7,797 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka
It was evident through the literature that the perceived value delivery of the global software
engineering industry is low due to various facts. Therefore, this research concerns global
software product companies in Sri Lanka to explore the software engineering methods and
practices in increasing the value addition. The overall aim of the study is to identify the key
determinants for value addition in the global software engineering industry and critically
evaluate the impact of them for the software product companies to help maximise the value
addition to ultimately assure the sustainability of the industry.
An exploratory research approach was used initially since findings would emerge while the
study unfolds. Mixed method was employed as the literature itself was inadequate to
investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the
disciplines from the targeted organisations which was combined with the literature findings as
well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings
of the existing literature were verified through the exploratory study and the outcomes were
used to formulate the questionnaire for the public survey. 371 responses were considered after
cleansing the total responses received for the data analysis through SPSS 21 with alpha level
0.05. Internal consistency test was done before the descriptive analysis. After assuring the
reliability of the dataset, the correlation test, multiple regression test and analysis of variance
(ANOVA) test were carried out to fulfil the requirements of meeting the research objectives.
Five determinants for value addition were identified along with the key themes for each area.
They are staffing, delivery process, use of tools, governance, and technology infrastructure.
The cross-functional and self-organised teams built around the value streams, employing a
properly interconnected software delivery process with the right governance in the delivery
pipelines, selection of tools and providing the right infrastructure increases the value delivery.
Moreover, the constraints for value addition are poor interconnection in the internal processes,
rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team
arrangements and inadequate focus for the technology infrastructure. The findings add to the
existing body of knowledge on increasing the value addition by employing effective processes,
practices and tools and the impacts of inaccurate applications the same in the global software
engineering industry
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on peopleās lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
Towards A Graphene Chip System For Blood Clotting Disease Diagnostics
Point of care diagnostics (POCD) allows the rapid, accurate measurement of analytes near to a patient. This enables faster clinical decision making and can lead to earlier diagnosis and better patient monitoring and treatment. However, despite many prospective POCD devices being developed for a wide range of diseases this promised technology is yet to be translated to a clinical setting due to the lack of a cost-eļ¬ective biosensing platform.This thesis focuses on the development of a highly sensitive, low cost and scalable biosensor platform that combines graphene with semiconductor fabrication tech-niques to create graphene ļ¬eld-eļ¬ect transistors biosensor. The key challenges of designing and fabricating a graphene-based biosensor are addressed. This work fo-cuses on a speciļ¬c platform for blood clotting disease diagnostics, but the platform has the capability of being applied to any disease with a detectable biomarker.Multiple sensor designs were tested during this work that maximised sensor ef-ļ¬ciency and costs for diļ¬erent applications. The multiplex design enabled diļ¬erent graphene channels on the same chip to be functionalised with unique chemistry. The Inverted MOSFET design was created, which allows for back gated measurements to be performed whilst keeping the graphene channel open for functionalisation. The Shared Source and Matrix design maximises the total number of sensing channels per chip, resulting in the most cost-eļ¬ective fabrication approach for a graphene-based sensor (decreasing cost per channel from Ā£9.72 to Ā£4.11).The challenge of integrating graphene into a semiconductor fabrication process is also addressed through the development of a novel vacuum transfer method-ology that allows photoresist free transfer. The two main fabrication processes; graphene supplied on the wafer āPre-Transferā and graphene transferred after met-allisation āPost-Transferā were compared in terms of graphene channel resistance and graphene end quality (defect density and photoresist). The Post-Transfer pro-cess higher quality (less damage, residue and doping, conļ¬rmed by Raman spec-troscopy).Following sensor fabrication, the next stages of creating a sensor platform involve the passivation and packaging of the sensor chip. Diļ¬erent approaches using dielec-tric deposition approaches are compared for passivation. Molecular Vapour Deposi-tion (MVD) deposited Al2O3 was shown to produce graphene channels with lower damage than unprocessed graphene, and also improves graphene doping bringing the Dirac point of the graphene close to 0 V. The packaging integration of microļ¬uidics is investigated comparing traditional