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In the context of carbon peaking and neutrality, effectively controlling agricultural carbon emissions has gained academic attention. As an essential form of agricultural service scale management, this study investigates whether and how trusteeship affects the carbon emission behavior in planting production.

The authors established a theoretical framework to analyze the impact of agricultural production trusteeship on carbon emissions from planting. China's provincial panel data in the 2012–2021 period were selected to test the impact, mechanisms and heterogeneity of agricultural production trusteeship on carbon emissions from planting using the bidirectional fixed effect model and the panel correction standard error regression model.

The findings indicate that agricultural production trusteeship significantly inhibits carbon emissions from planting, especially in the dimensions of fertilizer input, pesticide application, agricultural film use and mechanical fuel. Agricultural production trusteeship primarily affects the intensity of these carbon emissions through contiguous farmland management and planting structure adjustment. Further examinations revealed that the influence of agricultural production trusteeship on carbon emissions from planting was heterogeneous with respect to geographical location, proportion of non-farming income and scale of agricultural production.

This study is the first to systematically evaluate the impact of agricultural production trusteeship on carbon emissions from planting.

The purpose of this paper is to understand the effect of platform characteristics (i.e. media richness and interactivity) on individual perception (i.e. outcome expectations) and consequent behavioral response (i.e. user participation in online health communities (OHCs)) based on the stimulus-organism-response (S-O-R) model.

This study developed a research model to test the proposed hypotheses, and the proposed model was tested using partial least squares structural equation modeling (PLS-SEM) for which data were collected from 321 users with OHC experience using an online survey.

The empirical results show the following: (1) the three dimensions of media richness significantly affect the three outcome expectations, except that richness of expression has no significant effect on the outcome expectation of health self-management competence. (2) Human-to-human interaction significantly affects the three outcome expectations. Moreover, compared with human-to-human interaction, human-to-system interaction has a stronger impact on the outcome expectation of health self-management competence. (3) The three outcome expectations have a significant influence on user participation in OHCs.

This study extends the understanding about how platform characteristics (i.e. media richness and interactivity) motivate user participation in the context of OHCs. Drawing on the S-O-R model, this study reveals the underlying mechanisms by which media richness and interactivity are associated with outcome expectations and by which outcome expectations is associated with user participation in OHCs. This study enriches the literature on media richness, interactivity, outcome expectations and user participation in OHCs, providing insights for developers and administrators of OHCs.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

The aim of this paper is to argue for the values of familial caring and relationships in addition to the provision of social media technology during the COVID-19 pandemic in Hong Kong.

The discussion of this paper has adopted an inter-disciplinary approach by integrating health care system and psychological analysis, based on cultural philosophical argument through the hermeneutic approach of classical texts and critical analysis.

The COVID-19 pandemic has exposed the dilemma between the public health measures for COVID-19 and sustaining elderly social psychological health through familial connection. From a Confucian perspective, the practice of filial piety (xiao, 孝), which demands taking care of parents, is essential for one’s moral formation, and for one’s becoming a virtuous (ren, 仁) person. The necessity of taking care of elderly parents by adult children is not something that can be explained in terms of consequentialism. Indeed, the rising trend of instrumental rationality seems to weaken rather than strengthen the sense of filial obligation. In the face of the COVID-19 pandemic which tends to separate connections between family members, the author argues that we should emphasize the values of familial caring and relationship because it enhances the elderly’s characteristic of resilience.

This paper shows that while social media technology has mitigated the negative effect of social distancing, such online relationships should never replace the bodily connections between the elderly and their family members from a Confucian perspective.

The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems.

The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison.

The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms.

The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems.

The proposed algorithm can be applied to different inverse design problems.

The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.

Language barriers have always been obstacles to traveling abroad, making travelers less interested. This study aims to investigate how ChatGPT, as a translator, affects travelers’ behavioral intentions based on the perceived language barriers using the ChatGPT voice translation app.

A total of 531 responses were collected over a specific survey period using a cross-sectional time frame. This study proposed a research model based on technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT), and the proposed hypotheses were investigated using bootstrapping techniques and Smart-PLS analysis.

This study’s results are significant as they reveal that ChatGPT enables translated service to travelers and enhances their willingness to travel abroad. This finding indicates a considerable enhancement in the traveler’s confidence in communicating with residents, thereby emphasizing ChatGPT’s pivotal role in overcoming language barriers in the travel industry.

This research is novel in its approach as it delineates the influence of ChatGPT’s translation services on travelers’ willingness and behavioral intentions regarding international travel. By filling a significant gap in the existing literature, this study provides a fresh perspective on the role of technology in overcoming language barriers in the travel industry.

语言障碍一直是出国旅行的障碍, 使旅行者不太感兴趣。本研究调查了作为翻译工具的ChatGPT, 基于ChatGPT语音翻译应用程序感知到的语言障碍, 如何影响旅行者的行为意图。

使用横断面时间框架共收集了531份调查问卷。本研究提出了一个基于TAM和UTAUT的研究模型, 并使用bootstrapping技术和Smart-PLS分析对提出的假设进行了研究。

该研究结果具有重要意义, 因为它们揭示了ChatGPT可以为旅行者提供翻译服务, 并增强了他们出国旅游的意愿。这一发现表明旅行者与当地居民沟通的信心大大增强, 从而强调了ChatGPT在克服旅游行业语言障碍方面的关键作用。

本研究在方法上是新颖的, 讨论了ChatGPT翻译服务对旅行者国际旅行意愿和行为意图的影响。本研究为了解技术在克服旅行语言障碍方面的作用提供了一个新的视角, 填补了相关研究缺口。

Las barreras lingüísticas han sido siempre un obstáculo para viajar al extranjero, lo que ha restado interés a los viajeros. Este estudio investiga cómo ChatGPT, como traductor, afecta a las intenciones de comportamiento de los viajeros en función de las barreras lingüísticas percibidas utilizando la aplicación de traducción de voz ChatGPT.

Se recogieron un total de 531 respuestas a lo largo de un periodo de encuesta específico utilizando un marco temporal transversal. Este estudio propuso un modelo de investigación basado en Modelo de Aceptación de la Tecnología (TAM) y Modelo Unificado de Aceptación y Uso de Tecnología (UTAUT), y las hipótesis propuestas se investigaron mediante técnicas de bootstrapping y análisis Smart-PLS.

Los resultados de este estudio son significativos, ya que revelan que ChatGPT permite ofrecer un servicio traducido a los viajeros y aumenta su disposición a viajar al extranjero. Este hallazgo indica una mejora considerable de la confianza del viajero a la hora de comunicarse con los residentes, lo que subraya el papel fundamental de ChatGPT para superar las barreras lingüísticas en el sector de los viajes

Esta investigación es novedosa en su planteamiento, ya que describe la influencia de los servicios de traducción de ChatGPT en la disposición y las intenciones de comportamiento de los viajeros respecto a los viajes internacionales. Al llenar una laguna importante en la literatura existente, este estudio aporta una nueva perspectiva sobre el papel de la tecnología en la superación de las barreras lingüísticas en el sector de los viajes.