Integration of cooperation for development in Survey Engineering curricula at the European Higher Education Area (EHEA) framework

Survey Engineering curricula involves the integration of many formal disciplines at a high level of proficiency. The Escuela de Ingenieros en Topografía, Cartografía y Geodesia at Universidad Politécnica de Madrid (Survey Engineering) has developed an intense and deep teaching on so-called Applied Land Sciences and Technologies or Land Engineering. However, new approaches are encouraged by the European Higher Education Area (EHEA). This fact requires a review of traditional teaching and methods. Furthermore, the new globalization and international approach gives new ways to this discipline to teach and learn about how to bridge gap between cultures and regions. This work is based in two main needs. On one hand, it is based on integration of basic knowledge and disciplines involved in typical Survey Engineering within Land Management. On the other, there is an urgent need to consider territory on a social and ethical basis, as far as a part of the society, culture, idiosyncrasy or economy. The integration of appropriate knowledge of the Land Management is typically dominated by civil engineers and urban planners. It would be very possible to integrate Survey Engineering and Cooperation for Development in the framework of Land Management disciplines. Cooperation for Development is a concept that has changed since beginning of its use until now. Development projects leave an impact on society in response to their beneficiaries and are directed towards self-sustainability. Furthermore, it is the true bridge to reduce gap between societies when differences are immeasurable. The concept of development has also been changing and nowadays it is not a purely economic concept. Education, science and technology are increasingly taking a larger role in what is meant by development. Moreover, it is commonly accepted that Universities should transfer knowledge to society, and the transfer of knowledge should be open to countries most in need for developing. If the importance of the country development is given by education, science and technology, knowledge transfer would be one of the most clear of ways of Cooperation for Development. Therefore, university cooperation is one of the most powerful tools to achieve it, placing universities as agents of development. In Spain, the role of universities as agents of development and cooperation has been largely strengthened. All about this work deals to how to implement both Cooperation for Development and Land Management within Survey Engineering at the EHEA framework.

The EEA Review and Liechtenstein’s Integration Strategy. CEPS Paperbacks. March 2013

This study focuses on the European Economic Area (EEA), which is a very ‘deep’ instance of market integration between the European Union, Iceland, Liechtenstein and Norway. The EEA in fact is just the ‘internal-market-minus’, i.e. minus agriculture and fisheries, for its three non-EU members. As an extension to the single market of a mere 1% in GDP, the EEA has attracted little attention, not least because it functions quite well. Even less known, however, is the role of Liechtenstein within the EEA, which this book also attempts to clarify.

Could Transnistria block Moldova's integration with the EU? OSW Commentary No. 95, 2012-10-23

Moldova’s progress in its negotiations on an Association Agreement with the European Union, with a Deep and Comprehensive Free Trade Area (DCFTA) as its key element, has become a source of tension between Chisinau and the breakaway Republic of Transnistria. An almost certain refusal by Transnistria to join the DCFTA, will deprive the region of the benefits it currently enjoys under the EU Autonomous Trade Preferences (ATP) worsening its already precarious economic situation. It is to be expected that the issue will become an additional source of tension between the two sides of the Transnistrian conflict, and might also have a negative impact on the EU–Russia relationship. The signing of the Association Agreement, which is scheduled for the autumn of 2013, will be an important step towards Moldova’s integration with the EU. Both sides assign great importance to the speediest possible finalisation of the Agreement, and so far the negotiations have been described as progressing very smoothly. Transnistria’s highly sceptical attitude towards its possible accession to the DCFTA, however, is consistent with the interests of its main ally, Moscow. It is highly probable that Russia intends to thwart Moldova’s EU association process. Moscow’s objective seems to be to draw Moldova permanently into its own sphere of influence, and therefore it perceives Chisinau’s movement towards the EU as a transgression against its geopolitical interests. Consequently, in order to hinder this process, Russia may instrumentally exploit its extensive influence over Transnistria to provoke a crisis between Tiraspol and Chisinau. An apparent increase in Russian presence in the region over the last few months (including tighter control over Transnistria’s KGB and the Ministry of Information) may suggest that the Kremlin is preparing to implement such a scenario.

