Colloquiam: Documents published in 2025

Ethos Without Source: Algorithmic Identity and the Simulation of Credibility

Agustin V. Startari — Tue, 29 Jul 2025 14:15:43 +0200

Generative language models increasingly produce texts that simulate authority without a verifiable author or institutional grounding. This paper introduces synthetic ethos: the appearance of credibility constructed by algorithms trained to replicate human-like discourse without any connection to expertise, accountability, or source traceability. Such simulations raise critical risks in high-stakes domains including healthcare, law, and education. We analyze 1500 AI-generated texts produced by large-scale models such as GPT-4, collected from public datasets and benchmark repositories. Using discourse analysis and pattern-based structural classification, we identify recurring linguistic features,such as depersonalized tone, adaptive register, and unreferenced assertions,that collectively produce the illusion of credible voice. In healthcare, for instance, generative models produce diagnostic language without citing medical sources, risking patient misguidance. In legal context, generated recommendations mimic normative authority while lacking any basis in legislation or case law. In education, synthetic essays simulate scholarly argumentation without verifiable references. Our findings demonstrate that synthetic ethos is not an accidental artifact, but an engineered outcome of training objectives aligned with persuasive fluency. We argue that detecting such algorithmic credibility is essential for ethical and epistemically responsible AI deployment. To this end, we propose technical standards for evaluating source traceability and discourse consistency in generative outputs. These metrics can inform regulatory frameworks in AI governance, enabling oversight mechanisms that protect users from misleading forms of simulated authority and mitigate long-term erosion of public trust in institutional knowledge.

From Obedience to Execution: Structural Legitimacy in the Age of Reasoning Models

Agustin V. Startari — Mon, 28 Jul 2025 14:15:32 +0200

This article formulates a structural transition from Large Language Models (LLMs) to Language Reasoning Models (LRMs), redefining authority in artificial systems. While LLMs operated under syntactic authority without execution, producing fluent but functionally passive outputs, LRMs establish functional authority without agency. These models do not intend, interpret, or know. They instantiate procedural trajectories that resolve internally, without reference, meaning, or epistemic grounding. This marks the onset of a post-representational regime, where outputs are structurally valid not because they correspond to reality, but because they complete operations encoded in the architecture. Neutrality, previously a statistical illusion tied to training data, becomes a structural simulation of rationality, governed by constraint, not intention. The model does not speak. It acts. It does not signify. It computes. Authority no longer obeys form, it executes function.

Finite Element Modeling and Construction Aspects of Masonry Walls: An Overview

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:35:33 +0200

Masonry walls are a cornerstone of building construction worldwide, providing both structural and non-structural functions in diverse settings. Yet, masonry’s inherent heterogeneity, arising from variations in units, mortar joints, and interfaces, presents significant challenges in accurately predicting its performance under service and extreme loads. This review focuses on two complementary perspectives: the construction aspects of various masonry wall systems (including infill, unreinforced, cavity, confined, and interlocking mortarless walls) and the finite element modeling techniques employed to capture masonry’s mechanical behavior. The review found that the wall type has a significant impact on overall structural performance and seismic response. Moreover, it is found that micro-modeling approaches (both detailed and simplified) capture localized stress and strain states in the brick–mortar interface but entail higher computational costs. Macro-modeling provides a more practical, homogenized view for large-scale analysis, treating masonry as an anisotropic continuum. Numerous researchers have demonstrated that well-calibrated models can replicate experimental load-displacement behavior in both compressive and out-of-plane (OOP) bending scenarios. In particular, advanced contact formulations have been developed to address the progressive closure of mortarless joints, while plasticitybased models are widely employed to simulate cracking and crushing in conventional masonry under seismic loading. Collectively, these studies underscore that the choice of modeling strategy depends on the level of detail required, available computational resources, and the target performance metrics. By merging robust numerical methods with proven construction practices, researchers and engineers can better predict masonry’s structural response.OPEN ACCESS Received: 10/05/2025 Accepted: 25/06/2025 Published: 14/07/2025

