Non-Destructive Early Detection of Drosophila Suzukii Infestation in Sweet Cherries (c.v. <i>Sweet Heart</i>) Based on Innovative Management of Spectrophotometric Multilinear Corre... by Giuseppe Altieri, Mahdi Rashvand Avaei, Attilio Matera, Francesco Genovese, Vincenzo Verrastro, Naouel Admane, Orkhan Mammadov, Sabina Laveglia, Giovanni Carlo Di Renzo

“…The identified procedure of management of regression algorithms allowed the selection of a very performant and robust model using the partial least squares regression algorithm: its false negative rate and false positive rate, after 500 Monte Carlo runs, were 0.004% +/− 0.003 and 0.02% +/− 0.01, respectively, and, in addition, the 50% of samples were used for the external cross-validation set.…”
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Projected background and sensitivity of AMoRE-II by A. Agrawal, V. V. Alenkov, P. Aryal, J. Beyer, B. Bhandari, R. S. Boiko, K. Boonin, O. Buzanov, C. R. Byeon, N. Chanthima, M. K. Cheoun, J. S. Choe, Seonho Choi, S. Choudhury, J. S. Chung, F. A. Danevich, M. Djamal, D. Drung, C. Enss, A. Fleischmann, A. M. Gangapshev, L. Gastaldo, Y. M. Gavrilyuk, A. M. Gezhaev, O. Gileva, V. D. Grigorieva, V. I. Gurentsov, C. Ha, D. H. Ha, E. J. Ha, D. H. Hwnag, E. J. Jeon, J. A. Jeon, H. S. Jo, J. Kaewkhao, C. S. Kang, W. G. Kang, V. V. Kazalov, S. Kempf, A. Khan, S. Khan, D. Y. Kim, G. W. Kim, H. B. Kim, Ho-Jong Kim, H. J. Kim, H. L. Kim, H. S. Kim, M. B. Kim, S. C. Kim, S. K. Kim, S. R. Kim, W. T. Kim, Y. D. Kim, Y. H. Kim, K. Kirdsiri, Y. J. Ko, V. V. Kobychev, V. Kornoukhov, V. V. Kuzminov, D. H. Kwon, C. H. Lee, DongYeup Lee, E. K. Lee, H. J. Lee, H. S. Lee, J. Lee, J. Y. Lee, K. B. Lee, M. H. Lee, M. K. Lee, S. W. Lee, Y. C. Lee, D. S. Leonard, H. S. Lim, B. Mailyan, E. P. Makarov, P. Nyanda, Y. Oh, S. L. Olsen, S. I. Panasenko, H. K. Park, H. S. Park, K. S. Park, S. Y. Park, O. G. Polischuk, H. Prihtiadi, S. Ra, S. S. Ratkevich, G. Rooh, E. Sala, M. B. Sari, J. Seo, K. M. Seo, B. Sharma, K. A. Shin, V. N. Shlegel, K. Siyeon, J. So, N. V. Sokur, J. K. Son, J. W. Song, N. Srisittipokakun, V. I. Tretyak, R. Wirawan, K. R. Woo, H. J. Yeon, Y. S. Yoon, Q. Yue, The AMoRE Collaboration

“…We have intensively performed Monte Carlo simulations using the GEANT4 toolkit in all the experimental configurations with potential sources. …”
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Simulating a travel-related origin of Candida auris in New York–New Jersey by Rita R. Verma, Edward Kiegle, Alexander C. Keyel, Sudha Chaturvedi, Vishnu Chaturvedi

“…We tested the hypothesis by simulating introductions to NY-NJ with a Monte Carlo simulation based on travel from South Asia, proportion of US population in NY-NJ, proportion of hospitals in NY-NJ, and finally, proportion of all travelers entering the United States through NY-NJ. …”
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Bayesian inverse analysis with field observation for slope failure mechanism and reliability assessment under rainfall accounting for nonstationary characteristics of soil properti... by Xian Liu, Shui-Hua Jiang, Jiawei Xie, Xueyou Li

“…The probabilities of slope failure and distributions of critical slip surface for various rainfall durations are then evaluated within a Monte-Carlo simulation framework. Based on these, the slope failure mechanism induced solely by the rainfall is investigated. …”
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Evaluation of Failure Probability in Series System of Three-Axle Trucks under Strong Crosswind by Yahui Hu, Yingshi Guo, Rui Fu, Qingjin Xu

“…The model is used to obtain the dynamic response of the three-axle truck under strong crosswind conditions as per the time-varying curves of the vertical load of the truck, the time-varying curves of the lateral displacement of the center of mass, and the time-varying curves of the heading angle. An advanced Monte Carlo simulation algorithm based on importance sampling is used to determine the probability of a three-axle truck with FPSS under strong crosswinds; the given acceptable probability of failure (accident) is used to obtain the critical safety speed. …”
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A Covert &#x03B1;-Stable Noise-Based Extended Random Communication by Incorporating Multiple Inverse Systems by Areeb Ahmed, Zoran Bosnic

“…The results from Monte Carlo simulations show that deploying the proposed ERCS not only leads to better Bit Error Rate (BER) performance but also provides increased covertness values, which proves the design&#x2019;s ability to enhance performance and security compared to earlier models. …”
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Self-adaptive fuzzing optimization method based on distribution divergence by XU Hang, JI Jiangan, MA Zheyu, ZHANG Chao

