Global Gradient Algorithm Extension to Distributed Pressure Driven Pipe Demand Model

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INFLUENZA DELLA NITROCARBURAZIONE IONICA SULLA MICROSTRUTTURA E LA RESISTENZA A FATICA DI ACCIAI SINTERIZZATI

La nitrocarburazione al plasma di acciai sinterizzati conferisce resistenza superficiale e limita le variazioni dimensionali. Mentre le caratteristiche microstrutturali degli strati nitrurati, in linea generale, dipendono poco dal processo di produzione dell’acciaio sinterizzato, le proprietà risultanti, che sono una sintesi delle caratteristiche del materiale base e delle modifiche introdotte dalla nitrurazione, possono esserne influenzate anche significativamente. In questo lavoro, acciai sinterizzati di interesse per il settore dei trasporti, sono stati prodotti con parametri diversi, e sottoposti allo stesso trattamento di nitrocarburazione. E’ stata condotta l’analisi microstrutturale e sono state determinate le proprietà meccaniche, con particolare riferimento alla resistenza a fatica

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction

Open Data and media literacies: educating for democracy

Data Journalism techniques, in conjunction with Open Data[1], can form the basis for open educational practices in both formal and informal learning spaces.[2] This presentation aims to introduce an innovative pedagogical approach that enables learners to build capabilities in critically assessing information – for example, news media reporting – in order to verify whether the information presented is reliable, accurate and trustworthy. Via this approach, students can further be supported to develop their skills in presenting information, generating evidence of their claims and findings, and communicating their research, using traditional or more innovative means to present research outcomes. For Zembylas (2012)[3], when equipped with a critical attitude, learners can become agents of change who recognise and challenge stereotypes and transform social structures. But in order to develop such approaches, learners must be capable of critically analysing information from various sources and formats, including data. Capabilities in analysing and interpreting raw data are becoming understood as increasingly important both in and out of the workplace, contributing to a person’s range of transversal skills, which are defined by UNESCO[4] (2015) as “critical and innovative thinking, interpersonal skills; intrapersonal skills, and global citizenship”. Data literacy is therefore understood as growing in importance, alongside but certainly not displacing much-needed information, digital and media literacies. Students, researchers and academics, along with fellow citizens, are exposed to a wide range of ostensibly factual information from the media amongst other sources. Recent global political events and their links to notions of fake news and social media filter bubbles have highlighted the question of the degree to which citizens are able to evaluate this information in a critical manner. According to Kellner & Share (2009)[5], media education has transformative potential to become a powerful instrument to challenge oppression and strengthen democracy; and for Kahne, Lee & Feezell (2012)[6], media literacies assist learners to construct their political views by critically assessing the information presented in the press and in social media. Media literacies have been described as providing “a framework to access, analyze, evaluate, create and participate with messages in a variety of forms — from print to video to the Internet. Media literacies build an understanding of the role of media in society as well as essential skills of inquiry and self-expression necessary for citizens of a democracy”[7]. Our presentation will showcase how the use of journalistic techniques in civic and data-led research such as fact-checking[8] and data-expeditions[9], can support learners to understand socio-political phenomena such as the refugee crisis. Our focus will be a case study of Open Migration[10], an Italian project, which supports learners’ understanding of migration data with the aim of challenging stereotypes and influencing the direction of public opinion and policy. By providing data, and developing competences and knowledge of migrants and migration issues, the project has trained students, researchers and citizens to understand the real numbers and issues of refugees in Italy, with the aim of supporting their integration in the community. Open Migration is therefore an Open Data-led project which demonstrates a replicable pedagogical pathway to improved media and data literacy. [1] Open Knowledge International: Open Data Definition: http://opendatahandbook.org/guide/en/what-is-open-data/ [2] Atenas, J & Havemann, L (Eds.), Open Data As Open Educational Resources: Case Studies of Emerging Practice. London: Open Knowledge, Open Education Working Group. http://doi.org/http://dx.doi.org/10.6084/m9.figshare.1590031 [3] Zembylas, M. (2013). Critical Pedagogy and Emotion: Working Through ‘Troubled Knowledge’ in Posttraumatic Contexts. Critical Studies in Education, (54)2, 176-189. DOI: 10.1080/17508487.2012.743468 [4] UNESCO: Transversal Skills http://unesdoc.unesco.org/images/0023/002347/234738E.pdf [5] Kellner, A. D., & Share, J. (2009). Critical Media Literacy , Democracy , and the Reconstruction of Education. Media Literacy: A Reader, 3–23. Retrieved from http://gseis.ucla.edu/sudikoff/archive/pdfs/philosophy/Summary_Kellner_CritLitDemocracy.pdf [6] Kahne, J., Lee, N.-J. N., & Feezell, J. T. (2012). Digital media literacy education and online civic and political participation. International Journal of Communication, 6(1), 1–24. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84859851229&partnerID=40&md5=58b0d363ecf686165af37c359201bf4e [7]Media literacies definition http://www.medialit.org/media-literacy-definition-and-more [8] Fact-checking: https://www.factcheck.org/about/our-mission/ [9] School of Data, Data expeditions: http://schoolofdata.org/data-expeditions/ [10] Open Migration http://openmigration.org/en