Free-Strain Consolidation of Soft Ground Improved with Alternating Length PVDs

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:34:43 +0200

In practice, installation of alternating length prefabricated vertical drains (PVDs) cannot only satisfy the strength requirements for the upper soil layer, but also improve the lower soil layer to a certain extent, which is conducive to improving the overall strength and stability of the ground. In this paper, the original three-dimensional consolidation model for alternating length PVDs is simplified into a plane equivalent model, considering only well resistance by the model equivalence technique. Subsequently, a novel semi-analytical solution for soft ground with alternating length PVDs is derived using the boundary transform and local discretization methods. Accuracy analysis of local discretization is conducted to determine the appropriate segment number for PVD-soil interface boundaries. The effectiveness of the proposed solution is demonstrated by comparing the results from numerical analyses. Finally, the influence of the penetration depth of short and long PVDs on the consolidation efficiency is studied. Results show that the installation of alternating length PVDs is more effective compared to the conventional scheme of equal length installation. Both the average degrees of consolidation within and below a design depth for the ground with alternating length PVDs are higher than in the case with equal length PVDs.OPEN ACCESS Received: 20/03/2025 Accepted: 22/05/2025 Published: 14/07/2025

Fractional Hermite Functions: Power Series Solutions, Rodrigues Representation, and Orthogonality Properties

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:26:20 +0200

T his paper develops a rigorous framework for fractional Hermite func tions using Caputo derivatives. We establish three fundamental contribu tions. First, we derive a complete power series solution to the fractional Hermite equation expressed through the basis {xkα}∞ k=0 , proving absolute convergence for all x ∈ C through careful asymptotic analysis of the coefficients. Second, we introduce and characterize both even and odd fractional Hermite functions H(α) n (x), deriving their explicit representa tions, recurrence relations, and a fractional Rodrigues-type formula that generalizes the classical case. Third, we demonstrate their orthogonality properties under the weight function wα(x) = |x|α−1e−|x|2α, obtaining exact normalization constants. The theoretical results are supported by comprehensive graphical analysis showing the systematic deformation of classical Hermite polynomial features as α varies. These developments provide new tools for fractional spectral methods and advance the under standing of orthogonal function systems in fractional calculus.

Coupled Thermal-Structural Analysis and Structural Optimization of the Diaphragm of a Diaphragm Compressor for a Hydrogen Refueling Station

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:25:23 +0200

Diaphragm fatigue fracture stands as one of the primary failure modes in hydrogen refueling station diaphragm compressors, urgently requiring systematic investigation into the mechanical properties and fatigue reliability of diaphragms under actual service conditions. However, due to the complex contact state between the diaphragm and the gas cavity cover plate (gas passage, gas hole)/oil cavity support plate and oil distribution plate (oil passage, oil hole), it is difficult to accurately obtain the stress distribution state of the diaphragm. In this study, we conducted material property experiments on the diaphragm to characterize its constitutive parameters. A refined numerical simulation model of the cover platediaphragm-support plate was established to analyze the mechanical properties of the diaphragm group under different conditions. The influence of diaphragm thickness on stress distribution and fatigue life was also explored. The results indicated that high stress distribution areas were mainly concentrated in the cavity transition zone and in the overlapping area of the diaphragm and gas passage/oil passage. The temperature inside the diaphragm compressor cavity had a significant impact on the stress level of the diaphragm. When the cavity temperature reached 300°C, the maximum von Mises stress reached 1286 MPa. The optimization results of diaphragm thickness indicated that when the diaphragm thickness was within the range of [0.3, 0.5 mm], the diaphragm was at a low stress service level; considering both the fatigue life and deflection performance of the diaphragm, the diaphragm thickness should be selected as thick as possible within the range of [0.3, 0.5 mm]. The numerical simulation model of the diaphragm compressor and the structural optimization design method of the diaphragm proposed in this article have significant engineering implications for improving the fatigue life of diaphragm compressor diaphragms.OPEN ACCESS Received: 02/03/2025 Accepted: 13/05/2025 Published: 14/07/2025

