7,359 research outputs found
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
A direct-laser-written heart-on-a-chip platform for generation and stimulation of engineered heart tissues
In this dissertation, we first develop a versatile microfluidic heart-on-a-chip model to generate 3D-engineered human cardiac microtissues in highly-controlled microenvironments. The platform, which is enabled by direct laser writing (DLW), has tailor-made attachment sites for cardiac microtissues and comes with integrated strain actuators and force sensors. Application of external pressure waves to the platform results in controllable time-dependent forces on the microtissues. Conversely, oscillatory forces generated by the microtissues are transduced into measurable electrical outputs. After characterization of the responsivity of the transducers, we demonstrate the capabilities of this platform by studying the response of cardiac microtissues to prescribed mechanical loading and pacing.
Next, we tune the geometry and mechanical properties of the platform to enable parametric studies on engineered heart tissues. We explore two geometries: a rectangular seeding well with two attachment sites, and a stadium-like seeding well with six attachment sites. The attachment sites are placed symmetrically in the longitudinal direction. The former geometry promotes uniaxial contraction of the tissues; the latter additionally induces diagonal
fiber alignment. We systematically increase the length for both configurations and observe a positive correlation between fiber alignment at the center of the microtissues and tissue length. However, progressive thinning and “necking” is also observed, leading to the failure of longer tissues over time. We use the DLW technique to improve the platform, softening
the mechanical environment and optimizing the attachment sites for generation of stable microtissues at each length and geometry. Furthermore, electrical pacing is incorporated into the platform to evaluate the functional dynamics of stable microtissues over the entire range of physiological heart rates. Here, we typically observe a decrease in active force and contraction duration as a function of frequency.
Lastly, we use a more traditional ?TUG platform to demonstrate the effects of subthreshold electrical pacing on the rhythm of the spontaneously contracting cardiac microtissues. Here, we observe periodic M:N patterns, in which there are ? cycles of stimulation for every ? tissue contractions. Using electric field amplitude, pacing frequency, and homeostatic beating frequencies of the tissues, we provide an empirical map for predicting the emergence of these rhythms
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Credible to Whom? The Organizational Politics of Credibility in International Relations
Why do foreign policy decision makers care about the credibility of their own state’s commitments? How does organizational identity shape policymakers’ concern for credibility, and in turn, their willingness to use force during crises? While much previous research examines how decision makers assess others’ credibility, only recently have scholars questioned when and why leaders or their advisers prioritize their own state’s credibility.
Building on classic scholarship in bureaucratic politics, I argue that organizational identity affects the dimensions of credibility that national security officials value, and ultimately, their policy advocacy around the use of force. Particular differences arise between military and diplomatic organizations; while military officials equate credibility with hard military capabilities, diplomats view credibility in terms of reputation, or demonstrating reliability and resolve to external parties.
During crises, military officials confine their advice on the use of force to what can be achieved given current capabilities, while diplomats exhibit higher willingness to use force as a signal of a strong commitment. I test these propositions using text analysis of archival records from two collections of U.S. national security policy documents, eight case studies of American, British, and French crisis decision making, and an original survey experiment involving more than 400 current or former U.S. national security officials. I demonstrate that credibility concerns affect the balance of hawkishness in advice that diplomats and military officials deliver to leaders as a function of organizational identity
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After Creation: Intergovernmental Organizations and Member State Governments as Co-Participants in an Authority Relationship
This is a re-amalgamation of what started as one manuscript and became two when the length proved to be more than any publisher wanted to consider. The splitting consisted of removing what are now Parts 3, 4, and 5 so that the manuscript focused on the outcome-related shared beliefs holding an authority relationship together. Those parts were last worked on in 2018. The rest were last worked on in late 2021 but also remain incomplete.
The relational approach adopted in this study treats intergovernmental organizations and the governments of member states as co-participants in an authority relationship with the governments of their member states. Authority relationships link two types of actor, defined by their authority-holder or addressee role in the relationship, through a set of shared beliefs about why the relationship exists and how the participants should fulfill their respective roles. The IGO as authority holder has a role that includes a right to instruct other actors about what they should or should not do; the governments of member states as addressees are expected to comply with the instructions. Three sets of shared beliefs provide the conceptual “glue” holding the relationship together. The first defines the goal of the collective effort, providing both the rationale for having the authority relationship and providing a lode star for assessments of the collective effort’s success or lack of success. The second set defines the shared understanding about allocation of roles and the process of interaction by establishing shared expectations about a) the selection process by which particular actors acquire authority holder roles, b) the definitions identifying one or more categories of addressees expected to follow instructions, and c) the procedures through which the authority holder issues instructions. The third set focus on the outcomes of cooperation through the relationship by defining a) the substantive areas in which the authority holder may issue instructions, b) the bases for assessing the relevance actions mandated in instructions for reaching the goal, and c) the relative efficacy of action paths chosen for reaching the goal as compared to other possible action paths.
