Archivio della ricerca - Fondazione Bruno Kessler
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Mapping the Habitat Suitability of Culex pipiens in Europe Using Ensemble Bioclimatic Modelling
No full textAim
Anthropogenic pressure on natural ecosystems has profoundly influenced the dynamics of disease vectors, altering their distribution, phenology, and increasing the risk of vector-borne diseases. This study aims to investigate the climatic and environmental determinants of Culex pipiens distribution in continental Europe, to inform and support surveillance and mitigation strategies for vector-borne disease risk.
Location
Europe.
Time Period
2008–2018.
Major Taxa Studied
Culex pipiens (Diptera: Culicidae).
Methods
We used an ensemble species distribution modelling approach, integrating high-resolution occurrence data from entomological surveillance with a suite of bioclimatic, topographic and anthropogenic predictors. We acknowledged the potential sampling bias due to higher surveillance in more anthropised areas and addressed this limitation during both model calibration and validation.
Results
Imperviousness emerged as the most influential predictor of Cx. pipiens distribution, highlighting a strong association with human-modified, low-elevation areas. The ensemble modelling approach outperformed individual models in terms of predictive accuracy and spatial transferability.
Main Conclusions
These results emphasise the need to incorporate anthropogenic factors into disease vector distribution models to support evidence-based surveillance and control strategies, while also offering updated, robust and spatially explicit predictions of the habitat suitability for Cx. pipiens in Europe. Overall, this study highlights the role of human modification of the natural environment in shaping Cx. pipiens distribution, extending previous knowledge on the role of urban areas
RF-MEMS-controlled Minkowski fractal antenna for dual-band applications
No full textThis paper presents the design and analysis of a dual-band patch antenna that leverages the reconfigurability of Minkowski fractal geometry to achieve frequency agility. The proposed antenna incorporates Radio Frequency Micro-Electro-Mechanical Systems (RF-MEMS) switches to selectively activate different iterations of the Minkowski fractal, thereby enabling dynamic tuning of the operating frequency bands. This reconfiguration mechanism allows for effective miniaturization, particularly at lower frequencies, without altering the overall physical dimensions of the antenna. Each additional fractal iteration results in a shift of the resonance towards lower frequencies, a behavior that is exploited to maintain compactness while achieving dual-band functionality. The antenna has been specifically optimized to operate in the 1 GHz and 4 GHz frequency bands, and its performance has been validated through full-wave electromagnetic simulations using a commercial software tool. The resulting design is characterized by a compact footprint, structural simplicity, and low manufacturing cost, making it an excellent candidate for space-constrained platforms such as small satellites and CubeSats. Furthermore, the integration of RF-MEMS technology offers high precision in switching and improved reliability compared to traditional tuning elements, further enhancing the antenna’s applicability in modern reconfigurable communication systems
Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression
No full textBackground: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system. Aims: The main task is tailoring robust and consolidated machine learning models to detect which neuropsychological tests are more effective in predicting a patient's mental status. In a translational medicine perspective, such identification tool should find its place in the clinician's toolbox as a support throughout his daily diagnostic routine. A second objective involves predicting the patient's diagnosis based on the results of the cognitive assessment. Methods: 281 patients with MCI or dementia diagnosis were assessed through 14 commonly administered neuropsychological tests designed to evaluate different cognitive domains. A suite of machine learning models, trained on different subsets of data, was used to detect the most informative tests and to predict the patient's diagnosis. Two external validation datasets containing MMSE and FAB tests were involved in this second task. Results: The tests qualitatively and statistically associated to a cognitive decline are MMSE, FAB, BSTR, AM, and VSF, of which at least three were considered the most informative also by machine learning. 73% average accuracy was obtained in the diagnosis prediction on three subsets of original and external data. Discussion: Detecting the most informative tests could reduce the visits' time and prevent the cognitive assessment from being biased by external factors. Machine learning models' prediction represents a useful baseline for the clinician's actual diagnosis and a reliable insight into the future development of the patient's cognitive status
Isolated in connection. Un progetto di ricerca multidisciplinare sulla Silver Age e la sua cultura digitale
No full textInvestigating the New Ultracam Dragon Hybrid Aerial Mapping System
Get PDFHybrid airborne systems, integrating imaging and ranging sensors within the same platform, were introduced almost ten years on the market as the new frontier of aerial mapping. Recently Vexcel Imaging developed the ULTRACAM DRAGON 4.1 hybrid airborne system which integrates a Riegl LiDAR scanner, a multi-camera imaging system and a GNSS/IMU unit. This paper investigates the characteristics and quality of derivable geospatial products, (i) examining different aerial triangulation (AT) strategies for imagery data (leveraging hand-crafted and learning-based image matching approaches for tie point extraction and (ii) evaluating the benefits of the integration of LiDAR and imaging as complementary data in supporting the 3D reconstruction of different urban environments. Moreover, the work introduces a powerful measuring tool (MEASUREE) developed by AVT-Airborne System, designed for photogrammetric analyses of large aerial blocks composed of oblique imagery