Numerical Simulation of the Influence of Different Opening Parameters on the Flexural Performance of Fixed Open-Web Steel Beams

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:24:57 +0200

Based on the ABAQUS finite element analysis software platform, this study investigated the effects of opening parameters, including end distance, opening rate, and opening distribution, on the flexural performance of fixed open-web steel beams. The results indicate that an open-web steel beam with fixed support exhibits a flexural shear failure mode, which initiates yielding at the hole corners in the flexural shear zone. As the load increases, the plastic zone progressively extends to the flange, ultimately forming a four-hinge plastic mechanism around the hole. Specifically, a mid-span hole reduces the flexural performance by approximately 10%, while an end hole reduces it by approximately 47%. It is recommended that mid-span openings with end distances no less than 1.75 times the beam height be prioritized in engineering design. Additionally, the flexural performance of fixed open-web steel beams decreases as the opening rate increases. Optimizing the opening geometry by reducing its length-toheight ratio under invariant opening ratio conditions provides a more than doubled flexural capacity gain, offering practical insights for beam design.OPEN ACCESS Received: 25/02/2025 Accepted: 24/03/2025 Published: 14/07/2025

The Laplace Residual Power Series Method for Solving Fractional Fifth-Order Korteweg–de Vries Equations

Jesús Sánchez Pinedo — Mon, 28 Jul 2025 10:22:33 +0200

This study explores a modern analytical approach for solving the fractional fifth-order Korteweg–de Vries (KdV) equations, which describe intricate wave phenomena influenced by nonlinearity, dispersion, and memory effects. Specifically, the Laplace residual power series method (LRPSM) is utilized to obtain accurate approximate analytical solutions for three fundamental fractional equations: the fractional Sawada–Kotera (SK) equation, the fractional Caudrey–Dodd–Gibbon (CDG) equation, and the fractional Kaup–Kuperschmidt (KK) equation. These equations represent special cases of the broader fractional fifth-order KdV equation. The novelty of this study lies in the application of LRPSM, which addresses the limitations of traditional methods by combining analytical precision with computational efficiency. The method successfully captures fractional dynamics, including soliton-like behaviors and memory effects, demonstrating its capability to model wave attenuation and smoothness influenced by fractional orders. The numerical results demonstrate that this method achieves minimal error margins, validating its robustness and precision in solving nonlinear fractional systems. Numerical examples validate the efficiency and robustness of this method, achieving high accuracy in solving nonlinear fractional systems. The results establish LRPSM as a versatile and reliable tool for solving fractional differential equations, paving the way for advancements in modern wave theory and applications across disciplines such as plasma physics, fluid mechanics, and nonlinear optics.OPEN ACCESS Received: 12/10/2024 Accepted: 21/01/2025 Published: 14/07/2025

Sovereign Syntax in Financial Disclosure: How LLMs Shape Trust in Tokenized Economies

Agustin V. Startari — Fri, 25 Jul 2025 17:07:32 +0200

Through structural analysis of LLM-generated or LLM-refined whitepapers, this study identifies a recurring pattern in tokenized finance: legitimacy is simulated through formal syntactic depth rather than verifiable disclosure. It introduces the Syntactic Deception Risk Index (SDRI), a quantitative measure of non-referential persuasion derived from syntactic volatility. Grounded in Algorithmic Obedience and The Grammar of Objectivity, the findings show that high-risk disclosures converge on a formal grammar that substitutes substantive content with surface coherence. The concept of sovereign syntax is formalized as the regla compilada (type-0 production) that governs trust independently of source or reference. From this model follow concrete pathways for audit automation, exchange-side filtration, and real-time regulatory screening. SDRI thus exposes how non-human authority embeds in financial language without a traceable epistemic anchor.

