Documents published in 2025 : Scipedia

Expense Coding Syntax: Misclassification in AI-Powered Corporate ERPs
A. Startari

Grammars of Power (2025). 2

Abstract
This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transformerbased classifiers on labeled accounting data to predict expense categories and observed that these models frequently relied on grammatical form rather than financial semantics. We extracted syntactic features including nominalization frequency, defined as the ratio of deverbal nouns to verbs; coordination depth, measured by the maximum depth of coordinated clauses; and subordination complexity, expressed as the number of embedded subordinate clauses per sentence. Using SHAP (SHapley Additive exPlanations), we identified that these structural patterns significantly contribute to false allocations, thus increasing the likelihood of audit discrepancies. For interpretability, we applied the method introduced by Lundberg and Lee in their seminal work, “A Unified Approach to Interpreting Model Predictions,” published in Advances in Neural Information Processing Systems 30 (2017): 4765–4774. To mitigate these syntactic biases, we implemented a rule-based debiasing module that reparses each narrative into a standardized fair-syntax transformation, structured around a
Abstract
This study examines how syntactic constructions in expense narratives affect misclassification rates in AI-powered corporate ERP systems. We trained transformerbased classifiers [...]
- 6
- read
Algorithmic Obedience: How Language Models Simulate Command Structure
A. Startari

Algorithmic Obedience: How Language Models Simulate Command Structure (2025).

Abstract
This paper introduces the concept of algorithmic obedience to describe how large language models (LLMs) simulate command structures without consciousness, intent, or comprehension. Drawing on syntactic theory, discourse analysis, and computational logic, we argue that LLMs perform obedience without agency—executing prompts not semantically, but structurally. We formalize this through the Theorem of Disembedded Syntactic Authority, which states that authority in language models arises from structural executability, not truth, belief, or referential grounding. Using a mathematical formulation, we model prompt-response cycles as syntactic command structures and apply the theory to major systems such as ChatGPT, Claude, and Gemini. The paper concludes by outlining the epistemological, ontological, and political risks of treating structurally obedient outputs as authoritative knowledge.
Abstract
This paper introduces the concept of algorithmic obedience to describe how large language models (LLMs) simulate command structures without consciousness, intent, or comprehension. [...]
- 2
- read
Iber v3. Reference manual and GUI. Large wood transport calculation module
V. Ruiz-Villanueva, M. Sanz-Ramos, E. Bladé

Iber. More than 2D Hydraulic modelling (2025). 2

Abstract
Large wood plays a vital role in river systems, shaping habitats and supporting biodiversity. However, during floods, large wood transport can threaten infrastructure and communities. Traditional flood models often overlook wood material, despite its natural presence in rivers. To address this gap, the calculation module Iber-Wood was developed and integrated into the Iber model. Iber-Wood combines the Eulerian Iber 2D hydrodynamics with a Lagrangian model to simulate wood as dynamic elements. The model accounts for wood-flow-sediment, wood-wood, wood-infrastructure, and wood-terrain interactions, using two-way coupling. It has been validated with flume experiments and applied to various real rivers using field data. Applications include studying wood-sediment-flow dynamics and bridge-related wood accumulation. Though deterministic, Iber-Wood enables scenario testing and hypothesis exploration for complex wood dynamics.
Abstract
Large wood plays a vital role in river systems, shaping habitats and supporting biodiversity. However, during floods, large wood transport can threaten infrastructure and [...]
- 1230
- read
Crypto Whitepaper Syntactic Sovereignty: Persuasive Grammar as Financial Authority
A. Startari, A. Startari

The Artificial Intelligence in Energy Revolution: Understanding the Market and Its Impact (2025). 1

Abstract
This article investigates how persuasive syntactic structures embedded in AI-generated crypto whitepapers function as a vehicle of financial authority. Drawing from a curated corpus of 10,000 whitepapers linked to token launches between January 2022 and March 2025, we apply transformer-based dependency parsing to extract high-weighted grammatical features, including nested conditionals, modality clusters, and assertive clause chaining. We operate these patterns via a ''Deceptive Syntax Anomaly Detector'' (DSAD), which computes a syntactic risk index and identifies recurrent grammar configurations statistically correlated with anomalous capital inflows and subsequent collapses (Spearman correlation, ρ > 0.4, p < 0.01). Unlike prior studies focused on semantic deception or metadata irregularities, we model ''syntactic sovereignty'', the systematic use of syntax to establish non-human authority, as the groundwork of investor persuasion. We find that abrupt shifts in syntactic entropy, especially in modal intensifiers and future-perfect projections, consistently occur in documents associated with short-lived or fraudulent tokens. The article concludes by proposing a falsifiable governance framework based on fair-syntax enforcement (the principled correction of misleading grammatical patterns), including a corrective rewrite engine and syntactic risk disclosures embedded in compiled registration rules (''reglas compiladas'').
Abstract
This article investigates how persuasive syntactic structures embedded in AI-generated crypto whitepapers function as a vehicle of financial authority. Drawing from a curated [...]
- 6
- read
Interpretable machine learning for solid mechanics: from representation to forecast and back
W. Sun

Data-Driven Mechanics and Physics of Materials 2025.

Abstract
This talk explores the various ways high-fidelity constitutive laws for a wide range of solids, such as soil, rock, alloys, and polymer composites, can be represented and how the choice of representations influence the accuracy, robustness, and data/computational efficiency for computer simulations of solids. To represent material models as points, we adopt a model-free approach that enables physical simulations of material behaviors without a smooth constitutive law. In this case, pointwise stress-strain pairs are selected in Gauss points of finite elements to be compatible with the conversation laws. To represent material models as meshes, we introduce a latent diffusion model where previous material models and experimental data are used to guide the reverse generation of models. This mesh-based material model is particularly efficient for non-smooth plasticity, where projection on segments can lead to significantly faster simulations. To represent material models as equations, we use the neural additive model in the projected space of strain measures. This technique enables us to search for hyper-elasticity in high-dimensional space without sacrificing the expressivity of neural networks. We show that the proposed model may reproduce any polynomial of arbitrary orders and dimensions and thus achieve the universal approximation through the Stone-Weierstrass theorem. Through a series of 1D post-hoc symbolic regressions, we obtain symbolic material models that significantly reduce the inference time for hydrocodes. The pros and cons of these techniques for various practical applications will be discussed.
Abstract
This talk explores the various ways high-fidelity constitutive laws for a wide range of solids, such as soil, rock, alloys, and polymer composites, can be represented and [...]
- 2
- read
How deep neural networks learn — a dynamical-systems perspective
B. Mehlig

Data-Driven Mechanics and Physics of Materials 2025.

