Considering the role of tourism in promoting sustainable practices in destinations, this study aims to map the scientific literature on footprint calculators in the last three years (2020–2023) with a focus on the tourism context. The method adopted is a scoping review with a qualitative and exploratory approach, using the Scopus database. The originality of this research lies in the study of publications related to footprint calculators with a focus on the tourism sector. Based on the analysis carried out, the main results show that the study of footprint calculators applied to the tourism sector has had little prominence in the indexed research in the Scopus database during the specific period considered for this study. Consequently, the conclusion of the study highlights the marginality of the tourism sector in the discussion of footprint calculators in the last 3 years of scientific publications.

This study analyses the dynamic development of soybean (Glycine max (L.) Merr.) breeding in Russia, particularly examining its historical development, status, and future predictions. With the global demand for vegetable protein rising, understanding Russia’s potential contribution becomes crucial. This research provides valuable insights, offering precise data that may be unfamiliar to international researchers and the private sector. The authors trace the history of soybean selection in Russia, emphasizing its expansion from the Far East to other regions in Russia. The expansion is primarily attributed to the pioneering work of Soviet breeder V. A. Zolotnitsky and the development of the soybean variety in the Amur region in the 1930s. The study highlights the main areas of soybean variety originators, with approximately 40% of foreign varieties registered. The Krasnodar and Amur regions emerge as critical areas for breeding soybean varieties. In Russia, the highest yield potential of soybeans is in the Central Federal District. At the same time, the varieties registered in the Volga Federal District have higher oil content, and the Far Eastern Federal District has high protein content in the registered soybean varieties. The research outlines the state’s pivotal role in supporting soybean breeding and fostering a competitive market with foreign breeders. The study forecasts future soybean breeding development and the main factors that can influence the industry.

Low enrollment intention threatens the funding pools of rural insurance schemes in developing countries. The purpose of this study is to investigate how social capital enhances the enrollment of health insurance among rural middle-aged and elderly. We propose that social capital directly increases health insurance enrollment, while indirectly influences health insurance through health risk avoidance. We used data from the China Health and Retirement Longitudinal Study (wave 4) dating the year of 2018, instrumental variable estimation was introduced to deal with the endogeneity problem, and the mediation analysis was used to examine the mechanism of social capital on insurance enrollment. The results show that social capital is positively related to social health insurance enrollment, and the relationship between social capital and social health insurance enrollment is mediated by health risk avoidance.

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.

Onion (Allium cepa L.) is one of the important vegetables in Egypt. The study was conducted in the vegetable field to study the effect of different rates of phosphorus fertilizers and foliar application of Nano-Boron, Chitosan, and Naphthalene Acidic Acid (NAA) on growth and seed productivity of Onion plant (Allium cepa L., cv. Giza 6 Mohassan). The experiments were carried out in a split-plot design with three replicates. The main plot contains 3 rates of phosphorus treatments (30, 45 and 60 kg P₂O₅/feddan), Subplot includes foliar application of Nano-Boron, Nano-Chitosan and Naphthalene Acidic Acid (NAA) at a concentration of 50 ppm for each and sprayed at three times (50, 65 and 80 days after transplanting). Increasing the phosphorus fertilizers rate to 60 kg P₂O₅/fed significantly affects the growth and seed production of the Onion plant. Foliar application of nano-boron at 50 ppm concentration gave maximum values of onion seed yield in both seasons. Results stated that the correlation between yield and yield contributing characters over two years was highly significant. It could be recommended that P application at a rate of 60 kg P₂O₅ and sprayed onion plants at 50 ppm nano-boron three times (at 50, 65, and 80 days from transplanting) gave the highest seed yield of onion plants. Moreover, the maximum increments of inflorescence diameter (94.4%) were recorded to nano-boron foliar spray (60 p × nB) compared to the other treatments in both seasons.