Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks by Agung Wahyu Setiawan

“…In addition, this study also compares the performance of two optimizers, i.e. Adam and Stochastic Gradient Descent (SGD) for each CNN architecture. …”
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Outbreak of carbapenem resistant Klebsiella pneumoniae in a neurorehabilitation unit: Genomic epidemiology reveals complex transmission pattern in a tertiary care hospital by Maria Grazia Silvotti, Erika Scaltriti, Luca Bolzoni, Beatrice Zerbi, Gabriella Tocci, Andrea Zappavigna, Gianfranco Lamberti, Federico Donati, Franco Federici, Stefano Pongolini, Giuliana Lo Cascio

“…The most likely transmission networks of KPC-Kp were reconstructed with Bayesian discrete-time stochastic models. Results: Nineteen patients were colonized by KPC-Kp. …”
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Targeted reproductive management for lactating Holstein cows: Economic return by Ricardo C. Chebel, Tomas Gonzalez, Ana B. Montevecchio, Klibs N. Galvão, Albert de Vries, Rafael S. Bisinotto

“…According to a Monte Carlo stochastic simulation, the mean (±SD) difference in gross profit was $87.8 ± 12.6/cow in favor of the TRM treatment, and 95% of the scenarios ranged from $67.2/cow to $108.5/cow (minimum = $30.2/cow, maximum = $141.1/cow). …”
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Genomic selection strategies to overcome genotype by environment interactions in biosecurity-based aquaculture breeding programs by Ziyi Kang, Jie Kong, Qi Li, Juan Sui, Ping Dai, Kun Luo, Xianhong Meng, Baolong Chen, Jiawang Cao, Jian Tan, Qiang Fu, Qun Xing, Sheng Luan

“…Fortunately, genomic selection shows promise in improving genetic gain by taking within-family variance into account. Stochastic simulation was employed to evaluate genetic gain and G × E trends in BS for improving the body weight of L. vannamei, considering selective genotyping strategies for test group (TG) at a commercial farm environment (CE), the number individuals of the selection group (SG) genotyped at nucleus breeding center (NE), and varying levels of G × E. …”
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The impact of E × B shear on microtearing based transport in spherical tokamaks by B.S. Patel, M.R. Hardman, D. Kennedy, M. Giacomin, D. Dickinson, C.M. Roach

“…At higher $\hat{s}$ , MTMs drive more stochastic transport due a stronger overlap of magnetic islands centred on neighbouring rational surfaces, but equilibrium E  ×  B shear acts to mitigate this. …”
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A Metapopulation Model to Assess Water Management Impacts on the Threatened Australian Lungfish, <i>Neoceratodus forsteri</i> by Charles R. Todd, Andrew J. McDougall, Scott M. C. Raymond, Robin Hale, Timothy R. Brown, John D. Koehn, Henry F. Wootton, Steven G. Brooks, Adrian M. Kitchingman, Tom Espinoza, Benjamin G. Fanson, Peter K. Kind, Sharon M. Marshall, David T. Roberts

“…As this long-lived species (at least 77 years) has delayed maturity (mature at 10 years), field monitoring alone will not be enough to inform the challenge of ensuring sustainable populations. A stochastic metapopulation model was developed for the Burnett River (Southeast Queensland, Australia), an important habitat for the lungfish that is a highly regulated system with extensive water infrastructure. …”
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ICOS Potenza (Italy) Atmospheric Station: A New Spot for the Observation of Greenhouse Gases in the Mediterranean Basin by Emilio Lapenna, Antonella Buono, Alessandro Mauceri, Isabella Zaccardo, Francesco Cardellicchio, Francesco D’Amico, Teresa Laurita, Davide Amodio, Canio Colangelo, Gianluca Di Fiore, Antonella Gorga, Ermann Ripepi, Francesco De Benedictis, Silvana Pirelli, Liborio Capozzo, Vincenzo Lapenna, Gelsomina Pappalardo, Serena Trippetta, Lucia Mona

“…Preliminary data collected during a short campaign are compared with STILT (Stochastic Time-Inverted Lagrangian Transport) model results as a first test of the measurements and to provide a first insight of the specific Potenza situation in terms of GHG concentrations.…”
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Human Capital, Economic Growth and Environmental Quality with Special Emphasis on Public Education Expenditures: An Application of DSGE Models by Yahya Mohaghegh, hashem zare, Mehrzad Ebrahimi

“…Methodology To achieve the goal of the research, a dynamic stochastic general equilibrium (DSGE) model and Bayesian approach have been used. …”
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Spatial heterogeneity determines the gastrointestinal microbiome signatures and ecological processes that govern bacterial community assembly in sheep by Yukun Zhang, Jiangbo Cheng, Changchun Lin, Fadi Li, Xiaoxue Zhang, Chong Li, Deyin Zhang, Xiaobin Yang, Dan Xu, Yuan Zhao, Liming Zhao, Xiaolong Li, Huibin Tian, Xiuxiu Weng, Weimin Wang