The South Caucasus - Between integration and fragmentation

The South Caucasus is situated at the intersection of Eurasia’s major transport and energy corridors, making it an important geostrategic region. Traditional regional actors Iran, Turkey and Russia have jostled for influence and power in the region for centuries, and are now faced with competition from the EU, China, the US and NATO. Although Armenia, Azerbaijan and Georgia have been independent for more than two decades, they still continue to feel the sway, and sometimes threat, of external actors. As a response, the three South Caucasus states have chosen very different geostrategic paths since the collapse of the Soviet Union, leaving the region more fragmented and volatile than ever. In this book, various authors offer a deep and broad understanding of the developments in the South Caucasus, analyse the different foreign trajectories that each of the three state is following, and highlight the impact of external actors’ policies.

The Great Slump of 2008–9 and Ukraine's integration with the European Union

For a number of years following the Orange revolution of 2004, Ukraine aspired to join the European Union. Although full integration was never a short-term prospect, European integration, through the Association Agreement and the Deep and Comprehensive Free Trade Area, offers considerable benefits to Ukraine. However, the country was severely affected by the Great Slump of 2008–9 in the global economy, and this profoundly negative experience has shaped Ukrainian domestic and foreign policy in the subsequent period, putting paid to aspirations to EU membership and influencing the Ukrainian government's decision to seek a closer relationship with Russia immediately following the presidential election of 2010. Nevertheless, closer relations with Russia should not adversely affect Ukraine's efforts at EU integration.

Relational diversity, social integration and individual effectiveness: a social self-regulation perspective

Relational demographers and dissimilarity researchers contend that group members who are dissimilar (vs. similar) to their peers in terms of a given diversity attribute (e.g. demographics, attitudes, values or traits) feel less attached to their work group, experience less satisfying and more conflicted relationships with their colleagues, and consequently are less effective. However, qualitative reviews suggest empirical findings tend to be weak and inconsistent (Chattopadhyay, Tluchowska and George, 2004; Riordan, 2000; Tsui and Gutek, 1999), and that it remains unclear when, how and to what extent such differences (i.e. relational diversity) affect group members social integration (i.e. attachment with their work group, satisfaction and conflicted relationships with their peers) and effectiveness (Riordan, 2000). This absence of meta-analytically derived effect size estimates and the lack of an integrative theoretical framework leave practitioners with inconclusive advice regarding whether the effects elicited by relational diversity are practically relevant, and if so how these should be managed. The current research develops an integrative theoretical framework, which it tests by using meta-analysis techniques and adding two further empirical studies to the literature. The first study reports a meta-analytic integration of the results of 129 tests of the relationship between relational diversity with social integration and individual effectiveness. Using meta-analytic and structural equation modelling techniques, it shows different effects of surface- and deep-level relational diversity on social integration Specifically, low levels of interdependence accentuated the negative effects of surface-level relational diversity on social integration, while high levels of interdependence accentuated the negative effects of deep-level relational diversity on social integration. The second study builds on a social self-regulation framework (Abrams, 1994) and suggests that under high levels of interdependence relational diversity is not one but two things: visibility and separation. Using ethnicity as a prominent example it was proposed that separation has a negative effect on group members effectiveness leading for those high in visibility and low in separation to overall positive additive effects, while to overall negative additive effects for those low in visibility and high in separation. These propositions were sustained in a sample of 621 business students working in 135 ethnically diverse work groups in a business simulation course over a period of 24 weeks. The third study suggests visibility has a positive effect on group members self-monitoring, while separation has a negative effect. The study proposed that high levels of visibility and low levels of separation lead to overall positive additive effects on self-monitoring but overall negative additive effects for those low in visibility and high in separation. Results from four waves of data on 261 business students working in 69 ethnically diverse work groups in a business simulation course held over a period of 24 weeks support these propositions.

Compilation of downward flux observations from sediment trap deployments in the Atlantic Ocean - Contribution to EURO-BASIN's Data integration

We provide a compilation of downward fluxes (total mass, POC, PON, BSiO2, CaCO3, PIC and lithogenic/terrigenous fluxes) from over 6000 sediment trap measurements distributed in the Atlantic Ocean, from 30 degree North to 49 degree South, and covering the period 1982-2011. Data from the Mediterranean Sea are also included. Data were compiled from different sources: data repositories (BCO-DMO, PANGAEA), time series sites (BATS, CARIACO), published scientific papers and/or personal communications from PI's. All sources are specifed in the data set. Data from the World Ocean Atlas 2009 were extracted to provide each flux observation with contextual environmental data, such as temperature, salinity, oxygen (concentration, AOU and percentage saturation), nitrate, phosphate and silicate.

Deep Decline

Thesis (Ph.D.)--University of Washington, 2016-08

The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in Web menus

International audience

Multiple solutions and numerical analysis to the dynamic and stationary models coupling a delayed energy balance model involving latent heat and discontinuous albedo with a deep ocean

We study a climatologically important interaction of two of the main components of the geophysical system by adding an energy balance model for the averaged atmospheric temperature as dynamic boundary condition to a diagnostic ocean model having an additional spatial dimension. In this work, we give deeper insight than previous papers in the literature, mainly with respect to the 1990 pioneering model by Watts and Morantine. We are taking into consideration the latent heat for the two phase ocean as well as a possible delayed term. Non-uniqueness for the initial boundary value problem, uniqueness under a non-degeneracy condition and the existence of multiple stationary solutions are proved here. These multiplicity results suggest that an S-shaped bifurcation diagram should be expected to occur in this class of models generalizing previous energy balance models. The numerical method applied to the model is based on a finite volume scheme with nonlinear weighted essentially non-oscillatory reconstruction and Runge–Kutta total variation diminishing for time integration.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Integration of Multiple Simultaneous Stimuli within and between Somatosensory sub-Modalities

Psychological characterisation of the somatosensory system often focusses on minimal units of perception, such as detection, localisation, and magnitude estimation of single events. Research on how multiple simultaneous stimuli are aggregated to create integrated, synthetic experiences is rarer. This thesis aims to shed a light on the mechanisms underlying the integration of multiple simultaneous stimuli, within and between different sub-modalities of the somatosensory system. First, we investigated the ability of healthy individuals to perceive the total intensity of composite somatosensory patterns. We found that the overall intensity of tactile, cold, or warm patterns was systematically overestimated when the multiple simultaneous stimuli had different intensities. Perception of somatosensory totals was biased towards the most salient element in the pattern. Furthermore, we demonstrated that peak-biased aggregation is a genuine perceptual phenomenon which does not rely on the discrimination of the parts, but is rather based on the salience of each stimulus. Next, we studied a classical thermal illusion to assess participants’ ability to localise thermal stimuli delivered on the fingers either in isolation, or in uniform and non-uniform patterns. We found that despite a surprisingly high accuracy in reporting the location of a single stimulus, when participants were presented with non-uniform patterns, their ability to identify the thermal state of a specific finger was completely abolished. Lastly, we investigated the perceptual and neural correlates of thermo-nociceptive interaction during the presentation of multiple thermal stimuli. We found that inhibition of pain by warmth was independent from both the position and the number of thermal stimuli administered. Our results suggest that nonlinear integration of multiple stimuli, within and between somatosensory sub-modalities, may be an efficient way by which the somatosensory system synthesises the complexity of reality, providing an extended and coherent perception of the world, in spite of its deep bandwidth limitations.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Deep and near space tracking stations in support of lunar and planetary exploration missions

The aim of this dissertation is to describe the methodologies required to design, operate, and validate the performance of ground stations dedicated to near and deep space tracking, as well as the models developed to process the signals acquired, from raw data to the output parameters of the orbit determination of spacecraft. This work is framed in the context of lunar and planetary exploration missions by addressing the challenges in receiving and processing radiometric data for radio science investigations and navigation purposes. These challenges include the designing of an appropriate back-end to read, convert and store the antenna voltages, the definition of appropriate methodologies for pre-processing, calibration, and estimation of radiometric data for the extraction of information on the spacecraft state, and the definition and integration of accurate models of the spacecraft dynamics to evaluate the goodness of the recorded signals. Additionally, the experimental design of acquisition strategies to perform direct comparison between ground stations is described and discussed. In particular, the evaluation of the differential performance between stations requires the designing of a dedicated tracking campaign to maximize the overlap of the recorded datasets at the receivers, making it possible to correlate the received signals and isolate the contribution of the ground segment to the noise in the single link. Finally, in support of the methodologies and models presented, results from the validation and design work performed on the Deep Space Network (DSN) affiliated nodes DSS-69 and DSS-17 will also be reported.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.