Light-Output Enhancement of GaN-Based Light-Emitting Diodes with Three-Dimensional Backside Reflectors Patterned by Microscale Cone Array by Huamao Huang, Jinyong Hu, Hong Wang

“…The LED chips with flat reflectors or 3D reflectors were simulated using Monte Carlo ray tracing method. It is shown that the LEE increases as the reflectivity of backside reflector increases, and the light-output can be significantly improved by 3D reflectors compared to flat counterparts. …”
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PWR Containment Shielding Calculations with SCALE6.1 Using Hybrid Deterministic-Stochastic Methodology by Mario Matijević, Dubravko Pevec, Krešimir Trontl

“…The capabilities of the SCALE6.1/MAVRIC hybrid shielding methodology (CADIS and FW-CADIS) were demonstrated when applied to a realistic deep penetration Monte Carlo (MC) shielding problem of a full-scale PWR containment model. …”
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Three-Dimensional Virtual Model for Robot-Assisted Partial Nephrectomy (RAPN): Development of Study Protocol for Evaluation of the Learning Curve to Optimize the Precision and Accu... by Traverso P, Mantica G, Giasotto V, Terrone C

“…Paolo Traverso,1,2,* Guglielmo Mantica,1,2,* Veronica Giasotto,2 Carlo Terrone1,2 1Department of Surgical and Diagnostic Integrated Sciences (DISC), University of Genova, Genova, Italy; 2IRCCS Policlinico San Martino, Genova, Italy*These authors contributed equally to this workCorrespondence: Guglielmo Mantica, Department of Surgical and Diagnostic Integrated Sciences (DISC), University of Genova, Largo Rosanna Benzi 20, Genova, 16132, Italy, Tel +39010555964, Email guglielmo.mantica@gmail.comAbstract: 3D models have been introduced as tools to improve surgeon’s precision during Robotic-Assisted Partial Nephrectomy (RAPN). …”
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Probabilistic assessment of the cumulative risk from dietary heavy metal exposure in Chongqing, China using a hazard-driven approach by Jiahui Chen, Jingrong Chen, Mintao Li, Ping Feng, Mei Qin, Ting Chen, Shuquan Luo, Jian Zhao, Xiaofeng Yan, Jinyao Chen, Xuemei Lian, Huadong Zhang, Jiao Huo

“…The cumulative risk was calculated and evaluated using Monte Carlo modeling and the modified Reference Point Index (mRPI) method. …”
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Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation by Joseph P. Yurko, Jacopo Buongiorno, Robert Youngblood

“…Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. …”
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Risk Decision-Making Technology in Gas Reservoir Development at Sichuan Basin by Guo Yu, Haitao Li, Yanru Chen, Linqing Liu, Dongming Zhang

“…Regarding the specific production capacity conditions in gas wells (horizontal and vertical wells) and gas reservoir water invasion, the probability method with Monte Carlo stochastic simulation was used to calculate the production and water invasion volumes. …”
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Feasibility Study of an Alcohol-Based Liquid Scintillation Detector and Its Application by Byoung Chan Kim, Ji Young Choi, Kyung Kwang Joo, Seon Yeong Park, Ye Sung Song, Hee Jin Woo

“…The measurement results were compared with a Monte Carlo simulation, Novalis Tx, a phantom, and a CT image. …”
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METROLOGICAL SUPPORT OF DOSIMETRY GAMMA-RAY WITH ENERGY TO 10 MEV FOR RADIATION PROTECTION DEVICES by D. I. Komar, R. V. Lukashevich, V. D. Guzov, S. A. Kutsen

“…The aim of this work was to determine the metrological characteristics of capture gamma-ray fields from titanium target and nickel target obtained at the AT140 Neutron Calibration Facility to provide dosimetry up to 10 MeV.We have chosen energy intervals in which we can calibrate dosimetry devices taking into account the accompanying generation of gamma-ray neutrons by the fast neutron source 238PuBe, the capture radiation of collimator materials and capture radiation from targets.We measured air kerma rate with the aid of the reference AT5350 dosimeter with the ionization chamber TM32002. Using the Monte-Carlo simulation, we obtained the energy distribution of the air kerma rate for targets. …”
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Impact of Strain on Sub-3 nm Gate-All-Around CMOS Logic Circuit Performance Using a Neural Compact Modeling Approach by Ji Hwan Lee, Kihwan Kim, Kyungjin Rim, Soogine Chong, Hyunbo Cho, Saeroonter Oh

“…The transport models were calibrated against advanced Monte Carlo simulations to ensure accuracy. The resulting neural compact model demonstrated a close approximation to the original simulation results, achieving a minimal error of 1%. …”
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Unsupervised deep learning method for single image super-resolution of the thick pinhole imaging system using deep image prior by Guoguang Li, Liang Sheng, Baojun Duan, Yang Li, Dongwei Hei, Qingzi Xing

