Research on the Upper Limit of Accuracy for Predicting Theoretical Tandem Mass Spectrometry

In recent years, numerous theoretical tandem mass spectrometry prediction methods have been proposed, yet a systematic study and evaluation of their theoretical accuracy limits have not been conducted. If the accuracy of current methods approaches this limit, further exploration of new prediction techniques may become redundant. Conversely, a need for more precise prediction methods or models may be indicated. In this study, we have experimentally analyzed the limits of accuracy at different numbers of ions and parameters using repeated spectral pairs and integrating various similarity metrics. Results show significant achievements in accuracy for backbone ion methods with room for improvement. In contrast, full-spectrum prediction methods exhibit greater potential relative to the theoretical accuracy limit. Additionally, findings highlight the significant impact of normalized collision energy and instrument type on prediction accuracy, underscoring the importance of considering these factors in future theoretical tandem mass spectrometry predictions.

Spectral decomposition method for predicting magmatic intrusion into a coal bed

Accurate prediction of magmatic intrusion into a coal bed is illustrated using the method of seismic spectral decomposition.The characteristics of coal seismic reflections are first analyzed and the effect of variable time windows and domain frequencies on the spectral decomposition are examined.The higher domain frequency of coal bed reflections using the narrower STFT time window,or the smaller ST scale factor,are acceptable.When magmatic rock intrudes from the bottom of the coal bed the domain frequency of the reflections is decreased slightly,the frequency bandwidth is narrowed correspondingly,and the response from spectral decomposition is significantly reduced.Intrusion by a very thin magmatic rock gives a spectral decomposition response that is just slightly less than what is seen from a normal coal bed.Results from an actual mining area were used to validate the method.Predicting the boundary of magmatic intrusions with the method discussed herein was highly accurate and has been validated by observations from underground mining.

Total Transmission from Deep Learning Designs

Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.