5,701 research outputs found
A conceptual framework for developing dashboards for big mobility data
Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Towards ending incarceration of Indigenous peoples in Canada: A critical, narrative inquiry of hegemonic power in the Gladue report process
Abstract
This study is concerned with the possibility that Gladue perpetuates the hegemonic powers of settler colonialism, white supremacy, patriarchy, and neoliberalism. Gladue is intended to remediate systemic anti-Indigenous racism by requiring judges to consider all alternatives to incarceration when sentencing Indigenous peoples, yet Indigenous incarceration rates continue to rise precipitously. On the surface, Gladue does not appear to disrupt the hegemonic status quo. How is it that the Canadian state, even when ‘remediating,’ keeps producing the same – colonial, oppressive, and tyrannical – result?
This qualitative study used a critical, narrative methodology, interviewing Gladue report writers (n=9) and judges (n=12) about their perspectives and experiences with Gladue, particularly Gladue reports. The study purposefully emphasized settler accountability – research as reparation – in the research design, data collection, and analysis. A careful, ethical protocol for researching with Indigenous peoples (n=9) was followed, premised in Truth and Reconciliation ‘Call to Action’ number 30 to reduce Indigenous incarceration in Canada.
This study found that Gladue is falling short of achieving its systemic aim because of (a) a hyper-individualistic, dehumanizing configuration that discursively shifts judges away from dealing with the systemic issue of anti-Indigenous racism, towards judging the individual Indigenous person before the court; (b) colonial mentalities (e.g., whiteness and patriarchy) persisting in the process; (c) a lack of funding for Gladue writers, as well alternatives to incarceration, constraining judges’ capacities to divert Indigenous away from prisons. The study points towards the need for a more radical framework for Gladue that honours Indigenous self-determination and foundational treaties such as the Two Row Wampum
Human-Centered Approach to Technology to Combat Human Trafficking
Human trafficking is a serious crime that continues to plague the United States. With the rise of computing technologies, the internet has become one of the main mediums through which this crime is facilitated. Fortunately, these online activities leave traces which are invaluable to law enforcement agencies trying to stop human trafficking. However, identifying and intervening with these cases is still a challenging task. The sheer volume of online activity makes it difficult for law enforcement to efficiently identify any potential leads. To compound this issue, traffickers are constantly changing their techniques online to evade detection. Thus, there is a need for tools to efficiently sift through all this online data and narrow down the number of potential leads that a law enforcement agency can deal with. While some tools and prior research do exist for this purpose, none of these tools adequately address law enforcement user needs for information visualizations and spatiotemporal analysis. Thus to address these gaps, this thesis contributes an empirical study of technology and human trafficking. Through in-depth qualitative interviews, systemic literature analysis, and a user-centered design study, this research outlines the challenges and design considerations for developing sociotechnical tools for anti-trafficking efforts. This work further contributes to the greater understanding of the prosecution efforts within the anti-trafficking domain and concludes with the development of a visual analytics prototype that incorporates these design considerations.Ph.D
Perceptions of Organizational Politics and Interpersonal Relationships in Black Women\u27s Organizations and Sororities
Although much research has addressed the relationships between leaders and followers, none has focused on these relationships between Black women—specifically, in the context of Black women’s social service organizations and sororities—and the impact the leader’s chosen style of leadership has on the followers’ use of voice. Self silencing is a prominent response to the power dynamic in many leader–follower relationships. The purpose of this study was to describe the interactions between leaders and followers in Black women’s social service organizations and sororities and identify the influence leadership style has on follower engagement. This study extends the application of silencing the self theory to leader–follower dynamics in Black women’s organizations and sororities. This qualitative study employed a narrative design and semistructured interviews to ascertain the experiences of and gain insight from 15 members of Black women’s organizations and sororities on their interactions with leaders in their organization and the impact on their use of voice and their commitment to the organization. The multiple themes identified suggest that the leader–follower relationship—specifically the leader’s actions and leadership style—and the culture and interpersonal dynamics of the organization are critical to followers’ decision to moderate their voice and their commitment to the organization
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Denoising diffusion models, a class of generative models, have garnered
immense interest lately in various deep-learning problems. A diffusion
probabilistic model defines a forward diffusion stage where the input data is
gradually perturbed over several steps by adding Gaussian noise and then learns
to reverse the diffusion process to retrieve the desired noise-free data from
noisy data samples. Diffusion models are widely appreciated for their strong
mode coverage and quality of the generated samples despite their known
computational burdens. Capitalizing on the advances in computer vision, the
field of medical imaging has also observed a growing interest in diffusion
models. To help the researcher navigate this profusion, this survey intends to
provide a comprehensive overview of diffusion models in the discipline of
medical image analysis. Specifically, we introduce the solid theoretical
foundation and fundamental concepts behind diffusion models and the three
generic diffusion modelling frameworks: diffusion probabilistic models,
noise-conditioned score networks, and stochastic differential equations. Then,
we provide a systematic taxonomy of diffusion models in the medical domain and
propose a multi-perspective categorization based on their application, imaging
modality, organ of interest, and algorithms. To this end, we cover extensive
applications of diffusion models in the medical domain. Furthermore, we
emphasize the practical use case of some selected approaches, and then we
discuss the limitations of the diffusion models in the medical domain and
propose several directions to fulfill the demands of this field. Finally, we
gather the overviewed studies with their available open-source implementations
at
https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion
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