Sialons are the spotlight of current investigation as low-cost and outstanding alternatives to the currently used metal alloys in various applications. The excellent high-temperature properties, high mechanical properties, structural reliability, good sinterability, easy densification with its low-cost processing make them superior candidates in many applications such as automotive engines, high-performance bearings, wear components, and gas turbine blades. In addition, the existence of two interstitial crystallographic sites in their crystal structure gives the opportunity to accommodate some rare earth element activators such as Eu²⁺, Yb²⁺, Ce³⁺, and Pr³⁺. These types of sialon-based materials have recently found a new promising application as a luminescent material for white light-emitting diodes. The aim of this review is to survey and provide a comprehensive look at the most relevant and significant publications regarding the development of sialons and their processing into both structural and luminescent materials. Such information forms a database that could enable scholars and engineers to tailor a final product derived from sialons with specific characteristics for a certain application. This review article should be of concern to engineers and scientists interested in the development and utilization of sialons for structural and wLEDs applications.

Gallstone disease is a common condition affecting a substantial number of individuals globally. The risk factors for gallstones include obesity, rapid weight loss, diabetes, and genetic predisposition. Gallstones can lead to serious complications such as calculous cholecystitis, cholangitis, biliary pancreatitis, and an increased risk for gallbladder (GB) cancer. Abdominal ultrasound (US) is the primary diagnostic method due to its affordability and high sensitivity, while computed tomography (CT) and magnetic resonance cholangiopancreatography (MRCP) offer higher sensitivity and specificity. This review assesses the diagnostic accuracy of machine learning (ML) technologies in detecting gallstones. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting systematic reviews and meta-analyses. An electronic search was conducted in PubMed, Cochrane Library, Scopus, and Embase, covering literature up to April 2024, focusing on human studies, and including all relevant keywords. Various Boolean operators and Medical Subject Heading (MeSH) terms were used. Additionally, reference lists were manually screened. The review included all study designs and performance indicators but excluded studies not involving artificial intelligence (AI)/ML algorithms, non-imaging diagnostic modalities, microscopic images, other diseases, editorials, commentaries, reviews, and studies with incomplete data. Data extraction covered study characteristics, imaging modalities, ML architectures, training/testing/validation, performance metrics, reference standards, and reported advantages and drawbacks of the diagnostic models. The electronic search yielded 1,002 records, of which 34 underwent full-text screening, resulting in the inclusion of seven studies. An additional study identified through citation searching brought the total to eight articles. Most studies employed a retrospective cross-sectional design, except for one prospective study. Imaging modalities included ultrasonography (four studies), computed tomography (three studies), and magnetic resonance cholangiopancreatography (one study). Patient numbers ranged from 60 to 2,386, and image numbers ranged from 60 to 17,560 images included in the training, validation, and testing of the diagnostic models. All studies utilized neural networks, predominantly convolutional neural networks (CNNs). Expert radiologists served as the reference standard for image labelling, and model performances were compared against human doctors or other algorithms. Performance indicators such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were commonly used. In conclusion, while the reviewed machine learning models show promising performance in diagnosing gallstones, significant work remains to be done to ensure their reliability and generalizability across diverse clinical settings. The potential for these models to improve diagnostic accuracy and efficiency is evident, but the careful consideration of their limitations and rigorous validation are essential steps toward their successful integration into clinical practice.

This research presents the possibility of producing durable foam glasses from glass cullet using SiC/AlN foaming agent. The foaming agent generated by the SiC and AlN couple results in a more homogeneous microstructure and thus the emergence of foam glasses with better properties compared to the nitride foaming agent used alone in our previous work. The fabricated foam had a crack-free, 3-D cellular structure with macropores whose geometries varied between elliptical-, pentagonal-, and hexagonal-shaped constructions. It also had a lightweight (≥ 0.18 g/cm³), high cold crushing strength (≤ 4.5 MPa), low thermal conductivity (0.09–0.16 W/m K), and contained more than ~ 89 vol.% gas bubbles enclosed between 11 vol.% impervious glass walls. The properties accomplished by the foam prepared in this work conform with the requirements of international standard for commercial glass foams, demonstrating its strong capability to be utilized in potential applications in sustainable buildings and energy efficiency in industry.