Using an authority relationship framework for analyzing cooperation through IGOs highlights the inherently bi-directional nature of IGO-member government activity by viewing their interaction as involving a three-step process in which the IGO as authority holder decides when to issue what instruction, the member state governments as followers react to the instruction with anything from prompt and full compliance through various forms of pushback to outright rejection, and the IGO as authority holder responds to how the followers react with efforts to increase individual compliance with instructions and reinforce continuing acceptance of the authority relationship. Foregrounding the dynamics produced by the interaction of these two streams of perception and action reveals more clearly how far intergovernmental organizations acquire capacity to operate as independent actors, the dynamic ways they maintain that capacity, and how much they influence member governments’ beliefs and actions at different times. The approach fosters better understanding of why, when, and for how long governments choose cooperation through an IGO even in periods of rising unilateralism
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
REDESIGNING THE COUNTER UNMANNED SYSTEMS ARCHITECTURE
Includes supplementary material. Please contact [email protected] for access.When the Islamic State used Unmanned Aerial Vehicles (UAV) to target coalition forces in 2014, the use of UAVs rapidly expanded, giving weak states and non-state actors an asymmetric advantage over their technologically superior foes. This asymmetry led the Department of Defense (DOD) and the Department of Homeland Security (DHS) to spend vast sums of money on counter-unmanned aircraft systems (C-UAS). Despite the market density, many C-UAS technologies use expensive, bulky, and high-power-consuming electronic attack methods for ground-to-air interdiction. This thesis outlines the current technology used for C-UAS and proposes a defense-in-depth framework using airborne C-UAS patrols outfitted with cyber-attack capabilities. Using aerial interdiction, this thesis develops a novel C-UAS device called the Detachable Drone Hijacker—a low-size, weight, and power C-UAS device designed to deliver cyber-attacks against commercial UAVs using the IEEE 802.11 wireless communication specification. The experimentation results show that the Detachable Drone Hijacker, which weighs 400 grams, consumes one Watt of power, and costs $250, can interdict adversarial UAVs with no unintended collateral damage. This thesis recommends that the DOD and DHS incorporates aerial interdiction to support its C-UAS defense-in-depth, using technologies similar to the Detachable Drone Hijacker.DASN-OE, Washington DC, 20310Captain, United States Marine CorpsApproved for public release. Distribution is unlimited
Chinese Benteng Women’s Participation in Local Development Affairs in Indonesia: Appropriate means for struggle and a pathway to claim citizen’ right?
It had been more than two decades passing by aftermath the devastating Asia’s Financial Crisis in 1997, subsequently followed by Suharto’s step down from his presidential throne which he occupied for more than three decades. The financial turmoil turned to a political disaster furthermore has led to massive looting that severely impacted Indonesians of Chinese descendant, including unresolved mystery of the most atrocious sexual violation against women and covert killings of students and democracy activists in this country. Since then, precisely aftermath May 1998, which publicly known as “Reformasi”1, Indonesia underwent political reform that eventually corresponded positively to its macroeconomic growth. Twenty years later, in 2018, Indonesia captured worldwide attention because it has successfully hosted two internationally renowned events, namely the Asian Games 2018 – the most prestigious sport events in Asia – conducted in Jakarta and Palembang; and the IMF/World Bank Annual Meeting 2018 in Bali. Particularly in the IMF/World Bank Annual Meeting, this event has significantly elevated Indonesia’s credibility and international prestige in the global economic powerplay as one of the nations with promising growth and openness. However, the narrative about poverty and inequality, including increasing racial tension, religious conservatism, and sexual violation against women are superseded by friendly climate for foreign investment and eventually excessive glorification of the nation’s economic growth. By portraying the image of promising new economic power, as rhetorically promised by President Joko Widodo during his presidential terms, Indonesia has swept the growing inequality in this highly stratified society that historically compounded with religious and racial tension under the carpet of digital economy.Arte y Humanidade
Developing automated meta-research approaches in the preclinical Alzheimer's disease literature
