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This paper aims to study the direct synthesis of imino methyl ether amino resin using commercially available formaldehyde, melamine and methanol through one-step two-stage catalysis.

Initially, melamine undergoes a reaction with formaldehyde to form hydroxylmethylation melamine in a basic setting. Subsequently, hydrochloric acid is incorporated to facilitate the etherification process. The study delves into the impact of various factors during the etherification phase, including the quantity of methanol, the temperature at which etherification occurs, the number of etherification cycles and the amount of catalyst used, on the synthesis of imino methyl-etherified amino resins. Ultimately, the most favorable conditions for etherification are identified through comparative analysis to evaluate the resulting synthesized products.

The methyl-etherified amino resin, characterized by a stable structure and consistent performance, was efficiently synthesized through a one-step, two-stage catalytic process. Optimal conditions for the etherification stage were determined to be a reaction temperature of 35°C, a melamine to methanol ratio of 1:24 and an addition of hydrochloric acid ranging from 2.2 mL to 2.5 mL. Remarkably, the resulting resin notably enhanced the water resistance, salt resistance and gloss of the canned iron printing varnish coatings.

Amino resins, known for their broad applications across numerous industries, face sustainability and operational efficiency hurdles when produced through traditional methods, which predominantly involve the use of a 37% formaldehyde solution. To tackle these issues, our research introduces an innovative method that add 37% formaldehyde to facilitate industrial production. The use of 37% liquid formaldehyde in this paper has two benefits: first, it is convenient for industrial application and production; Second, it is convenient to provide mild reaction conditions at lower concentrations because the amino group is relatively active, which is convenient for the preservation of the amino group and integrates it with a one-step, two-stage catalytic process. The primary objective of our study is threefold: to reduce the environmental footprint of amino resin synthesis, to optimize the use of resources and to improve the economic viability for its large-scale production. By employing this new strategy, we try to provide a more sustainable and efficient manufacturing process for amino resins.

This study aims to introduce an innovative framework for mining tourism reviews that not only excels in sentiment analysis accuracy but also prioritizes user-friendly design for enhanced usability.

Online reviews of China’s Five Sacred Mountains were analyzed using an integrated methodology. Sentiment analysis was performed using ChatGPT, bidirectional encoder representations from transformers (BERT) and convolutional neural networks, with ChatGPT demonstrating superior performance. Latent Dirichlet allocation extracted key attributes. Models including importance–performance analysis (IPA), asymmetric impact-performance analysis (AIPA) and importance–performance competitor analysis (IPCA) then synthesized findings.

The results demonstrate that ChatGPT outperforms both machine learning and lexicon-based models in sentiment recognition, exhibiting performance comparable to that of the BERT model. In the case study, integrating sentiment analysis outcomes with IPA reveals deficiencies in both topics and attributes. Moreover, the synergistic combination of IPA, AIPA and IPCA furnishes actionable recommendations for resource management and enables nuanced monitoring of sustainability attributes.

Leveraging this framework in conjunction with the ChatGPT platform for application development can bring practical convenience to the tourism industry. It supports sentiment analysis, topic categorization and opinion mining. Equipped with monitoring capabilities, it provides valuable insights for sustainable improvement, aiding managers in formulating effective marketing strategies.

This research develops a novel multimodel framework integrating various ML/DL techniques and business models in a synergistic way. It provides an innovative and highly accurate yet simple approach to tourism review mining and enhances accessibility of advanced artificial intelligence for sustainable tourism monitoring, addressing limitations of prior methods.

本研究旨在引入一种创新的框架, 用于挖掘旅游评论, 不仅在情感分析准确性方面表现出色, 而且还优先考虑用户友好设计, 以提升可用性。

本研究使用综合方法分析了中国五岳的在线评论, 使用ChatGPT进行情感分析。LDA提取了关键属性。然后, 包括IPA、AIPA和IPCA在内的模型综合了研究结果。

结果表明, ChatGPT在情感识别方面优于机器学习和基于词典的模型, 表现与BERT模型相当。在案例研究中, 将情感分析结果与IPA结合起来揭示了主题和属性的不足。此外, IPA、AIPA和IPCA的协同组合为资源管理提供了可行的建议, 并实现了对可持续属性的细致监控

结合ChatGPT平台在应用开发中利用该框架可以为旅游业带来实际便利。它支持情感分析、主题分类和意见挖掘。配备了监控功能, 为可持续改进提供了宝贵的见解, 帮助管理者制定有效的营销策略。

本研究开发了一种新颖的多模型框架, 将各种ML/DL技术和商业模型以协同方式整合在一起。它提供了一种创新而高度准确但简单的方法, 用于旅游评论挖掘, 并提升了高级AI的可访问性, 以实现可持续旅游监测。

The utilization of mobile fitness applications (apps) is on the rise, making user retention and engagement critical factors in the commercial success of these apps. However, research in this area is limited and fragmented. The objective of this study is to conduct a thorough review of the available literature on the effects of digital innovations, gamification, artificial intelligence (AI) and machine learning (ML) on user engagement with fitness mobile apps. The findings reveal the relationships between gamification, the use of AI/ML and technology adoption on user engagement, interaction and intent to use. Additionally, the study highlights the importance of understanding how user experience, customer experience and brand experience impact customer retention and contribute to the overall success of mobile fitness apps. Furthermore, the study also identifies the gaps in the current research and recommends further studies to be conducted in these areas. Future research is encouraged to incorporate elements from the experience domains to provide consumers with engaging interactions and improve retention and commercial success for mobile fitness apps.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Conceptual and empirical research over the past 20 years has moved the social media (SM) literature beyond the embryotic stage to a well-developed academic discipline. As the lead article in the special issue in the Journal of Research in Interactive Marketing on Cutting-Edge Research in Social Media and Interactive Marketing, this review and agenda article has two key goals: (1) to review key SM and interactive marketing research over the past three years and (2) to identify the next wave of high priority challenges and research opportunities.

