Is digital twin technology supporting safety management? A bibliometric and systematic review

In the Industry 4.0 era, digital tools applied to production and manufacturing activities represent a challenge for companies. Digital Twin (DT) technology is based on the integration of different “traditional” tools, such as simulation modeling and sensors, and is aimed at increasing process performance. In DTs, simulation modeling allows for the building of a digital copy of real processes, which is dynamically updated through data derived from smart objects based on sensor technologies. The use of DT within manufacturing activities is constantly increasing, as DTs are being applied in different areas, from the design phase to the operational ones. This study aims to analyze existing fields of applications of DTs for supporting safety management processes in order to evaluate the current state of the art. A bibliometric review was carried out through VOSviewer to evaluate studies and applications of DTs in the engineering and computer science areas and to identify research clusters and future trends. Next, a bibliometric and systematic review was carried out to deepen the relation between the DT approach and safety issues. The findings highlight that in recent years, DT applications have been tested and developed to support operators during normal and emergency conditions and to enhance their abilities to control safety levels

Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas

In recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models, such as Agricultural Production Systems Simulator (APSIM), are tools that enable the evaluation of crop yield variability across a wide of environments. The objective of this study was to evaluate the APSIM ability to predict forage DM yield and water productivity (WP) of multiple continuous FCS. Thirteen continuous FCS, including winter and summer crops, were simulated by APSIM during two/three growing seasons in five locations across the Argentinian Pampas. Our modelling approach was based on the simulation of multiple continuous FCS, in which crop DM yields depend on the performance of the previous crop in the same sequence and the final soil variables of the previous crop are the initial conditions for the next crop. Overall, APSIM was able to accurately simulate FCS DM yield (0.93 and 3.2 Mg ha−1 for concordance correlation coefficient [CCC] and root mean square error [RMSE] respectively). On the other hand, the model predictions were better for annual (CCC = 0.94; RMSE = 0.4 g m−2 mm−1) than for seasonal WP (CCC = 0.71; RMSE = 1.9 g m−2 mm−1), i.e. at the crop level. The model performance to predict WP was associated with better estimations of the soil water dynamics over the long-term, i.e. at the FCS level, rather than the short-term, i.e. at the crop level. The ability of APSIM to predict WP decreased as seasonal WP values increased, i.e. for low water inputs. For seasonal water inputs, <200 mm, the model tended to under-predict WP, which was directly associated with crop DM yield under-predictions for frequently harvested crops. Even though APSIM showed some weaknesses in predicting seasonal DM yield and WP, i.e. at the crop level, it appears as a potential tool for further research on complementary forage crops based on multiple continuous FCS in the Argentinian livestock systems

Evaluation of Agricultural Production Systems Simulator (APSIM) as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re‐parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re‐parameterized APSIM modules. The re‐parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.Fil: Ojeda, Jonathan Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; ArgentinaFil: Volenec, Jeffrey J.. Purdue University; Estados UnidosFil: Brouder, Sylvie M.. Purdue University; Estados UnidosFil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Agnusdei, Mónica G.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentin

Soy protein supplementation does not cause lymphocytopenia in postmenopausal women

BACKGROUND: The health benefits of soy isoflavones have been widely investigated; however, there are some concerns as to whether soy isoflavones, similar to ipriflavone, a synthetic isoflavone, cause lymphocytopenia in postmenopausal women. Hence, the purpose of this study was to investigate the extent to which 12-month supplementation of 25 g soy protein containing 60 mg isoflavones alters lymphocyte counts or other hematological parameters in postmenopausal women who were not on hormone replacement therapy. METHODS: Eighty-seven postmenopausal women were randomly assigned to receive either soy protein or an equivalent amount of control protein devoid of isoflavones. Fasting venous blood was collected at baseline and at the end of twelve month study period for complete blood count analyses. RESULTS: Between the two treatment groups, the percent changes in hematological parameters, including lymphocytes, were not different. While women consuming the soy supplement had an increase in mean corpuscular hemoglobin concentration (MCHC) and red cell distribution width index (RDW; a marker of reticulocytes), women consuming the control diet had higher percentage of only MCHC. CONCLUSION: Overall, the results of the present study indicate that consumption of 25 g soy protein containing 60 mg isoflavones daily for one year does not cause lymphocytopenia