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Abstract Plasma wakefield acceleration revolutionized the field of particle accelerators by generating gigavolt-per-centimeter fields. To compete with conventional radio-frequency (RF) accelerators, plasma technology must demonstrate operation at high repetition rates, with a recent research showing feasibility at megahertz levels using an Argon source that recovered after about 60 ns. Here we report about a proof-of-principle experiment that demonstrates the recovery of a Hydrogen plasma at the sub-nanosecond timescale. The result is obtained with a pump-and-probe setup and has been characterized for a wide range of plasma densities. We observed that large plasma densities reestablish their initial state soon after the injection of the pump beam ( 13 ns). The results are supported with numerical simulations and represent a step forward for the next-generation of compact high-repetition rate accelerators.

Down-shifting (DS) has proven to be an effective technique to address thermalization losses in photovoltaic devices. It involves the modification of the incident solar spectrum by luminescent species to better suit solar cell’s optimal absorption regions. Despite the number of favorable characteristics of quantum dots (QDs) as DS materials, their incorporation into highly ordered close-packed films remains the major drawback of QD-based DS applications. This limitation can be overcome by the sequential integration of QDs into a multilayered film via the layer-by-layer assembly technique, that allows the precise control of composition and thickness. The experimental results discussed herein indicate that upon optimization employing a model-assisted design approach, the spectral response of a silicon solar cell benefits from the down-shifting and antireflection capabilities of the QD film, as evinced by their quantum efficiency. For the optimum design, the incorporation of QDs triggered an increment of 26.1% in the power conversion efficiency driven by an increase of 29.2% in the short circuit current density. The controlled assembly of QD multilayers offers unique opportunities in light harvesting for new hybrid photovoltaic technologies.

Wildfires are key not only to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by wildfires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean Basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time step, providing spatially and temporally explicit results through the k.LAB (Knowledge Laboratory) software. This software provides an environment for input data integration, combining methods and data such as geographic information systems, remote sensing and Bayesian network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (findable, accessible, interoperable and reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily have a high probability of fire occurrence in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aiding in landscape and fire risk management, under both current and climate change conditions.

The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle’s behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts.

Abstract Medical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.

Impulsive control systems have shown strong potential to represent and address challenging problems, especially in the biomedical field. In recent research, these problems have been tackled with advances in linear impulsive control systems. However, many biomedical applications are better described by nonlinear impulsive models, and therefore, it is necessary to develop analysis tools and control strategies in this context. In the literature, the main properties of nonlinear impulsive control systems have been fully understood, but there is no major development of control strategies. Particularly, there is no substantiation of model predictive control (MPC) strategies maintaining convexity of the optimization problem and closed-loop stability, and there is no control strategy to reduce the offset problem when there are parameter variations, which is a common situation in biological processes. Therefore, the main novelties of this paper are: (i) an MPC formulation extended to nonlinear impulsive systems that addresses non-zero tracking, (ii) the sufficient and necessary conditions to guarantee the stability of the closed-loop system at an equilibrium target, (iii) a comprehensive description of an offset-free MPC to handle low to moderate plant-model mismatches, (iv) the conditions to guarantee offset-free control. Finally, the MPC and offset-free MPC are tested to address the drug administration problem in two biomedical applications: oncolytic virus therapy, to regulate tumor dynamics using doses of oncolytic, and type 1 diabetes treatment, to regulate glycemia using insulin injections. Satisfactory results were obtained in simulation scenarios including parameter variations in nonlinear models that represent the corresponding dynamics.

Introduction Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by lack of self-regulation and deficits in organizing behaviors in response to emotional stimuli. Methylphenidate (MPH) is one of the most effective psychostimulant drugs for ADHD, however, a possible predictive utility of brain hemodynamic data related to MPH administration and its relation to clinical symptomatology is still not clear. To address these questions, we used Near Infrared Spectroscopy (NIRS) technology, a non-invasive optical technique that allows to investigate the effect of psychopharmacological treatment on cortical hemodynamics. Methods Twenty children with ADHD underwent a three-waves study and 25 healthy controls were recruited at W1. At W2 children with ADHD received first MPH administration and at W3 they reached the titration phase. At each phase children performed - during NIRS recording - an emotional continuous performance task with visual stimuli of different emotional content. Clinical data were also collected at W1 and W3. We investigated the relationship among the difference between NIRS activation at W2 and W1 (Delta1) and W3 and W2 (Delta2), for each subject, task condition and brain region. Lastly, we investigated correlations between the Delta1 and clinical symptomatology indexes at W1 and between Delta2 and clinical data at W3. Conclusions Our study results suggest that hemodynamic changes in right prefrontal region probably induced by first MPH administration could predict hemodynamic changes related to MPH titration phase. These biological indexes could be associated to clinical evidences related not only to core ADHD symptoms but also to affective correlates. Disclosure No significant relationships.

