Search

Repository of the Francesca Manni thesis degree in Astronomy and Astrophysics, 2022/2023. This archive contains the Retrieval data of the orbits 1 (North and South pole), 4, 7, 8, and 26 orbits (South pole). The files contain the following parameters: Spectrum number, Name of the analyzed file, Latitude, Longitude, Solar Zenith angle, χ^2, Column density of CH_4, Error on the Column Density of CH_4, Temperature of the CH_4, Error on the Temperature of CH_4, Column Density on H_3^+, Error on the Column Density of H_3^+, Temperature of H_3^+, Error on the Temperature of H_3^+. Read with the program Fortran. The repository contains also the data to plot the images of the orbits 1, 4, 15 (North and South poles), 9 (North pole), 7, 8, 19, 20, 21, 22, 26, 31, and 36 (South pole) orbits and the routines of Spices metakernel. Read with the program Matlab. &nbsp

Federated Learning (FL) is a machine learning technique that enables to collaboratively learn valuable information across devices or sites without moving the data. In FL, the model is trained and shared across decentralized locations where data are privately owned. After local training, model updates are sent back to a central server, thus enabling access to distributed data on a large scale while maintaining privacy, security, and data access rights. Although FL is a well-studied topic, existing frameworks are still at an early stage of development. They encounter challenges with respect to scalability, data security, aggregation methodologies, data provenance, and production readiness. In this paper, we propose a novel FL framework that supports functionalities like scalable processing with respect of data, devices, sites and collaborators, monitoring services, privacy, and support for use cases. Furthermore, we integrate multi party computation (MPC) within the FL setup, preventing reverse engineering attacks. The proposed framework has been evaluated in diverse use cases both in cross-device and cross-silo settings. In the former case, in-device FL is leveraged in the context of an AI-driven internet of medical things (IoMT) environment. We demonstrate the framework suitability for a range of AI techniques while benchmarking with conventional centralized training. Furthermore, we prove the feasibility of developing a user-friendly pipeline that enables an efficient implementation of FL in diverse clinical use cases.

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis. In this proof-of-concept study, we evaluate diffuse reflectance spectroscopy (DRS) for discrimination between glial tumors and normal brain ex vivo. DRS spectra and histology were acquired from 22 tumor samples and nine brain tissue samples retrieved from 30 patients. The content of biological chromophores and scattering features were estimated by fitting a model derived from diffusion theory to the DRS spectra. DRS parameters differed significantly between tumor and normal brain tissue. Classification using random forest yielded a sensitivity and specificity for the detection of low-grade gliomas of 82.0% and 82.7%, respectively, and the area under curve (AUC) was 0.91. Applied in a hand-held probe or biopsy needle, DRS has the potential to provide intra-operative tissue analysis.

Fluorescent light modulation by small electric potentials has gained huge interest in the past few years. This phenomenon, called electrofluorochromism, is of the utmost importance for applications in optoelectronic devices. Huge efforts are being addressed to developing electrofluorochromic systems with improved performances. One of the most critical issue is their low cyclability, which hampers their widespread use. It mostly depends on the intrinsic reversibility of the electroactive/fluorophore molecular system and on device architecture. Here we show a novel fluorene-based mixed-valence electrofluorochromic system that allows direct electrofluorochromic switching and exhibits incomparable electrochemical reversibility and device cyclability of more than 10 000 cycles.

