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

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

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

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

Three-Dimensional Human iPSC-Derived Artificial Skeletal Muscles Model Muscular Dystrophies and Enable Multilineage Tissue Engineering

Summary: Generating human skeletal muscle models is instrumental for investigating muscle pathology and therapy. Here, we report the generation of three-dimensional (3D) artificial skeletal muscle tissue from human pluripotent stem cells, including induced pluripotent stem cells (iPSCs) from patients with Duchenne, limb-girdle, and congenital muscular dystrophies. 3D skeletal myogenic differentiation of pluripotent cells was induced within hydrogels under tension to provide myofiber alignment. Artificial muscles recapitulated characteristics of human skeletal muscle tissue and could be implanted into immunodeficient mice. Pathological cellular hallmarks of incurable forms of severe muscular dystrophy could be modeled with high fidelity using this 3D platform. Finally, we show generation of fully human iPSC-derived, complex, multilineage muscle models containing key isogenic cellular constituents of skeletal muscle, including vascular endothelial cells, pericytes, and motor neurons. These results lay the foundation for a human skeletal muscle organoid-like platform for disease modeling, regenerative medicine, and therapy development. : Maffioletti et al. generate human 3D artificial skeletal muscles from healthy donors and patient-specific pluripotent stem cells. These human artificial muscles accurately model severe genetic muscle diseases. They can be engineered to include other cell types present in skeletal muscle, such as vascular cells and motor neurons. Keywords: skeletal muscle, pluripotent stem cells, iPS cells, myogenic differentiation, tissue engineering, disease modeling, muscular dystrophy, organoid