2,611 research outputs found
Feminisms in Movement: Theories and Practices from the Americas
Feminist movements from the Americas provide some of the most innovative, visible, and all-encompassing forms of organizing and resistance. With their diverse backgrounds, these movements address sexism, sexualized violence, misogyny, racism, homo- and transphobia, coloniality, extractivism, climate crisis, and neoliberal capitalist exploitation as well as the interrelations of these systems. Fighting interlocking axes of oppression, feminists from the Americas represent, practice, and theorize a truly "intersectional" politics. Feminisms in Movement: Theories and Practices from the Americas brings together a wide variety of perspectives and formats, spanning from the realms of arts and activism to academia. Black and decolonial feminist voices and queer/cuir perspectives, ecofeminist approaches and indigenous women's mobilizations inspire future feminist practices and inform social and cohabitation projects. With contributions from Rita Laura Segato, Mara Viveros Vigoya, Yuderkys Espinosa-Miñoso, and interviews with Anielle Franco (Brazilian activist and minister) and with the Chilean feminist collective LASTESIS
Review of next generation hydrogen production from offshore wind using water electrolysis
\ua9 2023 The Author(s)Hydrogen produced using renewable energy from offshore wind provides a versatile method of energy storage and power-to-gas concepts. However, few dedicated floating offshore electrolyser facilities currently exist and therefore conditions of the offshore environment on hydrogen production cost and efficiency remain uncertain. Therefore, this review focuses on the conversion of electrical energy to hydrogen, using water electrolysis located in offshore areas. The challenges associated with the remote locations, fluctuating power and harsh conditions are highlighted and recommendations for future electrolysis system designs are suggested. The latest research in polymer electrolyte membrane, alkaline and membraneless electrolysis are evaluated in order to understand their capital costs, efficiency and current research status for achieving scaled manufacturing to the GW scale required in the next three decades. Operating fundamentals that govern the performance of each device are investigated and future recommendations of research specifically for the integration of water electrolysers with offshore wind turbines is presented
Choreographing tragedy into the twenty-first century
What makes a tragedy? In the fifth century BCE this question found an answer through the conjoined forms of song and dance. Since the mid-twentieth century, and the work of the Tanztheater Wuppertal Pina Bausch, tragedy has been variously articulated as form coming apart at the seams. This thesis approaches tragedy through the work of five major choreographers and a director who each, in some way, turn back to Bausch. After exploring the Tanztheater Wuppertal’s techniques for choreographing tragedy in chapter one, I dedicate a chapter each to Dimitris Papaioannou, Akram Khan, Trajal Harrell, Ivo van Hove with Wim Vandekeybus, and Gisèle Vienne.
Bringing together work in Queer and Trans* studies, Performance studies, Classics, Dance, and Classical Reception studies I work towards an understanding of the ways in which these choreographers articulate tragedy through embodiment and relation. I consider how tragedy transforms into the twenty-first century, how it shapes what it might mean to live and die with(out) one another. This includes tragic acts of mythic construction, attempts to describe a sense of the world as it collapses, colonial claims to ownership over the earth, and decolonial moves to enact new ways of being human.
By developing an expanded sense of both choreography and the tragic one of my main contributions is a re-theorisation of tragedy that brings together two major pre-existing schools, to understand tragedy not as an event, but as a process. Under these conditions, and the shifting conditions of the world around us, I argue that the choreography of tragedy has and might continue to allow us to think about, name, and embody ourselves outside of the ongoing catastrophes we face
The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments
In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident.
In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion.
This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture.
Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data.
As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis
Towards a centralized multicore automotive system
Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them.
This thesis first introduces an integrated vehicle management system (VMS), called DriveOS™, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle
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Understanding the Impact of Covid-19 on Ethnic Minority Students: a Case Study of Open University Level 1 Computing Modules
As reported in [1] ‘Of the disparities that exist within higher education, the gap between the likelihood of White students and students from Black, Asian or minority ethnic backgrounds getting a first- or upper-second-class degree is among the starkest’. In the Open University (OU) for example, a recent research [2] found students from ethnic minorities to be at least 20% less likely to achieve excellent grades and to spend 4-12% more of study time to achieve the same performance as white students. Moreover, with the advent of COVID-19, a growing body of research suggested that students from these groups of the population, suffer disproportionally from the impacts of the pandemic [3], which inevitably impacts on their study experiences. However, recent research in the OU found that some COVID-19 arrangements such as the change of examination mode and change in work-life patterns have impacted students from ethnic minority backgrounds differently. In this paper we present findings from a project aiming to understand the impact of COVID-19 on ethnic minority students’ study experiences and performance. By means of a combination of qualitative and quantitative data analytics we first analysed the study performance and the patterns of progression, then by conducting focus groups with the teaching staff we assessed the impact of COVID-19 on the lived experiences of the students.
[1] Black, Asian and Minority Ethnic Student Attainment at UK Universities (2022). Available at: https://www.universitiesuk.ac.uk.
[2] Nguyen Q., Rienties B. Richardson J.T.E. (2020) Learning analytics to uncover inequality in behavioural engagement and academic attainment in a distance learning setting, Assessment & Evaluation in Higher Education, 45:4, 594-606.
[3] Arday, J. and Jones, C. (2022) “Same storm, different boats: The impact of covid-19 on black students and academic staff in UK and US higher education,” Higher Education. Available at:
https://doi.org/10.1007/s10734-022-00939-0
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability
Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far.
In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs.
We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes.
We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research
Integrated Approaches to Digital-enabled Design for Manufacture and Assembly: A Modularity Perspective and Case Study of Huoshenshan Hospital in Wuhan, China
Countries are trying to expand their healthcare capacity through advanced construction, modular innovation, digital technologies and integrated design approaches such as Design for Manufacture and Assembly (DfMA). Within the context of China, there is a need for stronger implementation of digital technologies and DfMA, as well as a knowledge gap regarding how digital-enabled DfMA is implemented. More critically, an integrated approach is needed in addition to DfMA guidelines and digital-enabled approaches.
For this research, a mixed method was used. Questionnaires defined the context of Huoshenshan Hospital, namely the healthcare construction in China. Then, Huoshenshan Hospital provided a case study of the first emergency hospital which addressed the uncertainty of COVID-19. This extreme project, a 1,000-bed hospital built in 10 days, implemented DfMA in healthcare construction and provides an opportunity to examine the use of modularity. A workshop with a design institution provided basic facts and insight into past practice and was followed by interviews with 18 designers, from various design disciplines, who were involved in the project. Finally, multiple archival materials were used as secondary data sources.
It was found that complexity hinders building systems integration, while reinforcement relationships between multiple dimensions of modularity (across organisation-process-product-supply chain dimensions) are the underlying mechanism that allows for the reduction of complexity and the integration of building systems. Promoting integrated approaches to DfMA relies on adjusting and coupling multi-dimensional modular reinforcement relationships (namely, relationships of modular alignment, modular complement, and modular incentive). Thus, the building systems integrator can use these three approaches to increase the success of digital-enabled DfMA
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