“…Furthermore, we found that the assembly processes of bacterial communities in the four-chambered stomach and small intestine were mainly stochastic, while those in the large intestine were mainly shaped by deterministic processes, with a higher ecological niche width than other GIT regions. …”
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The NANOGrav 15 Yr Data Set: Removing Pulsars One by One from the Pulsar Timing Array by Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Jeremy G. Baier, Paul T. Baker, Bence Bécsy, Laura Blecha, Adam Brazier, Paul R. Brook, Sarah Burke-Spolaor, J. Andrew Casey-Clyde, Maria Charisi, Shami Chatterjee, Tyler Cohen, James M. Cordes, Neil J. Cornish, Fronefield Crawford, H. Thankful Cromartie, Kathryn Crowter, Megan E. DeCesar, Paul B. Demorest, Heling Deng, Lankeswar Dey, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Emiko C. Gardiner, Nate Garver-Daniels, Peter A. Gentile, Kyle A. Gersbach, Joseph Glaser, Deborah C. Good, Lydia Guertin, Kayhan Gültekin, Jeffrey S. Hazboun, Ross J. Jennings, Aaron D. Johnson, Megan L. Jones, Andrew R. Kaiser, David L. Kaplan, Luke Zoltan Kelley, Matthew Kerr, Joey S. Key, Nima Laal, Michael T. Lam, William G. Lamb, Bjorn Larsen, T. Joseph W. Lazio, Natalia Lewandowska, Tingting Liu, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Chung-Pei Ma, Dustin R. Madison, Alexander McEwen, James W. McKee, Maura A. McLaughlin, Natasha McMann, Bradley W. Meyers, Patrick M. Meyers, Hannah Middleton, Chiara M. F. Mingarelli, Andrea Mitridate, Christopher J. Moore, Cherry Ng, David J. Nice, Stella Koch Ocker, Ken D. Olum, Timothy T. Pennucci, Benetge B. P. Perera, Nihan S. Pol, Henri A. Radovan, Scott M. Ransom, Paul S. Ray, Joseph D. Romano, Jessie C. Runnoe, Alexander Saffer, Shashwat C. Sardesai, Ann Schmiedekamp, Carl Schmiedekamp, Kai Schmitz, Brent J. Shapiro-Albert, Xavier Siemens, Joseph Simon, Magdalena S. Siwek, Sophia V. Sosa Fiscella, Ingrid H. Stairs, Daniel R. Stinebring, Kevin Stovall, Abhimanyu Susobhanan, Joseph K. Swiggum, Stephen R. Taylor, Jacob E. Turner, Caner Unal, Michele Vallisneri, Alberto Vecchio, Sarah J. Vigeland, Haley M. Wahl, Caitlin A. Witt, David Wright, Olivia Young

“…Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15 yr pulsar-timing data set compiled by the NANOGrav collaboration. …”
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Designing supply chain scenarios and investigating how it affects relevant variables in the National Company of Southern Oil-bearing Regions by Amin Mirshekar, Ghanbar Amirnejad, Mohammad Hemati, Ali Kangarani Farahani

“…Ramezani et al, (2022) in a research entitled "Designing a closed-loop supply chain of perishable products: a scenario-based two-stage stochastic planning approach" used discrete scenarios for scenario creation and assumed that the probabilities and the conditions of each scenario are predetermined. …”
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SEIR epidemiological model with varying infectivity and infinite delay by Gergely Röst, Jianhong Wu

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Bidding Strategy of Virtual Power Plant with Energy Storage Power Station and Photovoltaic and Wind Power by Zhongfu Tan, Qingkun Tan, Yuwei Wang

“…Assuming that the upper and lower limits of the combined output of photovoltaic and wind power are stochastically variable, the fluctuation range of the day-ahead energy market and capacity price is stochastically variable. …”
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Noise-augmented chaotic Ising machines for combinatorial optimization and sampling by Kyle Lee, Shuvro Chowdhury, Kerem Y. Camsari

“…Abstract Ising machines are hardware accelerators for combinatorial optimization and probabilistic sampling, using stochasticity to explore spin configurations and avoid local minima. …”
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Global Stability of Two-Group Epidemic Models with Distributed Delays and Random Perturbation by Xiaoming Fan, Zhigang Wang, Xuelian Xu

“…Our research shows that the endemic equilibrium of the model with distributed delays and random perturbation is stochastically asymptotically stable in the large. In addition, a sufficient stability condition is obtained by constructing suitable Lyapunov function.…”
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Mechanisms of How Random Input Controls Bursting Gene Expression by Sijia Xiao, Yan Wang, Zhigang Wang, Haohua Wang

“…The process of gene expression is affected by many extracellular stimulus signals, and the stochasticity of these signals reshapes gene expression. …”
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A Simple Wide Range Approximation of Symmetric Binomial Distribution by Tamás Szabados

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Analysis and Design of Networked Control Systems with Random Markovian Delays and Uncertain Transition Probabilities by Li Qiu, Chengxiang Liu, Fengqi Yao, Gang Xu

“…Using Lyapunov stability theory, a result is established on the Markovian structure and ensured that the closed-loop system is stochastically stable. A simulation example illustrates the validity and feasibility of the results.…”
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Finite-Time Passivity and Passification Design for Markovian Jumping Systems with Mode-Dependent Time-Varying Delays by Chao Ma

“…By employing the Lyapunov-Krasovskii functional method, delay-dependent sufficient criteria are derived to ensure the mean-square stochastically finite-time passivity. Based on the established results, mode-dependent passification controller is further designed in terms of linear matrix inequalities, such that the prescribed passive performance index of the resulting closed-loop system can be satisfied. …”
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Observer-Based Control Design for Nonlinear Networked Control Systems with Limited Information by Yilin Wang, Zhengrong Xiang, Hamid Reza Karimi

“…Based on the obtained model, a stability criterion is developed by constructing an appropriate Lyapunov-Krasovskii functional and sufficient conditions for the existence of a dynamic quantized output feedback controller which are given in terms of linear matrix inequalities (LMIs) such that the augmented error system is stochastically stable with an performance level. An example is presented to illustrate the effectiveness of the proposed method.…”
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