Modelling the Marginal Abatement Cost of Mitigating Nitrogen Loss from Agricultural Land

working paperWith the deadline identified by the Water Framework Directive (2000/60/EC) approaching in 2015 there is increasing pressure on policymakers to introduce new regulations to achieve water quality targets. Agriculture is one of the contributors of diffuse pollution entering watercourses and will come under pressure to reduce pollutant loads. This paper produces Marginal Abatement Cost (MAC) Curves for eight policy measures that could potentially reduce nitrate leaching from agricultural land on Irish dairy farms. These include: 1) reduction of fertiliser application by 10%; 2) reduction of fertiliser application by 20%; 3) livestock unit reduction to limit organic N to 170 kg ha-1; 4) reduction of livestock units by 20%; 5) change of feed mix to reduce cow dietary N intake; 6) fencing off watercourses to introduce a buffer zone; 7) improved dairy cow genetic merit by introducing higher performing dairy breeds; 8) more efficient slurry application. Results from this study indicate that there will be reductions in farm gross margins across nearly all policy measures. However, MAC and the ranking of MAC vary across individual farms and aggregate MAC does not reflect the heterogeneity of impacts across individual farms. This paper shows that any measure introduced in a “one size fits all command-control” fashion will not yield efficient economic results

Comparing Thirty30 Tennis with Traditional Tennis

Thirty30 is a shorter format of tennis where games start at 30-30. This means that a greater proportion of points are game points or break points than would be the case in traditional tennis. The purpose of the current paper is to compare the probability of players of different abilities winning games, sets and matches between Thirty30 tennis and traditional tennis. This is done using probabilistic models of each format of tennis. The results show that there is reduced dominance of the serve and a greater probability of upsets in Thirty30 tennis than in traditional tennis. The models are also experimented with, adjusting the probability of winning points where the point is a game point or a break point. The paper shows that such scoreline effects have a greater impact in Thirty30 tennis than they do in traditional tennis. This has implications for player preparation for Thirty30 tennis

Variable interactions in risk factors for dementia

Current estimates predict 1 in 3 people born today will develop dementia, suggesting a major impact on future population health. As such, research needs to connect specialist clinicians, data scientists and the general public. The In-MINDD project seeks to address this through the provision of a Profiler, a socio-technical information system connecting all three groups. The public interact, providing raw data; data scientists develop and refine prediction algorithms; and clinicians use in-built services to inform decisions. Common across these groups are Risk Factors, used for dementia-free survival prediction. Risk interactions could greatly inform prediction but determining these interactions is a problem underpinned by massive numbers of possible combinations. Our research employs a machine learning approach to automatically select best performing hyperparameters for prediction and learns variable interactions in a non-linear survival-analysis paradigm. Demonstrating effectiveness, we evaluate this approach using longitudinal data with a relatively small sample size

Seeing Things: Inventive Reasoning with Geometric Analogies and Topographic Maps

This paper examines two seemingly unrelated qualitative spatial reasoning domains; geometric proportional analogies and topographic (land-cover) maps. We present a Structure Matching algorithm that combines Gentner’s structuremapping theory with an attributematching process. We use structure matching to solve geometric analogy problems that involve manipulating attribute information, such as colors and patterns. Structure matching is also used to creatively interpret topographic (land-cover) maps, adding a wealth of semantic knowledge and providing a far richer interpretation of the raw data. We return to the geometric proportional analogies, identify alternate attribute matching processes that are required to solve different categories of problems. Finally, we assess some implications for computationally creative and inventive models

A configurable deep network for high-dimensional clinical trial data

Clinical studies provide interesting case studies for data mining researchers, given the often high degree of dimensionality and long term nature of these studies. In areas such as dementia, accurate predictions from data scientists provide vital input into the understanding of how certain features (representing lifestyle) can predict outcomes such as dementia. Most research involved has used traditional or shallow data mining approaches which have been shown to offer varying degrees of accuracy in datasets with high dimensionality. In this research, we explore the use of deep learning architectures, as they have been shown to have high predictive capabilities in image and audio datasets. The purpose of our research is to build a framework which allows easy reconfiguration for the performance of experiments across a number of deep learning approaches. In this paper, we present our framework for a configurable deep learning machine and our evaluation and analysis of two shallow approaches: regression and multi-layer perceptron, as a platform to a deep belief network, and using a dataset created over the course of 12 years by researchers in the area of dementia

GeneRepair - A Repair Operator for Genetic Algorithms

In this paper we present the outcome of two recent sets of experiments to evaluate the effectiveness of a new adjunct genetic operator GeneRepair. This operator was developed to correct invlaid tours which may be generated following crossover or mutation of our particular implementation of the genetic algorithm. Following implementation and testing of our genetic algotihm with GeneRepair we found a significant positive side in our results. Using GeneRepair along side traditional corsover and mutation operators we have been able to travers the search space of a problem and generate very good results in an extremely efficent manner, in both time and number of evaluations required