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This paper presents a multifactor approach for performance assessment of Water Users Associations (WUAs) in Uzbekistan in order to identify the drivers for improved and effi cient performance of WUAs. The study was carried out in the Fergana Valley where the WUAs were created along the South Fergana Main Canal during the last 10 years. The farmers and the employees of 20 WUAs were questioned about the WUAs’ activities and the quantitative and qualitative data were obtained. This became a base for the calculation of 36 indicators divided into 6 groups: Water supply, technical conditions, economic conditions, social and cultural conditions, organizational conditions and information conditions. All the indicators assessed with a differentiated point system adjusted for subjectivity of several of them give the total maximal result for the associations of 250 point. The WUAs of the Fergana Valley showed the score between 145 and 219 points, what refl ects a highly diverse level of the WUAs performance in the region. The analysis of the indicators revealed that the key points of the WUA’s success are the organizational and institutional conditions including the participatory factors and awareness of both the farmers and employees about the work of WUA. The research showed that the low performance of the WUAs is always explained by the low technical and economic conditions along with weak organization and information dissemination conditions. It is clear that it is complicated to improve technical and economic conditions immediately because they are cost-based and cost-induced. However, it is possible to improve the organizational conditions and to strengthen the institutional basis via formal and information institutions which will gradually lead to improvement of economic and technical conditions of WUAs. Farmers should be involved into the WUA Governance and into the process of making common decisions and solving common problems together via proper institutions. Their awareness can also be improved by leading additional trainings for increasing farmers’ agronomic and irrigation knowledge, teaching them water saving technologies and acquainting them with the use of water measuring equipment so it can bring reliable water supply, transparent budgeting and adequate as well as equitable water allocation to the water users.

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Software developers often ask questions about software systems and software ecosystems that entail exploration and navigation, such as who uses this component?, and where is this feature implemented?. Software visualisation can be a great aid to understanding and exploring the answers to such questions, but visualisations require expertise to implement effectively, and they do not always scale well to large systems. We propose to automatically generate software visualisations based on software models derived from open source software corpora and from an analysis of the properties of typical developers queries and commonly used visualisations. The key challenges we see are (1) understanding how to match queries to suitable visualisations, and (2) scaling visualisations effectively to very large software systems and corpora. In the paper we motivate the idea of automatic software visualisation, we enumerate the challenges and our proposals to address them, and we describe some very initial results in our attempts to develop scalable visualisations of open source software corpora.

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Various avours of a new research field on (socio-)physical or personal analytics have emerged, with the goal of deriving semantically-rich insights from people's low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental (e.g. weather, infrastructure) and application-specific data, to better capture the interactions between users and their context, in addition to those among users. To illustrate our proposed concept of synergistic user <-> context analytics, we first provide some example use cases. Then, we present our ongoing work towards a synergistic analytics platform: a testbed, based on mobile crowdsensing and the Internet of Things (IoT), a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights.

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We present a novel graphical user interface program GrafLab (GRAvity Field LABoratory) for spherical harmonic synthesis (SHS) created in MATLAB®. This program allows to comfortably compute 38 various functionals of the geopotential up to ultra-high degrees and orders of spherical harmonic expansion. For the most difficult part of the SHS, namely the evaluation of the fully normalized associated Legendre functions (fnALFs), we used three different approaches according to required maximum degree: (i) the standard forward column method (up to maximum degree 1800, in some cases up to degree 2190); (ii) the modified forward column method combined with Horner's scheme (up to maximum degree 2700); (iii) the extended-range arithmetic (up to an arbitrary maximum degree). For the maximum degree 2190, the SHS with fnALFs evaluated using the extended-range arithmetic approach takes only approximately 2-3 times longer than its standard arithmetic counterpart, i.e. the standard forward column method. In the GrafLab, the functionals of the geopotential can be evaluated on a regular grid or point-wise, while the input coordinates can either be read from a data file or entered manually. For the computation on a regular grid we decided to apply the lumped coefficients approach due to significant time-efficiency of this method. Furthermore, if a full variance-covariance matrix of spherical harmonic coefficients is available, it is possible to compute the commission errors of the functionals. When computing on a regular grid, the output functionals or their commission errors may be depicted on a map using automatically selected cartographic projection.

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