed.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0247

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

Smallholders are essential in ensuring food security; however, smallholder-dominated food production often involves high resource-environmental costs. This study analyzed the factors that differentiate horticultural practices, willingness to adopt technology and social networks between optimized practices (OPT) and farmer practices (FP) to provide localized and systematic solutions for the sustainable apple production.

To explore the approach of smallholder-dominated sustainable apple production, 257 apple producers in the Bohai Bay region, a major apple planting area in China, were investigated. Life cycle assessment (LCA), emergy analysis and social network analysis methods were used for evaluation.

The results showed that the net economic profit and emergy sustainability index (ESI) in OPT was 15.8 × 104 RMB·ha-1 and 1.2, respectively, which were 126.9 and 128.0% higher than FP. In contrast, greenhouse gas (GHG) emissions under OPT was 29.3% lower than those under FP. OPT has a higher percentage of adoption of scientific fertilizer application and water-saving irrigation technologies compared to FP. OPT has strong learning abilities, more social resources (such as technical training and sharing technical experience with others) and connections with stakeholders in the apple supply chain. Optimizing smallholders' social capital, willingness to adopt technology, behavioral willingness and technological awareness can promote sustainable apple production.

Considering the horticultural practices employed by smallholders in conjunction with their social networks, these factors contributed to the transition of smallholder-led apple production toward sustainability. The findings provided viable options and a theoretical basis for smallholder-dominated crop production to move toward sustainability, with significant implications for policymakers.

The issue of excessive carbon emissions continues to be a critical global challenge. As a prominent mode of transportation for long-distance travel, aircraft is widely acknowledged as a significant source of these emissions. Carbon offset initiatives function as ecological measures, helping to reduce the negative environmental impact. This study aims to explore how benefit appeals (BA) within the aviation industry impact tourists’ carbon offset payment intentions (COPIt).

In Studies 1 and 2, scenario-based experiments were conducted to explore how goal framing (GF) and (BA) interact to influence COPIt. Study 2 further investigated the mediating roles of moral responsibility and trust in airlines within this interaction. Study 3 used real-world surveys to examine the moderating influence of moral elevation, thereby supporting the interactive effects and mediation mechanisms identified in the earlier studies.

Across three studies, the authors consistently identified pivotal factors shaping COPIt in the context of air travel. Study 1 revealed that the combination of BA and GF significantly impacts COPIt, with egoistic appeals linked to loss framing and altruistic appeals connected to gain framing being particularly effective in encouraging COPIt. Study 2 extended these insights by showing that moral responsibility and trust in airlines serve as mediators between BA, GF and COPIt. In Study 3, moral elevation was found to moderate the influence of BA and GF on both moral responsibility and COPIt, deepening the understanding of these dynamics.

This study expands the range of factors affecting COPIt and delves into the underlying mechanisms through which BA and GF shape COPIt. Additionally, it advances current understanding by revealing the intricate processes influenced by moral elevation. The findings not only contribute to the existing knowledge on COPIt determinants but also offer practical guidance for the aviation industry and related sectors in promoting tourists’ participation in carbon offset programs.

本研究探讨航空业中不同利益诉求（利他 vs. 利己）和目标框架（收益 vs. 损失）如何影响游客的碳补偿支付意向, 以应对航空业作为主要碳排放源的环境挑战。

通过三项实验分析利益诉求与目标框架对碳补偿支付意向的影响, 前两项研究探讨利益诉求与目标框架的交互作用以及道德责任感和信任的中介作用, 第三项在机场调研中考察道德提升感的调节作用。

结果显示, 利他诉求与收益框架、利己诉求与损失框架的匹配显著提高游客的支付意向, 道德责任感和信任为中介, 且道德提升感调节了这一影响。

本研究丰富了碳补偿支付意向的理论, 揭示利益诉求与目标框架的交互作用机制, 并为航空业提供了有效推动碳补偿项目的实践建议。

Este estudio analiza cómo los intereses (altruismo vs. egoísmo) y el marco objetivo (ganancia vs. pérdida) influyen en la intención de pago de compensación de carbono de los turistas en la industria aérea.

Se emplearon métodos experimentales en dos estudios iniciales para explorar la interacción entre intereses y marco objetivo, y el papel mediador de la responsabilidad moral y la confianza en la aerolínea. Un tercer estudio en un aeropuerto evaluó el efecto moderador de la elevación moral.

Los intereses y el marco objetivo influyen conjuntamente en la intención de pago de compensación de carbono. La combinación de altruismo con un marco de ganancia y egoísmo con un marco de pérdida aumenta esta intención. La responsabilidad moral y la confianza en la aerolínea median estos efectos, mientras que la elevación moral modera su impacto.

Este estudio amplía la investigación sobre la compensación de carbono, revelando cómo la interacción entre intereses y marco objetivo afecta la intención de pago, ofreciendo recomendaciones prácticas para la industria aérea.

Workers' unsafe behavior is the main cause of construction safety accidents, thereby highlighting the critical importance of behavior-based management. To compensate for the limitations of computer vision in tackling knowledge-intensive issues, semantic-based methods have gained increasing attention in the field of construction safety management. Knowledge graph provides an efficient and visualized method for the identification of various unsafe behaviors.

This study proposes an unsafe behavior identification framework by integrating computer vision and knowledge graph–based reasoning. An enhanced ontology model anchors our framework, with image features from YOLOv5, COCO Panoptic Segmentation and DeepSORT integrated into the graph database, culminating in a structured knowledge graph. An inference module is also developed, enabling automated the extraction of unsafe behavior knowledge through rule-based reasoning.

A case application is implemented to demonstrate the feasibility and effectiveness of the proposed method. Results show that the method can identify various unsafe behaviors from images of construction sites and provide mitigation recommendations for safety managers by automated reasoning, thus supporting on-site safety management and safety education.

Existing studies focus on spatial relationships, often neglecting the diversified spatiotemporal information in images. Besides, previous research in construction safety only partially automated knowledge graph construction and reasoning processes. In contrast, this study constructs an enhanced knowledge graph integrating static and dynamic data, coupled with an inference module for fully automated knowledge-based unsafe behavior identification. It can help managers grasp the workers’ behavior dynamics and timely implement measures to correct violations.