X‐ray free‐electron lasers (XFELs) have been widely used for applications such as X‐ray crystallography and magnetic spin probes because of their unprecedented performance. Recently, time‐resolved X‐ray magnetic circular dichroism (XMCD) with ultrafast XFEL pulses have made it possible to achieve an instantaneous view of atomic de‐excitation. However, owing to the narrow bandwidth and coherence of XFELs, X‐ray absorption spectroscopy (XAS) and XMCD are time‐ and effort‐consuming for both machine scientists and users of XFELs. In this work, an efficient scheme using a broadband XFEL pulse and single‐shot X‐ray spectrometer is proposed, in which the XAS and XMCD measurements can be accomplished with the same machine conditions. An evolutionary multi‐objective optimization algorithm is used to maximize the XFEL bandwidth offered by the Shanghai Soft X‐ray FEL user facility without additional hardware. A numerical example using MnO is demonstrated, showing that, by using approximately 1000 consecutive XFEL shots with a central photon energy of 650 eV and full bandwidth of 4.4%, precise spectral measurements for XAS and XMCD can be achieved. Additional considerations related to single‐shot XAS and XMCD are discussed.

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Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.