Soft set theory based decision support system for mining electronic government dataset

Electronic government (e-gov) is applied to support performance and create more efficient and effective public services. Grouping data in soft-set theory can be considered as a decision-making technique for determining the maturity level of e-government use. So far, the uncertainty of the data obtained through the questionnaire has not been maximally used as an appropriate reference for the government in determining the direction of future e-gov development policy. This study presents the maximum attribute relative (MAR) based on soft set theory to classify attribute options. The results show that facilitation conditions (FC) are the highest variable in influencing people to use e-government, followed by performance expectancy (PE) and system quality (SQ). The results provide useful information for decision makers to make policies about their citizens and potentially provide recommendations on how to design and develop e-government systems in improving public services

Advances in the Assessment of Climate Change Impact on the Forest Landscape

Changing climates threaten the habitats and ecosystems at variable extents throughout the world. Forests are unique habitats and ecosystems that are vulnerable by the consequences of climate change. The climate change causes disturbances, alterations. and shifting on the forests that can be diagnosed at the tree and stand scales, as well as can be monitored and analyzed at the landscape scale. Furthermore, some recent researches concentrate on conveying the forest tree and stand-level shifting and disturbances to the forest landscape level by upscaling. In this study, the climate change impacts on the forest landscapes; principally, the disturbances including the drought-induced mortality, growth and productivity failures, and insect outbreaks are evaluated. Secondarily, climate change-induced alterations of the forest species distributions and forest landscape compositions, dynamics of the forest biodiversity, and tree migrations are discussed by focusing particularly on the relatively recent advances involving the modeling procedures. Ultimately, monitoring the climate change-driven shifting phenology of the forest landscape through the remote sensing techniques is referred in this study. Moreover, the study examples dependent upon the climate-ecological modeling and satellite data assessment of the forest landscapes throughout the world are also referenced. The landscape-scale assessment of the climate change impacts on the forest ecosystems provides integrated and comprehensive approach toward the proposal of sustainable mitigations and solutions to the phenomenon

Environmental Objects for Authoring Procedural Scenes

International audienceWe propose a novel approach for authoring large scenes with automatic enhancement of objects to create geometric decoration details such as snow cover, icicles, fallen leaves, grass tufts or even trash. We introduce environmental objects that extend an input object geometry with a set of procedural effects that defines how the object reacts to the environment, and by a set of scalar fields that defines the influence of the object over of the environment. The user controls the scene by modifying environmental variables, such as temperature or humidity fields. The scene definition is hierarchical: objects can be grouped and their behaviours can be set at each level of the hierarchy. Our per object definition allows us to optimize and accelerate the effects computation, which also enables us to generate large scenes with many geometric details at a very high level of detail. In our implementation, a complex urban scene of 10 000 m², represented with details of less than 1 cm, can be locally modified and entirely regenerated in a few seconds

Manipulating Attributes of Natural Scenes via Hallucination

In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods.Comment: Accepted for publication in ACM Transactions on Graphic

Machine Learning Based Massive Leaf Falling Detection For Managing The Waste Disposal Efficiently

 Massive falling of leaves in dense tree region during autumn environment often creates huge collection of waste each year. To maintain and manage a clean environment, it is necessary to collect the dried leaf waste regularly at rapid intervals. In order of claiming this, it is very essential to development a leaf falling simulator using objective detection principle that measures the size of the leaf, color and other relevant features for efficient feature selection, detection and collection of waste. In order to accommodate these three tasks, the study uses a machine learning detection that essentially identifies the leaves based on input datasets. The study considers various input features like size of the leaf, color, falling rate of a dried leaf and moment of inertia. These features are utilized for detecting the falling leaves and providing the input for clearing the leaf waste in that region. The experiments are conducted to test the real-time applicability of the model against various trees and in different regions

The Value of a Properly Maintained Hiking Trail Network and a Traditional Landscape for Mountain Recreation in the Dolomites

Alpine mountains represent one of the most important tourist destinations in the world, constituting approximately 3.1% of the global tourism market when considering the tourist flows coming from abroad. While there may be numerous factors that motivate tourists to choose rural areas, an important role is played by the opportunity to visit well-conserved landscapes and uncontaminated natural areas. The purpose of this study was to make a monetary valuation of the social benefits generated by the adoption of three measures of the Rural Development Plan (RDP) of Veneto (Italy) aimed specifically at enhancing the recreational usability of the mountain territory. In this regard, a discrete choice experiment (DCE) was applied for the economic valuation, and a qualitative survey was used to collect the opinion of respondents related to the measures to protect the meadows and mountain hiking trails. According to the DCE estimates, on average, the benefits due to the conservation of the existing meadows and pastures was equal to \u20ac851 per hectare, those due to the conservation and improvement of the trail network were \u20ac12,260 per km, and the benefits due to the recovery of the meadows and pastures of uncultivated and abandoned areas for naturalistic purposes amounted to \u20ac6,852 per hectare. Comparing the estimates obtained with the expenditure incurred by the RDP to finance the three actions considered in our DCE, it can be seen that the benefits are considerably higher than the costs, especially with regard to the conservation of paths and the recovery of abandoned areas for naturalistic purposes