Introduction Attention Deficit / Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition characterized by inattention, motor hyperactivity and impulsivity. ADHD cognitive and behavioral presentation is characterized by a high heterogeneity (APA, 2013). Indeed, a complex diagnostic process, that considers several validated tools, is, to date, necessary. Objectives The main aim is to develop supervised machine learning (ML) algorithms that could be used to support the diagnostic process for ADHD, by identifying the most relevant features in discriminating between the presence or absence of the ADHD diagnosis in children. Methods We analyzed data from 342 children (Mean age: 8y 8m ± 1y; 61 F) referred for possible ADHD symptomatology. Assessments were performed by an expert clinician and through questionnaires: Social Responsiveness Scale (SRS), Child Behavior Checklist (CBCL), Conners Rating Scale for Parents (CPRS) and for Teachers (CTRS). Data were analyzed using a decision tree classifier and random forest algorithms. Results The decision tree model performed an accuracy of 0.71. The random forest model that was identified as the best tested, performed an accuracy of 0.77 (Figure 1) and it identified as most informative parent- and teacher-rated DSM-oriented ADHD symptoms (Figure 2). Figure 1: Random forest confusion matrix and statistics. Figure 2: Ranking of variables importance. Conclusions A random forest classifier could represent an effective algorithm to support the identification of ADHD children and to simplify the diagnostic process as an initial step. The use of supervised machine learning algorithms could be useful in helping the diagnostic process, highlighting the importance of a personalized medicine approach. Disclosure No significant relationships.

Abstract Proportional electroluminescence (EL) in noble gases is used in two-phase detectors for dark matter searches to record (in the gas phase) the ionization signal induced by particle scattering in the liquid phase. The “standard” EL mechanism is considered to be due to noble gas excimer emission in the vacuum ultraviolet (VUV). In addition, there are two alternative mechanisms, producing light in the visible and near infrared (NIR) ranges. The first is due to bremsstrahlung of electrons scattered on neutral atoms (“neutral bremsstrahlung”, NBrS). The second, responsible for electron avalanche scintillation in the NIR at higher electric fields, is due to transitions between excited atomic states. In this work, we have for the first time demonstrated two alternative techniques of the optical readout of two-phase argon detectors, in the visible and NIR range, using a silicon photomultiplier matrix and electroluminescence due to either neutral bremsstrahlung or avalanche scintillation. The amplitude yield and position resolution were measured for these readout techniques, which allowed to assess the detection threshold for electron and nuclear recoils in two-phase argon detectors for dark matter searches. To the best of our knowledge, this is the first practical application of the NBrS effect in detection science.

One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.

The impact of graphene oxide on hepatic functional cells represents a crucial evaluation step for its potential application in nanomedicine. Primary human hepatocytes are the gold standard for studying drug toxicity and metabolism; however, current technical limitations may slow down the large-scale diffusion of this cellular tool for in vitro investigations. To assess the potential hepatotoxicity of graphene oxide, we propose an alternative cell model, the second-generation upcyte® hepatocytes, which show metabolic and functional profiles akin to primary human hepatocytes. Cells were acutely exposed to sub-lethal concentrations of graphene oxide (≤80 μg/ml) for 24 h and stress-related cell responses (such as apoptosis, oxidative stress, and inflammatory response) were evaluated, along with a broad investigation of graphene oxide impact on specialized hepatic functions. Results show a mild activation of early apoptosis but not oxidative stress or inflammatory response in our cell model. Notably, while graphene oxide clearly impacted phase-I drug-metabolism enzymes (e.g., CYP3A4, CYP2C9) through the inhibition of gene expression and metabolic activity, conversely, no effect was observed for phase-II enzyme GST and phase-III efflux transporter ABCG2. The GO-induced impairment of CYP3A4 occurs concomitantly with the activation of an early acute-phase response, characterized by altered levels of gene expression and protein production of relevant acute-phase proteins (i.e., CRP, Albumin, TFR, TTR). These data suggest that graphene oxide induces an acute phase response, which is in line with recent in vivo findings. In conclusion, upcyte® hepatocytes appear a reliable in vitro model for assessing nanomaterial-induced hepatotoxicity, specifically showing that sub-lethal doses of graphene oxide have a negative impact on the specialized hepatic functions of these cells. The impairment of the cytochrome P450 system, along with the activation of an acute-phase response, may suggest potential detrimental consequences for human health, as altered detoxification from xenobiotics and drugs.

