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The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

Owing to the toxicity, biodegradability, and cost of most corrosion inhibitors, research attention is now focused on the development of environmentally benign, biodegradable, cheap, and efficient options. In consideration of these facts, chrysin, a phytocompound of Populus tomentosa (Chinese white poplar) has been isolated and investigated for its anticorrosion abilities on carbon steel in a mixed acid and chloride system. This highlights the main purpose of the study.

Chrysin was isolated from Populus tomentosa using column chromatography and characterized using Fourier Transform Infrared Spectroscopy and Nuclear Magnetic Resonance Spectroscopy. The investigations are outlined based on theory (Fukui indices, condensed density functional theory and molecular dynamic simulation) and experiments (electrochemical, gravimetry and surface morphology examinations).

Theoretical evaluations permitted the description of the adsorption characteristics, and molecular interactions and orientations of chrysin on Fe substrate. The interaction energy for protonated and neutral chrysin on Fe (110) were −149.10 kcal/mol and −143.28 kcal/mol, respectively. Moreover, experimental investigations showed that chrysin is a potent mixed-type corrosion inhibitor for steel, whose effectiveness depends on its surrounding temperature and concentration. The optimum inhibition efficiency of 78.7% after 24 h for 1 g/L chrysin at 298 K indicates that the performance of chrysin, as a pure compound, compares favorably with other phytocompounds and plant extracts investigated under similar conditions. However, the inhibition efficiency decreased to 62.5% and 51.8% at 318 K after 48 h and 72 h, respectively.

The novelty of this study relies on the usage of a pure compound in corrosion suppression investigation, thus eliminating the unknown influences obtainable by the presence of multi-phytocompounds in plant extracts, thereby advancing the commercialization of bio-based corrosion inhibitors.

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction. However, this study extracts tourists’ online reviews to explore asymmetric relationships and identifies island tourism satisfiers, hybrids and dissatisfiers.

The research uses 3,523 reviews from Tripadvisor to examine Langkawi Island’s tourist satisfaction. Latent Dirichlet allocation (LDA) machine-learning approach, penalty–reward contrast analysis and asymmetric impact-performance analysis (AIPA) were employed to extract and analyse the data.

Langkawi’s dissatisfiers included “hotel and restaurant”, “beach leisure”, “water sport”, “snorkelling”, “commanding view”, “waterfall”, “sky bridge walk”, “animal show”, “animal feeding”, “history culture”, “village activity” and “duty-free mall”. Amongst these, five were low performers. Hybrids encompassed “ticket purchasing”, “amenity” “traditional food market” and “gift and souvenir”, all of which were low performers. Only one attribute was categorised as a satisfier: “nature view” which performed exceptionally well.

This study provides recommendations to enhance tourist satisfaction and address tourist dissatisfaction. The elements requiring immediate attention for enhancement are the five low-performance dissatisfiers, as they represent tourists’ fundamental expectations. Conversely, the satisfier or excitement factor (i.e. nature views – mangroves and wildlife) could be prominently featured in promotional materials.

This research constitutes an early endeavour to categorise attributes of island tourism into groups of satisfaction, hybrid or dissatisfaction based on user-generated data. It is underpinned by two-factor and three-factor theories.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

This study aims to examine how the initial ambiguity of COVID-19 contributed to tourists' intentions for visiting a once-viral outbreak site in the future.

The present study (N = 248) used partial least-squares structural equation modeling (PLS-SEM) to examine whether perceptions of ambiguity and mismanagement of COVID-19 are indirectly related to intentions to travel to Wuhan in a post-pandemic world through perceptions of risk and tourism value. Further, whether the model effects differed as a function of individual safety orientation was examined.

Perceptions of COVID-19 risk and tourism value serially mediated the effects of perceived COVID-19 ambiguity on post-pandemic travel intentions. Safety orientation did not moderate any paths. Perceived risk was a negative direct correlate of post-pandemic travel intentions.

The current study's strength is rooted in its specific targeting of post-pandemic travel intentions to Wuhan—the first city to experience a widescale outbreak of COVID-19 and subsequent international stigma—compared to general travel inclinations.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

This paper explores how the manufacturer’s strategic choice (acquisition or investment) impacts product quality in a supply chain comprising two complementary suppliers and a common manufacturer.

The manufacturer faces six strategic choices to improve product quality: acquiring or investing in the high-capable supplier, the low-capable supplier, or both. As the Stackelberg leader, the manufacturer determines which strategy is adopted, while suppliers are separately responsible for components’ quality and wholesale prices. The authors use game theory and calculate the model with Mathematica.

The authors develop analytical models to analyze how acquisition costs, investment proportions, component importance and quality improvement coefficients influence decision-makers. The results show that the highest quality may not benefit the manufacturer. Investing in or acquiring a low-capable supplier is better than a high-capable supplier under certain conditions. If the gaps between two suppliers’ quality improvement coefficients and the importance of two components are dramatic, the manufacturer should choose an investment strategy.

This study contributes to the complementary supply chain management by comparing two kinds of strategies-acquisition and investment, with a high-capable supplier and a low-capable supplier.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

This study examines the impact of debt maturity structure on stock price crash risk (SPCR) in Asian economies and the moderating effect of firm age on this relationship.

The study utilized annual data from 432 nonfinancial firms publicly listed in six Asian countries: China, Hong Kong, Japan, Singapore, Pakistan and India. The observation period covers 14 years, from 2007 to 2020. The sample was categorized into three groups: the entire sample and one group each for developing and developed Asian economies. A generalized least squares panel regression method was employed to test the research hypotheses.

The results suggest that long-term debt has a significant negative influence on SPCR in Asian economies, indicating that firms with high long-term debt experience lower future SPCR. Moreover, firm age negatively moderates this relationship, implying that older firms may experience a more pronounced reduction in SPCR due to high long-term debt. Finally, firms in developed Asian economies with high long-term debt are more effective in mitigating the risk of a significant drop in their stock prices than firms in developing Asian economies.

This study contributes to the literature in several ways. To the best of the researcher’s knowledge, this is the first of such efforts to investigate the relationship between debt maturity structure and crash risk in Asia. Additionally, it reveals that long-term debt influences SPCR directly and indirectly in Asia through the moderating role of firm age. Lastly, it is likely one of the first studies by a research team in Asia to compare the nonfinancial markets of developed and developing Asian countries.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.