scholarly journals Decentralized Economic Complexity in Switzerland and Its Contribution to Inclusive and Sustainable Change

2021 ◽  
Vol 13 (8) ◽  
pp. 4181
Author(s):  
Philipp Aerni
Keyword(s):  
High Tech ◽  
Collaborative Culture ◽  
Global Knowledge ◽  
Global Business ◽  
Economic Complexity ◽  
Success Stories ◽  
First Case ◽  
The Federalist ◽  
Development Goals

The UN Sustainable Development Goals (SDGs) aim at harnessing economic complexity for sustainable and inclusive economic growth by calling for a decade of joint action. In this paper, we show how the action-oriented collaborative culture of complex and competitive economic ecosystems in places outside the major population centers may generate significant positive external effects for society and the environment at large. We illustrate this by means of two small case studies in Switzerland, a country with a federal system that enables decentralized economic development. The first case study investigates the economic ecosystem of the small town Monthey to show how productive migrants and embedded multinational companies increase the knowledge and know-how of local small and medium-sized enterprises (SMEs). The successful collaboration of insiders and outsiders accounts for the internal economic complexity that makes the region innovative and competitive. The second case study highlights the importance of the federalist system by showing how the canton of Solothurn succeeded in nurturing globally competitive export-oriented SMEs. We conclude that the success of these inclusive economic ecosystems in unexpected places may only be understood in the specific geographical, historical and political context, as well as the general openness of these regions toward entrepreneurial migrants and global business. The importance of local social capital makes it hard to replicate such success stories. Nevertheless, they indicate that the global knowledge economy may not just pose a threat, but also offer great opportunities for productive regions beyond the major global high-tech clusters of economic complexity.

scholarly journals The Role of the Documents and Published Sources in Assessment and Attribution: The Case Study of the Malachite History

2020 ◽  
Author(s):  
Ludmila Budrina
Keyword(s):  
High Tech ◽  
Sources Of Information ◽  
The Third ◽  
First Case ◽  
Archival Documents ◽  
The Media ◽  
Traditional Approaches ◽  
World Exhibition

The newly developed high-tech methods of attribution and assessment have sometimes been viewed as replacements for more traditional approaches. This article uses three case studies to examine the role of documents and published resources as the important sources of information for the attribution of malachite pieces commissioned to the European artists by Nikolay Demidov. The first case highlights the role of archival documents in the identification of the elements and complete reconstruction of an important table centrepiece. The second example uses the materials published by the media of different countries made accessible by the digitalization process, and the placement of this digital copy in the open databases. With the support from the three articles published in the English, French and Vatican journals, it was possible to identify author, date of creation and the relation to the client for one pair of columns, which are the first example of the architectural use of malachite. The third case shows the role of iconographic sources – original pieces and printed graphics – in the attribution of the pieces from the presumably lost collection of Russian malachite created for the First World’s Exhibition in London in 1851. In conclusion, the author discusses the importance of the traditional methods of assessment and attribution based on the documents and printed sources. Keywords: malachite, stonecutting art, Demidov family, Thomire, Sibilio, private malachite manufacture of Demidov, The World Exhibition in London (1851)

scholarly journals Universities as the Engine of Transformational Sustainability in Delivering Against the Sustainable Development Goals: Livitabng Labs for Sustainability

2018 ◽  
Author(s):  
Wendy M. Purcell ◽  
Heather A. Henriksen ◽  
Jack D. Spengler
Keyword(s):  
Higher Education ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Social Innovation ◽  
Critical Role ◽  
Well Being ◽  
The Sustainable Development ◽  
First Case ◽  
Development Goals

Universities can do more to deliver against the Sustainable Development Goals (SDGs), working with faculty, staff and students as well as their wider stakeholder community and alumni body. They play a critical role in helping shape new ways for the world, educating global citizens and delivering knowledge and innovation into society – universities can be engines of societal transformation. Here, using a case study approach, different ways of strategizing sustainability in a university setting are explored with an example from the UK, Europe and USA. The first case is a public UK university that adopted enterprise and sustainability as its academic mission to secure differentiation in a disrupted and increasingly marketized global higher education sector which then became a source of inspiration for change in regional businesses and the local community. The second case study is a business sector-led sustainability-driven transformation working with a private university in Bulgaria to catalyze economic regeneration and social innovation. Finally, the case of Harvard’s Office for Sustainability engagement program is given to show how this approach connects faculty and students with institutional sustainability plans and external partners.  Each case is a living lab, positioning sustainability as an intentional strategy. Leadership at all levels, and by students, was key to success in acting with purpose. Partnerships within and with universities can help accelerate delivery of the SDGs, with higher education making a fuller contribution to sustaining the economic, cultural and intellectual well-being of our global communities. 

China and the Geopolitics of Rare Earths

2017 ◽  
Author(s):  
Sophia Kalantzakos
Keyword(s):  
Rare Earth ◽  
Rare Earths ◽  
Resource Competition ◽  
Renewable Energy Sources ◽  
High Tech ◽  
Trade Dispute ◽  
Policy Responses ◽  
Economic Statecraft ◽  
The Rare Earth

In 2010, because of a geopolitical incident between China and Japan, seventeen elements of the periodic table known as rare earths became notorious overnight. An “unofficial” and temporary embargo of rare-earth shipments to Japan alerted the world to China’s near monopoly position on the production and export of these indispensable elements for high-tech, defense, and renewable energy sources. A few months before the geopolitical confrontation, China had chosen to substantially cut export quotas of rare earths. Both events sent shockwaves across the markets, and rare-earth prices skyrocketed, prompting reactions from industrial nations and industry itself. The rare-earth crisis is not a simple trade dispute, however. It also raises questions about China’s use of economic statecraft and the impacts of growing resource competition. A detailed and nuanced examination of the rare-earth crisis provides a significant and distinctive case study of resource competition and its spill-over geopolitical effects. It sheds light on the formulation, deployment, longevity, effectiveness, and, perhaps, shortsightedness of policy responses by other industrial nations, while also providing an example of how China might choose to employ instruments of economic statecraft in its rise to superpower status.

Scales and Units

2018 ◽  
Author(s):  
Kathryn M. de Luna
Keyword(s):  
Language Change ◽  
Historical Linguistics ◽  
Bantu Languages ◽  
The North ◽  
First Case ◽  
Linguistic Evidence ◽  
River Region ◽  
History Of ◽  
Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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