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Molecular dynamics simulation method was used to study the influence of co-lattice twin boundary (TB) spacing on the deformation behavior of Al0.1CoCrFeNi high-entropy alloy single crystal under uniaxial tension. Studies have shown that there is a “critical distance” between the twin boundaries, and the sensitivity of the two sides to the change of the twin spacing is different, and the influence of the evolution of the deformation mechanism of the different twin spacing is analyzed from the number of defects and the evolution of the dislocation density. The results show that as the distance between twins decreases, the deformation mechanism gradually transforms from dislocation slip accompanied by defects, such as stacking faults and secondary twins, to a deformation mode of amorphous phase transition. The research aims to provide guidance and reference for the design of high-performance high-entropy alloys.

Additional file 1: Table S1. The evaluation metrics of confusion matrix of three univariables. Table S2. The result of multivariate regressions of eICU-CRD dataset. Table S3. The result of multivariate regressions of MIMIC-IV dataset.

Additional file 1. TRIPOD checklist is provided for the prediction model development and validation.

Distributed X-ray sources provide a solid foundation for the invention of new-generation computed tomography (CT). This study proposes a novel multi-focal spot electron gun scheme for distributed X-ray sources. The source in this study uses a dispenser cathode as the electron emitter, a mesh grid to control emission current, and two electrostatic lenses for beam shaping, focusing, and deflecting. Novel focusing and deflecting electrodes were designed to increase the number of focal spots in the distributed source. It consists of two identical half-rectangle opening electrodes by adjusting the potential of the two electrodes to control the trajectory of the electron beam and then obtain multi-focal spots on the anode target. The number of X-ray sources in the distributed source is limited by the amount of cathodes. The multi-focal spot electron gun can break this limitation and provide more possibilities and options for distributed X-ray sources and stationary CT. Experimental results reflect the excellent repeatability of the multi-spot electron gun. The dynamic effective focal spot sizes of the deflected (deflected amplitude, 10.5mm) and non-deflected electron beams at full with half max (FWHM) are 1.30mm × 0.60mm and 1.15mm × 0.55mm, respectively (anode voltage, 160kV; beam current, 30 mA).

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.

Additional file 1: Figure S1. Data missing before multiple imputation. SOFA, Sequential Organ Failure Assessment; SAPSII, Simplify Acute Physiological Scores II; CRRT, continuous renal replacement therapy; AG, anion gap; BUN, blood urea nitrogen; RBC, red blood cells; WBC, white blood cells; RDW, red blood cell distribution width. Figure S2. SMD of covariable before and after IPTW. SMD, standardized mean difference; IPTW, inverse probability of treatment weighting; BUN, blood urea nitrogen; SAPSII, Simplify Acute Physiological Scores II; RDW, red blood cell distribution width; SOFA, Sequential Organ Failure Assessment; CRRT, continuous renal replacement therapy;; AG, anion gap; RBC, red blood cells; WBC, white blood cells. Figure S3. The contribution of each covariate to the XGBoost model. WBC, white blood cells; BUN, blood urea nitrogen; RBC, red blood cells; RDW, red blood cell distribution width; SAPSII, Simplify Acute Physiological Scores II; AG, anion gap; SOFA, Sequential Organ Failure Assessment; CRRT, continuous renal replacement therapy. Figure S4. Cumulative incidence curves of IPTW cohorts. (A) by multinomial logistic regression, (B) by XGBoost. Table S1. Mean of posterior probabilities in each class. Table S2. Fixed effects in the longitudinal five classes model. Table S3. Results of multinomial logistic regression. Table S4. Results of subgroup analysis