Experimental Study on a Laboratory Test Bench for Sea Wave Generation Systems

Abstract The paper presents a laboratory test bench specifically designed for sea wave generation systems. In particular a DC Micro Grid is realized to experimentally validate the energy performance of a PM Brushless ball screw actuator, during motor-regenerative operative conditions, which is representative of an oscillating body wave generation system. The proposed architecture is based on a DC bus, which features the integration of renewable energy sources and buffered storage systems, with the aim of smoothing the natural power fluctuations of wave energy generation systems. The wave generation is simulated in laboratory by controlling an electric motor, which is directly coupled with the PM brushless generator. The experimental validation phase is mainly devoted to verify the design criteria of the architecture scheme and the control strategies of the power fluxes related to power converters

GAM Forest Explanation

Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In this work we focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests. We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions. We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset

Hybrid Storage System Control Strategy for All-Electric Powered Ships

Abstract In marine applications all-electric propulsion systems are employed on surface ships that are subjected to particular constraints, generally due to environmental restrictions. The technological advancement of electrochemical batteries, which are today characterized by higher capacity and efficiency, has widened their fields of application, although these storage systems require an accurate design to limit their initial and maintenance costs. In order to reduce battery charge and discharge peak currents, supercapacitor modules are generally adopted with the aim to extend batteries expected life. The proper management of energy fluxes within the hybrid architecture, and in particular among batteries, capacitors and loads requires a specific control, called EMS – Energy Management Strategy. In this paper, a novel EMS, based on constrained minimization problem, is proposed and verified with reference to a case study of a waterbus operating in restricted waterways on different routes. The procedure is based on a preliminary solution of an off-line optimization with respect to a known mission profile. Hence, a real-time control strategy is properly evaluated, in order to guarantee robustness against the unavoidable uncertainties, which occur during the operating conditions. In the last part of the paper, a numerical application is presented with the purpose to emphasize the feasibility of the proposal

Interpretable Ranking Using LambdaMART (Abstract)

In this talk we present the main results of a short paper appearing at SIGIR 2022 [1]. Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this talk we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models, as Neural RankGAM [2], address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, we propose Interpretable LambdaMART, an interpretable LtR solution based on LambdaMART that is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that our approach outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%

Effect of the Austempering Process on the Microstructure and Mechanical Properties of 27MnCrB5-2 Steel

AbstractThe effect of austempering parameters on the microstructure and mechanical properties of 27MnCrB5-2 steel has been investigated by means of: dilatometric, microstructural and fractographic analyses; tensile and Charpy V-notch (CVN) impact tests at room temperature and a low temperature.Microstructural analyses showed that upper bainite developed at a higher austempering temperature, while a mixed bainitic-martensitic microstructure formed at lower temperatures, with a different amount of bainite and martensite and a different size of bainite sheaf depending on the temperature. Tensile tests highlighted superior yield and tensile strengths (≈30%) for the mixed microstructure, with respect to both fully bainitic and Q&T microstructures, with only a low reduction in elongation to failure (≈10%). Impact tests confirmed that mixed microstructures have higher impact properties, at both room temperature and a low temperature

Understanding the Role of Nature in Urban-Rural Linkages: Identifying the Potential Role of Rural Nature-Based Attractive Clusters That Serve Human Well-Being

Rural areas provide unique amenities for recreational purposes which are highly appreciated by urban inhabitants. This generates an important but often hidden relationship between the urban and the rural. The aim of our study is first to provide empirical evidence for this linkage and then to identify for Italy, at the municipal level, those rural areas which actually function as nature-based attractive clusters. We used the data coming from a participatory webGIS survey that asked 1632 Italian respondents to mark attractive nature related places locally, regionally, nationally and world-wide to explain quantitatively and qualitatively the relationship between urban and rural. From the survey, indicators were developed to rank the nature-based attractive clusters. Our results pointed out a major (almost double) flow from urban to rural for natural amenities, which increased with the spatial level at which attractive nature areas were marked. This analysis allowed for the identification rural clusters of Italian municipalities that form nodal points for nature-based urban well-being; shedding light on an often neglected urban-rural relationship. The method is applicable in other countries and may stimulate better planning and management strategies for improving rural areas, taking an urban-rural perspective

Can Embeddings Analysis Explain Large Language Model Ranking?

Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP)

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

Gender-Dependent Specificities in Cutaneous Melanoma Predisposition, Risk Factors, Somatic Mutations, Prognostic and Predictive Factors: A Systematic Review.

Over the last decades, the incidence of melanoma has been steadily growing, with 4.2% of the population worldwide affected by cutaneous melanoma (CM) in 2020 and with a higher incidence and mortality in men than in women. We investigated both the risk factors for CM development and the prognostic and predictive factors for survival, stratifying for both sex and gender. We conducted a systematic review of studies indexed in PUB-MED, EMBASE, and Scopus until 4 February 2021. We included reviews, meta-analyses, and pooled analyses investigating differences between women and men in CM risk factors and in prognostic and predictive factors for CM survival. Twenty-four studies were included, and relevant data extracted. Of these, 13 studies concerned potential risk factors, six concerned predictive factors, and five addressed prognostic factors of melanoma. The systematic review revealed no significant differences in genetic predisposition to CM between males and females, while there appear to be several gender disparities regarding CM risk factors, partly attributable to different lifestyles and behavioral habits between men and women. There is currently no clear evidence of whether the mutational landscapes of CM differ by sex/gender. Prognosis is justified by a complex combination of phenotypes and immune functions, while reported differences between genders in predicting the effectiveness of new treatments are inconsistent. Overall, the results emerging from the literature reveal the importance of considering the sex/gender variable in all studies and pave the way for including it towards precision medicine. Men and women differ genetically, biologically, and by social construct. Our systematic review shows that, although fundamental, the variable sex/gender is not among the ones collected and analyzed

SARS-CoV-2 has been circulating in northern Italy since december 2019: evidence from environmental monitoring

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease COVID-19, a public health emergency worldwide, and Italy is among the most severely affected countries. The first autochthonous Italian case of COVID-19 was documented on February 21, 2020. We investigated the possibility that SARS-CoV-2 emerged in Italy earlier than that date, by analysing 40 composite influent wastewater samples collected - in the framework of other wastewater-based epidemiology projects - between October 2019 and February 2020 from five wastewater treatment plants (WWTPs) in three cities and regions in northern Italy (Milan/Lombardy, Turin/Piedmont and Bologna/Emilia Romagna). Twenty-four additional samples collected in the same WWTPs between September 2018 and June 2019 (i.e. long before the onset of the epidemic) were included as ‘blank’ samples. Viral concentration was performed according to the standard World Health Organization procedure for poliovirus sewage surveillance, with modifications. Molecular analysis was undertaken with both nested RT-PCR and real-rime RT-PCR assays. A total of 15 positive samples were confirmed by both methods. The earliest dates back to 18 December 2019 in Milan and Turin and 29 January 2020 in Bologna. Virus concentration in the samples ranged from below the limit of detection (LOD) to 5.6 × 104 genome copies (g.c.)/L, and most of the samples (23 out of 26) were below the limit of quantification of PCR. Our results demonstrate that SARS-CoV-2 was already circulating in northern Italy at the end of 2019. Moreover, it was circulating in different geographic regions simultaneously, which changes our previous understanding of the geographical circulation of the virus in Italy. Our study highlights the importance of environmental surveillance as an early warning system, to monitor the levels of virus circulating in the population and identify outbreaks even before cases are notified to the healthcare system