On combining Big Data and machine learning to support eco-driving behaviours

A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver\u2019s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver\u2019s braking style through visual elements (displayed in the vehicle dashboard, as a Human\u2013Machine Interface), actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers\u2019 awareness of eco-behaviours

Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles

Purpose. This paper introduces the Research Articles in Simplified HTML (or RASH), which is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the RASH Framework, a set of tools for interacting with RASH-based articles. The paper also presents an evaluation that involved authors and reviewers of RASH articles submitted to the SAVE-SD 2015 and SAVE-SD 2016 workshops. Design. RASH has been developed aiming to: be easy to learn and use; share scholarly documents (and embedded semantic annotations) through the Web; support its adoption within the existing publishing workflow. Findings. The evaluation study confirmed that RASH is ready to be adopted in workshops, conferences, and journals and can be quickly learnt by researchers who are familiar with HTML. Research Limitations. The evaluation study also highlighted some issues in the adoption of RASH, and in general of HTML formats, especially by less technically savvy users. Moreover, additional tools are needed, e.g., for enabling additional conversions from/to existing formats such as OpenXML. Practical Implications. RASH (and its Framework) is another step towards enabling the definition of formal representations of the meaning of the content of an article, facilitating its automatic discovery, enabling its linking to semantically related articles, providing access to data within the article in actionable form, and allowing integration of data between papers. Social Implications. RASH addresses the intrinsic needs related to the various users of a scholarly article: researchers (focussing on its content), readers (experiencing new ways for browsing it), citizen scientists (reusing available data formally defined within it through semantic annotations), publishers (using the advantages of new technologies as envisioned by the Semantic Publishing movement). Value. RASH helps authors to focus on the organisation of their texts, supports them in the task of semantically enriching the content of articles, and leaves all the issues about validation, visualisation, conversion, and semantic data extraction to the various tools developed within its Framework

Anisotropic optical conductivity of Sr4Ru3O10

n/

Quantitative analysis of CT-perfusion parameters in the evaluation of brain gliomas and metastases

<p>Abstract</p> <p>Background</p> <p>The paper reports a quantitative analysis of the perfusion maps of 22 patients, affected by gliomas or by metastasis, with the aim of characterizing the malignant tissue with respect to the normal tissue. The gold standard was obtained by histological exam or nuclear medicine techniques. The perfusion scan provided 11 parametric maps, including Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Average Perfusion (P_mean) and Permeability-surface area product (PS).</p> <p>Methods</p> <p>The perfusion scans were performed after the injection of 40 ml of non-ionic contrast agent, at an injection rate of 8 ml/s, and a 40 s cine scan with 1 s interval was acquired. An expert radiologist outlined the region of interest (ROI) on the unenhanced CT scan, by using a home-made routine. The mean values with their standard deviations inside the outlined ROIs and the contralateral ROIs were calculated on each map. Statistical analyses were used to investigate significant differences between diseased and normal regions. Receiving Operating Characteristic (ROC) curves were also generated.</p> <p>Results</p> <p>Tumors are characterized by higher values of all the perfusion parameters, but after the statistical analysis, only the <it>PS</it>, <it>Pat</it>_{<it>Rsq </it>}(Patlak Rsquare) and <it>T</it>_{<it>peak </it>}(Time to Peak) resulted significant. ROC curves, confirmed both <it>Pat</it>_{<it>Rsq </it>}and <it>PS </it>as equally reliable metrics for discriminating between malignant and normal tissues, with areas under curves (AUCs) of 0.82 and 0.81, respectively.</p> <p>Conclusion</p> <p>CT perfusion is a useful and non invasive technique for evaluating brain neoplasms. Malignant and normal tissues can be accurately differentiated using perfusion map, with the aim of performing tumor diagnosis and grading, and follow-up analysis.</p

Low dose rate brachytherapy (LDR-BT) as monotherapy for early stage prostate cancer in Italy: practice and outcome analysis in a series of 2237 patients from 11 institutions

OBJECTIVE: Low-dose-rate brachytherapy (LDR-BT) in localized prostate cancer is available since 15 years in Italy. We realized the first national multicentre and multidisciplinary data collection to evaluate LDR-BT practice, given as monotherapy, and outcome in terms of biochemical failure. METHODS: Between May 1998 and December 2011, 2237 patients with early-stage prostate cancer from 11 Italian community and academic hospitals were treated with iodine-125 ((125)I) or palladium-103 LDR-BT as monotherapy and followed up for at least 2 years. (125)I seeds were implanted in 97.7% of the patients: the mean dose received by 90% of target volume was 145 Gy; the mean target volume receiving 100% of prescribed dose (V100) was 91.1%. Biochemical failure-free survival (BFFS), disease-specific survival (DSS) and overall survival (OS) were estimated using Kaplan-Meier method. Log-rank test and multivariable Cox regression were used to evaluate the relationship of covariates with outcomes. RESULTS: Median follow-up time was 65 months. 5- and 7-year DSS, OS and BFFS were 99 and 98%, 94 and 89%, and 92 and 88%, respectively. At multivariate analysis, the National Comprehensive Cancer Network score (p < 0.0001) and V100 (p = 0.09) were correlated with BFFS, with V100 effect significantly different between patients at low risk and those at intermediate/high risk (p = 0.04). Short follow-up and lack of toxicity data represent the main limitations for a global evaluation of LDR-BT. CONCLUSION: This first multicentre Italian report confirms LDR-BT as an excellent curative modality for low-/intermediate-risk prostate cancer. ADVANCES IN KNOWLEDGE: Multidisciplinary teams may help to select adequately patients to be treated with brachytherapy, with a direct impact on the implant quality and, possibly, on outcome

In-vehicle human machine interface: An approach to enhance eco-driving behaviors

In the context of behavioral change for a more sustainability mobility, we designed and implemented an in-vehicle human machine interface for electric vehicles, on the basis of an approach we propose that exploits gamification and machine learning techniques. Our main goal is to equip the driver with instant and accurate ecodriving strategies, obtaining an optimization of the energy consumption. More specifically, we have developed a prototype that collects data related to the driver's braking style and makes use of a machine learning model to forward-predict the resulting energy gain. It then accordingly fosters custom ecodriving behaviour by means of gamified interactions provided on an infotainment dashboard on the car. We have conducted some tests and this paper presents the preliminary and promising results we obtained

Continuously updated e-learning material through easy authoring processes

The «Anywhere, Anytime, Anyway» slogan is frequently associated to e-learning with the aim to emphasize the wide access offered by on-line education to students. Our aim is to extend the reach of this sentence to content authors as well. Our idea is to produce tools to simplify drastically the task of creating, updating and publishing content for e-learning courses, and to allow them to produce learning objects of extremely high technical sophistication directly from off-the-shelf desktop application. Thus we introduce ISA-BeL, a conversion engine that can generate standard-based, visually homogeneous, accessible and graphically sophisticated SCORM learning objects by analyzing the internal structure and content of word processing &#64257; les and generating the required output without requiring particular technical awareness by the user