Application of a GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants by Jiayun JI, Xiao XIAO, Pinlu YANG, Yang LIU, Yahong ZHOU

“…To overcome this, two neural network models—GA-BP and standard BP—were applied to predict Mn2+ concentrations at unsampled locations. …”
Get full text

Traffic Capacity Assessment of the Urban Elevated Bridge after Near-Field Explosion Based on the Response Surface Method by Jinghui Jiang, Chaoyi Xia, Kunpeng Wang, He Xia, Qikai Sun

“…First, the finite element model of a reinforced concrete slab under near-field explosion is established by ANSYS/LS-DYNA software and compared with the experimental results, which verifies the effectiveness of the ALE (arbitrary Lagrangian–Eulerian) algorithm and the accuracy of the mesh size and material properties. …”
Get full text

Different sets of QTLs influence fitness variation in yeast by Gal Hagit Romano, Yonat Gurvich, Ofer Lavi, Igor Ulitsky, Ron Shamir, Martin Kupiec

“…In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein‐interaction networks, candidate genes were defined and validated by gene disruption. …”
Get full text

THREE-DIMENSIONAL GIS MODELING OF THE STRELTSOVSKAYA CALDERA’S COVER ON THE BASIS OF SURFACE RELIEF AND THE BASEMENT’S TOP DIGITAL MODELS by I. O. NAFIGIN, S. A. USTINOV, V. A. PETROV

“…The space between the constructed surfaces due  to the algorithm for creating closed framework of surfaces has been  transformed into a new object representing three-dimensional  volumetric model of the caldera volcano-sedimentary cover. …”
Get full text

Abstraction-based segmental simulation of reaction networks using adaptive memoization by Martin Helfrich, Roman Andriushchenko, Milan Češka, Jan Křetínský, Štefan Martiček, David Šafránek

“…Conclusion  We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.…”
Get full text

Ecological niche modelling using MaxEnt for riparian species in a Mediterranean context by Jean Stephan, Melissa Korban

“…Despite their key environmental role, riparian tree and shrub species gained little attention in ecological niche modeling (ENM), especially in semi-arid environments.This study examines the performance of selected climatic, topographic, and geographic predictors in ENM of obligate and non-obligate riparian tree and shrub species in perennial and intermittent streams in the Mediterranean biome.MaxEnt algorithm was used for ENM. Three models were designed with different sets of predictors with cropped and non-cropped backgrounds around the riparian zone.The models generated different predicted distribution maps by species and compared them with the presence points of the studied species. …”
Get full text

Analyzing key users’ behavior trends in volunteer-based networks by Nofar Piterman, Tamar Makov, Michael Fire

“…These algorithms allowed us to analyze the factors that significantly influence behavior predictions. …”
Get full text

A critical evaluation of Pure Pursuit, MPC and MPCC: Balancing simplicity, performance and constraints by Kong Xiangyiming, Fisher Callen, Evans Benjamin

“…Here we present an investigation into three prominent control strategies in a critical comparative analysis. These control algorithms include Pure Pursuit (PP), Model Predictive Control (MPC) and Model Predictive Contouring Control (MPCC). …”
Get full text

Adaptive Weighted Access Point Technique for Robust Dynamic Indoor Positioning by Aqilah Binti Mazlan, Yin Hoe Ng

“…This approach involves constructing a radio map based on the wireless signal properties and employing matching algorithms, such as the nearest neighbor, to predict the position of target. …”
Get full text

Weighted frequent gene co-expression network mining to identify genes involved in genome stability. by Jie Zhang, Kewei Lu, Yang Xiang, Muhtadi Islam, Shweta Kotian, Zeina Kais, Cindy Lee, Mansi Arora, Hui-Wen Liu, Jeffrey D Parvin, Kun Huang

“…In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. …”
Get full text

Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes by Jia Qu, Mei-Huan Wang, Yue-Hua Gao, Hua-Wei Zhang

“…Methods: The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). …”
Get full text

Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review by Arnick Abdollahi, Marta Yebra

“…The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. …”
Get full text

Combination treatment optimization using a pan-cancer pathway model. by Robin Schmucker, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, Tuomas Sandholm

“…In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. …”
Get full text

Modelling and optimisation of the surface‐mounted permanent magnet machine with multi‐level array magnets by Longxuan Li, Zhaoliang Chen, Wenliang Zhao, Chengwu Diao, Byung‐il Kwon

“…Then, the back electromotive force is predicted by analysing the winding distribution and radial air‐gap flux density. …”
Get full text

Multi‐Model Ensembles for Upper Atmosphere Models by S. Elvidge, S. R. Granados, M. J. Angling, M. K. Brown, D. R. Themens, A. G. Wood

“…Abstract Multi‐model ensembles (MMEs) are used to improve the forecasts of thermospheric neutral densities. A variety of algorithms for constructing the model weights for the MMEs are described and have been implemented including: performance weighting, independence weighting, and non‐negative least squares. …”
Get full text

Calibration and uncertainty quantification for deep learning-based drought detection by Mengxue Zhang, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Gustau Camps-Valls

“…To address this shortcoming, we introduce methodologies for model calibration and entropy-based uncertainty quantification for deep learning-based drought detection. The calibration algorithm can deal with calibration errors by reducing distributional shifts and alleviating overconfident predictions. …”
Get full text

Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models by Jinfeng Guo, Dong Cui, Jinxing Guo, Umut Hasan, Fengqi Lv, Zixing Li

“…In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. …”
Get full text

Investigating the Effectiveness of Option Pricing Using Machine learning models Compared to the Black-Scholes model by Reza Mahdavi, Eslam Fakher, Hasanali Sinaei

“…The results indicate that machine learning algorithms outperformed the Black-Scholes model in predicting option prices. …”
Get full text

Machine learning developed immune-related exosome signature for prognosis and immunotherapy benefit in bladder cancer by Xiaoting Luo, Yi Luo

“…To investigate the performance of IES in predicting the immunotherapy benefit, three immunotherapy datasets (GSE91061, GSE78220 and IMvigor210) and several predicting scores were used. …”
Get full text

The Interconnection Between Pharmaceutical Development and Preclinical Research (Review) by I. N. Ampilogova, M. V. Karlina, V. G. Makarov, M. N. Makarova

“…These examples made it possible to describe a clearer algorithm of actions in the laboratory pharmaceutical development of a new drug from the moment a drug candidate molecule is selected.Conclusion. …”
Get full text