Fabricating a Polysulfone-ZIF-8 Composite Membrane for Separating CO2 and CH4 by Pouneh Ramezani, Omid Alizadeh, Masoud Mokhtary

“…In this research, a mixed matrix membrane based on polysulfone and zinc nitrate-methylimidazole fillers was synthesized to improve the ability of the polymer membrane for the separation of CO2 and CH4. …”
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From Tension to Triumph: Design and Implementation of an Innovative Algorithmic Metric for Quantifying Individual Performance in Women Volleyball’s Critical Moments by Carlos López-Serrano, María Zakynthinaki, Daniel Mon-López, Juan José Molina Martín

“…This study introduces the critical individual contribution coefficient (CR-ICC), a novel metric that evaluates player effectiveness in critical moments of the game. …”
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The Relationship between Anthropometric, Biomotor, and Psychomotor Factors on the Performance of Paralympic 100 Meters Freestyle Swimming Athletes in National Paralympic Committee... by Alviyan Yoga Pratama, Agus Kristiyanto, Rony Syaifullah, Slamet Riyadi, Slamet Raharjo

“…The data collection includes seven independent variable tests there are anthropometric factors (height, weight, body mass index, arm length, and leg length); biomotor factors (abdominal muscle strength and arm muscle power); psychomotor factors by measuring balance using Balance Tests; and dependent variables by measuring swimming speed using the 100-meter freestyle swimming ability test. Statistical analysis used the Pearson correlation coefficient test with a significance level of 5%. …”
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Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning by Yongjie Ma, Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan, Yanjun Zhang

“…To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. …”
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A scale for measuring graduate nursing students’ academic resilience: a development and psychometric testing study by Yang Shen, Hanbo Feng, Xiaohan Li

“…Confirmatory factor analysis showed that all the fitting indices met the standard. The Cronbach’s α coefficient of the scale was 0.933, and the test-retest reliability coefficient was 0.910. …”
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Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study by Mujgan Firincioglulari, Mehmet Boztuna, Omid Mirzaei, Tolgay Karanfiller, Nurullah Akkaya, Kaan Orhan

“…The current research was conducted with the aim of measuring the ability of artificial intelligence algorithms to accurately diagnose pulp and pulp stone calcifications on panoramic radiographs. …”
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Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. …”
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An approach of rock blasting simulation of equivalent blasting dynamic-static action by Hao Zhang, Xueyang Xing, Yiteng Du, Tingchun Li, Jianxin Yu

“…When the decoupling coefficient is increased, an optimal decoupling coefficient is discovered, which reflects the consistency between the blasting results and the actual situation. …”
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MICROSTRUCTURAL AND TRIBOLOGICAL PROPERTIES OF NICKEL-BASED HARDFACING COATINGS APPLIED ON NITRIDING STEELS BY PLASMA TRANSFERRED ARC WELDING by Abdullah Sert, Fatih Hayati Çakır, Gökçe Mehmet Ay

“…Plasma Transferred Arc (PTA) coatings are widely used for the surface modification of metals due to their ability to achieve high coating thickness, low thermal stress, and high energy density. …”
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Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework by Liang Yu, Yuanshen Hong, Hua Lin, Xu Jiang, Ziming Song

“…Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. …”
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MRI-based deep transfer learning models for predicting progesterone receptor expression in meningioma by Song Gao, Li Zhao, Nan Li, Xiaoming Zhou, Chongfeng Duan

“…The DTL features were extracted via the fine-tuned ResNet 50 model and selected by the intraclass correlation coefficient (ICC), spearman correlation coefficient and least absolute shrinkage and selection operator (LASSO). …”
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Prediction of COD Degradation in Fenton Oxidation Treatment of Kitchen Anaerobic Wastewater Based on IPSO-BP Neural Network by Tianpeng Zhang, Pengfei Ji, Dayong Tian, Rui Xu

