INFLUENCE OF ORIENTATION ERRORS ASSOCIATED WITH THE USE OF A MAGNETIC COMPASS ON THE ACCURACY OF DETERMINING THE POSITION OF THE PALEOMAGNETIC POLE AND THE AMPLITUDE OF PALEOSECULA... by D. A. Ushakov, I. E. Lebedev, V. E. Pavlov

“…The use of a magnetic compass in paleomagnetic studies of highly magnetic rocks (for instance, basalts) can lead to large errors in the orientation of paleomagnetic samples. On the other hand, alternative methods of orientation are relatively time-consuming, and in the case of using a solar compass, they also require sunny weather – a condition that is rarely met, especially when sampling at high and subpolar latitudes. …”
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A Random Compressive Sensing Method for Airborne Clustering WSNs by Wei Zhou, Bo Jing, Yifeng Huang

“…In this scheme, hardware resource limited cluster members sample the input signals with random sampling sequence and then transmit the sampling signals to the cluster head or Sink to reconstruct. …”
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Testing the Dual-State-Process assumption in the preventive care services use by Dimitris Zavras

“…The survey used stratified random sampling, and the sample selection strata were defined by age, gender, urbanity status of permanent residence and prefecture. …”
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Effect of Glass Fiber Reinforcement on the Mechanical Properties of Polyester Composites by I. R. Antipas

“…The technique of creating samples and methods of their testing were described. …”
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Facets of Random Symmetric Edge Polytopes, Degree Sequences, and Clustering by Benjamin Braun, Kaitlin Bruegge, Matthew Kahle

“…We investigate the facet structure of symmetric edge polytopes for various models of random graphs. For an Erd\H{o}s-Renyi random graph, we identify a threshold probability at which with high probability the symmetric edge polytope shares many facet-supporting hyperplanes with that of a complete graph. …”
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Modeling student satisfaction in online learning using random forest by Jinlei Li, Xiaowei Chen

“…To address this gap, this study employs a Random Forest–based framework to model satisfaction using a multidimensional dataset from 782 university students. …”
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Saúde mental, uso de medicamentos e mudanças de hábitos em idosos durante a pandemia de COVID-19: uma análise com amostra aleatória = Mental Health, Medication usage and Habit Changes Among the Elderly During the COVID-19 Pandemic: An Analysis with a Random Sample = Salud mental, uso de medicamentos y cambios de hábitos en ancianos durante la pandemia de COVID-19: un análisis con muestra aleatoria by Costa, Fernanda Fagundes, Alves, Vicente Paulo

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Optimizing credit card fraud detection with random forests and SMOTE by P. Sundaravadivel, R. Augustian Isaac, D. Elangovan, D. KrishnaRaj, V. V. Lokesh Rahul, R. Raja

“…The dataset, highly imbalanced with fraudulent transactions representing less than 0.2% of the total, was processed using techniques like Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. …”
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Application of random forests methods to diabetic retinopathy classification analyses. by Ramon Casanova, Santiago Saldana, Emily Y Chew, Ronald P Danis, Craig M Greven, Walter T Ambrosius

“…We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. …”
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Resolving the confusions on the complete spatial randomness of species by Youhua Chen, Yongbin Wu, Tsung-Jen Shen

“…Complete spatial randomness (CSR) describes a fully homogeneous random distribution of species across space, where every location within a study area has an equal probability of hosting individuals. …”
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Random k conditional nearest neighbor for high-dimensional data by Jiaxuan Lu, Hyukjun Gweon

“…The proposed approach aggregates multiple kCNN classifiers, each constructed from a randomly sampled feature subset. We also develop a score metric to weigh individual classifiers based on the level of separation of the feature subsets. …”
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A framework for disentangling ecological mechanisms underlying the island species–area relationship by Jonathan M. Chase, Leana Gooriah, Felix May, Wade A. Ryberg, Matthew S. Schuler, Dylan Craven, Tiffany M. Knight

“…Nevertheless, there is contention about exactly how to estimate the ISAR and the influence of the three primary ecological mechanisms that drive it — random sampling, disproportionate effects, and heterogeneity. …”
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Randomness as a driver of inactivity in social groups. by Abel Bernadou, Raphaël Jeanson

“…We developed a model to explore the conditions under which variations in the scaling of workers' production and maintenance costs, along with activity costs, allow colonies to sustain a fraction of inactive workers. We sampled individual performances according to different random distributions in order to simulate the variability associated with worker efficiency. …”
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Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction by Andres Ramirez-Jaime, Gonzalo R. Arce, Nestor Porras-Diaz, Oleg Ieremeiev, Andrii Rubel, Vladimir Lukin, Mateusz Kopytek, Piotr Lech, Jarosław Fastowicz, Krzysztof Okarma

“…We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. …”
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Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs by Yuan Ge, Yan Zhang, Wengen Gao, Fanyong Cheng, Nuo Yu, Jincenzi Wu

“…This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. …”
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Quantum Advantage from Measurement-Induced Entanglement in Random Shallow Circuits by Adam Bene Watts, David Gosset, Yinchen Liu, Mehdi Soleimanifar

“…For circuits composed of Haar-random two-qubit gates, it is also believed that this coincides with a quantum advantage phase transition in the classical hardness of sampling from the output distribution. …”
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Random priming PCR strategy to amplify and clone trace amounts of DNA by Nianxiang Zou, Susan Ditty, Bingjie Li, Shyh-Ching Lo

“…The second PCR is carried out with a single 19-nucleotide primer that matches the specific 5′ sequence of the partial random primer. Using human and Mycoplasma genitalium DNA as examples, we demonstrated the efficiency of this approach by effectively cloning target DNA fragments from 1 pg DNA sample. …”
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BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification by Yaoyao Zhu, Xiuding Cai, Xueyao Wang, Xiaoqing Chen, Zhongliang Fu, Yu Yao

“…Current SDA methods typically sample the amount of shifting from a Gaussian distribution or the sample variance. …”
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Design of an Encrypted Serial Communication System Based on Pseudo-random Sequences by LI Miao, FAN Linbin, WANG Yan

“…To enhance data security during transmission and communication reliability, this paper proposes an encrypted serial communication system based on pseudo-random sequences. The system adopts the technologies of arbitrary series pseudo-random sequence generation, dynamic sampling window adjustment, and serial code stream scrambling to realize non-transparent data transmission via channels. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…Hyper-parameter tuning was conducted using Bayesian optimization and cross-validation, while the performance of random forests was evaluated in comparison to gradient boosting, extreme gradient boosting, artificial neural networks, and the generalized linear model.FINDINGS: The random forests models, particularly those utilizing under sampling and cost-sensitive learning, demonstrated superior performance, achieving sensitivity of 0.81, Specificity of 0.85, accuracy of 0.85, and an area under the receiver operating characteristic curve of 0.89 on an independent test set. …”
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