Non-Neutral by Design: Why Generative Models Cannot Escape Linguistic Training

Agustin V. Startari — Fri, 25 Jul 2025 15:36:43 +0200

This article investigates the structural impossibility of semantic neutrality in large language models (LLMs), using GPT as a test subject. It argues that even under strictly formal prompting conditions, such as invented symbolic systems or syntactic proto-languages, GPT reactivates latent semantic structures drawn from its training corpus. The analysis builds upon prior work on syntactic authority, post-referential logic, and algorithmic discourse (Startari, 2025), and introduces empirical tests designed to isolate the model from known linguistic content. These tests demonstrate GPT’s consistent failure to interpret or generate structure without semantic interference. The study proposes a falsifiable framework to define and detect semantic contamination in generative systems, asserting that such contamination is not incidental but intrinsic to the architecture of probabilistic language models. The findings challenge prevailing narratives of user-driven interactivity and formal control, establishing that GPT(and similar systems) are non-neutral by design.

When Language carries form, Not meaning

Agustin V. Startari — Thu, 24 Jul 2025 14:43:43 +0200

This article is not about semantics. It is about its disappearance. The central claim is epistemologically direct: in generative language models, language is not produced to represent; it is produced to continue. The shift is not from intention to structure, but from intention to extinction. What remains is not an utterance with referential anchoring, but a formal residue, syntactically valid, semantically inert. We propose that large language models (LLMs) do not operate on meaning, but on distributive compatibility. Each word is not a sign pointing to an idea, but a structurally permissible unit in a predictive chain. In this sense, language follows form, not as a stylistic choice, but as a computational necessity. Meaning is not misrepresented; it is bypassed. This article positions itself as a rupture with the dominant assumption that semantic drift in LLMs is a temporary misalignment. We argue instead that it is structural and permanent. The phenomenon is not semantic degradation, but syntactic sovereignty. The theoretical background is grounded in prior works that identified the collapse of agency within AIgenerated language. The Passive Voice (2025) demonstrated that algorithmic outputs simulate neutrality by erasing subjects in surface grammar. Ethos and Artificial Intelligence (2025) extended this to show that authority in generative models is not derived from enunciative subjectivity, but from repetition, syntax, and structural legitimacy. Building on these foundations, this article isolates the final stage of that displacement: form without reference, language without intention, activation without meaning. We introduce the model of Formal Syntactic Activation (FSA), a logical framework for understanding how language can be generated in the total absence of semantic operations. This is not a metaphor. It is a system.

Natural Gas Heating in Serbian and Czech Towns: The Role of Urban Topologies and Building Typologies

Dejan Brkić — Thu, 24 Jul 2025 13:30:44 +0200

This article presents an analysis on natural gas heating in residential areas, focusing on two primary systems: (1) local heating, where piped gas is delivered directly to individual dwellings equipped with autonomous gas boilers, and (2) district heating, where gas or an alternative fuel powers a central heating plant, and the generated heat is distributed to buildings via a thermal network. The choice between these systems should first consider safety and environmental factors, followed by the urban characteristics of the settlement. In particular, building typology—such as size, function, and spatial configuration—and urban topology, referring to the relative positioning of buildings, play a crucial role. For example, very tall buildings often exclude the use of piped gas due to safety concerns, whereas in other cases, economic efficiency becomes the determining factor. To support decisionmaking, a comparative cost analysis is conducted, assessing the required infrastructure for both systems, including pipelines, boilers, and associated components. The study identifies representative residential building types in selected urban areas of Serbia and Czechia that are suitable for either heating approach. Additionally, the article examines the broader energy context in both countries, with emphasis on recent developments in the natural gas sector and their implications for urban heating strategies.

