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.

Current study examines the intervening role of team creativity for the relationship of four kinds of KM practice with innovation and the moderating effect of proactiveness in IT companies based on a Knowledge-Based View (KBV). Data was collected from 316 employees of IT companies who engage in software development in teams with the help of a simple random sampling method. Results indicate that KM practices have a positive impact on innovation. Also, team creativity plays mediating role in the relation of two KM practices i.e., knowledge sharing and knowledge application with innovation. Whereas proactiveness plays a positive moderating role in the relation of knowledge application and knowledge generation with innovation. Moreover, it plays a negative moderating role in relation of Knowledge sharing with innovation. This research adds to the body of literature by suggesting a framework of knowledge diffusion, knowledge storage, knowledge generation, knowledge application, team creativity, proactiveness, and innovation in a single model. This research also adds to the body of literature by proposing the intervening role of team creativity in the relationships of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. The results of this research help the managers to use the team creativity concept to intervene in relation of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. The results of the current study also give valuable insights to managers into why they can use the proactiveness to moderate the relations of knowledge diffusion, knowledge storage, knowledge generation, and knowledge application, with innovation. Current study adds in the body of literature by proposing the entire manuscript on the basis of two theories i.e., Knowledge-Based View (KBV) builds on and expands the RBV.

The proposed scientific article aims to analyze the application of Lean Six Sigma in the food industry. To this end, a detailed methodology has been designed that ranges from the selection of the works to the synthesis and presentation of the results obtained. The methodology is based on rigorous inclusion criteria to ensure the relevance and quality of the selected sources, including books, academic articles, theses, and other relevant documents. Through extensive searches of academic databases and other reliable sources, key works were identified that specifically address the implementation of Lean Six Sigma in the context of food production. Once the relevant papers were collected, a critical analysis was conducted to identify common themes, trends, and key findings. The works were classified according to their main focus, such as process improvement, waste reduction, supply chain optimization and food safety assurance. This categorization allowed the information to be organized in a coherent way and to facilitate the synthesis of the results. The results obtained were presented in a table that included details about each selected work, such as title, author, year of publication, abstract and links to the original source. This structured and rigorous approach provides a clear and comprehensive view of the topic, contributing to the advancement of knowledge in this area and offering practical guidance for practitioners and researchers interested in the application of Lean Six Sigma in the food industry. The literature on Lean Six Sigma in the food industry highlights its importance in improving efficiency, quality, and safety. Key recommendations include gradual implementation, appropriate training, focus on quality, and continuous improvement.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.