University of Bacău

The CNFIS-FDI-2025-0489 project, “Integrated strategies for counselling young people at academic risk” (PRO-EDU@UBc) consolidates the efforts previously made by “Vasile Alecsandri” University of Bacău (UBc) regarding social inclusion policy and reducing university dropout rates, especially among young people from disadvantaged backgrounds. The problems faced by the academic environment regarding inclusive education have led universities to develop their own intervention mechanisms to increase access to education, combined with packages of measures to support students from disadvantaged backgrounds in completing their studies. This study uses a multi-method qualitative design, combining document analysis and literature review with a descriptive institutional micro-case study. The latter aims to illustrate some of the strategies for addressing university dropout over the last 14 years at the UBc, at the Bachelor’s and Master’s degree levels. The results presented reflect the trends in university dropout and contribute to the identification of possible future strategies that could support intervention in academic management.

Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models-including the linear-quadratic (LQ) formula and the Jones-Hopewell single-session repair model-do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. We developed AI-BED-Fx, the first multi-fraction extension of the Jones-Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on-time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts-arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)-were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain-metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60-210 Gy2.47; MEN 41-85 Gy3.5; VS 46-68 Gy3; BM 37-75 Gy10) and biologically coherent dose-response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R2 = 0.9991). AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose-response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned.

The ECOSTAND project (no 2023-1-RO01-KA220-SCH-000156665) was a European Union–funded strategic partnership implemented between October 2023 and September 2025, with the aim of strengthening students’ understanding of climate change, natural hazards, and human–environment interactions through meaningful school-based learning. The project was developed in response to the increasing visibility of climate-related hazards and to the need for educational approaches that connect Earth system science with disaster risk reduction and everyday decision-making.Within ECOSTAND, the partners designed and implemented an interdisciplinary educational framework entitled Natural Disasters Response and Climate Action, tailored for secondary school education. The framework brings together key concepts related to climate processes, natural hazards, Earth’s resources, and human impact, and places strong emphasis on inquiry-based and experiential learning. Central project outputs include curriculum-aligned learning modules, methodological guidelines for teachers, and an interactive simulation environment developed using the Minecraft Education platform. This digital environment allows students to explore realistic scenarios involving floods, earthquakes, wildfires, droughts, and extreme weather events, while analysing risk, vulnerability, and response strategies in a safe and engaging context.A core pedagogical principle of the project was the use of simulation-based learning to support systems thinking and to help students make connections between geoscientific concepts and real-world challenges. Classroom implementation focused on active student involvement, collaborative problem-solving, and reflection on the role of human actions in both amplifying and reducing disaster risk. Teachers participating in the project engaged in targeted professional development activities, which supported the effective integration of the ECOSTAND resources into everyday teaching practice.Feedback from classroom implementation and evaluation activities indicates increased student awareness of climate-related hazards, improved understanding of disaster preparedness measures, and greater confidence in discussing complex environmental issues. Teachers reported that the simulation-based approach enhanced student engagement and facilitated meaningful discussions about sustainability and resilience.As a completed project, ECOSTAND offers a practical and transferable model for integrating natural hazards education and climate change topics into secondary school curricula. The project contributes directly to the aims of EOS 5.5 by providing classroom-tested approaches that link Earth science content with human impact, sustainability, and community resilience. Partners in the ECOSTAND project:Colegiul National "Vasile Alecsandri" Iasi, Romania - CoordinatorINDEPCIE SCA, Cordoba, SpainAsociatia DEMETRIUS, Iasi, RomaniaOzel Adalya Anadolu Lisesi, TürkiyeATERMON B.V., Rotterdam, NetherlandsPOWOW SAS Camarsac, FranceIstituto Comprensivo Maneri - Ingrassia - Don Milani, Palermo, Italy

Bien qu’ils constituent une catégorie sociale prépondérante pendant lapremière modernité, les paysans ne bénéficient pas d’une place privilégiée dans lalittérature de l’époque. Leur représentation littéraire s’avère être, en revanche,particulièrement complexe, surtout dans les recueils hybrides et facétieux, où l’onmet en relief plusieurs facettes du personnage campagnard. Décrit comme étant soitrusé ou sage, soit absolument stupide, le paysan est souvent associé au mondeanimal, ce qui le place en dehors de la société civilisée. Néanmoins, il réussitparfois à accéder à un statut plus élevé, en se servant d’une méthode insolite - laréplique facétieuse. Celle-ci lui permet de renverser l’ordre social établi, au moinslors d’un échange verbal généralement à but comique. L’article vise ainsi àinterroger la place sociale du paysan par le biais du mot d’esprit, en considérantsurtout la fonction de défense de la repartie facétieuse dans le Chasse-ennuy deLouis Garon.

