Search in Publications

Found 6525 publications

Investigation of the Tribological Effects of Nano-Sized Transition Metal Oxides on a Base Oil Containing Pour Point Depressant and Viscosity Modifier

Publication Name: Chemengineering

Publication Date: 2025-02-01

Volume: 9

Issue: 1

Page Range: Unknown

Description:

This study investigates the tribological effects of nano-sized metal oxides (ZrO2, CuO, Y2O3 and TiO2) in Group III type base oil containing 0.3% pour point depressant (PPD) and 5% viscosity modifier (VM) to enhance friction and wear performance. The homogenized lubricant samples with varying concentrations of oxide nanoparticles (0.1–0.5 wt%) on a linear oscillating tribometer performed static and dynamic frictional tests. Optical and confocal microscopy surface analysis evaluated the wear of the specimen, and SEM and EDX analyses characterized the wear tracks, nanoparticle distributions, and quantification. The cooperation between PPD and nanoparticles significantly improved friction and wear values; however, the worn surface suffered extensively from fatigue wear. The collaboration between VM and nanoparticles resulted in a nanoparticle-rich tribofilm on the contact surface, providing excellent wear resistance that protects the component while also favorably impacting friction reduction. This study found CuO reduced wear volume by 85% with PPD and 43% with VM at 0.5 wt%, while ZrO2 achieved 80% and 63% reductions, respectively. Y2O3 reduced wear volume by 82% with PPD, and TiO2 reduced friction by 20% with VM. These nanoparticles enhanced tribological performance at optimal concentrations, but high concentrations caused tribofilm instability, highlighting the need for precise optimization.

Open Access: Yes

DOI: 10.3390/chemengineering9010001

Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference

Publication Name: Applied Sciences Switzerland

Publication Date: 2025-02-01

Volume: 15

Issue: 3

Page Range: Unknown

Description:

The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to identify key geotechnical parameters influencing Vs predictions. Additionally, a Bayesian GLM is developed to explicitly account for uncertainties arising from geotechnical variability. The effectiveness and predictive performance of the proposed models were validated through comparison with real case scenarios. The results highlight the unique advantages of each model. The XGBoost model demonstrates good predictive performance, achieving high coefficient of determination ((Formula presented.)), index of agreement (IA), Kling–Gupta efficiency (KGE) values, and low error values while effectively explaining the impact of input parameters on Vs. In contrast, the Bayesian GLM provides probabilistic predictions with 95% credible intervals, capturing the uncertainty associated with the predictions. The integration of these two approaches creates a comprehensive framework that combines the strengths of high-accuracy ML predictions with the uncertainty quantification of Bayesian inference. This hybrid methodology offers a powerful and interpretable tool for Vs prediction, providing engineers with the confidence to make informed decisions.

Open Access: Yes

DOI: 10.3390/app15031409

A novel multimodal communication framework using robot partner for aging population

Publication Name: Expert Systems with Applications

Publication Date: 2015-06-01

Volume: 42

Issue: 9

Page Range: 4540-4555

Description:

In developed country such as Japan, aging has become a serious issue, as there is a disproportionate increasing of elderly population who are no longer able to look after themselves. In order to tackle this issue, we introduce human-friendly robot partner to support the elderly people in their daily life. However, to realize this, it is essential for the robot partner to be able to have a natural communication with the human. This paper proposes a new communication framework between the human and robot partner based on relevance theory as the basis knowledge. The relevance theory is implemented to build mutual cognitive environment between the human and the robot partner, namely as the informationally structured space (ISS). Inside the ISS, robot partner employs both verbal as well as non-verbal communication to understand human. For the verbal communication, Rasmussen's behavior model is implemented as the basis for the conversational system. While for the non-verbal communication, environmental and human state data along with gesture recognition are utilized. These data are used as the perceptual input to compute the robot partner's emotion. Experimental results have shown the effectiveness of our proposed communication framework in establishing natural communication between the human and the robot partner.

Open Access: Yes

DOI: 10.1016/j.eswa.2015.01.016

Review on the occurrence of the mcr-1 gene causing colistin resistance in cow's milk and dairy products

Publication Name: Heliyon

Publication Date: 2021-04-01

Volume: 7

Issue: 4

Page Range: Unknown

Description:

Both livestock farmers and the clinic use significant amount of antibiotics worldwide, in many cases the same kind. Antibiotic resistance is not a new phenomenon, however, it is a matter of concern that resistance genes (mcr - Mobilized Colistin Resistance - genes) that render last-resort drugs (Colistin) ineffective, have already evolved. Nowadays, there is a significant consumption of milk and dairy products, which, if not treated properly, can contain bacteria (mainly Gram-negative bacteria). We collected articles and reviews in which Gram-negative bacteria carrying the mcr-1 gene have been detected in milk, dairy products, or cattle. Reports have shown that although the incidence is still low, unfortunately the gene has been detected in some dairy products on almost every continent. In the interest of our health, the use of colistin in livestock farming must be banned as soon as possible, and new treatments should be applied so that we can continue to have a chance in fighting multidrug-resistant bacteria in human medicine.

