Nanofriction behavior of cluster-assembled carbon films

We have characterized the frictional properties of nanostructured (ns) carbon films grown by Supersonic Cluster Beam Deposition (SCBD) via an Atomic Force-Friction Force Microscope (AFM-FFM). The experimental data are discussed on the basis of a modified Amonton's law for friction, stating a linear dependence of friction on load plus an adhesive offset accounting for a finite friction force in the limit of null total applied load. Molecular Dynamics simulations of the interaction of the AFM tip with the nanostructured carbon confirm the validity of the friction model used for this system. Experimental results show that the friction coefficient is not influenced by the nanostructure of the films nor by the relative humidity. On the other hand the adhesion coefficient depends on these parameters.Comment: 22 pages, 6 figures, RevTex

Align-then-abstract representation learning for low-resource summarization

Generative transformer-based models have achieved state-of-the-art performance in text summarization. Nevertheless, they still struggle in real-world scenarios with long documents when trained in low-resource settings of a few dozen labeled training instances, namely in low-resource summarization (LRS). This paper bridges the gap by addressing two key research challenges when summarizing long documents, i.e., long-input processing and document representation, in one coherent model trained for LRS. Specifically, our novel align-then-abstract representation learning model (ATHENA) jointly trains a segmenter and a summarizer by maximizing the alignment between the chunk-target pairs in output from the text segmentation. Extensive experiments reveal that ATHENA outperforms the current state-of-the-art approaches in LRS on multiple long document summarization datasets from different domains

Multi-language transfer learning for low-resource legal case summarization

Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries

Efficient Memory-Enhanced Transformer for Long-Document Summarization in Low-Resource Regimes

Long document summarization poses obstacles to current generative transformer-based models because of the broad context to process and understand. Indeed, detecting long-range dependencies is still challenging for today’s state-of-the-art solutions, usually requiring model expansion at the cost of an unsustainable demand for computing and memory capacities. This paper introduces Emma, a novel efficient memory-enhanced transformer-based architecture. By segmenting a lengthy input into multiple text fragments, our model stores and compares the current chunk with previous ones, gaining the capability to read and comprehend the entire context over the whole document with a fixed amount of GPU memory. This method enables the model to deal with theoretically infinitely long documents, using less than 18 and 13 GB of memory for training and inference, respectively. We conducted extensive performance analyses and demonstrate that Emma achieved competitive results on two datasets of different domains while consuming significantly less GPU memory than competitors do, even in low-resource settings

FLARE: A Framework for the Finite Element Simulation of Electromagnetic Interference on Buried Metallic Pipelines

The functionality of buried metallic pipelines can be compromised by the electrical lines that share the same right-of-way. Given the considerable size of shared corridors, computer simulation is an important tool for performing risk assessment and mitigation design. In this work, we introduce an open-source computational framework for the analysis of electromagnetic interference on large earth-return structures. The developed framework is based on FLARE-an efficient finite element solver developed by the authors in MATLAB((R)). FLARE includes solvers for problems involving static electric and magnetic fields, and DC and time-harmonic AC currents. Quasi-magnetostatic transient problems can be studied through time-marching or-for linear problems-with an efficient inverse-Laplace approach. In this work, we succinctly describe the optimization of time-critical operations in FLARE, as well as the implementation of a transient solver with automatic time-stepping. We validate the numerical results obtained with FLARE via a comparison with the commercial software COMSOL Multiphysics((R)). We then use the validated time-marching analysis results to test the accuracy and efficiency of three numerical inverse-Laplace algorithms. The test problem considered is the assessment of the inductive coupling between a 500 kV transmission line and a metallic pipeline buried in the soil

Potentials of the waste-to-energy sector for an unconventional district heating system

