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Gradient nano-grained (GNG) high-entropy alloys (HEAs) generally exhibit an excellent balance of plasticity and strength compared to homogeneous alloys, but their mechanical behaviors vary significantly with grain size distributions. This work has systematically investigated the mechanical behaviors and deformation mechanisms of GNG Al0.1CoCrFeNi HEAs with various grain-size gradients using molecular dynamics. The results have indicated that the grain-size gradient induces stress and strain gradients, and results in more dislocations and a unique multiaxial stress state, which contribute to the strength-plasticity synergy in the GNG structure. The deformation mechanism of GNG Al0.1CoCrFeNi HEA involves dislocation slip in low strain level, martensitic transformation, and twinning in high strain level. The GNG Al0.1CoCrFeNi HEA with a grain size gradient rate of n = 3 exhibits the best strength-plasticity synergy due to its significant strain and stress gradients, alongside notable deformation-induced twins and martensite. These simulation results are consistent with the strain gradient theory and some experimental reports. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality. Currently, detailed characterization of early autonomic dysfunction in the acute phase after sTBI is lacking. This study aims to use physiological waveform data collected from patients with sTBI to characterize early autonomic dysfunction and its association with clinical outcomes to prevent multi-organ dysfunction and improving patient outcomes. Objective: This data analysis protocol describes our pre-planned protocol using cardiac waveforms to evaluate early autonomic dysfunction and to inform multi-dimensional characterization of the autonomic nervous system (ANS) after sTBI. Methods: We will collect continuous cardiac waveform data from patients managed in an intensive care unit within a clinical trial. We will first assess the signal quality of the electrocardiogram (ECG) using a combination of the structural image similarity metric and signal quality index. Then, we will detect premature ventricular contractions (PVC) on good-quality ECG beats using a deep-learning model. For arterial blood pressure (ABP) data, we will employ a singular value decomposition (SVD)-based approach to assess the signal quality. Finally, we will compute multiple indices of ANS functions through heart rate turbulence (HRT) analysis, time/frequency-domain analysis of heart rate variability (HRV) and pulse rate variability, and quantification of baroreflex sensitivity (BRS) from high-quality continuous ECG and ABP signals. The early autonomic dysfunction will be characterized by comparing the values of calculated indices with their normal ranges. Conclusion: This study will provide a detailed characterization of acute changes in ANS function after sTBI through quantified indices from cardiac waveform data, thereby enhancing our understanding of the development and course of eAD post-sTBI. [ABSTRACT FROM AUTHOR]