Strong gravitational lensing in a rotating Kaluza-Klein black hole with squashed horizons

We have investigated the strong gravitational lensing in a rotating squashed Kaluza-Klein (KK) black hole spacetime. Our result show that the strong gravitational lensings in the rotating squashed KK black hole spacetime have some distinct behaviors from those in the backgrounds of the four-dimensional Kerr black hole and of the squashed KK G\"{o}del black hole. In the rotating squashed KK black hole spacetime, the marginally circular photon radius $\rho_{ps}$, the coefficient $\bar{a}$, $\bar{b}$, the deflection angle $\alpha(\theta)$ in the $\phi$ direction and the corresponding observational variables are independent of whether the photon goes with or against the rotation of the background, which is different with those in the usual four-dimensional Kerr black hole spacetime. Moreover, we also find that with the increase of the scale of extra dimension $\rho_0$, the marginally circular photon radius $\rho_{ps}$ and the angular position of the relativistic images $\theta_\infty$ first decreases and then increases in the rotating squashed KK black hole for fixed rotation parameter $b$, but in the squashed KK G\"{o}del black hole they increase for the smaller global rotation parameter $j$ and decrease for the larger one. In the extremely squashed case $\rho_0=0$, the coefficient $\bar{a}$ in the rotating squashed KK black hole increases monotonously with the rotation parameter, but in the squashed KK G\"{o}del black hole it is a constant and independent of the global rotation of the G\"{o}del Universe.Comment: 20 pages; 7 figures. Accepted for publication in JHEP. arXiv admin note: substantial text overlap with arXiv:1102.008

Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Both linear (alin) and rotational (arot) accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant alin and arot as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model consistency and dependency in results: the Dartmouth Head Injury Model (DHIM) and Simulated Injury Monitor (SIMon). For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated to the product of the magnitude and duration of arot, or effectively, the rotational velocity, but not to alin. Responses from arot-only were comparable to the full-DOFs counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., <1%) at typical difference percentage levels of 1–4% on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics, and demonstrate the feasibility of estimating strain-related responses from isolated arot for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes

High-conductive protonated layered oxides from H2O vapor-annealed brownmillerites

Protonated 3d transition-metal oxides often display low electronic conduction, which hampers their application in electric, magnetic, thermoelectric, and catalytic fields. Electronic conduction can be enhanced by co-inserting oxygen acceptors simultaneously. However, the currently used redox approaches hinder protons and oxygen ions co-insertion due to the selective switching issues. Here, a thermal hydration strategy for systematically exploring the synthesis of conductive protonated oxides from 3d transition-metal oxides is introduced. This strategy is illustrated by synthesizing a novel layered-oxide SrCoO3H from the brownmillerite SrCoO2.5. Compared to the insulating SrCoO2.5, SrCoO3H exhibits an unprecedented high electronic conductivity above room temperature, water uptake at 250 °C, and a thermoelectric power factor of up to 1.2 mW K-2 m-1 at 300 K. These findings open up opportunities for creating high-conductive protonated layered oxides by protons and oxygen ions co-doping.CC acknowledges support from the Spanish Ministry of Science, Innovation, and Universities under the “Ramón y Cajal” fellowship RYC2018-024947-I.Peer ReviewedPostprint (author's final draft