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Found 6383 publications

Numerical Study of Maxwell and Navier–Stokes Equations for Fluid Flow Over a Curvilinear Surface Subject to Buoyancy Forces

Muhammad Shuaib Muhammad Bilal Hijaz Ahmad Hassan Ali Ghazwani Sabahat Khan

Publication Name: ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik

Publication Date: 2026-04-01

Volume: 106

Issue: 4

Page Range: Unknown

Description:

Buoyancy-driven viscous fluid flow across a curved surface is investigated numerically in this work using the coupled Maxwell and Navier–Stokes equations, with variable fluid characteristics represented as nonlinear functions of temperature. Realistic magneto-hydrodynamic effects are captured by including the Lorentz force and the influence of a fluctuating magnetic field in curvilinear coordinates. The governing partial differential equations are solved using the parametric continuation method (PCM) after being converted into a system of ordinary differential equations by similarity transformations. Results demonstrate excellent agreement when compared to previously published data using MATLAB's PCM solver to confirm correctness. According to the parametric study, buoyancy ((Formula presented.)) improves fluid motion by around 15%, whereas greater curvature factors (Formula presented.), Stuart numbers (Formula presented.), and Prandtl numbers (Formula presented.) result in a 12%–16% drop in radial and arc-length velocities. The temperature profile falls by more than 23% as (Formula presented.) and (Formula presented.) increase, indicating the significance of thermal diffusivity in preventing heat buildup. It increases by 25% with higher magnetic interaction ((Formula presented.), (Formula presented.)). The induced magnetic field is strengthened by 6%–7% with a little increase in the magnetic interaction parameter (Formula presented.), whereas the magnetic field intensity is reduced by about 25% with a larger (Formula presented.). Skin friction falls by almost 10% with greater (Formula presented.) at moderate (Formula presented.), but increases by 4% under larger Lorentz forces ((Formula presented.), (Formula presented.)). Overall, the results show that velocity, temperature, magnetic field distribution and surface forces are strongly influenced by buoyancy, curvature and electromagnetic parameters. The findings shed light on efficient energy optimisation, thermal control, and electromagnetic regulation of MHD flows over curved geometries.

Open Access: Yes

DOI: 10.1002/zamm.70423

Equitable Economic Development in Global South through Sustainable Mineral Policy: Role of Political and Governance Factors

Gagan Deep Sharma Umer Shahzad

Publication Name: Politicka Ekonomie

Publication Date: 2025-01-01

Volume: 73

Issue: 5

Page Range: 769-777

Description:

No description provided

Open Access: Yes

DOI: 10.18267/j.polek.1514

The Relationship Between Situational Crime Prevention and Road Safety Defending Against Environmental Threats

József Krenner

Publication Name: Advanced Sciences and Technologies for Security Applications

Publication Date: 2025-01-01

Volume: Part F136

Issue: Unknown

Page Range: 461-467

Description:

The aim of the research is to examine the relationship between situational crime prevention and road safety, with a specific focus on environmental factors and pedestrian safety. The research endeavors to develop a model that aids in identifying risk factors and their underlying causes within the traffic environment. The model analyzes the built environment, accident statistics, and legal regulations, with particular emphasis on pedestrians. Current police methods, campaigns, and responses to pedestrian safety issues are also analyzed. According to the WHO report, millions of individuals lose their lives in road accidents annually, predominantly pedestrians, cyclists, and motorcyclists. Environmental criminology and situational crime prevention offer approaches that analyze the interplay between human behavior and environmental factors, thereby enhancing road safety. The situational prevention method can be effectively applied to enhance road safety, considering the combined influence of various factors in the occurrence of traffic accidents.

Open Access: Yes

DOI: 10.1007/978-3-031-78544-3_36

Advanced Examination Systems: Applying Fuzzy Logic and Machine Learning Methods in Education

Eniko Nagy Karl Eva Gyorgy Molnar

Publication Name: Iccc 2025 IEEE 12th International Joint Conference on Cybernetics and Computational Cybernetics Cyber Medical Systems Proceedings

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: 231-235

Description:

In our paper, we present an innovative educational assessment system that attempts to overcome the limitations of traditional educational evaluation methods by applying machine learning, artificial intelligence, fuzzy logic, and advanced mathematical techniques. This system can provide a more objective and personalized assessment of students' knowledge. Current examination systems in educational institutions are outdated and do not meet modern societal and technological requirements. A central element of the system is the application of fuzzy logic, which allows for handling uncertainties in knowledge assessment. Using the FP-growth algorithm and the fuzzy analytical hierarchy process (AHP) aids in optimizing the evaluation process and enables a more profound analysis of students' performance. During the system's development, we aim to adaptively manage the difficulty level of questions, taking into account students' prior performance and individual capabilities. The study highlights the security risks and efficiency issues of current 'manual' question compilation methods. The new system aims to minimize these risks while improving the quality of education and reducing the workload of educators.

