Enhancing Undrained Shear Strength Prediction through Innovative Hybridization Techniques by Chisom Samuel, Damilare Adewunmi

“…An innovative approach for predicting USS in soft clays using machine learning methods is pioneered in this study, with a specific focus on a set of five crucial feature variables, such as pre-consolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W), which serve as inputs for the data-driven models. …”
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Digital twin for real-time pressure vessels fatigue life prediction by Izat Khaled, Mohamed Bennebach, Dmytro Vasiukov, Modesar Shakoor, Salim Chaki

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Analytical prediction of groundwater loss in deep coal mines induced by ground vibration by Pieride Mabe Fogang, Bingjie Huo, Hervé Losaladjome Mboyo, Rong Hai, Songtao Zhang, Lesly Dasilva Wandji Djouonkep, Dieudonné Bisso

“…This study develops an analytical model coupling Fourier’s heat conduction and Cauchy’s momentum equations to predict groundwater depletion under dynamic stress from vibrations (0–6 MPa). …”
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Developing serum proteomics based prediction models of disease progression in ADPKD by Hande Ö. Aydogan Balaban, Sita Arjune, Franziska Grundmann, Jan-Wilm Lackmann, Thomas Rauen, Philipp Antczak, Roman-Ulrich Müller

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Automated diabetes detection prediction system based on patients’ medical data by S.V. Pidopryhora, Yu.V. Bogoyavlenska

“…The development of an automated system for predicting diabetes detection is an extremely relevant task, especially given the rapid increase in the number of diabetes cases in Ukraine and worldwide. …”
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Prediction of instability of formwork concrete pier based on big data machine learning for secondary mining without coal pillar mining by Yanhui Zhu, Ye Tian, Peilin Gong, Kang Yi, Tong Zhao

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Predicting stroke with machine learning techniques in a sub-Saharan African population by Benjamin Segun Aribisala, Deirdre Edward, Godwin Ogbole, Onoja M. Akpa, Segun Ayilara, Fred Sarfo, Olusola Olabanjo, Adekunle Fakunle, Babafemi Oluropo Macaulay, Joseph Yaria, Joshua Akinyemi, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Osaigbovo, Akinkunmi Paul Okekunle, Arti Singh, Philip Ibinaye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu Ogah, Ruth Y. Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi

“…These are addition of salt to food at table during eating, cardiac disease, diabetes mellitus, dyslipidemia, education, family history of cardiovascular disease, hypertension, income, low green leafy vegetable consumption, obesity, physical inactivity, regular meat consumption, regular sugar consumption, smoking, stress and use of tobacco. …”
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A novel formula for predicting the ultimate compressive strength of the cylindrically curved plates by Do Kyun Kim, Andy Ming King Wong, Jinha Hwang, Shen Li, Nak-Kyun Cho

“…''The present study aims to develop an empirical formula to predict the ultimate compressive strength of unstiffened cylindrically curved plates. …”
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Prediction of Crushing Response for Metal Hexagonal Honeycomb under Quasi-Static Loading by Xinyu Geng, Yufei Liu, Wei Zheng, Yongbin Wang, Meng Li

“…The mathematical models of mean crushing stress and peak crushing stress for metal hexagonal honeycombs were predicted on the basis of simplified super element theory. …”
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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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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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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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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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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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