Alzheimer’s disease is a devastating neurodegenerative disorder for which there is no cure. A crucial part of the drug development pipeline involves testing therapeutic interventions in animal disease models. However, promising findings in preclinical experiments have not translated into clinical trial success. Reproducibility has often been cited as a major issue affecting biomedical research, where experimental results in one laboratory cannot be replicated in another. By using meta-research (research on research) approaches such as systematic reviews, researchers aim to identify and summarise all available evidence relating to a specific research question. By conducting a meta-analysis, researchers can also combine the results from different experiments statistically to understand the overall effect of an intervention and to explore reasons for variations seen across different publications. Systematic reviews of the preclinical Alzheimer’s disease literature could inform decision making, encourage research improvement, and identify gaps in the literature to guide future research. However, due to the vast amount of potentially useful evidence from animal models of Alzheimer’s disease, it remains difficult to make sense of and utilise this data effectively. Systematic reviews are common practice within evidence based medicine, yet their application to preclinical research is often limited by the time and resources required. In this thesis, I develop, build-upon, and implement automated meta-research approaches to collect, curate, and evaluate the preclinical Alzheimer’s literature. I searched several biomedical databases to obtain all research relevant to Alzheimer’s disease. I developed a novel deduplication tool to automatically identify and remove duplicate publications identified across different databases with minimal human effort. I trained a crowd of reviewers to annotate a subset of the publications identified and used this data to train a machine learning algorithm to screen through the remaining publications for relevance. I developed text-mining tools to extract model, intervention, and treatment information from publications and I improved existing automated tools to extract reported measures to reduce the risk of bias. Using these tools, I created a categorised database of research in transgenic Alzheimer’s disease animal models and created a visual summary of this dataset on an interactive, openly accessible online platform. Using the techniques described, I also identified relevant publications within the categorised dataset to perform systematic reviews of two key outcomes of interest in transgenic Alzheimer’s disease models: (1) synaptic plasticity and transmission in hippocampal slices and (2) motor activity in the open field test.
Over 400,000 publications were identified across biomedical research databases, with 230,203 unique publications. In a performance evaluation across different preclinical datasets, the automated deduplication tool I developed could identify over 97% of duplicate citations and a had an error rate similar to that of human performance. When evaluated on a test set of publications, the machine learning classifier trained to identify relevant research in transgenic models performed was highly sensitive (captured 96.5% of relevant publications) and excluded 87.8% of irrelevant publications. Tools to identify the model(s) and outcome measure(s) within the full-text of publications may reduce the burden on reviewers and were found to be more sensitive than searching only the title and abstract of citations. Automated tools to assess risk of bias reporting were highly sensitive and could have the potential to monitor research improvement over time. The final dataset of categorised Alzheimer’s disease research contained 22,375 publications which were then visualised in the interactive web application. Within the application, users can see how many publications report measures to reduce the risk of bias and how many have been classified as using each transgenic model, testing each intervention, and measuring each outcome. Users can also filter to obtain curated lists of relevant research, allowing them to perform systematic reviews at an accelerated pace with reduced effort required to search across databases, and a reduced number of publications to screen for relevance. Both systematic reviews and meta-analyses highlighted failures to report key methodological information within publications. Poor transparency of reporting limited the statistical power I had to understand the sources of between-study variation. However, some variables were found to explain a significant proportion of the heterogeneity. Transgenic animal model had a significant impact on results in both reviews. For certain open field test outcomes, wall colour of the open field arena and the reporting of measures to reduce the risk of bias were found to impact results. For in vitro electrophysiology experiments measuring synaptic plasticity, several electrophysiology parameters, including magnesium concentration of the recording solution, were found to explain a significant proportion of the heterogeneity. Automated meta-research approaches and curated web platforms summarising preclinical research could have the potential to accelerate the conduct of systematic reviews and maximise the potential of existing evidence to inform translation
Maximising the Circularity and Sustainability: An End-of-Life Tyre Recycling Outlets Selection Model
The stuff of strategy: the potential of the material turn in strategy studies
This thesis explores the potential of the material turn in strategy studies to explore how nonhuman ‘things’ contribute to strategy production. Drawing on the ontologies and methodologies of the strategy-as-practice and actor-network theory domains, the empirical research informing this thesis is an immersive mixed methods ethnographic study in a higher education school of art and design conducted over a period of 24 months. The study combines observation and a qualitative interview protocol to build four explorative case study narratives that consider various aspects of material agency in strategy production. Analysis and discussion inform a re-theorising of strategy production that foregrounds the agency of materials beyond that of human intent, providing a counterpoint to prevailing approaches that centre the affordances ‘things’ offer to human action and suggesting instead a novel extension to strategy studies that emphasises emancipatory critique of normative organisational practices and ontologies
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