A brief study of some aspects of early father-child relationship

Thesis (M.S.)--Boston Universit

Markov Chain Monitoring

In networking applications, one often wishes to obtain estimates about the number of objects at different parts of the network (e.g., the number of cars at an intersection of a road network or the number of packets expected to reach a node in a computer network) by monitoring the traffic in a small number of network nodes or edges. We formalize this task by defining the 'Markov Chain Monitoring' problem. Given an initial distribution of items over the nodes of a Markov chain, we wish to estimate the distribution of items at subsequent times. We do this by asking a limited number of queries that retrieve, for example, how many items transitioned to a specific node or over a specific edge at a particular time. We consider different types of queries, each defining a different variant of the Markov chain monitoring. For each variant, we design efficient algorithms for choosing the queries that make our estimates as accurate as possible. In our experiments with synthetic and real datasets we demonstrate the efficiency and the efficacy of our algorithms in a variety of settings.Comment: 13 pages, 10 figures, 1 tabl

An integrated approach project for the revaluation of a traditional sourdough bread production chain

The influence of organic and conventional farming systems on the performance of a panel of old and modern Italian bread wheat varieties has been evaluated, with the aim to individuate an agronomic protocol suitable for the production of a sourdough bread traditionally prepared in a hill zone of Emilia-Romagna. The agronomic and technological characterisation of the wheat samples obtained in organic and conventional farming conditions has been done and the sensorial qualities of the sourdough bread obtained have been evaluated

Multivariate SCADA data analysis methods for real-world wind turbine power curve monitoring

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible

Sleeve lobectomy for non-small cell lung cancer and carcinoids: results in 160 cases

n/

Residual stress of as-deposited and rolled Wire + Arc Additive Manufacturing Ti–6Al–4V components

Wire + arc additive manufacturing components contain significant residual stresses, which manifest in distortion. High-pressure rolling was applied to each layer of a linear Ti–6Al–4V wire + arc additive manufacturing component in between deposition passes. In rolled specimens, out-of-plane distortion was more than halved; a change in the deposits' geometry due to plastic deformation was observed and process repeatability was increased. The Contour method of residual stresses measurements showed that although the specimens still exhibited tensile stresses (up to 500 MPa), their magnitude was reduced by 60%, particularly at the interface between deposit and substrate. The results were validated with neutron diffraction measurements, which were in good agreement away from the baseplate

Wind turbine systematic yaw error: Operation data analysis techniques for detecting IT and assessing its performance impact

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4◦ affects the performance up to the order of the 1.5% of the Annual Energy Production

Stochastic system dynamics modelling for climate change water scarcity assessment of a reservoir in the Italian Alps

Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions.Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021-2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999-2004 and 2009-2016; -24.9 % of turbined outflow and -19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041-2070; -26.2 % of turbined outflow and -20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041-2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021-2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to -56.3 % in August) and from May to December for stored volume (down to -44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions