Sheath motion in a capacitively coupled radio frequency discharge

The sheath motion in a capacitively coupled RF discharge is highly nonlinear. The voltage waveform on a cylindrical probe placed in the sheath region is measured as a function of position and time. A circuit model of the probe-discharge system relates the observed probe voltage to the sheath motion. The equations derived from this circuit model are solved numerically with varying nonlinear sheath motions; the resulting waveforms are compared with the experimental observations to determine the actual sheath motion. The time-varying plasma potential is also determined, indirectly, from the comparison. The authors also report observation of oscillations related to the plasma frequency, whose peak harmonic component can be calculated from a single plasma model. These oscillations can be a useful plasma diagnostic for determining plasma density. The presence of these high-frequency oscillations may significantly enhance the rate of stochastic heating of electrons     

Correlation of Moments and Critical Phenomenon in Disassembly of Hot Nuclei

We used Monte Carlo method to generate the configurations of disassembly of hot nucleus Au* based on the Atomic Mass Table and the conservation of mass and charge number.The resulted charge distribution of fragments was then used to calculate the conditional moments.The logarithm correlation between second and third moments of experimental charge distribution is very well reproduced by theory.It seems that no connection to the critical phenomenon of concerned system exists.     

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.     

Effects of soil and operational parameters on soil-tire interface stress vectors

Normal and tangential stress vectors were measured at the soil-tire interface of a pneumatic tractor tire on firm and soft soils. Stress magnitudes were determined with a transducer which was designed to measure both normal and tangential stresses. The orientation of the transducer was determined using a 3-dimensional, sonic digitizing system which was mounted inside the air cavity of the tire. Data are presented from tests conducted at zero input torque, zero net traction, and 0.15 net traction ratio which show the effects of inflation pressure, dynamic load, and soil conditions on the stress vectors.