The tetravalent cations with similar sizes (Zr ⁴⁺ , Hf ⁴⁺ ) supress the Ti ⁴⁺ reduction while the bulky divalent cations (Ca ²⁺ , Sr ²⁺ ) promote it even at relatively low concentrations.

Breast cancer comprises a serious public health concern. The three primary techniques for detecting breast cancer are ultrasound, mammography, and magnetic resonance imaging (MRI). However, the existing methods of diagnosis are not practical for regular mass screening at short time intervals. Thermography could be a solution to this issue because it is a non-invasive and low-cost method that can be used routinely as a self-screening method. The research significance of this work lies in the implementation and integration of multiple different AI techniques for achieving diagnosis based on breast thermograms from several data sources. The data sources contain 306 images. The concept of transfer learning with several pre-trained models is implemented. Bayesian Networks (BNs) are also used to have interpretability of the diagnosis. A novel feature extraction from images (related to temperature) has been implemented and feeds the BNs. Finally, all methods and the classification results of pre-trained models are compared. It is found that the best result amongst the transfer learning concept is achieved with MobileNet, which delivered 93.8% accuracy. Furthermore, the BN achieves an accuracy of 90.20%, and finally, the expert model that combines CNNs and BNs gives an accuracy of 90.85%, even with a limited amount of data available. The integration of CNN and BN aims to overcome the hardship of interpretability. These approaches demonstrate high performance with added interpretability compared to previous works. In conclusion, the deep neural network provides promising results in breast cancer detection. It could be an ideal candidate for Breast Self-Exam (BSE), the goal recommended by WHO for mass screening.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy.

Breast cancer remains one of the most prevalent cancers worldwide, necessitating reliable, efficient, and precise diagnostic methods. Meanwhile, the rapid development of artificial intelligence (AI) presents significant opportunities for integration into various fields, including healthcare, by enabling the processing of medical data and the early detection of cancer. This review examines the major medical imaging techniques used for breast cancer detection, specifically mammography, ultrasound, and thermography, and identifies widely used publicly available datasets in this domain. It also surveys traditional machine learning and deep learning approaches commonly applied to the analysis of mammographic, ultrasound, and thermographic images, discussing key studies in the field and evaluating the potential of different AI techniques for breast cancer detection. Furthermore, the review highlights the development and integration of explainable artificial intelligence (XAI) to enhance transparency and trust in medical imaging-based diagnoses. Finally, it considers potential future directions, including the application of large language models (LLMs) and multimodal LLMs in breast cancer diagnosis, emphasizing recent research aimed at advancing the precision, accessibility, and reliability of diagnostic systems.

The advancement of miniaturized energy storage systems is essential for the next generation of electronics. Lithium–sulfur (Li–S) microbatteries are able to offer exceptional theoretical capacity and energy density for microdevices. However, their practical implementation is hindered by challenges in material stability and electrode design. In this study, we introduced a spin-coated sulfur–carbon nanotube–Super P (S/CNT/SP) cathode integrated with a spin-coated polyethylene oxide (PEO)/polyvinylidene fluoride (PVDF)/lithium lanthanum zirconium oxide (LLZO) composite electrolyte. The spin-coating technique ensured the formation of uniform electrode and electrolyte thin films, which could work without a separator. The polymer-ceramic composite electrolyte with nanopores effectively suppressed polysulfide dissolution, improved ionic conductivity, and stabilized the electrode–electrolyte interface. Electrochemical evaluation revealed that the quasi-solid-state Li–S battery achieved near-theoretical capacity with enhanced cycling stability, retaining approximately 1000 mA h g⁻¹ (60% of its initial capacity) after 150 cycles across various C-rates. In a pouch-cell configuration, the cell retained 64% of its initial capacity over 60 cycles. These findings underscore the potential of spin-coating and composite quasi-solid electrolytes in enabling high-performance, safe, and compact Li–S battery technologies for next-generation energy storage applications.