Particle sizing in the process industry using Hertz-Zener impact theory and acoustic emission spectra

The cost of implementing real-time monitoring and control of industrial processes is a significant barrier for many companies. Acoustic techniques provide complementary information to optical spectroscopic sensors and have a number of advantages: they are relatively inexpensive, can be applied non-invasively, are non-destructive, multi-point measurements are possible, opaque samples can be analysed in containers that are made from opaque materials (e.g. steel or concrete) and the analysis can be conducted in real-time. In this paper a new theoretical model is proposed which describes the transport of particles in a stirred reactor, their collision with the reactor walls, the subsequent vibrations which are then transmitted through the vessel walls, and their detection by an ultrasonic transducer. The particle-wall impact is modelled using Hertz-Zener impact theory. Experimental data is then used in conjunction with this (forward) model to form an inverse problem for the particle size distribution using a least squares cost function. Application of an integral smoothing operator to the power spectra greatly enhances the accuracy and robustness of the approach. One advantage of this new approach is that since it operates in the frequency domain, it can cope with the industrially relevant case of many particle-wall collisions. The technique will be illustrated using data from a set of controlled experiments. In the first instance a set of simplified experiments involving single particles being dropped in air onto a substrate are utilised. The second set of experiments involves particles in a carrier fluid being stirred in a reactor vessel. In each case the approach is able to successfully recover the associated particle size

Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements

Separating the components of ecosystem-scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO2) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO2-based methods use traditional flux partitioning and artificial neural networks to separate the net CO2 flux into respiration and photosynthesis. The COS-based methods make use of a unique 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO2 (LRU), of which one (LRUCAP) was developed in this study. LRUCAP was based on a previously tested stomatal optimization theory (CAP), while LRUPAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO2-based estimates on daily (23 % and 7 % higher, using LRUPAR and LRUCAP, respectively) and monthly scales (20 % and 3 % higher), as well as a higher cumulative sum over 3 months in all years (on average 25 % and 3 % higher). LRUCAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRUCAP gave closer agreement with CO2-based estimates of GPP than use of LRUPAR. When extended to other sites, LRUCAP may be more robust than LRUPAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRUPAR may be more site-specific. However, this requires further testing at other measurement sites.Peer reviewe

Evidence of rehabilitative impact of progressive resistance training (PRT) programs in Parkinson disease: an umbrella review

Parkinson disease (PD) is a chronic neurodegenerative condition that leads to progressive disability. PD-related reductions in muscle strength have been reported to be associated with lower functional performance and balance confidence with an increased risk of falls. Progressive resistance training (PRT) improves strength, balance, and functional abilities. This umbrella review examines the efficacy of PRT regarding muscular strength in PD patients. The PubMed, PEDro, Scopus, and Cochrane Library databases were searched from January 2009 to August 2019 for systematic reviews and meta-analyses conducted in English. The populations included had diagnoses of PD and consisted of males and females aged >18 years old. Outcomes measured were muscle strength and enhanced physical function. Eight papers (six systematic reviews and meta-analyses and two systematic reviews) were considered relevant for qualitative analysis. In six of the eight studies, the reported severity of PD was mild to moderate. Each study analyzed how PRT elicited positive effects on muscle strength in PD patients, suggesting 10 weeks on average of progressive resistance exercises for the upper and lower limbs two to three times per week. However, none of the studies considered the postworkout follow-up, and there was no detailed evidence about the value of PRT in preventing falls. The possibility of PRT exercises being effective for increasing muscle strength in patients with PD, but without comorbidities or severe disability, is discussed. Overall, this review suggests that PRT should be included in rehabilitation programs for PD patients, in combination with balance training for postural control and other types of exercise, in order to preserve cardiorespiratory fitness and improve endurance in daily life activities

Understanding the Distributions of Benthic Foraminifera in the Adriatic Sea with Gradient Forest and Structural Equation Models

Abstract: In the last three decades, benthic foraminiferal ecology has been intensively investigated to improve the potential application of these marine organisms as proxies of the effects of climate change and other global change phenomena. It is still challenging to define the most important factors affecting foraminiferal communities and derived faunistic parameters. In this study, we examined the abiotic-biotic relationships of foraminiferal communities in the central-southern area of the Adriatic Sea using modern machine learning techniques. We combined gradient forest (Gf) and structural equation modeling (SEM) to test hypotheses about determinants of benthic foraminiferal assemblages. These approaches helped determine the relative effect of sizes of different environmental variables responsible for shaping living foraminiferal distributions. Four major faunal turnovers (at 13–28 m, 29–58 m, 59–215 m, and >215 m) were identified along a large bathymetric gradient (13–703 m water depth) that reflected the classical bathymetric distribution of benthic communities. Sand and organic matter (OM) contents were identified as the most relevant factors influencing the distribution of foraminifera either along the entire depth gradient or at selected bathymetric ranges. The SEM supported causal hypotheses that focused the factors that shaped assemblages at each bathymetric range, and the most notable causal relationships were direct effects of depth and indirect effects of the Gf-identified environmental parameters (i.e., sand, pollution load Index–PLI, organic matter–OM and total nitrogen–N) on foraminifera infauna and diversity. These results are relevant to understanding the basic ecology and conservation of foraminiferal communitie

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia.Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2  0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.European Union (EU) GA 283080 283080 640176European Research Council (ERC) 647423Ministry of the Environment, Japan 2-1401JAXA Global Change Observation Mission (GCOM) project 115National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08ANatural Sciences and Engineering Research Council of CanadaGEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-REuropean Commission Joint Research Centre 300083United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911FAO-GTOS-TCOiLEAPSMax Planck Institute for BiogeochemistryNational Science Foundation (NSF)University of Tusci

Variation-based design of an AM demodulator in a printed complementary organic technology

n/

Design of the ELIMAIA ion collection system

A system of permanent magnet quadrupoles (PMQs) is going to be realized byINFNLNS to be used as a collection system for the injection of laser driven ionbeams up to 60 AMeV in an energy selector based on four resistive dipoles. Thissystem is the first element of the ELIMED (ELI-Beamlines MEDical andMultidisciplinary applications) beam transport, dosimetry and irradiation linethat will be developed by INFN-LNS (It) and installed at the ELI-Beamlinesfacility in Prague (Cz). ELIMED will be the first users open transportbeam-line where a controlled laser-driven ion beam will be used formultidisciplinary researches. The definition of well specified characteristics,both in terms of performances and field quality, of the magnetic lenses iscrucial for the system realization, for the accurate study of the beam dynamicsand for the proper matching with the magnetic selection system which will bedesigned in the next months. Here, we report the design of the collection system and the adopted solutionsin order to realize a robust system form the magnetic point of view. Moreover,the first preliminary transport simulations are also described

Modeling house price dynamics with heterogeneous speculators

This paper investigates the impact of speculative behavior on house price dynamics. Speculative demand for housing is modeled using a heterogeneous agent approach, whereas ‘real’ demand and housing supply are represented in a standard way. Together, real and speculative forces determine excess demand in each period and house price adjustments. Three alternative models are proposed, capturing in different ways the interplay between fundamental trading rules and extrapolative trading rules, resulting in a 2D, a 3D, and a 4D nonlinear discretetime dynamical system, respectively. While the destabilizing effect of speculative behavior on the model’s steady state is proven in general, the three specific cases illustrate a variety of situations that can bring about endogenous dynamics, with lasting and significant price swings around the ‘fundamental ’ price, as we have seen in many real markets