A SPAD detection strategy based on the inter-arrival time of photons for enhanced long distance LiDAR applications
No full textIn this work, we present a detection strategy for Single Photon Avalanche Diode (SPAD)-based direct Time of Flight (d-ToF) depth measurement systems designed to improve the state of the art in terms of capability to withstand intense background light and thus increasing the maximum achievable measurement range. The proposed detection strategy differs with respect to existing approaches as it is based on an active search of the photon-related timestamp with the maximum likelihood of belonging to the reflected laser pulse. Designed to be implemented with an asynchronous SPAD driving scheme, the technique is based on the selection of the photon timestamp associated to the shortest inter-arrival time, therefore inherently maximizing the probability of detection of the correct ToF value. The technique is demonstrated by means of simulation results, based on a physical model for the computation of the optical power budget and a numerical Monte Carlo engine for the generation of the simulated train of timestamps. We consider a set of realistic parameters for a wide range, SPAD-based, d-ToF sensor for an autonomous driving scenario, showing an increase in the maximum achieved measurement range of up to +46% compared to a standard detection approach, under extremely challenging background illumination of ≈ 100 kiloLux
E-Voting with Confidence: Usability Challenges in Manual Integrity Checks
No full textWhen dealing with a digital service, it is often necessary to compare hashes manually to certify the integrity of data. This study evaluates two alternative encodings - emoji sequences and word sequences - to improve the usability of integrity checks. We conducted a web-based user study to assess participants’ ability to recognize attacks, collecting insights from both a pilot test and then a main test. Results show that users achieved over 70% accuracy in anomaly detection, demonstrating the potential of these encodings to enhance verifiability while maintaining usability
Flexible microfluidics-integrated electrochemical system for detection of tumor necrosis factor-alpha under continuous flow of sweat
No full textCytokines play a vital role in immune system signaling, making their detection crucial for continuous health monitoring. Among the various cytokines, tumor necrosis factor-alpha (TNF-α) stands out as a key regulator of the immune response. Notably, TNF-α can be detected in sweat at concentrations as low as pg/mL, with levels strongly correlated with those in blood. Despite its importance, sensitive, wearable, and continuous monitoring of TNF-α in sweat remains limited. To address this gap, this study presents a flexible electrochemical sensor integrated into a microfluidic system for the sensitive and selective detection of TNF-α under continuous sweat flow. First, we present the fabrication of two distinct, miniaturized designs of flexible screen-printed carbon three-electrode platforms, which are subsequently biofunctionalized with gold nanoparticles (AuNPs) coated with TNF-α-specific thiolated aptamers. Next, we compare the two geometrically distinct AuNP-aptamer-functionalized sensors, utilizing experimental and novel simulation-based characterization techniques. Finally, the sensors are integrated into a custom-built microfluidic system enabling the detection of TNF-α ranging from 0.2 to 1000 pg/mL under constant artificial sweat flow conditions, exhibiting high selectivity with negligible responses to non-specific analytes. These findings highlight the feasibility of integrating wearable cytokine sensors for detecting TNF-α under continuous sweat flow conditions, achieving clinically relevant sensitivity within the pg/mL range
Linking Player Types to User Experience: Considerations for the Design of a Platform for the Education on Sensitive Topics
No full textGamification has been widely applied in educational contexts to enhance students' motivation and engagement. Its effectiveness has been shown to vary based on individual user preferences, such as age, gender, and player type. This study investigates the relationship between player types and user experience in StandByMe, a gamified educational platform designed to raise awareness about gender-based violence. A total of 61 high school students used the platform for about 35 minutes and completed a user experience questionnaire and the Hexad scale for player types. Results indicate that Free spirits reported higher motivation and overall user experience, while Achievers exhibited lower engagement, possibly due to a lack of clearly structured challenges. Socializers and Disruptors showed no significant relation with user experience. Additionally, demographic factors such as age and gender were related to participants' fun levels and perception of challenge. These findings highlight how users' player type predicts the user experience of a gameful system and should be considered during the design phase. Future research should explore adaptive gamification approaches and specific design modifications to enhance user experience across all player types, both within the StandByMe platform and, more broadly, in gameful systems for the education of sensitive topics