Environmental Impacts and Housing Deprivation: A Study of the Effects of Industrial Polluting Sites in the Italian Context

The disadvantaged populations often bear a disproportionate burden of environmental pollution, especially when air quality exceeds regulatory standards. This link is particularly pronounced in areas with housing deprivation. As deprivation levels rise, the number of people exposed to excessive pollution increases. The industrial sector is one of the major contributors to air pollution and greenhouse gas emissions. Given the close relationship between industrial sites and socioeconomic, environmental, and health factors in cities, analyzing the real estate market can provide valuable insights for guiding sustainable development strategies. The present research aims to determine if a correlation exists between residential property prices and polluting sites, by considering factors that could represent housing deprivation. By examining the type of relationship and changes in property prices, the study intends to inform decision-making on housing deprivation policies. Through a genetic algorithm (Multi-Case Strategy of Evolutionary Polynomial Regression) applied to a sample of a limited available sample of polluting sites in Italy, first results have been obtained, that align with empirical observations and local user expectations. The results highlight the importance of implementing effective housing deprivation policies that address the environmental impacts of polluting industrial sites on real estate market dynamics. The innovative contribution of this work lies in identifying critical ‘pollution-poverty’ areas that require urgent remedial action

Quantitative Analysis of Urban Heat Island and ISA Density Effects on Land Surface Temperature: A Remote Sensing Study Using Local Morphological Density Analysis and Hybrid Optimization Model

Urban Heat Island (UHI) phenomenon, driven by rapid urbanization and increased Impervious Surface Areas (ISA) density, is a significant challenge to sustainable urban development and climate resilience. A robust understanding of the relationship between ISA density and Land Surface Temperature (LST) is critical for mitigating UHI effects. A key component in UHI analysis is the accurate extraction of UHI zones, which depends on the chosen threshold values. Traditional approaches often rely on static methods to identify UHI thresholds and simple regression models, which may not fully capture the complex, nonlinear interactions between ISA density and LST. Therefore, this study introduces a hybrid optimization model that integrates Genetic Algorithm-Simulated Annealing with Generalized Additive Models to assess the impact of ISA density on LST and thus define UHI thresholds. The model is applied to Algiers (2012-2021) and Taranto (2000-2014) cities using ASTER data. A local morphological density analysis was employed to quantify ISA and green space densities. Findings reveal a distinct positive nonlinear relationship between the ISA density and LST, identifying critical thresholds beyond which temperature escalates sharply. UHI zones exhibited significantly higher mean LST values, reaching 42.11°C in Taranto and 38.38°C in Algiers. Moreover, green space density was found to mitigate LST, reinforcing the pivotal role of vegetation in urban climate regulation. The findings of this adaptive modeling framework highlight the necessity of sustainable urban planning and the potential of data-driven approaches for refining climate adaptation strategies

Enhancing Maintenance Operations in Industry 5.0: A Conceptual User Interface Design for Task Assignment

The Fifth Industrial Revolution, or Industry 5.0, fosters an innovative, resilient, competitive, and society-centered industry. This era emphasizes enhanced human-machine interactions, enabling individuals to manifest their creativity through personalized products and services. As smart factories evolve, the demand for flexibility and adaptability necessitates increased cognitive efforts, particularly in maintenance tasks critical to the flexibility of production systems. Despite the potential of emerging technologies like Augmented Reality and Artificial Intelligence to aid operators, the complexity of tasks combined with the novelty of such technologies can overwhelm workers, thereby impacting workplace well-being. To tackle these challenges, the DESDEMONA project, funded by the European Union through PRIN as part of NextGenerationEU, is developing a Decision Support System (DSS). This system aims to provide real-time suggestions for assigning the most suitable operators for maintenance tasks characterized by high cognitive demands. The DSS considers three primary factors: the operator’s profile (including skills and age), their emotional state, and the availability of smart devices. This manuscript details the project’s initial results, presenting a simplified mathematical model capable of ranking the optimal list of operators. To demonstrate the effectiveness of the DSS, it is compared, through a simulation approach, with a simulated maintenance supervisor. This comparison highlights the system’s ability to identify, from the k-permutations of N operators, the number of optimal tuples that best fit the operational needs

Optical synchronous signal demodulation-based quartz-enhanced photoacoustic spectroscopy for remote, multi-point methane detection in complex environments