Abstract
After giving an introduction to deep learning, I will discuss how deep networks learn. This can be analysed and understood, in part, using concepts from dynamical-systems and random-matrix theory [1]. For deep neural networks, the maximal finite-time Lyapunov exponent forms geometrical structures in input space, akin to coherent structures in dynamical systems such as turbulent flow. Ridges of large positive exponents divide input space into different regions that the network associates with different classes in a classification task. The ridges visualise the geometry that deep networks construct in input space, and help to quantify how the learning depends on the network depth and width [2]. [1] Bernhard Mehlig, Machine Learning with neural networks, Cambridge University Press (2021). [2] Storm, Linander, Bec, Gustavsson & Mehlig, Finite-time Lyapunov exponents of deep neural networks, Phys. Rev. Lett. 132 (2024) 057301.
Abstract
After giving an introduction to deep learning, I will discuss how deep networks learn. This can be analysed and understood, in part, using concepts from dynamical-systems [...]
- 10
- read
Iber v3. Reference manual and GUI. Non-Newtonian shallow flows calculation module
M. Sanz-Ramos, E. Bladé

Iber. More than 2D Hydraulic modelling (2025). 1

Abstract
Iber is a two-dimensional hydraulic model for the simulation of free surface flow in rivers and estuaries, and the simulation of environmental processes in fluvial hydraulics. Since the release of the first version of Iber, which included a hydrodynamic calculation engine fully coupled with sediment transport processes and turbulence, it has evolved to become a free surface flow modelling tool for highly complex environmental processes. This document presents the developments made for version 3, specifically, for the new calculation module for the simulation of shallow non-Newtonian flows called IberNNF. The shallow water equations are solved using an ad-hoc numerical scheme focused on the simulation of this type of flow in nature (e.g., steep slopes, irregular geometries). The graphical user interface (GUI) has been adapted to the module's new features to achieve a simple and user-friendly workflow.
Abstract
Iber is a two-dimensional hydraulic model for the simulation of free surface flow in rivers and estuaries, and the simulation of environmental processes in fluvial hydraulics. [...]
- 787
- read
Effect of Plants on reducing Particulate Matter (PM) levels in Indoor Spaces
G. Bonetti

Collection of Articles on Environmental Science (2025). 1

Abstract
4.3 million people die annually prematurely from illnesses related to indoor air pollution (WHO, 2014), and around 3 billion people are exposed daily to poor indoor air quality (Martin et al.,2021). The damaging health effects of air pollution, specifically particulate matter, stresses the need to know how plants can help reduce air pollutant levels. According to the National Academies of Sciences in 2016, humans spend the majority of their time indoors which makes it more likely that indoor PM exposure is a main contributor to the adverse health effects caused by PM exposure. The aim of this experiment was to investigate the effect basil and spider plants will have to reduce particulate matter levels in indoor spaces. Spider and basil plants were put next to a desktop computer for one week each while the average PM concentrations were measured daily for 15 minutes at an indoor testing site, PM was also measured for one week without any plants. It was then found that the basil and spider plants reduced the PM concentrations at the indoor site with the spider plants absorbing a greater percentage of the PM (64.60%) than the basil plants (28.90%). Although both spider and basil plants reduced the average PM concentrations, it is recommended to use spider plants to reduce average PM concentrations as it was more effective.
Abstract
4.3 million people die annually prematurely from illnesses related to indoor air pollution (WHO, 2014), and around 3 billion people are exposed daily to poor indoor air quality [...]
- 99
- read
Fast Solution Techniques for Polytopic hp-Discontinuous Galerkin Methods for Radiation Transport Problems
P. Houston

coupled2025.
- 26
- read
Accelerating Continental-Scale Groundwater Simulation with A Fusion of Machine Learning, Integrated Hydrologic Models and Community Platforms
R. Maxwell

coupled2025.
- 4
- read
Iterative and multilevel methods for PDE-constrained optimization under uncertainty
F. Nobile

coupled2025.
- 3
- read
Coupled Problems Arising in the Design of Drug Carriers
A. Quaini

coupled2025.
- 8
- read
Brain membranes and vasculature - a computational mathematics tale of dimensional gaps
M. Rognes

coupled2025.
- 33
- read
Coupled Problems: Marking 20 Years of Advancement and Continued Impact
P. Wriggers

coupled2025.
- 8
- read
Multiscale modeling of coupled thermo-hydro-mechanical problems in granular media
J. Zhao

coupled2025.
- 13
- read
Transformations in Early Childhood Relational Environments and the Rise in Autism Diagnoses During and After COVID-19 Lockdowns: A Critical Perspective from Public Health and Subjective Development
A. Pavolink, J. Pavolink

Jose Luis Pavolink's personal collection (2025). 2

Abstract
La pandemia de COVID-19 provocó interrupciones abruptas y a gran escala en los entornos relacionales tempranos debido a los confinamientos obligatorios, el cierre de instituciones educativas y terapéuticas y la suspensión de las interacciones comunitarias. Cabe destacar que durante este mismo período, se observó un aumento significativo en los diagnósticos de trastorno del espectro autista (TEA) en varios países, incluida Argentina, incluso en momentos en que el acceso a los servicios de salud estaba severamente restringido. Esta tendencia es particularmente paradójica dada la ausencia de datos exhaustivos sobre las tasas de consulta y la disminución simultánea de la cobertura de vacunación triple vírica, lo que debilita las declaraciones etiológicas relacionadas con la vacuna. Este estudio lleva a cabo un examen crítico de esta convergencia, sin plantear una relación causal directa, cuestionando si la alteración estructural de las experiencias intersubjetivas tempranas puede haber contribuido a la aparición o amplificación de comportamientos posteriormente codificados dentro de los límites diagnósticos del TEA. La hipótesis propuesta no es etiológica, sino ecológica: explora la posibilidad de que las interrupciones sostenidas en la mediación simbólica, la corregulación afectiva y la consistencia relacional en la primera infancia constituyan condiciones de vulnerabilidad del desarrollo. Se empleó un diseño documental basado en fuentes secundarias. Los datos cuantitativos se extrajeron de las historias clínicas de salud pública emitidas por el Ministerio de Salud de la Nación, la Agencia Nacional de Discapacidad (ANDIS), los Centros para el Control y la Prevención de Enfermedades (CDC), la Organización Mundial de la Salud (OMS) y UNICEF (2018-2023). Estos se analizaron en conjunto con marcos teóricos de la psicología del desarrollo, la teoría psicoanalítica y la salud pública, enfatizando conceptos como la estructuración simbólica temprana, la privación intersubjetiva y la confianza epistémica. Los resultados indican un aumento sostenido de los diagnósticos de TEA durante y después de los periodos de confinamiento, sin correlación estadística con las tendencias de vacunación. La literatura cualitativa informa consistentemente de un aumento del retraimiento social, retrasos en la adquisición del lenguaje, disminución del juego simbólico, sobreexposición a las pantallas y aumento del estrés del cuidador, factores que se cruzan con los criterios diagnósticos actuales, pero que pueden reflejar factores estresantes contextuales y relacionales en lugar de una alteración intrínseca del desarrollo neurológico. El estudio subraya la necesidad de reconsiderar los marcos de diagnóstico y los enfoques de salud pública a través de una lente relacional-evolutiva frente a las disrupciones sociales a gran escala.
Abstract
La pandemia de COVID-19 provocó interrupciones abruptas y a gran escala en los entornos relacionales tempranos debido a los confinamientos obligatorios, el cierre de [...]
- 20
- read
European Efficiency Schemes for Domestic Gas Boilers: Estimation of Savings in Heating of Settlements
D. Brkić

