493 research outputs found
The reality of media freedom in Swaziland under the new constitutional dispensation
No full textThe study concludes that there is still lack of media freedom in Swaziland under the new constitutional dispensation. Its significant finding is that the lack of media freedom is a consequence of constitutional, legal and extra-legal constraints
Raw and real: How travel influencers package the nation.
Get PDFThis paper delineates how Indian travel influencers construct and morph the tourist gaze of the domestic viewer. The author argues that through Instagram reels, YouTube shorts and vlogs, domestic travel Social Media Influencers (SMIs) propagate a skewed understanding of the nation—one that necessarily (a) claims to offer a glimpse of unseen and authentic India that exists outside of where the viewer resides; (b) can be discovered only in remote, interior parts of the country such as towns/villages or larger neglected, peripheral regions such as the North-East; (c) consists of people who are uni-dimensionally kind, generous and happy—living in peace and harmony with no conflict. Anything that does not fit this paradigm is edited out and not presented to the viewers. Thereby these SMIs articulate an imaginative geography of the nation that not only echoes their ideology but frequently melds to become a
constitutive component of the very spaces they imagine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/177343/1/22-Chaudhary-Travel-Social Media and Society in India Proceedings-125-132-10.73027940.pdfSEL
Resin and steel-reinforced resin used as injection materials in bolted connections
No full textInjection bolts are bolts in which the cavity produced by the clearance between the bolt and the wall of the hole is completely filled up with a two-component resin. Filling of the clearance is carried out through a small hole in the head of the bolt. After injection and complete curing, the connection is slip resistant. Recently the injection material, typically an epoxy resin, was modified at TU Delft by adding steel shots (spherical particles) to mitigate the effects of resin compliance in the shear connection of reusable composite (steel-concrete) structures. Experimental compressive material tests on unconfined/confined resin and steel-reinforced resin are evaluated in this chapter. The uniaxial model which combines damage mechanics and the Ramberg-Osgood relationship is proposed to describe the uniaxial compressive behavior of resin and steel-reinforced resin. First-order numerical homogenization is employed as a high-fidelity model, where a combined nonlinear isotropic/kinematic cyclic hardening model is employed to define the steel plasticity, the linear Drucker-Prager plastic criterion was used to simulate resin damage, and the cohesive surfaces reflecting the relationship between traction and displacement at the interface. The linear Drucker-Prager plastic model is used as a low-fidelity model.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Steel & Composite Structure
Graph Coloring Using Heat Diffusion
Get PDFGraph coloring is a problem with varied applications in industry and science
such as scheduling, resource allocation, and circuit design. The purpose of
this paper is to establish if a new gradient based iterative solver framework
known as heat diffusion can solve the graph coloring problem. We propose a
solution to the graph coloring problem using the heat diffusion framework. We
compare the solutions against popular methods and establish the competitiveness
of heat diffusion method for the graph coloring problem.Comment: 5 Pages, 3 Figure
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning
Get PDFIn this paper, we explore the graph partitioning problem, a pivotal
combina-torial optimization challenge with extensive applications in various
fields such as science, technology, and business. Recognized as an NP-hard
prob-lem, graph partitioning lacks polynomial-time algorithms for its
resolution. Recently, there has been a burgeoning interest in leveraging
machine learn-ing, particularly approaches like supervised, unsupervised, and
reinforce-ment learning, to tackle such NP-hard problems. However, these
methods face significant hurdles: supervised learning is constrained by the
necessity of labeled solution instances, which are often computationally
impractical to obtain; reinforcement learning grapples with instability in the
learning pro-cess; and unsupervised learning contends with the absence of a
differentia-ble loss function, a consequence of the discrete nature of most
combinatorial optimization problems. Addressing these challenges, our research
introduces a novel pipeline employing an unsupervised graph neural network to
solve the graph partitioning problem. The core innovation of this study is the
for-mulation of a differentiable loss function tailored for this purpose. We
rigor-ously evaluate our methodology against contemporary state-of-the-art
tech-niques, focusing on metrics: cuts and balance, and our findings reveal
that our is competitive with these leading methods.Comment: 2 Tables, 2 Figure
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