PathOrganic - Identification of Critical Control Points for organic vegetable crops

The aim of this work is the identification of Critical Control Points1 (CCPs) for organic farms, which use manure for the production of organic lettuce, cabbage, carrots and spinach in Austria, Switzerland, Sweden, and Denmark. Due to the application of manure, vegetables are at risk to be contaminated with enteropathogens such as Escherichia coli pathogenic strains (i. e. O157:H7), Salmonella enterica serovar Typhimurium, Campylobacter jejuni, Listeria monocyto-genes, and Staphylococcus aureus. This work applies part of the principles and steps described for HACCP2 to identify the CCPs. The steps described hereafter are reported in the course guidance document “HACCP in agri-culture – Organic milk production” (1) and were adapted to the agricultural production of field vegetables. The hazard analysis is based on the characteristics of the above mentioned enteropathogens and the agricultural practices applied by the organic farmers in the four countries when growing lettuce, cabbage, carrots, and spinach. The assessment of the actual agricultural practices uses the evaluation of interviews conducted with organic farmers growing these vegetables and using manure as fertiliser. In total were interviewed 16 farmers in Austria, 16 farmers in Switzerland, 13 farmers in Sweden, and 9 farmers in Denmark. In general terms, there is no agricultural practice common to the majority of organic farmers concerning the management of animal ma-nure, fertilisation, irrigation, harvest and postharvest management when growing lettuce, cab-bage, spinach, and carrots in Austria, Switzerland, Denmark, and Sweden. Using the decision tree of the HACCP system, CCPs were identified for the primary production of organic field vegetables. Where appropriate, instead of CCPs were defined PRP-CPs3 or OP-PRPs4. Four CCPs were identified for the process steps ‘storage of animal manure’, ‘fertilisation practices’, ‘prevention of runoff and flooding’, and ‘irrigation practices’

Semi-parametric Regression under Model Uncertainty: Economic Applications

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi-parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects. The presented approach enables the simultaneous identification of robust linear and nonlinear effects. The additional insights gained are illustrated on applications in empirical economics, namely willingness to pay for housing, and cross-country growth regression

Auctioning risk: The all-pay auction under mean-variance preferences

We analyse the all-pay auction with incomplete information and variance-averse bidders. We characterise the symmetric equilibrium for general distributions of valuations and any number of bidders. Variance aversion is a sufficient assumption to predict that high-valuation bidders increase their bids relative to the risk-neutral case while low types decrease their bid. Considering an asymmetric two-player environment with uniform valuations, we show that a more variance-averse type always bids higher than her less variance-averse counterpart. Utilising our analytical bidding functions we discuss all-pay auctions with variance-averse bidders from a designer's perspective. We extend our basic model to include noisy signals and allow for the possibility of variance-seeking preferences and type-dependent attitudes towards risk

Auctioning risk: The all-pay auction under mean-variance preferences

We develop the idea of using mean-variance preferences for the analysis of the first-price, all-pay auction. On the bidding side, we characterise the optimal strategy in symmetric all-pay auctions under mean-variance preferences for general distributions of valuations and any number of bidders. We find that, in contrast to winner-pay auction formats, only hightype bidders increase their bids relative to the risk-neutral case while low types minimise variance exposure by bidding low. Introducing asymmetric variance aversions across bidders into a Uniform valuations, two-player framework, we show that a more variance-averse type bids always higher than her less variance-averse counterpart. Taking mean-variance bidding behaviour as given, we show that an expected revenue maximising seller may want to optimally limit the number of participants. Although expected revenue for risk-neutral bidders typically dominates revenue under mean-variance bidding, if the seller himself takes account of the variance of revenue, he may find it preferable to attract bidders endowed with mean-variance preferences

Individual Differences in Personality Moderate the Effects of Perceived Group Deprivation on Violent Extremism: Evidence From a United Kingdom Nationally Representative Survey