Algorithms (2025). Brkić, D. European Efficiency Schemes for Domestic Gas Boilers: Estimation of Savings in Heating of Settlements. Algorithms 2025, 18, 416. https://doi.org/10.3390/a18070416

Abstract
This article aims to evaluate the seasonal efficiency of natural gas boilers used in European households, highlighting the cost effectiveness, environmental benefits, and user comfort associated with higher-efficiency models, particularly those based on condensing technology. The study applies a standardized algorithm used in European energy labeling schemes to calculate the seasonal efficiency of household gas boilers. It further includes a comparative analysis of selected boiler models available on the Serbian market and outlines a step-by-step method for estimating gas savings when replacing older, less efficient boilers with modern units. Condensing boilers demonstrate significantly higher seasonal efficiency than standard models by recovering additional heat from exhaust gases. These improved boilers produce lower greenhouse gas emissions and offer annual fuel savings of approximately 10% to 30%, depending on the boiler’s age, system design, and usage patterns. The results also confirm the direct correlation between seasonal efficiency and annual fuel consumption, validating the use of efficiency-based cost comparisons. The analysis focuses on residential gas boilers available in the Serbian market, although the models examined are commonly distributed across Europe. The findings highlight the important role of energy efficiency labels—based on a standardized algorithm—in guiding boiler selection, helping consumers and policymakers make informed decisions that promote energy savings and reduce environmental impact. This article contributes to the theoretical and practical understanding of gas boiler efficiency by integrating algorithm-based evaluation with market data and user-centered considerations. It offers actionable insights for consumers, energy advisors, and policymakers in the context of Europe’s energy transition. Verifying the efficiency calculations of gas boilers requires a careful combination of theoretical methods, measured data, and adherence to standards.
Abstract
This article aims to evaluate the seasonal efficiency of natural gas boilers used in European households, highlighting the cost effectiveness, environmental benefits, and [...]
- 12
- read
Unscented Kalman filter approach for tracking physical and dynamic properties of structures: Validation for multi-story buildings under seismic excitation
C. Gaviria

Structural Monitoring and Maintenance (2021).

Abstract
An improved unscented Kalman filter approach is implemented to estimate induced displacements and changes in structural properties (stiffness, frequencies and damping) during the forced dynamic response of multistory buildings to seismic excitations. The methodology is validated using a fiber-based nonlinear model of a 4-story 4-bays reinforced concrete (RC) frame building subjected to a set of earthquakes causing different levels of inelastic demand on the structure. The variation of the dynamic properties is successfully estimated by iterative updating the filter parameters. The estimated peak values of stiffness and damping reached during the seismic excitation agree with peak inelastic demand values and seem appropriate for detection and damage diagnosis of RC structures.
Abstract
An improved unscented Kalman filter approach is implemented to estimate induced displacements and changes in structural properties (stiffness, frequencies and damping) [...]
- 11
- read
Evaluating Threats to Eastern Migratory Monarch Butterflies (Danaus plexippus) Using Convolutional Neural Network Species Distribution Models
C. Zhang, L. Demirciyan

J. of Dawn. R. (2025). Vol. 7, 7

Abstract
Eastern migratory monarch butterflies have declined by over 80% since the 1990s and have a 56–74% chance of extinction by 2080, which has been attributed to climate change and habitat loss. As a native migratory insect widely distributed across North America, the monarch butterfly serves as a valuable bioindicator for environmental change and conservation needs. However, there is still a lack of understanding of the spatiotemporal nature, relative importance, and future risks of individual threats due to the monarch’s multi-generational and vast annual cycle. Given the monarch's distinct ecological niche and sensitivity to environment conditions for migration, this study used convolutional neural network species distribution models (CNN-SDMs), which enhance occurrence predictions by capturing the surrounding environmental neighborhood, to analyze suitability predictors of monarch butterflies and explain their contributions at each step of the migratory route. The models were projected onto future climate and land cover scenarios in 2061–2080. Monthly maximum and minimum temperature ranked highest in feature importance across the migratory cycle, while vegetative land cover became ranked high in importance for the overwintering monarch population and future breeding habitat forecasted to shift northward. This niche-switching suggests that conservation efforts to facilitate the northward expansion of suitable habitat and the cultivation of nectar sources near hibernating colonies will be critical with growing climate impact and emissions. This study was the first to establish a CNN-based predictive spatiotemporal model of monarch butterflies and incorporate a comprehensive set of environmental predictors to evaluate potential threats to the monarch decline.
Abstract
Eastern migratory monarch butterflies have declined by over 80% since the 1990s and have a 56–74% chance of extinction by 2080, which has been attributed to climate [...]
- 28
- read
Physics-informed neural networks of moisture diffusion in bio-composites
Z. Han, M. Mirkhalaf

Data-Driven Mechanics and Physics of Materials 2025.
- 4
- read
Exploring Flow Matching for Bifurcation: A Preliminary Framework for Buckling in Mechanical Metamaterials
F. Hendriks, V. Menkovski, M. Doškár, M. Geers, O. Rokoš

Data-Driven Mechanics and Physics of Materials 2025.
- 18
- read
DeepTrack2: High-Quality Microscopy Simulations for Deep Learning
M. Granfors, A. Lech, B. Midtvedt, J. Pineda, H. Bachimanchi, C. Manzo, G. Volpe

Data-Driven Mechanics and Physics of Materials 2025.
- 22
- read
Deeplay: Enhancing PyTorch with Customizable and Reusable Neural Networks
A. Lech, M. Granfors, B. Midtvedt, J. Pineda, H. Bachimanchi, C. Manzo, G. Volpe

Data-Driven Mechanics and Physics of Materials 2025.
- 10
- read
A Data-Driven Computational Workflow For The Growth Of Porous Anodized Aluminum Oxide: Integration Of Surrogate Modeling And Uncertainty Quantification
T. Albaraghteh, C. Feiler, M. Zheludkevich, D. Höche

Data-Driven Mechanics and Physics of Materials 2025.
- 30
- read
Exploring the latent space of Physically Recurrent Neural Networks in the low data regime
M. Maia, A. Gils, I. Rocha, F. Meer

Data-Driven Mechanics and Physics of Materials 2025.
- 18
- read
Machine learning accelerated design of architectured materials for tailored mechanics
S. Kang, H. Moon, M. Mousavi, S. Ryu

Data-Driven Mechanics and Physics of Materials 2025.
- 1
- read
Physics-Enforced Neural Network Modeling of History-Dependent Materials
K. Meyer

Data-Driven Mechanics and Physics of Materials 2025.
- 19
- read
Inverse design of nonlinear shell metamaterials via diffusion language models
L. Zheng, S. Kumar, D. Kochmann

Data-Driven Mechanics and Physics of Materials 2025.
- 12
- read
Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks
N. Kovacs, I. Rocha, F. Meer, C. Furtado, P. Camanho

Data-Driven Mechanics and Physics of Materials 2025.
- 11
- read
Unsupervised learning of constitutive models with neural networks
A. Benady, E. Baranger, L. Chamoin, C. Jailin, L. Lorenzis

