Electromechanical Analogies for the Theory of Consolidation by R. Uklejewski, M. Krakowski

“…As an example of such application, an algorithm of the method of determination of elastic co­efficients of a porous medium is presented. …”
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Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models by Q. Hou, Z. Gao, Z. Gao, Z. Duan, M. Yu

“…Experimental outcomes revealed that the interpolations from Transformer_CNN surpassed the traditional single artificial intelligence model approaches. The coefficient of determination (<span class="inline-formula"><i>R</i>²</span>) reached 0.95 in the sensible heat flux test set and 0.90 in the latent heat flux test set, thereby confirming the applicability of the Transformer_CNN model for data interpolation of turbulent heat flux on the Tibetan Plateau. …”
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Relation between maxillary sinus pneumatization and the thickness of the mandibular inferior border in panoramic radiographs by Nasim Jafari-pozve, Sayed Hanif EmamJome, Mahshad Saei

“…If a relation between the thickness of the mandibular cortex and maxillary sinus pneumatization is proven, future bone availability can be deducted. Material and methods: In this cross-sectional study, 85 panoramic radiographs were evaluated. …”
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Rapid Analysis of Heavy Metal Element Adsorption by SCG Based on LIBS Technology by Xie Hairui, You Libing, Fang Xiaodong, Li Leyi, Ding Haokai, Zhou Zhengqi, Zhang Guanyang, Zhang Dongjian

“…Using Cu as the internal standard element, the Cd/Cu intensity ratios from the spectral tests of 16 calibration samples were fitted with the different Cd mass fractions in SCG to obtain a calibration model, with the polynomial fitting determination coefficient (R²) reaching 0.998. Eight adsorption samples were tested, and the calibration model was used to analyze the adsorption capacity of SCG for Cd solutions of different concentrations. …”
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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples by Abdullahi G. Usman, Sagiru Mati, Hanita Daud, Ahmad Abubakar Suleiman, Sani I. Abba, Hijaz Ahmad, Taha Radwan

“…The performance of the models was assessed via mean square error, correlation coefficient, and Nash–Sutcliffe efficiency. The SVR-HHO model outperformed existing methods by 4–7% in both the two learning (training and testing) phases respectively. …”
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Assessing Discrimination and Acceptance for Lightness and Chroma During Shade Selection: A Comparison of Dental and Non-Dental Professionals by Haruna Konishi, Yuichi Ishida, Takaharu Goto, Tetsuo Ichikawa

“…<b>Background/Objectives</b>: Tooth color is key in determining aesthetic appearance during restorative and prosthodontic treatments. …”
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Simulation Study on the Accuracy of Modified Distributed Beamforming in Phased Array Weather Radars for Various Precipitation Events by Steffy Benny, Swaroop Sahoo, S. Athira, V. Chandrasekar

“…The simulated measurements are then used to estimate the radar variables from DB, which in turn are used to determine accuracy and effectiveness. The modified DB technique radar variables have been found to have error less than 5 dBZ for reflectivity, 1 dB for differential reflectivity and 0.08 for correlation coefficient. …”
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Ability of Side Hop Test to Predict Deficits in Dynamic Balance Control and Isometric Strength of Hip and Ankle Muscles in Recreational Male Athletes with Chronic Ankle Instability by Neda Orakifar, Fatemeh varezardi, Razieh Mofateh, maryam Seyedtabib

“…The aim is to examine the SHT’s ability to predict deficits in dynamic balance control and isometric strength of hip and ankle muscles in these individuals. Methods: Sixty male athletes with CAI (mean age 29.6 ± 6.2 years) participated in this cross-sectional study. …”
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Prediction and verification of thin liquid film thickness on salt-deposited copper surface in an atmospheric hygrothermal environment by Rongdie Zhu, Binxia Ma, Hongbin Zhang, Zhihao Qu, Jinyang Zhu

“…In addition, artificial neural networks (ANNs) and support vector machine models were constructed based on machine learning methods for predicting liquid film thickness. Comparative results indicated that the ANN prediction model exhibited higher accuracy, with a model determination coefficient (R2) reaching 0.99. …”
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Spatial interpolation of cropland soil bulk density by increasing soil samples with filled missing values by Aiwen Li, Jinli Cheng, Dan Chen, Wendan Li, Yaruo Mao, Xinyi Chen, Bin Zhao, Wenjiao Shi, Tianxiang Yue, Qiquan Li

“…The RBFNN model, tailored for each sub-watershed, yielded the highest accuracy in filling missing BD, with an increase in coefficient of determination (R 2) by 19.54–37.36% and reductions in mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) by 8.91–14.81%, 9.02–16.22% and 7.71–13.61%, respectively. …”
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Gustatory thresholds and obesity: a comparative study of five main tastes by Hamidreza Khalighi, Hamed Mortazavi, Fahimeh Anbari, Masoumeh Sadat Eftekhari, Nahid Mohammadnia, Homa Mirzaei, Sara Nashibi

