MXenes are one of the most important classes of materials discussed worldwide by many researchers of diverse fields for diverse applications in recent years. It is a nanomaterial with a wide range of applications due to its multiple forms and structures with fascinating properties, for example, high surface area and porosity, biocompatibility, ease of fictionalizing with various active chemical moieties, benefit of high metallic conductivity, activated metallic hydroxide sites, and sensitivity to moisture. MXenes have great chances for potential applications in environmental issues, water purification, biological applications, and energy storage devices and sensors. MXenes show great selectivity towards the absorption of heavy metals and a good capability to reduce chemical and biological pollutants present in the water. The present review article critically analyzed advancements in water purification using the adsorption and reduction abilities of MXenes and their composites. The mechanism of various procedures, important challenges, and associated problems using MXene and their composites are discussed in detail. The future research directions can be extracted from this article efficiently and comprehensively. The energy storage issues of rechargeable lithium-ion batteries, batteries other than lithium-ion batteries, and electrochemical capacitors are also discussed in detail.

The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

This study uses the UTAUT2 (Unified theory of acceptance and use of technology) model as well as adding other factors such as Platform Usability, User Autonomy to determine the behavioral intention and behavior of online shoppers using e-commerce applications (ECAs) in Vietnam. Using the analysis results from structural equation modeling, it was shown that Social Influence, Use Proficiency, Hedonic Motivation, User Skill, Effort Expectancy positively affect Behavioral Intention. At the same time, Behavioral Intention is negatively affected by Performance Expectancy. Behavioral Intention and Facilitating Conditions are two factors that positively affect Use Behavior. Besides, User Autonomy negatively affects Use Behavior. The research results are an important basis for ECAs providers, managers and stakeholders to apply in assessing the behavioral intentions and behaviors of online shopping customers using ECAs in Vietnam to promote the use of ECAs in online shopping.

The effective drainage radius of coal seam is an important basis for the spacing of pre-drainage gas boreholes. To quickly and accurately determine the effective drainage radius, a new method was proposed. For the coal face where the desorbable gas content before mining has met the standard, the compliance of mine gas drainage rate was used as the basis to determine the effective drainage radius. The fluid-structure interaction model was constructed, numerical simulation of coal seam gas drainage was carried out by using COMSOL software, and the model was validated by combining the field test results. The results show that the new method has the advantage of short cycle. With the extension of drainage time, the increase of effective drainage radius gradually decreases, and finally reaches a relatively stable limit value, which conforms to the Langmuir function. The average error between numerical simulation and field test values of effective drainage radius is 4.9%, which proves that the model is reliable. This model can accurately predict the effective drainage radius under different coal seam gas contents and drainage times. The research results provide a new mean for determining the effective drainage radius of coal seam and the layout of gas drainage boreholes.

This article delves into the application of blockchain technology in enhancing intellectual property (IP) protection within the e-commerce sector, providing a comprehensive analysis of its future prospects. By examining the core characteristics and working principles of blockchain, the paper reveals the unique advantages it offers in strengthening IP protection for e-commerce. The article elaborates on how blockchain’s features of decentralization, data immutability, and timestamping contribute to a secure, transparent, and efficient IP protection mechanism in the e-commerce field. Furthermore, the paper discusses the practical application of blockchain technology in IP registration, management, transaction, and rights protection, highlighting its significant impact on security traceability, transaction cost reduction, and efficiency improvement. Lastly, the article anticipates the future role of blockchain technology in IP protection in e-commerce and believes that with continued technological advancements and enhanced policy support, blockchain will play an increasingly pivotal role in this domain. The paper also proposes potential challenges and solutions that require attention, aiming to foster the healthy and sustainable development of blockchain technology.

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.