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An intelligent machine learning paradigm for gyrotactic micro-organism-induced squeezing flow of Eyring–Powell hybrid nanofluids with entropy generation

A. Imran, Muna Elsadig, Jongsuk Ro • AIP Advances (2026)

A novel unsteady squeezing flow of a non-Newtonian ternary hybrid nanofluid is investigated by focusing Eyring–Powell fluid rheology in an infinite channel. The nanofluid consists of AA1072, AA1075, and ZrO2 nanoparticles suspended in blood...

DOAJ Open Access (DOAJ) DOI: 10.1063/5.0337148

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Jongho Lee, Jiuk Shin, Jaewook Lee, Chorong Park et al. • Applied Sciences (2023)

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

DOAJ Open Access (DOAJ) DOI: 10.3390/app132111866

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Tomasz Rokicki, Piotr Bórawski, Aneta Bełdycka-Bórawska, Bogdan Klepacki • Applied Sciences (2026)

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16189017

A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data

Longfei Cui, Xinyu Niu, Haizhong Qian, Xiao Wang et al. • Applied Sciences (2025)

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tail...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15052383

Inferring Markovian quantum master equations of few-body observables in interacting spin chains

Francesco Carnazza, Federico Carollo, Dominik Zietlow, Sabine Andergassen et al. • New Journal of Physics (2022)

Full information about a many-body quantum system is usually out-of-reach due to the exponential growth—with the size of the system—of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phen...

DOAJ Open Access (DOAJ) DOI: 10.1088/1367-2630/ac7df6

A feature selection and scoring scheme for dimensionality reduction in a machine learning task

PHILEMON UTEN EMMOH, christopher ifeanyi Eke, Timothy Moses • Journal of Nigerian Society of Physical Sciences (2025)

Selection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. It helps in reducing the dimensionality of a dataset and improving model performance. Most of the feature select...

DOAJ Open Access (DOAJ) DOI: 10.46481/jnsps.2025.2273

The Integration of Cone Beam Computed Tomography, Artificial Intelligence, Augmented Reality, and Virtual Reality in Dental Diagnostics, Surgical Planning, and Education: A Narrative Review

Aida Meto, Gerta Halilaj • Applied Sciences (2025)

(1) Background: Advancements in dental imaging technologies have significantly transformed diagnostic and surgical practices. The integration of cone beam computed tomography (CBCT), artificial intelligence (AI), augmented reality (AR), and...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15116308

Artificial Empathy and Imprecise Communication in a Multi-Agent System

Joanna Siwek, Konrad Pierzyński, Przemysław Siwek, Adrian Wójcik et al. • Applied Sciences (2024)

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applicatio...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15010008

Evidence for Dynamical Filtering: High Binary Fraction, Hard-binary Excess, and Unresolved Triples in the Surviving Core of NGC 6791

Huanbin Chi, Zhi Li, Feng Wang, Xuefen Tian et al. • The Astrophysical Journal (2026)

We present a deep photometric analysis of the main-sequence (MS) population in the old, metal-rich open cluster (OC) NGC 6791 using Gaia Data Release 3 data. After correcting for differential reddening, we use the Bayesian model comparison ...

DOAJ Open Access (DOAJ) DOI: 10.3847/1538-4357/ae690f

Design of the Digital Pathology Workspace for Artificial Intelligence Integration

Elena Guerini-Rocco, Chiara Frascarelli, Joana Sorino, Francesca Maria Porta et al. • Applied Sciences (2026)

Designing an optimal digital pathology workspace is essential to ensure diagnostic accuracy and safeguard the long-term well-being of pathologists. While digital pathology improves reproducibility, facilitates multidisciplinary collaboratio...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16126021

Convergence of Artificial Intelligence and Wearables in Strength Training and Performance Monitoring: A Scoping Review

Eleftherios Fyntikakis, Spyridon Plakias, Themistoklis Tsatalas, Minas A. Mina et al. • Applied Sciences (2026)

Background: Strength training (ST) is essential for enhancing athletic performance and reducing injury risk, yet traditional monitoring relies heavily on subjective assessment, limiting objective and individualized evaluation. Objective: Th...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16073565

Micro‐/Nanohierarchical Surfaces for Enhanced Pool Boiling in Large‐Area Silicon Multichips

Youngseob Lee, Kiwan Kim, Yunseo Kim, Daeyoung Kong et al. • Small Structures (2025)

With the rising demand for data centers, the need for an efficient thermal management approach becomes increasingly critical. This study examines the enhancement in pool boiling heat transfer on a customized multichip module, designed to mi...

DOAJ Open Access (DOAJ) DOI: 10.1002/sstr.202400512

Integrating Merkle Trees with Transformer Networks for Secure Financial Computation

Xinyue Wang, Weifan Lin, Weiting Zhang, Yiwen Huang et al. • Applied Sciences (2024)

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of th...

DOAJ Open Access (DOAJ) DOI: 10.3390/app14041386

Restoring the Fluctuation–Dissipation Theorem in Kardar–Parisi–Zhang Universality Class through a New Emergent Fractal Dimension

Márcio S. Gomes-Filho, Pablo de Castro, Danilo B. Liarte, Fernando A. Oliveira • Entropy (2024)

The Kardar–Parisi–Zhang (KPZ) equation describes a wide range of growth-like phenomena, with applications in physics, chemistry and biology. There are three central questions in the study of KPZ growth: the determination of height probabili...

DOAJ Open Access (DOAJ) DOI: 10.3390/e26030260