Search

In this paper, I explore socio-spatial segregation from a particular perspective, which may probably be considered a novelty in the investigation of spatial social patterns, as it focuses on questions such as: What kind of distinction occurs between socio-spatial patterns designated by statistical data and the cognitive representations of those patterns in people’s minds? And what explains these differences, and what kind of impact can they generate?

The European cultural landscape and its traditional values represent a significant condition for recreation and tourism. However, we are aware of the negative consequences of human activity in the past years in rural areas and pay more attention to protecting these values. Modern rural recreation and tourism must be built on scientific background. That is why the presented contribution brings an innovative integrated approach to sustainable rural recreation and tourism model by applying landscape ecological planning methodology with a combination of ecological stability and landscape character assessment. It deals with the qualitative evaluation of the landscape potential of the foothill village of Podkonice in Slovakia and the possibilities of its use in sustainable rural tourism. Determining the potential in confrontation with different forms of tourism activities can help achieve a sustainable tourism concept. The main result of the presented research is the final optimal selection of activities and use of the landscape with regard to the development of tourism. Such a proposal can be used in spatial planning and strategic decision-making processes not only in the study area, but it can be applied in the European context.

Public participation is increasingly important in flood and environmental management planning. Accordingly , understanding the attitude of local society to natural values and their relations with the environment is highly important to realize successful development projects. This study aimed to analyze the human-nature relationship, the public perceptions of environmental hazards and people's engagement with water management related and human interventions at Maros/Mureş River through a public survey. The survey was carried out in 11 Romanian and Hungarian settlements using the random walking method to interview the local public. The results show that people are a little pessimistic concerning the state of the river, and there are misbeliefs about the general problems affecting its present environmental status. Meanwhile, the perception of flood hazard is governed by the fading memory of the last high-risk flood event in 1970. The engagement of residents is mostly affected by socio-demographic parameters. However, the regularity they visit the river is also very important. Consequently, informing people on apparent environmental issues and processes can greatly help the socially inclusive implementation of water management measures along the river.

The data acquisition system (DAQ) of the CMS experiment at the CERN Large Hadron Collider (LHC) assembles events of 2MB at a rate of 100 kHz. The event builder collects event fragments from about 750 sources and assembles them into complete events which are then handed to the High-Level Trigger (HLT) processes running on O(1000) computers. The aging eventbuilding hardware will be replaced during the long shutdown 2 of the LHC taking place in 2019/20. The future data networks will be based on 100 Gb/s interconnects using Ethernet and Infiniband technologies. More powerful computers may allow to combine the currently separate functionality of the readout and builder units into a single I/O processor handling simultaneously 100 Gb/s of input and output traffic. It might be beneficial to preprocess data originating from specific detector parts or regions before handling it to generic HLT processors. Therefore, we will investigate how specialized coprocessors, e.g. GPUs, could be integrated into the event builder. We will present the envisioned changes to the event-builder compared to today’s system. Initial measurements of the performance of the data networks under the event-building traffic pattern will be shown. Implications of a folded network architecture for the event building and corresponding changes to the software implementation will be discussed.

The Compact Muon Solenoid (CMS) is one of the experiments at the CERN Large Hadron Collider (LHC). The CMS Online Monitoring system (OMS) is an upgrade and successor to the CMS Web-Based Monitoring (WBM)system, which is an essential tool for shift crew members, detector subsystem experts, operations coordinators, and those performing physics analyses. The CMS OMS is divided into aggregation and presentation layers. Communication between layers uses RESTful JSON:API compliant requests. The aggregation layer is responsible for collecting data from heterogeneous sources, storage of transformed and pre-calculated (aggregated) values and exposure of data via the RESTful API. The presentation layer displays detector information via a modern, user-friendly and customizable web interface. The CMS OMS user interface is composed of a set of cutting-edge software frameworks and tools to display non-event data to any authenticated CMS user worldwide. The web interface tree-like component structure comprises (top-down): workspaces, folders, pages, controllers and portlets. A clear hierarchy gives the required flexibility and control for content organization. Each bottom element instantiates a portlet and is a reusable component that displays a single aspect of data, like a table, a plot, an article, etc. Pages consist of multiple different portlets and can be customized at runtime by using a drag-and-drop technique. This is how a single page can easily include information from multiple online sources. Different pages give access to a summary of the current status of the experiment, as well as convenient access to historical data. This paper describes the CMS OMS architecture, core concepts and technologies of the presentation layer.

We investigated the predictability of mean reverting portfolios and the VAR(1) model in several aspects. First, we checked the dependency of the accuracy of VAR(1) model on different data types including the original data itself, the return of prices, the natural logarithm of stock and on the log return. Then we compared the accuracy of predictions of mean reverting portfolios coming from VAR(1) with different generative models such as VAR(1) and LSTM for both online and o ine data. It was eventually shown that the LSTM predicts much better than the VAR(1) model. The conclusion is that the VAR(1) assumption works well in selecting the mean reverting portfolio, however, LSTM is a better choice for prediction. With the combined model a strategy with positive trading mean profit was successfully developed. We found that online LSTM outperforms all VAR(1) predictions and results in a positive expected profit when used in a simple trading algorithm.