Mineralization Dynamics of Pineapple Harvest Residues as Affected by Placement on Acrisoil in Benin by Elvire Line Sossa, Codjo Emile Agbangba, Pierre Gbènoukpo Tovihoudji, Jamali Oladédji Ayifimi, Bana Donsaré Nadège Bouko, Oloushègun Isidore Achille Falolou, Guillaume Lucien Amadji

“…The residues decomposition was monitored through random sampling of eight litter bags each 60 days for the determination of residual weight, carbon (C), nitrogen (N), phosphorus (P), and potassium (K) contents. …”
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Meso-Complexity Computer Simulation Investigation on Antiexplosion Performance of Double-Layer Foam Aluminum under Pore Grading by Zhen Wang, Wen Bin Gu, Xing Bo Xie, Yu Tian Chen, Lei Fu

“…Based on the Voronoi algorithm, a three-dimensional foam aluminum generation algorithm with random pore size and random wall thickness was written by using the Python language and Fortran language. …”
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Accuracy of Estimating Periodontitis and Its Risk Association Using Partial-Mouth Recordings for Surveillance Studies: A Systematic Review and Meta-Analysis by Yasmine N. Alawaji, Abdulsalam Alshammari, Jolanta Aleksejuniene

“…Overall, the PRP with the highest accuracy and precision mainly included (1) a full-mouth protocol at the following partial sites: mesiobuccal-midbuccal-distolingual (MB-B-DL), mesiobuccal-distolingual (MB-DL), mesiobuccal-midbuccal-distobuccal (MB-B-DB), mesiobuccal-distobuccal (MB-DB), and 84 sites using the random site selection method (RSSM) and (2) random-half-mouth (RHM) protocols. …”
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The method for evaluation errors of automated measurements of base tangent length by three-axis indicator on five-axis CNC machine by D. T. Safarov, A. G. Kondrashov

“…The data of experimental estimation of random and systematic errors of measurement of the length of the base tangent on a five-coordinate CNC machine with a three-coordinate indicator of Mahr are presented. …”
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IoT Network Anomaly Detection in Smart Homes Using Machine Learning by Nadeem Sarwar, Imran Sarwar Bajwa, Muhammad Zunnurain Hussain, Muhammad Ibrahim, Khizra Saleem

“…Attack categories include binary class, multiclass class, and subclasses. Results show Random Forest algorithm outperforms enough to use this methodology in smart environments.…”
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Modulation of Corticospinal Excitability Depends on the Pattern of Mechanical Tactile Stimulation by Sho Kojima, Hideaki Onishi, Shota Miyaguchi, Shinichi Kotan, Ryoki Sasaki, Masaki Nakagawa, Hikari Kirimoto, Hiroyuki Tamaki

“…MS intervention was defined as simple, lateral, rubbing, vertical, or random. Simple intervention stimulated the entire finger pad at the same time. …”
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Machine Learning Algorithm for Estimating Surface PM2.5 in Thailand by Pawan Gupta, Shanshan Zhan, Vikalp Mishra, Aekkapol Aekakkararungroj, Amanda Markert, Sarawut Paibong, Farrukh Chishtie

“…The integrated data then used to train and validate a supervised MLA’ random forest’ to estimate hourly and daily PM2.5 concentrations. …”
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Prevalence of diabetes and prediabetes in South Asian countries: a systematic review and meta-analysis by Masum Ali, Md Mahbub Alam, M. A. Rifat, Sonjida Mesket Simi, Sneha Sarwar, Md Ruhul Amin, Sanjib Saha

“…The quality of the included articles was assessed using the Newcastle–Ottawa Scale. Both random-effect (Der Simonian-Laird inverse variance) and fixed-effect models were used to perform meta-analyses followed by meta-regression. …”
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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…This was 35.56%, 37.41%, and 32.03% lower than the random forest, support vector machine, and neural network models, respectively. …”
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The Effects of Emotional Schema Therapy on Social Health and Attitude Towards Social Harms Among Female Students by Fatemeh Eskandari, Milad Abedi ghlich ghashlaghi

“…Subsequently, using a random dice roll participants were allocated at random to either the experimental (n=15) or the control group (n=15). …”
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Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning by D. Kothandaraman, N. Praveena, K. Varadarajkumar, B. Madhav Rao, Dharmesh Dhabliya, Shivaprasad Satla, Worku Abera

“…In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM2.5 pollutants in polluted cities. …”
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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features by Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, Tariq Shahzad, Wasim Ahmad, Afsha Bibi, Mamoon M. Saeed, Habib Hamam

“…Abstract This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, and SVR—aimed at precisely estimating solar and wind power generation to enhance renewable energy forecasting. …”
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The Application of Structural Reliability and Sensitivity Analysis in Engineering Practice by Urszula Radoń, Paweł Zabojszcza

“…Furthermore, we use Sobol indices to describe the sensitivity of the failure probability to input random variables. Computations of the Sobol indices are carried out using the classic Monte Carlo method. …”
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METHOD AND ALGORITHMS OF CALCULATION OF PARAMETERS OF RELIABILITY IN ACCORDANCE WITH MULTIVARIATE DATA by E. M. Farhadzadeh, A. Z. Muradaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…Statistical functions of fiducial distributions are obtained by imitating modeling of samples of random variables for a series of numbers of random variables samples. …”
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TS-PAID: A Two-Stage PUF-Based Lightweight Authentication Protocol for Internet of Drones by Shehzad Ashraf Chaudhry, Azeem Irshad, Bander A. Alzahrani, Ahmad Alhindi, Mohd Shariq, Ashok Kumar Das

“…The security properties of TS-PAID are proven under formal analysis based on Real-or-Random (RoR) random oracle model. The performance findings exhibit practicality and effectiveness for the proposed scheme against the comparative studies.…”
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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. …”
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Attenuation Correction Effects in Rainfall Estimation at X-Band Dual-Polarization Radar: Evaluation with a Dense Rain Gauge Network by Young-A Oh, DaeHyung Lee, Sung-Hwa Jung, Kyung-Yeub Nam, GyuWon Lee

“…This result indicates that temporal accumulation or spatial average (aggregation) is important to reduce random error.…”
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A Millimeter-Wave Single-Bit Reconfigurable Intelligent Surface With High-Resolution Beam-Steering and Suppressed Quantization Lobe by Aditya S. Shekhawat, Bharath G. Kashyap, Russell W. Raldiris Torres, Feiyu Shan, Georgios C. Trichopoulos

“…We incorporate fixed, random phase delays at each unit cell of the surface which breaks the periodicity of the phase quantization error and suppresses side lobes. …”
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Multimodal Zero-Shot Shelf Deformation Detection Based on MEMS Sensors and Images by Hong Yan, Jingjing Fan, Yajun Liu

“…The experimental results demonstrate that both Random Forest and GBDT achieved precision, recall, and F1 scores exceeding 95%, confirming the effectiveness and accuracy of the proposed method in shelf deformation detection. …”
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Biodiversity and integration of ecological characteristics of species in spatial pattern analysis by Z. Mohebi, H. Mirzaei

“…While, in high-diversity communities with a predominant cover of gross, spatial distribution was random and distance and quadrat indices were more convergent. …”
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