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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.

This study aims to apply the predictive processing theory to examine the influence of artificial intelligence (AI)-driven robotic performers on audience emotions and the audience’s resulting electronic word-of-mouth (eWOM) behaviors during tourism service encounters.

Using a quantitative research methodology, survey responses from 339 regular customers of performing arts in tourism destinations were analyzed. The respondents were recruited through Prolific, a professional data collection platform. SPSS 23.0 was used for the preliminary analysis, from which a research model to achieve the aim was proposed. SmartPLS 3 was used for partial least squares structural equation modeling to test the model.

Interactive and novel robotic performances significantly encouraged the consumers to share their experiences online, thereby enhancing eWOM. However, melodic resonance had no significant impact on eWOM intentions. The consumers’ emotional responses fully mediated the relationship of the novelty and interactivity of the performances to the consumers’ eWOM intentions but did not mediate the relationship of the musical elements to their eWOM intentions.

This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors. It extends the application of the predictive processing theory to the domain of consumer behavior, offering valuable insights for enhancing audience engagement in performances through technological innovation.

本研究旨在运用预测处理理论, 考察人工智能（AI）驱动的机器人表演对观众情感及其在旅游服务接触中的电子口碑（eWOM）行为的影响。。

采用定量研究方法, 分析了339名经常观看旅游景点表演艺术的常客的调查问卷。受访者通过专业数据收集平台Prolific招募。初步分析使用SPSS 23.0进行, 从中提出了实现研究目标的研究模型。使用SmartPLS 3进行偏最小二乘结构方程模型测试该模型。

互动性和新颖性的机器人表演显著鼓励消费者在线分享他们的体验, 从而增强电子口碑。然而, 旋律共鸣对电子口碑意图没有显著影响。消费者的情感反应完全中介了表演的新颖性和互动性与消费者电子口碑意图之间的关系, 但没有中介音乐元素与电子口碑意图之间的关系。

本研究丰富了对AI驱动表演如何影响消费者情感参与和分享行为的理解。将预测处理理论的应用扩展到消费者行为领域, 为通过技术创新增强观众参与度提供了宝贵的见解。

The purpose of this paper is to reveal the mechanism of graphene low-temperature friction and provide a theoretical basis for the application of graphene.

A probe etching model of graphene on the copper substrate was established to obtain the friction pattern of graphene with different layers in the temperature interval from 100 to 300 K. The friction mechanism was also explained from a microscopic perspective based on thermal lubrication theory. Low-temperature friction experiments of graphene were carried out by atomic force microscopy to further verify the graphene low-temperature friction law.

Graphene nanofriction experiments were conducted at 230–300 K. Based on this, more detailed simulation studies were performed. It is found that the combined effect of thermolubricity and thermal fluctuations affects the variation of friction. For monolayer graphene, thermolubricity is the main influence, and friction decreases with increasing temperature. For multilayer graphene, thermal fluctuations gradually become the main influencing factor as the temperature rises, and the overall friction becomes larger with increasing temperature.

Graphene with excellent mechanical properties provides a new way to reduce the frictional wear of metallic materials in low-temperature environments. The friction laws and mechanisms of graphene in low-temperature environments are of great significance for the expansion of graphene application environments.

People often need to make intertemporal choices in their daily life, such as savings and spending, but their decisions are not always entirely rational. The purpose of this paper is to study the effect of hunger on intertemporal choices and the moderating effect of sensitivity to reward.

Two studies verified these two hypotheses. The first study confirmed the existence of the main effect by manipulating food aroma. In the second study, by manipulating hunger with images, the authors increased external validity of the study and confirmed the regulation of the sensitivity of rewards.

The authors found that hungry people prefer to reap the benefits as early as possible in an intertemporal choice; this effect is significant only for those people who are sensitive to reward.

The research contributes to understand more about which factors will influence Chinese residents’ decisions on savings and spending. It also has practical implication for government policy, for example, proposing new ideas for reducing household savings rate and stimulating consumption.

The results confirmed that hunger significantly affects consumers’ intertemporal choices, which broadened the scope of researches on the factors that influence intertemporal choice, and advanced the study on the influence of individual’s physiological state on intertemporal choices. This study filled the gaps in previous researches, and opened up new research ideas for interdisciplinary study.

The purpose of this study is to establish a new temperature set for characterizing the frictional temperature rise (FTR) of disc brakes. The FTR produced by braking is an important factor which directly affects the tribological properties of disc brakes. Presently, most existing researches characterize the FTR only by several static parameters such as average temperature or maximum temperature, which cannot reflect accurately the dynamic characteristics of temperature variation in the process of braking. In this paper, a new temperature parameter set was extracted and the influences of braking conditions on these parameters were investigated by experiments.