Colonization of Time: How Predictive Models Replace the Future as a Social Structure

Agustin V. Startari — Wed, 23 Jul 2025 13:59:43 +0200

This article proposes that contemporary predictive models do not merely anticipate the future but structurally replace it. Drawing from computational epistemology, formal grammar theory, and digital sociology, we introduce the concept of algorithmic colonization of time. In this framework, predictive algorithms function not as neutral tools of foresight but as agents that overwrite the horizon of uncertainty with pre-modeled sequences. Where human temporal experience traditionally revolved around openness, chance, and decision, algorithmic systems reduce the future to a field of executable outcomes. This replacement is not metaphoric: it manifests in social platforms, recommendation engines, and predictive analytics in justice, health, and security. The model becomes a closure device, optimizing trajectories rather than interpreting possibilities. We present a formal operator to describe this transition: ΔS(x)⇒Af(x) where ΔS(x) represents the syntactic derivation of input structures and Af(x) their optimized anticipatory execution. This formulation accounts for the structural shift from contingency to iteration, from human agency to algorithmic deployment. Finally, the paper argues that while this colonization is expansive, it is not total. Anomalous margins—spaces not yet computed—persist. These residual gaps are the new sites of resistance against temporal closure. Thus, the future no longer arrives: it is executed.\

Expense Coding Syntax: Misclassification in AI-Powered Corporate ERPs

Agustin V. Startari — Tue, 22 Jul 2025 17:56:34 +0200

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

Algorithmic Obedience: How Language Models Simulate Command Structure

Agustin V. Startari — Tue, 22 Jul 2025 15:14:43 +0200

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.

Iber v3. Reference manual and GUI. Large wood transport calculation module

Virginia Ruiz-Villanueva — Tue, 22 Jul 2025 15:01:43 +0200

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.

Crypto Whitepaper Syntactic Sovereignty: Persuasive Grammar as Financial Authority

Agustin V. Startari — Mon, 21 Jul 2025 17:49:43 +0200

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'').

Interpretable machine learning for solid mechanics: from representation to forecast and back

Scipedia content — Mon, 21 Jul 2025 13:14:21 +0200

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.

How deep neural networks learn — a dynamical-systems perspective

Scipedia content — Mon, 21 Jul 2025 13:07:03 +0200

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.

Iber v3. Reference manual and GUI. Non-Newtonian shallow flows calculation module

Marcos Sanz-Ramos — Mon, 21 Jul 2025 07:44:43 +0200

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.

Effect of Plants on reducing Particulate Matter (PM) levels in Indoor Spaces

Gregorio Bonetti — Wed, 16 Jul 2025 02:46:24 +0200

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.

Fast Solution Techniques for Polytopic hp-Discontinuous Galerkin Methods for Radiation Transport Problems

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 11:06:07 +0200

Accelerating Continental-Scale Groundwater Simulation with A Fusion of Machine Learning, Integrated Hydrologic Models and Community Platforms

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:48:47 +0200

Iterative and multilevel methods for PDE-constrained optimization under uncertainty

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:44:21 +0200

Coupled Problems Arising in the Design of Drug Carriers

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:40:13 +0200

Brain membranes and vasculature - a computational mathematics tale of dimensional gaps

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:36:47 +0200

Coupled Problems: Marking 20 Years of Advancement and Continued Impact

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:32:40 +0200

Multiscale modeling of coupled thermo-hydro-mechanical problems in granular media

Jesús Sánchez Pinedo — Tue, 15 Jul 2025 10:25:31 +0200

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

Jose Luis Pavolink — Fri, 11 Jul 2025 19:15:33 +0200

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.

European Efficiency Schemes for Domestic Gas Boilers: Estimation of Savings in Heating of Settlements

Dejan Brkić — Fri, 11 Jul 2025 14:21:43 +0200

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.

Unscented Kalman filter approach for tracking physical and dynamic properties of structures: Validation for multi-story buildings under seismic excitation

Carlos Gaviria — Thu, 10 Jul 2025 15:51:43 +0200

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.

Evaluating Threats to Eastern Migratory Monarch Butterflies (Danaus plexippus) Using Convolutional Neural Network Species Distribution Models

Cynthia Zhang — Mon, 07 Jul 2025 15:42:17 +0200

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.

Physics-informed neural networks of moisture diffusion in bio-composites

Scipedia content — Mon, 07 Jul 2025 11:55:40 +0200

Exploring Flow Matching for Bifurcation: A Preliminary Framework for Buckling in Mechanical Metamaterials

Scipedia content — Mon, 07 Jul 2025 11:54:01 +0200

DeepTrack2: High-Quality Microscopy Simulations for Deep Learning

Scipedia content — Mon, 07 Jul 2025 11:51:46 +0200

Deeplay: Enhancing PyTorch with Customizable and Reusable Neural Networks

Scipedia content — Mon, 07 Jul 2025 11:49:59 +0200

A Data-Driven Computational Workflow For The Growth Of Porous Anodized Aluminum Oxide: Integration Of Surrogate Modeling And Uncertainty Quantification

Scipedia content — Mon, 07 Jul 2025 11:48:24 +0200

Exploring the latent space of Physically Recurrent Neural Networks in the low data regime

Scipedia content — Mon, 07 Jul 2025 11:46:29 +0200

Machine learning accelerated design of architectured materials for tailored mechanics

Scipedia content — Mon, 07 Jul 2025 11:44:19 +0200

Physics-Enforced Neural Network Modeling of History-Dependent Materials

Scipedia content — Mon, 07 Jul 2025 11:39:33 +0200

Inverse design of nonlinear shell metamaterials via diffusion language models

Scipedia content — Mon, 07 Jul 2025 11:37:37 +0200

Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks

Scipedia content — Mon, 07 Jul 2025 11:35:37 +0200

Unsupervised learning of constitutive models with neural networks

Scipedia content — Mon, 07 Jul 2025 11:30:36 +0200

Cycle domain plasticity modeling using machine learning

Scipedia content — Mon, 07 Jul 2025 11:28:49 +0200

Efficient Surrogate Modelling of Fully Coupled Thermomechanical Behavior of Composite Materials

Scipedia content — Mon, 07 Jul 2025 11:27:08 +0200

Multiscale Thermodynamics-Informed Neural Networks (MuTINN) for Nonlinear Prediction of Recycled Composite Behavior

Scipedia content — Mon, 07 Jul 2025 11:01:42 +0200

A comparison between transformers and recurrent neural networks for non linear elastoplastic behavior in 3D micro-mechanical modeling

Scipedia content — Mon, 07 Jul 2025 11:00:07 +0200

Accelerating Multiscale Simulations using Uncertainty-Driven Phase-Field Mixtures of Constitutive Models

Scipedia content — Mon, 07 Jul 2025 10:58:28 +0200

Physics informed holomorphic neural networks for fracture mechanics

Scipedia content — Mon, 07 Jul 2025 10:56:50 +0200

A methodology to estimate the fiber tow position during the fiber placement manufacturing processes of FRP laminates to homogenize the laminate’s permeability

Scipedia content — Mon, 07 Jul 2025 10:55:04 +0200

A Machine Learning-Based Constitutive Model Incorporating History Dependent Features for Cyclic Loading Scenarios

Scipedia content — Mon, 07 Jul 2025 10:50:32 +0200

Machine learning-based uncertainty quantification of failure predictions

Scipedia content — Mon, 07 Jul 2025 10:48:53 +0200

Reinforcement Learning for Optimizing 3D Printing Paths

Scipedia content — Mon, 07 Jul 2025 10:47:06 +0200

Modeling corrosion-fatigue in bcc metals with unified mechanics theory

Scipedia content — Mon, 07 Jul 2025 10:45:24 +0200

Graph Neural Networks for Efficient Prediction of Mechanical Response in Composite Structures with Models using Unstructured Meshes

Scipedia content — Mon, 07 Jul 2025 10:43:52 +0200

Numerical Simulation and Optimization of Grey Relational Analysis Models for Panel Data

Jesús Sánchez Pinedo — Thu, 03 Jul 2025 12:02:33 +0200

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

Optimization of Support for Deeply Buried Diversion Tunnels with Bad Geology Based on MOMVO Algorithm

Jesús Sánchez Pinedo — Thu, 03 Jul 2025 12:01:43 +0200

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

Fundamental Theorems and Wave Dynamics in Rotating Thermoelastic Diffusion with Multi-Phase Delays and Temperature-Dependent Properties

Jesús Sánchez Pinedo — Thu, 03 Jul 2025 11:56:33 +0200

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

Simplified Electromagnetic Method for the Analytical Euclidean Modeling of Multiple Electrical Conductors in Arbitrary Directions

Jesús Sánchez Pinedo — Thu, 03 Jul 2025 11:55:43 +0200

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

Bidirectional Fluid-Structure Coupling Analysis of Steering Tool Performance with Spring Stress Consideration

Jesús Sánchez Pinedo — Thu, 03 Jul 2025 11:53:32 +0200

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

Multi-hazard stochastic time-dependent tropical cyclone fragilities for coastal structures

Scipedia content — Mon, 30 Jun 2025 13:35:42 +0200

Seismic risk of isolated highway bridges with high damping rubber bearings in cold regions

Scipedia content — Mon, 30 Jun 2025 13:32:45 +0200

Reliability-based multilatticetopologyoptimizationfor structuraldesign

Scipedia content — Mon, 30 Jun 2025 13:31:13 +0200

A new framework for the quantification of sustainability, inclusiveness, and aesthetics of structures and urban areas

Scipedia content — Mon, 30 Jun 2025 13:29:49 +0200

Sensitivity analysis of hydrodynamic journal bearing failure modes due to lubricant contamination

Scipedia content — Mon, 30 Jun 2025 13:28:30 +0200

Numerical Investigations of Fractional Complex Coupled Maccari and Cahn-Hilliard Equations Using Controlled Picard Iteration with ρ-Laplace Transform

Jesús Sánchez Pinedo — Mon, 30 Jun 2025 10:46:33 +0200

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

Investigation of the Effect of Harmonics Caused by Transformer-Induced Nonlinear Loads on Electrical Energy

Jesús Sánchez Pinedo — Mon, 30 Jun 2025 10:43:54 +0200

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

Vectorization for Tower Sketches Using Large Model and Deep Learning

Jesús Sánchez Pinedo — Mon, 30 Jun 2025 10:29:23 +0200

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

Exploring the Flow Dynamics of MHD Hybrid Nanofluid over a Non-Flat Porous Surface Using Neural Network Approach

Jesús Sánchez Pinedo — Mon, 30 Jun 2025 10:25:43 +0200

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.

Stability and Chaotic Analysis of Nonlinear Fractional Model Using Novel Analytical Technique: Soliton Solutions for Wazwaz Kaur Boussinesq Equation

Jesús Sánchez Pinedo — Mon, 30 Jun 2025 10:04:03 +0200

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

The Sealing Failure Mechanism of Old Well Plugging Section under Thermo-Mechanical Coupling in Salt Cavern Hydrogen Storage

Jesús Sánchez Pinedo — Fri, 27 Jun 2025 11:40:43 +0200

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

DISEÑANDO OPORTUNIDADES: EL ROL DEL ESPACIO EN LA ACTIVIDAD PRODUCTIVA DEL ADULTO MAYOR

Daniel Arturo García Arista — Mon, 23 Jun 2025 20:18:33 +0200

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.

media managementT

Hayder Abbas Hussein Al Mahdi — Sat, 14 Jun 2025 22:51:44 +0200

A Critical Analysis of Pakistan’s 2021 National Climate Change Policy: Ambition vs. Implementation

Noor Ul Hassan Khan — Thu, 12 Jun 2025 17:29:28 +0200

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.

PROPUESTA TRABAJO DE GRADO

Laura Gutiérrez — Thu, 05 Jun 2025 00:16:33 +0200

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.

