AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact. by Monica Golumbeanu, Olivier Briët, Clara Champagne, Jeanne Lemant, Munir Winkel, Barnabas Zogo, Maximilian Gerhards, Marianne Sinka, Nakul Chitnis, Melissa Penny, Emilie Pothin, Tom Smith

“…Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. …”
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Social contact patterns in Vietnam and implications for the control of infectious diseases. by Peter Horby, Quang Thai Pham, Niel Hens, Thi Thu Yen Nguyen, Quynh Mai Le, Dinh Thoang Dang, Manh Linh Nguyen, Thu Huong Nguyen, Neal Alexander, W John Edmunds, Nhu Duong Tran, Annette Fox, Tran Hien Nguyen

“…Although there is a long history of using mathematical models to understand these transmission dynamics, there are still remarkably little empirical data on contact behaviors with which to parameterize these models. Even starker is the almost complete absence of data from developing countries. …”
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Modeling mixing and melting in laminar seawater intrusions under grounded ice by M. S. Mamer, A. A. Robel, C. C. K. Lai, E. Wilson, P. Washam

“…The magnitude of modeled basal melt rates from seawater intrusion can be replicated by existing sub-ice-shelf melt parameterizations by modifying the traditionally used transfer coefficients. …”
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Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks by David Anton, Jendrik-Alexander Tröger, Henning Wessels, Ulrich Römer, Alexander Henkes, Stefan Hartmann

“…Finally, we consider experimental data and show that the results are in good agreement with a finite element method-based calibration. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…The over-parameterized convolutional layers enhance the interactive ability of feature representation and context information. …”
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Comparison of Simulations of Updraft Mass Fluxes and Their Response to Increasing Aerosol Concentration between a Bin Scheme and a Bulk Scheme in a Deep-Convective Cloud System by Seoung Soo Lee, Chang-Hoon Jung, Sen Chiao, Junshik Um, Yong-Sang Choi, Won Jun Choi

“…For this investigation, comparisons of these parameterizations are made between a bin scheme and a bulk scheme. …”
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The Signatures of Density Fluctuations and Mixing Gas in Circumgalactic Absorption Systems by Yakov Faerman, Daniel R. Piacitelli, Matthew McQuinn, Jessica K. Werk

“…Focusing on these physically motivated models, we parameterize each with a single parameter for simplicity and provide empirical and theoretical estimates for reasonable parameter values. …”
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Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks by Dong Liang, Guirong Li, Xiaoyu Liu, Lin Zeng, Hsiao-Dong Chiang, Shouxiang Wang

“…Test results on balanced and unbalanced distribution networks using field data show that the proposed method can achieve better estimation accuracy than the Bayesian SE method without power flow informing. …”
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Drainage Ditches (“Hot Spots”) and Storms (“Hot Moments”) Define Aquatic Greenhouse Gas (CO2, CH4, N2O) Emissions From the Land‐to‐Ocean Aquatic Continuum by Naomi S. Wells, Mustefa Yasin Reshid, Karl Hennig, Matthew Hipsey, Peisheng Huang, Bradley D. Eyre

“…Combining measurements with hydrodynamic modeling enabled us to parameterize physical gas transfer uncertainties, revealing artificial waterways contributed disproportionately to emissions. …”
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Multi-Objective Optimization Design of Dual-Spindle Component Based on Coupled Thermal–Mechanical–Vibration Collaborative Analysis by Xiaoliang Lin, Yiming Xie, Xiaolei Deng, Jing Tian, Yue Han, Peng Wang

“…And the multiple objectives of improving the thermal, static and dynamic characteristics, as well as lightweighting, are achieved. The results show that the mass of the optimized component is reduced by 10.1%; the first-order natural frequency is increased by 3.9%; the coupling deformation of the end face of the left spindle seat is reduced by 5.3%; and the coupling deformation of the end face of the right left spindle seat is reduced by 9.0%, while the temperature of the component hardly increases. …”
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DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature by R. Börner, R. Börner, J. O. Haerter, J. O. Haerter, J. O. Haerter, J. O. Haerter, R. Fiévet, R. Fiévet

“…In spite of this nuanced interaction, many idealized modeling studies of tropical convection either assume a constant, homogeneous SST or, in the case of a responsive sea surface, represent the upper ocean by a slab with fixed thickness. Here we show that slab ocean models with constant heat capacity fail to capture the wind-dependence of observed diurnal sea surface warming. …”
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Coupled Alternating Neural Networks for Solving Multi-Population High-Dimensional Mean-Field Games by Guofang Wang, Jing Fang, Lulu Jiang, Wang Yao, Ning Li

“…In multi-group quadcopter trajectory-planning numerical experiments, the convergence results of HJB residuals, control, and average speed show the effectiveness of the CA-Net algorithm, and the comparison with baseline methods—cluster game, HJB-NN, Lax–Friedrichs, ML, and APAC-Net—shows the progressiveness of our solution method.…”
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WeatherClean: An Image Restoration Algorithm for UAV-Based Railway Inspection in Adverse Weather by Kewen Wang, Shaobing Yang, Zexuan Zhang, Zhipeng Wang, Limin Jia, Mengwei Li, Shengjia Yu

“…This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. …”
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Transit Analysis Package: An IDL Graphical User Interface for Exoplanet Transit Photometry by J. Zachary Gazak, John A. Johnson, John Tonry, Diana Dragomir, Jason Eastman, Andrew W. Mann, Eric Agol

“…Reanalysis of ground-based observations of TrES-1b, WASP-4b, and WASP-10b (Winn et al., 2007, 2009; Johnson et al., 2009; resp.) and space-based Kepler 4b–8b (Kipping and Bakos 2010) show good agreement between TAP and those publications. …”
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Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems by S. Isaac, C. Kadurumba, H. Ugwu, F. Abam

“…Thus, the pumping process was parameterized and parametrically characterized to define the performance and control parameters quantitatively. …”
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Research on the Real-Time Scoring of Chest Ring Target Based on Transfer Learning and Improved Lightweight Neural Network by Minghui Meng, Chuande Zhou

“…First, the Repvit-MobileNet block (re-parameterized depth wise separable mobile network module) is introduced into its backbone network to reduce model size and computational complexity while improving detection speed. …”
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Thickness Model of the Adhesive on Spacecraft Structural Plate by Yanhui Guo, Peibo Li, Yanpeng Chen, Xinfu Chi, Yize Sun

“…The simulation and experimental results show that the variation trend of coating thickness with the influence of variables is consistent. …”
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The surface tension and cloud condensation nuclei (CCN) activation of sea spray aerosol particles by J. Kleinheins, N. Shardt, U. Lohmann, C. Marcolli

“…Since common climate models use parameterizations that are based on classical Köhler theory, these results suggest that the CCN activity of small SSA particles might be underestimated in climate models.…”
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Aerodynamic Optimization Method for Propeller Airfoil Based on DBO-BP and NSWOA by Changjing Guo, Zhiling Xu, Xiaoyan Yang, Hao Li

“…The CST (Class Function/Shape Function Transformation) airfoil parametrization method is used to parameterize the original airfoil, and Latin hypercube sampling is employed to perturb the original airfoil within a certain range to generate a sample space. …”
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Reduced Geostatistical Approach With a Fourier Neural Operator Surrogate Model for Inverse Modeling of Hydraulic Tomography by Quan Guo, Yu He, Ming Liu, Yue Zhao, Yuzheng Liu, Jian Luo

“…FNO is a deep learning model that directly parameterizes the integral kernel in Fourier space to learn solution operators for parametric partial differential equations (PDEs). …”
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