Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

peer-reviewedAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises

Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

peer-reviewedThe global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practice

Anderson et al. Reply (to the Comment by Murphy on Pioneer 10/11)

We conclude that Murphy's proposal (radiation of the power of the main-bus electrical systems from the rear of the craft) can not explain the anomalous Pioneer acceleration.Comment: LaTex, 3 pages, Phys. Rev. Lett. (to be published

Effect of introducing weather parameters on the accuracy of milk production forecast models

peer-reviewedThe objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy

Mitochondrial ROS cause motor deficits induced by synaptic inactivity:implications for synapse pruning

Developmental synapse pruning refines burgeoning connectomes. The basic mechanisms of mitochondrial reactive oxygen species (ROS) production suggest they select inactive synapses for pruning: whether they do so is unknown. To begin to unravel whether mitochondrial ROS regulate pruning, we made the local consequences of neuromuscular junction (NMJ) pruning detectable as motor deficits by using disparate exogenous and endogenous models to induce synaptic inactivity en masse in developing Xenopus laevis tadpoles. We resolved whether: (1) synaptic inactivity increases mitochondrial ROS; and (2) antioxidants rescue synaptic inactivity induced motor deficits. Regardless of whether it was achieved with muscle (α-bugarotoxin), nerve (α-latrotoxin) targeted neurotoxins or an endogenous pruning cue (SPARC), synaptic inactivity increased mitochondrial ROS in vivo. The manganese porphyrins MnTE-2-PyP5+ and/or MnTnBuOE-2-PyP5+ blocked mitochondrial ROS to significantly reduce neurotoxin and endogenous pruning cue induced motor deficits. Selectively inducing mitochondrial ROS—using mitochondria-targeted Paraquat (MitoPQ)—recapitulated synaptic inactivity induced motor deficits; which were significantly reduced by blocking mitochondrial ROS with MnTnBuOE-2-PyP5+. We unveil mitochondrial ROS as synaptic activity sentinels that regulate the phenotypical consequences of forced synaptic inactivity at the NMJ. Our novel results are relevant to pruning because synaptic inactivity is one of its defining features

Age Differences in Central (Semantic) and Peripheral Processing: The importance of Considering Both Response Times and Errors

In this project we examined the effect of adult age on visual word recognition by using combined reaction time (RT) and accuracy methods based on the Hick–Hyman law. This was necessary because separate Brinley analyses of RT and errors resulted in contradicting results. We report the results of a lexical decision task experiment (with 96 younger adults and 97 older adults). We transformed the error data into entropy and then predicted RT by using entropy values separately for exposure duration (thought to influence peripheral processes) and word frequency (thought to influence central processes). For exposure duration, the entropy–RT functions indicate that older adults show higher intercepts and slopes than do younger adults, suggesting an encoding decrement for older adults. However, for word frequency, older adults show higher intercepts but not steeper slopes than younger adults. Older adults thus show a peripheral processing decrement but not a central processing decrement for lexical decision

Age Differences in Central (Semantic) and Peripheral Processing: The importance of Considering Both Response Times and Errors

In this project we examined the effect of adult age on visual word recognition by using combined reaction time (RT) and accuracy methods based on the Hick–Hyman law. This was necessary because separate Brinley analyses of RT and errors resulted in contradicting results. We report the results of a lexical decision task experiment (with 96 younger adults and 97 older adults). We transformed the error data into entropy and then predicted RT by using entropy values separately for exposure duration (thought to influence peripheral processes) and word frequency (thought to influence central processes). For exposure duration, the entropy–RT functions indicate that older adults show higher intercepts and slopes than do younger adults, suggesting an encoding decrement for older adults. However, for word frequency, older adults show higher intercepts but not steeper slopes than younger adults. Older adults thus show a peripheral processing decrement but not a central processing decrement for lexical decision

Investigating the prevalent security techniques in wireless sensor network protocols

The radio architectures of and protocols used by wireless sensor networks (WSNs) are, typically, very similar and are based on IEEE 802.15.4. By concentrating on this standard and the associated employed security techniques, the possibility of designing a transferable safety and privacy enhancement across protocols and services, becomes a reality. WSN applications have expanded significantly over the past decade or so and adopt commercial off-the-shelf (COTS) devices and publicly available standards, which inherently creates intruder incentives and security challenges. Securing WSNs is a critical requirement due to the challenging burden of protecting the transmitted sensitive information across various applications, while operating under unique security vulnerabilities and a fluctuating radio frequency (RF) spectrum and physical environment. Couple this aspect with establishing a level of trust among network nodes, while providing resilience to interference, it becomes clear that maintaining security is challenging. This paper identifies unique vulnerabilities in WSNs, which have a direct impact on privacy and safety. The prevalent security techniques used in the common PHY and MAC layers of various WSN protocols are discussed in terms of providing the essential security requirements. An experimental visualization of the coexistence issues in the industrial, scientific and medical (ISM) RF band, which is integral for IoT operations, is provided as an introduction to a new perspective on attacking WSNs. Fundamental attack styles and spectrum sharing/coexistence based intrusions are presented. Typical methods, which use COTS devices and open source software to exploit WSN security holes, are also discussed