The digital twin (DT) paradigm provides a purpose-built digital representation of a physical system. DTs are often composed by several interconnected models, which need to be specifically tailored to fit the DT purposes. The objective of this work is the development of an urban digital twin (UDT) for the city of Matera, following urban intelligence (UI) paradigm. The latter leverages the multi-disciplinary integration and optimization of the city systems and subsystems to develop purpose-driven DTs and support the decision-making process. The UDT is intended to support governance, stakeholders, and citizens, integrating morphological data for the city representation, reliable simulation tools and data-driven methods for the city state prediction (e.g., traffic, solar irradiation), optimization algorithms for planning and emergency response, sensors for vehicle and pedestrian traffic volumes and environmental monitoring (e.g., pollutant distributions), and a participatory data collection process from the citizens. A Data Lake is used to make available to the UDT modules the data produced by the morphological representation, simulations/data-driven methods, sensors, and the participatory data. The Data Lake provides a standardized approach for the aggregation and extraction of information. Finally, an Urban Sensing Engine supports the decision-making process with artificial intelligence forms of reasoning, to effectively combine the information produced by the UDT components.

Radio Frequency IDentification (RFID) technology and low power electronic sensors may be combined to build wireless passive sensors which might cover many applications ranging from Internet of Things to Healthcare. This work presents a multi-perspective overview of RFID-based sensor tags and then proceed to experimentally characterize the sensing capabilities of two commercial fully-passive RFID sensor tags for acceleration and relative humidity data acquisition, respectively. Measurements are compared with data acquired through commercial traditional non-RFID wired sensors

Oxidative stress is one of the main causes of cell damage, leading to the onset of several diseases, and antioxidants represent a barrier against the production of reactive species. Saliva is receiving increasing interest as a promising biofluid to study the onset of diseases and assess the overall health status of an individual. The antioxidant capacity of saliva can be a useful indicator of the health status of the oral cavity, and it is nowadays evaluated mainly through spectroscopic methods that rely on benchtop machines and liquid reagents. We developed a low-cost screen-printed sensor based on cerium oxide nanoparticles that can be used to assess the antioxidant capacity of biofluids as an alternative to traditional methods. The sensor development process was investigated via a quality-by-design approach to identify the most critical parameters of the process for further optimization. The sensor was tested in the detection of ascorbic acid, which is used as an equivalent in the assessment of overall antioxidant capacity. The LoDs ranged from 0.1147 to 0.3528 mM, while the recoveries varied from 80% to 121.1%, being therefore comparable with those of the golden standard SAT test, whose recovery value was 96.3%. Therefore, the sensor achieved a satisfactory sensitivity and linearity in the range of clinical interest for saliva and was validated against the state-of-the-art equipment for antioxidant capacity evaluation.

Band selection techniques are widely explored in spectroscopic analysis to enhance predictive performance. This study aims to inform the design of the spectral dispersion component of a low-cost smartphone microspectrometer by identifying the most informative bands within practical constraints on the number of bands and spectral range. The goal is to enable rapid, affordable, and accurate leaf Nitrogen (N) assessment as part of the DEMETRA project, which promotes methodological and technological innovation for smart nutrient estimation in agriculture. Three band selection methods were evaluated: Permutation Feature Importance (PFI), Recursive Feature Elimination (RFE), and Genetic Algorithms (GA), each integrated with machine learning regression models. Among these, GA proved most effective, outperforming models based on the full spectral range. The results highlight the potential of targeted band selection for cost-effective, in-field spectroscopic tools.