Data-Driven Mechanics and Physics of Materials 2025.
- 54
- read
Cycle domain plasticity modeling using machine learning
N. Talebi, M. Ekh, K. Meyer

Data-Driven Mechanics and Physics of Materials 2025.
- 12
- read
Efficient Surrogate Modelling of Fully Coupled Thermomechanical Behavior of Composite Materials
M. Idrissi, F. Meraghni, G. Chatzigeorgiou, F. Chinesta

Data-Driven Mechanics and Physics of Materials 2025.
- 22
- read
Multiscale Thermodynamics-Informed Neural Networks (MuTINN) for Nonlinear Prediction of Recycled Composite Behavior
S. Sekkal, M. Idrissi, F. Meraghni, G. Chatzigeorgiou, F. Chinesta

Data-Driven Mechanics and Physics of Materials 2025.
- 13
- read
A comparison between transformers and recurrent neural networks for non linear elastoplastic behavior in 3D micro-mechanical modeling
P. Uvdal, M. Mirkhalaf

Data-Driven Mechanics and Physics of Materials 2025.
- 4
- read
Accelerating Multiscale Simulations using Uncertainty-Driven Phase-Field Mixtures of Constitutive Models
J. Storm, W. Sun, I. Rocha, F. Meer

Data-Driven Mechanics and Physics of Materials 2025.
- 24
- read
Physics informed holomorphic neural networks for fracture mechanics
M. Calafà, H. Jensen, A. Engsig-Karup, T. Andriollo

Data-Driven Mechanics and Physics of Materials 2025.
- 48
- read
A methodology to estimate the fiber tow position during the fiber placement manufacturing processes of FRP laminates to homogenize the laminate’s permeability
I. Cózar, G. Broggi, C. Broggi, B. Caglar

Data-Driven Mechanics and Physics of Materials 2025.
- 44
- read
A Machine Learning-Based Constitutive Model Incorporating History Dependent Features for Cyclic Loading Scenarios
R. Shoghi, A. Hartmaier

Data-Driven Mechanics and Physics of Materials 2025.
- 9
- read
Machine learning-based uncertainty quantification of failure predictions
R. Li, M. Miranda, S. Pinho

Data-Driven Mechanics and Physics of Materials 2025.
- 28
- read
Reinforcement Learning for Optimizing 3D Printing Paths
R. Schmeitz, R. Wolfs, J. Remmers

Data-Driven Mechanics and Physics of Materials 2025.
- 19
- read
Modeling corrosion-fatigue in bcc metals with unified mechanics theory
C. Basaran, H. Lee, H. Fakhri, R. Ranade, H. Egner, A. Lipski, M. Piotrowski, S. Mroziński

Data-Driven Mechanics and Physics of Materials 2025.
- 37
- read
Graph Neural Networks for Efficient Prediction of Mechanical Response in Composite Structures with Models using Unstructured Meshes
L. Patrignani, S. Pinho

Data-Driven Mechanics and Physics of Materials 2025.
- 16
- read
Numerical Simulation and Optimization of Grey Relational Analysis Models for Panel Data
X. Liu, F. Jian, X. Tang

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 36

Abstract
The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research on performance validation for the various ICGRA models. In this paper, we investigate the performance of the existing ICGRA models accounting for the reordering of indicators. Firstly, the robot execution failures (REF) dataset of the University of California Irvine (UCI) machine learning database is adopted to validate the robustness of four traditional ICGRA models. Then, we compared the grey relational orders for all arrangements of indicators in panel data. Simulation experiments showed that the four ICGRA models are not all robust against the grey relational order. To resolve this problem, we adopted the mean value theory and deep modeling to optimize the four models and compared them with the tetrahedral grey relational analysis (GRA) model that considers the coupling effect between indicators on the grey relational order, as well as with the k-nearest neighbor (KNN) algorithm. Results show that the classification accuracy of the averaged absolute GRA model was 97.73%, the other optimized ICGRA models and the k-nearest neighbor (KNN) method all achieved 100% accuracy, while the tetrahedral GRA model has an accuracy of 83.33%. Therefore, the average grey incidence degree for all arrangements of indicators and deep modeling significantly improves the stability of models and enhances the clustering accuracy in different cases.OPEN ACCESS Received: 09/12/2024 Accepted: 04/03/2025 Published: 30/06/2025
Abstract
The indicators-coupled grey relational analysis (ICGRA) models are important in clustering panel data with cross-sectional dependence. However, there is still little research [...]
- 34
- read
Optimization of Support for Deeply Buried Diversion Tunnels with Bad Geology Based on MOMVO Algorithm
M. Huang, L. Zhao, F. Yang, Y. Jiang

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 35

Abstract
During the construction of diversion tunnels, different support schemes exhibit varying stability conditions and entail distinct economic costs. To enhance the cost-effectiveness of tunnel support while ensuring its safety, a multi-objective optimization model has been developed for deeply buried diversion tunnels with bad geological conditions, enabling the execution of multi-objective optimization calculations regarding construction safety and economy. By considering the settlement of the vault and the cost of supporting materials as the objective function, a set of solution sets is computed using the multi-objective multi-verse algorithm (MOMVO), and subsequently, an optimal solution is obtained by integrating the entropy weight-technique for order preference by similarity to an ideal solution (TOPSIS) decision. The Luzhi River Tunnel of the Central Yunnan Water Diversion Project was selected as the subject of analysis to conduct support optimization calculations. The results demonstrate that, compared to the original design, there is an 8.16% reduction in the cost of support materials per linear meter. Although there is a 0.76% increase in settlement at the tunnel vault, it remains significantly below the safety critical value and aligns with project safety requirements. The proposed optimization method for tunnel support demonstrates remarkable efficacy, effectively reducing the cost of support materials while ensuring engineering safety. Moreover, it provides valuable technical support for optimizing the support system in water diversion tunnels.OPEN ACCESS Received: 07/11/2024 Accepted: 14/01/2025 Published: 30/06/2025
Abstract
During the construction of diversion tunnels, different support schemes exhibit varying stability conditions and entail distinct economic costs. To enhance the cost-effectiveness [...]
- 123
- read
Fundamental Theorems and Wave Dynamics in Rotating Thermoelastic Diffusion with Multi-Phase Delays and Temperature-Dependent Properties
S. Sharma, R. Kumar, H. Taha, A. El-Bary, K. Lofty