Efficient modeling of entangled details for natural scenes

Proceedings of Pacific Graphics 2016 (Okinawa)International audienceDigital landscape realism often comes from the multitude of details that are hard to model such as fallen leaves, rock piles orentangled fallen branches. In this article, we present a method for augmenting natural scenes with a huge amount of details suchas grass tufts, stones, leaves or twigs. Our approach takes advantage of the observation that those details can be approximatedby replications of a few similar objects and therefore relies on mass-instancing. We propose an original structure, the GhostTile, that stores a huge number of overlapping candidate objects in a tile, along with a pre-computed collision graph. Detailsare created by traversing the scene with the Ghost Tile and generating instances according to user-defined density fields thatallow to sculpt layers and piles of entangled objects while providing control over their density and distribution

Neo-spaces for urban livability? Urbanites' versatile mental images of green roofs in the Helsinki metropolitan area, Finland

Within the context of enhancing sustainable and livable urban environments, one aim is to establish multifunctional green infrastructure (GI). We argue that in order to successfully plan and manage the development of GI, an inclusive and future-oriented stance concerning the needs and expectations of urbanites is required. By using green roofs as an example, the aim of this paper was to offer insights into how people envisage novel GI in urban environments and to reveal the scope of meanings and values people attach to these kinds of green infrastructure. We present results based on 149 stories collected with the method of empathy-based stories. Respondents were asked to use their imagination to produce mental images of not-yet-existing green roofs in different urban situations. Our results reflect a rich set of dimensions of green roofs that the respondents vividly imagined. Green roofs may contribute to the livability of urban areas in multiple ways, such as strengthening social cohesion, providing space for everyday renewal and restoration, offering interesting sceneries and multisensory experiences, softening the hard cityscape, showing ephemeral events and making experiences of "height" possible, as well as increasing the "contact with nature" experiences for residents, e.g. through biodiverse nature in the middle of built environments. Furthermore, the need for local, customized solutions that offer different benefits and experiences was expressed. Using both qualitative and quantitative analyses, we idealized four green roof meta-types for understanding the diverse expectations people may have for green roofs in urban area: Urban farm, Oasis, Urban hill and Meadow. Based on our results we suggest that comprehensive experiences and needs of people should be taken into account when designing urban green roofs or urban green in general not only, e.g. visual pleasure. Also, site- and user-specific solutions should be considered instead of generally applied ones. Our results offer tools for, e.g. urban planners to understand the value of diverse green roof solutions to the user. (C) 2016 The Authors. Published by Elsevier Ltd.Peer reviewe

Estimate tourism model choice for Pilgrim in the Way of St. James: The Portuguese Way

The purpose of this study is to examine how the characteristics of the pilgrims and the attributes of travel influence the choose of the Portuguese. It is important to understand the choices tourists make. This study identifies the factors that influence the tourists’ choice of way. The analysis is based on the official statistics of the Portuguese Way of Santiago between 2003 and 2020. The study provides several important findings concerning tourism’s model choice. Understanding the tourists’ modal choice behavior help public and private organizations to develop appropriate marketing strategies. The probit model is used to model a relationship between a dependent variable and the independent variables: Sex, Age and Continent. The independent variables are assumed to affect the choice of Portuguese Way. The model has been estimated by the maximum likelihood method. The estimated coefficients and standard errors disclose the factors that influence Choice of Portuguese. Discrete choice experiments are used for measuring and predicting individuals’ preferences and choices of alternatives and provide quantitative measures of the relative importance of attributes of chooses of the Portuguese way. The study provides several important findings concerning the pilgrim’s modal choice. The social stratum, family cycle, origin country, start city are key elements in explaining the tourist model choice decision. The results of the estimate model reveal that the variables social stratum has a significant and positive impact in the choose of Portuguese way, but the life cycle doesn’t have an impact the dependent variables. Finally, the o religious motivations are a significant and negative effect in the Portuguese Way. Understanding the tourists’ modal choice behavior may help tourism organizations to develop appropriate marketing strategies