Experimental Study on the Property Degradation and Failure Mechanism of Weakly Cemented Sandstone under Dry-Wet Cycles by Zhaoyang Song, Lihui Sun, Shouye Cheng, Zhiqiang Liu, Jie Tan, Fangbo Ning

“…The test results provide new methods and a basis for the damage evolution mechanism and fracture prediction of weakly cemented sandstone under dry-wet cycles.…”
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Monte Carlo Simulation to Evaluate Mould Growth in Walls: The Effect of Insulation, Orientation, and Finishing Coating by Ricardo M. S. F. Almeida, Eva Barreira

“…Mould growth is a very complex process that depends on many factors such as temperature and relative humidity, presence of nutrients, and exposure time. Several mould prediction models, which allow estimating mould growth in building components and performing risk analysis, are available in the literature, such as the updated VTT model or the Biohygrothermal model. …”
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Clinical considerations on antimicrobial resistance potential of complex microbiological samples by Norbert Solymosi, Adrienn Gréta Tóth, Sára Ágnes Nagy, István Csabai, Csongor Feczkó, Tamás Reibling, Tibor Németh

“…ARG detection showed a phenotypic prediction with at least 90% confidence in 67% of ABs. …”
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Depth-electrode stimulation and concurrent functional MRI in humans: Factors influencing heating with body coil transmission by Hiroyuki Oya, Ralph Adolphs, Matthew A. Howard, J. Michael Tyszka

“…In particular, the method requires reliable prediction and minimization of local tissue heating close to the electrodes, which will vary with imaging parameters and hardware configurations. …”
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An Empirical Model of the Occurrence Rate of Low Latitude Post‐Sunset Plasma Irregularities Derived From CHAMP and Swarm Magnetic Observations by C. Stolle, T. A. Siddiqui, L. Schreiter, S. K. Das, I. Rusch, M. Rother, E. Doornbos

“…Abstract The prediction of post‐sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. …”
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A review of characteristic lengths in the coupled criterion framework and advanced fracture models by Molnár, Gergely, Doitrand, Aurélien, Estevez, Rafael,  Gravouil, Anthony

“…This integrative strategy would allow for more accurate predictions and a deeper insight into the mechanics of fracture.…”
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The Impact of Length-Scale Variation When Diagnosing the Standard Deviations of Background Error in a 4D-Var System and Filtering Method Investigation by Xiang Xing, Bainian Liu, Weimin Zhang, Xiaoqun Cao, Hongze Leng

“…The four-dimensional variational data assimilation (4D-Var) method has been widely employed as an operational scheme in mainstream numerical weather prediction (NWP) centers. In addition to the ensemble data assimilation method, the randomization technique is still used to diagnose the standard deviations of background error in variational data assimilation (VAR) systems; however, such randomization techniques induce sampling noise, which may contaminate the quality of the standard deviations. …”
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Evaluation Method of the Vertical Well Hydraulic Fracturing Effect Based on Production Data by Debin Xia, Zhengming Yang, Daolun Li, Yapu Zhang, Ying He, Yutian Luo, Anshun Zhang, Wenming Wang, Xinli Zhao

“…As the progress develops, the equivalent permeability and the area of the fracture gradually decrease as the fracturing effect gradually weakens, and so does the conductivity of the network decreasing exponentially; a good correlation is observed between the conductivity of the fracture network, the cumulative production, and fracturing construction parameters, which can serve as the evaluation parameters for the fracturing effects and the basis for fracturing productivity prediction and provide a guidance for fracturing optimization design.…”
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Influence of Wellbore Dogleg Severity on Drilling Friction in Horizontal Wells by Shitang Chen, Gui Hu, Xueqin Huang, Guohui Zhang, Xinyun Liu

“…The influence of the rate of change in the overall hole trajectory angle (dogleg severity) is often ignored in the prediction of frictional force in drilling program design, and there is a shortage of quantitative analysis. …”
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Hybridize Machine Learning Methods and Optimization Techniques to Analyze and Repair Welding Defects via Digital Twin of Jidoka Simulator by Ahmed M. Abed, Tamer S. Gaafar

