Dynamic interaction between valve-closure water hammer wave and centrifugal pump

Water hammer is a principal cause of pipeline and equipment failure in pumping systems. The numerical method and experiments are used to investigate the dynamic interaction between the valve-closure water hammer wave and pump, to study the pressure variation and fluid-induced force on the pump components. The impeller-volute interaction and impeller position are taken into consideration. Results show that the valve-closure water hammer wave generates a substantial fluid-induced force on the pump and leads to a pressure surge in the pipeline. Meanwhile, the impeller-volute interaction also causes pressure and force variation in the pipeline and pump. The force amplitude caused by this factor in the axial direction is similar to that caused by the water hammer wave but is much smaller in the radial direction. The different interaction position of the water hammer wave on the blades can weaken the force, by 13.11% and 13.18% for the impeller and volute when the blades are under the most optimal position, respectively.

Bisphenol A derivatives act as novel coactivator binding inhibitors for estrogen receptor β

Bisphenol A and its derivatives are recognized endocrine disruptors based on their complex effects on estrogen receptor (ER) signaling. While the effects of bisphenol derivatives on ERα have been thoroughly evaluated, how these chemicals affect ERβ signaling is not well understood. Herein, we identified novel ERβ ligands by screening a chemical library of bisphenol derivatives. Many of the compounds identified showed intriguing dual activities as ERα agonists and ERβ antagonists. Docking simulations suggested that these compounds act as coactivator binding inhibitors (CBIs). Direct binding experiments using wild-type and mutated ERβ demonstrated the presence of a second ligand interaction position at the coactivator binding site in ERβ. Our study is the first to propose that bisphenol derivatives act as CBIs, presenting a critical view point for future ER signaling-based drug development.

Direct Annihilation Position Classification Based on Deep Learning Using Paired Cherenkov Detectors: A Monte Carlo Study

To apply deep learning to estimate the three-dimensional interaction position of a Cherenkov detector, an experimental measurement of the true depth of interaction is needed. This requires significant time and effort. Therefore, in this study, we propose a direct annihilation position classification method based on deep learning using paired Cherenkov detectors. The proposed method does not explicitly estimate the interaction position or time-of-flight information and instead directly estimates the annihilation position from the raw data of photon information measured by paired Cherenkov detectors. We validated the feasibility of the proposed method using Monte Carlo simulation data of point sources. A total of 125 point sources were arranged three-dimensionally with 5 mm intervals, and two Cherenkov detectors were placed face-to-face, 50 mm apart. The Cherenkov detector consisted of a monolithic PbF2 crystal with a size of 40 × 40 × 10 mm3 and a photodetector with a single photon time resolution (SPTR) of 0 to 100 picosecond (ps) and readout pitch of 0 to 10 mm. The proposed method obtained a classification accuracy of 80% and spatial resolution with a root mean square error of less than 1.5 mm when the SPTR was 10 ps and the readout pitch was 3 mm.