1,550 research outputs found
An Effective Semiclassical Approach to IR Spectroscopy
We present a novel approach to calculate molecular IR spectra based on
semiclassical molecular dynamics. The main advance from a previous
semiclassical method [M. Micciarelli, R. Conte, J. Suarez, M. Ceotto J. Chem.
Phys. 149, 064115 (2018)] consists in the possibility to avoid state-to-state
calculations making applications to systems characterized by sizable densities
of vibrational states feasible. Furthermore, this new method accounts not only
for positions and intensities of the several absorption bands which make up the
IR spectrum, but also for their shapes. We show that accurate semiclassical IR
spectra including quantum effects and anharmonicities for both frequencies and
intensities can be obtained starting from semiclassical power spectra. The
approach is first tested against the water molecule, and then applied to the
10-atom glycine aminoacid
Assessing the Impact of Real-Time Machine Translation on Multilingual Meetings in Global Software Projects
Communication in global software development is hindered by language differences in countries with a lack of English speaking professionals. Machine translation is a technology that uses software to translate from one natural language to another. The progress of machine translation systems has been steady in the last decade. As for now, machine translation technology is particularly appealing because it might be used, in the form of cross-language chat services, in countries that are entering into global software projects. However, despite the recent progress of the technology, we still lack a thorough understanding of how real-time machine translation affects communication. In this paper, we present a set of empirical studies with the goal of assessing to what extent real-time machine translation can be used in distributed, multilingual requirements meetings instead of English. Results suggest that, despite far from 100% accurate, real-time machine translation is not disruptive of the conversation flow and, therefore, is accepted with favor by participants. However, stronger effects can be expected to emerge when language barriers are more critical. Our findings add to the evidence about the recent advances of machine translation technology and provide some guidance to global software engineering practitioners in regarding the losses and gains of using English as a lingua franca in multilingual group communication, as in the case of computer-mediated requirements meetings
An Efficiency-Based Power Management Strategy for an Isolated Microgrid Project
The microgrids design for remote locations represents one of the most important and critical applications of the microgrid concept. It requires the correct sizing and the proper utilization of the different sources to guarantee the economical feasibility and the reliability of the supply. This study illustrates an efficiency-based power management strategy, designed for an undergoing microgrid project, where the sizing process of the resources (diesel generators, battery energy storage system, and PV plant) is obtained using a mixed-integer optimization algorithm. The proposed power management strategy guarantees the efficient exploitation of the power sources, which is one of the key elements of the optimal sizing process, being naturally included in the definition of the energy cost functions. The effectiveness of the power control strategy is validated by means of quasi-dynamic simulations on the complete microgrid model, where sources are defined by the optimal problem solution, while the cabling (size and length) and the main switchboards location reflect the expected system layout. Results obtained from the simulation of the microgrid electrical system include losses, and allow to verify and to highlight the desired quantities, such as the quality of supply at each busbar (voltage magnitude), and the state of charge of the energy storage system
Anticipating New Treatments for Cystic Fibrosis: A Global Survey of Researchers
Cystic fibrosis is a life-threatening disease that affects at least 100,000 people worldwide. It is caused by a defect in the cystic fibrosis transmembrane regulator (CFTR) gene and presently, 360 CFTR-causing mutations have been identified. Since the discovery of the CFTR gene, the expectation of developing treatments that can substantially increase the quality of life or even cure cystic fibrosis patients is growing. Yet, it is still uncertain today which developing treatments will be successful against cystic fibrosis. This study addresses this gap by assessing the opinions of over 524 cystic fibrosis researchers who participated in a global web-based survey. For most respondents, CFTR modulator therapies are the most likely to succeed in treating cystic fibrosis in the next 15 years, especially through the use of CFTR modulator combinations. Most respondents also believe that fixing or replacing the CFTR gene will lead to a cure for cystic fibrosis within 15 years, with CRISPR-Cas9 being the most likely genetic tool for this purpose
Optimal Management of a Smart Port with Shore-Connection and Hydrogen Supplying by Stochastic Model Predictive Control
The paper proposes an optimal management strategy for a Smart Port equipped
with renewable generation and composed by an electrified quay, operating
Cold-Ironing, and a Hydrogen-based quay, supplying Zero-Emission Ships. One
Battery Energy Storage System and one Hydrogen Energy Storage System are used
to manage renewable energy sources and to supply electric and hydrogen-fueled
ships. A model predictive control based algorithm is designed to define the
best economic strategy to be followed during operations. The control algorithm
takes into account the uncertainties of renewable energy generation using
stochastic optimization. The performance of the approach is tested on a
potential future Smart Port equipped with wind and photovoltaic generation
A comprehensive approach to dark matter studies: exploration of simplified top-philic models
Studies of dark matter lie at the interface of collider physics, astrophysics
and cosmology. Constraining models featuring dark matter candidates entails the
capability to provide accurate predictions for large sets of observables and
compare them to a wide spectrum of data. We present a framework which, starting
from a model lagrangian, allows one to consistently and systematically make
predictions, as well as to confront those predictions with a multitude of
experimental results. As an application, we consider a class of simplified dark
matter models where a scalar mediator couples only to the top quark and a
fermionic dark sector (i.e. the simplified top-philic dark matter model). We
study in detail the complementarity of relic density, direct/indirect detection