We present a novel optical synchronized signal demodulation (OSSD) method applied in quartz-enhanced photoacoustic spectroscopy (QEPAS) for remote gas sensing. Using 1 % of the laser source as an optical synchronization signal, kilometer-scale remote gas detection was achieved, overcoming the challenges of long-distance real-time detection in complex environments with conventional QEPAS. A time-sharing OSSD-QEPAS system for sewer methane detection was subsequently developed. The system’s modulation depth was optimized, and the catalytic effect of water vapor on photoacoustic signals was validated, resulting in a CH4 sensor achieving a detection limit of 445 ppb with a 300-ms averaging time, and an excellent linear dynamic range with a R2 = 0.999. To demonstrate the stability, robustness, and accuracy of the OSSD-QEPAS system, continuous methane measurements covering a 14-hour period at two different sewer locations on campus were performed

A novel mode shape identification approach for structures having planes with rigid-like behavior

The identification of mode shapes of structures through Operational Modal Analysis (OMA) often requires the application of data merging techniques to compensate for the lack of information on mode shapes scaling factors, which is inherent in OMA. In this paper, we propose a novel mode shape identification approach for structures having planes with rigid- like behavior, such as steel or reinforced concrete buildings with rigid floors. The approach is based on a theoretical model that generalizes the mechanical features of the structures under considerations. We show that the mode shapes of the model can be reconstructed starting from two components, i.e., modal centers of rotation and modal rotations; modal rotations depend on scaling factors of mode shapes, while modal centers of rotation turn out to be invariant with respect to mode shape scaling. Afterwards, we develop a method for identifying modal centers of rotation and modal rotations from experimental data, and then for reconstructing mode shapes. Numerical experiments have been performed to assess the capability of the approach with respect to a structural specimen having known modal properties. Compared with classic merging techniques, our approach enables a significant simplification of the experimental setup and a deeper analysis of mode shapes

Cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies

Bridges and viaducts are crucial components of transportation network, and activities as risk assessment and structural maintenance are crucial to avoid economic losses and casualties due to their collapse. External actions, natural hazards, material degradation and aging affect safety and serviceability of bridges. The effort required by national guidelines, as the one issued in 2020 in Italy, pose questions about the resources required for large-scale screening and risk assessment of large bridge portfolios, mostly populated by simply supported concrete girder bridges, both reinforced and prestressed (PSC). The main challenge is to leverage cutting-edge technologies for a cost-effective near-continuous monitoring of structural risk of bridges. In the field of remote-sensing technologies, Interferometry via Synthetic Aperture Radar and Unmanned Aerial Vehicle photogrammetry are two emerging technologies that meet requirements to achieve the goal. Large coverage, adequate resolution and accuracy, and acquisition frequency are them strengthen points at the base of two proposed frameworks. The combination with basic structural knowledge of bridges and environmental data represents a promising mix for portfolio structural risk assessment of existing bridges and structures as demonstrated in the explored case studies. Although these technologies are affected by limitations, their constant use can help to identify bridges requiring a more refined structural risk assessment based on traditional structural health monitoring systems (i.e., sensor-based). Some bridge typologies as PSC box-girder bridges pose additional challenges requiring more effective strategies to monitor prestressing force reduction. Machine learning surrogate models as Artificial Neural Networks coupled with eXplainability approaches are demonstrated to be effective when integrated with traditional sensor-based systems. The experimental campaign on a scaled PSC box bridge is a great example of this combination. The cost-effective monitoring of structural risk of bridges supported by cutting-edge technologies is a true example of the ongoing change in civil engineering that will flourish in the years to come thanks to the technical and scientific progress

Three Albanian cultural centers in comparison under an acoustic perspective

After the World War II, Albania was governed by a dictatorship that lasted for about 50 years. The cultural life restarted to be one of the main centers of the society community. Many buildings styles reflected the influence of governors as leaders of countries, in combination with the spread of armed concrete used as the main material for new constructions, given its flexibility compared to brickwork that was not yet developed as it is nowadays. In Albania, many cultural centers were constructed for the local community where citizens can have access to libraries, coffee shops, and auditoria. These latest ones represent the places where live shows are performed, along with international conferences. This paper deals with the assessment of the acoustic response gathered within three auditoria as part of cultural centers in Albania. The acoustic measurements were carried out in accordance with ISO 3382-1. The acoustic response recorded inside the three case studies indicate a good listening condition for both speech and music performance. This outcome has been found in all three auditoria, despite the room volume between each other is different