Sensitivity of Self‐Aggregation and the Key Role of the Free Convection Distance by A. Casallas, A. M. Tompkins, C. Muller, G. Thompson

“…Abstract Recently, Biagioli and Tompkins (2023, https://doi.org/10.1029/2022ms003231) used a simple stochastic model to derive a dimensionless parameter to predict convective self aggregation (SA) development, which was based on the derivation of the maximum free convective distance dclr expected in the pre‐aggregated, random state. Our goal is to test and further investigate this hypothesis, namely that dclr can predict SA occurrence, using an ensemble of 24 distinct combinations of horizontal mixing, planetary boundary layer (PBL), and microphysical parameterizations. …”
Get full text

A Two-Phase Embedding Approach for Secure Distributed Steganography by Kamil Woźniak, Marek R. Ogiela, Lidia Ogiela

“…Initially, the secret message is divided into shares using Shamir’s Secret Sharing and embedded into distinct media containers via pseudo-random LSB paths determined by a unique internal stego key. …”
Get full text

A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

“…This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. …”
Get full text

Investigation of Free Vibration Behavior for Composite Sandwich Beams with a Composite Honeycomb Core by Zainab Majid Jasim, Husam Jawad Abdulsamad

“…The typical sandwich structure consists of three layers: face sheets, core, and adhesive bonding, and in this research, the adhesive layer between the face sheets and core was abolished by preparing the overall mold with fibers inside and casting the resin to fill the face sheet and core parts. …”
Get full text

Homeostatic synaptic normalization optimizes learning in network models of neural population codes by Jonathan Mayzel, Elad Schneidman

“…We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This ‘reshaping’ of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. …”
Get full text

Investigation of dual memory behavior in RRAM: coexistence of resistive and capacitive switching phenomena by Hyoseob Kim, Suhan Kim, Jae-Yeong Cho, Sin-Hyung Lee, Min-Hwi Kim

“…Abstract The basic structure of resistive random access memory (RRAM), with an insulator between two metal electrodes, closely resembles that of a capacitor. …”
Get full text

A machine learning approach for wind turbine power forecasting for maintenance planning by Hariom Dhungana

“…The interpretable ML includes Linear Regression (LR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Random Forest (RF); the explainable ML consists of graphical Neural network (GNN); and the blackbox model includes Multi-layer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). …”
Get full text

Robust asphaltene onset pressure prediction using ensemble learning by Jafar Khalighi, Alexey Cheremisin

“…This paper adopts a robust approach to training three machine learning models—Multi-Layer Perceptron (MLP), CatBoost, and Random Forest (RF)—to predict AOP as a function of oil composition, SARA fractions, saturation pressure, and temperature. …”
Get full text

Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks by Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He, Zebing Mao, Xia Huang, Jun Ding

“…Inspired by the Kolmogorov–Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. …”
Get full text

Non-destructive Weight Prediction Model of Spherical Fruits and Vegetables using U-Net Image Segmentation and Machine Learning Methods by Halil Kayra, Savaş Koç

“…Machine learning models such as Multi-Layer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Linear and Stochastic Gradient Descent (SGD) regression models were used for weight predictions. …”
Get full text

ML modeling of ultimate and relative bond strength for corroded rebars based on concrete and steel properties by Alireza Hosseinzadeh Kashani, Mansour Ghalehnovi, Hossein Etemadfard

“…A comprehensive dataset was compiled from experimental studies, and six ML algorithms, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost), were trained to forecast UBS and RBS simultaneously. …”
Get full text

Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings by Boubker Najdi, Mohammed Benbrahim, Mohammed Nabil Kabbaj

“…The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. …”
Get full text

An Improved Fault Diagnosis Method and Its Application in Compound Fault Diagnosis for Paper Delivery Structure Coupling by Fu Liu, Haopeng Chen, Yan Wang

“…Furthermore, the dimensionality reduction results of each network layer are visualized using the T-stochastic neighbor embedding (T-SNE) method, which reveals clear feature patterns and confirms the model’s reliability and effectiveness.…”
Get full text

Optimization of SA-Gel Hydrogel Printing Parameters for Extrusion-Based 3D Bioprinting by Weihong Chai, Yalong An, Xingli Wang, Zhe Yang, Qinghua Wei

“…Process parameters (nozzle diameter <i>d</i>, layer height <i>h</i>, printing speed v₁, extrusion speed v₂) significantly influence hydrogel deposition and structure formation. …”
Get full text

Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

“…Compared to traditional random forest and neural network models, the MSE and MAE show average reductions of 32.3% and 25.3%. …”
Get full text

Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces by Mitar Simić, Milan Medić, Milan Radovanović, Vladimir Risojević, Patricio Bulić

“…EIS data obtained at the estimated characteristic frequency is processed with three equations in the closed form for the parameter estimation of series resistance, charge transfer resistance, and double-layer capacitance. The proposed recursive filter enhances estimation accuracy in the presence of random noise. …”
Get full text

Predicting long-term deposit customers using convolutional neural network and data conversion technique by Adebayo Abdulganiyu Keji, Oluwafemi FAKEYE, Nneka N. ONOCHIE, Olumide SANGOTOKI

“…The highest performance reached by the conventional machine learning models, Random Forest (RF), is 90.78% for accuracy, 59.37% precision, 96.78% recall, and 85.28% F1 score, tested on 412 test samples. …”
Get full text

Uncertainty Modeling of Fouling Thickness and Morphology on Compressor Blade by Limin Gao, Panpan Tu, Guang Yang, Song Yang

“…The FLH model effectively simulates the morphology characteristics of actual blade fouling and elucidates how parameters influence fouling roughness, morphology, and randomness. Based on the uncertainty modeling method, models for dense fouling layer thickness and loose fouling layer morphology are constructed, followed by numerical calculations and aerodynamic performance uncertainty quantification. …”
Get full text

Identifying the Combined Impacts of Sensor Quantity and Location Distribution on Source Inversion Optimization by Shushuai Mao, Jianlei Lang, Feng Hu, Xiaoqi Wang, Kai Wang, Guiqin Zhang, Feiyong Chen, Tian Chen, Shuiyuan Cheng

“…The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. …”
Get full text

Graph Neural Network Learning on the Pediatric Structural Connectome by Anand Srinivasan, Rajikha Raja, John O. Glass, Melissa M. Hudson, Noah D. Sabin, Kevin R. Krull, Wilburn E. Reddick

“…Two GNN models (GCN simple and GCN residual), a deep neural network (multi-layer perceptron), and two standard machine learning models (random forest and support vector machine) were trained. …”
Get full text