Local structural modelling and local pair distribution function analysis for Zr-Pt metallic glass.

In disordered glass structures, the structural modelling and analyses based on local experimental data are not yet established. Here we investigate the icosahedral short-range order (SRO) in a Zr-Pt metallic glass using local structural modelling, which is a reverse Monte Carlo simulation dedicated to two-dimensional angstrom-beam electron diffraction (ABED) patterns, and local pair distribution function (PDF) analysis. The local structural modelling invariably leads to the icosahedral SRO atomic configurations that are similarly distorted by starting from some different initial configurations. Furthermore, the SRO configurations with 11-13 coordination numbers reproduce almost identical ABED patterns, indicating that these SRO structures are similar to each other. Further local PDF analysis explicitly indicates the presence of the wide distribution of atomic bond distances, which is comparable to the global PDF profile, even at the SRO level. The SRO models based on the conventional MD simulation can be strengthened by comparison with those obtained by the present local structural modelling and local PDF analysis based on the ABED data.

A novel approach to predict acute radiation dermatitis in patients with head and neck cancer using a model based on Bayesian probability.

PURPOSE: In this study, we aimed to establish a method for predicting the probability of each acute radiation dermatitis (ARD) grade during the head and neck Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning phase based on Bayesian probability. METHODS: The skin dose volume >50 Gy (V50), calculated using the treatment planning system, was used as a factor related to skin toxicity. The empirical distribution of each ARD grade relative to V50 was obtained from the ARD grades of 119 patients (55, 50, and 14 patients with G1, G2, and G3, respectively) determined by head and neck cancer specialists. Using Bayes' theorem, the Bayesian probabilities of G1, G2, and G3 for each value of V50 were calculated with an empirical distribution. Conversely, V50 was obtained based on the Bayesian probabilities of G1, G2, and G3. RESULTS: The empirical distribution for each graded patient group demonstrated a normal distribution. The method predicted ARD grades with 92.4 % accuracy and provided a V50 value for each grade. For example, using the graph, we could predict that V50 should be ≤24.5 cm3 to achieve G1 with 70 % probability. CONCLUSIONS: The Bayesian probability-based ARD prediction method could predict the ARD grade at the treatment planning stage using limited patient diagnostic data that demonstrated a normal distribution. If the probability of an ARD grade is high, skin care can be initiated in advance. Furthermore, the V50 value during treatment planning can provide radiation oncologists with data for strategies to reduce ARD.

Bayesian modeling of pattern formation from one snapshot of pattern.

Partial differential equations (PDEs) have been widely used to reproduce patterns in nature and to give insight into the mechanism underlying pattern formation. Although many PDE models have been proposed, they rely on the pre-request knowledge of physical laws and symmetries, and developing a model to reproduce a given desired pattern remains difficult. We propose a method, referred to as Bayesian modeling of PDEs (BM-PDEs), to estimate the best dynamical PDE for one snapshot of a objective pattern under the stationary state without ground truth. We apply BM-PDEs to nontrivial patterns, such as quasicrystals (QCs), a double gyroid, and Frank-Kasper structures. We also generate three-dimensional dodecagonal QCs from a PDE model. This is done by using the estimated parameters for the Frank-Kasper A15 structure, which closely approximates the local structures of QCs. Our method works for noisy patterns and the pattern synthesized without the ground-truth parameters, which are required for the application toward experimental data.

Estimation of mental workload using saccadic eye movements in a free-viewing task.

This study proposes a new method to automatically estimate a person's mental workload (MWL) using a specific type of eye movements called saccadic intrusions (SI). Previously, the most accurate existing method to estimate MWL was the pupil diameter measure [1]. However, pupil diameter is not practical in a vehicle driving environment because it is overly sensitive to brightness changes. A new method should be independent from environment brightness changes, robust in most driving environments, and accurately reflect MWL. This study used SI as an indicator of MWL because eye movements, including SI, are independent from brightness changes. SI are a specific type of eye-gaze deviations. SI are known to be closely related to cognitive activities [2], [3]. This means that SI may be also closely related to MWL. Eye movements were recorded using a non-intrusive eye tracking camera, located 550 mm away from a participant. Participants were instructed to move their eye gaze to examine a highway driving scenery picture. In the data set of the recorded eye movements, our new algorithm detected SI and quantified SI behavior into a SI measure. Participants were also engaged in a secondary N-back task. The N-back task is a popular task used in cognitive sciences to systematically control a MWL level of participants. In our results, all 14 participants exhibited more SI eye movements when their MWL level was high compared to when their MWL level was low. Moreover, our results showed that the SI measure was a more accurate measure of MWL than the pupil diameter measure. This finding indicates that MWL of the person can be estimated by observation of SI eye movements. This new method has a wide range of applications. One of them is to predict a person's MWL, thus predicting when a person is capable of driving a vehicle in a safe or dangerous manner.