Compact Laser Communication Terminal for Small Satellites

The New Space Economy is booming, as more than 7000 small satellites will be launched in the next decade. Dominant applications such as telecommunication & Earth imaging will generate data volumes that are simply not manageable with classic RF telecom payloads. Optical communication is a crucial technology of the future commercial space assets, granting stand-alone satellites and constellations the ability to route in space and transmit to ground unprecedented amounts of data, with limited spacecraft’s complexity and cost

High Precision Dual-Stage Pointing Mechanism for Miniature Satellite Laser Communication Terminals

This paper presents an innovative mechatronic design of a high-accuracy pointing mechanism for orbital laser communication terminals. The system is based on a dual-stage architecture and is miniaturized to fit nanosatellite-class spacecraft, aiming to enable optical communication on small-size space platforms. The focus is on control design aspects and on the performance assessment of an experimental prototype under emulated external environmental disturbances

LaserCube optical communication terminal for nano and micro satellites

This paper presents the design and testing of LaserCube, a miniature optical communication terminal conceived for nano and microsatellites. The system architecture has been designed for both the downlink and intersatellite link version of the system. Then, a complete engineering model of LaserCube in its intersatellite link configuration has been developed and tested. It features (1) a dual stage pointing and tracking system based on a coarse pointing mechanism patented by Stellar Project, (2) an optical head with a full-duplex telecom channel with transmission and reception on the same wavelength for two-way links, (3) a transceiver section with telecom laser source and optical receiver and (4) the terminal control unit with onboard computer, actuator drivers and data interface. Experimental validation of the system is achieved through a laboratory simulation of an intersatellite link scenario with realistic dynamic disturbance coming from the host satellite attitude jitter

Trading-Off Machine Learning Algorithms towards Data-Driven Administrative-Socio-Economic Population Health Management

Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users of the healthcare systems. The early identification of this sub-population can really help to improve people’s quality of life and reduce healthcare costs. In this paper, we describe a population health management tool based on state-of-the-art intelligent algorithms, starting from administrative and socio-economic data, for the early identification of high-risk patients. The study refers to the population of the Local Health Unit of Central Tuscany in 2015, which amounts to 1,670,129 residents. After a trade-off on machine learning models and on input data, Random Forest applied to 1-year of historical data achieves the best results, outperforming state-of-the-art models. The most important variables for this model, in terms of mean minimal depth, accuracy decrease and Gini decrease, result to be age and some group of drugs, such as high-ceiling diuretics. Thanks to the low inference time and reduced memory usage, the resulting model allows for real-time risk prediction updates whenever new data become available, giving General Practitioners the possibility to early adopt personalised medicine

Prefronto-cerebellar transcranial direct current stimulation increases amplituded and decreases latency of P3b component in patients with euthymic bipolar disorder

INTRODUCTION: Neurocognitive impairments have been observed in patients with bipolar disorder (BD) even during the euthymic phase of the disease, potentially representing trait-associated rather than state-associated characteristics of the disorder. In the present study, we used transcranial direct current stimulation (tDCS) applied to cerebellar and prefrontal cortices to improve the neurophysiological performances of patients with euthymic BD. METHODS: Twenty-five outpatients with BD underwent open-label prefrontocerebellar tDCS for 3 consecutive weeks. Neurophysiological performances were assessed through the examination of the P3b and P3a subcomponents of P300 event-related potential at baseline and after stimulation. RESULTS: Compared to baseline, P3b component after tDCS showed significantly higher amplitude and shorter latency (latency: Fz P=0.02, Cz P=0.03, and Pz P=0.04; amplitude: Fz P=0.24, Cz P=0.02, and Pz P=0.35). CONCLUSION: In our sample of patients with euthymic BD, concomitant prefrontoexcitatory and cerebellar-inhibitory modulations led to improved brain information processing stream. This improvement may at least partially result from neuroplastic modulation of prefrontocerebellar circuitry activity