Abstract Background Obstructive sleep apnea (OSA) is associated with excessive daytime sleepiness (EDS), which may go undiagnosed and can significantly impair a patient’s health-related quality of life (HRQOL). This qualitative research examined timing and reasons patients sought medical care for their EDS and OSA symptoms, and the impact of EDS on HRQOL. Methods Focus groups were conducted in 3 US cities with 42 participants currently experiencing EDS with OSA. Transcripts were coded and analyzed using an adapted grounded theory approach common to qualitative research. Results Over three-fifths of study participants (n = 26, 62%) were currently using a positive airway pressure (PAP) or dental device; one-third (n = 14, 33%) had previously used a positive airway pressure (PAP) or dental device, and the remainder had either used another treatment (n = 1, 2%) or were treatment naïve (n = 1, 2%). Twenty-two participants (52%) reported experiencing OSA symptoms for ≥1 year, with an average duration of 11.4 (median 8.0, range 1–37) years before seeking medical attention. Several (n = 7, 32%) considered their symptoms to be “normal,” rather than signaling a serious medical condition. Thirty participants (71%) discussed their reasons for ultimately seeking medical attention, which included: input from spouse/partner, another family member, or friend (n = 20, 67%); their own concern about particular symptoms (n = 7, 23%); and/or falling asleep while driving (n = 5, 17%). For all 42 participants, HRQOL domains impacted by EDS included: physical health and functioning (n = 40, 95%); work productivity (n = 38, 90%); daily life functioning (n = 39, 93%); cognition (n = 38, 90%); social life/relationships (n = 37, 88%); and emotions (n = 30, 71%). Conclusions Findings suggest that patients may be unaware that their symptoms could indicate OSA requiring evaluation and treatment. Even following diagnosis, EDS associated with OSA can continue to substantially affect HRQOL and daily functioning. Further research is needed to address diagnostic delays and unmet treatment needs for patients with EDS associated with OSA.

El desarrollo del Páncreas Artificial, el cual consiste en la implementación de una bomba de infusión continua de insulina y de sensores para el monitoreo de glucosa en la sangre, es una prioridad para el tratamiento de personas con diabetes tipo 1. Para lograr la automatización completa de este proceso se han propuesto varias estrategias de control con el objetivo de mantener los niveles de glucosa en la sangre en el rango de normoglucemia. Entre las estrategias desarrolladas se destacan las basadas en control adaptable, para contrarrestar el efecto de las variaciones fisiológicas inherentes al paciente. Sin embargo, estas estrategias requieren la medición completa del estado, lo cual no es una tarea fácil debido al costo de los sensores y a la dificultad inherente en la medición directa de variables fisiológicas. Por lo tanto, en este trabajo se usa una estrategia de control adaptable con estabilidad garantizada para un modelo de control de insulina y se diseña un observador de estado no lineal por modos deslizantes, con el fin de estimar las variables de estado que requiere el controlador. Con el sistema completo control-observador se logran resultados satisfactorios al compensar la ingesta de alimentos y los errores entre los parámetros del modelo y del sujeto.

The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient’s response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.