In spite of being a process that exploits a renewable source of energy, the combustion of wood-based biomass contributes to deteriorate outdoor and indoor air quality. Critical situations for human exposure may occur in mountainous areas, where wood-based biomass is usually abundant and the complex morphology may favour the stagnation of air pollutants in valleys. Replacing wood/pellet stoves with centralised systems would reduce the impact, but the construction of district heating systems may not be convenient in areas with low density of houses. A possible solution could rely on direct electrical heating (DEH) systems, preferably fed by thermochemical processes that help achieve environmental goals for the local community, like the reduction of waste landfilling and the valorisation of the energy content of waste. This paper aims at presenting a comparison between the impacts expected by household wood/pellet stoves and by a modern waste-to-energy (WtE) plant, in terms of emissions of air pollutants into the atmosphere, when replacing wood stoves with a DEH system fed by the electric energy generated by the WtE plant. The comparison shows that the replacement of household stoves with an equivalent DEH system would be beneficial in terms of impacts on the local air quality. Such an approach could be considered to reduce the health impacts from biomass burning in critical areas like the Alpine region

Measurement of Atmospheric Neutrino Oscillations with a High-Density Detector

We propose an experiment to test the hypothesis that the reported anomaly on atmospheric neutrino fluxes is due to nu_mu nu_x oscillations. It will rely both on a disappearance technique, exploiting the method of the dependence of the event rate on L/E, which was recently shown to be effective for detection of neutrino oscillation and measurement of the oscillation parameters, and on an appearance technique, looking for an excess of muon-less events at high energy produced by upward-going tau neutrinos. The detector will consist of iron planes interleaved by limited streamer tubes. The total mass will be about 30 kt. The possibility of recuperating most of the instrumentation from existing detectors allows to avoid R&D phases and to reduce construction time. In four years of data taking, this experiment will be sensitive to oscillations nu_mu nu_x with Delta m^2 > 10^-4 eV^2 and a mixing near to maximal, and answer the question whether nu_x is a sterile or a tau neutrino

Perspectives of stack and environmental monitoring in the surroundings of a waste-to-energy plant

Waste-to-Energy (WtE) processes respond both to the emerging need for reducing the flow of waste into the environment and, at the same time, to the increasing demand for energy. Despite, these evident advantages, WtE plants still present some critical issues regarding the emissions of heavy metals into the atmosphere, which are regulated by the environmental legislations but, concerning the European Union, are regarded as groups of metals with no consideration of the different carcinogenic potential of each metal. In addition, there are uncertainties on the estimation of the balance of carbon dioxide, which depends on several factors like transportation, type of incoming waste, processes in use and secondary emissions. Despite great improvements in air pollution control from this sector, persistent organic pollutants are still emitted by WtE plants. Therefore, the implementation of adequate environmental monitoring (EM) plans is essential to monitor the impact of WtE plants in their surroundings, especially in the presence of the population, fields and pastures. In view of these considerations, this paper aims to provide guidance on basic and novel approaches that are necessary for a comprehensive monitoring of the impacts of a new WtE plant in terms of air quality and public health. A case study regarding the EM plan proposed for a new WtE plant will also be reported as an example

Risk assessment in a materials recycling facility: Perspectives for reducing operational issues

Mechanical separation of light packaging waste is a useful practice for improving the quality of the recyclable waste flows and its exploitation in a frame of the circular economy. Materials Recovery Facilities can treat from 3000 to 5000 tons per year of light packaging waste. Concerning the plastic content, this is divided in four flows: PET, HDPE, other plastics, and waste rejects. The last two are generally used for energy recovery. For improving the quality of the recyclable plastic waste, a manual separation is required for reducing the impurities detectable in the final products. However, this practice could enhance the risk at work of the operators, which should be constantly monitored. This article explores the main differences of a manual separation and of a mechanical separation, assessing the costs and the health risk for the workers. The analysis started from the situation in an Italian Materials Recovery Facility, generalizing the context; a future scenario with the application of a mechanical separation is theoretically introduced. The main results obtained suggest that the manual separation plant improves the quality of the material, though increasing the risk of the operators due to the possible contact with sharp waste, sanitary danger, and risk of injuries for the mismanagement of machines, among others. The mechanical separation can be considered a real advantage from an economic point of view, since the operating costs are lower and the investment could be recovered in around 10 years, in an Italian-like context. On balance, on the one hand, the article provides indications for the private sector for improving the management of a Materials Recovery Facility, while, on the other hand, it detects the main pros and cons of both methodologies. © 2018 by the authors