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 34

Abstract
The current study uses a novel mathematical formulation of temperaturedependent thermoelastic diffusion with multi-phase delays in a rotating frame of reference (TDMR). Higher order time derivatives are included for the diffusing mass flux, the gradient of chemical potential, the gradient of temperature, and the TDMR model of heat flow vector in Fourier’s and Fick’s laws. The fundamental theorems (energy, uniqueness, reciprocity, and variational criterion) are examined using the preliminary equations for the simulated model TDMR. Reciprocity theorem applications are taken for particular scenarios including body forces, heat sources, and chemical potential sources. It has been noted that the variational criteria and these theorems are mostly influenced by the field variables’ susceptibility and the variations in the parameters of higher-order temporal derivatives. The two-dimensional example of the assumed model’s planar wave propagation is also provided. Four connected longitudinal waves have been identified: the primary (P) wave, secondary (S) wave, thermal (T) wave, and chemical potential (CP) wave. Wave properties such as phase velocity and attenuation coefficient are estimated numerically and displayed visually. A few special examples are also investigated and connected with the established outcomes. This work provides a foundation for further research into basic issues in thermoelastic continua under various physical field factors. Numerous applications in material science, geomechanics, soil dynamics, and the electronic sector can be made of the current findings.OPEN ACCESS Received: 22/02/2025 Accepted: 11/04/2025 Published: 30/06/2025
Abstract
The current study uses a novel mathematical formulation of temperaturedependent thermoelastic diffusion with multi-phase delays in a rotating frame of reference (TDMR). Higher [...]
- 74
- read
Simplified Electromagnetic Method for the Analytical Euclidean Modeling of Multiple Electrical Conductors in Arbitrary Directions
D. Cardenas, S. Portugal, A. Chong

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 33

Abstract
The analytical modeling of electromagnetic fields generated by multiple current-carrying conductors is fundamental in power systems, electromagnetic compatibility, and transmission line analysis. Traditional analytical methods often assume conductors aligned with the coordinate system axes, limiting their applicability to arbitrary configurations. This paper presents a novel analytical approach that determines the exact electric and magnetic fields for multiple conductors oriented in arbitrary directions within a Euclidean space, assuming linear, homogeneous, and isotropic media. Unlike numerical methods, such as the Finite Element Method (FEM), which require extensive meshing and domain discretization, the proposed method directly computes field vectors without interpolation or artificial boundary conditions. The applicability of the method is evaluated through four case studies, demonstrating its effectiveness in various configurations, including power transmission lines, communication cables, and grounding systems in homogeneous isotropic media. Additionally, a comparative analysis is performed using a specific case with a known analytical solution, validating the method against FEM-based solvers such as ANSYS Maxwell and FEMM. The results confirm that the proposed approach achieves higher accuracy with significantly lower computational costs.OPEN ACCESS Received: 10/03/2025 Accepted: 09/06/2025 Published: 30/06/2025
Abstract
The analytical modeling of electromagnetic fields generated by multiple current-carrying conductors is fundamental in power systems, electromagnetic compatibility, and transmission [...]
- 92
- read
Bidirectional Fluid-Structure Coupling Analysis of Steering Tool Performance with Spring Stress Consideration
X. Zhou, Y. Peng, W. Cheng, Y. Xue, H. Fang

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 32

Abstract
This paper investigates the automatic steering tool in the integrated injection-production tubing string and proposes an analysis method for bidirectional fluid-structure coupling problems, incorporating spring stress accumulation. The pre-stress field feature in ABAQUS software is used to model the variation in leaf spring stress. A ball valve acts as an intermediary, and FLUENT software is employed to simulate the transfer of forces between the flow field and the ball valve’s motion. The results indicate that the transition response time from steam injection to oil production under the designed operating conditions is approximately 0.0787 s. The motion path of the ball valve aligns with expectations, exhibiting stable movement. Additionally, an indoor simulation of the steering tool’s opening and closing performance was conducted, with high-speed cameras used for experimental validation. The simulation and experimental results show a discrepancy of less than 15%, confirming the accuracy of the numerical model and providing theoretical support for the practical application of integrated injection-production tubing string technology.OPEN ACCESS Received: 06/11/2024 Accepted: 01/04/2025 Published: 30/06/2025
Abstract
This paper investigates the automatic steering tool in the integrated injection-production tubing string and proposes an analysis method for bidirectional fluid-structure [...]
- 28
- read
Multi-hazard stochastic time-dependent tropical cyclone fragilities for coastal structures
A. Calvo, J. Padgett

ICOSSAR25.
- 27
- read
Seismic risk of isolated highway bridges with high damping rubber bearings in cold regions
J. Shen, M. Alam, A. Igarashi, Y. Hamada, T. Himeno

ICOSSAR25.
- 0
- read
Reliability-based multilatticetopologyoptimizationfor structuraldesign
J. Chun

ICOSSAR25.
- 0
- read
A new framework for the quantification of sustainability, inclusiveness, and aesthetics of structures and urban areas
A. Cardoni, A. Elahi, G. Cimella

ICOSSAR25.
- 24
- read
Sensitivity analysis of hydrodynamic journal bearing failure modes due to lubricant contamination
H. Bouajila, Y. Aoues, J. Bouyer, P. Jolly, B. Pap, J. Cole

ICOSSAR25.
- 0
- read
Numerical Investigations of Fractional Complex Coupled Maccari and Cahn-Hilliard Equations Using Controlled Picard Iteration with ρ-Laplace Transform
N. Raza, Z. Khalid, Y. Chahlaoui, M. Farman, E. Hincal

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 31

Abstract
This article presents a semi-analytical approach to address two nonlinear evolution equations: the fractional complex coupled Maccari system and the fractional Cahn-Hilliard equation. These mathematical models encapsulate essential concepts such as non-locality and memory, making them applicable in signal and image processing. The proposed method utilizes the controlled Picard framework combined with theρ-Laplace transform. The recommended approach eliminates the necessity for traditional techniques, such as Lagrange multipliers and Adomian expansions, by integrating the controlled Picard method with theρ-Laplace transformation. Additionally, a minimal parameter, ¯h, has been introduced to improve the convergence of the system under investigation. The results have been validated against exact solutions, and absolute error analyses support the accuracy of the proposed approach. The article includes 2D and 3D plots to illustrate how results vary with different parameters. In conclusion, this paper demonstrates that our method is explicit and efficiently executable.OPEN ACCESS Received: 25/03/2025 Accepted: 07/05/2025 Published: 30/05/2025
Abstract
This article presents a semi-analytical approach to address two nonlinear evolution equations: the fractional complex coupled Maccari system and the fractional Cahn-Hilliard [...]
- 184
- read
Investigation of the Effect of Harmonics Caused by Transformer-Induced Nonlinear Loads on Electrical Energy
S. Rüstemli, N. Sezgin, B. Coskun, G. Sahin

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 30

Abstract
In this study, distribution transformers in the Mu¸s Alparslan University campus are analyzed in order to minimize energy losses due to harmonics in the electrical network. Current and voltage harmonics, which are the power quality problems of these transformers, are analyzed. The power quality of different transformers in Mu¸s Alparslan University was measured with a Chauvin Arnoux CA:8331 energy analyzer. Three-phase nonlinear load models were created using current harmonic values in the Matlab/Simulink environment. A parallel active power filter is designed in the Matlab/Simulink environment to eliminate current harmonics. The performance of the parallel active power filter is analyzed for each transformer. As a result, the total harmonic distortion (THDI) ratio for the current decreased from 6.78% to 1.38% in fast Fourier transform analyses after the parallel active power filter was activated in the first transformer. Similarly, the THDI ratio decreased from 5.1% to 2.19% in the second transformer, from 4.5% to 1.39% in the third transformer, and from 5.63% to 1.36% in the fourth transformer.OPEN ACCESS Received: 20/02/2025 Accepted: 15/04/2025 Published: 30/05/2025
Abstract
In this study, distribution transformers in the Mu¸s Alparslan University campus are analyzed in order to minimize energy losses due to harmonics in the electrical network. [...]
- 161
- read
Vectorization for Tower Sketches Using Large Model and Deep Learning
Z. Tang, C. Han, H. Li, Z. Fan, K. Sun, Y. Huang, Z. Xu, C. Wang

