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This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

With the rapid development of social media, many organizations have begun to attach importance to social media platforms. This research studies the management and the use of social media in academic music libraries, taking the Center for Chinese Music Studies of the Chinese University of Hong Kong (CCMS) as a case study.

We conducted a sentiment analysis of posts on Facebook’s public page to analyze the reaction to the posts with some exploratory analysis, including the communication trend and relevant factors that affect user interaction.

Our results show that the Facebook channel for the library has a good publicity effect and active interaction, but the number of posts and interactions has a downward trend. Therefore, the library needs to pay more attention to the management of the Facebook channel and take adequate measures to improve the quality of posts to increase interaction.

Few studies have analyzed existing data directly collected from social media by programming based on sentiment analysis and natural language processing technology to explore potential methods to promote music libraries, especially in East Asia, and about traditional music.

The failure rate of international construction joint venture (ICJV) projects has been noted to be high in developing countries due to the complexity and risky nature of construction projects in the international market. The purpose of this study is to identify and evaluate the risks facing ICJV projects in Ghana.

A risk register was developed through a comprehensive literature review. The identified risks were then used in a questionnaire survey involving local and foreign partners in ICJV projects in Ghana.

From a total of 74 risks identified, categorized into country-level risks, market-level risks and project-level risks, the “top ten” risks found to be the most critical risks facing ICJV projects in Ghana include unstable currency exchange rates, inflation, design changes, high-interest rate, budget overrun, cash flow problems of the client, economy fluctuation, difficulty in obtaining approval of projects from host government authorities/bureaucracy, potential financial distress of JV partner and bribery and corruption.

This study provides a comprehensive list of risks ICJV partners are likely to encounter on their projects in developing countries. Furthermore, this study improves on one of the major limitations of previous ICJV studies by collecting data from both partners of the ICJV, appropriate for cross-cultural examination and comparison.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

Given the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.

This research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).

The study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.

Prophet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.

Our study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

This research aims to investigate how absorptive capacity impacts artisan businesses' innovation performance in Jammu and Kashmir, India. Additionally, the study examines the role of strategic orientation (customer and technological orientation) as a mediator.

The study analysed data from 408 artisan entrepreneurs using partial least squares structural equation modelling. The research model was built on the “Dynamic-Capability Theory” of absorptive capacity and the “Resource-Based Theory” of performance.

The study’s findings suggest that both realised and potential absorptive capacity positively and significantly impact innovation performance. Moreover, customer and technology orientations positively and strongly influence innovation performance. Additionally, potential and realised absorptive capacity has a favourable impact on customer and technology orientation. The mediation analysis results indicate that customer and technological orientation have complementary partial mediation between potential absorptive capacity and innovation performance. Finally, mediating variables like customer and technological orientation show complementary partial mediation for realised absorptive capacity.

The research model would enrich the existing literature and offer an improved understanding of how absorptive capacity enhances the innovation performance among artisan entrepreneurs and concurrently validates the theory of “Dynamic-Capability Theory” of absorptive capacity and the “Resource Based Theory” of innovation performance of a firm.

The aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.

Existing studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.

Identified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.

The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.

This study examines the existing literature on generative artificial intelligence (Gen AI) and its impact across many sectors. This analysis explores the potential, applications, and challenges of Gen AI in driving innovation and creativity and generating ideas.

The study adopts a comprehensive literature review approach, carefully assessing current scientific articles on Gen AI published from 2022 to 2024. The analysis examines trends and insights derived from research.

The review indicates that Gen AI has significant potential to augment human creativity and innovation processes as a collaborative partner. However, it is imperative to prioritize responsible development and ethical frameworks in order to effectively tackle biases, privacy concerns, and other challenges. Gen AI is significantly transforming business models, processes, and value propositions in several industries, but with varying degrees of effect. Findings indicate also that despite the theory-driven approach to investigating Gen AI's creative and innovative potential, cutting-edge applications research prioritizes examining the possibilities of Gen AI models.

Although this review offers a picture of great possibilities, it concurrently underlines the necessity for a deep knowledge of Gen AI nuances to fully harness its capabilities. The findings indicate that continuous research and exploration efforts are required to address the challenges of Gen AI and assure its responsible and ethical implementation. Therefore, more study is needed on enhancing human-AI collaboration and defining ethical norms for varied circumstances.

This study presents a relevant analysis of Gen AI's transformational potential as an innovation catalyst. It emphasizes major potential, applications across industries, and ethical issues for responsible integration.

The landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.

In this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.

The results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.

The theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.

This study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

This study investigates the digital competence (DC) of undergraduate students, considering three key dimensions: use of Information and Communication Technologies (ICT) resources, knowledge about ICT resources, and attitude towards ICT. We also examined the relationship between these aspects in order to build a DC assessment framework.

A structured questionnaire was administered to students from several universities in Vietnam in order to assess their DC. A PLS analysis was conducted based on data from 551 valid responses.

Statistical analyses showed that the DC of students was quite low. The findings certify that knowledge of ICT resources is a total intermediate variable in the impact of attitude towards ICT on the use of ICT resources by students.

Our findings provide important implications for policymakers and teachers' insights for authorities, universities, and scholars to develop strategies for DC of students in higher education.

This study is the first to explore the DC assessment framework for students in higher education in an emerging market, Vietnam.