35,970 research outputs found
Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology
INE/AUTC 10.0
A hydro-environmental optimization for assessing sustainable carrying capacity
The present study proposes an applicable method to determine the population carrying capacity of urban areas in which ecological impacts of river ecosystem as the source of water supply and sustainable population growth are linked. A multiobejctive optimization method was developed in which two objectives were considered: 1) minimizing the fish population loss as the environmental index of the river ecosystem and 2) minimizing the difference between initial population carrying capacity and the sustainable population carrying capacity. The ecological impacts of the river ecosystem were assessed through the potential fish population as an environmental index using several artificial intelligence and regression models. Based on case study results, the initial plan of development is not reliable because ecological impacts on the river ecosystem are remarkable. The proposed method is able to reduce the ecological impacts. However, the sustainable population carrying capacity is considerably lower than the initial planned population. It is needed to reduce the planned population more than 45% in the case study. Habitat loss is less than 35% which means the optimization model is able to find an optimal solution for balancing environmental requirements and humansâ needs. In other words, the optimization model balances the needs of environment and water supply by reducing 45% of population and decreasing habitat loss to 35%
Proceedings of Abstracts Engineering and Computer Science Research Conference 2019
© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care
ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects
This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.TelefĂłnica Chair âIntelligence in Networksâ of the University of Seville (Spain
Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil
This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (ÎŽ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearsonâs correlation coefficient, PâŻ=âŻ2.22âŻĂâŻ10â32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications
CEPS Task Force on Artificial Intelligence and Cybersecurity Technology, Governance and Policy Challenges Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version). CEPS Task Force Report 22 January 2020
The Centre for European Policy Studies launched a Task Force on Artificial Intelligence (AI) and
Cybersecurity in September 2019. The goal of this Task Force is to bring attention to the market,
technical, ethical and governance challenges posed by the intersection of AI and cybersecurity,
focusing both on AI for cybersecurity but also cybersecurity for AI. The Task Force is multi-stakeholder
by design and composed of academics, industry players from various sectors, policymakers and civil
society.
The Task Force is currently discussing issues such as the state and evolution of the application of AI
in cybersecurity and cybersecurity for AI; the debate on the role that AI could play in the dynamics
between cyber attackers and defenders; the increasing need for sharing information on threats and
how to deal with the vulnerabilities of AI-enabled systems; options for policy experimentation; and
possible EU policy measures to ease the adoption of AI in cybersecurity in Europe.
As part of such activities, this report aims at assessing the High-Level Expert Group (HLEG) on AI Ethics
Guidelines for Trustworthy AI, presented on April 8, 2019. In particular, this report analyses and
makes suggestions on the Trustworthy AI Assessment List (Pilot version), a non-exhaustive list aimed
at helping the public and the private sector in operationalising Trustworthy AI. The list is composed
of 131 items that are supposed to guide AI designers and developers throughout the process of
design, development, and deployment of AI, although not intended as guidance to ensure
compliance with the applicable laws. The list is in its piloting phase and is currently undergoing a
revision that will be finalised in early 2020.
This report would like to contribute to this revision by addressing in particular the interplay between
AI and cybersecurity. This evaluation has been made according to specific criteria: whether and how
the items of the Assessment List refer to existing legislation (e.g. GDPR, EU Charter of Fundamental
Rights); whether they refer to moral principles (but not laws); whether they consider that AI attacks
are fundamentally different from traditional cyberattacks; whether they are compatible with
different risk levels; whether they are flexible enough in terms of clear/easy measurement,
implementation by AI developers and SMEs; and overall, whether they are likely to create obstacles
for the industry.
The HLEG is a diverse group, with more than 50 members representing different stakeholders, such
as think tanks, academia, EU Agencies, civil society, and industry, who were given the difficult task of
producing a simple checklist for a complex issue. The public engagement exercise looks successful
overall in that more than 450 stakeholders have signed in and are contributing to the process.
The next sections of this report present the items listed by the HLEG followed by the analysis and
suggestions raised by the Task Force (see list of the members of the Task Force in Annex 1)
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
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