Soil Amendment with Biochar, Hydrochar and Compost Mitigates the Accumulation of Emerging Pollutants in Rocket Salad Plants

The uptake of organic pollutants by agricultural plants and their accumulation in edible parts cause serious health problems to animals and humans. In this study, we used carbon-rich materials, such as biochar (BC), hydrochar (HC), and green compost (GC), to reduce the absorption and accumulation of three pesticides, imidacloprid (IMI), boscalid (BOS), and metribuzin (MET) and two endocrine disruptors, 4-tert-octylphenol (OP) and bisphenol A (BPA), in rocket salad plants (Eruca vesicaria L.). After an experimental period of 35 days, compared to unamended soil, the addition of BC, HC, and GC significantly reduced chemical phytotoxicity, increasing the elongation of the aerial plant parts by 26, 25, and 39%, respectively, whereas GC increased the fresh biomass by 21%. The assessment of residual chemicals in both soil and plant tissues indicated that any amendment was very effective in enhancing the retention of all compounds in soil, thus reducing their uptake by plants. Averagely for the five compounds, the reduction of plant absorption followed the trend BC > HC > GC. In particular, the presence of BC decreased the chemical residues in the plants from a minimum of 71% (IMI) to a maximum of 91% (OP). The overall results obtained encourage the incorporation in soil of C-rich materials, especially BC, to protect leafy food plants from the absorption and toxicity of organic pollutants of a wide range of hydrophobicity, with relevant benefits for consumers

Use of the Solid By-Product of Anaerobic Digestion of Biomass to Remove Anthropogenic Organic Pollutants with Endocrine Disruptive Activity

Anaerobic digestion of biomass has increasing implementation for bioenergy production. The solid by-product of this technology, i.e., the digestate, has relevant potential in agricultural and environmental applications. This study explored the capacity of a digestate from mixed feedstock to remove from water four endocrine-disrupting chemicals, namely the pesticides metribuzin (MET) and boscalid (BOS) and the xenoestrogens bisphenol A (BPA) and 4-tert-octylphenol (OP). The surface micromorphology and functional groups of the digestate were investigated using scanning electron microscopy (SEM) and Fourier-transform infrared (FTIR) spectroscopy, respectively. Results of sorption kinetics showed that all compounds reached the steady state in a few hours according to a pseudo-first-order model in the cases of MET and OP, a pseudo-second-order model for BOS and both models in the case of BPA. Data of adsorption isotherms were fitted to the Henry, Freundlich, Langmuir and Temkin equations. The adsorption of MET preferentially followed the non-linear Freundlich model, whereas the adsorption of the other compounds was properly described by both the linear and Freundlich models. The organic carbon partition coefficients, KOC, were 170, 1066, 256 and 2180 L kg1 for MET, BOS, BPA and OP, respectively. The desorption of BOS, BPA and OP was slow and incomplete, indicating a phenomenon of hysteresis. In conclusion, the digestate showed a remarkable efficiency in the removal of the compounds, especially those with high hydrophobicity, thus behaving as a promising biosorbent for environmental remediation

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Quantum ESPRESSO toward the exascale

Quantum ESPRESSO is an open-source distribution of computer codes for quantum-mechanical materials modeling, based on density-functional theory, pseudopotentials, and plane waves, and renowned for its performance on a wide range of hardware architectures, from laptops to massively parallel computers, as well as for the breadth of its applications. In this paper, we present a motivation and brief review of the ongoing effort to port Quantum ESPRESSO onto heterogeneous architectures based on hardware accelerators, which will overcome the energy constraints that are currently hindering the way toward exascale computing

Tavola rotonda “Le declinazioni della formazione nella sicurezza organizzata”

Convegno "La cultura della sicurezza fra organizzazione e formazione" Bari 20 novembre 2015 Tavola rotonda: “Le declinazioni della formazione nella sicurezza organizzata

An integrated experimental and quantum-chemical investigation on the vibrational spectra of chlorofluoromethane

The vibrational analysis of the gas-phase infrared spectra of chlorofluoromethane (CH2ClF, HCFC-31) was carried out in the range 200-6200 cm(-1). The assignment of the absorption features in terms of fundamental, overtone, combination, and hot bands was performed on the medium-resolution (up to 0.2 cm(-1)) Fourier transform infrared spectra. From the absorption cross section spectra accurate values of the integrated band intensities were derived and the global warming potential of this compound was estimated, thus obtaining values of 323, 83, and 42 on a 20-, 100-, and 500-year horizon, respectively. The set of spectroscopic parameters here presented provides the basic data to model the atmospheric behavior of this greenhouse gas. In addition, the obtained vibrational properties were used to benchmark the predictions of state-of-the-art quantum-chemical computational strategies. Extrapolated complete basis set limit values for the equilibrium geometry and harmonic force field were obtained at the coupled-cluster singles and doubles level of theory augmented by a perturbative treatment of triple excitations, CCSD(T), in conjunction with a hierarchical series of correlation-consistent basis sets (cc-pVnZ, with n = T, Q, and 5), taking also into account the core-valence correlation effects and the corrections due to diffuse (aug) functions. To obtain the cubic and quartic semi-diagonal force constants, calculations employing second-order Moller-Plesset perturbation (MP2) theory, the double-hybrid density functional B2PLYP as well as CCSD(T) were performed. For all anharmonic force fields the performances of two different perturbative approaches in computing the vibrational energy levels (i.e., the generalized second order vibrational treatment, GVPT2, and the recently proposed hybrid degeneracy corrected model, HDCPT2) were evaluated and the obtained results allowed us to validate the spectroscopic predictions yielded by the HDCPT2 approach. The predictions of the deperturbed second-order perturbation approach, DVPT2, applied to the computation of infrared intensities beyond the double-harmonic approximation were compared to the accurate experimental values here determined. Anharmonic DFT and MP2 corrections to CCSD(T) intensities led to a very good agreement with the absorption cross section measurements over the whole spectral range here analysed. (C) 2013 AIP Publishing LLC

Electronic absorption spectra of pyridine and nicotine in aqueous solution with a combined molecular dynamics and polarizable QM/MM approach

The electronic absorption spectra of pyridine and nicotine in aqueous solution have been computed using a multistep approach. The computational protocol consists in studying the solute solvation with accurate molecular dynamics simulations, characterizing the hydrogen bond interactions, and calculating electronic transitions for a series of configurations extracted from the molecular dynamics trajectories with a polarizable QM/MM scheme based on the fluctuating charge model. Molecular dynamics simulations and electronic transition calculations have been performed on both pyridine and nicotine. Furthermore, the contributions of solute vibrational effect on electronic absorption spectra have been taken into account in the so called vertical gradient approximation. \ua9 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc