Theory and Applications of Mathematics and Computer Science

Manufacturing Process of the SAV PALET Dacia Renault

Alexandru Puiu Barbos — 2026-09-01

The paper analyzes the technological workflow used to manufacture the welded SAV PALET G assembly intended for the
Renault/Dacia application, starting with the procurement of semi-finished materials and continuing with cutting, machining on
numerically controlled equipment, turning of the parts, welding, stress relieving, hot-dip galvanizing and final inspection. The
study uses technical data from the attached technological documentation and focuses on the engineering interpretation of the
manufacturing parameters. Calculations are presented for spindle speed, feed rate, theoretical machining time and an example
of dimensional verification. The results show that the sequence of operations must be established so that geometric accuracy,
welded-assembly stability and corrosion protection are maintained throughout the entire manufacturing cycle.

Mathematical Analysis and Structural Optimization of an Insulated Support for the 25 kv–50 hz Railway Contact Line

Alexandru Puiu Barbos — 2026-09-01

This paper presents a structural analysis of an insulated support assembly used in 25 kV–50 Hz railway overhead contact
lines. The study is based on the supplied engineering drawings of the stay rod, brace, direct steady arm, reverse fixing device,
bar, couplings, fixing plate and contact-wire clamp. The dimensions and materials explicitly readable in the drawings are incorporated
into a simplified analytical model for bending, shear and elastic displacement. The reference configuration mainly uses
S235/S235JR/FMN35 steels with corrosion protection by hot-dip galvanizing, while CuZn15 and other steels/stainless components
are present in the contact clamp and fasteners. AlCu4Mg is investigated as a lightweight alternative for selected structural members.
The analysis confirms the substantial mass-saving potential of aluminium alloys, but also the associated loss of sti ness;
therefore, a material substitution must be accompanied by geometric redesign. For the ?34 4 mm tubular direct steady arm, with
L = 400–1600 mm, the section properties are calculated and a conservative Fd = 90 N case is used to quantify the sensitivity of
bending moment and elastic displacement to length. The proposed methodology combines mass, strength and sti ness criteria and
can be extended by finite-element analysis.

Mathematical Modeling, Structural Resistance Calculation, and Technological Itinerary for Manufacturing the Y25 Bogie Frame

Paul Ilie Varsendan — 2026-09-01

This paper presents an analytical and mathematical approach to the dynamic stress phenomena acting on the supports and
protection devices of railway track inductors. The main objective of the research is the mathematical substantiation of the transition
from the use of conventional structural steels (S235J2) to superior alloys (S355J2) in railway infrastructure, through a rigorous
analysis of stress tensors and safety coecients. Based on equilibrium equations and bending models under transient shocks and
accelerations, it has been demonstrated that adopting the S355J2 material increases the structural safety coecient by over 50%,
ensuring the continuous operation of trac safety systems (PZB/Indusi).

Mathematical Analysis of Dynamic Stresses and Structural Optimization of Protection Devices for Railway Track Inductors

Paul Ilie Varsendan — 2026-09-01

TrueVision: A Human-Aligned Explainable Visual Completion Dataset for Incomplete Artworks

Roxana Buzuriu — 2026-09-18

Large vision-language models are increasingly used in domains where the input image is incomplete, including damaged
paintings, cropped artworks, and restoration scenarios. In these settings, a plausible answer is not sucient: the model should
distinguish between visible evidence and inferred content, communicate uncertainty, and abstain when the available cues are too
weak. This paper presents the current repository-backed stage of TrueVision, a bilingual English-Romanian dataset and annotation
platform for explainable visual completion on artworks and other incomplete images. The current reportable experimental bundle
contains a shared 100-task overlap annotated by three production annotators, yielding 300 bilingual annotation instances and
deterministic 76/10/14 train-validation-test splits. The live platform already supports a richer structured workflow that separates
visible cues from inferred hidden content in both languages, but the committed gold overlap still follows the earlier single-prediction
annotation format and is therefore the basis of the quantitative results reported here. The paper includes four reproducible local
baselines: zero-shot and few-shot Qwen2-VL 2B prompting, a metadata-aware retrieval baseline, and a lightweight supervised
visual baseline trainable on Apple Silicon. On the held-out test split, retrieval remains the strongest non-learned reference in
English similarity (0.4820), while the trained baseline provides the strongest learned result, with 0.4746 Romanian similarity,
0.4507 English token F1, 0.3223 Romanian token F1, 0.5093 confidence macro-F1, and 0.4853 diculty macro-F1. These results
show that the TrueVision pipeline is operational end to end and already supports controlled comparison of explainable visual
completion methods in a low-resource bilingual setting.

Convolutional Neural Networks for Cardiac MRI Analysis: An Overview

Marcela Florea — 2026-09-18

Cardiac magnetic resonance imaging (cardiac MRI) provides detailed information about cardiac anatomy and function, but
analysing a large number of images can be time-consuming when performed manually. Deep learning methods, particularly convolutional
neural networks (CNNs), are widely used for automated medical image analysis. This paper provides an overview of
CNNs and their applications in cardiac MRI analysis, with a focus on cardiac image segmentation. The paper first presents the
basic principles of CNNs, including convolution, pooling and activation functions, followed by a discussion of U-Net and its use
in biomedical image segmentation. Applications of CNNs in cardiac MRI, including cardiac structure segmentation, disease classification
and quantitative assessment, are also discussed. Recent developments such as 2D and 3D CNNs, attention mechanisms
and hybrid CNN-Transformer architectures are presented. The paper also discusses current challenges, including limited annotated
datasets, di erences between imaging settings, generalization, interpretability and clinical validation. Overall, CNNs remain important
tools for cardiac MRI analysis, while recent research is exploring more robust models that can be applied across di erent
clinical settings.

