Bioceramics Development and Applications

Two high-level fusion and two sequential hybrid strategies were also tested. MIR outperformed XRF when it came to inorganic properties (RPIQV for clay=3.4, silt=3.0, and sand=1.8) in the field under investigation, while MIR was superior for organic properties (RPIQV for total OC=7.7 and N=5.0). For these properties, there was little to no improvement in accuracy with even the optimal fusion approach. The large number of elements with variable importance in the projection scores >1 (Fe, Ni, Si, Al, Mg, Mn, K, Pb (clay only), and Cr) and strong spearman correlations (0.57 rs 0.90) with clay and silt account for the high XRF accuracy for these materials. When comparing the best single spectrometer to the optimal fusion method, relative improvements in spectrally inactive properties based on indirect prediction mechanisms were marginal for pH (3.2% increase in RPIQV versus MIR alone), but more pronounced for labile OC (9.3% vs. MIR) and CEC (12%). Performance was worse when a subpar spectrometer dominated in a fusion approach compared to the best single spectrometer. 

The foundation of global food production is soil, which also serves as a habitat, regulates the hydrological cycle, and mitigates climate change through carbon sequestration. However, precision agriculture, soil mapping, contamination monitoring, and documentation of soil C sequestration all require a high spatial and temporal density of soil information due to the heterogeneous and dynamic nature of soils. In this context, sensors that make use of various parts of the electromagnetic spectrum offer a quicker, less expensive and nondestructive alternative to conventional laboratory procedures. Models can be used to predict a variety of soil properties after they have been calibrated with paired reference data and spectral measurements. However, the prediction mechanisms for the soil property of interest determine the accuracy of the resulting model.

 Multi-sensor fusion for the collection of soil information has been the subject of varying improvements in previous studies, but the
underlying prediction mechanisms for spectrally active and inactive properties are still poorly understood. By measuring Mid-Infrared (MIR) and X-ray Fluorescence (XRF) spectra, texture, total and labile Organic Carbon (OC) and Nitrogen (N) content, pH, and Cation Exchange Capacity (CEC) for n=117 soils from an arable field in Germany, our goal was to investigate the prediction mechanisms and benefits of model fusion. Using MIR spectra or elemental concentrations derived from XRF spectra, partial least squares regression models went through a three-step training and testing process. Two high-level fusion and two sequential hybrid strategies were also tested. MIR outperformed XRF when it came to inorganic properties (RPIQV for clay=3.4, silt=3.0, and sand=1.8) in the field under investigation, while MIR was superior for organic properties (RPIQV for total OC=7.7 and N=5.0). For these properties, there was little to no improvement in accuracy with even the optimal fusion approach. The large number of elements with variable importance in the projection scores >1 (Fe, Ni, Si, Al, Mg, Mn, K, Pb (clay only), and Cr) and strong spearman correlations (0.57 rs 0.90) with clay and silt account for the high XRF accuracy for these materials.

Implantology, a specialized field of dentistry, has revolutionized tooth restoration by providing patients with durable and aesthetically pleasing solutions for missing teeth. This article explores the history, science, and process of dental implants, highlighting their benefits over traditional tooth replacement options. It discusses advanced techniques, challenges, and considerations in implantology, emphasizing the role of technology in enhancing treatment outcomes. Furthermore, it discusses the aesthetic aspects of implantology, including full-arch restorations, patient selection, and long-term care. The article concludes by discussing the future of implantology and its potential for further advancements in the field.