Numerous studies argue that perceived group deprivation is a risk factor for radicalization and violent extremism. Yet, the vast majority of individuals, who experience such circumstances do not become radicalized. By utilizing models with several interacting risk and protective factors, the present analysis specifies this relationship more concretely. In a large United Kingdom nationally representative survey (n = 1,500), we examine the effects of group-based relative deprivation on violent extremist attitudes and violent extremist intentions, and we test whether this relationship is contingent upon several individual differences in personality. The results show that stronger group-based injustices lead to increased support for and intentions to engage in violent extremism. However, some of the effects are much stronger for individuals who exhibit a stronger need for uniqueness and for status and who demonstrate higher levels of trait entitlement. Conversely, several effects are lessened for those individuals high in trait forgiveness, demonstrating a strong capacity for self-control and for those who are exerting critical as well as open-minded thinking styles, thus constituting buffering protective factors, which dampen the adverse effects of perceived group injustice on violent extremism. The results highlight the importance of considering (a) the interaction between individual dispositions and perceptions of contextual factors (b) the conditional and cumulative effects of various risk and protective factors and (c) the functional role of protective factors when risk factors are present. Collectively, these findings bring us one step closer to understanding who might be more vulnerable to violent extremism as well as how. Overall, the study suggests that preventing and countering violent extremism (P/CVE) programs must take account of the constellation of multiple factors that interact with (and sometimes enable or disable) one another and which can be targeted in preventions strategies

Conspiracy Theories and Violent Extremism

Slides for the presentation presented at the ENVISION24 Conference Session 5: Galvanizing Events: The U.S. Electio

Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature -- including COMPAS -- where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We also illustrate our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.Comment: 15 pages, 3 figures, 1 tabl

AGILE PORTFOLIO MANAGEMENT: DESIGN GOALS AND PRINCIPLES

Digital transformation and the resulting volatile and unpredictable business environments challenge traditional enterprises to continuously fulfill and surpass customers’ expectations. They need to become agile in its organization by proactively sensing the unpredictable change and responding accordingly with speed and dexterity. While many organizations are quite advanced in realizing adaptivity at the operational level, strategic agility in general and in portfolio management in particular as linking op-erations and strategy for satisfying the customer needs is in its nascence. To identify the baseline for portfolio management for achieving agility, we derive four design goals for an effective agile portfolio management system, six design principles on how to achieve these goals and show an exemplary setup with design features. Our results are based on a research study with empirical insights from six com-panies and theoretical input from thirteen existing case studies and eight frameworks for scaling agility to the portfolio level. By deriving design principles for an agile portfolio management system, our work closes a gap in existing research, which focuses on principles for adaptive IT portfolio management processes instead of proactive enterprise systems, insights on individual portfolio practices or non-generalizable blueprints for an agile organizational setup without showing alternative approaches

An Experimental Test of Design Alternatives for the British 3G / UMTS Auction

In spring 2000, the British government auctioned off licences for Third Generation mobile telecommunications services. In the preparation of the auction, two designs involving each a hybrid of an English and a sealed-bid auction were suggested by the government: a discriminatory and a uniform price variant. We report an experiment on these two designs, and also compare the results to those with a pure English auction. Both hybrids are similar in efficiency, revenue differences disappear as bidders get experienced. Compared to the discriminatory format, the pure English auction gives new entrants better chances.Spectrum auctions, UMTS, experiments

Bovine serum albumin as the dominant form of dietary protein reduces subcutaneous fat mass, plasma leptin and plasma corticosterone in high fat-fed C57/BL6J mice

Acknowledgements The authors thank Harriett Schellekens from the University College Cork and Paula O’Connor from Teagasc Moorepark Food Research Centre for their assistance in procuring laboratory space and equipment. The present study was funded by Teagasc. B. L. M. was funded by the Walsh Fellowship Program. J. R. S. was supported by a 1000-talents professorship from the Chinese government. The funding bodies had no input on the design of the study or in the interpretation of the data.Peer reviewedPublisher PD