Demand-Orientated Power Production from Biogas: Modeling and Simulations under Swedish Conditions

The total share of intermittent renewable electricity is increasing, intensifying the need for power balancing in future electricity systems. Demand-orientated combined heat and power (CHP) production from biogas has potential for this purpose. An agricultural biogas plant, using cattle manure and sugar beet for biogas and CHP production, was analyzed here. The model Dynamic Biogas plant Model (DyBiM) was developed and connected to the Anaerobic Digestion Model No. 1 (ADM1). Flexible scenarios were simulated and compared against a reference scenario with continuous production, to evaluate the technical requirements and economic implications of demand-orientated production. The study was set in Swedish conditions regarding electricity and heat price, and the flexibility approaches assessed were increased CHP and gas storage capacity and feeding management. The results showed that larger gas storage capacity was needed for demand-orientated CHP production but that feeding management reduced the storage requirement because of fast biogas production response to feeding. Income from electricity increased by 10%, applying simple electricity production strategies to a doubled CHP capacity. However, as a result of the currently low Swedish diurnal electricity price variation and lack of subsidies for demand-orientated electricity production, the increase in income was too low to cover the investment costs. Nevertheless, DyBiM proved to be a useful modeling tool for assessing the economic outcome of different flexibility scenarios for demand-orientated CHP production

Finding Topic-Specific Trends and Influential Users in Social Networks

Social networks (SNs) have become an integral part of contemporary life, as they are increasingly used as a basic means for communication with friends, sharing of opinions and staying up to date with news and current events. The general increase in the usage and popularity of social media has led to an explosion of available data, which creates opportunities for various kinds of utilization, such as predicting, finding or even creating trends. We are thus interested in exploring the following questions: (a) Which are the most influential - popular internet publications posted in SNs, for a specific topic? (b) Which members of SNs are experts or influential regarding a specific topic? Our approach towards answering the above questions is based on the functionality of hashtags, which we use as topic indicators for posts, and on the assumption that a specific topic is represented by multiple hashtags. We present a neighborhood-based recommender system, which we have implemented using collaborative filtering algorithms in order to (a) identify hashtags, urls and users related with a specific topic, and (b) combine them with SN-based metrics in order to address the aforementioned questions in Twitter. The recommender system is built on top of Apache Spark framework in order to achieve optimal scaling and efficiency. For the verification of our system we have used data sets mined from Twitter and tested the extracted results for influential users and urls concerning specific topics in comparison with the influence scores produced by a state of the art influence estimation tool for SNs. Finally, we present and discuss the results regarding two distinct topics and also discuss the offered and potential utility of our system. © 2018, Springer Nature Switzerland AG

Reusable Defense Components for Online Reputation Systems

Attacks on trust and reputation systems (TRS) as well as defense strategies against certain attacks are the subject of many research papers. Although proposing valuable ideas, they all exhibit at least one of the following major shortcomings. Firstly, many researchers design defense mechanisms from scratch and without reusing approved ideas. Secondly, most proposals are limited to naming and theoretically describing the defense mechanisms. Another issue is the inconsistent denomination of attacks with similar characteristics among different researchers. To address these shortcomings, we propose a novel taxonomy of attacks on TRS focusing on their general characteristics and symptomatology. We use this taxonomy to assign reusable, clearly described and practically implemented components to different classes of attacks. With this work, we aim to provide a basis for TRS designers to experiment with numerous defense mechanisms and to build more robust systems in the end