1,720,954 research outputs found
Ghost Kitchen Location Problem for Meal Delivery Services
No full textMeal delivery are a key part of on-demand logistics in cities worldwide. Ghost kitchens (“delivery-only
kitchens”) have emerged as facilities for preparing and distributing meals to meet online demand.
This thesis develops an actor-classification framework covering ghost kitchens, platforms, couriers,
and customers. Despite their rise, research on ghost kitchens remains limited, particularly regarding
courier behaviour, on-demand delivery and location optimisation.
Couriers on e-bikes, bikes, or scooters collect from ghost kitchens and deliver to one or more
customers. Since delivery speed depends on kitchen location, site selection is strategically important.
It affects private-sector profitability through delivery efficiency and demand fulfillment and influences
public planning through zoning, infrastructure, and urban policy. Demand randomness and order
variability are key features. Entropy maximisation leads to a Markov model that reproduces pick up,
delivery frequencies and mean delivery time. Key indicators such as mean and variance of delivery
times are derived from model parameters. The model is irreducible, ensuring a unique steady state.
Two parameter estimation methods are proposed: one uses an urgency input to produce delivery
time. The model is calibrated using a public Grubhub dataset and validated with a likelihood ratio
test.
The model also supports street network trip assignment and when combined with a route choice
model, estimates demand for bike lanes or signals. This thesis examines optimal kitchen location
using an entropy-based derivation. Grid search shows how relocation affects delivery time and
demand. Two solution methods are tested: a modified Weiszfeld algorithm and an Adaptive Step
Size Gradient (ASG) method. ASG yields better delivery times and demand outcomes. The thesis
offers practical guidance for kitchen siting and represents a novel contribution by optimising dynamic
pickup and delivery using a Markov model of courier behaviour
Emulated Autoencoder: A Time-Efficient Image Denoiser for Defense of Convolutional Neural Networks against Evasion Attacks
No full textThesis (Master's)--University of Washington, 2022As Convolutional Neural Networks (CNN) have become essential to modern applications such as image classification on social networks or self-driving vehicles, evasion attacks targeting CNNs can lead to damage for users. Therefore, there has been a rising amount of research focusing on defending against evasion attacks. Image denoisers have been used to mitigate the impact of evasion attacks; however, there is not a sufficiently broad view of the use of image denoisers as adversarial defenses in image classification due to a lack of trade-off analysis. Thus, image denoisers' costs, including training time, image reconstruction time, and loss of benign F1 scores of CNN classifiers, are explored in this thesis. Additionally, Emulated Autoencoder (EAE), which is the proposed method of this thesis to optimize trade-offs for high volume classification tasks, is evaluated alongside state-of-the-art image denoisers in the gray-box and white-box threat models. EAE outperforms most image denoisers in both the gray-box and white-box threat models while drastically reducing training and image reconstruction time compared to the state-of-the-art denoisers. As a result, EAE is more appropriate for securing high-volume classification applications of images
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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