7,784 research outputs found
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Landfill site trees: Potential source or sink of greenhouse gases?
Tree stems can transport greenhouse gases (GHGs) produced belowground to the atmosphere. Previous studies in natural wetland and upland ecosystems have quantified tree stem fluxes of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O). However, tree stem GHG fluxes have not previously been measured in the context of managed environments. The work presented in this thesis aimed to quantify GHG fluxes from tree stems on closed landfill sites.
To investigate the potential for trees growing on closed landfill sites to act as conduits for GHGs produced belowground to the atmosphere, GHG fluxes were measured from tree stem and soil surfaces. In situ measurements from a closed landfill site in the UK were examined for spatial and temporal patterns and evaluated against data from a comparable non-landfill area. Measurements were also conducted from landfill sites in the UK with varying management practices and different tree species present. The resulting flux values were scaled up to estimate the magnitude of tree stem GHG fluxes from closed landfills at a national level.
The findings presented here show evidence of tree mediated GHG transport on closed landfill sites and temporal variations in fluxes from tree stems were also observed, with generally higher fluxes in the summer months. Stem CH4 fluxes varied between trees growing on landfill sites with different management practices. Additionally, stem N2O fluxes displayed spatial patterns, with decreasing emissions at increased height from the forest floor, indicating an underground source. Evidence suggested that GHG fluxes from closed landfills are influenced by factors including the quantity of GHG produced in the waste (linked to the age of the site), the susceptibility of the area to waterlogging and landfill management techniques put in place upon closure (for example, clay caps, cover soils and gas extraction). Upscaled CH4 and N2O flux values from tree stems on closed landfill sites corresponded to less than 1% of the total CH4 and N2O emissions reported from UK landfills in 2020.
Overall, results indicated that measuring soil fluxes alone from forested landfill sites would result in an underestimation of the total surface fluxes. However, the emission rates from tree stems on closed landfills observed in this thesis do not exceed those in natural ecosystems. Therefore, with careful planning and management, the recommendation is that trees can be planted on closed landfill sites in the UK without emitting atypical levels of GHGs. However, including gas fluxes from tree stems on closed landfills would increase the accuracy of GHG budgets at national and global levels
The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review
With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs
Interactive visualizations of unstructured oceanographic data
The newly founded company Oceanbox is creating a novel oceanographic forecasting system to provide oceanography as a service. These services use mathematical models that generate large hydrodynamic data sets as unstructured triangular grids with high-resolution model areas. Oceanbox makes the model results accessible in a web application. New visualizations are needed to accommodate land-masking and large data volumes.
In this thesis, we propose using a k-d tree to spatially partition unstructured triangular grids to provide the look-up times needed for interactive visualizations. A k-d tree is implemented in F# called FsKDTree. This thesis also describes the implementation of dynamic tiling map layers to visualize current barbs, scalar fields, and particle streams. The current barb layer queries data from the data server with the help of the k-d tree and displays it in the browser. Scalar fields and particle streams are implemented using WebGL, which enables the rendering of triangular grids. Stream particle visualization effects are implemented as velocity advection computed on the GPU with textures.
The new visualizations are used in Oceanbox's production systems, and spatial indexing has been integrated into Oceanbox's archive retrieval system. FsKDTree improves tree creation times by up to 4x over the C# equivalent and improves search times by up to 13x compared to the .NET C# implementation. Finally, the largest model areas can be viewed with current barbs, scalar fields, and particle stream visualizations at 60 FPS, even for the largest model areas provided by the service
FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Extruded Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders
In many practical fluid dynamics experiments, measuring variables such as
velocity and pressure is possible only at a limited number of sensor locations,
\textcolor{black}{for a few two-dimensional planes, or for a small 3D domain in
the flow}. However, knowledge of the full fields is necessary to understand the
dynamics of many flows. Deep learning reconstruction of full flow fields from
sparse measurements has recently garnered significant research interest, as a
way of overcoming this limitation. This task is referred to as the flow
reconstruction (FR) task. In the present study, we propose a convolutional
autoencoder based neural network model, dubbed FR3D, which enables FR to be
carried out for three-dimensional flows around extruded 3D objects with
different cross-sections. An innovative mapping approach, whereby multiple
fluid domains are mapped to an annulus, enables FR3D to generalize its
performance to objects not encountered during training. We conclusively
demonstrate this generalization capability using a dataset composed of 80
training and 20 testing geometries, all randomly generated. We show that the
FR3D model reconstructs pressure and velocity components with a few percentage
points of error. Additionally, using these predictions, we accurately estimate
the Q-criterion fields as well lift and drag forces on the geometries.Comment: 29 pages, 10 figures. Accepted at International Journal of Heat and
Fluid Flo
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Sign Language Translation from Instructional Videos
The advances in automatic sign language translation (SLT) to spoken languages
have been mostly benchmarked with datasets of limited size and restricted
domains. Our work advances the state of the art by providing the first baseline
results on How2Sign, a large and broad dataset.
We train a Transformer over I3D video features, using the reduced BLEU as a
reference metric for validation, instead of the widely used BLEU score. We
report a result of 8.03 on the BLEU score, and publish the first open-source
implementation of its kind to promote further advances.Comment: Paper accepted at WiCV @CVPR2
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
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