Living systems exhibit anticipation, adaptability, and resilience that cannot be fully explained by stimulus–response models, static homeostasis, or convergence-based optimization. This work addresses this gap by proposing a theoretical framework in which a central aspect of biological function is understood through the geometry and stability of distributions over unrealized but accessible future trajectories. We formalize these distributions as a counterfactual manifold, defined as a probabilistically supported subset of path space induced by a system’s effective internal dynamics. Using tools from information geometry and dynamical systems theory, we analyze adaptive systems that modify the laws governing their own future trajectories and construct explicit dual-channel adversarial dynamics that couple processes expanding future possibilities with antagonistic processes enforcing feasibility constraints. We show that adaptive systems of this kind are generically unstable, tending toward either collapse of accessible futures or unbounded sensitivity to perturbation. Constructive adversarial dynamics are sufficient to stabilize counterfactual geometry without requiring convergence to a fixed point. A minimal adversarial model reveals three generic regimes: collapse, runaway sensitivity, and bounded non-convergent regulation. The framework yields operational, falsifiable predictions through measurable proxies based on response diversity, perturbation sensitivity, recovery geometry, and boundary residence, allowing these regimes to be discriminated using finite observations without reconstructing underlying state-space dynamics. Interpreting disease as instability of counterfactual geometry provides a unifying language for understanding rigidity, volatility, and context dependence across biological domains. Rather than replacing mechanistic models, the proposed framework offers a higher-level geometric and dynamical perspective in which such models can be embedded and compared, shifting attention from component-level dysfunction to the stability of biological futures and establishing a principled foundation for analyzing disease, intervention, and adaptability across scales.

Generative Adversarial Networks (GANs) perform well on natural images but often fail in domains governed by strict geometric or symbolic constraints. This work focuses on convolutional GANs and studies how their inductive biases interact with two contrasting types of synthetic image data: fractal patterns, characterized by self-similarity and scale-invariant local structure, and Euclidean shapes, defined by simple geometric primitives and rigid global constraints. Using multiple convolutional GAN architectures (DCGAN, WGAN-GP, and SNGAN), two resolutions (64 × 64 and 128 × 128), and a suite of evaluation metrics, we compare adversarial training behavior on these datasets under tightly controlled conditions. Fractal datasets yield stable training dynamics and perceptually plausible generations, whereas Euclidean shape datasets consistently exhibit structural failure modes that persist under higher resolution, smoother shape representations, and architectural stabilization. Geometry-aware metrics reveal severe violations of global shape consistency in Euclidean outputs that are not reliably captured by standard perceptual or distributional measures such as FID, SSIM, or LPIPS. We argue that these findings reflect a fundamental inductive bias of convolutional generative models toward a locally rich, scale-repeating structure rather than globally constrained geometry. Rather than indicating that fractals are intrinsically easier to model, our results show that Euclidean geometry exposes limitations of adversarial generative learning that remain hidden under conventional evaluation. From this perspective, fractal datasets serve as informative diagnostic benchmarks for probing how adversarially trained convolutional generators handle scale-invariant structure versus globally constrained geometry, and our results highlight the need for domain-aware metrics and alternative architectural biases when applying generative models to structured or symbolic data.