Development of a multi-payload 2U CubeSat: the Alba project

Alba CubeSat UniPD is a student team of University of Padova with the aim to participate to the ESA Fly Your Satellite! (FYS!) programme and to launch for the first time at University of Padova a CubeSat made by students. The proposed mission has three independent objectives: (1) to collect in-situ measurements of the sub-mm space debris environment in LEO, (2) to study the micro-vibration environment on the satellite throughout different mission phases, (3) to do precise orbit determination through laser ranging and evaluate procedures for fast satellite Pointing, Acquisition and Tracking (PAT) from ground. The proposed technological experiments aim to obtain data that will enrich the current knowledge of the space environment and will provide precious information useful for the further development of some research projects currently performed at University of Padova. In order to reach the objectives, in these years the activities of the teams aimed to develop a 2U CubeSat equipped with three payloads. The first payload is an impact sensor that will be placed on one of the outer faces of the satellite and will be able to count the number of debris impacting the spacecraft thus being able to measure the energy/momentum transferred to the satellite. The second one is a Commercial Off The Shelf (COTS) sensor that measures the micro-vibrations experienced by payloads in a CubeSat in different mission phases. The third one consists in a number of COTS Corner Cube Retroreflectors that will be placed onboard the satellite. Thanks to this, Satellite Laser Ranging (SLR) will be done to collect data on the satellite range and range rate using a facility currently under development at University. This paper presents the mission objectives and motivations. In addition, the mission phases and the preliminary design of the CubeSat reached during the activities of the project are shown. Particular attention is given to the payloads which are the most challenging aspect of this project

Phosphate binding by a novel Zn(II) complex featuring a trans-1,2-diaminocyclohexane ligand. Effective anion recognition in water

Excellent affinities and selectivities toward triphosphates are achieved through an adaptive ditopic receptor featuring a metal ion and a macrocyclic polyammonium cation binding sites, concertedly bridging phosphate anions

Biomimetic Tweezers for N-Glycans: Selective Recognition of the Core GlcNAc(2) Disaccharide of the Sialylglycopeptide SGP

In recent years, glycomics have shown how pervasive the role of carbohydrates in biological systems is and how chemical tools are essential to investigate glycan function and modulate carbohydrate-mediated processes. Biomimetic receptors for carbohydrates can carry out this task but, although significant affinities and selectivities toward simple saccharides have been achieved, targeting complex glycoconjugates remains a goal yet unattained. In this work we report the unprecedented recognition of a complex biantennary sialylglycopeptide (SGP) by a tweezers-shaped biomimetic receptor, which selectively binds to the core GlcNAc2 disaccharide of the N-glycan with an affinity of 170 μM. Because of the simple structure and the remarkable binding ability, this biomimetic receptor can represent a versatile tool for glycoscience, opening the way to useful applications.We thank MIUR-Italy “Progetto Dipartimenti di Eccellenza 2018–2022” allocated to Department of Chemistry Ugo Schiff, COST Action (CA18132), MIUR-Italy PRIN2017 (2017XZ2ZBK) for granting a fellowship to F.M. and Ente Cassa di Risparmio di Firenze (Italy) is acknowledged for granting an ITC nanocalorimeter and a high-field NMR spectrometer. Open Access funding provided by Università degli Studi di Firenze within the CRUI-CARE Agreement

Incidenza della sclerosi multipla in Toscana: uno studio basato su dati amministrativi

