The rapid shift to online learning during COVID-19 posed challenges for students. This investigation explored these hurdles and suggested effective solutions using mixed methods. By combining a literature review, interviews, surveys, and the analytic hierarchy process (AHP), the study identified five key challenges: lack of practical experience, disruptions in learning environments, condensed assessments, technology and financial constraints, and health and mental well-being concerns. Notably, it found differences in priorities among students across academic years. Freshmen struggled with the absence of hands-on courses, sophomores with workload demands, and upperclassmen with mental health challenges. The research also discussed preferred strategies for resolution, emphasizing independent learning methods, managing distractions, and adjusting assessments. By providing tailored insights, this study aimed to enhance online learning. Governments and universities should support practical work, prioritize student well-being, improve digital infrastructure, adapt assessments, foster innovation, and ensure resilience.

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.

Background: Traditional education in neurosurgery primarily relies on observation, giving residents and interns limited opportunities for clinical practice. However, the development of 3D printing has the potential to improve this situation. Based on bibliometrics, we analyze the application of 3D printing technology in neurosurgery medical education and surgical training. Methods: We searched the publications in this field in Web of Science core collection database from September 2000 to September 2023. VOS viewer, Citespace and Microsoft Office Excel were used to visually analyze and draw knowledge graphs. Results: A total of 231 articles and reviews were included. The United States is the country with the largest volume of articles and Mayo Clinic is the leading organization in this field. Partnership between countries, authors and institutions is also presented. World Neurosurgery is the journal with the highest number of publications. The top three key words by occurrence rate are “3D printing”, “surgery” and “simulation”. Conclusions: In recent years, more and more attention has been paid to the research in this field. According to bibliometric analysis, “accuracy” and “surgery simulation” are the research focuses in this field, while “augment reality” is the potential research target.

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.

Richard’s equation was approximated by finite-difference numerical scheme to model water infiltration profile in variably unsaturated soil^[1]. The published data of Philip’s semi-analytical solution was used to validate the simulated results from the numerical scheme. A discrepancy was found between the simulated and the published semi-analytical results. Morris method as a global sensitivity tool was used as an alternative to local sensitivity analysis to assess the results discrepancy. Morris method with different sampling strategies were tested, of which Manhattan distance method has resulted a better sensitivity measures and also a better scan of input space than Euclidean method. Moreover, Morris method at p = 2 , r = 2 and Manhattan distance sampling strategy, with only 2 extra simulation runs than local sensitivity analysis, was able to produce reliable sensitivity measures (μ^*, σ). The sensitivity analysis results were cross-validated by Sobol’ variance-based method with 150,000 simulation runs. The global sensitivity tool has identified three important parameters, of which spatial discretization size was the sole reason of the discrepancy observed. In addition, a high proportion of total output variance contributed by parameters β and θ_s is suggesting a greater significant digits to reduce its input uncertainty range.