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An Improved Machine Learning Model for Pure Component Property Estimation

Xinyu Cao Anjan Tula Xi Chen Ming Gong Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Engineering

Publication Date: 2024-08-01

Volume: 39

Issue: Unknown

Page Range: 61-73

Description:

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

Open Access: Yes

DOI: 10.1016/j.eng.2023.08.024

Real-time Emotion Recognition in Smart Homes

Zoltan Balogh Kristian Fodor Gyorgy Molnar

Publication Name: Saci 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 71-76

Description:

To make machines emotionally more intelligent, it is necessary to choose the right measuring devices that are able to collect physiological biosignals, which can be used to recognize the emotional state of the user. These devices should not restrict the users in their everyday life, therefore commercial, non-invasive Internet of Things (IoT) devices must be used. Thanks to the interoperability of IoT devices, it is possible to send the obtained data to a server, where it can be used in the predict method of a trained machine learning algorithm. Based on the recognized emotion, it is possible to create different instructions for smart home elements.

Open Access: Yes

DOI: 10.1109/SACI58269.2023.10158664

Advanced Numerical Simulation of Scour around Bridge Piers: Effects of Pier Geometry and Debris on Scour Depth

Muhanad Al-Jubouri Ethar H. Abbas Richard P. Ray

Publication Name: Journal of Marine Science and Engineering

Publication Date: 2024-09-01

Volume: 12

Issue: 9

Page Range: Unknown

Description:

Investigating different pier shapes and debris Finteractions in scour patterns is vital for understanding the risks to bridge stability. This study investigates the impact of different shapes of pier and debris interactions on scour patterns using numerical simulations with flow-3D and controlled laboratory experiments. The model setup is rigorously calibrated against a physical flume experiment, incorporating a steady-state flow as the initial condition for sediment transport simulations. The Fractional Area/Volume Obstacle Representation (FAVOR) technique and the renormalized group (RNG) turbulence model enhance the simulation’s precision. The numerical results indicate that pier geometry is a critical factor influencing the scour depth. Among the tested shapes, square piers exhibit the most severe scour, with depths reaching 5.8 cm, while lenticular piers show the least scour, with a maximum depth of 2.5 cm. The study also highlights the role of horseshoe, wake, and shear layer vortices in determining scour locations, with varying impacts across different pier shapes. The Q-criterion study identified debris-induced vortex generation and intensification. The debris amount, thickness, and pier diameter (T/Y) significantly affect the scouring patterns. When dealing with high wedge (HW) debris, square piers have the largest scour depth at T/Y = 0.25, while lenticular piers exhibit a lower scour. When debris is present, the scour depth rises at T/Y = 0.5. Depending on the form of the debris, a significant fluctuation of up to 5 cm was reported. There are difficulties in precisely estimating the scour depth under complicated circumstances because of the disparity between numerical simulations and actual data, which varies from 6% for square piers with a debris relative thickness T/Y = 0.25 to 32% for cylindrical piers with T/Y = 0.5. The study demonstrates that while flow-3D simulations align reasonably well with the experimental data under a low debris impact, discrepancies increase with more complex debris interactions and higher submersion depths, particularly for cylindrical piers. The novelty of this work lies in its comprehensive approach to evaluating the effects of different pier shapes and debris interactions on scour patterns, offering new insights into the effectiveness of flow-3D simulations in predicting the scour patterns under varying conditions.

Open Access: Yes

DOI: 10.3390/jmse12091637

Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs

Dragan Pamucar Rajesh Kumar Dhanaraj C. N. Vanitha Mahmoud Ahmad Al-Khasawneh P. Anusuya

Publication Name: Methodsx

Publication Date: 2025-12-01

Volume: 15

Issue: Unknown

Page Range: Unknown

Description:

Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. The proposed approach utilizes a multi-objective reward model to adaptively balance energy consumption, task success rate, and network lifetime. Extensive simulation results demonstrate that ETORL-UAV outperforms five state-of-the-art methods Meta-RL, g-MAPPO, Backscatter Optimization, Hierarchical Optimization, and Game Theory based Pricing achieving up to 9.3 % higher task offloading success, 18.75 % improvement in network lifetime, and 27 % reduction in energy consumption. These results validate the framework's scalability, reliability, and practical applicability for real-world disaster-response WSN deployments. • Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs • Leverages PPO-based reinforcement learning and a multi-objective reward model • Demonstrates superior performance over five benchmark approaches in disaster-response simulations

Open Access: Yes

DOI: 10.1016/j.mex.2025.103472

Design and control for torque ripple reduction of a 3-phase switched reluctance motor

Miklós Kuczmann Daniel Marcsa

Publication Name: Computers and Mathematics with Applications

Publication Date: 2017-07-01

Volume: 74

Issue: 1

Page Range: 89-95

Description:

A major problem in switched reluctance motor is torque ripple, which causes undesirable acoustic noise and vibration. This work focuses on reducing the undesirable torque ripple in 6/4-pole three-phase switched reluctance motor by geometry modification and using control technique. The proposed method combined the specially skewed rotor pole shape with instantaneous torque control with sinusoidal torque sharing function. The results of geometry modification are analysed through the three-dimensional finite element simulation to determine the appropriate skewing angle. The drive performances of conventional and modified motor are compared through the simulations. The effectiveness of the proposed method is also demonstrated and verified by the simulations.

