Boiling heat transfer has maintained a high degree of interest due to the range of its applications in the energy sector. In recent years, much research has focused on improving the nucleate pool boiling by modifying the fluid properties. In this review article, the basic properties and characteristics of Al2O3 nanofluids and few other nanofluids are explored and discussed through past research findings. Next, previous studies that involved pool boiling heat transfer enhancement using Al2O3 nanofluid and its performance in terms of critical heat flux (CHF) and heat transfer coefficient (HTC) are further highlighted. These studies have employed methods that affected the performance of CHF and HTC such as electric field and surface modification. Maximum enhancement in CHF measured is approximately 200%. On the other hand, usage of prediction models to predict enhancements are also discussed thoroughly. Regardless of boiling performance enhancements with the deployment of nanofluids, several concerns must first be addressed before it is able to be deployed for practical use.

Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and cold-start issues. The proposed model includes two phases. In the first—offline—phase, a deep learning technique is implemented to learn hidden features from a large image dataset (targeting new item cold start), and a multimodal data embedding is used to produce dense user feature and item feature vectors (targeting user cold start). This phase produces three different similarity matrices that are used as inputs for the second—online—phase to generate a list of top-n relevant items for a target user. We analyzed the accuracy and effectiveness of the proposed model against the existing baseline RSs using a Brazilian E-commerce dataset. The results show that our model scored 0.5882 for MAE and 0.4011 for RMSE which is lower than baseline RSs which indicates that the model achieved an improved accuracy and was able to minimize the typical cold start and data sparseness issues during the recommendation process.