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MARTINA CASARI
Dottorando
Dipartimento di Ingegneria "Enzo Ferrari"
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Pubblicazioni
2024
- ANFIS_PM_adjustment: ANFIS low-cost PM adjustment
[Software]
Casari, Martina
abstract
MATLAB code implementing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to adjust low-cost PM (Particulate Matter) data. The code consists of several MATLAB scripts and functions that perform data preprocessing, ANFIS modelling, training, and evaluation.
2024
- MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors
[Articolo su rivista]
Casari, Martina; Po, Laura
abstract
2023
- AirMLP - SPS30 low-cost sensors and Tecora reference station PM 2.5 data
[Banca dati]
Casari, Martina; Po, Laura
abstract
Dataset related to a study conducted in Turin, Italy, involving low-cost laser-scattering SPS30 sensors placed by Wiseair SRL and a Tecora reference station placed by Arpa Piemonte (Italian Air Quality Agency). This dataset spans two different time periods in 2022, specifically from March 1, 2022, to April 29, 2022, and from October 26, 2022, to December 30, 2022. The data in this dataset pertains to the mass concentration of PM2.5 (particulate matter with a diameter of 2.5 micrometres or less).
2023
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin
[Articolo su rivista]
Casari, Martina; Po, Laura; Zini, Leonardo
abstract
2023
- AirMLP: PM 2.5 Correction MLP Network - Source Code v1.0.1
[Software]
Casari, Martina; Zini, Leonardo; Po, Laura
abstract
This repository houses a Multilayer Perceptron (MLP) network designed to correct PM 2.5 data affected by hygroscopicity, gathered from low-cost SPS30 sensors.
2023
- Mitigating the Impact of Humidity on Low-Cost PM Sensors
[Relazione in Atti di Convegno]
Casari, Martina; Po, Laura
abstract
This preliminary study, conducted in Italy, aims to investigate the potential of growth functions and multi-layer perceptron neural networks (MLP NN) in reducing the impact of humidity on low-cost particulate matter (PM) sensors, with a focus on the sustainability of low-cost sensors compared to reference stations. All over the world, low-cost sensors are increasingly being used for air quality monitoring due to their cost-effectiveness and portability. However, low-cost sensors are susceptible to high humidity, which can lead to inaccurate measurements due to their hygroscopic property. This issue is particularly relevant in Italy, where many cities such as Rome, Milan, Naples, and Turin experience high mean relative humidity levels (>70%) for most months of the year. To improve data quality and gain useful data for quantitative analysis, techniques must be developed to reduce the impact of humidity on the final data. The sensors used in this study were placed in proximity to a reference station, solely for validation purposes in the case of corrective functions and involved in the training phase in the case of MLP NN.
2021
- A Fast and Effective Method to Identify Relevant Sets of Variables in Complex Systems
[Articolo su rivista]
D’Addese, Gianluca; Casari, Martina; Serra, Roberto; Villani, Marco
abstract
In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case