Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations by Yuhang Zhang, Ming Ou, Liang Chen, Yi Hao, Qinglin Zhu, Xiang Dong, Weimin Zhen

“…This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

“…The first layer applies three different ensemble machine-learning models (random forests, extra random trees, and LightGBM), trained on the modern taxon assemblage and associated environmental data to make reconstructions based on the three different models, while the second layer uses multiple linear regression to integrate these three reconstructions into a consensus reconstruction. …”
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Enhancing Wi-Fi 6 spectrum access control with a heuristic OFDMA back-off mechanism by Abdul Rehman, Faisal Bashir Hussain, Rashid Ali, Hassan Jalil Hadi, Naveed Ahmad

“…The IEEE 802.11ax standard, known as Wi-Fi 6, employs a centralized, multiuser, uplink Orthogonal Frequency Division Multiple Access (OFDMA)-based Random Access (UORA) mechanism to improve user efficiency and network capacity in dense environments. …”
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Evaluating Diverse Meta-modeling Approaches for Predicting Performance Characteristics of a Twin Air Intake Based on Experimental Data by Human AMIRI, U. C. Kucuk, O. Kucukoglu, Y. F. Kuscu, O. V. Ozdemır

“…Their diverse designs, ranging from conventional shapes to innovative configurations, coupled with the intricate interplay of fluid dynamics, boundary layer effects, and structural considerations, render the determination of their performance characteristics a time-consuming task. …”
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Subliminal Channels in CRYSTALS-Kyber Key-Encapsulation Mechanism and Their Use in Quantum-Resistant TLS Protocols by Roberto Roman, Rosario Arjona, Iluminada Baturone

“…In the found subliminal channels, the covert message is embedded in the random data needed by the encapsulation or the key generation algorithms. …”
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A warning model for predicting patient admissions to the intensive care unit (ICU) following surgery by Li Li, Hongye He, Linjun Xiang, Yongxiang Wang

“…Subsequently, the effectiveness of logistic regression, random forest, support vector machine, and multi-layer perceptron algorithms was compared using ROC curves. …”
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Tunable Thin Film Periodicities by Controlling the Orientation of Cylindrical Domains in Side Chain Liquid Crystalline Block Copolymers by Lei Dong, Alvin Chandra, Kevin Wylie, Yuta Nabae, Teruaki Hayakawa

“…In the thin film study, poly(methyl methacrylate-random-2,2,2-trifluoroethyl methacrylate-random-methacrylic acid) (PMMA-ran-PTFEMA-ran-PMAA) solution was used as BSLs for tuning the desired periodicities. …”
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Eco-Friendly Materials for Temporary Use in Architecture and Decorations by Walanrak Poomchalit, Ponlapath Tipboonsri, Boonsong Chongkolnee, Supaaek Pramoonmak, Watthanaphon Cheewawuttipong, Anin Memon

“…Two processing methods were studied: (1) random dispersion of SC at 0, 2, 4, 6, 8, and 10 wt%, and (2) single and double-layer SC composite sheets made with 6 wt% SC. …”
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Recognition of Conus species using a combined approach of supervised learning and deep learning-based feature extraction. by Noshaba Qasmi, Rimsha Bibi, Sajid Rashid

“…In this report, we propose an ensemble learning strategy based on the combination of Random Forest (RF) and XGBoost (XGB) methods. We used 47,600 cone shell images of uniform size (224 x 224 pixels), which were split into an 80:20 train-test ratio. …”
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Effect of work-rate on synergy of corrosion and wear of nickel-based 690 alloys under fretting conditions by Wenjie Pei, Shengzan Zhang, Mengyuan Ma, Wei Tan, Guorui Zhu

“…The work-rate can characterize random vibrations over time. The effect of work-rate under random vibration on the fretting corrosion of nickel-based 690 alloys in high-temperature and high-pressure environments was studied. …”
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A Simple Numerical Modeling of the Effect of the Incoherent Thick Substrate in Thin-Film Solar Cells Based on the Equispaced Thickness Method by Kyungnam Kang, Sanghwa Lee, Jungho Kim, Sungchul Kim, Younho Han, Seungin Baek

“…For comparison, the reflectance spectra in the same structures are calculated by taking the average over coherent calculation results for a large number of random thicknesses of the incoherent layer. According to the comparison of the calculated statistical deviations from the exact solution between the ETM and the random thickness method, the ETM reduces the number of simulations by at least a factor of 50 with the same accuracy.…”
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Forecasting Wind Farm Production in the Short, Medium, and Long Terms Using Various Machine Learning Algorithms by Gökhan Ekinci, Harun Kemal Ozturk

“…Five machine learning algorithms were employed—Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors Regression (KNN), and Multi-Layer Perceptron (MLP ANN)—utilizing both MinMax and Standard Scaling methods. …”
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Routing algorithm for heterogeneous computing force requests based on computing first network by ZHANG Gang, LI Xi

“…This algorithm was designed from both local and global perspectives: to ensure fast convergence to the target solution locally, a single parameter satisfying the randomness strategy was used to initialize the population, making it widely dispersed in the solution space; adopting a multi-parameter solution (or path) balanced selection strategy for selection operations, making the selected population rich and diverse; adopting a two-layer crossover strategy for crossover operations, with the aim of expanding the breadth of global search; adopting a multi parameter random single point mutation strategy for mutation operations, with the aim of deepening local search capabilities. …”
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Potentiodynamic polarization analysis with various corrosion inhibitors on A508/IN-182/IN-52M/308L/316L welds by Chaur-Jeng Wang, Prihatno Kusdiyarto, Yi-Hong Li

“…Additionally, Inconel 52M served as an overlay layer, and material 308 acted as a buffer layer to facilitate bonding between 316L and the overlay welding layer. …”
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BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs. by Xiyang Sun, Fumiyasu Komaki

“…Additional analyses confirm that optimizing message components, model layer and teleportation proportion further enhances the model performance. …”
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Medical Specialty Classification Using Large Language Models (LLMs) by Surya Kathirvel, Lenin Mookiah

“…Machine Learning approaches, such as Random Forest and Multi-Layer Perceptron, also exhibited strong performance, whereas BERT-based models achieved lower accuracy, around 63%. …”
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Interlayer interactions in La_{3}Ni_{2}O_{7} under pressure: From s^{±} to d_{xy}-wave superconductivity by Lauro B. Braz, George B. Martins, Luis G. G. V. Dias da Silva

“…We investigate the role of interlayer interaction terms in the competition between different superconducting gap symmetries in the bilayer nickelate La_{3}Ni_{2}O_{7} under high pressure. We study a two-layer, two-orbital electron model that encompasses both intra- and interlayer Coulomb interaction terms within the matrix random-phase approximation. …”
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Self-Organization-Based Fabrication of Stable Noble-Metal Nanostructures on Large-Area Dielectric Substrates by Victor Ovchinnikov, Andriy Shevchenko

“…A cost-effective fabrication of random noble-metal nanostructures with a feature size of the order of 10 nm on a large-area dielectric substrate is described. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…The model integrates random forest decision-making with multi-layer perceptron feature learning, incorporating sentiment analysis and advanced data processing pipelines to generate personalized learning trajectories while maintaining model transparency. …”
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An improved extreme learning machine algorithm for prospectivity mapping of copper deposits using multi-source remote sensing data: a case study in the North Altyn Tagh, Xinjiang,... by Boqi Yuan, Qinjun Wang, Wentao Xu, Chaokang He, Wenyue Xie

“…Traditional extreme learning machine (ELM) model suffers from instability due to random initialization of input weights and hidden-layer bias, often resulting in suboptimal predictive performance. …”
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