The Impacts of Animal Disease Crises on the Korean Meat Market

Employing the error correction method and historical decomposition with direct acyclic graphs, we quantify the impacts of domestic and oversea animal disease crises on the Korean meat markets. We find that (a) the market partially recovered 16 months after the foot-and-mouth outbreak in 2000, and 13 months after the avian influenza and the U.S. BSE incidents in 2003; (b) animal disease outbreaks have differentiate impacts by disease type and supply chain level. Retailers likely to have windfall profits as the retail price margin increased relative to the farm and wholesale levels; and (c) disease outbreaks affect dynamic price interdependence.Animal disease outbreak, Error correction model, Direct acyclic graphs, Korean meat market, Historical Decomposition, Price margins, Livestock Production/Industries, C32, Q11, L11,

Local movement: agent-based models of pedestrian flows

Modelling movement within the built environment has hitherto been focused on rather coarse spatial scales where the emphasis has been upon simulating flows of traffic between origins and destinations. Models of pedestrian movement have been sporadic, based largely on finding statistical relationships between volumes and the accessibility of streets, with no sustained efforts at improving such theories. The development of object-orientated computing and agent-based models which have followed in this wake, promise to change this picture radically. It is now possible to develop models simulating the geometric motion of individual agents in small-scale environments using theories of traffic flow to underpin their logic. In this paper, we outline such a model which we adapt to simulate flows of pedestrians between fixed points of entry - gateways - into complex environments such as city centres, and points of attraction based on the location of retail and leisure facilities which represent the focus of such movements. The model simulates the movement of each individual in terms of five components; these are based on motion in the direction of the most attractive locations, forward movement, the avoidance of local geometric obstacles, thresholds which constrain congestion, and movement which is influenced by those already moving towards various locations. The model has elements which enable walkers to self-organise as well as learn from their geometric experiences so far. We first outline the structure of the model, present a computable form, and illustrate how it can be programmed as a variant of cellular automata. We illustrate it using three examples: its application to an idealised mall where we show how two key components - local navigation of obstacles and movement towards points of global locational attraction - can be parameterised, an application to the more complex town centre of Wolverhampton (in the UK West Midlands) where the paths of individual walkers are used to explore the veracity of the model, and finally it application to the Tate Gallery complex in central London where the focus is on calibrating the model by letting individual agents learn from their experience of walking within the environment

The 2004 Niger Food Crisis: What Role Can Price Discovery Play in Famine Early Warning Systems?

The 2005 food crisis centered in Niger received worldwide attention and extensive media coverage. Crops suffered from poor rainfall and were plagued by locusts throughout the growing season. Malnutrition flourished with the sudden disruption in food supplies. One-fifth of Niger's children suffered from moderate to severe forms of malnutrition by the summer of 2005. In an era of increased awareness and the introduction of famine early warning systems, the development community was left wondering why they were for the most part caught off guard by the food crisis. This paper tests whether market prices and price discovery could have played an active role in detecting the food crisis. Directed acyclic graphs are used to test whether price discovery mechanisms within Niger's millet markets were ahead of the early warning systems. Results suggest that as early as October 2005 markets in Arlit and the Dosso province had price anomalies that appeared to begin signaling the upcoming food crisis. This market based discovery came about two months earlier than the warnings issued by the regional early warning networks.Food Security and Poverty,

GruMon: Fast and Accurate Group Monitoring for Heterogeneous Urban Spaces

Singapore National Research Foundation under IDM Futures Funding Initiativ

Dynamics of Price Transmission in the Presence of a Major Food Safety Shock: Impact of H5N1 Avian Influenza on the Turkish Poultry Sector

This article addresses the dynamic impact of the 2005 H5N1 avian influenza outbreak on the Turkish poultry sector. Contemporary time-series analyses with historical decomposition graphs are used to address differences in monthly price adjustments between market levels along the Turkish poultry supply channel. The empirical results show that price adjustments are asymmetric with respect to both speed and magnitude along the marketing channel. Results also reveal a differential impact of the exogenous shock on producers and retailers. The findings have critical efficiency and equity implications for the supply-chain participants.avian influenza, chicken, food safety shock, price transmission dynamics, supply chain, Turkey, Agribusiness, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, International Development, Livestock Production/Industries, Q11, Q13,

On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehousePartial funding for open access charge: Universidad de Málaga. This work has been partially developed within SA3IR (an experiment funded by EU H2020 ESMERA Project under Grant Agreement 780265), the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the B1-2021_26 project, funded by the University of Málaga

Area Based Alarm System using 3D Cameras

Depth map cameras provide new ways of designing surveillance systems. In this thesis we evaluate three different cameras from two different depth sensor techniques, and propose a complete method for detecting thefts over a counter in a retail environment. Our algorithm covers pre-processing with noise reduction and background segmentation using the reflected signals amplitude as a confidence measurement. A plane is fitted both to the 3D points of the top of the retail counter as well as to the 3D points on the side (cashiers side) of the retail counter. The algorithm determines which foreground pixels are on the wrong side of both these planes. By running this result through a few methods to improve rigidity, we show that it is possible to detect thefts with a very high detection rate and low false positive rate. Finally we present the results from our testing of different versions on a database of activities with known ground-truth (theft/no theft)