Given the “cutting-edge” research focus of the special issue, this review and research agenda paper focused on articles published in 25 key marketing journals between January 2021 and March 2024. Initially, the search request was for articles with “social media, social selling, social commerce” located in the article title, author-selected key words and journal-selected keywords. Later, we conducted searches based on terminology from articles presented in the final review. In total, over 1,000 articles were reviewed across the 25 journals, plus additional ones that were cited in those journals that were not on the initial list.

Our review uncovered eight key content areas: (1) data sources, methodology and scale development; (2) emergent SM technologies; (3) artificial intelligence; (4) virtual reality; (5) sales and sales management; (6) consumer welfare; (7) influencer marketing; and (8) social commerce. Table I provides a summer of key articles and research findings for each of the content areas.

As a literature review and research agenda article, this paper is one of the most extensive to date on SM marketing, and particularly with regard to emergent research over the past three years. Recommendations for future research are integrated through the paper and summarized in Figure 2.

Consumer welfare is one of the eight emergent content areas uncovered in the literature review. Specific focus is on SM privacy, misinformation, mental health and misbehavior.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Reducing carbon emissions in agriculture is vital for fostering sustainable agricultural growth and promoting ecological well-being in rural areas. The adoption of Low-Carbon Agriculture (LCA) by farmers holds great potential to accomplish substantial reductions in carbon emissions. The purpose of this study is to explore the farmers' preference and willingness to engage in LCA.

This study employs the Choice Experiment (CE) method to examine farmers' preferences and willingness to adopt LCA, using field survey data of 544 rural farmers in the Weihe River Basin between June and July 2023. We further investigate differences in willingness to pay (WTP) and personal characteristics among different farmer categories.

The empirical results reveal that farmers prioritize government-led initiatives providing pertinent technical training as a key aspect of the LCA program. Farmers' decisions to participate in LCA are influenced by factors including age, gender, education and the proportion of farm income in household income, with their evaluations further shaped by subjective attitudes and habits. Notably, we discovered that nearly half of the farmers exhibit indifference towards LCA attributes.

To the best of the authors' knowledge, this study is the first to investigate farmers' attitudes toward LCA from their own perspectives and to analyze the factors influencing them from both subjective and objective standpoints. This study presents a fresh perspective for advocating LCA, bolstering rural ecology and nurturing sustainable development in developing nations.

The purpose of this paper is to explore the operating characteristics of gallium-based liquid metals (GLMs) by directly adding them as lubricants in real mechanical equipment.

This paper conducts an analysis of the rotor-bearing system under GLM lubrication using a constructed test rig, focusing on vibration signals, surface characteristics of the friction pair, contact resistance and temperature rise features.

The study reveals that GLM can effectively improve the lubrication condition of the tribo-pair, leading to a more stable vibration signal in the system. Surface analysis demonstrates that GLM can protect the sample surface from wear, and phase separation occurs during the experimental process. Test results of contact resistance indicate that, in addition to enhancing the interfacial conductivity, GLM also generates a fluid dynamic pressure effect. The high thermal conductivity and anti-wear effects of GLM can reduce the temperature rise of the tribo-pair, but precautions should be taken to prevent oxidation and the loss of its fluidity.

The overall operating characteristics of the rotor-bearing system under GLM lubrication were investigated to provide new ideas for the lubrication of the rotor-bearing system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0067/

This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ at 900°C.

The GTD222 superalloy and TiC/GTD222 nickel-based composite were prepared using selective laser melting (SLM). Subsequently, the hot corrosion behavior of the two alloys was systematically investigated in a salt mixture consisting of 75% Na₂SO₄ and 25% K₂SO₄ (Wt.%) at 900°C.

The TiC/GTD222 composite exhibited better hot corrosion resistance compared to the GTD222 superalloy. First, the addition of alloying elements led to the formation of a protective oxide film on the TiC/GTD222 composites 20 h before hot corrosion. Second, TiC/GTD222 composite corrosion surface has a higher Ti content, after 100 h of hot corrosion, the composite corrosion surface Ti content of 10.8% is more than two times the GTD222 alloy 4% Ti. The Ti and Cr oxides are tightly bonded, effectively resisting the erosion of corrosive elements.

The hot corrosion behavior of GTD222 superalloy and TiC/GTD222 composites prepared by SLM in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ was studied for the first time. This study provides insights into the design of high-temperature alloys resistant to hot corrosion.

The purpose of this study is to evaluate the tannase-assisted extraction of tea stem pigment from waste tea stem, after which the stability of the purified pigment was determined and analyzed.

The extracting process was optimized using the response surface methodology (RSM) approach. Material-liquid ratio, temperature and time were chosen as variables and the absorbance as a response. The stability of the tea stem pigment at the different conditions was tested and analyzed.

The optimized extraction technology was as follows: material-liquid ratio 1:20 g/ml, temperature 50°C and time 60 min. The stability test results showed that tea stem pigment was sensitive to oxidants, but the reducing agents did not affect it. The tea stem pigment was unstable under strong acid and strong alkali and was most stable at pH 6. The light stability was poor. Tea stem pigment would form flocculent precipitation under the action of Fe²⁺ or Fe³⁺ and be relatively stable in Cu²⁺ and Na²⁺ solutions. The tea stem pigment was relatively stable at 60°C and below.

No comprehensive and systematic study reports have been conducted on the extraction of pigment from discarded tea stem, and researchers have not used statistical analysis to optimize the process of tannase-assisted tea stem pigment extraction using RSM. Additionally, there is a lack of special reports on the systematic study of the stability of pigment extracted from tea stem.