“…The root mean square error (RMSE), RMSE coefficient of variation (CV-RMSE), and coefficient of determination (R2) were used to evaluate the prediction performance of the model. …”
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Component Prediction of Antai Pills Based on One-Dimensional Convolutional Neural Network and Near-Infrared Spectroscopy by Tuo Guo, Fengjie Xu, Jinfang Ma, Fahuan Ge

“…Taking wogonoside as an example, the indices such as the correction coefficient of determination (Rv2), the root mean-squared error of cross validation (RMSECV) for calibration set, the prediction coefficient of determination (Rp2), and the root mean-squared error of prediction (RMSEP) obtained by PLSR modeling were 0.9340, 0.5568, 0.9491, and 0.5088; the indices obtained by SVR modeling were 0.9520, 0.4816, 0.9667, and 0.4117; and the indices obtained by 1DCNN modeling were 0.9683, 0.3397, 0.9845, and 0.2807, respectively. …”
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FLA-UNet: feature-location attention U-Net for foveal avascular zone segmentation in OCTA images by Wei Li, Li Cao, He Deng

“…Although the U-Net and its existing improvement methods have achieved good performance on FAZ segmentation, their generalization ability and segmentation accuracy can be further improved by exploring more effective improvement strategies.MethodsWe propose a novel improved method named Feature-location Attention U-Net (FLA-UNet) by introducing new designed feature-location attention blocks (FLABs) into U-Net and using a joint loss function. …”
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Investigation of Gamma Radiation Shielding in NiMnGa-Doped Multifunctional Smart Polymer Composites Using Geant4 and WinXCOM by Mediha Kök, Mustafa Ersin Pekdemir, Seçil Niksarlıoğlu, Serpil Yalçın Kuzu, Mustafa Kaya

“…The radiation protection ability of the composites and the pure alloy were assessed by calculating key parameters, including the mass attenuation coefficient (μm), linear attenuation coefficient (μ), half value layer (HVL), tenth value layer (TVL), and mean free path (MFP). …”
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Conjoint effect of nanofluids and baffles on a heat exchanger thermal performance: Numerical approach by hayder Al-Lami, Dheyaa S.J. Al-Saedi, Ali A.H. AlMaidib, Qudama Al-Yasiri

“…The Al2O3 -water nanofluid showed the most notable enhancement, with a 4.5% increase in the heat transfer coefficient. This improvement is due to the superior thermal conductivity of Al2O3 nanoparticles and their ability to induce localized turbulence within the fluid. …”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Combined with particle swarm optimization (PSO), the hyper-parameter combination optimization of support vector machine, decision tree and random forest models is carried out to obtain the optimal parameter combination of the model and improve the prediction performance and generalization ability of the model. The prediction performance of each model was compared by using three evaluation indexes coefficient of determination (R2), mean square error (EMS) and mean absolute error (EMA). …”
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Comparison of the Regression Method and The Neural Network Method in Specific Cases of Engineering Practice by Križan Peter, Svátek Michal, Matúš Miloš, Hanko Lukáš

“…Subsequently, through the steps of statistical analysis, we carried out estimates of effects, individual tests of hypotheses about the significance of the model and effects and, last but not least, also the predictive ability of the models thus obtained. The obtained models, which were created using the two methods already mentioned, were subsequently compared based on their predictive ability, specifically through the so-called predictive ability of the model, which is represented by the coefficient of determination R2, which is defined as the ratio of the sum of squares SSM explained by the model to the total sum of squares SST. …”
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Carotid peak flow velocity variation as a surrogate of aortic peak flow velocity variation in a pediatric population by Federico Cristiani, Juan Pablo Bouchacourt, Juan Riva, Pablo Motta

“…We hypothesize that the ability of ΔVpeakCar as a surrogate of ΔVpeakAo changes throughout childhood. …”
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Evaluation of SSM-Wheat Model in Simulating the Growth and Development of Wheat (Triticum aestivum L.) Cultivars in Different Planting Densities by Ali Rahemi karizki, Faramarz Sayyedi, Habib allah Soghi, Arazqlych Marfy, Mojtaba Salehi SHaikhi, Saeed Bagherikia

“…To evaluate the model, the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE), and the 1:1 line were used.  …”
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