To address the study of the deployment of publicly accessible Automated External Defibrillators (AED), Geomatics allows computing their limited area of effectiveness (i.e. ‘catchment area’, CA), traditionally set as circular surfaces with a 100m-radius. Exploiting open geospatial data related to roads network, also ‘realistic’ CAs, based on the effective walking distance, can be computed. Aim of this study (performed on the territory of Lombardy, Italy, total surface 23,863.65 km2, with open source software as QGIS, PostGIS, pgRouting) was to compare the two approaches, and to evaluate if the territory analysis could support case-by-case decision-making about the preferable mapping technique.Setting a limit of 200 m, realistic CAs were computed for 7702 known AEDs on the territory (at 28/02/2018). The mean area obtained resulted close to that of the traditional 100m-radius circular area (33,665m2 against 31,415m2), but the spatial coverage of 45043 OHCAs - Out-of-Hospital Cardiac Arrests (Lombardy, 1/1/2015 to 31/12/2018) is very different considering realistic or circular areas (15.35% vs 9.43%). The distribution of the mapping error (realistic-CA – circular-CA) and the computation failures of realistic areas were studied and correlated with the characteristics of the surrounding territory considering attributes related to streets, buildings, and land-use, computing linear correlation coefficients and performing Mann-Whitney U-tests. Results suggest that realistic CAs are not always correctly computable and circular areas are preferable when AEDs are far from the streets in less urbanized and more uniform territories. An automatized decision-making about the best case-by-case mapping technique is therefore feasible with open data and open source software.

The European Union and the European Space Agency (EU/ESA) have promoted since 1998 (Baveno Manifesto*) the GMES Programme (Global Monitoring for Environment and Security), nowadays called Copernicus (www.copernicus.eu). In the agriculture domain, the use of Copernicus Sentinel imagery and its services are providing several new opportunities. The knowledge of fundamentals of Earth Observation/Geographic Information EO/GI, namely Geomatics, for the development of innovative strategies for professional skills adequacy and capacity building, supporting Copernicus user uptake, becomes mandatory (Gomarasca, 2009). The target is to help bridging gaps between supply and demand of education and training for geospatial sector (www.eo4geo.eu). The innovative and strategical novelties are the complete free access to Sentinel time series imagery and digital image processing software “Sentinel toolboxes” such as SNAP (Sentinel Application Platform) for different environments (Windows, Mac, Unix). The paper introduce topics as crop mapping and monitoring, biophysical parameters, phenology and yield estimations, through several concluded or ongoing international projects such as: ERMES -FP7 (http://www.ermes-fp7space.eu/it/homepage/, Busetto et al. 2017) and SATURNO (https://www.progettosaturno.it/, Nutini et al., 2018) devoted to the regional agricultural monitoring. As conclusion, SNAP software for image processing of Sentinel data was demonstrated and tested together with Earth Engine software for specific vertical agriculture applications. The topics reported in this paper have been part of the Summer School ‘Sentinel for Applications in Agriculture’ supported by the Copernicus programme, several scientific associations (AIT, ASITA, EARSeL - European Association of Remote Sensing Laboratories), the European Erasmus+ project EO4GEO, University Departments and Geo-Information Companies.

Phononic Crystals (PnCs) are novel synthetic periodic materials for controlling and manipulating the propagation of elastic (or acoustic) waves. In this work, we have considered systems formed by three-dimensional (3D) acoustic rectangular waveguides, which have internal periodic structures. Also, the case of a two-dimensional (2D) rectangular waveguide with a periodic array of circular cylindrical inclusions is exhibited, to carry out a comparative study with the 3D case. Integral numerical methods used to obtain phononic band structures involve a Boundary Element Method, which requires the use of periodic Green’s functions. An interesting detail that resulted from this study was the appearance of band gaps of the proposed structure for different values of the filling fractions. The system considered constitutes itself a PnC whose band structure corresponds in many ways to a conventional one-dimensional PnC. This fact is an alternative to manufacture a PnC that exhibits a double function: acting as a PnC and as an acoustic waveguide. The main result of this work is that discrete modes for some frequency range were obtained, which indicates that Phononic Crystal Waveguides (PnCWs) considered act as unimodal filters when the filling fraction is large. These properties exhibit some interest from a technological point of view. Keywords: Phononic Crystal Waveguide, Periodic Green’s Function Method, Band gap, Discrete mode

