Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.

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.

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.

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.

Open-source software (OSS) has emerged as a transformative tool whose implementation has the potential to modernise many libraries around the world in the digital age. OSS is a type of software which permits its users to inspect, share, modify, and enhance through its freely accessed source code. The accessibility and openness of the source code permits users to manipulate, change, and improve the way in which a piece of software, program, or application works. OSS solutions therefore provide cost-effective alternatives that enable libraries to enhance their technological infrastructure without being constrained by proprietary systems. Hence, many countries have initiated and formulated policies and legislative frameworks to support the implementation and use of OSS library solutions such as DSpace, Alfresco, and Greenstone. The purpose of the study reported on was to investigate the leveraging of OSS to modernise public libraries in South Africa. Content analysis was adopted as the research methodology for this qualitative study, which was based on a literature review integrating insights from the researchers’ experiences with the use of OSS in libraries The findings of the study reveal that the use of OSS has the potential to modernise public libraries, especially those located outside cities or urban areas. These libraries are often less well equipped with the necessary technology infrastructure to meet the demands of the digital age, such as online books and open access materials. The study culminated in an OSS framework that may be implemented to modernise public libraries. This framework may help public libraries to integrate OSS solutions and further allow users access to digital services.