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Giving back to the community. Educare città che imparano a restituire
No full textAccording to the Global Solidarity Report, in 2024 the world scored just 36 out of 100 on the Global
Solidarity Scorecard, indicating a dangerously weak level of solidarity, far below what is required
to build an effective trajectory toward a united world. This data invites us to rethink the
transformation of the places we inhabit as spaces and times of existential affirmation, capable of
strengthening opportunities for education and lifelong learning. The essay questions the forms and
modalities through which formal education can be returned to the community through both formal
and non-formal educational spaces. What is at stake is an active commitment on the part of
institutions, which must know how to make their mission a pervasive process, capable of involving
every member of the community and contributing to their individual and collective growth, in the
service of the common good. The Performing project is an emblematic example of this, adopting
service-learning as a powerful device to foster the interconnection between education and
community
Pointwise Stabilization in the Laminated Beam Model
No full textWe consider two identical beams on top of each other with an adhesive in the middle. A slip occurs naturally in the structure. In this work, we take this slip into account and show that we can stabilize the system exponentially using pointwise controls applied on the axial force and the bending moment. The model consists of three coupled equations. The first two equations are related to the Timoshenko system and the third equation describes the dynamics of the slip. Our result improves previous results in the sense that it addresses the smaller dissipative effect, the point mechanism. To do this, we introduce a new method that allows us to show the exponential stability of systems of partial differential equations with point dissipation
Interplay Between Protein Phosphatase 2A (PP2A) and SE Translocation (SET) as Macromolecular Target of Anticancer Compounds: A Combined Computational and Experimental Study
No full textCancer represents a leading cause of mortality globally, with its complex biological nature posing significant challenges for treatment. Central to cancer progression are molecular pathways that govern cellular function, among which protein phosphatase 2A (PP2A) plays a vital role. As a serine/threonine phosphatase, PP2A maintains cellular homeostasis by dephosphorylating a broad range of protein substrates and has emerged as a key tumor suppressor. However, PP2A activity can be physiologically inhibited by endogenous regulators such as the SE Translocation (SET) protein. Overexpression of SET has been associated with the loss of PP2A function, promoting hallmark features of cancer. Interestingly, targeting the PP2A/SET interaction has shown therapeutic potential. Indeed, inhibiting SET to reactivate PP2A may restore cellular regulation, induce apoptosis in tumor cells, and attenuate cancer progression. Research efforts have explored compounds such as the endogenous D-erythro-C18-ceramide and the drug fingolimod (FTY720), both known for their ability to reactivate PP2A. In this work, PP2A/SET complex models were generated through a computational approach and, using molecular docking, the interaction of potential SET inhibitors from a library of 26 alkoxy phenyl 1-propan-one derivatives (APPDs) was characterized. Additionally, absorption, distribution, metabolism, and excretion (ADME) predictions were performed to assess pharmacokinetic properties and therapeutic potential. Eventually, the predicted binding affinities were then correlated with biological data to assess the reliability of the models. These findings provide valuable insights into molecule–receptor interactions and lay the groundwork for developing inhibitors with encouraging therapeutic implications
Early Insights into Argumentation-Guided Causal Evaluation with the Help of LLMs
No full textThe rapid growth of Deep Neural Networks (DNNs) has brought substantial advances in artificial intelligence across domains such as vision, language, and recommendation systems. However, this progress comes at a steep energy cost, with model training and deployment contributing significantly to global computational energy consumption. Understanding what drives this energy demand requires more than empirical correlation- it demands causal explanations. In this work, we investigate the causal factors underlying energy use in DNN training, using structure learning algorithms such as the PC algorithm to derive candidate causal graphs. Recognising the limitations of such methods-particularly in terms of assumptions and finite data-we introduce a novel approach to evaluate each inferred link through formal argumentation. We treat each proposed causal relationship as a dialectical object, generating arguments and counterarguments that articulate its plausibility, underlying mechanisms, and possible confounders. We operationalise this reasoning using large language models in a zero-shot prompting setup, surfacing the evidential and conceptual assumptions behind each causal claim. This hybrid approach, combining causal discovery with structured argumentative evaluation, promotes interpretability and critical scrutiny in data-driven causal modelling. Preliminary results demonstrate its potential for rendering causal claims more transparent and contestable
Market Barriers to RNAi Adoption in Agriculture: Evidence From a Multi‐Country Discrete Choice Experiment Amongst European Consumers
