Homework is an indispensable basic link in classroom teaching, an important link in the consolidation of knowledge after class, and an important way for students to understand knowledge, digest knowledge, and improve their problem-solving ability. In the practice of mathematics teaching in primary schools, attention should be paid to the effectiveness of homework assignments in different links before, during and after class, and the content of homework should take into account the reality of students at different levels. This paper expounds the strategy of hierarchical design of mathematics homework from the aspects of the hierarchical design and arrangement of mathematics homework in the upper grades of primary school, aiming to effectively improve the quality of mathematics classroom teaching in the upper grades of primary school.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

This study explores the factors that affect consumer adoption of reusable packaging in South Korea’s food delivery market. Adopting a mixed-method that includes interviews and an online survey of 137 consumers aged 18 to 30, the analysis, using an ordered probit model, reveals key drivers of the likelihood of switching to food delivery services using reusable packaging. Positive influences include environmental concerns, intention to take action on disposable packaging, willingness to pay extra, and awareness that reusable packaging does not require washing. However, challenges such as hygiene concerns and higher delivery fees deter consumers from switching to reusable package option. Demographic factors like living arrangements and gender show minimal impact. In response to the findings, the study suggests strategic solutions, including a pilot program, to overcome barriers and effectively demonstrate the benefits of reusable containers.

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.

Molybdenum (Mo) is considered and described as an essential element for living organisms' development. Until now, no studies have been performed on genes involved in the Mo transporter in ancestral Ipomoea species. This study aimed to identify potential Mo genes in Ipomoea trifida and I. triloba genomes using bioinformatics tools. We identified four Mo transporter genes, two in I. trifida and two in I. triloba. Based on the RNA-seq datasets, we observed that Mo genes are expressed (in silico) and present different mechanisms between the tissues analyzed. The information generated in this study fills missing gaps in the literature on the Mo gene in an important agronomic crop.