3,943 research outputs found
Morphology of Salina offshore (Southern Tyrrhenian Sea)
In this paper, we present the first complete morphological map of the Salina offshore at a scale
of 1:100,000. The submarine flanks of the Salina edifice extend down to −650 to −1300 m, are
steep and characterized by an uneven morphology due to the presence of volcanic and erosivedepositional
features. The volcanic features cover ∼30% of the submarine portion and include
volcanic cones and bedrock outcrops. The remaining ∼70% is affected by a wide series of
erosive-depositional features. Among these, features related to Late Quaternary sea level
fluctuations comprise the insular shelf surrounding the island and overlying submarine
terraced depositional sequences. Mass-wasting features include landslide scars, channels, fanshaped
deposits and waveforms. The presented map provides useful insights for a better
understanding of the morphological evolution of the edific
Morphology of Lipari offshore (Southern Tyrrhenian Sea)
High-resolution multibeam bathymetry was recently collected around Lipari, the largest and
most densely populated island of the Aeolian Archipelago (Southern Tyrrhenian Sea). The
data were acquired within the context of marine geological studies performed in the area
over the last 10 years. We present the first detailed morphological map of the Lipari
offshore at 1:100,000 scale (Main Map). A rugged morphology characterizes the submarine
portions of Lipari volcano, reflecting both volcanic and erosive-depositional processes. The
volcanic features include cones, lava flows and bedrock outcrops. Erosive-depositional
features include an insular shelf topped by submarine depositional terraces related to LateQuaternary
sea-level fluctuations, as well as landslide scars, channelized features, fanshaped
deposits and wavy bedforms. The different distribution of volcanic and erosivedepositional
features on the various sectors of Lipari is mainly related to the older age of the
western flank with respect to the eastern one. The map also provides insights for a first
marine geohazard assessment of this active volcanic area
Evaporative CO2 cooling using microchannels etched in silicon for the future LHCb vertex detector
The extreme radiation dose received by vertex detectors at the Large Hadron
Collider dictates stringent requirements on their cooling systems. To be robust
against radiation damage, sensors should be maintained below -20 degree C and
at the same time, the considerable heat load generated in the readout chips and
the sensors must be removed. Evaporative CO2 cooling using microchannels etched
in a silicon plane in thermal contact with the readout chips is an attractive
option. In this paper, we present the first results of microchannel prototypes
with circulating, two-phase CO2 and compare them to simulations. We also
discuss a practical design of upgraded VELO detector for the LHCb experiment
employing this approach.Comment: 12 page
The beneficial role of green bonds as a new strategic asset class: Dynamic dependencies, allocation and diversification before and during the pandemic era
The paper proposes a full comprehensive analysis of green bond diversification benefits, their co-movement with multiple market indices, and the corresponding implications for portfolio allocation. Based on a time frame of seven years, divided into four sub-periods, the co-movements of green-bond indices, i.e. Solactive Green Bond Index and Bloomberg Barclays MSCI Green Bond Index, and the stock/bond market have been described, shedding light on the connections with sectors most affected by the Covid-19 pandemic. The Solactive Green Bond Index is found to provide the greater diversification benefit of the two green-bond indices, on average during the seven years and also during the pandemic. Allocation strategies and risk performances have also been analyzed to assess the impact of green-bond indices on otherwise traditional portfolios; their diversification power is discussed by use of traditional measures and an additional behavioral approach, drawing attention to its evolution in time and its consistency in terms of diminished risks and increased returns. Portfolios constructed with the inclusion of green bonds prove preferable in terms of risk, in all periods and for all strategies, while the superiority of returns depends on the allocation strategy
Open Biomedical Ontologies Applied to Prostate Cancer
In this presentation we survey preliminary results from the Interdisciplinary Prostate Ontology Project (IPOP), in which ontologies from the Open Biomedical Ontologies (OBO) library have been used to annotate clinical reports about prostate cancer. First we discuss why we rejected several controlled vocabularies, including SNOMED, DICOM, and RadLex, preferring instead to use the OBO library. We then briefly describe the database-backed website we have created around the relevant OBO ontologies, and provide excerpts of reports from radiology, surgery, and pathology which we have hyperlinked to the ontology terms. This method allows us to discover which relevant terms exist in the OBO library, and which do not. The final section of this paper discusses these gaps in the OBO library and considers methods of filling them
On the prediction of psd in antisolvent mediated crystallization processes based on fokker-planck equations
A phenomenological model for the description of antisolvent mediated crystal growth processes is presented. The crystal size growth dynamics is supposed to be driven by a deterministic growth factor coupled to a stochastic component. Two different models for the stochastic component are investigated: a Linear and a Geometric Brownian motion terms. The evolution in time of the particle size distribution is then described in terms of the Fokker-Planck equation. Validations against experimental data are presented for the NaCl-water-ethanol anti-solvent crystallization system. It was found that a proper modeling of the stochastic component does have an impact on the model capabilities to fit the experimental data. In particular, the GBM assumption is better suited to describe the antisolvent crystal growth process under examination
Optimal strategies to control particle size and variance in antisolvent crystallization operations using deep RL
Solution crystallization operations have complex dynamics that are typically lumped into two competing processes namely nucleation and growth. Mathematical models can be used to describe these two processes and their effect on the crystal population when subject to variables like temperature, addition of anti-solvent, etc. To ensure that the crystals meet specific performance objectives, the models need to be solved and the control variables need to be optimized. This has largely been done until now using algorithms from dynamic programming or optimal control theory. Recently, however, it has been shown that learning frameworks like Reinforcement Learning can solve large optimization problems efficiently while offering distinct advantages. In this work, we explore the possibility of computing the optimal profiles of a semi-batch crystallizer to control the mean size and variance using four different deep RL algorithms. The performance on one of the tasks is evaluated experimentally on the anti-solvent crystallization of NaCl in a water-ethanol system
Time-domain Fourier optics for polarization-modedispersion compensation
We report on a novel technique to compensate for all-order polarization-mode dispersion. By means of this
technique, based on a suitable combination of phase modulation and group-velocity dispersion, we compensated
for as much as 60 ps of differential group delay that affected a 10-Gbit/s return-to-zero data stream
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