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Found 6383 publications

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

ENERGY MANAGEMENT POLICY SELECTION IN SMART GRIDS: A CRITIC-CoCoSo METHOD WITH Lq* q-rung ORTHOPAIR MULTI-FUZZY SOFT SETS

Dragan Pamucar J. Vimala S. Rajareega P. Mahalakshmi Tamil Vizhi Mariappan Tamil Vizhi Mariappan

Publication Name: Applied Engineering Letters

Publication Date: 2025-03-01

Volume: 10

Issue: 1

Page Range: 35-47

Description:

In response to the energy crisis and the global push for sustainability, modern power grids are increasingly integrating renewable energy, plug-in electric vehicles, and energy storage systems. This evolution demands an advanced energy management system capable of handling the variability of renewable resources, uncertainties in electric vehicle performance, fluctuating electricity prices, and dynamic load conditions. To address these challenges, our study introduces a novel decision-making framework that leverages a new score function for comparing q-rung orthopair multi-fuzzy soft numbers. This approach employs the Criteria Importance Through Inter-criteria Correlation (CRITIC) method to determine objective weights while simultaneously incorporating subjective preferences through an integrated weighting scheme. The framework is further enhanced by applying the Combined Compromise Solution (CoCoSo) method within the Lq* q-rung orthopair multi-fuzzy soft decision-making structure to select optimal energy management policies. Extensive sensitivity analysis confirms the robustness and effectiveness of the proposed methodology, offering a promising solution for efficient energy management in modern power systems.

Open Access: Yes

DOI: 10.46793/aeletters.2025.10.1.4

Eimeria Oocysts and Passalurus ambiguus Infection of Farmed Rabbits Depending on the Age

Zsolt Gerencsér Zsolt Matics Zoltán Német Erika Lencsés-Varga Boróka Bárdos Csongor Demeter Anett Demeter-Jeremiás Róbert Juráskó András Mayer

Publication Name: Journal of Animal Health and Production

Publication Date: 2025-03-01

Volume: 13

Issue: 1

Page Range: 178-184

Description:

The aim of the study was to investigate the Eimeria spp. and Passalurus ambiguus infections of rabbits by day of life. From 2018 to 2024, pooled faecal samples were collected from 29 Hungarian and 2 Slovakian rabbit farms. Low level of Eimeria oocysts infection was observed during lactation. In the week following the weaning the proportion of positive samples increased. Between day 43 and slaughter age, the proportion of positive samples was consistently high. The average OPG values (number of Eimeria oocysts per gram faeces), never reached 5,000 during the lactation period, but a critical period started at 42 days of age, with average OPG values above 10,000 in several cases. High number of oocysts were observed until the end of fattening period. The exponential smoothing model estimated the onset of Eimeria infection at 38-40 days of age (p=0.023). The proportion of P. ambiguus eggs positive samples reached 50% already in the lactation period. From day 29 to slaughter age, a relatively low rate of infected samples was detected. For P. ambiguus, the model estimated 7 days of age as the increase (p=0.001) of infection. It can be concluded that P. ambiguus eggs and Eimeria oocysts can be detected in the faeces of rabbits during their whole life cycle. During the fattening period (5-11 weeks of age), the Eimeria infection is on high level while the P. ambiguus infection is in low level.

Open Access: Yes

DOI: 10.17582/journal.jahp/2025/13.1.178.184

Hypertension and sports activity

Zsolt Szelid Ágnes Sziva

Publication Name: Cardiologia Hungarica

Publication Date: 2025-03-01

Volume: 55

Issue: 1

Page Range: 34-39

Description:

This summary article reviews the complex relationship between hypertension and sports, addressing key aspects of prevalence, risk factors, and management strategies. Hypertension is a leading cause of cardiovascular disease and mortality, and while regular exercise generally provides cardioprotective effects, certain sports disciplines and lifestyle choices may elevate blood pressure levels in athletes. The article highlights the role of isometric training, high body mass, and the use of performance-enhancing substances as contributors to increased risk. Additionally, it explores the implications of high blood pressure on both athletic performance and long-term cardiovascular health. Diagnostic challenges are discussed, emphasizing the limitations of routine measurements and the need for advanced tools such as ambulatory blood pressure monitoring. Updated European guidelines are presented as a framework for accurate hypertension diagnosis and risk assessment among athletes. Management approaches prioritize lifestyle interven-tions, including dietary changes, stress reduction, and tailored exercise programs. When necessary, pharmacological treatments are recommended with careful consideration of doping regulations and potential impacts on athletic perfor-mance. This article underscores the importance of individualized care in addressing hypertension in athletes, advocat-ing for a multidisciplinary approach that integrates medical, nutritional, and training expertise. By consolidating current evidence, the article aims to provide practical guidance for clinicians, treating athletes and patients with regular physical activities, to better understand and manage hypertension in this population.

Open Access: Yes

DOI: 10.26430/CHUNGARICA.2025.55.1.34

Numerical and Experimental Analysis of Impact Force and Impact Duration with Regard to Radiosondes: Is a PUR Foam Shell an Effective Solution?

Gusztáv Fekete János Jósvai Norbert Hegyi

Publication Name: Applied Mechanics

Publication Date: 2025-03-01

Volume: 6

Issue: 1

Page Range: Unknown

Description:

This study investigates the effect of a polyurethane (PUR) foam layer on impact force, impact duration, and deformation with regard to radiosondes during drop tests. Numerical (Finite Element Method) and experimental approaches were used to model collisions with and without protective PUR layers. The numerical results demonstrated that adding a soft PUR foam layer reduced peak impact force by 10% while it increased impact duration up to 71%. Experimental drop tests confirmed the numerical outcomes as peak impact force difference was 7% between simulations and experiments, while impact duration differed only by 11%. Besides force and duration, impact deformation was also investigated by an FEM model and high-speed camera footage on radiosondes with a PUR foam layer. The FEM model was able to approximate well the deformation magnitude since the numerical deformation was only 2% lower compared to the experimental data. In summary, a reliable and validated FEM model was created. On the one hand, this model allows the analysis of different protective layers around a radiosonde. On the other hand, it can adequately predict the impact behavior of radiosondes by incorporating multiple important factors. In addition, it has been confirmed that incorporating a soft PUR foam layer significantly improves safety by reducing impact force and extending impact duration.

Open Access: Yes

DOI: 10.3390/applmech6010019

Models, modeling and model-based systems in the era of computers, machine learning and AI

Seyed Soheil Mansouri Abhishek Sivaram Christopher J. Savoie Rafiqul Gani

Publication Name: Computers and Chemical Engineering

Publication Date: 2025-03-01

Volume: 194

Issue: Unknown

Page Range: Unknown

Description:

Models, representing a system under study with respect to problems such as process design, process control, product synthesis and many more, are at the core of most computer-aided solution techniques. The representation of a system through a model is done in different ways, such as, symbols, data, mathematical equations, and/or some combination of these. The workflow or process of creating a proxy mathematical representation (model) of a given target system is referred to as modeling. Model-based software tools incorporate the developed models within the steps of their systematic workflow through simultaneous or decomposed solution strategies related to synthesis, design, analysis, etc., of specific systems. In this perspective paper we highlight the various ways systems can be represented by models, the different ways the required models are developed through modeling techniques, and examples of model-based software tools developed to solve different process and product engineering problems. Two types of systems - process systems and chemical systems, are considered. Important issues and challenges are highlighted and perspectives on how they can be addressed are presented.

Open Access: Yes

DOI: 10.1016/j.compchemeng.2024.108957

TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors

Viktor Remeli Adonisz Dimitriu Tamas V. Michaletzky

Publication Name: Big Data and Cognitive Computing

Publication Date: 2025-03-01

Volume: 9

Issue: 3

Page Range: Unknown

Description:

Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing effective adversarial methodologies is increasingly challenging. We present Truck Adversarial Camouflage Optimization (TACO), a novel framework that generates adversarial camouflage patterns on 3D vehicle models to deceive state-of-the-art object detectors. Adopting Unreal Engine 5, TACO integrates differentiable rendering with a Photorealistic Rendering Network to optimize adversarial textures targeted at YOLOv8. To ensure the generated textures are both effective in deceiving detectors and visually plausible, we introduce the Convolutional Smooth Loss function, a generalized smooth loss function. Experimental evaluations demonstrate that TACO significantly degrades YOLOv8’s detection performance, achieving an AP@0.5 of 0.0099 on unseen test data. Furthermore, these adversarial patterns exhibit strong transferability to other object detection models such as Faster R-CNN and earlier YOLO versions.

Open Access: Yes

DOI: 10.3390/bdcc9030072

Mathematical Modeling of the Rail Track Superstructure–Subgrade System

Szabolcs Fischer Dmytro Kurhan Vladyslav Khmelevskyi

Publication Name: Geotechnics

Publication Date: 2025-03-01

Volume: 5

Issue: 1

Page Range: Unknown

Description:

The “rail track superstructure–subgrade” system is a sophisticated engineering structure critical in ensuring safe and efficient train operations. Its analysis and design rely on mathematical modeling to capture the interactions between system components and the effects of both static and dynamic loads. This paper offers a detailed review of contemporary modeling approaches, including discrete, continuous, and hybrid models. The research’s key contribution is a thorough comparison of five primary methodologies: (i) quasi-static analytical calculations, (ii) multibody dynamics (MBD) models, (iii and iv) static and dynamic finite element method (FEM) models, and (v) wave propagation-based models. Future research directions could focus on developing hybrid models that integrate MBD and FEM to enhance moving load predictions, leveraging machine learning for parameter calibration using experimental data, investigating the nonlinear and rheological behavior of ballast and subgrade in long-term deformation, and applying wave propagation techniques to model vibration transmission and evaluate its impact on infrastructure.

Open Access: Yes

DOI: 10.3390/geotechnics5010020

Hybrid ML models for volatility prediction in financial risk management

Monika Dhochak Amar Rao Satish Kumar

Publication Name: International Review of Economics and Finance

Publication Date: 2025-03-01

Volume: 98

Issue: Unknown

Page Range: Unknown

Description:

Predicting volatility in financial markets is an important task with practical uses in decision-making, regulation, and academic research. This study focuses on forecasting realized volatility in stock indices using advanced machine learning techniques. We examine three key indices: the Shanghai Stock Exchange Composite (SSE), Infosys (INFY), and the National Stock Exchange Index (NIFTY). To achieve this, we propose a hybrid model that combines optimized Variational Mode Decomposition (VMD) with deep learning methods like Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Using data from 2015 to 2022, we analyse how well these models predict volatility. Our findings reveal distinct patterns: the SSE shows high unpredictability, INFY is prone to extreme positive volatility, and NIFTY is relatively moderate. Among the models tested, the Q-VMD-ANN-LSTM-GRU hybrid model consistently performs best, providing highly accurate predictions for all three indices. This model has practical benefits for financial institutions. It improves risk management, supports investment decisions, and provides real-time insights for traders and risk managers. Additionally, it can enhance stress testing and inspire innovative trading strategies. Overall, our study highlights the potential of advanced machine learning, especially hybrid models, to address financial market complexities and improve risk management practices.

Open Access: Yes

DOI: 10.1016/j.iref.2025.103915