Open Access: Yes

DOI: 10.1016/j.heliyon.2021.e06800

Implicit a posteriori error estimation using patch recovery techniques

Publication Name: Central European Journal of Mathematics

Publication Date: 2012-02-01

Volume: 10

Issue: 1

Page Range: 55-72

Description:

We develop implicit a posteriori error estimators for elliptic boundary value problems. Local problems are formulated for the error and the corresponding Neumann type boundary conditions are approximated using a new family of gradient averaging procedures. Convergence properties of the implicit error estimator are discussed independently of residual type error estimators, and this gives a freedom in the choice of boundary conditions. General assumptions are elaborated for the gradient averaging which define a family of implicit a posteriori error estimators. We will demonstrate the performance and the favor of the method through numerical experiments. © 2012 Versita Warsaw and Springer-Verlag Wien.

Open Access: Yes

DOI: 10.2478/s11533-011-0119-7

Criteria-Based Selection of Cycling Infrastructure: Comparative Analysis of European Guidelines †

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

Determining suitable cycling infrastructure is essential for transport planners in European countries seeking to improve safety, promote sustainability, and encourage active travel. This paper compares national cycling infrastructure guidelines from Ukraine, Hungary, the Netherlands, the United Kingdom, and Slovakia. The analysis focuses on key aspects such as infrastructure se-lection criteria (traffic volume, speed), threshold values, design flexibility, contextual integration, and safety performance. Although motor traffic volume and speed are regarded as essential parameters in all countries, implementation and thresholds differ significantly. The Dutch CROW manual enforces the strictest separation guidelines, while Ukraine allows mixed traffic even in higher-speed environments. The UK’s LTN 1/20 emphasizes contextual design and quality, while Hungary and Slovakia rely on matrix-based methods. The findings highlight the need for standardization and alignment with global safety norms.

Open Access: Yes

DOI: 10.3390/engproc2025113072

Extended Measurement Methods for Onboard Detection of Brake Disc Deformation †

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

Runout is a common failure of brake discs. The detection of this fault usually depends on the driver, as there is a vibration in the car and on the brake pedal. As Advanced Driver Assistant Systems are implemented and autonomous driving modes are available, braking is carried out by the car instead. Brake disc runout can cause longer braking distance, so it is essential to recognize and repair it. NVH measurements have been validated to be one of the solutions to detect the fault immediately without disassembling the brake unit. In this article, the previous vibration measurements are extended with other methods that can also be used for fault detection. Brake fluid pressure measurement and integration of the disc rotation angle sensor enable the detection of faults without additional sensors. The aim of the research is to design a measurement method that can be compared with previously validated measurements.

Open Access: Yes

DOI: 10.3390/engproc2025113078

User-Specific Load Profile Clustering for Automotive Battery Applications †

Publication Name: Engineering Proceedings

Publication Date: 2025-01-01

Volume: 113

Issue: 1

Page Range: Unknown

Description:

In applied battery research, use-case-driven prediction is becoming increasingly important, particularly for predicting real-life load profiles. This study proposes techniques to forecast lifetime load profiles for traction batteries, comparing urban- and highway-dominated vehicular use cases. Both charging and discharging scenarios are analyzed. We examine the uncertainty in these profiles and conduct a sensitivity analysis to understand the relationship between load profiles and user behavior. In this study, we introduce a novel methodology that maps behavioral and environmental parameters to battery load clusters, enabling us to identify high-risk aging scenarios. Based on parameter studies, we perform load profile clustering to identify critical use case groups and observe key parameter interactions. We present a case study of an idealized driver under Hungarian environmental conditions to predict outlier battery usage in fleets. This novel approach enables more robust predictions of aging and performance degradation for automotive traction batteries across different user clusters.

Open Access: Yes

DOI: 10.3390/engproc2025113074

Trade-offs in production supply systems

Publication Name: International Conference on Industrial Logistics Icil 2012 Conference Proceedings

Publication Date: 2012-01-01

Volume: Unknown

Issue: Unknown

Page Range: 356-361

Description:

The cost price of a final product depends on - among others - the cost of value added processes such as production supply. When planning the supply strategy and defining the parameters of the processes we face a lot of trade-off situations (e.g.: decision between kanban or other supply strategy, kanban quantity, production line loading cycle time, maximal number of picking items on picking lists, etc.). The aim of this paper is to list these decision situations and give adequate solutions on them. By means of mathematical modeling we define the optimal system parameter configuration of a production supply system where the total cost is minimal.

Open Access: Yes

DOI: DOI not available