No full textRNA interference (RNAi) technology offers promising alternatives to conventional chemical pesticides, particularly for fungal resistance. It can be applied in agriculture either through topical delivery, such as spray-induced gene silencing (SIGS), or via genetic modification, as in host-induced gene silencing (HIGS). While the European public has shown consistent aversion toward genetically modified (GM) technologies, the level of acceptance for topical RNAi applications remains largely unexplored. This study primarily investigates public acceptance for strawberries produced with Topical RNAi, GM RNAi, or Traditional breeding. A discrete choice experiment was conducted in Italy, France, Germany, and Spain including additional attributes for organic production, local origin, and price. Mixed logit and willingness-to-pay estimates were used to analyze preferences and identify socio-demographic and attitudinal determinants. Results consistently reveal a marked consumer aversion to products developed using RNAi technologies relative to conventional breeding, with particularly strong skepticism in France and Germany. By contrast, organic and local attributes exert strong positive influence, confirming the enduring salience of “natural” and provenance-related cues. Key factors that could facilitate a shift toward greater acceptance include improving understanding of biotechnology innovations, enhancing consumer confidence in safety assessments, and—most importantly—increasing awareness of the alignment between RNAi technologies and sustainability goals. Acceptance is higher among men, urban residents, and individuals with greater knowledge of biotechnology and trust in regulatory assessments. Conversely, older consumers, women, and those strongly committed to sustainability-oriented behaviors display lower acceptance, perceiving RNAi as incompatible with their values. The findings underscore that the social readiness of RNAi technologies lags behind their scientific potential. Building consumer trust, improving understanding, and reframing RNAi within broader sustainability goals will be the themes on which to base future policy decisions
Study of and its higher moments, and extraction of the speed of sound in Pb-Pb collisions with ALICE
No full textUltrarelativistic heavy-ion collisions produce a state of hot and dense strongly interacting QCD matter called quark-gluon plasma (QGP). On an event-by-event basis, the volume of the QGP in ultracentral collisions is mostly constant, while its total entropy can vary significantly due to quantum fluctuations, leading to variations in the temperature of the system. Exploiting this unique feature of ultracentral collisions allows for the interpretation of the correlation of the mean transverse momentum of produced charged hadrons and the number of charged hadrons as a measure for the speed of sound, cs. This speed is related to the rate at which compression waves travel in the QGP and is determined by fitting the relative increase in with respect to the relative change in the average charged-particle density measured at mid-rapidity. This study reports the event-average of charged particles as well as the variance, skewness, and kurtosis of the event-by-event transverse momentum per charged particle distribution in ultracentral Pb-Pb collisions at a center-of-mass energy of 5.02 TeV per nucleon pair using the ALICE detector. Different centrality estimators based on charged-particle multiplicity or the transverse energy of the event are used to select ultracentral collisions. By ensuring a pseudorapidity gap between the region used to define the centrality and the region used to perform the measurement, the influence of biases and their potential effects on the rise of the mean transverse momentum is tested. The measured is found to strongly depend on the exploited centrality estimator and ranges between 0.1146±0.0028 (stat.)±0.0065 (syst.) and 0.4374±0.0006 (stat.)±0.0184 (syst.) in natural units. The self-normalized variance shows a steep decrease towards ultracentral collisions, while the self-normalized skewness variables show a maximum, followed by a fast decrease. These non-Gaussian features are understood in terms of the vanishing of the impact-parameter fluctuations contributing to the event-to-event [pT] distribution
Impact of the Inter-Fin Space on Stress Modulation and FinFET Transistor Performance
No full textAs microelectronic devices continue to scale down, the complexities of their integration and design become increasingly intricate. This study investigates the impact of fin pitch variability on stress modulation and performance in 7nm FinFET transistors. Variations in critical dimension (CD) during patterning can modulate fin width and spacing, causing undesired effects and change of device characteristics. In this work we measured device sensitivity using a comprehensive set of test structures (designed to electrically quantify the effect of fin pitch variability) as well as performed TCAD simulation of the measured devices. The results confirm device sensitivity to inter-fin space and the simulation highlights the dominant role of stress modulation due to variation of Source/Drain stressor volume. NMOS devices show gradual degradation of performance with larger inter-fin space and PMOS devices show a weaker response due to counterbalancing stress components. The study underscores the importance of fin pitch control in reduction of transistor performance variability, providing valuable insights for optimization of FinFET technologies