On the Expressiveness of Relative-Timed Coordination Models

n/

Nitrogen use efficiency and N2O and NH3 losses attributed to three fertiliser types applied to an intensively managed silage crop

Three different nitrogen (N) fertiliser types, ammonium nitrate, urea and urea coated with a urease inhibitor (Agrotain®), were applied at standard rates (70 kg N ha−1) to experimental plots in a typical and intensively managed grassland area at the Easter Bush Farm Estate (Scotland). The nitrogen use efficiency of the fertilisers was investigated as well as nitrogen losses in the form of nitrous oxide fluxes (N2O) and ammonia (NH3) during fertilisation events in the 2016 and 2017 growing seasons. Nitrous oxide was measured by the standard static chamber technique and analysed using Bayesian statistics. Ammonia was measured using passive samplers combined with the Flux Interpretation by Dispersion and Exchange over Short Range (FIDES) inverse dispersion model. On average, fertilisation with ammonium nitrate supported the largest yields and had the highest nitrogen use efficiency, but as large spatial and seasonal variation persisted across the plots, yield differences between the three fertiliser types and zero N control were not consistent. Overall, ammonium nitrate treatment was found to increase yields significantly (p value < 0.05) when compared to the urea fertilisers used in this study. Ammonium nitrate was the largest emitter of N2O (0.76 % of applied N), and the urea was the largest emitter of NH3 (16.5 % of applied N). Urea coated with a urease inhibitor did not significantly increase yields when compared to uncoated urea; however, ammonia emissions were only 10 % of the magnitude measured for the uncoated urea, and N2O emissions were only 47 % of the magnitude of those measured for ammonium nitrate fertiliser. This study suggests that urea coated with a urease inhibitor is environmentally the best choice in regards to nitrogen pollution, but because of its larger cost and lack of agronomic benefits, it is not economically attractive when compared to ammonium nitrate

ESSAYS IN (I) STRATEGIC ORDERING WITH ENDOGENOUS SEQUENCE OF EVENTS IN SUPPLY CHAIN (II) STRATEGIC MANAGEMENT OF NEW PRODUCT INNOVATION AND PROCESS IMPROVEMENT

This dissertation discusses two research problems. First topic is strategic information management in supply chain, and second topic is analytical modeling approach in productivity dilemma. The first two chapters of dissertation discuss the impact of information asymmetry and competition on vertical contractual relationships, and risk neutral firms' strategic ordering decisions with minimal assumptions. Modern business environment caused by competition and information asymmetry plagues most firms across industries, often leading to suboptimal outcomes. Given the lead times in planning capacity, suppliers prefer earlier orders from their downstream partners (retailers). Much attention has been given in the literature to Advance Purchase Discount (APD), where the supplier lowers the wholesale price to entice the retailers to order early. In this dissertation, we suggest another avenue of early purchase model considering more realistic ways - competition between downstream retailers and information flows (from information acquisition to dissemination) in supply chain. We show that with one retailer having "better" market demand information on uncertain demand than the other, the supplier can induce earlier ordering from the better-informed retailer without any reduction in the wholesale price, or creating rationing risk. In addition, we investigate firm's information investment decisions corresponding to the timing of the orders. We extend the model with different information structures of firms such as imperfect and evolving information. In reality, firms can have more accurate market information near the selling season by acquiring it from more diverse resources. Consistent with practice, we explorer firm's equilibrium outcomes of endogenous sequencing game with this setting. The third chapter of dissertation is in the trade-off between production efficiency and new product innovation. A firm's ability to compete over time has been rooted not only in improved efficiency, but also in its ability to be simultaneously innovative (Abernathy (1978)). This trade-off between efficiency and innovation has long been discussed in the business context, but limited analytical research has been done using the `extreme value theory' (Dahan & Mendelson (2001)) to investigate this issue. Our model considers important exogenous innovation factors such as innovation characteristics (Benner & Tushman (2003)) and degree of competition, which has yielded the following theoretical results and practical implications. First, we highlight new product characteristics. If R&D projects are paradigm-shifting innovations, there is a stronger adverse effect between efficiency and innovation than incremental innovation. Second, competition results in underinvestment effort in innovation performance for the firms. For example, in the symmetric firms' competition, the optimal size of R&D projects decreased, as competition increases. On the other hand, firms are more likely to focus on process improvement activities

