Effects of the ruminal comminution rate and microbial contamination of particles on accuracy of in situ estimates of ruminal degradability and intestinal digestibility of feedstuffs.

Effects of considering the comminution rate -kc- and the correction of microbial contamination -using 15N techniques- of particles in the rumen on estimates of ruminally undegraded fractions and their intestinal digestibility were examined generating composite samples -from rumen-incubated residues- representative of the undegraded feed rumen outflow. The study used sunflower meal -SFM- and Italian ryegrass hay -RGH- and three rumen and duodenum cannulated wethers fed with a 40:60 RGH to concentrate diet -75 g DM/kgBW0.75-. Transit studies up to the duodenum with Yb-SFM and Eu-RGH marked samples showed higher kc values -/h- in SFM than in RGH -0.577 vs. 0.0892, p = 0.034-, whereas similar values occurred for the rumen passage rate -kp-. Estimates of ruminally undegraded and intestinal digestibility of all tested fractions decreased when kc was considered and also applying microbial correction. Thus, microbial uncorrected kp-based proportions of intestinal digested undegraded crude protein overestimated those corrected and kc-kp-based by 39% in SFM -0.146 vs. 0.105- and 761% in RGH -0.373 vs. 0.0433-. Results show that both kc and microbial contamination correction should be considered to obtain accurate in situ estimates in grasses, whereas in protein concentrates not considering kc is an important source of error.

Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

Effect of positioning on the accuracy of decision making of association football top-class referees and assistant referees during competitive matches

The aim of this study was to examine the effect of positioning on the correctness of decision making of top-class referees and assistant referees during international games. Match analyses were carried out during the Fe´de´ration Internationale de Football Association (FIFA) Confederations Cup 2009 and 380 foul play incidents and 165 offside situations were examined. The error percentage for the referees when indicating the incidents averaged 14%. The lowest error percentage occurred in the central area of the field, where the collaboration of the assistant referee is limited, and was achieved when indicating the incidents from a distance of 11–15 m, whereas this percentage peaked (23%) in the last 15-min match period. The error rate for the assistant referees was 13%. Distance of the assistant referee to the offside line did not have an impact on the quality of the offside decision. The risk of making incorrect decisions was reduced when the assistant referees viewed the offside situations from an angle between 46 and 608. Incorrect offside decisions occurred twice as often in the second as in the first half of the games. Perceptual-cognitive training sessions specific to the requirements of the game should be implemented in the weekly schedule of football officials to reduce the overall error rate.

Performance of pressure control valves and flow meters precision in operational irrigation-water balance accuracy

In pressure irrigation-water distribution networks, pressure regulating devices for controlling the discharged flow rate by irrigation units are needed due to the variability of flow rate. In addition, applied water volume is used controlled operating the valve during a calculated time interval, and assuming constant flow rate. In general, a pressure regulating valve PRV is the commonly used pressure regulating device in a hydrant, which, also, executes the open and close function. A hydrant feeds several irrigation units, requiring a wide range in flow rate. In addition, some flow meters are also available, one as a component of the hydrant and the rest are placed downstream. Every land owner has one flow meter for each group of field plots downstream the hydrant. Its lecture could be used for refining the water balance but its accuracy must be taken into account. Ideal PRV performance would maintain a constant downstream pressure. However, the true performance depends on both upstream pressure and the discharged flow rate. The objective of this work is to asses the influence of the performance on the applied volume during the whole irrigation events in a year. The results of the study have been obtained introducing the flow rate into a PRV model. Variations on flow rate are simulated by taking into account the consequences of variations on climate conditions and also decisions in irrigation operation, such us duration and frequency application. The model comprises continuity, dynamic and energy equations of the components of the PRV.

Discrete element analysis for the assessment of the accuracy of load cell-based dynamic weighing systems in grape harvesters under different ground conditions

Dynamic weighing systems based on load cells are commonly used to estimate crop yields in the field. There is lack of data, however, regarding the accuracy of such weighing systems mounted on harvesting machinery, especially on that used to collect high value crops such as fruits and vegetables. Certainly, dynamic weighing systems mounted on the bins of grape harvesters are affected by the displacement of the load inside the bin when moving over terrain of changing topography. In this work, the load that would be registered in a grape harvester bin by a dynamic weighing system based on the use of a load cell was inferred by using the discrete element method (DEM). DEM is a numerical technique capable of accurately describing the behaviour of granular materials under dynamic situations and it has been proven to provide successful predictions in many different scenarios. In this work, different DEM models of a grape harvester bin were developed contemplating different influencing factors. Results obtained from these models were used to infer the output given by the load cell of a real bin. The mass detected by the load cell when the bin was inclined depended strongly on the distribution of the load within the bin, but was underestimated in all scenarios. The distribution of the load was found to be dependent on the inclination of the bin caused by the topography of the terrain, but also by the history of inclination (inclination rate, presence of static periods, etc.) since the effect of the inertia of the particles (i.e., representing the grapes) was not negligible. Some recommendations are given to try to improve the accuracy of crop load measurement in the field.