“…The point spread function (PSF) of the imaging system is obtained by analytical calculation and Monte Carlo simulation methods, and the mathematical model of the imaging system is established using a linear equation. …”
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Performance analysis of generalized orthogonal code index modulation system by Lina BIAN, Huizhen FENG, Xiaoping JIN, Weizhong LOU, Chongxiao QU

“…In order to improve the transmission efficiency of the code index modulation (CIM) system, a generalized orthogonal code index modulation (GQCIM) system with lower complexity was proposed, which was a single input single output (SISO) system.CIM system used spreading codes and constellation symbols to transmit information, but only activated two spreading code indexes and one modulation symbol.GQCIM system overcame the limitation of only activating one modulation symbol at a time in a novel way, at the same time, made full use of the orthogonality of modulation symbols, increased the spreading code index to transmit more additional information bits, and improved the transmission efficiency of the system.In addition, the theoretical performance of GQCIM system was analyzed and derived the upper bound of bit error rate performance.Then the performance of GQCIM was verified by Monte Carlo simulation, and the theoretical and simulation performance of GQCIM system was found to be consistent.And under the same transmission efficiency, the results show that the performance of GQCIM system is better than the same orthogonal modulation system, such as generalized code index modulation (GCIM) system, CIM system, code index modulation aided quadrature spatial modulation (CIM-QSM) system, code index modulation aided spatial modulation (CIM-SM) system, pulse index modulation (PIM) system.…”
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Machine‐Learned HASDM Thermospheric Mass Density Model With Uncertainty Quantification by Richard J. Licata, Piyush M. Mehta, W. Kent Tobiska, S. Huzurbazar

“…These models leverage Monte Carlo dropout to provide uncertainty estimates, and the use of the NLPD loss function results in well‐calibrated uncertainty estimates while only increasing error by 0.25% (<10% mean absolute error) relative to MSE. …”
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PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy by Lovro Palaversa, Željko Ivezić, Neven Caplar, Karlo Mrakovčić, Bob Abel, Oleksandra Razim, Filip Matković, Connor Yablonski, Toni Šarić, Tomislav Jurkić, Sandro Campos, Melissa DeLucchi, Derek Jones, Konstantin Malanchev, Alex I. Malz, Sean McGuire, Mario Jurić

“…The computation speed is about 10 ms per star on a single core for both optimized grid search and Markov Chain Monte Carlo methods; we show in a companion paper by K. …”
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Relative Permeability of Porous Media with Nonuniform Pores by He Chen, Minghui Yang, Ke Chen, Chao Zhang

“…Based on these, a fractal-based continuous analytical model and a fractal-based Monte Carlo model of relative permeability as well as a permeability-porosity model are established. …”
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Comparison of Weighted Lag Adaptive LASSO with Autometrics for Covariate Selection and Forecasting Using Time-Series Data by Sara Muhammadullah, Amena Urooj, Faridoon Khan, Mohammed N Alshahrani, Mohammed Alqawba, Sanaa Al-Marzouki

“…For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. …”
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The Dilution Dependency of Multigroup Uncertainties by M. R. Ball, C. McEwan, D. R. Novog, J. C. Luxat

“…The second is based on a more rigorous approach involving the Monte Carlo sampling of resonance parameters in evaluated nuclear data using the TALYS software. …”
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Cancer phylogenetic tree inference at scale from 1000s of single cell genomes by Salehi, Sohrab, Dorri, Fatemeh, Chern, Kevin, Kabeer, Farhia, Rusk, Nicole, Funnell, Tyler, Williams, Marc J., Lai, Daniel, Andronescu, Mirela, Campbell, Kieran R., McPherson, Andrew, Aparicio, Samuel, Roth, Andrew, Shah, Sohrab P., Bouchard-Côté, Alexandre

“…The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. …”
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Assessment of soil classification based on cone penetration test data for Kaifeng area using optimized support vector machine by Hanliang Bian, Zhongxun Sun, Jiahan Bian, Zhaowei Qu, Jianwei Zhang, Xiangchun Xu

“…Moreover, the models were thoroughly evaluated using Monte Carlo simulations, confusion matrices, ROC curves, and 10 key performance metrics. …”
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Modeling the Magnetic Connection from Earth to Solar Corona during the May 11 Geomagnetic Superstorm by Alessandro Ippolito, Tommaso Alberti, Fabio Giannattasio

“…This study aims at improving our understanding of the magnetic connectivity between Earth and the solar corona by reconstructing the magnetic footpoint of the Earth on the solar surface during this geomagnetic superstorm. A Monte Carlo simulation was applied to model the random walk of interplanetary magnetic field lines due to low-frequency turbulence in the solar wind. …”
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Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties by Xueteng Wang, Jiandong Wang, Mengyao Wei, Yang Yue

“…Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. …”
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