Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data by David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

Subjects: “…Bag of Functions framework…”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…In this study, we implemented a machine learning (ML)-based predictive framework to identify MetS using serum liver function tests—Alanine Transaminase (ALT), Aspartate Aminotransferase (AST), Direct Bilirubin (BIL.D), Total Bilirubin (BIL.T)—and high-sensitivity C-reactive protein (hs-CRP). …”
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Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning by Amrit Kumar, Shweta Mishra, R. K. Gupta, V. Manjuladevi

“…This study presents a novel, label-free approach for E. coli detection using ultrathin Langmuir-Blodgett films of octadecylamine functionalized (ODA)-functionalized graphene on gold electrodes, with a detection range spanning $$10^{1}-10^{6}$$ colony-forming units/mL (CFU/mL). …”
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Does Speculation Matter in the Carbon Pricing Framework? Insights from the EU Emissions Trading System by Kazeem O. ISAH, Joy E. A. EBEH, Damilola ABOLUWODI, Monday ALIU, Salam S. MOHAMMED, Yusuf YAKUBU, Muhammad ALI

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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

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Ag(I) decorated isomeric triazine complexes as efficient hydrogen storage materials - A theoretical investigation by Abhishek Bag, Gobinda Chandra De, Shampa Bhattacharyya, Bikash Bepari, Himadri Sekhar Das, Sateesh Bandaru, Gourisankar Roymahapatra

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Slug Self-Propulsion in a Capillary Tube Mathematical Modeling and Numerical Simulation by M. I. Khodabocus, M. Sellier, V. Nock

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Slower Ageing of Cross-Frequency Coupling Mechanisms Across Resting-State Networks Is Associated with Better Cognitive Performance in the Picture Priming Task by Vasily A. Vakorin, Taha Liaqat, Hayyan Liaqat, Sam M. Doesburg, George Medvedev, Sylvain Moreno

“…The brain age gap (BAG), the divergence of an individual’s neurobiologically predicted brain age from their chronological age, is a key indicator of brain health. …”
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Revisiting the Group Classification of the General Nonlinear Heat Equation <i>u_t</i> = (<i>K</i>(<i>u</i>)<i>u_x</i>)<i>_x</i> by Winter Sinkala

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TEDVIL: Leveraging Transformer-Based Embeddings for Vulnerability Detection in Lifted Code by Gary A. McCully, John D. Hastings, Shengjie Xu

“…Notably, these results are achieved when the embedding model is trained with a dataset of just 48,000 functions, demonstrating effectiveness in resource-constrained settings. …”
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Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization by Xiang Bao, Fei Han, Qinghua Ling

“…Subsequently, the loss function of the framework is solved by an alternating optimization process where Support Vector Machine (SVM) classifiers are constructed for optimization. …”
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Designing Age-Friendly Paved Open Spaces: Key Green Infrastructure Features for Promoting Seniors’ Physical Activity by Wei Dong, Shuangyu Zhang, Jiayi Lin, Yue Wang, Xingyue Xue, Guangkui Wang

“…Existing studies often overlook factors like spatial configuration, planar morphology, and bag storage facilities, and lack a systematic analytical framework. …”
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Entrepreneurial Abilities and Business Performance: Enacting Business Survival Paradigm from Electronics Informal Market, Nigeria by Adebanji Adejuwon William Ayeni

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LSN-GTDA: Learning Symmetrical Network via Global Thermal Diffusion Analysis for Pedestrian Trajectory Prediction in Unmanned Aerial Vehicle Scenarios by Ling Mei, Mingyu Fu, Bingjie Wang, Lvxiang Jia, Mingyu Yu, Yu Zhang, Lijun Zhang

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Fermentation-enabled wellness foods: A fresh perspective by Huan Xiang, Dongxiao Sun-Waterhouse, Geoffrey I.N. Waterhouse, Chun Cui, Zheng Ruan

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Local nutrient addition drives plant diversity losses but not biotic homogenization in global grasslands by Qingqing Chen, Shane A. Blowes, W. Stanley Harpole, Emma Ladouceur, Elizabeth T. Borer, Andrew MacDougall, Jason P. Martina, Jonathan D. Bakker, Pedro M. Tognetti, Eric W. Seabloom, Pedro Daleo, Sally Power, Christiane Roscher, Peter B. Adler, Ian Donohue, George Wheeler, Carly Stevens, G. F. Ciska Veen, Anita C. Risch, Glenda M. Wardle, Yann Hautier, Catalina Estrada, Erika Hersch-Green, Yujie Niu, Pablo L. Peri, Anu Eskelinen, Daniel S. Gruner, Harry Olde Venterink, Carla D’Antonio, Marc W. Cadotte, Sylvia Haider, Nico Eisenhauer, Jane Catford, Risto Virtanen, John W. Morgan, Michelle Tedder, Sumanta Bagchi, Maria C. Caldeira, Miguel N. Bugalho, Johannes M. H. Knops, Chris R. Dickman, Nicole Hagenah, Anke Jentsch, Petr Macek, Brooke B. Osborne, Lauri Laanisto, Jonathan M. Chase

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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…Glucose levels, BMI, blood pressure, and Diabetes Pedigree Function emerged as the most significant predictors across all models. …”
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Quality-Aware PPG-Based Blood Pressure Classification for Energy-Efficient Trustworthy BP Monitoring Devices With Reduced False Alarms by Yalagala Sivanjaneyulu, M. Sabarimalai Manikandan, Srinivas Boppu, Linga Reddy Cenkeramaddi

“…Four SQA methods are based on the average magnitude difference function (AMDF/(SQA-M1)) features and the AMDF features with the total number of zero-crossings present in raw/original (SQA-M2), derivative (SQA-M3), and smoothed derivative (SQA-M4) PPG segment features. …”
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Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms by Seyed Morteza Javadpour, Erfan Kadivar, Zienab Heidary Zarneh, Ebrahim Kadivar, Mohammad Gheibi

“…The comparison of different machine learning algorithms indicates that the best ones for predicting DD, VFD, and VBD are function, SMOreg, Lazy-IBK, and Meta-Bagging, respectively.…”
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