INTRODUZIONE L’Italia è un’area ad elevato rischio di sclerosi multipla (SM) con una prevalenza stimata di 110.000 casi e un’incidenza di 3.400 casi annui [1]. Gli ultimi dati pubblicati sulla prevalenza sono 149 casi su 100.000 a Genova nel 2007 [2], 140 casi a Padova nel 2009 [3] e 210 nella parte meridionale della Sardegna nel 2007 [4]. Per quanto riguarda l’incidenza, i dati più recenti sono 5,5 casi su 100.000 a Padova nel periodo 2000-09 [3], 6,6 a Genova nel 1998-2007 [2], e 9,7 in Sardegna nel 2003-07 [4]. Dallo scorso anno è stato attivato in Italia un registro nazionale di SM il quale rappresenterà, nel prossimo futuro, un valido strumento per lo studio dell’epidemiologia di questa malattia. Anche in Toscana è presente, dal 2006, un registro regionale della SM ma, al momento, non è rappresentativo dell’intera popolazione di pazienti. Una possibile alternativa per studiare l’epidemiologia è attraverso i dati amministrativi. Questi, infatti, coprono l’intera popolazione residente e vengono raccolti di routine in un modo standardizzato ai fini della gestione del servizio sanitario. In un precedente lavoro, abbiamo creato e validato un algoritmo di cattura dei casi prevalenti basato su fonti amministrative [5]. La prevalenza, calcolata al 2011, è risultata pari a 188 casi per 100.000 [5]. Anche altre Regioni hanno utilizzato i dati amministrativi per stimare la prevalenza della SM, come il Lazio con 131 casi su 100.000 nel 2011 [6], la Puglia con 183 casi nel 2012, il Veneto con 170-180 casi nel 2015, la Sicilia con 110 casi nel 2010 e la Sardegna con 360 casi nel 2016 [1]. OBIETTIVI Calcolare l’incidenza della SM in Toscana utilizzando dati amministrativi. METODI Per il calcolo dell’incidenza abbiamo creato il seguente algoritmo: ospedalizzazione in reparto per acuti e con diagnosi primaria di SM, esenzione attiva per SM, e prescrizione di farmaci specifici. I casi incidenti sono stati identificati come quei casi catturati dall’algoritmo non tracciati in precedenza nei flussi amministrativi, e la data della prima traccia è stata considerata quale data di diagnosi della SM. Da questa coorte di soggetti abbiamo selezionato i pazienti con un’età ≤ 55 anni, residenti in Toscana al momento della diagnosi e presenti in anagrafe da almeno 10 anni (o nati in Toscana se età <10). Abbiamo calcolato i tassi grezzi e standardizzati e gli intervalli di confidenza (IC) al 95% per gli anni 2011-2015. RISULTATI Abbiamo identificato, dal 2011 al 2015, 1.056 nuovi casi in Toscana con un’incidenza che varia da 5,04 nel 2011 a 6,02 casi su 100.000 nel 2015 (Tabella 1). Nelle donne l’incidenza è circa due volte più alta rispetto agli uomini con un range che va da 6,48 nel 2011 a 7,96 su 100.000 nel 2015 nelle donne, e da 3,49 nel 2011 a 3,93 nel 2015 negli uomini (Tabella 2). Prendendo in considerazione l’ultimo anno di analisi (2015), abbiamo inoltre osservato delle differenze per ASL di residenza al momento della diagnosi, con aree in cui il tasso di incidenza è inferiore alla media regionale, come Grosseto (4,58), Pisa (4,33 casi/100.000), Siena (3,30), Lucca (3,07) e Viareggio (3,06), e aree in cui l’incidenza è più elevata rispetto alla media, come Empoli (7,99), Livorno (8,80) e Arezzo (9,78)

Increasing prevalence of multiple sclerosis in Tuscany: a study based on validated administrative data

AIMS Italy is a high-risk area for Multiple Sclerosis (MS) with a prevalence of around 140/105 (2009) with the exception of Sardinia, with about 224 cases/105 (2009). Nowadays, in Italy, prevalence is absolutely higher than the above estimates. Indeed, prevalence is rising due to annual incidence that is higher than annual mortality. In Tuscany a population MS register has been founded but, to date, it’s not yet completed. To monitor disease epidemiology, comorbidities and care pathways, but also to describe the disease burden and to plan its prevention, treatment and management strategies and resource allocation, population-based studies are preferable. Administrative data offer a unique opportunity for population-based prevalence study of chronic diseases such as MS. Our aim is to update the prevalence of MS in Tuscany and to demonstrate its progressive increment. METHODS The prevalence was calculated using a case-finding algorithm based on administrative data: hospitalization, specific MS drug dispensing, disease-specific exemptions from patient copayment, home and residential long-term care and inhabitant registry. To test algorithm sensitivity, we used a true-positive reference cohort of 302 MS patients from the Tuscan MS register. To test algorithm specificity, we used a general population cohort of 2,644,094 individuals who were presumably not affected by MS (who had never effectuated either cranial or spinal cord CT scan or MRI and had never received a neurological outpatient visit within the NHS). We calculated prevalence on three consecutive years (2011, 2012, 2013). RESULTS At prevalence date (31 December), we identified 6,890 cases in 2011, 7,057 in 2012 and 7,330 in 2013 with a rate of 187.9, 191.1 and 195.4/105, respectively. The female:male ratio slightly increased from 2.0 in 2011 to 2.1 in 2012-2013. The sensitivity of algorithm was 98% and its specificity was 99.99%. DISCUSSION We found a progressive increment of prevalence that confirmed our hypothesis of increasing prevalence. Although our validity study demonstrated a high level of sensibility, we could miss some patients, especially individuals with a severe MS, who did not access the healthcare system and who did not use the DMDs included in our algorithm. CONCLUSIONS We confirmed that Tuscany is a high-risk area for MS and that the prevalence is increasing over time. Despite some limitations, we also demonstrated that our algorithm can accurately identify patients and this cohort is suitable to monitor care pathways. Our future aim is to create an integrated dataset with administrative and clinical data from MS register