Calibration Procedure for Water Distribution Systems: Comparison among Hydraulic Models

Proper hydraulic simulation models, which are fundamental to analyse a water distribution system, require a calibration procedure. This paper proposes a multi-objective procedure to calibrate water demands and pipe roughness distribution in the context of an ill-posed problem, where the number of measurements is smaller than the number of variables. The proposed methodology consists of a two-steps procedure based on a genetic algorithm. Firstly, several runs of the calibrator are performed and the corresponding pressure and flow-rates values are averaged to overcome the non-uniqueness of the solutions problem. Secondly, the final calibrated model is achieved using the calibrator with the average values of the previous step as the reference condition. Therefore, the procedure enables to obtain physically based hydraulic parameters. Moreover, several hydraulic models are investigated to assess their performance on this optimisation procedure. The considered models are based either on concentrated at nodes or distributed along pipes demands approach, but also either on demand driven or pressure driven approach. Results show the reliability of the final calibrated model in the context of the ill-posed problem. Moreover, it is observed the overall better performance of the pressure driven approach with distributed demand in scarce pressure condition

Stochastic Generation of District Heat Load

Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district

Ali-Mikhail-Haq copula to detect low correlations in hierarchical clustering

In this work we introduce a new dissimilarity measure based on the AliMikhail-Haq copula, motivated by the empirical issue of detecting low correlations and discriminating variables with very similar rank correlation. This issue arises from the analysis of panel data concerning the district heating demand of the Italian city Bozen-Bolzano. In the hierarchical clustering framework, we empirically investigate the features of the proposed measure and compare it with a classical dissimilarity measure based on Kendall\u2019s rank correlation

Application of Machine Learning Models to Bridge Afflux Estimation

Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known as bridge backwater or afflux, can threaten the stability or service of bridges and riverbanks. It is postulated that applications of estimation models with more precise afflux predictions can enhance the safety of bridges in flood-prone areas. In this study, eight machine learning (ML) models were developed to estimate bridge afflux utilizing 202 laboratory and 66 field data. The ML models consist of Support Vector Regression (SVR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Gradient Boost Regressor (GBR), eXtreme Gradient Boosting (XGBoost) for Regression (XGBR), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN). To the best of the authors’ knowledge, this is the first time that these ML models have been applied to estimate bridge afflux. The performance of ML-based models was compared with those of artificial neural networks (ANN), genetic programming (GP), and explicit equations adopted from previous studies. The results show that most of the ML models utilized in this study can significantly enhance the accuracy of bridge afflux estimations. Nevertheless, a few ML models, like SVR and ABR, did not show a good overall performance, suggesting that the right choice of an ML model is important

Optimal Selection and Monitoring of Nodes Aimed at Supporting Leakages Identification in WDS

Many efforts have been made in recent decades to formulate strategies for improving the efficiency of water distribution systems (WDS), led by the socio-demographic evolution of modern society and the climate change scenario. The improvement of WDS management is a complex task that can be addressed by providing services to maximize revenues while ensuring that the quality standards required by national and international regulations are upheld. These two objectives can be fulfilled by utilizing optimized techniques for the operational and maintenance strategies of WDS. This paper proposes a methodology for assisting engineers in identifying water leakages in WDS, thus providing an effective procedure for ensuring high level hydraulic network functionality. The proposed approach is based on an inverse analysis of measured flow rates and pressure data, and consists of three steps: The analysis of measurements to select the most suitable period for leakage identification, the localization of the best measurement points based on a correlation analysis, and leakage identification with a hybrid optimization that combines the exploration capability of the differential evolution algorithm with the rapid convergence of particle swarm optimization. The proposed procedure is validated on a reference hydraulic network, known as the Apulian network