Human-AI Collaboration in West-Romanian SMEs: A Qualitative Study of Managers’ Early-Stage Adoption, Risks and Value

Miruna Maura Trocan — 2026-09-18

This study focuses on Small and Medium Enterprises (SMEs) from the western side of Romania. The current research starts
from the hypothesis that SMEs started to experiment with generative artificial intelligence at a growing rate, in order to reduce costs
and accelerate customer communication. However, evidence from owner-managed companies in Central and Eastern Europe is
limited. This study addresses this research gap by bringing evidence gathered using the qualitative methodology. Evidence on this
topic was collected in Arad, Romania, and examines how managers in the service-sector SMEs adopt AI in their day-to-day work,
what benefits and risks they perceive, and which routines enable a responsible, human-in-the-loop use. This research is conducted
from a managerial point of view, showing the challenges that managers try to address and the backlash that some of them faced
from their own employees. It is important to mention that this study focuses on how these managers and their employees use AI as
a tool in their companies, not as a replacement for their human employees.
This study provides understanding of the benefits and challenges that are encountered by real-life managers and why it is
important to proceed with caution and adopt new technologies responsibly. Limitations of this study include a single region and
reliance on self-reported commercial outcomes. Future research should include evidence from a wider region.

ESG Strategy Integration in SME Development: Pathways to Sustainable Growth

Oleg Bujor — 2026-10-20

In the context of the reconfiguration of the global economy and the establishment of Sustainable Development as the dominant
paradigm, small and medium-sized enterprises (SMEs) are becoming essential actors in the transition towards sustainable economic
models. This article investigates the integration of Environmental, Social and Governance (ESG) principles into SME development
strategies, critically analyzing both the constraints and opportunities associated with this process. The research is based on a mixed
methodological design, including a survey conducted among SMEs in the Republic of Moldova and a correlation of the results with
established European ESG indicators. The obtained results allow for the formulation of a conceptual and applicative framework
that can guide the elaboration and implementation of robust ESG strategies across various economic sectors. Through the proposed
approach, the study contributes to the academic literature on SME sustainability and provides empirical support for policymakers
involved in promoting a sustainable economy.

AI-Powered Leader Digital Twins as Identity-Governed Augmentation Systems: A Quantum-Entangled Framework for Leadership, XR, and Human–AI Coupling

Giammarco Tosi — 2026-10-20

This work develops a conceptual framework for AI-powered leader digital twins, defined as digital systems designed to emulate a specific leader’s knowledge, communication style, and decision logic. The paper addresses a central theoretical problem:
how such systems can remain meaningfully linked to a leader’s identity without reducing leadership to functional simulation. To
address this problem, the study integrates research on human digital twins, digital twin typologies, decentralized identity infrastructures, extended mind theory, trust and transparency, and quantum entanglement as a bounded metaphor for tightly coupled
human–AI systems. Extended reality (XR) is incorporated as the embodiment layer through which a leader digital twin becomes
socially present in organizational settings. The resulting Quantum-Entangled Leadership Digital Twin (QELDT) framework conceptualizes the twin as an identity-governed augmentation system anchored by verifiable linkage, closed-loop synchronization,
bounded delegation, and human accountability. The framework is interpreted against exploratory survey evidence from 38 leaders,
which suggests greater willingness to delegate analytical and monitoring activities than identity-laden tasks such as culture shaping,
inspiration, and ethically sensitive judgment. The paper contributes a governance-oriented foundation for theorizing leader digital
twins as extensions of leadership capacity rather than autonomous substitutes.

Virtual Reality and Serious Games: Innovative Tools for Inclusion and Accessibility

Roberta Barba — 2026-10-20

As digital technologies continue to shape education and healthcare, Virtual Reality (VR) emerges as a powerful medium for
immersive learning and therapeutic intervention. This paper explores the design and evaluation of a VR-based serious game prototype aimed at improving accessibility, cognitive training, and rehabilitation for persons with motor or cognitive impairments. The
proposed system employs immersive and adaptive environments powered by AI-driven interaction to enhance user motivation, engagement, and learning outcomes. A user-centered design approach was adopted, with iterative testing conducted in collaboration
with participants from accessibility-focused institutions. Preliminary results indicate improvements in user motivation and task
completion rates, highlighting the potential of VR-based serious games as tools for inclusive education and therapeutic practice.

Modeling and Statistical Analysis of Braking Parameters for Improving Traffic Safety

Dennis Frentiu — 2026-10-20

This paper examines, from a combined engineering and statistical perspective, how key braking parameters affect traffic
safety, with the aim of proposing a robust framework for modeling and evaluation. The main variables considered, initial speed,
tire-road adhesion, the condition and temperature of the braking system, axle load distribution, hydraulic pressure, and driver
reaction time, are used to estimate braking distance, average deceleration, and their variation under real operating conditions.
Illustrative calculations and comparative scenarios are used to quantify how these factors, individually and in combination, affect
stopping performance, and to identify parameter combinations with a disproportionate impact on outcomes. Based on the resulting
model, operational thresholds for preventive maintenance are proposed, along with scenarios for reducing the variability of braking
response. The results indicate that incorporating thermal and mechanical effects into the predictive model produces more stable
estimates of braking distance and more reliable inputs for decision-making in vehicle design, approval, and maintenance. The
paper’s main contribution is an integrated deterministic model that combines classical stopping-distance mechanics with correction
factors for adhesion, thermal effects, and load distribution, offering an objective basis for prioritizing safety related technical
interventions.