Offshore Marketers

Nandini Tyagi — Mon, 02 Jun 2025 13:46:14 +0200

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/

MULTI-HAZARD GEOTECHNICAL RELIABILITY MAPPING ALONG THE SAN ANDREAS FAULT ZONE: A DATA-DRIVEN FRAMEWORK

Umar Malik — Sat, 24 May 2025 01:41:34 +0200

''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.''

Comparison of stiffness and strength of flax, hemp and kenaf composites with other natural and synthetic fibers composites using fibre-specific parameters

Maria Asun Cantera — Wed, 21 May 2025 13:48:34 +0200

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.

Experimental Analysis of Magnetic Focusing of the Plasma Arc of a Cutting Torch

Dejan Brkić — Mon, 19 May 2025 15:11:23 +0200

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.

Probabilistic life-cycle performance, design, maintenance, optimization, and decision-making in asphalt pavement

Scipedia content — Sun, 18 May 2025 17:59:44 +0200

Seismic fragility and risk assessment of masonry infilled low ductile RC frames retrofitted with BRBs

Scipedia content — Sun, 18 May 2025 17:58:32 +0200

Probabilistic demand hazard assessment of a RC building with buckling restrained braces exposed to seismic loadings

Scipedia content — Sun, 18 May 2025 17:57:20 +0200

Vine-copula-based structural multi-hazard reliability assessment under post-earthquake fire

Scipedia content — Sun, 18 May 2025 17:56:10 +0200

Shifts in population and housing affordability after Louisiana’s 2016 floods

Scipedia content — Sun, 18 May 2025 17:54:59 +0200

Advancing geotechnical insights through geodatabases and data-driven methodologies

Scipedia content — Sun, 18 May 2025 17:53:47 +0200

Planning inspections to minimize uncertainty in thickness monitoring data

Scipedia content — Sun, 18 May 2025 17:52:35 +0200

Graph-based multi-agent reinforcement learning for railway infrastructure decision support

Scipedia content — Sun, 18 May 2025 17:51:23 +0200

Using the Life Quality Index as indicator for Social Life Cycle Assessment in engineering structures

Scipedia content — Sun, 18 May 2025 17:50:09 +0200

Beyond statistical fitting: improving reliability analysis through physically consistent bridge traffic load model

Scipedia content — Sun, 18 May 2025 17:43:49 +0200

Active learning Kriging methods for Bayesian updating

Scipedia content — Sun, 18 May 2025 17:42:37 +0200

Uncertainty quantification of the concrete Modulus of Elasticity through different experiment tests and sensors

Scipedia content — Sun, 18 May 2025 17:41:25 +0200

Influence of priors on estimation results in simultaneous geometry and spatial field estimation

Scipedia content — Sun, 18 May 2025 17:40:13 +0200

Bayesian uncertainty analysis of grouted anchors

Scipedia content — Sun, 18 May 2025 17:39:03 +0200

Bayesian nonlinear finite element model updating of a full-scale bridge column: insights into calibration of inherent damping model

Scipedia content — Sun, 18 May 2025 17:37:51 +0200

Global sensitivity evolution of vertical ride quality for high-speed maglev trains

Scipedia content — Sun, 18 May 2025 17:32:11 +0200

Global point-selection strategy based on the MF-discrepancy for stochastic response analysis of structures with independent random variables

Scipedia content — Sun, 18 May 2025 17:30:59 +0200

Resilience metric for post-flood road network

Scipedia content — Sun, 18 May 2025 17:26:10 +0200

Effects of uncertainties in the particle distribution of rubber-soil mixture on the performance of shallow foundations

Scipedia content — Sun, 18 May 2025 17:24:58 +0200

Review of Structural Health Monitoring methods for the assessment of probabilistic models of imposed loads

Scipedia content — Sun, 18 May 2025 17:23:47 +0200

A surrogate model approach for uncertainty treatment in performance-based engineering

Scipedia content — Sun, 18 May 2025 17:22:33 +0200