Enrico Zero

This study analyzes the impact of socio-demographic factors on citizens’ perceptions of the functioning of local-level inclusion mechanisms, focusing on four dimensions: information, participation, transparency, and effectiveness. A mixed-methods approach is employed, combining: (1) a large-scale survey with 885 residents in three municipalities (Patos, Elbasan, and Mat) and (2) in-depth interviews with mayors, municipal councilors, and social service managers. The quantitative analysis was conducted through binary logistic regression models in SPSS version 27, as well as ordered logistic regression, examining the impact of socio-demographic factors such as age, education level, gender, employment status, and area of residence on the four dimensions of the study and the Inclusion Index. The qualitative component analyzes how local officials address citizen inclusion in key social policy areas such as employment, education, housing, social assistance, and social services. The results show that residence is the strongest predictor, with citizens in urban areas reporting higher levels of information, transparency, and effectiveness of participatory processes. Employment status is also associated with more positive perceptions, while gender and educational level show limited and inconsistent effects. Qualitative findings suggest that these differences are mediated by structural and institutional factors, such as infrastructure, administrative capacity and access to information. The study contributes to the literature on democratic innovation and participatory governance by showing that the impact of demographic factors on civic engagement is mediated by institutional and territorial conditions, particularly in developing countries.

Motivated by a traffic management application, this work proposes a novel robust optimal control framework for networks subject to disruptions. It minimizes deviations from a target vehicle configuration while enforcing bounds on the expected values of quadratic constraints including states and controls, and fulfilling balance equations constraints. The problem uses a recent Linear-Quadratic (LQ) framework with stochastic quadratic constraints. Validation via two case studies shows that the method, despite disruptions, maintains proximity to references and satisfies all constraints, outperforming a traditional LQ controller and a heuristic controller, both of which lack the stochastic constraints. Furthermore, offline verification checks the covariance matrices' asymptotic limits to guarantee bound compliance.

Anomaly detection is vital for sustainable petroleum transportation, as faults, or irregular routes cause fuel losses, ecological risks, and resource waste. This paper applies machine learning methods-Logistic Regression, SVM, Random Forest, XGBoost, k-Means, Isolation Forest, and Autoencoders-to detect anomalies in smart fuel distribution. Results on real data show that combining supervised and unsupervised approaches improves accuracy and reliability. To address limited labeled datasets, transfer learning is employed to enhance generalization and reduce data dependency. The proposed approach strengthens operational safety while advancing sustainability by minimizing fuel losses, preventing environmental harm, and promoting efficient resource use.

The Dangerous Goods Transportation (DGT) presents significant challenges, requiring a strong and systematic risk assessment framework to ensure the safety and efficiency of the supply chain. This study addresses a critical gap by integrating a deterministic and holistic approach to risk assessment and management. Utilizing Geographic Information Systems (GIS), meteorological data, and material-specific information, the research develops a data-driven approach to identify analyze, evaluate, and mitigate risks associated with DGT. The main objectives include monitoring dangerous goods flows to identify critical risk areas, optimizing emergency response using a shared model, and providing targeted training for stakeholders involved in DGT. The study leverages Information and Communication Technologies (ICT) to systematically collect, interpret, and evaluate data, producing detailed risk scenario maps. These maps are instrumental in identifying vulnerable areas, predicting potential accidents, and assessing the effectiveness of risk management strategies. This work introduces an innovative GIS-based risk assessment model that combines static and dynamic data to address various aspects of DGT, including hazard identification, accident prevention, and real-time decision support. The results contribute to enhancing safety protocols and provide actionable insights for policymakers and practitioners aiming to improve the resilience of technological systems for road transport networks handling dangerous goods.

This article introduces a digital twin framework for greenhouse climate management, utilizing model predictive control (MPC) to enhance smart farming practices and tackle the challenge of maintaining optimal conditions in diverse weather scenarios. The proposed MPC integrates dynamic system modeling, predictive control, and constraint management to regulate critical variables such as ambient temperature, solar radiation, and airflow. To validate the control framework, historical meteorological data from the city of Savona are used for four seasonal scenarios: winter, spring, summer, and autumn. The main contributions include designing and tuning the MPC, applying soft constraints via equal concern for relaxation for feasibility, and implementing scaling factors to ensure numerical stability. Simulation results demonstrate the controller’s ability to maintain desired climatic conditions while meeting physical and operational constraints. This work highlights the potential of combining digital twin technology with advanced control systems to support sustainable and efficient smart farming.