Surgical navigation systems can enhance surgeon vision and form a reliable image-guided tool for complex interventions as spinal surgery. The main prerequisite is successful patient tracking which implies optimal motion compensation. Nowadays, optical tracking systems can satisfy the need of detecting patient position during surgery, allowing navigation without the risk of damaging neurovascular structures. However, the spine is subject to vertebrae movements which can impact the accuracy of the system. The aim of this paper is to investigate the feasibility of a novel approach for offering a direct relationship to movements of the spinal vertebra during surgery. To this end, we detect and track patient spine features between different image views, captured by several optical cameras, for vertebrae rotation and displacement reconstruction. We analyze patient images acquired in a real surgical scenario by two gray-scale cameras, embedded in the flat-panel detector of the C-arm. Spine segmentation is performed and anatomical landmarks are designed and tracked between different views, while experimenting with several feature detection algorithms (e.g. SURF, MSER, etc.).The 3D positions for the matched features are reconstructed and the triangulation errors are computed for an accuracy assessment. The analysis of the triangulation accuracy reveals a mean error of 0.38~mm, which demonstrates the feasibility of spine tracking and strengthens the clinical application of optical imaging for spinal navigation.

A series of biarylaminofluorene-based systems with donor-π-donor (D-π-D) structure have been designed and synthesized in order to study the dependence on the π-conjugated bridge length of the intervalence charge-transfer transitions (IV-CT) and of the electronic coupling between the redox centers. To this purpose cyclic voltammetry, UV/Vis-NIR, fluorescence spectroscopy and computational investigations have been carried out to characterize the electronic structure of the compounds in the neutral as well as in the mono- and dication states. Additionally, a study of related D-π compounds has been performed to elucidate the effect of the interaction between two redox centers. Interestingly it was observed that the mono- and dication species exhibit intense transition bands in the NIR region, in the 10000-15000 cm−1 range, whose intensity depends on the oxidation state and thus it can be reversibly tuned by an applied potential. In a similar way, all compounds show an oxidation state dependent fluorescence which leads to electrofluorochromism. Particularly significant is the mixed valence behavior that provides these systems singular optoelectronic properties, making them excellent active components for electrochromic and electrofluorochromic applications.

Functional electrochromic materials that allow energy modulation both in the visible and in the near-infrared (NIR) spectral ranges are attracting increasing interest both for the fundamental scientific aspects related to their spectroelectrochemistry and for their technological applications. Vis-NIR dimmable windows based on these materials are very promising for tunable shading, thus allowing lighting and heat energy use saving. Organic mixed valence compounds (MVs) are an interesting class of small molecules with NIR electrochromism arising from optically induced intervalence charge transfer transitions (IVCT). Here, we report the synthesis and vis-NIR electrochromic properties of new organic mixed valence systems, with two and three amino redox centers bridged by a dibenzofulvene (DBF) unit. We studied the neutral and charged state characteristics of these MVs in solution by spectroelectrochemical experiments, theoretical TD-DFT investigations, and, in the solid state, through electrochromic devices (...

In this work, the fluorenone molecule was symmetrically difunctionalised in the 3,6-positions with four different arylamine moieties. Using slow evaporation, the four fluorenone derivatives (FO1-4) exhibit good ability to arrange into microstructures in the solid state. The electronic and steric effects of arylamine substituents influence both optical features and aggregation processes, as evidenced by photophysical and XRD characterization. SEM investigations have shown that the four FO1-4 derivatives arrange in four different microstructures. More specifically, the self-assembled 1D microribbon – shaped structure of FO1 exhibited an excellent optical loss coefficient (α) as low as 0.006 dBμm−1, suggesting a potential use of these materials in the field of optical waveguide.

The main curative treatment for localized colon cancer is surgical resection. However when tumor residuals are left positive margins are found during the histological examinations and additional treatment is needed to inhibit recurrence. Hyperspectral imaging (HSI) can offer non-invasive surgical guidance with the potential of optimizing the surgical effectiveness. In this paper we investigate the capability of HSI for automated colon cancer detection in six ex-vivo specimens employing a spectral-spatial patch-based classification approach. The results demonstrate the feasibility in assessing the benign and malignant boundaries of the lesion with a sensitivity of 0.88 and specificity of 0.78. The results are compared with the state-of-the-art deep learning based approaches. The method with a new hybrid CNN outperforms the state-of the-art approaches (0.74 vs. 0.82 AUC). This study paves the way for further investigation towards improving surgical outcomes with HSI.