Optimalne potrebe juvenilnih morskih grgeča sebastes schlegeli u proteinima i lipidima

U ovom istraživanju su analizirane optimalne potrebe juvenilnih morskih grgeča Sebastes schlegeli u proteinima i lipidima. 810 juvenilnih riba je izabrano po principu slučajnosti i distribuirano u 27 tankova od po 50 L sa protočnih sistemom. Pripremljeno je 9 eksperimentalnih smeša u vidu 3x3 faktorijalne eksperimentalne postavke: tri nivoa proteina (45, 50 i 55%) x tri nivoa lipida (11, 15 i 19%). Nivo proteina je imao uticaj na prirast riba, dok nivo lipida nije. Prirast riba hranjenih smešom u odnosu 50P-15L (50% proteina i 15% lipida) je bio veći nego prirast riba hranjenih smešama sa 45% proteina, bez obzira na nivo lipida, ali je bio isti kao kod riba hranjenih sa smešama 50P-11L, 50P-19L, 55P-11L, 55P-15L i 55P-19L. Stopa efikasnosti hrane (FER) riba je bila pod uticajem proteina u hrani ali ne i nivoa lipida. Stopa efikasnosti proteina (PER) riba je takođe bila pod uticajem proteina u hrani ali ne i nivoa lipida. Može se zaključiti da je za juvenilne Sebastes schlegeli optimalan nivo proteina i lipida za dobar prirast i iskoristljivost hrane (PER and NRE) 50% i 15% odnosno 45% i 19%, dok je optimalan odnos proteina i energije 27.4 i 23.9 mg protein/kJ

Free Vibration of Axially Functionally Graded Timoshenko Circular Arch

Functionally graded materials are innovative composites of hybrid ceramics and metals that exhibit excellent mechanical performance in harsh temperature environments and under various external loads. In this study, the free vibrations of Timoshenko circular arches, made of functionally graded materials in the axial direction, are investigated. The material properties of Young's modulus and mass density of the arch vary according to a symmetric quadratic function along the arch axis. Differential equations governing the free vibration of the arch including the rotatory inertia and shear deformation, called the Timoshenko arch, are derived. A novel numerical solution method is developed to calculate the natural frequencies and mode shapes of the arch. Parametric studies of the modular ratio, shear correction factor, shear modulus ratio, and slenderness ratio on the natural frequencies are conducted, and the results are reported in the tables and figures

Machine Learning-Based Analysis of Adolescent Gambling Factors

Background and aims: Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. Methods: Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression. Results: The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set. Discussion: The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling. Conclusion: Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.11Ysciessciscopu

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure

Laser mode-hopping assisted all-optical single beam pulsed atomic magnetometer

We demonstrate an all-optical single beam pulsed atomic magnetometer assisted by laser mode-hopping in a distributed Bragg reflector (DBR) laser. We implement a temporal sequence of the laser current; sinusoidal current modulation including the laser mode-hop current for synchronous optical pumping and the following constant current for paramagnetic Faraday rotation measurements to probe the free induction decay (FID) of transverse $^{87}$Rb spin polarization. Repetitive sudden frequency shifts of 20 GHz around the pressure-broadened $^{87}$Rb spectra originating from laser mode-hopping enables discontinuous optical pumping modulation with a large depth which enhances transverse spin polarization. We achieve a sensitivity of 3.77 pT/Hz$^{1/2}$ in a magnetic field of 14 $\mu$T, limited by the performance of the frequency counter. The Cramer-Rao lower bound (CRLB) of the sensitivity due to the non-magnetic noise such as photon shot-noise is 191 fT/Hz$^{1/2}$. Our approach based on laser mode-hopping can be applied to miniaturization of all-optical atomic magnetometers with sub-pT/Hz$^{1/2}$ sensitivities.Comment: 10 pages, 7 figure