“…An interprocedural comparison flow graph was first constructed based on the interprocedural control flow graph to characterize the spatial random field corresponding to the branch condition variables of the program under test, and the distribution features of the random field generated by a fuzzing mutation strategy were extracted using the Monte Carlo method. Then, a deep graph convolutional neural network was constructed to extract the feature embeddings of the interprocedural comparison flow graph, and this neural network was used as the deep Q-network for deep reinforcement learning. …”
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Design Forces of Horizontal Braces Unlocated at Middle of Columns considering Random Initial Geometric Imperfections by Jinyou Zhao, Junming Wei, Jun Wang

“…In this paper, a large number of column-bracing systems with the horizontal braces unlocated at middle of columns were modelled and analyzed using the finite element method, in which the random initial geometric imperfections of both the columns and the horizontal braces unlocated at middle of columns were well considered by the Monte Carlo method. Based on the numerical calculations, parametric analysis, and probability statistics, the probability density function of the horizontal bracing forces was found, so that the corresponding design forces of horizontal braces unlocated at middle of columns were proposed which were compared with the design mid-height horizontal bracing forces in the previous study and the relevant codes. …”
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Approbation of the stochastic group virus protection model by R. Y. Sharykin

“…To analyze the system we use several metrics, such as the saturation time of the virus propagation, the proportion of infected nodes upon reaching saturation and the maximal virus propagation speed. We use Monte Carlo method with the computation of confidence intervals to obtain estimates of the selected metrics. …”
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Application of Random Dynamic Grouping Simulation Algorithm in PE Teaching Evaluation by Haitao Hao

“…To address this problem, a stochastic simulation-based comprehensive evaluation solution algorithm based on the idea of “Monte Carlo simulation” is proposed, and the corresponding ranking method is investigated, which is characterized by generating evaluation conclusions with probability (reliability) information, and thus has more advantages than the absolute conclusion form in terms of problem interpretability. …”
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Adsorption in Slit Pores and Pore-Size Distribution: A Molecular Layer Structure by E.A. Ustinov, D.D. Do

“…The model is less time-consuming than methods such as the DFT and Monte-Carlo simulation, and most importantly it can be readily extended to the adsorption of mixtures and capillary condensation phenomena.…”
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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling by Ayush Saurabh, Peter T. Brown, J. Shepard Bryan IV, Zachary R. Fox, Rory Kruithoff, Cristopher Thompson, Comert Kural, Douglas P. Shepherd, Steve Pressé

“…To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. …”
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Investigation of the structure of CaO-Al2O3-SiO2 melts as a basis for the development of new agglomerated welding fluxes and industrial refractories by Sokol’skii V.E., Pruttskov D.V., Busko V.M., Kazimirov V.P., Roik O.S., Chyrkin A.D., Galinich V.I., Goncharov I.A.

“…The structural parameters of short-range order were calculated using melt structure models obtained by Reverse Monte Carlo method. The existence of microparticle associates of the mullite or sillimanite type immersed in the slag matrix is assumed. …”
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An Efficient and Robust Method to Predict Multifractured Horizontal Well Production in Shale Oil and Gas Reservoirs by Ling Chen, Yuhu Bai, Bingxiang Xu, Yanzun Li, Zhiqiang Dong, Suran Wang

“…Secondly, the duration of the linear flow period is verified to be over 10~15 years by using an analytical model to do the calculation with the method of Monte Carlo random sampling taking a large amount of parameter combinations of Eagle Ford shale oil and gas reservoirs into calculation. …”
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Antibacterial action of penicillin against Mycobacterium avium complex by D. Deshpande, G. Magombedze, S. Srivastava, T. Gumbo

“…The exposure mediating E max was 84.6% (95% CI 76.91–82.98) of time concentration exceeded the minimum inhibitory concentration (MIC). In Monte Carlo experiments, 24 million international units of benzylpenicillin continuous infusion achieved the target exposure in lungs of >90% of 10,000 subjects until an MIC of 64 mg/L, designated the susceptibility breakpoint. …”
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Effective Partitioning Method With Predictable Hardness for CircuitSAT by Konstantin Chukharev, Irina Gribanova, Dmitry Ivanov, Stepan Kochemazov, Victor Kondratiev, Alexander Semenov

“…A distinctive feature of the proposed partitioning methods is that they make it possible to estimate the hardness (e.g. the total runtime of a SAT solver on all subproblems) of a partitioning via the Monte Carlo method. In the experimental evaluation we apply these methods to hard CircuitSAT instances and compare their performance with the well known Cube and Conquer approach. …”
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Simulation of SARS-CoV-2 Aerosol Emissions in the Infected Population and Resulting Airborne Exposures in Different Indoor Scenarios by Michael Riediker, Christian Monn

“…It uses the estimates of a Monte Carlo simulation for the viral emission strength of breathing, speaking softly and loudly. …”
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Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification by Masoud Muhammed Hassan, Halbast Rashid Ismail

“…We then applied the Bayesian CNN twice, once using Variational Inference (VI) and once using Monte Carlo dropout (MC-dropout) methods, to the same CNN architecture. …”
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The Lomax-Claim Model: Bivariate Extension and Applications to Financial Data by Jin Zhao, Humaira Faqiri, Zubair Ahmad, Walid Emam, M. Yusuf, A. M. Sharawy

“…The behaviors of the maximum likelihood estimators are examined by conducting a brief Monte Carlo study. The potentiality and applicability of the Lomax claim model are illustrated by analyzing a dataset taken from financial sciences representing the vehicle insurance loss data. …”
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Radiation Exposure and Shielding Effects on the Lunar Surface by Daniel Matthiä, Thomas Berger

“…This work investigated the expected dose rates for maximum GCR intensity and the total dose from several historical solar energetic particle events, including the NASA reference event, through the application of numerical simulations with the Geant4 Monte‐Carlo framework. An evaluation of the shielding effect of lunar regolith was carried out. …”
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