Giuseppe Aiello

Bio-based phase change materials (PCMs) can increase transient heat storage in lightweight building envelopes, but their performance depends on the climate, transition properties, layer design, and assumptions used to translate thermal loads into carbon and cost indicators. Although PCM optimization, machine learning surrogates, and lifecycle assessment have each been studied extensively, comparatively few studies combine them while explicitly separating simulation-derived thermal outputs from scenario-dependent environmental and economic post-processing and benchmarking bio-based candidates against paraffin on a common wall area basis. This study develops a simulation-based screening framework for a south-facing office wall model using 18,000 EnergyPlus cases, climate-specific machine learning surrogates, TreeSHAP interpretation, NSGA-II optimization, and scenario-based lifecycle carbon and cost accounting. XGBoost achieved pooled held-out R² values of 0.974 for occupied discomfort degree-hours and 0.978 for total annual thermal demand. For Palermo, the directly re-simulated balanced configuration (T_m = 24.8 °C, L_h = 178 kJ/kg, 22 mm thickness, intermediate position) reduced occupant discomfort by 42.6% and the modeled single-zone total thermal demand by 6.0%. Under the central all-electric scenario (SCOP = SEER = 3.0, grid factor = 0.233 kg CO₂eq/kWh, 25 years), scenario-based net lifecycle carbon was −22.1 kg CO₂eq/m² for the analyzed south wall with an 8.0-year environmental payback, compared with −7.4 kg CO₂eq/m² and 19.2 years for RT28 paraffin. Energy savings did not recover the additional investment; the incremental lifecycle cost was +24.1 EUR/m². The theoretical contribution is a transparent, climate-dependent screening logic that couples surrogate interpretation with explicit evidence boundaries; the applied outcome is a palmitic–capric target-property region prioritized for laboratory validation rather than a deployment-ready product. Only directly re-simulated configurations are used for quantitative applied thermal claims; surrogate-only Pareto points are retained as exploratory screening candidates and are not interpreted as validated optima. The numerical results are specific to the modeled south-wall, single-zone boundary; whole-building and cross-regional application requires local recalibration and direct validation. By linking passive comfort, carbon accounting, material innovation, and responsible pre-experimental selection, the workflow is relevant to the decarbonization objectives represented by SDGs 7, 9, 11, 12, and 13.

This study presents a comprehensive assessment of the Italian risk model used during the COVID-19 pandemic to guide regional mobility restrictions through a colour-coded classification system. The research evaluates the variables selected by the Italian Ministry of Health and their effectiveness in supporting public health decision-making. The analysis adopts a statistical framework which combines data reduction and regression modelling techniques to enhance interpretability and predictive accuracy. Dimensionality reduction is applied to address multicollinearity and simplify complex variable structures, while an ordinal regression model is employed to investigate the relationship between the reduced set of variables and the colour regional classifications. Model performance is evaluated using classification error metrics, providing insights into the adequacy of the selected variables in explaining the decision-making process. Results reveal significant redundancy among the chosen variables, suggesting that excessive predictors may compromise information by reducing the efficiency and clarity of the model. To address this, the study proposes refined and robust predictive models for regional classification, and corresponding empirical distribution of the model parameters, offering a reliable tool of the proposed framework and to support public health decision-makers. By highlighting methodological improvements and offering a generalizable approach to multi-indicator risk classification, this study contributes to the ongoing development of quantitative tools for evidence-based policy-making. The findings provide insights that can inform regional health strategies and support more effective, data-driven decision-making during health crises. Although applied here to the Italian COVID-19 risk classification, our framework is a generalizable methodological approach that can be applied to any context where multiple indicators are aggregated into ordinal risk levels.

This study presents a comprehensive assessment of the Italian risk model used during the COVID-19 pandemic to guide regional mobility restrictions through a colour-coded classification system. The research focuses on evaluating the variables selected by the Italian Ministry of Health for this purpose and their effectiveness in supporting public health decision-making. The analysis adopts a statistical framework which combines data reduction and regression modelling techniques to enhance interpretability and predictive accuracy. Dimensionality reduction is applied to address multicollinearity and simplify complex variable structures, while an ordinal regression model is employed to investigate the relationship between the reduced set of variables and the colour regional classifications. Model performance is evaluated using classification error metrics, providing insights into the adequacy of the selected variables in explaining the decision-making process. Results reveal significant redundancy within the variables chosen by the Italian Ministry of Health, suggesting that excessive predictors may compromise information. To address this, the study proposes refined and robust predictive models for regional classification, offering a reliable tool of the proposed framework and to support public health decision-makers. This study contributes to the ongoing development of quantitative methodologies aimed at improving the effectiveness of statistical models in guiding public health policies. The findings offer valuable insights for refining data-driven decision-making processes during health crises and improving the quality of information available to policymakers.

Despite their substantial economic and financial burden, modern healthcare systems are frequently criticized for their poor efficiency and inclusivity, and substantial concerns arise about their capability to provide adequate responses to the population's needs in the near future. In such context, new digital healthcare models based on advanced bio-sensing technologies are regarded as a possible solution to improve the screening and prevention processes thus improving the overall performance of the healthcare supply chains. This is particularly relevant for respiratory diseases, which are currently among the first causes of death and medical expenditures in industrialized countries. In this research, a new approach to the testing and diagnosis of respiratory diseases is proposed as a new e-health model based on a fog-based architecture and a smart device. According to such a model, the fog-based system that allows for patients’ testing through the analysis of the exhaled breath is collected using a smart device based on a novel nanostructured sensor integrated into a breathing mask, and capable of transferring relevant patient information to a centralized decision-making process. The objective of this new healthcare model is thus to improve the diagnosis process of respiratory diseases by minimizing under-diagnosed or late-diagnosed cases. The practical relevance of the approach proposed is the possibility of enabling remote diagnostics of respiratory diseases without the need for skilled medical staff and bulky equipment only available in specialized labs, thus allowing for decentralized patient testing and self-testing. The original testing method and system proposed have been validated in the lab using samples extracted from real patients.

In the last decade, with the increased concerns about the global environment, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen-propelled vehicles. However, the lack of adequate decision support approach makes the introduction of new propulsion technologies in the transportation sector a complex strategic decision problem where distorted non-optimal decisions may easily result in long-term negative effects on the performance of logistic operators. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach and takes into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the inherent scenario uncertainty. The proposed approach, based on the TOPSIS model, involves a novel decision framework referred to as a generalized life cycle evaluation of the environmental and cost objectives, which is necessary when comparing green and traditional propulsion systems in a long-term perspective to avoid distorted decisions. Since the objective of the study is to provide a practical methodology to support strategic decisions, the framework proposed has been validated against a practical case referred to the strategic fleet renewal decision process. The results obtained demonstrate how the decision maker’s perception of the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.

In the last decade, with the increased concerns about the global environmental situation, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen propelled vehicles. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach, taking into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the uncertainty related to the technological scenario, in a generalized Life Cycle framework. The proposed approach, based on the TOPSIS model, demonstrates how the optimality of the choice is related to the different technological scenarios and to the risky attitude of the decision maker. The proposed methodology is finally validated against a practical case referred the strategic fleet renewal decision process, and the results obtained demonstrate how the decision maker’s perception about the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.