This research demonstrates the use of an unmanned aerial vehicle (UAV) to characterize the gaseous (CO2) and particle (10–500 nm) emissions of a ship at sea. The field study was part of the research voyage “The Great Barrier Reef as a significant source of climatically relevant aerosol particles” on board the RV Investigator around the Australian Great Barrier Reef. Measurements of the RV Investigator exhaust plume were carried out while the ship was operating at sea, at a steady engine load of 30 %. The UAV system was flown autonomously using several different programmed paths. These incorporated different altitudes and distances behind the ship in order to investigate the optimal position to capture the ship plume. Five flights were performed, providing a total of 27 horizontal transects perpendicular to the ship exhaust plume. Results show that the most appropriate altitude and distance to effectively capture the plume was 25 m a.s.l. and 20 m downwind. Particle number emission factors (EFPNs) were calculated in terms of number of particles emitted (no.) per weight of fuel consumed (kgfuel). Fuel consumption was calculated using the simultaneous measurements of plume CO2 concentration. The calculated EFPN was 7.6±1.4×1015no. kgfuel-1 which is in line with those reported in the literature for ship emissions ranging from 0.2 to 6.2×1016 no. kgfuel-1. This UAV system successfully assessed ship emissions to derive EFPN under real world conditions. This is significant as it provides a novel, relatively inexpensive and accessible way to assess ship EFPN at sea.

El objetivo del estudio fue evaluar filtros domiciliarios para remover arsénico presente en agua proveniente del río Sama. El sistema estuvo compuesto de un filtro tradicional de arena y un filtro con el material adsorbente de hierro cerovalente estabilizado en quitosano. La evaluación del sistema fue por un período de 12 semanas en 18 puntos de uso; las primeras ocho semanas se determinó la concentración de arsénico del agua filtrada empleando tiras reactivas, y las cuatro últimas fueron a través de espectrometría de absorción atómica (EAA-FIAS). Durante las primeras ocho semanas se redujo la concentración de arsénico en el rango de 0,005 y 0,025 mg/L, y durante las cuatro últimas en el rango de 0,001 y 0,052 mg/L, a partir de una concentración promedio de 0,51 mg/L. Se demostró que el sistema de filtración es eficiente para remover arsénico a condiciones naturales.

Introduction Epigenetics hypothesizes a crucial link between postnatal risk factors, individual response to stress, DNA methylation and psychiatric symptomatology changes during life. Objectives We analyzed methylation within two gene exons: NR3C1 and SLC6A4, which are involved in responses to environmental stressors. We investigated the relationship between methylation, postnatal risk factors and psychopathology assessed by Child Behavior Checklist (CBCL) in our help-seeking sample evaluated in infancy (W1), preadolescence (W2) and adult life (W3). Methods Postnatal risk factors data were collected at W1 in 205 clinical subjects (156 M, 49 F; age=9,13±1,95). The CBCL scores were collected at W1 and W2 (W2 age=14,52±2,12). Data regarding methylation were collected at W2. At W3 we are also collecting clinical scores. A Spearman correlation coefficient was calculated between methylation percentage and clinical data at W2. The externalizing and internalizing trajectories were evaluated through repeated measure ANOVA with postnatal risk factors (presence/absence) as between-groups factor. Results Significant associations were found between methylation and internalizing and total clinical scores (Table 1). The rm-ANOVA results showed a significant interaction between the CBCL internalizing score and presence/absence of postnatal risk, with higher internalizing problems in subjects that were exposed to postnatal risk factors. This effect was significant at W2 but not at W1 (Figure 1). Conclusions Psychopathological symptoms trajectories could depend on epigenetics and early environmental risk factors. Further analyses will address a Linear Discriminant Analysis to proceed to a machine learning oriented approach. Disclosure No significant relationships.

Introduction Our 15-years follow-up ReMIND project aims to re-assess an epidemiological and a clinical sample of adults (Wave 3), who were assessed in preadolescence (Wave 1) and adolescence (Wave 2), to evaluate symptoms trajectories and their relationship with genetic/epigenetic data, environmental risk factors and neuroimaging measures. Objectives Here, we depict preliminary results regarding the epidemiological sample. Methods We assessed internalizing and externalizing symptoms in 40 italian subjects (25 F) from general population at three waves (W1 mean age: 12±0,82; W2 mean age: 17±0,88, W3 mean age: 28±1), through the Child Behavior Checklist (W1 and W2) or the Adult Self Report (W3), and perinatal risk factors through a socio-anamnestic questionnaire, by a new online platform (MedicalBit). We analyzed symptoms trajectories and their relation with perinatal risk factors through a repeated measures multivariate analysis of variance (rm-MANOVA). Results rm-MANOVA results show that high number of perinatal risks was significantly associated with higher internalizing symptomatology in preadolescence but not in adolescence and adult life. The mean difference was 8 T-points. The same trend is evident in adolescence but not in adult age (Graph 1). Perinatal risk factors did not have a significant effect on externalizing symptoms at any time point, despite a non-significant trend is evident (Graph 2). Conclusions Our preliminary results suggest a trend of increased internalizing symptoms from childhood to adulthood and a significant role of perinatal risk factors in pre-adolescence. Further investigations are necessary to better understand symptoms trajectories and the role of biological and environmental factors. Disclosure No significant relationships.

