Kinematic and Dynamic Study of Cam Mechanisms for Bottling Machines

The main objective of this study is to analyze and optimize the cam mechanisms of the cork capper station currently in use for wine bottling machines. For each machine model considered, current cam profiles and corresponding real trajectories performed during operation are analyzed. Subsequently, various alternative laws of motion are tested to implement the same process, respecting the same precision points but modifying other parts of trajectory to improve machine dynamic performances. A series of tests carried out on a reconfigurable prototype and using different types of cork have made it possible to verify the effectiveness of the new laws of motion and to obtain the load acting on the machine at different operating speeds

Differential effects on membrane permeability and viability of human keratinocyte cells undergoing very low intensity megasonic fields

Among different therapeutic applications of Ultrasound (US), transient membrane sonoporation (SP) - a temporary, non-lethal porosity, mechanically induced in cell membranes through US exposure - represents a compelling opportunity towards an efficient and safe drug delivery. Nevertheless, progresses in this field have been limited by an insufficient understanding of the potential cytotoxic effects of US related to the failure of the cellular repair and to the possible activation of inflammatory pathway. In this framework we studied the in vitro effects of very low-intensity US on a human keratinocyte cell line, which represents an ideal model system of skin protective barrier cells which are the first to be involved during medical US treatments. Bioeffects linked to US application at 1 MHz varying the exposure parameters were investigated by fluorescence microscopy and fluorescence activated cell sorting. Our results indicate that keratinocytes undergoing low US doses can uptake drug model molecules with size and efficiency which depend on exposure parameters. According to sub-cavitation SP models, we have identified the range of doses triggering transient membrane SP, actually with negligible biological damage. By increasing US doses we observed a reduced cells viability and an inflammatory gene overexpression enlightening novel healthy relevant strategies

Surge prevention for gas turbines connected with large volume size: Experimental demonstration with a microturbine

The aim of this work is the demonstration of a surge prevention technique for advanced gas turbine cycles. There is significant surge risk in dynamic operation for turbines connected with large volume size additional components, such as a fuel cell stack, a saturator, a solar receiver or a heat exchanger for external combustion. In comparison with standard gas turbines, the volume size generates different behaviour during dynamic operations (with significant surge risk), especially considering that such additional components are including important dynamic constraints. In order to prevent the surge events, a vibration analysis was carried out to develop precursors which are able to highlight the approach of this unstable operative zone. Since the sub-synchronous content of the measured vibrations is significantly increasing approaching the surge line, special attention was devoted to this parameter. The demonstration of a surge prevention system based on the sub-synchronous vibration content was carried out at the Innovative Energy Systems Laboratory of the University of Genoa. In this laboratory, a recuperated microturbine connected with a large size vessel was used. Starting from the stable operation, closing a valve in the main air line or increasing the compressor inlet temperature produced operative conditions with significant surge risk. The increase in sub-synchronous vibration content detected by the control system was used to perform an active operation (bleed valve opening) to avoid the approaching surge event

Vibrational analysis for surge precursor definition in gas turbines

Compressor behaviour analysis in critical working conditions, such as incipient surge, represents a significant aspect in the turbomachinery research field. Turbines connected with large-size volumes present critical issues related to surge prevention especially during transient operations. Investigations based on acoustic and vibrational measurements appear to provide an interesting diagnostic and predictive solution by adopting suitable quantifiers calculated from microphone and accelerometer signals. For this scope a wide experimental activity has been conducted on a T100 microturbine connected with different volume sizes. A machine dynamical characterisation has been useful for better interpretation of signals during its transient to the surge. Hence, different possible methods of incipient surge identification have been developed through the use of different signal processing techniques in time, frequency and angle domain. These results will be useful for control system development to prevent compressor failures

LP 400-22, A very low-mass and high-velocity white dwarf

We report the identification of LP 400-22 (WD 2234+222) as a very low-mass and high-velocity white dwarf. The ultraviolet GALEX and optical photometric colors and a spectral line analysis of LP 400-22 show this star to have an effective temperature of 11080+/-140 K and a surface gravity of log g = 6.32+/-0.08. Therefore, this is a helium core white dwarf with a mass of 0.17 M_solar. The tangential velocity of this white dwarf is 414+/-43 km/s, making it one of the fastest moving white dwarfs known. We discuss probable evolutionary scenarios for this remarkable object.Comment: accepted for publication in ApJ Letters, made minor correction

Ultrasound delivery of Surface Enhanced InfraRed Absorption active gold-nanoprobes into fibroblast cells: a biological study via Synchrotron-based InfraRed microanalysis at single cell level

Ultrasound (US) induced transient membrane permeabilisation has emerged as a hugely promising tool for the delivery of exogenous vectors through the cytoplasmic membrane, paving the way to the design of novel anticancer strategies by targeting functional nanomaterials to specific biological sites. An essential step towards this end is the detailed recognition of suitably marked nanoparticles in sonoporated cells and the investigation of the potential related biological effects. By taking advantage of Synchrotron Radiation fourier transform infrared micro-spectroscopy (SR-microftiR) in providing highly sensitive analysis at the single cell level, we studied the internalisation of a nanoprobe within fibroblasts (NIH-3T3) promoted by low-intensity US. To this aim we employed 20 nm gold nanoparticles conjugated with the IR marker 4-aminothiophenol. The significant Surface Enhanced Infrared Absorption provided by the nanoprobes, with an absorbance increase up to two orders of magnitude, allowed us to efficiently recognise their inclusion within cells. Notably, the selective and stable SR- microftiR detection from single cells that have internalised the nanoprobe exhibited clear changes in both shape and intensity of the spectral profile, highlighting the occurrence of biological effects. Flow cytometry, immunofluorescence and murine cytokinesis-block micronucleus assays confirmed the presence of slight but significant cytotoxic and genotoxic events associated with the US-nanoprobe combined treatments. our results can provide novel hints towards US and nanomedicine combined strategies for cell spectral imaging as well as drug delivery-based therapies

From natural language processing to neural databases

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases, a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy

Database reasoning over text

Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context