Open Access: Yes

DOI: 10.1109/ICCC64928.2025.10999148

Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling

L. Tarrason S. Janssen J. L. Pérez-Camanyo László Környei J. L. Santiago V. Rodrigues G. Sousa Santos P. Thunis E. Rivas C. Cuvelier I. Sakellaris M. G. Villani S. Hamilton N. Masey F. Martín N. Reiminger X. Jurado D. Barbero J. L. Pérez-Camanyo R. Jackson J. Sousa J. Bartzis R. San José G. Tinarelli J. Stocker Zoltán Horváth F. Russo

Publication Name: Science of the Total Environment

Publication Date: 2025-08-01

Volume: 988

Issue: Unknown

Page Range: Unknown

Description:

This study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO2 concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO2 concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO2 annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models. The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization. Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.

Open Access: Yes

DOI: 10.1016/j.scitotenv.2025.179824

Evaluating the life quality of the built environment by FRI method

Szilveszter Kovacs Gergely Molnarka László T. Kóczy

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2015-11-25

Volume: 2015-November

Issue: Unknown

Page Range: Unknown

Description:

The fuzzy rule interpolation (FRI) and the fuzzy signature methodology was successfully adapted for expressing the building condition. In this paper we extend this concept for estimating the life quality of the built environment, especially of the residential segment. The applied methodology is based on a hierarchical FRI as a straightforward implementation of the fuzzy signature concept. The paper also introduces some application details of a case study related to residential houses located in a historic district of Budapest, Hungary.

Open Access: Yes

DOI: 10.1109/FUZZ-IEEE.2015.7338029

Constrained optimization in physics-informed neural networks for singular three-point boundary value problems

Aamir Farooq Hijaz Ahmad Sadique Rehman Emad A.A. Ismail Uzma Nadeem Malik Ahsin Iqbal Fuad A. Awwad

Publication Name: Ain Shams Engineering Journal

Publication Date: 2026-04-01

Volume: 17

Issue: 4

Page Range: Unknown

Description:

Physics-informed neural networks represent a category of deep learning models that directly incorporate physical laws into the training process to solve differential equations, thereby diminishing the dependence on extensively labeled datasets. This study investigates a constrained optimization framework within PINNs to address singular three-point boundary value problems, which present significant challenges owing to singularities and internal boundary conditions that result in non-standard solution behavior. To address these complexities, we developed a customized Physics-informed neural network architecture that integrates constraint-driven regularization terms into the loss function to enhance the generalization and numerical stability. The proposed approach was evaluated across multiple benchmark problems, with performance assessed using statistical metrics and the mean squared error. The optimization and training PINN regular framework will stabilize the training and convergence in the presence of singularities to yield dependable TPS-BVP solutions. The predicted solutions were rigorously compared with exact analytical solutions. The results demonstrate that the constrained optimization-based Physics-informed neural networks framework provides highly accurate and stable approximations, validating its effectiveness in handling complex singular boundary value problems.

Open Access: Yes

DOI: 10.1016/j.asej.2026.104063

Adaptive Vehicle Trajectory Clustering Based on Computer Vision

András Horváth Áron Dávid Agg Bence Gábor Péter

Publication Name: Lecture Notes in Networks and Systems

Publication Date: 2025-01-01

Volume: 1258 LNNS

Issue: Unknown

Page Range: 344-360

Description:

Intelligent Transportation Systems is a rapidly evolving and extensively researched field. The development of tools and methods necessitated the appearance of a new concept, Cognitive Mobility, covers greater integration of transport related areas. Our research goes beyond simple image processing and statistical analysis and includes cognitive elements of mobility, for example, gives methods for adaptive analysis of typical vehicle behavior in junctions and helps in decision making for better utilization of infrastructure. In this work, we present an adaptive approach to interpreting traffic scenes. This approach uses the YOLOv7 object detector and DeepSORT tracking algorithm to generate trajectories of moving vehicles in video recordings captured from a stationary viewpoint. The paper examines and compares various techniques for clustering the vehicle trajectories, such as using clustering algorithms to group vehicles based on their entry and exit points within the video scene. Furthermore, we report the results of clustering parameter sensitivity tests performed on the video dataset we created. This control over the granularity of the clustering enables generating street-level or lane-level traffic data from the same recordings. Finally, we demonstrate some practical applicability of the method through illustrative examples, showcasing how the generated traffic data can be used to monitor and analyze traffic patterns, identify congestion points, and inform transportation planning and decision-making.

Open Access: Yes

DOI: 10.1007/978-3-031-81799-1_32

Fuzzy-based multi-stroke character recognizer

A. Tormási László T. Kóczy

Publication Name: 2013 Federated Conference on Computer Science and Information Systems Fedcsis 2013

Publication Date: 2013-12-01

Volume: Unknown

Issue: Unknown

Page Range: 671-674

Description:

In this paper an extension for multi-stroke character recognition of FUzzy BAsed handwritten character Recognition (FUBAR) algorithm will be presented. First the basic concept of a single-stroke version will be overviewed; in the second part of the paper the new version of the algorithm with multi-stroke symbol support will be introduced, which deploy the same algorithm overviewed in the first part and use flat and hierarchical rule bases. © 2013 Polish Information Processing Society.

Open Access: Yes

DOI: DOI not available