Current technological advances have brought closer to reality the project of a safe, portable, and efficient artificial pancreas for people with type 1 diabetes (T1D). Among the developed control strategies for T1D, model predictive control (MPC) has been emphasized in literature as a promising control for glucose regulation. However, these control strategies are commonly designed in a computer environment, regardless of the limitations of a portable device. In this paper, the performances of six embedded platforms and three open-source optimization solver algorithms are assessed for T1D treatment. Their advantages and limitations are clarified using four MPC formulations of increasing complexity and a hardware-in-the-loop methodology to evaluate glucose control in virtual adult subjects. The performance comparison includes the execution time, the difference concerning the evolution obtained in MATLAB, the processor temperature, energy consumption, time percentage in normoglycemia, and the number of hypo- and hyperglycemic events. Results show that Quadprog is the package that faithfully follows the results obtained with control strategies designed and tuned on a computer with the MATLAB software. In addition, the Raspberry Pi 3 and the Tinker Board S embedded systems present the appropriate characteristics to be implemented as portable devices in the artificial pancreas application according to the criteria set out in this work.

Oncolytic virus therapy aims to eradicate tumours using viruses which only infect and destroy targeted tumour cells. It is urgent to improve understanding and outcomes of this promising cancer treatment because oncolytic virus therapy could provide sensible solutions for many patients with cancer. Recently, mathematical modelling of oncolytic virus therapy was used to study different treatment protocols for treating breast cancer cells with genetically engineered adenoviruses. Indeed, it is currently challenging to elucidate the number, the schedule, and the dosage of viral injections to achieve tumour regression at a desired level and within a desired time frame. Here, we apply control theory to this model to advance the analysis of oncolytic virus therapy. The control analysis of the model suggests that at least three viral injections are required to control and reduce the tumour from any initial size to a therapeutic target. In addition, we present an impulsive control strategy with an integral action and a state feedback control which achieves tumour regression for different schedule of injections. When oncolytic virus therapy is evaluated in silico using this feedback control of the tumour, the controller automatically tunes the dose of viral injections to improve tumour regression and to provide some robustness to uncertainty in biological rates. Feedback control shows the potential to deliver efficient and personalized dose of viral injections to achieve tumour regression better than the ones obtained by former protocols. The control strategy has been evaluated in silico with parameters that represent five nude mice from a previous experimental work. Together, our findings suggest theoretical and practical benefits by applying control theory to oncolytic virus therapy.

Next-generation plasma-based accelerators can push electron beams to GeV energies within centimeter distances. The plasma, excited by a driver pulse, is indeed able to sustain huge electric fields that can efficiently accelerate a trailing witness bunch, which was experimentally demonstrated on multiple occasions. Thus, the main focus of the current research is being shifted towards achieving a high quality of the beam after the plasma acceleration. In this paper we present a beam-driven plasma wakefield acceleration experiment, where initially preformed high-quality witness beam was accelerated inside the plasma and characterized. In this experiment the witness beam quality after the acceleration was maintained on high level, with 0.2% final energy spread and 3.8 μm resulting normalized transverse emittance after the acceleration. In this article, for the first time to our knowledge, the emittance of the plasma wakefield accelerated beam was directly measured.

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

We characterized the single-photon timing response function of various silicon photomultipliers (SiPMs) over a broad (500-1100 nm) spectral range. We selected two SiPM manufacturers, and we investigated two active areas, i.e., a small (1-1.69 mm2) and a large (9 mm2) one, for each of them. We demonstrate that selected SiPMs are suitable for time-resolved diffuse optics (DO) applications where a very large detection area and sensitivity down to single photons are crucial to detecting the very faint return signal from biological tissues, like the brain, thus allowing replacement of photomultiplier tubes and opening the way to a novel generation of DO multichannel instrumentation. Due to our custom front-end electronics, we show the world's best single-photon timing resolution for SiPMs, namely, 57-ps full-width at half maximum for Hamamatsu 1.69 mm2 and 115 ps for Excelitas 9 mm2. Even further, we provide a thorough spectral investigation of the full single-photon timing response function, also detailing diffusion tails' time constants and dynamic range. The achieved insight and the reported performance open the way to a widespread diffusion of SiPMs not just in many-photon regimes (e.g., PET) but at single-photon counting regimes like DO as well.

