Objectives: The unprecedented COVID-19 pandemic has intensified the stress on blood banks and deprived the blood sources due to the containment measures that restrict the movement and travel limitations among blood donors. During this time, Malaysia had a significant 40% reduction in blood supply. Blood centers and hospitals faced a huge challenge balancing blood demand and collection. The health care systems need a proactive plan to withstand the uncertain situation such as the COVID-19 pandemic. This study investigates the psychosocial factors that affect blood donation behavior during a pandemic and aims to propose evidence-based strategies for a sustainable blood supply. Study design: Qualitative design using focus group discussion (FGD) was employed. Methods: Data were acquired from the two FGDs that group from transfusion medicine specialists (N = 8) and donors (N = 10). The FGD interview protocol was developed based on the UTM Research Ethics Committee’s approval. Then, the data was analyzed using Nvivo based on the General Inductive Approach (GIA). Results: Analysis of the text data found that the psychology of blood donation during the pandemic in Malaysia can be classified into four main themes: (i) reduced donation; (ii) motivation of donating blood; (iii) trends of donation; and (iv) challenges faced by the one-off, occasional, and non-donors. Conclusions: Based on the emerging themes from the FGDs, this study proposes four psycho-contextual strategies for relevant authorities to manage sustainable blood accumulation during the pandemic: (1) develop standard operating procedure for blood donors; (2) organize awareness campaigns; (3) create a centralized integrated blood donors database; and (4) provide innovative Blood Donation Facilities.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.

Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model's insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy's specific needs and challenges.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

This study aims to investigate what influences local workers over the age of 40 to work and stay employed in oil palm plantations. 414 individuals participated in a face-to-face interview that provided the study’s primary source of data. Exploratory Factor Analysis was used to analyse the given data. The study revealed that factors influencing local workers over the age of 40 years to leave or continue working in oil palm plantations can be classified as income factors, internal factors and external factors. The income factor was the most significant factor as the percentage variance explained by the factor was 26.792% and Cronbach Alpha was high at 0.870. Therefore, the study suggested that the oil palm plantation managements pay more attention to income elements such as basic salary, wage rate paid to the workers and allowance given to the workers since these elements contribute to the monthly total income received by the workers and in turn be able to attract more local workers to work and remain in the plantations.

Despite the current craze for e-commerce live streaming, its specific impact on consumer repurchase intentions and the underlying mechanisms remain insufficiently explored, creating a notable gap in existing research. The purpose of this study is to investigate the precise impact of e-commerce live streaming on consumers’ repurchase intentions and to uncover the path through which this influence occurs. Drawing on behavioral cognitive theory, this paper employs a contextual experimental method to examine how e-commerce live streaming affects consumer repurchase behavior. The experimental results show that e-commerce live can significantly improve consumer repurchase intention, consumer loyalty and market order can positively regulate the effect of e-commerce live. This paper not only verifies the effectiveness of e-commerce live broadcasting, but also provides new ideas for brands and governments to strengthen the ability of e-commerce live broadcasting to “bring goods”.