Toward Privacy-Aware Environmental Monitoring of CO2\textrm{CO}_2 and Air Pollutants in Southern Italy

The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns

The Shades of Beauty and Inclusivity in Cosmetics: The Fenty Beauty Case

This chapter discusses the renowned best practice of Fenty Beauty, showing how the company responded to and met the demand for diversity, describing the strategies it employed to achieve a dimension perceived as authentic by customers, and examining the impact of its marketing actions on consumer behaviour and market dynamics

Optimizing electronic cooling: Harnessing potentials of impinging jet flows with metal foam heat sinks for superior thermal management

Effective heat dissipation is essential for cooling power-intensive chips and densely packed electronics with significant heat output such as those used in data centers. This study explores the hybrid thermal management strategies that combine the use of impinging jet flow (IJF) and high-porosity metal foam (MF) heat sinks to enhance convective heat transfer. Unlike conventional jet cooling systems, the integration of metal foam introduces a highly conductive, porous medium that amplifies surface area, promotes flow mixing, and accelerates thermal diffusion — resulting in 1 - 2 times higher heat transfer compared to clear cases (CC). A 2D axisymmetric numerical model is developed in ANSYS Fluent using the Reynolds-Averaged Navier–Stokes (RANS) equations with the standard k-ε turbulence model. The setup features a single circular nozzle, aluminum target plate, aluminum metal foams with air as a cooling fluid. Key parameters including nozzle-to-plate spacing (IH = 2.8 – 8.5), Peclet number (Pe = 2200 – 17,000), and foam porosity (ε = 0.90 – 0.95) — are systematically varied. The findings indicate that minimizing porosity and internal heating (IH) substantially enhances heat transfer performance. A novel correlation for average Nusselt number (Nu ̅) has been developed, accompanied by an enhancement factor that quantifies the thermal improvement across the analyzed configurations. This work provides a robust framework for optimizing hybrid cooling systems and can be extended to explore alternative working fluids, foam geometries, and transient thermal loads in future studies

Promoting Inclusion by Bridging Gaps through “Story-Making:” The Fastweb Case

This chapter describes the case of Fastweb, one of the leading telecommunications operators in Italy, which has been repeatedly awarded for its commitment to DEI. Their chapter analyses, in particular, from a first-hand experience, the concept of “closing the gap” at the foundation of the brand identity by illustrating the example of the Punti Luce project and its “story-making” rationale

A line-search based SGD algorithm with Adaptive Importance Sampling

Stochastic Gradient methods are widely used in the field of supervised learning associated with big data. In this context, importance sampling-based algorithms have been proposed to minimize the variance of the stochastic gradient by introducing practical strategies to approximate the optimal sampling distribution, which is otherwise only theoretically accessible. In this paper, we propose a scheme that combines stochastic gradient descent with adaptive importance sampling with automatic step-size selection based on a stochastic Armijo-type line-search. This approach makes the method robust to the choice of the initial step-size, which would otherwise require a tuning phase that is computationally expensive or even impractical in certain big data scenarios. Moreover, we introduce different mini-batch variants to foster the practical acceleration of the original scheme. Finally, numerical experiments are presented on real datasets to validate the proposed method in the context of supervised classification problems

Steel exoskeletons for low-impact and Integrated seismic retrofit of existing buildings

A significant portion of Europe's building stock was constructed following World War II. The absence of updates and enhancements has left this constructed environment exceedingly vulnerable to seismic events and energy inefficient. Due to PNRR tax incentives, there is a distinct necessity to delineate and supply qualified professionals with integrated, efficient, prompt, and cost-effective retrofit solutions. These treatments ought to be formulated with a comprehensive approach and grounded in the principle of Life Cycle Thinking. Traditional seismic retrofitting is often hindered by high costs, long execution times requiring building halts, and environmental concerns stemming from excessive material usage. This thesis investigates modern, non-disruptive, and environmentally conscious alternatives, focusing on external steel frame structures, or exoskeletons, which facilitate an integrated approach to resilience and energy improvements. Exoskeletons leverage the simplicity of steel assembly to reduce costs, minimize operational disruptions, and improve structural resilience. They are also compatible with dissipative or self-centering devices for improved seismic performance. The integration of energy-dissipating devices allows for the absorption and dissipation of seismic energy via inelastic deformations, thereby decreasing stress on the existing reinforced concrete structure. Such a retrofit provides a precise strategy, preventing excessive strengthening and concentrating on seismic demands. This leads to lower loads for new foundations and minimal impact on the original building, potentially diminishing overall intervention costs. In this scenario, innovative devices have been selected that can dissipate energy resulting from seismic activity, as well as seek to improve the resilience of the structure through devices that allow the building to return to its plumb position. To achieve this objective, the devices analyzed within this thesis work are rectangular and hourglass-shaped Buckling-Restrained Aluminum Shear-Yielding Plates; Dumbbell-shaped Steel Slit Dampers; Shape-Memory-Alloy-based dampers. This thesis applies and sizes several exoskeleton systems for strategic RC structures. The investigated retrofit strategies, all characterized as steel exoskeletons, include Dissipative Eccentric Braced Frames, Self-Centering Concentrically Braced Frames, Self-Centering Dual-Rocking Frames, and Base-Rocking Dual-Core Frames. The design of these interventions utilizes the Displacement-based Design approach. This method features constraints that may emerge when employed to build a retrofit for an actual case study. This thesis addresses several issues arising from the retrofit system or the as-built structure. The considerations encompass the energy dissipation capacity of the installed device and the connection system between the structure and the exoskeleton, which must guarantee the complete transmission of stresses from seismic forces while preserving rigid behavior, including the validation of the initial rigid diaphragm assumption. The retrofit strategies investigated in the matter of this work were thought and designed for real-world strategic RC buildings case studies. Their capacity to handle seismic forces was assessed starting from the linear (eigenvalue) modal and nonlinear (pushover) static analyses by Eurocode 8. Based on collapse mechanisms of each structure, the target displacement was defined, which is useful for designing the various interventions. The effectiveness of the retrofit strategy and the design procedure have been assessed by nonlinear Time-History analyses under a set of earthquake-strong ground motions selected and scaled for the Collapse Prevention Limit State based on the spectrum-compatibility rules of the Italian Code (2018)

An algorithm for locally adaptive bivariate penalized splines

This paper introduces a penalized spline model for bivariate smoothing that locally adjusts to gridded data set affected by noise varying across different areas of the domain. As for all penalized spline methods, the approach requires the definition of suitable penalty terms and the selection of the regularization parameters. In our model, the regularization parameters are chosen anisotropically using a data-driven approach that adapts the roughness amount to the noise level. The proposed approach alternates the construction of univariate penalized spline based on B-spline basis functions along both coordinates, and uses a tensor product structure to capture interactions between the two dimensions. Numerical experiments confirm the efficacy of the approach, the anisotropy of the model, and the ability to locally adapt the amount of regression to different noise levels in different areas. The model is compared with two state-of-the-art smoothers, for which we also provide an original reformulation highlighting their construction as penalized splines