Single cell analysis of host response to helminth infection reveals the clonal breadth, heterogeneity, and tissue-specific programming of the responding CD4+ T cell repertoire. by Ivy K Brown, Nathan Dyjack, Mindy M Miller, Harsha Krovi, Cydney Rios, Rachel Woolaver, Laura Harmacek, Ting-Hui Tu, Brian P O'Connor, Thomas Danhorn, Brian Vestal, Laurent Gapin, Clemencia Pinilla, Max A Seibold, James Scott-Browne, Radleigh G Santos, R Lee Reinhardt

“…Antigen-reactivity of the broader repertoires was predicted to be shared across both tissues and individual mice. …”
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Anti‐interference lithium‐ion battery intelligent perception for thermal fault detection and localization by Luyu Tian, Chaoyu Dong, Rui Wang, Yunfei Mu, Hongjie Jia

“…After denoising by the autoencoder, the prediction results improved by 22% compared to non‐local mean denoising and by about 32% compared to noisy images.…”
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ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT by Ghardapaty Ghaly Ghiffary, Nabila Tri Amanda, Rizky Ardhani, Bagus Sartono, Aulia Rizki Firdawanti

“…The stacking method is an ensemble technique in machine learning that combines predictions from several base models to improve classification accuracy. …”
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Vibration Diagnostic Methods from Methodsof Obtaining Data to Processing It Using Modern Means by Anton O. Zhuravlev, Alexey O. Polyakov, Denis A. Andrikov

“…Thus, self-diagnosis, combined with a high level of automated analytics, makes it possible to predict a malfunction with a high degree of probability, warn about the timing of its occurrence and methods of preventive elimination. …”
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Integrating feedback control for improved human-structure interaction analysis by Santiago A. Lopez, Daniel Gomez, Albert R. Ortiz, Sandra Villamizar

“…The results of this study indicate that feedback controllers accurately predict the experimental structural response for different subjects. …”
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Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports by Ümit Gökkuş, Mehmet Sinan Yıldırım, Metin Mutlu Aydin

“…Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). …”
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A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics. by Jorge Duconge, Alga S Ramos, Karla Claudio-Campos, Giselle Rivera-Miranda, Luis Bermúdez-Bosch, Jessicca Y Renta, Carmen L Cadilla, Iadelisse Cruz, Juan F Feliu, Cunegundo Vergara, Gualberto Ruaño

“…<h4>Results</h4>The admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. …”
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BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis. by Allen W Bryan, Matthew Menke, Lenore J Cowen, Susan L Lindquist, Bonnie Berger

“…BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator) in beta-structure prediction and amyloid propensity prediction. …”
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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. The predicted accuracy scores of four supervised machine learning algorithms, namely, gradient boosting, logistic regression, random forest, and support vector machine were 0.87, 0.90, 0.81, and 0.77, respectively. …”
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Inversion model of stress state reconstruction for geological hazard pipelines based on digital twin by Xue Luning, Tian Mingliang, Zhao Juncheng

“…The mechanical state of the physical pipeline is mapped in real time by the digital twin, the numerical simulation and multi-source monitoring data are integrated, and the parameters of the twin model are dynamically optimized by combining the optimization algorithms of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM), so as to realize the real-time prediction of the pipeline stress state and the dynamic updating of the disaster scenario. …”
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Automated Fillet Weld Inspection Based on Deep Learning from 2D Images by Ignacio Diaz-Cano, Arturo Morgado-Estevez, José María Rodríguez Corral, Pablo Medina-Coello, Blas Salvador-Dominguez, Miguel Alvarez-Alcon

“…Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. …”
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Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries by Dongchen Yang, Weilin He, Xin He

“…By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. …”
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Multitarget Vehicle Tracking and Motion State Estimation Using a Novel Driving Environment Perception System of Intelligent Vehicles by Yuren Chen, Xinyi Xie, Bo Yu, Yi Li, Kunhui Lin

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Identifying and prioritizing antifragile tourism strategies in a Neutrosophic environment by Mahdi Ghaforiyan, Ali Sorourkhah, Seyyed Edalatpanah

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Dissertation council in the state scientific certification system: Organizational and management mechanisms of digital transformation by Sergey I. Pakhomov, Elena A. Loginova, Oleg V. Kulyamin, Mikhail P. Petrov

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Application of Neural Networks to Analyse the Spatial Distribution of Bicycle Traffic Before, During and After the Closure of the Mill Road Bridge in Cambridgeshire, United Kingdom by Shohel Amin

“…The application of wireless sensors and machine learning algorithms can enhance the analysis and prediction ability of traffic distribution and characteristics in the proximity of road closures. …”
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Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification by Romany M. Farag, Mahmoud Y. Shams, Dalia A. Aldawody, Hazem M. El-Bakry, Huda E. Khalid, Ahmed K. Essa, A. A. Salama

“…Traditionally, traditional methods such as multiple sequence alignment (MSA) algorithms or the NeedlemanWinch algorithm become extremely cumbersome when faced with large or unclear data. …”
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SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads by Ramesh Rajaby, Wing-Kin Sung

“…We also show that SurVIndel2 is able to complement small indels predicted by Google DeepVariant, and the two software used in tandem produce a remarkably complete catalogue of variants in an individual. …”
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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…Among these, green hydrogen—particularly via water electrolysis and biomass gasification—received the most attention, reflecting its central role in decarbonization strategies. ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Using the proposed system, it was demonstrated that predictions of diseases like powdery mildew and blight are improved compared to traditional methods both in terms of accuracy as well as in the speed of response. …”
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