593 research outputs found
The Emotional and Chromatic Layers of Urban Smells
People are able to detect up to 1 trillion odors. Yet, city planning is
concerned only with a few bad odors, mainly because odors are currently
captured only through complaints made by urban dwellers. To capture both good
and bad odors, we resort to a methodology that has been recently proposed and
relies on tagging information of geo-referenced pictures. In doing so for the
cities of London and Barcelona, this work makes three new contributions. We
study 1) how the urban smellscape changes in time and space; 2) which emotions
people share at places with specific smells; and 3) what is the color of a
smell, if it exists. Without social media data, insights about those three
aspects have been difficult to produce in the past, further delaying the
creation of urban restorative experiences.Comment: 11 pages, 18 figures, final version published in the Proceedings of
the Tenth International Conference on Web and Social Media (ICWSM 2016
Humidity Condenser for potable and non potable water generation, and water-metal-ground heatsink
This working hypothesis involves the use of a humidity condenser with heat sink that works from a depth of 5 meters and beyond (up to 10 meters depending on the sizing requirements), using water, thermal conductive metals and good conductive gravels or soil. The dissipator cools the water so that, with a circulation pump, it cools the exchanger placed in the humidity condenser. The exchanger/condenser, in copper or aluminum, with tube and tube bundle, on the one hand produces condensed water (distilled) and cold air, and on the other hand introduces into the cooling circuit the water slightly heated during the exchange with the air. This heated water is cooled by the heat sink in the following way: 1) A correct sizing of the heatsink (diameter and depth) is evaluated. 2) On the surface of the cooling water inside the heatsink, some floating copper spheres make a first distribution of the surface heat towards the external wall of the heatsink and above them, assisted by the flow of cold air from the condenser. 3) Inside the heat sink, the copper plates (a second exchanger) convey the heat absorbed by the water also towards the metal wall of the heat sink (also made of a good conductor metal). These plates also have the purpose of providing a counter-thrust to the ground surrounding the heatsink. 4) Outside the heat sink wall there are copper wings and sheets and granite gravels (or in any case with good thermal conduction), these sheets, wrapping the external wall of the heat sink, convey the heat from the internal wall of the heatsink to the surrounding ground. 5) Optionally, the cooling water of the exchanger can be engineered with additives to accelerate heat exchanges (only valid for the configuration with the collection of drinking water which has the cooling circuit separate from the condensate one). 6) Only for the purpose of optimizing the process it is possible to use a small peltier cell or a similar tecnology on the terminal section of the primary exchanger: in a cycle in which the air is already very cooled by the water of the dissipator, the use of a very low wattage peltier just to get a 2 or 3 degree dip, for the sole purpose of making small adjustments.
Note:
No prototype has yet been built (today is 06 January 2021) and therefore some working hypotheses are reported in this article. No responsibility can in any way be attributed to the author of this article for: 1) accidents (directly or indirectly related to this article). 2) economic loss related to the construction of the condenser (whether it works or not). 3) Any other possible damage may derive from this article
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
Reading the Source Code of Social Ties
Though online social network research has exploded during the past years, not
much thought has been given to the exploration of the nature of social links.
Online interactions have been interpreted as indicative of one social process
or another (e.g., status exchange or trust), often with little systematic
justification regarding the relation between observed data and theoretical
concept. Our research aims to breach this gap in computational social science
by proposing an unsupervised, parameter-free method to discover, with high
accuracy, the fundamental domains of interaction occurring in social networks.
By applying this method on two online datasets different by scope and type of
interaction (aNobii and Flickr) we observe the spontaneous emergence of three
domains of interaction representing the exchange of status, knowledge and
social support. By finding significant relations between the domains of
interaction and classic social network analysis issues (e.g., tie strength,
dyadic interaction over time) we show how the network of interactions induced
by the extracted domains can be used as a starting point for more nuanced
analysis of online social data that may one day incorporate the normative
grammar of social interaction. Our methods finds applications in online social
media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web
(WebSci'14
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
Smelly Maps: The Digital Life of Urban Smellscapes
Smell has a huge influence over how we perceive places. Despite its
importance, smell has been crucially overlooked by urban planners and
scientists alike, not least because it is difficult to record and analyze at
scale. One of the authors of this paper has ventured out in the urban world and
conducted smellwalks in a variety of cities: participants were exposed to a
range of different smellscapes and asked to record their experiences. As a
result, smell-related words have been collected and classified, creating the
first dictionary for urban smell. Here we explore the possibility of using
social media data to reliably map the smells of entire cities. To this end, for
both Barcelona and London, we collect geo-referenced picture tags from Flickr
and Instagram, and geo-referenced tweets from Twitter. We match those tags and
tweets with the words in the smell dictionary. We find that smell-related words
are best classified in ten categories. We also find that specific categories
(e.g., industry, transport, cleaning) correlate with governmental air quality
indicators, adding validity to our study.Comment: 11 pages, 7 figures, Proceedings of 9th International AAAI Conference
on Web and Social Media (ICWSM2015
Detecting Spatial Health Disparities Using Disease Maps
Epidemiologists commonly use regional aggregates of health outcomes to map
mortality or incidence rates and identify geographic disparities. However, to
detect health disparities across regions, it is necessary to identify
"difference boundaries" that separate neighboring regions with significantly
different spatial effects. This can be particularly challenging when dealing
with multiple outcomes for each unit and accounting for dependence among
diseases and across areal units. In this study, we address the issue of
multivariate difference boundary detection for correlated diseases by
formulating the problem in terms of Bayesian pairwise multiple comparisons by
extending it through the introduction of adjacency modeling and disease graph
dependencies. Specifically, we seek the posterior probabilities of neighboring
spatial effects being different. To accomplish this, we adopt a class of
multivariate areally referenced Dirichlet process models that accommodate
spatial and interdisease dependence by endowing the spatial random effects with
a discrete probability law. Our method is evaluated through simulation studies
and applied to detect difference boundaries for multiple cancers using data
from the Surveillance, Epidemiology, and End Results Program of the National
Cancer Institute
Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory
Social groups play a crucial role in social media platforms because they form
the basis for user participation and engagement. Groups are created explicitly
by members of the community, but also form organically as members interact. Due
to their importance, they have been studied widely (e.g., community detection,
evolution, activity, etc.). One of the key questions for understanding how such
groups evolve is whether there are different types of groups and how they
differ. In Sociology, theories have been proposed to help explain how such
groups form. In particular, the common identity and common bond theory states
that people join groups based on identity (i.e., interest in the topics
discussed) or bond attachment (i.e., social relationships). The theory has been
applied qualitatively to small groups to classify them as either topical or
social. We use the identity and bond theory to define a set of features to
classify groups into those two categories. Using a dataset from Flickr, we
extract user-defined groups and automatically-detected groups, obtained from a
community detection algorithm. We discuss the process of manual labeling of
groups into social or topical and present results of predicting the group label
based on the defined features. We directly validate the predictions of the
theory showing that the metrics are able to forecast the group type with high
accuracy. In addition, we present a comparison between declared and detected
groups along topicality and sociality dimensions.Comment: 10 pages, 6 figures, 2 table
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