Triple-negative breast cancer (TNBC) shows a very bad prognosis, even in early stages of disease. Metronomic chemotherapy refers to the minimum biologically effective dose of a chemotherapy agent given as a continuous dosing regimen with no prolonged drug-free breaks that leads to antitumor activity. In the present article, we review preclinical and clinical data of metronomic administration of chemotherapy agents with or without biological agents in TNBC cell lines and patients, contextually reporting data from the VICTOR-2 study in the subgroup of patients with TNBC, in order to stimulate new ideas for the design of clinical trials in this subset of patients.

In the present article, a review of the complete characterization in different aggressive media of a Ti-6Al-4V titanium alloy, performed by the Structural Mechanics Laboratory of the University of Bergamo, is presented. The light alloy has been investigated in terms of corrosion fatigue, by axial fatigue testing (R = 0.1) of smooth and notched flat dogbone specimens in laboratory air, 3.5% wt. NaCl–water mixture and methanol–water mixture at different concentrations. The first corrosive medium reproduced a marine environment, while the latter was used as a reference aggressive environment. Results showed that a certain corrosion fatigue resistance is found in a salt water medium, while the methanol environment caused a significant drop – from 23% to 55% in terms of limiting stress reduction – of the fatigue resistance of the Ti-6Al-4V alloy, even for a solution containing 5% of methanol. A Stress Corrosion Cracking (SCC) experimental campaign at different methanol concentrations has been conducted over slightly notched dog-bone specimens (Kt = 1.18), to characterize the corrosion resistance of the alloy under quasi-static load conditions. Finally, crack propagation models have been implemented to predict the crack propagation rates for smooth specimens, by using Paris, Walker and Kato-Deng-Inoue-Takatsu propagation formulae. The different outcomes from the forecasting numerical models were compared with experimental results, proposing modeling procedures for the numerical simulation of fatigue behavior of a Ti-6Al-4V alloy.

Objetivou-se com este trabalho quantificar a composição mineral das folhas de Oliveira a fim de diagnosticar o estado nutricional das plantas cultivadas em sistema convencional. O experimento foi conduzido com sete cultivares Arbequina, MGS Asc 315, MGS Mariense, MGS Grap 541, MGS Grap 561, Grappolo 575 e Leccino, em blocos casualizados com 4 repetições e parcelas com cinco plantas. Em maio de 2010 foram coletadas 60 folhas de cada cultivar (12 por planta) e submetidas a analise foliar. As cultivares apresentaram diferenças significativas entre si quanto aos teores foliares médios de nutrientes. Observou-se também que apesar da analise de solo apontar níveis elevados de Fe e Mn, a análise foliar acusou deficiência para esses elementos em todas as cultivares de Oliveira avaliadas. Os resultados alertam para os altos níveis de Cu, como consequência do uso abusivo de fungicidas a base de oxicloreto e calda bordalesa nos pomares. A concentração elevada desse elemento nas folhas pode causar fitotoxidez às plantas. Desta forma, sugere-se o uso racional de fertilizantes e de produtos fitossanitários no cultivo da oliveira a fim de evitar fitotoxidez ou deficiências nutricionais.

Under real-time operation, early detection of oscillations that lead to instability is of noteworthy importance for power system operators. This paper demonstrates how the relative angle (RA) obtained with online data from phasor measurement units (PMUs) and predefined thresholds of the relative angle (PTRA) obtained with offline simulations are valuable for the monitoring and prediction of transient stability. Primary features of the method consist of first calculating the maximum and minimum relative angles by offline simulations of different contingencies. Next, the voltage angles at buses that represent areas of the power system are measured to calculate the center of inertia (COI). Finally, the RA of the generators at each area is determined during the online operation to monitor stability behaviors and identify those that lead to a loss of synchronism. The method was validated in the New England 39-bus and the IEEE 118-bus power systems by performing contingencies, finding critical stability angles, monitoring areas and controlling the predicted unstable events with control actions, such as generation and load tripping, with enough time to return to stability.