• Energy Research
• 2025-2025close
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In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

All over the world, cancer death and prevalence are increasing. Breast cancer (BC) is the major cause of cancer mortality (15%) which makes it the most common cancer in women. BC is defined as the furious progression and quick division of breast cells. Novel nanotechnology-based approaches helped in improving survival rate, metastatic BC is still facing obstacles to treat with an expected overall 23% survival rate. This paper represents epidemiology, classification (non-invasive, invasive and metastatic), risk factors (genetic and non-genetic) and treatment challenges of breast cancer in brief. This review paper focus on the importance of nanotechnology-based nanoformulations for treatment of BC. This review aims to deliver elementary insight and understanding of the novel nanoformulations in BC treatment and to explain to the readers for enduring designing novel nanomedicine. Later, we elaborate on several types of nanoformulations used in tumor therapeutics such as liposomes, dendrimers, polymeric nanomaterials and many others. Potential research opportunities for clinical application and current challenges related to nanoformulations utility for the treatment of BC are also highlighted in this review. The role of artificial intelligence is elaborated in detail. We also confer the existing challenges and perspectives of nanoformulations in effective tumor management, with emphasis on the various patented nanoformulations approved or progression of clinical trials retrieved from various search engines.