Filling the Atlantic coastal tree-ring reconstruction gap: A 195-year record of growing season discharge of the Sainte-Anne River, Gaspésie, Québec, Canada by A.V. Pace, J.-M. St-Jacques, D.D. Noel, G. Fortin

“…A principal components analysis (PCA) and correlation analysis with GRUN (the 5°x5° gridded multi-model ensemble of runoff of Ghiggi et al. (2019. Earth Syst. …”
Get full text

Extensive composable entropy for the analysis of cosmological data by Constantino Tsallis, Henrik Jeldtoft Jensen

“…Being extensive means that in the micro-canonical, or uniform, ensemble where all micro-state occur with the same probability, the entropy becomes proportional to N asymptotically: S(N)∝N for N→∞. …”
Get full text

Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong

“…Sentinel-2 showed higher performance than PRISMA up to 20 m (~18% lower MAE), attributed to training with a larger number of images and employing an ensemble prediction, while PRISMA outperformed Sentinel-2 for depths between 25 m and 30 m (~19% lower MAE). …”
Get full text

Discovery and Characterization of Two Selective Inhibitors for a Mu-Class Glutathione S-Transferase of 25 kDa from <i>Taenia solium</i> Using Computational and Bioinformatics Tools by César Sánchez-Juárez, Roberto Flores-López, Lluvia de Carolina Sánchez-Pérez, Ponciano García-Gutiérrez, Lucía Jiménez, Abraham Landa, Rafael A. Zubillaga

“…Representative structures from these simulations and a putative binding site with low conservation relative to human GSTs, identified via the SILCS methodology, were employed for virtual screening through ensemble docking against a commercial compound library. …”
Get full text

Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest by Mohsin Ali Khan, Shazim Ali Memon, Furqan Farooq, Muhammad Faisal Javed, Fahid Aslam, Rayed Alyousef

“…The influential parameters that are considered as input variables for modelling are curing temperature T, curing time t, age of the specimen A, the molarity of NaOH solution M, percent SiO2 solids to water ratio % S/W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate ( % AG), fine aggregate to the total aggregate ratio F/AG, sodium oxide (Na2O) to water ratio N/W in Na2SiO3 solution, alkali or activator to the FA ratio AL/FA, Na2SiO3 to NaOH ratio Ns/No, percent plasticizer (% P), and extra water added as percent FA EW%. RFR is an ensemble algorithm and gives outburst performance as compared to GEP. …”
Get full text

Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. …”
Get full text

Multistability of synchronous modes in a multimachine power grid with a common load and their global and non-local stability by Khramenkov, Vladislav Anatolevich, Dmitrichev, Aleksej Sergeevich, Nekorkin, Vladimir Isaakovich

“…To study the dynamics of a power grid we use the effective network model in the form of an ensemble of globally coupled nodes-generators. The state of every node is described by the swing equation. …”
Get full text

Emergent constraints on global soil moisture projections under climate change by Lei Yao, Guoyong Leng, Linfei Yu, Hongyi Li, Qiuhong Tang, Andre Python, Jim W. Hall, Xiaoyong Liao, Ji Li, Jiali Qiu, Johannes Quaas, Shengzhi Huang, Yin Jin, Jakob Zscheischler, Jian Peng

“…Here we identify strong physically-based emergent relationships between soil moisture change (2070–2099 minus 1980–2014) and recent air temperature and precipitation trends across an ensemble of climate models. We extend the commonly used univariate Emergent Constraints to a bivariate method and use observed temperature and precipitation trends to constrain global soil moisture changes. …”
Get full text

EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations by David Camarena-Martinez, Juan P. Amezquita-Sanchez, Martin Valtierra-Rodriguez, Rene J. Romero-Troncoso, Roque A. Osornio-Rios, Arturo Garcia-Perez

“…This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. …”
Get full text

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

“…This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). …”
Get full text

Spatiotemporal characteristics of future precipitation variability in the Tianshan Mountain region of China by Xianglin Lyu, Junkai Du, Yaqin Qiu, Yangwen Jia, Chunfeng Hao, Hao Dong

“…Changes have affected the water cycle and resource stability in the Tianshan Mountain region in China (TM), the primary water source of Central Asia. Based on ensemble precipitation data from the NEX-GDDP-CMIP6 dataset, this study analyzed multiscale changes in precipitation variability in the TM for the near (2030–2065) and far future (2066–2100) using Shannon entropy, offering valuable insights for water resource management and future challenges. …”
Get full text

Multi‐Model Assessment of Groundwater Recharge Across Europe Under Warming Climate by Rohini Kumar, Luis Samaniego, Stephan Thober, Oldrich Rakovec, Andreas Marx, Niko Wanders, Ming Pan, Falk Hesse, Sabine Attinger

“…The findings highlight the importance of using multi‐model ensembles to assess future groundwater recharge changes in Europe and emphasize the need to mitigate impacts in higher warming scenarios.…”
Get full text

Blockchain-Driven Optimized Chaotic Encryption Scheme for Medical Image Transmission in IoT-Edge Environment by Goli Archana, Rajeev Goyal, K. M. V. Madan Kumar

“…This research study presents the block chain driven medical image encryption technique using modified honey badger optimization with the ensemble chaotic systems. The proposed block chain framework uses the divergent methods that integrates differential scroll, Hénon chaotic maps and modified honey badger optimization to generate the optimum keys and high secured image data. …”
Get full text

Les Archives photographiques Notman au Musée McCord by Noëlle Charpentier

“…Noëlle Charpentier présente les Archives Notman, un important ensemble de photographies, négatifs et matériel photographiques entreposé au musée McCord d’Histoire canadienne à Montréal. …”
Get full text

Improving drug repositioning with negative data labeling using large language models by Milan Picard, Mickael Leclercq, Antoine Bodein, Marie Pier Scott-Boyer, Olivier Perin, Arnaud Droit

“…We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. …”
Get full text

Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm by Kai Zhang, Yujun Zhang, Kun You, Ying He, Qiankun Gao, Guohua Liu, Chungui He, Yibing Lu, Boqiang Fan, Qixing Tang, Wenqing Liu

“…In this paper, an adaptive ensemble empirical mode decomposition (EEMD) denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NOX. …”
Get full text

Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms by Yucong Gu, Kaiwen Wang, Zhengyu Zhang, Yi Yao, Ziming Xin, Wenjun Cai, Lin Li

“…The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. …”
Get full text

بررسی دوره بازگشت و احتمال رخداد بارش در ایران بر اساس پایگاه داده بارش همادی-وزنی چند منبعی (MSWEP) by آذر زرین, عباسعلی داداشی رودباری

“…بیشینه عدم قطعیت در احتمال رخداد بارش روزانه در مناطق کوهستانی ایران مشاهده شد. [1] Multi-Source Weighted-Ensemble Precipitation (MSWEP)[2] Generalized Extreme Value (GEV)…”
Get full text

An analysis of observed and predicted extreme heat and precipitation trends across four pulse producing regions in North America: North Dakota, Montana, Saskatchewan, and Northeast... by Sophia Weiss, Erin Coughlan de Perez

“…We analyze climate trends using the Unprecedented Simulated Extreme Ensemble method with historical data, and we compare results to the average future change in the ssp370 scenario, which is a medium-high climate change scenario of 40 models of the the Coupled Model Intercomparison Project (CMIP6) in four pulse growing regions across North America: Montana, North Dakota, Saskatchewan, and the Northeast USA. …”
Get full text

Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin by Abdul-Muaizz Koray, Emmanuel Gyimah, Mohamed Metwally, Hamid Rahnema, Olusegun Tomomewo

“…Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. …”
Get full text