Sustainability and the City: New Kensington CDC's Sustainable 19125 Initiative

New Kensington Community Development Corporation (NKCDC), an organization long dedicated to revitalizing the East Kensington, Fishtown, and Port Richmond neighborhoods of Philadelphia, launched an urban sustainability initiative in 2009 called "Sustainable 19125." The initiative's goal is to make the 19125 ZIP code the most sustainable ZIP code in the city

Pregelix: Big(ger) Graph Analytics on A Dataflow Engine

There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow. Pregelix is a new open source distributed graph processing system that is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up to 35x speedup compared to distributed GraphLab), and makes more effective use of available machine resources to support Big(ger) Graph Analytics

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Oat variety characteristics for suppressing weeds

Oats are a valuable food source and useful in the crop rotation both in organic and conventional farming systems, partly because of their excellent weed suppression ability. Thomas Döring, Louisa Winkler and Nick Fradgley report new results that show how plant breeding can make oats even better

India and the Patent Wars: Pharmaceuticals in the New Intellectual Property Regime

[Excerpt] India and the Patent Wars contributes to an international debate over the costs of medicine and restrictions on access under stringent patent laws showing how activists and drug companies in low-income countries seize agency and exert influence over these processes. Murphy Halliburton contributes to analyses of globalization within the fields of anthropology, sociology, law, and public health by drawing on interviews and ethnographic work with pharmaceutical producers in India and the United States. India has been at the center of emerging controversies around patent rights related to pharmaceutical production and local medical knowledge. Halliburton shows that Big Pharma is not all-powerful, and that local activists and practitioners of ayurveda, India’s largest indigenous medical system, have been able to undermine the aspirations of multinational companies and the WTO. Halliburton traces how key drug prices have gone down, not up, in low-income countries under the new patent regime through partnerships between US- and India-based companies, but warns us to be aware of access to essential medicines in low- and middle-income countries going forward

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

In Things We Trust? Towards trustability in the Internet of Things

This essay discusses the main privacy, security and trustability issues with the Internet of Things