Open Access: Yes

DOI: 10.1016/j.camwa.2017.01.001

Charpy Impact Test Result Comparison on Reinforcing Materials used in Continuous Filament 3D Printing

Robert Magai Balazs Molnar

Publication Name: Engineering Technology and Applied Science Research

Publication Date: 2025-02-01

Volume: 15

Issue: 1

Page Range: 19354-19357

Description:

With the growing industrial demand for materials that can withstand dynamic loads, composite 3D printing, particularly utilizing continuous fiber reinforcements, presents a promising solution. This study investigates the toughness of three fiber-reinforced materials, namely carbon fiber, Kelvar, and fiberglass, by conducting Charpy impact tests. The results reveal that fiber-reinforced 3D materials significantly outperform standard 3D printed components, with fiberglass showing the highest toughness. These findings demonstrate that fiber-reinforced 3D printed materials offer a viable alternative for applications requiring high toughness and dynamic resistance.

Open Access: Yes

DOI: 10.48084/etasr.8740

Innovation through intelligent computer-aided formulation design

Naz P. Taskiran Kris Prasopsanti Thunyaras Phanusupawimol Rafiqul Gani Venkat Venkatasubramanian

Publication Name: Current Opinion in Chemical Engineering

Publication Date: 2025-03-01

Volume: 47

Issue: Unknown

Page Range: Unknown

Description:

This perspective paper presents a focused review of a selected topic of chemical-based products, namely, formulations. As formulations cover a wide range of chemical-based products, we highlight opportunities for innovation in three types of formulations — liquid blends, which are mixtures of chemicals that are in the liquid state at standard conditions; liquid formulations, which are mixtures of chemicals that may exist in different states but the final product is a single-phase liquid; and emulsions, which are also mixtures of chemicals that may exist in different states, but the final product is in the form of an emulsion. In each case, we discuss aspects of design, analysis, and innovation together with issues and challenges that could be tackled to find better and more sustainable products. In particular, the potential of hybrid artificial intelligence augmented computer-aided techniques that can aid in the design, analysis, and innovation of formulations is highlighted.

Open Access: Yes

DOI: 10.1016/j.coche.2025.101099

Metakaolin-Enhanced Laterite Rock Aggregate Concrete: Strength Optimization and Sustainable Cement Replacement

Adewumi John Babafemi Majid Movahedi Rad Muayad Habashneh Udeme Udo Imoh Sophia Chukwufumnanya Kaine Rauf Hassan

Publication Name: Buildings

Publication Date: 2025-12-01

Volume: 15

Issue: 24

Page Range: Unknown

Description:

The growing demand for concrete in tropical regions faces two unresolved challenges: the high carbon footprint of ordinary Portland cement (OPC) and limited understanding of how supplementary cementitious materials affect the mechanical performance of laterite rock aggregates concrete. Although metakaolin (MK) is a highly reactive pozzolan, its combined use with laterite rock aggregates concrete and its influence on strength development and microstructure have not been sufficiently clarified. This study investigates the mechanical behavior and sustainability potential of laterite rock aggregate concrete in which OPC is partially replaced by MK at 0%, 5%, 10%, 15%, and 20% by weight. All mixes were prepared at a constant water–binder ratio of 0.50 and tested for workability, compressive strength, split-tensile strength, and flexural strength at 7, 14, and 28 days, with and without a polycarboxylate-based superplasticizer. The results show that MK significantly enhances the mechanical performance of laterite rock concrete, with an optimum at 10% replacement: the 28-day compressive strength increased from 35.6 MPa (control) to 53.9 MPa in the superplasticized mix, accompanied by corresponding gains in tensile and flexural strengths. SEM–EDS analyses revealed microstructural densification, reduced portlandite, and a refined interfacial transition zone, explaining the improved strength and cracking resistance. From an environmental perspective, a 10% MK replacement corresponds to an approximate 10% reduction in clinker-related CO2 emissions, while the use of locally available laterite rock reduces the dependence on quarried granite and transportation impacts. The findings demonstrate that MK-modified laterite rock concrete is a viable and eco-efficient option for structural applications in tropical regions. The study concludes that MK-enhanced laterite rock aggregate concrete can deliver higher structural performance and improved sustainability without altering conventional mix design and curing practices.

Open Access: Yes

DOI: 10.3390/buildings15244553

Infection by SARS-CoV-2 in a rescued serval (Leptailurus serval, Schreber, 1776)

Anna Szilasi János Gál Tibor Papp Nándor Balogh Viktória Sós-Koroknai Márton Hoitsy Endre Sós Miklós Marosán

Publication Name: Acta Veterinaria Hungarica

Publication Date: 2025-11-24

Volume: 73

Issue: 4

Page Range: 228-236

Description:

In late November 2021, a male serval (Leptailurus serval) which had escaped from an unknown holding facility was observed in the Bükk Mountains of Northern Hungary. A few days were needed to capture the animal with a live trap, after which it was transported to a national Rescue Centre at the Budapest Zoo and Botanical Garden. The exact origin of the specimen was never identified and apart from being emaciated, it seemed only to be stressed and weakened by the cold weather and starvation. Contrary to the initial fair prognosis, 2.5 days after admittance, the animal rapidly developed pronounced respiratory and central nervous system signs and despite intensive treatment died within a few hours. The subsequent diagnostic investigation revealed that the cause of death was SARS-CoV-2 infection. These diagnostic steps excluded other possible, lethal felid pathogens as causative agents and confirmed that the disease process was attributed to this virus.

Open Access: Yes

DOI: 10.1556/004.2025.01178