Expertise-weighing of judgments in wisdom of crowds: Investigating independent judgments and sequential collaboration

In the past, collaboration to form an aggregate from individual judgments or preferences was mostly investigated for either wisdom of crowds or group decision making. While wisdom of crowds was typically examined by statistically aggregating independent individual judgments, group members form a consensus decision by information sharing and discussing in group decision making. Even though very different, both of these methods were demonstrated to profit from considering expert judgment in the aggregation process. However, the Internet vastly changed the way individuals can collaborate, share information, and form judgments. Large-scale online collaborative projects such as Wikipedia and OpenStreetMap rely on sequential collaboration, a process in which contributors consecutively adjust or maintain the latest versions of entries. In this thesis comprising three articles, I add to research showing that weighing independent individual judgments by expertise improves resulting estimates. Moreover, I demonstrate that sequential collaboration is a successful way of aggregating individual judgments which relies at least partially on an implicit expertise-weighing of judgments by contributors. In the first paper, I extend Cultural Consensus Theory to two-dimensional continuous data which allows to derive estimates from independent individual location judgments while simultaneously considering individuals’ competence. With this model extension, I show that aggregating location judgments with Cultural Consensus Theory yields more accurate estimates than unweighted averaging. In the second paper, I examine judgment aggregation with sequential collaboration showing that sequential collaboration is a successful way of integrating individual judgments which results in similar accurate estimates as unweighted averaging. Lastly, I investigate the role of expertise in sequential collaboration in the third paper. There I show that sequential collaboration allows contributors to implicitly weigh judgments by expertise which results in more accurate estimates the more and later experts enter sequential chains. With my thesis, I aim to further deepen and extend the understanding of how expertise influences judgments and estimates in wisdom of crowds and establish a theoretical framework of sequential collaboration. Thereby, I hope to contribute to the understanding of successful judgment aggregation and provide a theoretical underpinning for the success and high information quality in large-scale online collaborative projects

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here

Environmental Policy Across Terrestrial Space

This dissertation examines spatial heterogeneity that results from various environmental policies. In Chapter 1, I provide a comprehensive overview of each dissertation chapter. Chapter 2 (with Ed Rubin) demonstrates that most coal-fueled power plants are located on or near jurisdictional (county or state) borders. We find that coal-fired power plants are disproportionately sited on downwind borders (within county or state). Natural gas plants---much lower polluters---do not exhibit this behavior. Motivated by the inferred strategic siting, we use an atmospheric dispersion model developed by NOAA to estimate various aspects of the ``pollution transport problem.'' We find that nearly 90% of coal-based particulate matter leaves its state of origin within 48 hours of release. Chapter 3 (with Mark Colas) examines the effects of stringent land-use regulations on national carbon emissions. We develop and estimate a general equilibrium model of residential sorting and energy consumption. We find that relaxing land-use restrictions in California leads to a 0.6% drop in national carbon emissions. The mechanism behind this drop is straightforward. California cities have a temperate climate, carbon-efficient power plants, and high land-use regulations. These land-use regulations inflate housing prices, thus keeping households out of California cities. When households live outside of California, they emit more carbon on average, and therefore national carbon emissions are higher due to California's land-use regulations. In Chapter 4, I simulate the labor market effects of a carbon tax across the continental United States. To recover the welfare impacts of a carbon tax, I build and estimate a spatial equilibrium model that features heterogeneous households. I incorporate a rich level of heterogeneity into the model that allows me to answer: (1) who is most affected by a carbon tax, (2) how much the burden of a carbon tax is borne on different households, and (3) where the households are that bear the greatest burden from the tax. I find that workers without a college degree in manufacturing bear a disproportionate share of the tax incidence. Chapter 5 concludes this dissertation. This dissertation includes previously both previously published and unpublished and co-authored material

Nitrous oxide emissions from grazed grasslands: novel approaches to assessing spatial heterogeneity

Nitrous oxide (N2O) is a potent greenhouse gas mainly produced by microbial processes in the soil. Anthropogenic N2O is principally emitted from soils after nitrogen fertiliser and manure applications on agricultural land. This thesis focuses on emissions from grazing systems, which are known to be the largest source of uncertainty in global and national N2O emission inventories. Nitrogen-rich excreta deposits from grazing livestock are recognised as hotspots of N losses (N2O emissions in particular). The non-uniform distribution of these emissions hotspots within a typical field contributes significantly to the spatial heterogeneity of emissions often observed in addition to the natural variability of soil properties within the field such as pH, moisture and nutrient availability. However, it is extremely difficult to characterise the spatial and temporal pattern of these grazing inputs other than through the use of demanding and costly approaches such as manual observation or animal based-sensors. Two separate experiments were conducted during this study, in Scotland on sheep grazed grasslands and in Ireland on a dairy cow grazed grassland. Both sites were commercially used and were intensively managed with a nitrogen fertiliser application rate of 225 kg ha-1 yr-1 and 261 kg ha-1 yr-1, respectively. In Scotland, at Easter Bush fields the experiment was conducted during a 9 month campaign of gas, soil and grass sampling over the grazed field to study the spatial and temporal variability of the fluxes and soil properties to improve up-scaling of the fluxes from the plot scale to the field scale. In Ireland, at the Johnstown Castle farm, the experiment was conducted during an 11 month campaign on an experimental plot excluded from grazing. At the Scottish site, gas, soil and grass samples were collected regularly on soil which received different treatments within a randomised block design (e.g. urine deposition, fertiliser application, urine and fertiliser application or no N addition as a control). At both sites, Remotely Piloted Aerial System (RPAS) imagery was collected to study the spatial variability of the grass growth with the aim to map excreta depositions over the whole field. The Scottish site was used as a proof of concept of the method and the method was then used weekly on the Irish site over the entire grazing season. More generally, this thesis details the novel use of remote sensing techniques using high-resolution cameras linked to RPAS to improve our understanding of the spatial and temporal patterns of excreta deposition. This method proved to be repeatable for future studies as it can be automated, is easily deployable in the field, low-cost and the measurements are non-destructive (i.e. has no influence on the soil, vegetation or livestock). Excreta depositions contribute to very high emissions of N2O from relatively small areas of soil and can vary throughout the growing season in response to climatic conditions. Therefore, mapping of the excreta nitrogen inputs to the field facilitated a more accurate estimation of the annual field-scale N2O emission from grazing grasslands. Both experiments conducted in this study showed a high spatial and temporal N2O emissions variability due to the nature of N2O production within the soil and high variability of the soil properties (soil pH, soil moisture content, soil temperature) which influence the microbiological processes. Interaction on N2O emissions between fertiliser application and urine deposition was proved to be statistically significant and the magnitude of the interactions depended on the time of application within the year. The results showed a link between the variability of the emission factors of excreta deposition and fertiliser application and to the variation in weather conditions. This technique can be employed to up-scale emissions to a national level. This study plays a part in the on-going development of precision agricultural tools, based on image analysis of the grass sward to mitigate emissions from grazed grassland. Possible mitigation approaches, based on the methods presented in this thesis, include the use of RPAS technology to deliver nitrification inhibitors to newly deposited excreta within the field to reduce the potential nitrogen losses to the environment. This research indicates the future potential to better adjust fertiliser application using variable-rate fertiliser applications matching the vegetation nitrogen needs and limit nitrogen losses. This thesis identifies opportunities to develop innovative approaches to N2O mitigation by better evaluating emission estimations from agricultural practices, which could then be implemented in the national and global greenhouse gas inventories established by the Intergovernmental Panel on Climate Change