Whole-body voxel-based internal dosimetry using deep learning

In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms

An accurate RSS/AoA-based localization method for internet of underwater things

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors

Effect of Er:YAG Laser and Sandblasting in Recycling of Ceramic Brackets

Introduction: This study was performed to determine the shear bond strength of rebonded mechanically retentive ceramic brackets after recycling with Erbium-Doped Yttrium Aluminum Garnet (Er:YAG) laser or sandblasting.Methods: Twenty-eight debonded ceramic brackets plus 14 intact new ceramic brackets were used in this study. Debonded brackets were randomly divided into 2 groups of 14. One group was treated by Er:YAG laser and the other with sandblasting. All the specimens were randomly bonded to 42 intact human upper premolars. The shear bond strength of all specimens was determined with a universal testing machine at a crosshead speed of 0.5 mm/min until bond failure occurred. The recycled bracket base surfaces were observed under a scanning electron microscope (SEM). Analysis of variance (ANOVA) and Tukey tests were used to compare the shear bond strength of the 3 groups. Fisher exact test was used to evaluate the differences in adhesive remnant index (ARI) scores.Results: The highest bond strength belonged to brackets recycled by Sandblasting (16.83 MPa). There was no significant difference between the shear bond strength of laser and control groups. SEM photographs showed differences in 2 recycling methods. The laser recycled bracket appeared to have as well-cleaned base as the new bracket. Although the sandblasted bracket photographs showed no remnant adhesives, remarkable micro-roughening of the base of the bracket was apparent.Conclusion: According to the results of this study, both Er:YAG laser and sandblasting were efficient to mechanically recondition retentive ceramic brackets. Also, Er:YAG laser did not change the design of bracket base while removing the remnant adhesives which might encourage its application in clinical practic

Bis(2-amino-4-methyl­pyridinium) bis­(pyridine-2,6-dicarboxyl­ato)cuprate(II)

The asymmetric unit of the title compound, (C6H9N2)2[Cu(C7H3NO4)2], contains half of a [Cu(pydc)2]2− (pydcH2 is pyridine-2,6-dicarb­oxy­lic acid) anion and one protonated 2-amino-4-methyl­pyridine (2a4mpH)+ counter-ion. The anion is a six-coordinated complex with a distorted CuN2O4 octa­hedral geometry around the CuII ion. N—H⋯O and C—H⋯O hydrogen bonds along with π–π contacts between the pyridine rings of the (2a4mpH)+ cations [centroid–centroid distance = 3.573 (2) Å] stabilize the crystal structure

Instability and phase transitions of a rotating black hole in the presence of perfect fluid dark matter

In this paper, we study the thermodynamic features of a rotating black hole surrounded by perfect fluid dark matter. We analyze the critical behavior of the black hole by considering the known relationship between pressure and cosmological constant. We show that the black hole admits a first order phase transition and, both rotation and perfect fluid dark matter parameters have a significant impact on the critical quantities. We also introduce a new ad hoc pressure related to the perfect fluid dark matter and find a first order van der Waals like phase transition. In addition, using the sixth order WKB method, we investigate the massless scalar quasinormal modes (QNMs) for the static spherically symmetric black hole surrounded by dark matter. Using the finite difference scheme, the dynamical evolution of the QNMs is also discussed for different values of angular momentum and overtone parameters.Comment: 13 pages, 14 figures, version accepted for publication in Eur. Phys. J.

IP2P K-means: an efficient method for data clustering on sensor networks

Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2) and the preliminary results show that the algorithm works effectively and relatively more precisely