Higher Institute on Territorial Systems for Innovation
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Dynamic Cooperative Energy and Coverage Management for V2G-Enhanced RAN Resilience
No full texthe resilience of cellular communication is
paramount, given how widely its services are used. However,
it also depends on the resilience of the power grid, which
is increasingly threatened by extreme weather events and the
growing reliance on distributed, intermittent energy sources.
Current base station backup systems, typically limited to 2-6
hours of autonomy, operate in isolation and fail to leverage
spatial redundancy. This paper proposes the Multi-Site Resiliency
Cooperative (MSRC) framework, a unified control strategy that
transforms independent sites into a collaborative energy cluster.
We formulate a two-stage mixed-integer optimization problem
that jointly manages radio coverage adaptation switching sites
between helping, assisted, and deep-sleep modes and dynamic
Vehicle-to-Grid (V2G) energy injection. By proactively reshaping
cell boundaries and traffic loads based on real-time battery states,
MSRC maximizes network survival while prioritizing critical
service classes. Extensive simulations on a 19-site urban network
demonstrate that the proposed framework extends survival time
by 96% and maintains 94% service continuity compared to
conventional baselines. Crucially, MSRC requires no physical in-
frastructure upgrades, offering operators a deployable, software-
defined solution for outage-resilient green communications
"Characterization of AlSi10Mg interlocking structures additively manufactured via Laser Powder Bed Fusion"
Full text linkMechanical interlocking is a joining technique capable of mechanically bonding two dissimilar materials by means of protrusions on the component surfaces, as an alternative or complement to mechanical fasteners or adhesives. Proper adhesion between materials, and joint retention mainly depend on the shape and strength of the interlocking structures. In this respect, additive manufacturing is used to optimize the interlocking design and performance. The intent of this research is to evaluate the effectiveness of laser powder bed fusion and post-processing treatments on AlSi10Mg interlocking structures. The geometry, microstructure and density of the structures are investigated utilizing metallographic and tomographic techniques
Is it possible a `Reverse Flynn Effect'? Consideration for new opportunities in the context of sustainability
No full textRecently, some evaluations of the workers’ and students’ abilities brought
back to consider the negative (or reverse) Flynn effect. The reverse Flynn effect is an
empirically confirmed decrease in the Intelligence Quotient since the 1990s. It has been
proven to be related to environmental conditions only. Here, we consider this effect as an
environmental consequence of the social and education conditions, considering it a possible
resource, meaning the opportunity to reconsider the education pathway and the workers’
training. To do so, in this paper, we develop a thermodynamic analysis of the information
fluxes that enter the brain and the related stabilisation of the inflow of information itself.
Moreover, we develop a thermo-economic analysis of the consequences of the reverse Flynn
effect, pointing out the need to focus educational policies on the continuous stimulus of the
use of reasoning and problem-solving-based education, to develop the processing capacity
and foster the creative capabilities of young people, who build the backbone of the future
workforce
Measuring Firm Greenness: A Comprehensive Review of Corporate Sustainability Indicators
No full textThe major global challenges of climate change, environmental degradation, and resource scarcity have gained centrality in
policymaking and academia, with a growing body of research exploring the interlinkages among economic growth, trade,
environmental outcomes, and policy interventions. These analyses and theirresults draw fundamentally on the definitions used to
consider a firm, product, or process “green,” and on the related operationalization. Despite a proliferation of empirical approaches,
however, the literature lacks a comprehensive overview of firm-level sustainability indicators. This article addresses this gap by
systematically reviewing existing definitions and the most frequently employed indicators Our analysis shows that, according
to prevailing definitions of greenness, a firm can be considered green either because it is purpose-sustainable, meaning that it
primarily serves an environmental purpose, or because it is process-sustainable, meaning that its activities reduce or prevent
negative environmental impacts even if environmental goals are not its primary purpose. We further classify the existing firmlevel sustainability indicators into three groups: (1) product-level indicators; (2) resource and pollution management indicators;
and (3) investment, innovation, and commitment indicators. Our findings aim to support researchers in selecting indicators that
align with specific research objectives and dimensions of sustainability under investigatio
Investigation of selected invasion problems using analytical, numerical, and control approaches
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Advanced multiscale models for the analysis of composite structures with applications to curing simulation and multiphysics
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Estimating Temperature in a Permanent-Magnet Synchronous Motor Using Hammerstein and Nonlinear Autoregressive Models Initialized Via Thermal Networks
No full textMonitoring the temperature of permanent-magnet synchronous motors is crucial to prevent failures in sensitive components such as windings and permanent magnets. In this respect, machine learning techniques have been used to generate models to estimate the temperature of rotor and stator hotspots. However, the effective use of data-driven methods requires large datasets, extensive training time, and substantial computational power. Moreover, machine learning methods mostly operate with a black-box approach; they do not account for the physics of the system to be modeled. This paper proposes and compares Hammerstein and nonlinear autoregressive exogenous models to estimate the temperature of the permanent magnets and windings of an out-runner permanent-magnet synchronous motor. A linear time-invariant component, used for both the Hammerstein and nonlinear autoregressive exogenous models, is initialized via a previously identified fourth-order lumped parameter thermal network. This model accounts for the thermal behavior of the machine. The nonlinear component is modeled via a neuron sigmoid network. Results show that the Hammerstein model achieves a lower mean squared error for the winding temperature estimation than the nonlinear autoregressive exogenous model. The opposite is true for the magnet temperature estimation
Machine learning applied to high-entropy alloy coatings process parameters and composition optimization – A case study
No full textRecent research has indicated that Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi high entropy alloys (HEAs) exhibit superior mechanical and thermal properties under extreme conditions. This chapter provides an account of the wear and surface characteristics of cold-sprayed HEA coatings at various temperatures. The inputs of surface roughness and volume variation are analyzed by analysis of variance (ANOVA), employing formulas optimized by genetic algorithms. Gaussian process regression, support vector regression (SVR), and artificial neural networks are machine learning (ML) methods that predict surface roughness and volume variation with high accuracy. For surface roughness, SVR achieves a coefficient of determination of 0.97, which is lower than that determined from the other models. Furthermore, all three models achieve a coefficient of determination of 0.99 for volume variation. The results indicate the ability of ML to generalize effectively across datasets, capturing nonlinear patterns with precision. These findings emphasize the potential of HEAs for high-wear applications and the reliability of predictive modeling
Relaxation for a degenerate functional with linear growth in the onedimensional case
No full textIn this work, we study the relaxation of a degenerate functional with linear growth, depending on a weight w that does not exhibit doubling or Muckenhoupt-type conditions. In order to obtain an explicit representation of the relaxed functional and its domain, our main tools for are Sobolev inequalities with double weight
A Survey of Memory Models for Virtual Agents and Humans: From Psychological Foundations to Computational Architectures
No full textVirtual Humans are an advanced class of Virtual Agents characterized by human-like embodiment and cognitive capabilities. Central to their adaptivity is the integration of computational cognitive architectures, in which memory models play a key role in learning, continuity, and contextual reasoning. This review bridges psychological theories of memory and their computational implementations by comparing symbolic and connectionist approaches and exploring new paradigms such as Memory-Augmented Neural Networks and Large Language Models. We propose a unified framework for analyzing the components of artificial memory - Working, Semantic, Episodic, Procedural, Spatial, and Autobiographical Memory - and review their applications in domains such as education, games, and social simulation. Finally, we discuss open challenges and future works