soft lithography approaches and the new 3D printed microļ¬uidic approach. Speciļ¬c microļ¬uidic packaging for blood separation towards a blood sampling point of care sensor is examined to identify the laminar approach for lower blood cell count, as a method of pre-processing the blood sample before sensing.To test the sensitivity of the Post-Transfer MVD passivated graphene sensor de-veloped in this work, real-time IV measurements were performed to identify throm-bin protein binding in real-time on the graphene surface. The sensor was function-alised using a thrombin speciļ¬c aptamer solution and real-time IV measurements were performed on the functionalised graphene sensor with a range of biologically relevant protein concentrations. The resulting sensitivity of the graphene sensor was in the 1-100 pg/ml concentration range, producing a resistance change of 0.2% per pg/ml. Speciļ¬city was conļ¬rmed using a non-thrombin speciļ¬c aptamer as the neg-ative control. These results indicate that the graphene sensor platform developed in this thesis has the potential as a highly sensitive POCD. The processes developed here can be used to develop graphene sensors for multiple biomarkers in the future
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MODELING CHAIN PACKING IN COMPLEX PHASES OF SELF-ASSEMBLED BLOCK COPOLYMERS
Block copolymer (BCP) melts undergo microphase seperation and form ordered soft matter crystals with varying domain shapes and symmetries. We study the con- nection between diblock copolymer molecular designs and thermodynamic selection of ordered crystals by modeling features of variable sub-domain geometry filled with individual blocks within non-canonical sphere-like and network phases that together with layered, cylindrical and canonical spherical phases forms ānatural formsā of self- assembled amphiphilic soft matter at large. First, we present a model to revise our understanding of optimal Frank-Kasper sphere-like morphologies by advancing the- ory to account for varying domain volumes. We then develop generic approaches to quantify local changes to domain thickness or packing frustration using medial sets and show its application to morphologies with arbitrary domain topologies and sym- metries in both theoretical models and experimental data. We further use medial sets as a proxy for terminal boundaries of blocks within different domains and revise thermodynamic models of BCP assembly in the strong segregation limit. Finally, we use this revised model to study effect of elastic stiffness asymmetry on relaxing packing frustration experienced by BCPs in tubular and matrix domains leading to equilibrium double gyroid network morphology in diblock copolymers
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Novel delivery and sample mixing for synchrotron diffraction experiments using acoustic levitation with multi-transducer arrays
Acoustic levitation may utilise standing waves at ultrasonic frequencies to manipulate suspended substances and small objects in a contactless manner. These materials may be levitated in the positions in which the nodes are located, corresponding to positions of low acoustic pressure. In recent years, off the shelf transducer based acoustic levitators have been used for contactless manipulation of liquids. These systems benefit from requiring low power and low-cost components making acoustic levitation more accessible to the masses. Such a system was investigated in this work for presenting protein crystals, within their mother liquor, to the I24 beamline at Diamond Light Source for x-ray diffraction experiments. It was found that the crystals tended to sediment toward the bottom of the droplets, which were oblate in shape. The droplets which were levitated often became unstable and fell from their suspended position, or they would not detach from the pipette tip when they were being injected. To rectify this, a coating of silicone oil was added allowing the droplets to remain stable as well as limit the evaporation of the droplet whilst it was manually inserted and the area cleared of personnel before the x-ray beam was engaged. This silicone oil coating is non-crystalline and thus did not invalidate the results collected which showed the lysozyme crystal structure with a resolution of 1.69 A, confirming acoustic levitation as a good sample presentation method for these types of experiments. To remove the requirement for the silicone oil, a bespoke system was created named the DLS-Lev that allowed top-loading of the sample. The droplets of mother liquor which contained protein crystals were easily detached from the pipette tip into the traps within the DLS-Lev system owing to the increased strength of the traps in the modified design. This system, paired with an automated pipette, facilitated sample mixing experiments whilst the x-ray beam was engaged. The further development of the pipetting system was halted due to the COVID-19 pandemic. However, future work should see the permanent installation of these systems at the I24 beamline at Diamond Light Source, as well as additional bespoke acoustic levitators designed for the other beamlines specialising in the research of protein structure via x-ray scattering techniques
Industry 4.0: product digital twins for remanufacturing decision-making
Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a productās life cycle.
