5,295 research outputs found
Organizing sustainable development
The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility
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
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug
discovery has been a growing area of interdisciplinary scientific research.
However, conventional AI models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and
providing interpretations for outputs, which hinders their practical
application. As of late, Graph Machine Learning (GML) has gained considerable
attention for its exceptional ability to model graph-structured biomedical data
and investigate their properties and functional relationships. Despite
extensive efforts, GML methods still suffer from several deficiencies, such as
the limited ability to handle supervision sparsity and provide interpretability
in learning and inference processes, and their ineffectiveness in utilising
relevant domain knowledge. In response, recent studies have proposed
integrating external biomedical knowledge into the GML pipeline to realise more
precise and interpretable drug discovery with limited training instances.
However, a systematic definition for this burgeoning research direction is yet
to be established. This survey presents a comprehensive overview of
long-standing drug discovery principles, provides the foundational concepts and
cutting-edge techniques for graph-structured data and knowledge databases, and
formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug
discovery. A thorough review of related KaGML works, collected following a
carefully designed search methodology, are organised into four categories
following a novel-defined taxonomy. To facilitate research in this promptly
emerging field, we also share collected practical resources that are valuable
for intelligent drug discovery and provide an in-depth discussion of the
potential avenues for future advancements
Reasoning about quantities and concepts: studies in social learning
We live and learn in a ‘society of mind’. This means that we form beliefs not
just based on our own observations and prior expectations but also based on the
communications from other people, such as our social network peers. Across seven
experiments, I study how people combine their own private observations with other
people’s communications to form and update beliefs about the environment. I will
follow the tradition of rational analysis and benchmark human learning against optimal Bayesian inference at Marr’s computational level. To accommodate human
resource constraints and cognitive biases, I will further contrast human learning
with a variety of process level accounts. In Chapters 2–4, I examine how people
reason about simple environmental quantities. I will focus on the effect of dependent information sources on the success of group and individual learning across a
series of single-player and multi-player judgement tasks. Overall, the results from
Chapters 2–4 highlight the nuances of real social network dynamics and provide
insights into the conditions under which we can expect collective success versus
failures such as the formation of inaccurate worldviews. In Chapter 5, I develop a
more complex social learning task which goes beyond estimation of environmental
quantities and focuses on inductive inference with symbolic concepts. Here, I investigate how people search compositional theory spaces to form and adapt their
beliefs, and how symbolic belief adaptation interfaces with individual and social
learning in a challenging active learning task. Results from Chapter 5 suggest that
people might explore compositional theory spaces using local incremental search;
and that it is difficult for people to use another person’s learning data to improve
upon their hypothesis
Improving low latency applications for reconfigurable devices
This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed.
Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods.
Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture.
Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces
Agency and professionalism in translation and interpreting: navigating conflicting role identities among translation and interpreting practitioners working for local government in Japan
This thesis investigates the ethical choices of Coordinators for International Relations (CIRs), a group of largely non-professional translators and interpreters working for local government bodies in Japan. In addition to T&I, CIRs are tasked with engaging in intercultural relations, “internationalising” their local areas, and working with the public as members of the civil service. The thesis examines the different roles and particular circumstances of CIRs to describe and explain how they make ethical decisions in T&I.
This was explored using an ethnographic methodology featuring both traditional and online sites. Specifically, data was collected from participant observation of an internet forum created by CIRs, through online surveys, and also by employing focus groups and
interviews held with CIRs in Japan. Analysis of forum and survey data illuminated the ethical struggles experienced by CIRs in T&I. It indicated that professionalism and agency were of particular concern for these CIRs when dealing with questions of ethics. Through focus groups, more detailed data was elicited surrounding the ethical struggles faced by CIRs, with a particular focus on professionalism and agency. Forum and focus group data combined to create a set of hypothetical ethical scenarios discussed during semistructured interviews held to understand factors that influence CIR decision making.
A theoretical framework combining Agency Theory (Mitnick, 1975) and Role Identity Theory (Stryker, 1968) was used to describe and explain CIR ethical decision making; foregrounding their potential to effect change in their workplaces (agency) and the prioritisation afforded to different roles with which they identify in their work (role identity). Ultimately, CIRs were most disposed to translate or interpret in a manner that they believed was in keeping with the wishes of their employers, based on their superior ability to monitor and control the CIRs. However, in instances where the CIR operated with free will, their choices were a result of complex structuring of the various identities that they had normalised within themselves.
Keywords: translation, interpreting, Coordinator for International Relations (CIR), Japan Exchange and Teaching (JET) Programme, agency, professionalism, role identity
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
The Entrenched Political Limitations of Australian Refugee Policy: A Case Study of the Australian Labor Party (2007-2013)
This thesis deconstructs Australia’s asylum and refugee policy trajectory under the Labor government between 2007 and 2013. For a short time after the 2007 election, in accordance with its promise to abolish the LNP’s Pacific Solution, Labor began to unwind certain policy structures of externalisation and deterrence that had been in place since the introduction of mandatory detention in 1992. By 2013 however, the ALP had declared that asylum seekers arriving by boat had no prospect of resettlement in Australia. This thesis analyses the political strategy of the ALP in rhetoric, policy choices and policy justifications to derive lessons from Labor’s mitigated challenge to the deterrence/externalisation paradigm. Critical Discourse Analysis is used to examine the political strategies of lead actors, particularly the ALP and the LNP, and to reconcile these strategies with policy outcomes such as irregular arrivals, detention figures, deaths at sea and compliance with obligations under international law. A central argument of this thesis is that Labor’s attempt to sustainably depart from the dominant externalisation paradigm was impaired, not by a lack of commitment to its stated program of reform, but rather by entrenched political limitations of the Australian context. These limitations include the LNP’s rigid partisanship and lack of policy compromise, the deep-rooted nature of mandatory detention, and the Australian public’s historical and continued support for controlled migration. A precise and detailed analysis of the impact of these limitations on Labor’s proposed reform fills a gap in academic knowledge about the political influences on policy action in Australian asylum and refugee policy. I contend that these limitations must be effectively engaged with in any attempt to reform the Australian asylum and refugee policy space
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
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