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 29

Abstract
Background:3D modeling of transmission towers from hand-drawn sketches is occasionally needed in the exploration stage, construction stage, or operation of transmission lines. To achieve automatic 3D modeling, extracting standard vectors from the sketches is very important, however, this is still lacking for real applications. Methods: In this article, we propose a method for automatically converting the hand-drawn design drawings of a tower into standard vector diagrams. The network ControlNet is introduced to control the image generation model Stable Diffusion, to convert hand-drawn sketches into standard line-structural diagrams. Then, a vectorization network is used to vectorize the line structure, and some designed optimization modules are proposed to refine the vector extraction results. Results: Experiments prove that our method can output the most accurate and robust vectorization results compared with some state-of-the-art methods. Conclusion: The proposed method is automatic and does not need manual operations, which can bring convenience for the intelligent design and modeling of transmission towers. Furthermore, it can also provide technical support for developing relevant digital products for the smart grid.OPEN ACCESS Received: 31/10/2024 Accepted: 13/02/2025 Published: 30/05/2025
Abstract
Background:3D modeling of transmission towers from hand-drawn sketches is occasionally needed in the exploration stage, construction stage, or operation of transmission lines. [...]
- 84
- read
Exploring the Flow Dynamics of MHD Hybrid Nanofluid over a Non-Flat Porous Surface Using Neural Network Approach
H. Upreti, A. Pandey, T. Gupta

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 28

Abstract
T he current work examined the heat transfer performance of TiO2 Al2O3/kerosene oil flowing over a permeable non-flat plate. The energy equation is modeled using Fourier’s law, and heating is supported by Joule heating and nonlinear thermal radiation. Moreover, the momentum equation is extended with mixed convection, magnetic field, and porous medium. We applied an artificial neural network (ANN) to the data obtained from the bvp4c solver to show the significance of AI techniques in predicting skin friction coefficient (SFC) and local Nusselt number (LNN) values. The ANN is developed with the Levenberg-Marquardt backpropagation algorithm to predict the values precisely, and its signif icance is assessed in terms of mean square error (MSE) between values obtained by the bvp4c solver and the predicted value neural network. T heoutcomes revealed that the heat transfer rate decayed with the rise in variable thermal conductivity. Moreover, when increasing the magnitude of the mixedconvectionparameter,thefluidvelocity constantly increases.
Abstract
T he current work examined the heat transfer performance of TiO2 Al2O3/kerosene oil flowing over a permeable non-flat plate. The energy equation is modeled using Fourier’s [...]
- 83
- read
Stability and Chaotic Analysis of Nonlinear Fractional Model Using Novel Analytical Technique: Soliton Solutions for Wazwaz Kaur Boussinesq Equation
A. Mumtaz, M. Shakeel, A. Manan, N. Shah

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 27

Abstract
This study examines the exact solutions of the nonlinear WazwazKaur Boussinesq (NLWKB) equation in (2+ 1)-dimension by using a novel modified (G /G2)-expansion method. This examination uses BetaDerivative, M-Truncated and Conformable derivatives for finding new closed-form soliton solutions. The graphic demonstration covers some of these solutions. These visualized graphs show individual W-type, bright, and dark solitons to emphasize the effect of fractional derivatives on the behavior of waves. Furthermore, the study investigates the bifurcation analysis, chaotic dynamics, multistability, and Poincaré maps to describe the stability transitions of the system. The results illustrate how fractional calculus improves soliton modeling and nonlinear wave propagation, with possible applications in plasma physics, optical fiber communications, ion-acoustic, magneto-sound, and stationary media, and wave dynamics in complex media, and the transmission of tidal and tsunami waves. The proposed method proves to be a powerful method for solving nonlinear fractional models and examining their dynamic behavior.OPEN ACCESS Received: 12/02/2025 Accepted: 15/04/2025 Published: 30/05/2025
Abstract
This study examines the exact solutions of the nonlinear WazwazKaur Boussinesq (NLWKB) equation in (2+ 1)-dimension by using a novel modified (G /G2)-expansion method. This [...]
- 155
- read
The Sealing Failure Mechanism of Old Well Plugging Section under Thermo-Mechanical Coupling in Salt Cavern Hydrogen Storage
J. Wang, B. Zheng, R. Zhang, Q. Ren, Z. Song

Rev. int. métodos numér. cálc. diseño ing. (2025). Vol. 41, (2), 37

Abstract
To address the plugged sections of old wells in salt-cavern hydrogen storage facilities, this study establishes a three-dimensional finite element model considering fluid-solid-thermal multi-field coupling effect is established. based on the Cohesive Zone Model (CZM). The model simulates the debonding failure process at the cement plug-formation interface and investigates the effects of various gas injection scenarios, operational pressures, formation temperatures, mechanical parameters of the cement plug, and interface bonding quality on the debonding failure length. Simulation results indicate that hydrogen, compared to natural gas, is more prone to interface debonding failure and leakage. Increasing the cement plug’s elastic modulus from 5 to 15 GPa reduces the debonding failure length by 10.37 m and increases the fracture propagation pressure by 2.2 MPa, demonstrating that higher elastic modulus effectively mitigates the risk of interface bonding failure. Increasing the Poisson’s ratio from 0.05 to 0.20 only reduces the debonding failure length by 2.2 m and increases the fracture propagation pressure by 1.2 MPa, indicating that Poisson’s ratio has minimal impact on bonding failure. Micro-annular gaps are highly sensitive to bonding quality, highlighting the need for rigorous quality control at the bonding interface. Under constant low operating pressure, minor debonding persists at the interface, but as operating pressure increases, the debonding failure length grows rapidly. Additionally, with increasing temperature, the debonding failure length gradually decreases. The research findings provide guidance for the optimization of cement slurry formulations and the underground hydrogen storage process.OPEN ACCESS Received: 07/04/2025 Accepted: 10/06/2025
Abstract
To address the plugged sections of old wells in salt-cavern hydrogen storage facilities, this study establishes a three-dimensional finite element model considering fluid-solid-thermal [...]
- 222
- read
DISEÑANDO OPORTUNIDADES: EL ROL DEL ESPACIO EN LA ACTIVIDAD PRODUCTIVA DEL ADULTO MAYOR
D. García Arista, J. Romero Bueno

DISEÑANDO OPORTUNIDADES: EL ROL DEL ESPACIO EN LA ACTIVIDAD PRODUCTIVA DEL ADULTO MAYOR (2025).