“…Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {{R}_{s}}$ </tex-math></inline-formula>) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. …”
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Forecasting the Incidence and Prevalence of Patients with End-Stage Renal Disease in Malaysia up to the Year 2040 by Mohamad Adam Bujang, Tassha Hilda Adnan, Nadiah Hanis Hashim, Kirubashni Mohan, Ang Kim Liong, Ghazali Ahmad, Goh Bak Leong, Sunita Bavanandan, Jamaiyah Haniff

“…Four forecasting models were evaluated, and the model with the smallest error was selected for the prediction. Result. ARIMA (0, 2, 1) modeling with the lowest error was selected to predict both the incidence (RMSE = 135.50, MAPE = 2.85, and MAE = 87.71) and the prevalence (RMSE = 158.79, MAPE = 1.29, and MAE = 117.21) of dialysis patients. …”
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OSPREI: A Coupled Approach to Modeling CME‐Driven Space Weather With Automatically Generated, User‐Friendly Outputs by C. Kay, M. L. Mays, Y. M. Collado‐Vega

“…Current CME‐related space weather predictions rely on information reconstructed from coronagraphs, sometimes from only a single viewpoint, to drive a simple interplanetary propagation model, which only gives the arrival time or limited additional information. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…The predominant method for quantifying uncertainty was Monte Carlo dropout, producing predictions from multiple networks for which different neurons have dropped out and measuring variance across the distribution of resulting predictions. …”
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Optimization of maltodextrin coating for masking to maintain the characteristic of Spirulina powder using the Box-Behnken design: A Response Surface Methodology approach by Prasetiyo Himawan, Purwaningsih Sri, Setyaningsih Iriani, Sadi Uju, Nurilmala Mala, Tarman Kustiyariyah

“…The response analysis showed a non-significant lack of fit (p>0.05), indicating that the data response will match with the optimized prediction model. The optimized conditions of spirulina masking were determined in low-temperature heating at 33°C for 60 min, with 7 g/L maltodextrin concentration and a 1:1 ratio of spirulina and maltodextrin (m/v). …”
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Experimental and Numerical Evaluation of Dynamic Characteristics of 3DOF Reduced-Scale Model by Abderaouf Daci, Nassima Benmansour, Abdellatif Bentifour, Rachid Derbal

“… The dynamic behavior of structures subjected to seismic excitations is often predicted by numerical models based on the finite elements method. …”
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Graph Convolution for Large-Scale Graph Node Classification Task Based on Spatial and Frequency Domain Fusion by Junwen Lu, Lingrui Zheng, Xianmei Hua, Yankun Wang

“…Secondly, LEGNN incorporates a noise prediction mechanism that injects controlled perturbations into the node representations, improving the model&#x2019;s robustness, reducing overfitting, and enhancing generalization to unseen data. …”
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Assessing the health and economic burden of obesity-related complications in East-Asian populations: implementation of risk equations in the Core Obesity Model for Japan and model... by Ichiro Tatsuno, Mark Lamotte, Laetitia Gerlier, Anamaria-Vera Olivieri, James Baker-Knight

“…Conversely, the 10-year cumulative ACS incidences predicted in a Japanese population were less than half of those in a Western population.Conclusion The Japanese COM adaptation addresses ethnicity-specific patterns of overweight/obesity, with better sensitivity to lower BMIs for several associated complications. …”
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An Enhanced End-to-End Object Detector for Drone Aerial Imagery by Quan Yu, Qiang Tong, Lin Miao, Lin Qi, Xiulei Liu

“…However, we observe that their pipeline still suffer from several challenges, including unbalanced distribution of positive and negative samples, low-quality initial prediction boxes, and unreasonable gradient structure in the decoding stage. …”
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Exploring when to exploit: the cognitive underpinnings of foraging-type decisions in relation to psychopathy by D. V. Atanassova, J. M. Oosterman, A. O. Diaconescu, C. Mathys, V. I. Madariaga, I. A. Brazil

“…Additionally, higher levels of Interpersonal traits were associated with reduced learning from personalized rewards, as evidenced by reductions in the prediction errors (PEs) about rate of change. Higher Affective traits were associated with lower PEs and aberrant learning from painful punishments. …”
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Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities by Denis Leite, Emmanuel Andrade, Diego Rativa, Alexandre M. A. Maciel

“…While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. …”
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