We present a conceptual and computational investigation of vacuum memory within a discrete toy-model framework. In this phenomenological approach, we introduce an effective memory field that records virtual events and nonlocal couplings on a lattice, without claiming to derive a fundamental new field of nature. Using a discrete toy model, we simulate memory formation via virtual events, nonlocal links, and black-hole-like information sinks. The resulting dynamics exhibit long-range spatial correlations, curvature-induced accumulation, high-entropy retention zones, and distinct spectral features, indicating that the modeled memory field can store and organize information in a vacuum-like medium. Building on this foundation, we incorporate curvature-modulated vacuum memory fields into Bohmian particle dynamics. By varying the memory coupling strength λ, we demonstrate that memory gradients systematically bend particle trajectories toward curvature centers, illustrating an active role for structured memory in guiding quantum-like motion. We further show that when vacuum memory encodes the full quantum phase S(x, t) and particles are guided by the Bohmian relation x˙=m−1∂xS, the trajectories collapse onto a single path with machine-level precision, providing a numerical consistency check that our implementation reproduces exact pilot-wave guidance and minimal-action dynamics. Through a minimal two-site entangled-memory model, we demonstrate that coupled memory fields—without explicit particle dynamics—can spontaneously synchronize via weak informational coupling, generating robust nonlocal correlations reminiscent of entanglement. Finally, we simulate two-slit interference under vacuum memory perturbations. While random, unstructured memory preserves quantum coherence and fringe visibility, structured, phase-sensitive memory induces dephasing and suppresses interference, functioning as a phenomenological decoherence mechanism. Together, these results situate our toy model within emerging information-based views of quantum dynamics and spacetime, offering a computational platform and conceptual lens for exploring the informational dynamics of a vacuum-like medium.

Substations are the main elements of an energy system that must ensure evacuation of power produced in the power plants, connection of lines to transit the power, distribution electricity to consumers. Reliability of a substation is essential for the role it plays in the energy system. In this paper are determined the energy losses in power lines and transformers associated with an electrical distribution station. The real operating regime is analyzed and an optimal model is established to improve the energy efficiency of the power station.

BackgroundThis study investigates the impact of different smartphone use durations on postural balance in adolescents with moderate to mild intellectual disabilities.MethodsTwenty-two participants, aged 10 to 16 years, engaged in smartphone use sessions lasting 10, 20, and 40 min. Postural balance was assessed before, immediately after, 10 min after, and 30 min after each session under open eyes (OE) and closed eyes (CE) conditions on both firm and foam surfaces. Data were analyzed using repeated measures ANOVA to examine the effects of smartphone use duration, time, vision, and surface on postural balance.ResultsThe results revealed a significant decrease in postural balance immediately after 20 and 40 min of smartphone use compared to 10 min under OE conditions on firm and foam surfaces. Transitions from OE to CE were generally associated with decreased postural balance. However, exceptions were observed, particularly immediately after 20- and 40-min durations on the firm surface, and at the 20- and 40-min durations both immediately after and 10 min after on the foam surface.ConclusionSmartphone engagement over 20 min use disrupts postural balance in adolescents with moderate to mild intellectual disabilities. These findings highlight the importance of managing smartphone usage habits. Implementing guidelines on screen time limits, taking breaks, and adjusting posture may help mitigate immediate balance disturbances and support the overall well-being of this population.

The fractal extension of the time-dependent Ginzburg–Landau (TDGL) equation, formulated within the framework of Scale Relativity, generalizes superconducting dynamics to non-differentiable space–time. Although analytically well established, its numerical solution remains difficult because of the strong coupling between amplitude and phase curvature. Here we develop two complementary deep learning solvers for the fractal TDGL (FTDGL) system. The Fractal Physics-Informed Neural Network (F-PINN) embeds the Scale-Relativity covariant derivative through automatic differentiation on continuous fields, whereas the Fractal Graph Neural Network (F-GNN) represents the same dynamics on a sparse spatial graph and learns local gauge-covariant interactions via message passing. Both models are trained against finite-difference reference data, and a parametric study over the dimensionless fractality parameter D quantifies its influence on the coherence length, penetration depth, and peak magnetic field. Across multivortex benchmarks, the F-GNN reduces the relative L2 error on ψ2 from 0.190 to 0.046 and on Bz from approximately 0.62 to 0.36 (averaged over three seeds). This ≈4× improvement in condensate-density accuracy corresponds to a substantial enhancement in vortex-core localization—from tens of pixels of uncertainty to sub-pixel precision—and yields a cleaner reconstruction of the 2π phase winding around each vortex, improving the extraction of experimentally relevant observables such as ξeff, λeff, and local Bz peaks. The model also preserves flux quantization and remains robust under 2–5% Gaussian noise, demonstrating stable learning under experimentally realistic perturbations. The D—scan reveals broader vortex cores, a non-monotonic variation in the penetration depth, and moderate modulation of the peak magnetic field, while preserving topological structure. These results show that graph-based learning provides a superior inductive bias for modeling non-differentiable, gauge-coupled systems. The proposed F-PINN and F-GNN architectures therefore offer accurate, data-efficient solvers for fractal superconductivity and open pathways toward data-driven inference of fractal parameters from magneto-optical or Hall-probe imaging experiments.