Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model by Yan Lu, Hongze Zhao

“…Quantifying slope mechanical parameters as comprehensive indicators is crucial for predicting slope stability. The Mohr–Coulomb (M-C) criterion, a classical method for determining the relevant parameters of rock mass mechanics, effectively reflects the failure characteristics of rock masses in most types of slopes. …”
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Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens by Emmanouil-Marinos Mantalas, Vasileios D. Sagias, Paraskevi Zacharia, Constantinos I. Stergiou

“…Experimental data collected from AM processes are used to train the ANFIS model, allowing it to predict outputs such as stress, strain, and Young’s modulus under various printing parameters values. …”
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Risk Factor Analysis and Prediction Model Construction and Validation of Depression During Pregnancy by Huiling Qu, Yanna Zhou, Yi Yu

“…Conclusions: A nomogram model, which incorporates indicators such as spousal and parental disharmony, changes in sleep and dietary habits, work-study stress, adverse maternal history, unsatisfactory living environment, assisted reproduction, unplanned pregnancy, interference from adverse life events, and lack of maternity knowledge, can effectively predict depression during pregnancy.…”
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Research on Accelerated Degradation Test Design and Life Prediction for Aluminum Electrolytic Capacitor by YANG Tao, WANG Xu, XIAO Jianglin

“…Starting from the structure and degradation mechanism of aluminum electrolytic capacitor, this paper designs an accelerated degradation test with temperature as the accelerated sensitive stress, uses the accelerated degradation data for life prediction, and gives a top-down design direction for extending the life of aluminum electrolytic capacitors. …”
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Do Depressive Traits and Hostility Predict Age-Related Decline in General Intelligence? by Erik Lykke Mortensen, John Calvin Barefoot, Kirsten Avlund

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Rockburst Prediction Based on the KPCA-APSO-SVM Model and Its Engineering Application by Yuefeng Li, Chao Wang, Jiankun Xu, Zonghong Zhou, Jianhui Xu, Jianwei Cheng

“…This makes it essential to accurately predict rockburst intensity. In this paper, the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (σθ/σc), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (σc/σt), and the elastic energy index of rock (Wet) were chosen as input indices, and rockbursts were graded as level I (none rockburst), level II (light rockburst), level III (medium rockburst), and level IV (strong rockburst). …”
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Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…In response to the need for rapid and precise predictions of pile bearing capacity, this study introduces a kernel extreme learning machine (KELM) prediction model optimized through a multi-strategy improved beetle optimization algorithm (IDBO), referred to as the IDBO-KELM model. …”
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Harmful algal blooms are preceded by a predictable and quantifiable shift in the oceanic microbiome by Miranda C. Mudge, Michael Riffle, Gabriella Chebli, Deanna L. Plubell, Tatiana A. Rynearson, William S. Noble, Emma Timmins-Schiffman, Julia Kubanek, Brook L. Nunn

“…Abstract Harmful algal blooms (HABs) have become a worldwide environmental and human health problem, stressing the urgent need for a reliable forecasting tool. …”
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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…The deep beam in load transfer is very important as well as difficult to design due to its shear stress problems. Accurate estimation of shear stress would help engineers to get a safer design. …”
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Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach by Zhuocheng Xie, Achraf Atila, Julien Guénolé, Sandra Korte-Kerzel, Talal Al-Samman, Ulrich Kerzel

“…The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. …”
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An illustration of multi-class roc analysis for predicting internet addiction among university students. by Nishat Tasnim Thity, Atikur Rahman, Adisha Dulmini, Mst Nilufar Yasmin, Rumana Rois

“…University students' backgrounds, depression, anxiety, stress, participation in physical activity, misbehaving with family members, memory loss symptoms, and being COVID-19-positive were selected as significant features for predicting IA. …”
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Study and development of methods for predicting the physical and mechanical properties of thin wire and metal cord by E. S. Eltsova, Yu. L. Bobarikin, Yu. V. Martyanov

“…Predicting the physical and mechanical properties of thin wire and metal cord will ensure an increase in the quality of latuned thin wire, a reduction in the number and time of technological pauses, and increase the manufacturability of metal cord twisting. …”
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A dataset of psychological emotional expressions relating to depression, anxiety and stress for Malay language model trainingOSF by Ruhaila Maskat, Nor Hapiza Mohd Ariffin, Nurul Akhmal Dzulkefli

“…The dataset can benefit research works in the areas of emotional speech recognition, emotional intelligence understanding and emotional prediction. The dataset consists of raw and pre-processed posts which include normalized and tokenized words suitable for training Malay Large Language Models and predictive analytics.…”
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Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival by Razan N. Alnahhas, Virgile Andreani, Mary J. Dunlop

“…We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impacts ciprofloxacin survival in Escherichia coli. …”
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Relationship Between 8-iso-prostaglandin-F_2α and Predicted 10-Year Cardiovascular Risk in Hypertensive Patients by Giulio Geraci, Alessandra Sorce, Luca Zanoli, Giuseppe Cuttone, Vincenzo Calabrese, Francesco Pallotti, Valentina Paternò, Pietro Ferrara, Ligia J. Dominguez, Riccardo Polosa, Jacob George, Giuseppe Mulè, Caterina Carollo

“…The aim of this study was to assess the relationship between 8-iso-PGF_2α and 10-year CV risk, as predicted by validated equations in hypertension patients without CV diseases. …”
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Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models by Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

“…Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km2 from 2021 and 2022 in New Mexico, USA. …”
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Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network by Min Zhang

“…In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. …”
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Aging Analysis and Degradation Prediction of PLL Circuits in 14-nm FinFET Technology by Meng Li, Xin Xu, Xianghui Li, Yunpeng Li, Yiqun Shi, Qingqing Sun, Hao Zhu

“…The quasi-static-approximation (QSA) model built with DC stress test data, was employed for device and circuit degradation predictions, and its limitations and applicability were discussed. …”
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Creep behavior of clayey soil and its model prediction in the Cangzhou land subsidence area by Jianfeng Qi, Yongjie Xie, Chen Li, Haipeng Guo, Yunlong Wang

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Study on the Prediction of the Height of Two Zones in the Overlying Strata under a Strong Shock by Xingping Lai, Bowei Liu, Pengfei Shan, Feng Cui, Yun Zhang, Xudong Zhang, Rui Bai, Xuan Wu

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