First, several simulated braking experiments of disc brakes were conducted to reveal the dynamic variation rules and mechanisms of the FTR in braking. Second, the characteristic parameter subset of the FTR was extracted with five significant parameters, namely, initial temperature, average temperature, end temperature, maximum temperature and the ratio of maximum temperature time. Furthermore, the fitting parameter subset of the FTR was constructed based on the temperature rise curve. Finally, the influence and mechanisms of initial braking velocity and braking pressure on the new temperature parameter set were investigated through braking experiments.

This paper extracted a new temperature parameter set including a characteristic parameter subset and a fitting parameter subset and revealed the influences of braking conditions on it by experiments.

The results showed that the new temperature parameter set extracted in this paper can characterize the dynamic characteristics of disc brake’s FTR variations more objectively and comprehensively. The research results will provide a theoretical basis for extracting the fault feature of friction properties.

Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co₃O₄ is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.

Herein, Co₃O₄ cathode material based on carbon cloth (Co₃O₄/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.

The results show that the Co₃O₄/CC material synthesized at 120°C has good performance. Co₃O₄/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.

The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

The purpose of this study is to use a multi-methodological approach to investigate how puns in texts and pictorial elements comprising human figures influence viewer engagement and potential consumer conversion in tourism advertising.

This study used an experiment with the EyeLink 1000 Plus. The research team curated 24 advertisements with homonymic puns from online travel agencies and Chinese tourism websites.

The findings of this study reveal several insights: Eye saccade trajectories among participants were generally consistent with the image-text-image pattern when exposed to advertisements incorporating textual puns and human figures. Pictorial representations featuring human figures independently garnered heightened viewer attention. Textual elements presenting pun expressions also induced greater visual attention from participants. The combination of textual puns and pictorial human figures in advertisements, although not attracting the most visual attention from participants, successfully enhanced their memory of the advertisements and fostered positive attitudes toward them.

Using human figures in advertisements is likely to improve viewer engagement and attitude toward the brand, which could be strategically used to enhance campaign effectiveness. Furthermore, the use of puns should be considered carefully, as they can increase attention and retention when used effectively, suggesting a tactical deployment in advertising content to maximize impact.

These results contribute to the existing literature by offering empirical evidence on the effectiveness of textual puns and pictorial human figures in advertising. Additionally, this study provides actionable insights for tourism marketing practitioners seeking to optimize advertisement design.

本研究采用多元方法, 探讨文本中的双关语与包含人物形象的图片元素如何影响观众的参与度及潜在消费者的转化率。

本研究通过EyeLink 1000 Plus进行实验。研究团队从在线旅行社和中国旅游网站精选了24个包含谐音双关语的广告。

研究结果揭示了以下几点：(1) 当参与者观看包含文本双关语和人物形象的广告时, 其眼球运动轨迹普遍呈现“图像-文本-图像”模式; (2) 独立展示人物形象的图片能够显著吸引观众注意力; (3) 表现双关语的文本元素同样能够引发更高的视觉关注度; (4) 尽管包含文本双关语和人物形象的广告未能吸引最多的视觉注意力, 但其显著增强了观众对广告的记忆, 并促进了对广告的积极态度。

本研究通过实证数据, 为文本双关语和图片人物形象在广告中的有效性提供了新的见解。同时, 为寻求优化广告设计的旅游营销从业者提供了可操作的建议。

在广告中使用人物形象有助于提升观众参与度和对品牌的态度, 可作为增强广告活动效果的战略手段。此外, 双关语的使用应当谨慎设计。有效使用双关语可提高观众的注意力与记忆力, 表明在广告内容中策略性地应用双关语可最大化影响力。

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.

This study aims to improve understanding of how coastal maritime transport system of Vancouver Island would be disrupted in disaster events, and the strategies could be used to address such risks. Any transport disruption at the maritime leg of the supply chain can affect the needs of vulnerable residents and thus, the supply of many goods to coastal communities.

This case study focuses on the disruption that can be expected to occur for ferries that serves coastal communities of Vancouver Island in Canada. A landslide scenario in the Fraser River (which connects coastal communities) is developed, and interviews and focus groups are used to gain understanding of the vulnerability and resilience of shipping.

The findings show that the maritime leg of the supply chain for the coastal communities of Vancouver Island is resilient to a landslide disruption of ferries. Besides, there would be no impact on the operability of tugs and barges. This study also offers suggestions for creating the conditions for increasing resilience of maritime supply chains to any such disruption.

A research gap exists with respect to minimizing disruption in maritime supply chains, mainly in regard to lessening the impact on the vulnerable residents of coastal communities. This study contributes to filling this gap in the literature.

The findings have significant implications for maritime service providers and for people working on disaster preparedness, emergency response and recovery.

Studies which focus on alleviating the impact of disruptions in the maritime supply chains and the mitigation strategies for coastal communities are scarce in the literature.