and collider searches in constraining the multi-dimensional model parameter
space, and efficiently identify regions where individual approaches to dark
matter detection provide the most stringent bounds. In the context of collider
studies of dark matter, we point out the complementarity of LHC searches in
probing different regions of the model parameter space with final states
involving top quarks, photons, jets and/or missing energy. Our study of dark
matter production at the LHC goes beyond the tree-level approximation and we
show examples of how higher-order corrections to dark matter production
processes can affect the interpretation of the experimental results.Comment: 52 pages, 23 figure
Metabolomics investigation of post-mortem human pericardial fluid
Introduction Due to its peculiar anatomy and physiology, the pericardial fluid is a biological matrix of particular interest in the forensic field. Despite this, the available literature has mainly focused on post-mortem biochemistry and forensic toxicology, while to the best of authors' knowledge post-mortem metabolomics has never been applied. Similarly, estimation of the time since death or post-mortem interval based on pericardial fluids has still rarely been attempted.ObjectivesWe applied a metabolomic approach based on H-1 nuclear magnetic resonance spectroscopy to ascertain the feasibility of monitoring post-mortem metabolite changes on human pericardial fluids with the aim of building a multivariate regression model for post-mortem interval estimation.MethodsPericardial fluid samples were collected in 24 consecutive judicial autopsies, in a time frame ranging from 16 to 170 h after death. The only exclusion criterion was the quantitative and/or qualitative alteration of the sample. Two different extraction protocols were applied for low molecular weight metabolites selection, namely ultrafiltration and liquid-liquid extraction. Our metabolomic approach was based on the use of H-1 nuclear magnetic resonance and multivariate statistical data analysis.ResultsThe pericardial fluid samples treated with the two experimental protocols did not show significant differences in the distribution of the metabolites detected. A post-mortem interval estimation model based on 18 pericardial fluid samples was validated with an independent set of 6 samples, giving a prediction error of 33-34 h depending on the experimental protocol used. By narrowing the window to post-mortem intervals below 100 h, the prediction power of the model was significantly improved with an error of 13-15 h depending on the extraction protocol. Choline, glycine, ethanolamine, and hypoxanthine were the most relevant metabolites in the prediction model.ConclusionThe present study, although preliminary, shows that PF samples collected from a real forensic scenario represent a biofluid of interest for post-mortem metabolomics, with particular regard to the estimation of the time since death
Pain treatment with high-dose, controlled-release oxycodone: an Italian perspective
To investigate the possible role and tolerability of high-dose (>160 mg/day) oxycodone controlled release (CR) for the treatment of cancer and non-cancer pain. 227 patients with cancer or non-cancer pain were enrolled in an open-label, multi-center, Italian study in order to assess the adequacy of their existing pain management (using a numerical rating scale [NRS]) and the possible benefit high-dose oxycodone CR may offer patients experiencing uncontrolled pain.
Results: Pain was poorly controlled at baseline, with only 18.1% of patients reporting adequate pain relief (NRS <3.5). All other patients reported uncontrolled pain, with an average NRS of 7.81. At baseline assessment, 47.89% of patients had been in pain for up to 3 months, 32.82% for 3â6 months, and 19.19% for more than 6 months. After baseline assessment, patients were switched to oxycodone CR monotherapy. The starting dose was individualized to each patient and titrated up over a 3- to 4-day period until effective pain management was achieved. Treatment was continued for an average of 37.24 days during the study. Pain control (final mean NRS of 2.85) was attained with an average dose of oxycodone CR 221.84 mg/day. Standard adverse events (including constipations, nausea, and vomiting) were recorded in 39.64% of patients receiving high-dose oxycodone CR monotherapy. Side-effects tended to subside after the initial week of treatment and did not result in any participants leaving the study. High-dose oxycodone CR can achieve rapid and effective management of moderate to severe cancer and non-cancer pain with minimum side-effects
Amyotrophic lateral sclerosis phenotypes significantly differ in terms of magnetic susceptibility properties of the precentral cortex
The aim of our study was to investigate whether the magnetic susceptibility varies according to the amyotrophic lateral sclerosis (ALS) phenotypes based on the predominance of upper motor neuron (UMN)/lower motor neuron (LMN) impairment. We retrospectively collected imaging and clinical data of 47 ALS patients (12 with UMN predominance (UMN-ALS), 16 with LMN predominance (LMN-ALS), and 19 with no clinically defined predominance (Np-ALS)). We further enrolled 23 healthy controls (HC) and 15 ALS mimics (ALS-Mim). These participants underwent brain 3-T magnetic resonance imaging (3-T MRI) with T1-weighted and gradient-echo multi-echo sequences. Automatic segmentation and quantitative susceptibility mapping (QSM) were performed. The skewness of the susceptibility values in the precentral cortex (SuscSKEW) was automatically computed, compared among the groups, and correlated to the clinical variables. The Kruskal-Wallis test showed significant differences in terms of SuscSKEW among groups (Ï2(3) = 24.2, p < 0.001), and pairwise tests showed that SuscSKEW was higher in UMN-ALS compared to those in LMN-ALS (p < 0.001), HC (p < 0.001), Np-ALS (p = 0.012), and ALS-Mim (p < 0.001). SuscSKEW was highly correlated with the Penn UMN score (Spearman's rho 0.612, p < 0.001). This study demonstrates that the clinical ALS phenotypes based on UMN/LMN sign predominance significantly differ in terms of magnetic susceptibility properties of the precentral cortex. Combined MRI-histopathology investigations are strongly encouraged to confirm whether this evidence is due to iron overload in UMN-ALS, unlike in LMN-ALS. âą Magnetic susceptibility in the precentral cortex reflects the prevalence of UMN/LMN impairment in the clinical ALS phenotypes. âą The degree of UMN/LMN impairment might be well described by the automatically derived measure of SuscSKEW in the precentral cortex. âą Increased SuscSKEW in the precentral cortex is more relevant in UMN-ALS patients compared to those in Np-ALS and LMN-ALS patients
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