94,760 research outputs found
Prioritization methodology for roadside and guardrail improvement: Quantitative calculation of safety level and optimization of resources allocation
The attention to road safety-related issues has grown fast in recent decades. The experience gained with these themes reveals the importance of considering these aspects in the resource allocation process for roadside and guardrail improvement, which is a complex process often involves conflicting objectives. This work consists on defining an innovative methodology, with the objective of calculating and analysing a numerical risk factor of a road. The method considers geometry, accident rate, traffic of the examined road and four categories of elements/defects where the resources can be allocated to improve the road safety (safety barriers, discrete obstacles, continuous obstacles, and water drainage). The analysis allows the assessment of the hazard index, which could be used in decision-making processes. A case study is presented to analyse roadsides of a 995 km long road network, using the cost-benefit analysis, and to prioritize possible rehabilitation work. The results highlighted that it is suitable to intervene on roads belonging to higher classes of risk, where it is possible to maximize the benefit in terms of safety as consequence of rehabilitation works (i.e., new barrier installation, removal and new barrier installation, and new terminal installation). The proposed method is quantitative; therefore, it avoids providing weak and far from reliable results; moreover, it guarantees a broad vision for the problem, giving a useful tool for road management body
Application of the Procedure for Institutional Compatibility Assessment (PICA) to the implementation of the EU Nitrate Directive in Midi-Pyrenees. Evaluation and suggestions for further improvement and integration into the final version of SEAMLESS-IF
Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
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The digital transformation of business models in the creative industries: A holistic framework and emerging trends
This paper examines how digital technologies facilitate business model innovations in the creative industries. Through a systematic literature review, a holistic business model framework is developed, which is then used to analyse the empirical evidence from the creative industries. The research found that digital technologies have facilitated pervasive changes in business models, and some significant trends have emerged. However, the reconfigured business models are often not ânewâ in the unprecedented sense. Business model innovations are primarily reflected in using digital technologies to enable the deployment of a wider range of business models than previously available to a firm. A significant emerging trend is the increasing adoption of multiple business models as a portfolio within one firm. This is happening in firms of all sizes, when one firm uses multiple business models to servedifferent markets segments, sell different products, or engage with multi-sided markets, or to use different business models over time. The holistic business model framework is refined and extended through a recursive learning process, which can serve both as a cognitive instrument for understanding business models and a planning tool for business model innovations. The paper contributes to our understanding of the theory of business models and how digital technologies facilitate business model innovations in the creative industries. Three new themes for future research are highlighted
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
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