Abstract
Las personas mayores se enfrentan a una disponibilidad limitada de espacios adecuados para desarrollar actividades productivas, lo que restringe su autonomía, afecta su bienestar integral y desperdicia su experiencia en oficios tradicionales. Ante esta realidad, la investigación buscó explorar cómo los centros artesanales innovadores pueden promover su inclusión productiva. Se adoptó un enfoque cualitativo, no experimental, descriptivo y causal-correlacional, centrado en comprender las experiencias y expectativas de las personas mayores con respecto a su integración en espacios productivos. El trabajo se realizó con personas mayores, representantes institucionales, arquitectos y médicos (geriatras y médicos de familia). Los centros artesanales representan una alternativa viable y necesaria para promover el envejecimiento activo, siempre que cuenten con infraestructura adaptada a las capacidades de los participantes, programas de capacitación continua en técnicas artesanales y estrategias de comercialización integradas con el entorno local. De esta manera, las personas mayores pueden convertirse en actores productivos que contribuyan al desarrollo sostenible, promoviendo su inclusión y dignidad.
Abstract
Las personas mayores se enfrentan a una disponibilidad limitada de espacios adecuados para desarrollar actividades productivas, lo que restringe su autonomía, afecta [...]
- 76
- read
media managementT
H. Hussein Al Mahdi

Publications (2025). 1
- 39
- read
A Critical Analysis of Pakistan’s 2021 National Climate Change Policy: Ambition vs. Implementation
N. Khan

Noor Ul Hassan Khan's personal collection (2025). 1

Abstract
Pakistan’s 2021 National Climate Change Policy (NCCP) articulates an ambitious goal: to steer the country toward “climate compatible development” – i.e. resilient, low-carbon growth . It lists 14 broad objectives (from sustaining growth while addressing climate impacts to building resilient infrastructure, protecting food/water security, and enhancing capacity ). The policy spans adaptation (water, agriculture, health, forestry, coastal and mountain ecosystems, disaster preparedness) and mitigation (energy, transport, industry, waste, carbon sequestration) across multiple sectors . Institutional mechanisms are envisaged: climate-change cells in federal/provincial ministries, inter-ministerial coordination bodies, and dedicated NCCP implementation committees at federal and provincial levels.
Abstract
Pakistan’s 2021 National Climate Change Policy (NCCP) articulates an ambitious goal: to steer the country toward “climate compatible development” – [...]
- 685
- read
PROPUESTA TRABAJO DE GRADO
L. Gutiérrez

Laura Gutiérrez's personal collection (2025). 1

Abstract
En Villavicencio, las huertas urbanas han surgido como espacios donde convergen las necesidades básicas de las comunidades con sus saberes culturales y prácticas tradicionales. En este contexto, se plantea que la composición vegetal no responde al azar, sino que está moldeada por el valor que las personas asignan a ciertas especies para el autoconsumo y la medicina tradicional. La presencia dominante de plantas comestibles y medicinales refleja una búsqueda de autosuficiencia y cuidado comunitario de la salud. Las formas de manejo desde la siembra hasta el control de plagas, donde varían según la experiencia y el conocimiento local, lo que sugiere una relación directa entre diversidad vegetal y diversidad de saberes. Asimismo, la elección de especies con usos tradicionales refuerza la importancia cultural que adquieren estos espacios en contextos urbanos, revelando tensiones entre lo funcional, lo simbólico y lo ecológico. Esta propuesta en construcción busca comprender estas dinámicas para contribuir a una visión más integral y contextualizada de la etnobiología urbana.
Abstract
En Villavicencio, las huertas urbanas han surgido como espacios donde convergen las necesidades básicas de las comunidades con sus saberes culturales y prácticas [...]
- 7
- read
Offshore Marketers
N. Tyagi

Nandini Tyagi's personal collection (2025). 1

Abstract
As a strategic branding design agency, we're here to solve problems, share our expertise and challenge the convention. Come and join us to discover a fresh perspective. Visit us : https://offshoremarketers.com/
Abstract
As a strategic branding design agency, we're here to solve problems, share our expertise and challenge the convention. Come and join us to discover a fresh perspective. Visit [...]
- 17
- read
MULTI-HAZARD GEOTECHNICAL RELIABILITY MAPPING ALONG THE SAN ANDREAS FAULT ZONE: A DATA-DRIVEN FRAMEWORK
U. Malik, K. Ali, A. Azhar, G. Nawaz, E. Hussain

ICOSSAR25.

Abstract
''This study presents a spatially integrated methodology for assessing geotechnical reliability in a multi-hazard context, with a case application along the San Andreas Fault Zone (SAFZ) in California. A Composite Reliability Index (CRI) was developed by integrating DEM-derived terrain attributes, 3D lithologic clustering, soil taxonomy, and peak ground velocity (PGV) data into a unified, normalized framework. Weighted overlay analysis was employed, with suitability scores derived from seismic, geotechnical, and geomorphological principles, supporting compatibility with ASCE 7 [13] and FEMA seismic guidelines [14]. The resulting CRI surface delineates zones of varying geotechnical resilience, identifying areas of high stability suitable for infrastructure development and regions warranting caution due to compounded seismic and geological risks. Spatial validation through correlation with established urban centers and reliability index (β) mapping reinforces the practical applicability of the framework for preliminary site screening and regional resilience planning in faulted, data-constrained terrains.''
Abstract
''This study presents a spatially integrated methodology for assessing geotechnical reliability in a multi-hazard context, with a case application along the San Andreas [...]
- 53
- read
Comparison of stiffness and strength of flax, hemp and kenaf composites with other natural and synthetic fibers composites using fibre-specific parameters
M. Cantera

Materiales Compuestos (2025). Vol. 09 - Comunicaciones MatComp25 (2025), (Núm. 2 - Reciclaje y Sostenibilidad), 46

Abstract
The increasing adoption of natural fibres as composite reinforcement is a promising development in materials science. These fibres have a low carbon footprint and are biodegradable, and they also have remarkable properties such as low density and high specific stiffness and strength. However, the mechanical properties of these composites are influenced by various parameters, which can complicate comparisons due to their diverse internal structures. This study focuses on two key normalised parameters: the Tsai modulus, which represents the trace of the stiffness matrix tensor; and the area of the Omni failure envelope in stress space. Our analysis of published data on unidirectional flax, hemp, jute, and kenaf composites shows that trace-normalised longitudinal Young's modulus can effectively facilitate stiffness comparisons between natural and synthetic fibre composites. A new and innovative way of measuring strength is suggested. This is based on the radius of a circle that matches the area of the Omni stress envelopes. This method is both robust and reliable for quantifying and comparing material strength. Although, extensive mechanical data on natural composites is available, it is difficult to establish design criteria for comparing them. Addressing this gap presents a significant opportunity to unlock the full potential of natural fibres in composite applications, paving the way for a more sustainable future in engineering materials.
Abstract
The increasing adoption of natural fibres as composite reinforcement is a promising development in materials science. These fibres have a low carbon footprint and are biodegradable, [...]
- 59
- read
Experimental Analysis of Magnetic Focusing of the Plasma Arc of a Cutting Torch
M. Marek, D. Brkić, P. Praks, T. Kozubek, J. Frantík