“…Abstract Background Obesity is an important public health concern, which has dramatically grown in the last decades. Taste sensation determines food preferences and could contribute to obesity. …”
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Prevalence of Dysfunctional Breathing in Subjects Undergoing Maintenance Dialysis: A Cross-sectional Study by Neha D Bora, Abhishek S Mishra, Preeti S Ganachari, Santosh P Dobhal

“…Aim: To determine the prevalence of dysfunctional breathing (DB) in subjects undergoing maintenance dialysis. …”
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Advanced intelligent techniques for modeling oxygen storage in zeolite-based porous materials by Arefeh Naghizadeh, Ahmadreza Jafari-Sirizi, Behnam Amiri-Ramsheh, Saeid Atashrouz, Dragutin Nedeljkovic, Ahmad Mohaddespour, Abdolhossein Hemmati-Sarapardeh

“…The findings demonstrated the superior performance of the GRNN model, achieving an exceptional root mean square error of 0.03 and a coefficient of determination (R2) of 0.9991, outperforming the other techniques. …”
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Relationship job embeddedness to turnover intention among nurses: A cross-sectional, correlational study by Benyamin Saadatifar, Zahra Ebrahimi Rigi, Parichehr Sabaghzadeh Irani, Chiman Ghaderi, Omolbanin Akbari

“…Methods & Materials: This descriptive-analytic study included 242 nurses from hospitals affiliated with Zahedan University of Medical Sciences, Iran. …”
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PSOA-LSTM: a hybrid attention-based LSTM model optimized by particle swarm optimization for accurate lung cancer incidence forecasting in China (1990–2021) by Nannan Xu, Guang Yang, Linlin Ming, Jiefei Dai, Kun Zhu

“…The proposed model was compared against traditional models including ARIMA, standard LSTM, Support Vector Regression (SVR), and Random Forest (RF).ResultsThe PSOA-LSTM model achieved superior performance across five key evaluation metrics: mean squared error (MSE) = 0.023, coefficient of determination (R2) = 0.97, mean absolute error (MAE) = 0.152, normalized root mean squared error (NRMSE) = 0.025, and mean absolute percentage error (MAPE) = 0.38%. …”
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Ship motion identification model based on enhanced Bi-LSTM by Haozhe ZHANG, Zhibo YANG, Xuguo JIAO, Chengxing LÜ, Peng LEI

“…Finally, using the navigation data of KLVCC2 ships, the prediction effects of the enhanced Bi-LSTM model are compared with those of the Support Vector Machine (SVM), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) models.ResultsThe Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indicators of the enhanced Bi-LSTM model in the test set are lower than 0.015 and 0.011 respectively, and the coefficient of determination（R2）is higher than 0.99913, demonstrating prediction accuracy significantly higher than that of the SVM, GRU, and LSTM models.ConclusionThe proposed enhanced Bi-model has excellent generalization performance and excellent prediction stability and precision, and effectively realizes ship motion identification.…”
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The Variability and Trend of Harvest Dates of Table and Pisco Grapes in Northern Chile Are Independently Influenced by Bioclimatic Indices by Nicolás Verdugo-Vásquez, Antonio Ibacache-González, Gastón Gutiérrez-Gamboa

“…The trend in the harvest date of Thompson Seedless was statistically significant, reaching a coefficient of determination of 0.42. (4) Conclusions: Understanding the influence of bioclimatic indices on harvest date in long-term periods is critical in the context of climatic variability since producers can make more informed decisions to optimize grape quality and maintain sustainability in production systems.…”
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Effects of blood glucose level on 18F-FDG uptake for PET/CT in normal organs: A systematic review. by Clarice Sprinz, Stephan Altmayer, Matheus Zanon, Guilherme Watte, Klaus Irion, Edson Marchiori, Bruno Hochhegger

“…To objectively measure the magnitude of the association between glycemia and 18F-FDG uptake in different organs, we calculated the effect size (ES) and the coefficient of determination (R2) whenever possible.…”
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What is the test-retest reliability of the Malay version of the Hypertension Self-Care Profile self efficacy assessment tool? A validation study in primary care by Ngiap Chuan Tan, Yi Ling Eileen Koh, Kai Cong Seow, Diana Mohamed Yusoff

“…A Malay version of the HTN-SCP Tool was developed to assess self-efficacy of Malay-literate patients with hypertension in the multi-ethnic Asian population in Singapore. The study aimed to determine the test-retest reliability of this tool which has been translated in Malay language.Methods and Material 145 Malay-literate patients, aged 41-70 years, with essential hypertension were recruited in a polyclinic (primary care clinic) in Singapore. …”
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Relationship between optical coherence tomography sector peripapillary angioflow-density and Octopus visual field cluster mean defect values. by Gábor Holló

“…No significant difference between the corresponding inferotemporal and superotemporal parameters was seen. The coefficient of determination (R2) calculated for the relationship between inferotemporal sector PAFD and superotemporal cluster MD (0.5141, P<0.0001) was significantly greater than that between inferotemporal sector RNFLT and superotemporal cluster MD (0.2546, P = 0.0001). …”
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