The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The modelās architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an assetās through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary āsmartā capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored
The Neural Mechanisms of Value Construction
Research in decision neuroscience has characterized how the brain makes decisions by assessing the expected utility of each option in an abstract value space that affords the ability to compare dissimilar options. Experiments at multiple levels of analysis in multiple species have localized the ventromedial prefrontal cortex (vmPFC) and nearby orbitofrontal cortex (OFC) as the main nexus where this abstract value space is represented. However, much less is known about how this value code is constructed by the brain in the first place. By using a combination of behavioral modeling and cutting edge tools to analyze functional magnetic resonance imaging (fMRI) data, the work of this thesis proposes that the brain decomposes stimuli into their constituent attributes and integrates across them to construct value. These stimulus features embody appetitive or aversive properties that are either learned from experience or evaluated online by comparing them to previously experienced stimuli with similar features. Stimulus features are processed by cortical areas specialized for the perception of a particular stimulus type and then integrated into a value signal in vmPFC/OFC.
The project presented in Chapter 2 examines how food items are evaluated by their constituent attributes, namely their nutrient makeup. A linear attribute integration model succinctly captures how subjective values can be computed from a weighted combination of the constituent nutritive attributes of the food. Multivariate analysis methods revealed that these nutrient attributes are represented in the lateral OFC, while food value is encoded both in medial and lateral OFC. Connectivity between lateral and medial OFC allows this nutrient attribute information to be integrated into a value representation in medial OFC.
In Chapter 3, I show that this value construction process can operate over higher-level abstractions when the context requires bundles of items to be valued, rather than isolated items. When valuing bundles of items, the constituent items themselves become the features, and their values are integrated with a subadditive function to construct the value of the bundle. Multiple subregions of PFC including but not limited to vmPFC compute the value of a bundle with the same value code used to evaluate individual items, suggesting that these general value regions contextually adapt within this hierarchy. When valuing bundles and single items in interleaved trials, the value code rapidly switches between levels in this hierarchy by normalizing to the distribution of values in the current context rather than representing all options on an absolute scale.
Although the attribute integration model of value construction characterizes human behavior on simple decision-making tasks, it is unclear how it can scale up to environments of real-world complexity. Taking inspiration from modern advances in artificial intelligence, and deep reinforcement learning in particular, in Chapter 4 I outline how connectionist models generalize the attribute integration model to naturalistic tasks by decomposing sensory input into a high dimensional set of nonlinear features that are encoded with hierarchical and distributed processing. Participants freely played Atari video games during fMRI scanning, and a deep reinforcement learning algorithm trained on the games was used as an end-to-end model for how humans evaluate actions in these high-dimensional tasks. The features represented in the intermediate layers of the artificial neural network were found to also be encoded in a distributed fashion throughout the cortex, specifically in the dorsal visual stream and posterior parietal cortex. These features emerge from nonlinear transformations of the sensory input that connect perception to action and reward. In contrast to the stimulus attributes used to evaluate the stimuli presented in the preceding chapters, these features become highly complex and inscrutable as they are driven by the statistical properties of high-dimensional data. However, they do not solely reflect a set of features that can be identified by applying common dimensionality reduction techniques to the input, as task-irrelevant sensory features are stripped away and task-relevant high-level features are magnified.</p
The Digital Continent:Placing Africa in Planetary Networks of Work
Only ten years ago, there were more internet users in countries like France or Germany than in all of Africa put together. But much has changed in a decade. The year 2018 marks the first year in human history in which a majority of the worldās population are now connected to the internet. This mass connectivity means that we have an internet that no longer connects only the worldās wealthy. Workers from Lagos to Johannesburg to Nairobi and everywhere in between can now apply for and carry out jobs coming from clients who themselves can be located anywhere in the world. Digital outsourcing firms can now also set up operations in the most unlikely of places in order to tap into hitherto disconnected labour forces. With CEOs in the Global North proclaiming that ālocation is a thing of the pastā (Upwork, 2018), and governments and civil society in Africa promising to create millions of jobs on the continent, the book asks what this ānew world of digital workā means to the lives of African workers. It draws from a year-long fieldwork in South Africa, Kenya, Nigeria, Ghana, and Uganda, with over 200 interviews with participants including gig workers, call and contact centre workers, self-employed freelancers, small-business owners, government officials, labour union officials, and industry experts. Focusing on both platform-based remote work and call and contact centre work, the book examines the job quality implications of digital work for the lives and livelihoods of African workers
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