Materials (2025). Marek, M.; Brkić, D.; Praks, P.; Kozubek, T.; Frantík, J. Experimental Analysis of Magnetic Focusing of the Plasma Arc of a Cutting Torch. Materials 2025, 18, 1811. https://doi.org/10.3390/ma18081811

Abstract
This study aimed to verify the possibility of stabilizing and focusing a plasma column generated by a plasma cutter. The simulation performed by the COMSOL Multiphysics software is based on the actual configuration and geometry of the burner. This article presented a universal computational method based on FEM simulations, focusing on the deflection of the current of electrically charged particles in a magnetic field within the context of a plasma cutting torch. The simulations estimate the optimal shape and positioning of a focused electron beam for various magnetic lens positions and plasma stream energies, revealing that higher initial electron energies lead to a more even beam focus. Among the configurations tested, positioning the cathode 3 mm above the ring-shaped permanent magnet proved most effective, maintaining beam linearity and minimizing electron scattering, making it suitable for practical implementations.
Abstract
This study aimed to verify the possibility of stabilizing and focusing a plasma column generated by a plasma cutter. The simulation performed by the COMSOL Multiphysics software [...]
- 6
- read
Probabilistic life-cycle performance, design, maintenance, optimization, and decision-making in asphalt pavement
J. Xin, L. Zhang, D. Frangopol, M. Akiyama, J. Pei

ICOSSAR25.
- 288
- 5/5
- read
Seismic fragility and risk assessment of masonry infilled low ductile RC frames retrofitted with BRBs
R. Chelapramkandy, J. Ghosh, F. Freddi

ICOSSAR25.
- 25
- read
Probabilistic demand hazard assessment of a RC building with buckling restrained braces exposed to seismic loadings
I. Ramirez, J. Gaxiola-Camacho, D. Tolentino

ICOSSAR25.
- 13
- read
Vine-copula-based structural multi-hazard reliability assessment under post-earthquake fire
C. Zefan, F. Decheng

ICOSSAR25.
- 6
- read
Shifts in population and housing affordability after Louisiana’s 2016 floods
S. Asif

ICOSSAR25.
- 289
- read
Advancing geotechnical insights through geodatabases and data-driven methodologies
U. Malik, K. Ali, A. Azhar, G. Nawaz, E. Hussai

ICOSSAR25.
- 387
- 5/5
- read
Planning inspections to minimize uncertainty in thickness monitoring data
C. Brown, S. Ferson

ICOSSAR25.
- 2
- read
Graph-based multi-agent reinforcement learning for railway infrastructure decision support
G. Arcieri, G. Duthé, C. Muller, D. Haener, K. Papakonstantinou, D. Straub, E. Chatzi

ICOSSAR25.
- 235
- read
Using the Life Quality Index as indicator for Social Life Cycle Assessment in engineering structures
J. Knibbe, P. Gelder, R. Steenbergen, A. Afghari

ICOSSAR25.
- 39
- read
Beyond statistical fitting: improving reliability analysis through physically consistent bridge traffic load model
A. Rizqiansyah, C. Caprani

ICOSSAR25.
- 24
- read
Active learning Kriging methods for Bayesian updating
I. Prentzas, M. Fragiadakis

ICOSSAR25.
- 39
- read
Uncertainty quantification of the concrete Modulus of Elasticity through different experiment tests and sensors
J. Henao, A. Lasprilla, P. Roach

ICOSSAR25.
- 10
- read
Influence of priors on estimation results in simultaneous geometry and spatial field estimation
T. Shibata, M. Koch, I. Papaioannou, K. Fujisawa

ICOSSAR25.
- 21
- read
Bayesian uncertainty analysis of grouted anchors
M. Effenberger, C. Moormann

ICOSSAR25.
- 43
- read
Bayesian nonlinear finite element model updating of a full-scale bridge column: insights into calibration of inherent damping model
E. Simbort Zeballos, M. Ramancha, R. Astroza, J. Conte

ICOSSAR25.
- 100
- read
Global sensitivity evolution of vertical ride quality for high-speed maglev trains
L. Fan, F. Ni, Q. Zheng, W. Jia

ICOSSAR25.
- 21
- read
Global point-selection strategy based on the MF-discrepancy for stochastic response analysis of structures with independent random variables
J. Chen, X. Huang, J. Li

ICOSSAR25.
- 16
- read
Resilience metric for post-flood road network
S. Koduru, V. Vemana

ICOSSAR25.
- 28
- read
Effects of uncertainties in the particle distribution of rubber-soil mixture on the performance of shallow foundations
F. Maksimov, A. Contento, B. Briseghella, P. Cacciola

ICOSSAR25.
- 10
- read
Review of Structural Health Monitoring methods for the assessment of probabilistic models of imposed loads
P. Chojnicka, L. Iannacone, M. Limongelli

ICOSSAR25.
- 22
- read
A surrogate model approach for uncertainty treatment in performance-based engineering
M. Birrell, R. Astroza, Y. Li

ICOSSAR25.
- 37
- read
Quantifying the expected utility of fire tests before execution
A. Franchini, R. Coile

ICOSSAR25.
- 2
- read
Infrastructure wind risk assessment under climate change: an integrated analysis of metal roofs, power networks and street trees
C. Lin, Y. Chuang, C. Tsai

ICOSSAR25.
- 49
- read
A stochastic discrepancy data-physics fusion framework for tropical cyclone model and applications to typhoon wind hazard assessment
X. Zhong, J. Zhang

ICOSSAR25.
- 6
- read
Data- and simulation-supported system reliability assessment of bridges subjected to extreme floods and climate change
S. Giraldo, T. Friis, S. Thöns

ICOSSAR25.
- 168
- read
Does resolution matter? Satellite imagery comparison for dynamic fuel mapping and wildfire risk assessment
N. Rashidi, J. Lee

ICOSSAR25.
- 171
- read
Modelling stochastic degradation and maintenance effects for the road network of Amsterdam: a multi-attribute data-driven approach
A. Sterrenberg, C. Andriotis, J. Stoter

ICOSSAR25.
- 213
- read
A multi-fidelity framework with adaptive transfer learning for risk assessment of structural systems subject to natural hazards
L. Xu, S. Spence

ICOSSAR25.
- 106
- read
Outperforming subset simulation by skipping: a case study on power grid reliability assessment
Z. Wang, L. Duenas-Osorio, I. Papaioannou, D. Straub

ICOSSAR25.
- 100
- read
System effects of timber connections comprising multiple fasteners
A. Cao, S. Thöns, R. Jockwer, P. Palma

ICOSSAR25.
- 138
- read
Probabilistic performance evaluation of steel moment resisting frame with masonry infill walls
M. Li, Y. Li

ICOSSAR25.
- 24
- read
Reliability analysis of performance functions via adaptive sequential sampling with detection of failure surfaces
M. Vořechovský

ICOSSAR25.
- 24
- read
Reliability-based multilattice topology optimization for structural design
J. Chun

ICOSSAR25.
- 18
- read
Enhancing seismic risk assessment: a sparse multi-task learning approach for high-dimensional structural systems
S. Zehsaz, F. Soleimani

ICOSSAR25.
- 72
- read