The Costs of Biosecurity at the Farm Level: the Case of Finnish Broiler

In the European Union, the animal health and food safety strategy includes managing biosecurity along the entire production chain. Farm-level biosecurity provides the foundation for this. However, the farm-level costs of preventive biosecurity have rarely been assessed. Yet many risk management practices are in place constantly regardless of whether there is a disease outbreak or not. We contribute towards filling this information gap by studying the costs incurred in preventive biosecurity by the Finnish poultry farms. In a preliminary analysis, we find that the cost of biosecurity is some 3.55 cents per bird for broiler producers and 75.7 cents per bird for hatching egg producers. The results indicate that work-time devoted to biosecurity represents some 8% of total work time on broiler farms and about 5% on breeder farms.Biosecurity, on-farm costs, poultry, Livestock Production/Industries,

Pathogenic Yersinia and Listeria monocytogenes in organic pork production

The goal of this study is to determine the prevalence of pathogenic Yersinia and Listeria monocytogenes in organic pork production and assess risks in different steps of the pork production chain

Edge computing assisted adaptive mobile video streaming

Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially

Aspiration, Achievement and Abandonment in ‘The World’s Best Country’: Merit and Equity or Smoke and Mirrors?

Finland is internationally valorised for its education system, quality of life and high-tech, innovative, competitiveness. However, a critical focus on institutional dynamics and trajectories of higher education careers illuminates questions about the reproduction of global inequities, rather than the societal transformation Finland’s education system was once noted for. The purpose of this self-ethnography of career trajectories within Finnish higher education is designed to call attention to institutional social dynamics that have escaped the attention of scholarly literature and contemporary debates about academic work and practice within highly situated research groups, departments and institutes. Our analysis illuminates emergent stratification, in a country and institution previously characterized by the absence of stratification and the ways in which this reinforces - and is reinforced by – the tension between transnational academic capitalism, methodological nationalism and the resulting global division of academic labour that now cuts across societies, manifesting within the one institution Finland’s general population trusts to explain, engage and ameliorate stratification: Higher Education

Energy-aware QoE and backhaul traffic optimization in green edge adaptive mobile video streaming

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks

Mobile edge computing assisted green scheduling of on-move electric vehicles

Mobile edge computing (MEC) has been proposed as a promising solution, which enables the content processing at the edges of the network helping to significantly improve the quality of experience (QoE) of end users. In this article, we aim to utilize the MEC facilities integrated with time-varying renewable energy resources for charging/discharging scheduling known as green scheduling of on-move electric vehicles (EVs) in a geographical wide area comprising of multiple charging stations (CSs). In the proposed system, the charging/discharging demands and the contextual information of EVs are first transmitted to nearby edge servers. With instantaneous electricity load/pricing and the availability of renewable energy at nearby CSs collected by aggregators, a weighted social-welfare maximization problem is then solved at the edges using greedy-based algorithms to choose the best CS for the EV’s service. From the system point of view, our results reveal that compared to cloud-based scheme, the proposed MEC-assisted EVs scheduling system significantly improves the complexity burden, boosts the satisfaction (QoE) of EVs’ drivers by localizing the traffic at nearby CSs, and further helps to efficiently utilize the renewable energy across CSs. Furthermore, our greedy-based algorithm, which utilizes the internal updating heuristics, outperforms some baseline solutions in terms of social welfare and power grid ancillary services

A First Look at Quality of Mobile Live Streaming Experience: the Case of Periscope

Live multimedia streaming from mobile devices is rapidly gaining popularity but little is known about the QoE they provide. In this paper, we examine the Periscope service. We first crawl the service in order to understand its usage patterns. Then, we study the protocols used, the typical quality of experience indicators, such as playback smoothness and latency, video quality, and the energy consumption of the Android application