Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

In recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.

Sports competition is one of the important contents and forms of sports activities and physical education. It plays a full range of valuable functions in promoting the all-round development of college students. Specifically, it can better help college students enjoy fun, enhance their physique, and improve their physical fitness during physical exercise. Personality and tempering the will. Countries around the world attach great importance to youth sports competitions, and use national strategies as the top-level design and sports events as activity carriers to create a series of youth sports competitions such as graded competitions, championships, and campus events, providing more opportunities for young people to watch and participate in sports. Opportunities and platforms for competition. College student sports competitions are an important part of youth sports competitions and shoulder multiple missions such as physical health promotion, competitive talent training, and sports industry development. In recent years, the development of college sports competitions around the world has achieved remarkable results, and the scale and quality of Chinese college sports competitions have also been significantly improved. However, compared with developed countries, overall, there is still a weak awareness of participation, poor competition experience, and competitive competition. Prominent problems such as low levels and high activity withdrawal rates have, to a certain extent, restricted the high-quality development of college student sports competitions. In fact, it is not as easy as imagined for college students to participate in sports competitions regularly for a long time. In addition to requiring college students to possess certain basic conditions such as time, energy, and skills, it also requires support and promotion from all walks of life, especially It is inseparable from the material, spiritual and technical support provided by family, friends, coaches and other important groups. Just as the social ecological model believes that individual physical activity behavior is closely related to social support at the interpersonal level, especially social support from important groups such as family and friends has a positive impact on individual physical activity behavior. At the same time, although social support is very important, not all social support received can promote college students to form good sports competition behaviors. Self-determination theory emphasizes that only effective social support can regulate and optimize individual sports motivation by meeting the individual’s basic psychological needs, and ultimately promote the formation of positive, long-term sports behavior. However, most of the current sports academic circles continue the research context of traditional college student sports management, focusing on the contemporary value, practical issues, system construction, etc. of college student sports competitions. They are more subjective qualitative theoretical research and relatively lack the influence of social support. Empirical research on the sports competition behavior of college students, so that the internal mechanism of social support affecting the sports competition behavior of college students is not clear enough and understood. Therefore, from the perspective of social ecology, this study explores the internal mechanism of social support affecting college students’ sports competition behavior, in order to provide certain theoretical reference for improving the level of college students’ sports competition behavior.

The WRKY gene family plays a very diverse role in plant growth and development. These genes contained an evolutionarily conserved WRKY DNA binding domain, which shows functional diversity and extensive expansion of the gene family. In this study, we conducted a genome-wide comparative analysis to investigate the evolutionary aspects of the WRKY gene family across various plant species and revealed significant expansion and diversification ranging from aquatic green algae to terrestrial plants. Phylogeny reconstruction of WRKY genes was performed using the Maximum Likelihood (ML) method; the genes were grouped into seven different clades and further classified into algae, bryophytes, pteridophytes, dicotyledons, and monocotyledons subgroups. Furthermore, duplication analysis showed that the increase in the number of WRKY genes in higher plant species was primarily due to tandem and segmental duplication under purifying selection. In addition, the selection pressures of different subfamilies of the WRKY gene were investigated using different strategies (classical and Bayesian maximum likelihood methods (Data monkey/PAML)). The average dN/dS for each group are less than one, indicating purifying selection. Our comparative genomic analysis provides the basis for future functional analysis, understanding the role of gene duplication in gene family expansion, and selection pressure analysis.

The relationship between transport infrastructure and accessibility has long stood as a central research area in regional and transport economics. Often invoked by governments to justify large public spending on infrastructure, the study of this relationship has led to conflicting arguments on the role that transport plays in productivity. This paper expands the existing body of knowledge by adopting a spatial analysis (with spillover effects) that considers the physical effects of investment in terms of accessibility (using distinct metrics). The authors have used the Portuguese experience at regional level over the last 30 years as a case study. The main conclusions are as follows: i) the choice of transport variables matters when explaining productivity, and more complex accessibility indicators are more correlated with; ii) it is important to account for spill-over effects; and iii) the evidence of granger causality is not widespread but depends on the regions.

Virtual environments like the Metaverse have been gaining popularity in recent years. Live streaming has gained popularity as a favorite way to entertain among social network users, thanks to its real-time authenticity. This study will utilize the Extended Unified Theory of Acceptability and Use of Technology (UTAUT2) to examine the factors influencing the adoption of live streaming in the Metaverse, a new platform with greater immersion, among citizens in Vietnam. The research used a quantitative approach, collected data from a sample of participants through a structured questionnaire including Performance Expectancy (PEE), Effort Expectancy (EEF), Social Influence (SCI), Hedonic Motivation (HEM), and Experience (EXP). Additionally, technological Self-Efficacy (TSE) as an extended alternative is thought to influence that relationship as well. Results from the PLS-SEM technique was used to examine perception, acceptance, and adoption differences among demographic groups. Remarkably, the results show experience has a remarkable impact on the relationship between behavioral intention and the adoption use Metaverse for livestreaming. This study contributes theoretical value for investors and researchers on the entertainment and technology sectors due to the abilities of the live-streaming industry and the advanced features of metaverse in this digital world.