922017 research outputs found
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Elective Amputation and Neuroprosthetic Limbs
No full textThis paper explores the impact that developments in the field of neuroprosthetics will have on the ethical viability of healthy limb amputation, specifically in cases of Body Integrity Identity Disorder (BIID). Developments in the field have meant that the prospect of such artificial components matching the utility of their biological counterparts is now a possibility. As such, arguments against the provision of therapeutic, healthy limb amputation which are grounded in the perceived resultant harm of disability need to be reconsidered. Drawing on philosophical insights, as well as the field of disability studies and BIID research, this paper argues that such neuroprosthetics presents a challenge for the fundamental dichotomy between the disabled and ‘abled’, including the latter’s perceived superiority. It goes on to suggest that healthy limb amputation, for those with BIID, should not be dismissed simply because of the distastefulness of the procedure, but rather be evaluated based upon its own merits
Elective Amputation and Neuroprosthetic Limbs
No full textThis paper explores the impact that developments in the field of neuroprosthetics will have on the ethical viability of healthy limb amputation, specifically in cases of Body Integrity Identity Disorder (BIID). Developments in the field have meant that the prospect of such artificial components matching the utility of their biological counterparts is now a possibility. As such, arguments against the provision of therapeutic, healthy limb amputation which are grounded in the perceived resultant harm of disability need to be reconsidered. Drawing on philosophical insights, as well as the field of disability studies and BIID research, this paper argues that such neuroprosthetics presents a challenge for the fundamental dichotomy between the disabled and ‘abled’, including the latter’s perceived superiority. It goes on to suggest that healthy limb amputation, for those with BIID, should not be dismissed simply because of the distastefulness of the procedure, but rather be evaluated based upon its own merits
Digitalisation as a Catalyst for Supplier Diversity, Equity and Inclusion
No full textPurpose:This article investigates digital technologies’ potential to enable supplier diversity, equity and inclusion (SDEI) outcomes by improving procurement processes.Design/Methodology/Approach:An in-depth qualitative case study approach was used to analyse a global energy organisation (Energy-Co) and two of its technology service providers (TSPs), which form two dyads embedded in one case. Thirty-nine interviews were conducted, and internal secondary data was also examined. Line-by-line inductive coding was employed to extract 873 participant insights, leading to first-order codes and six second-order themes.Findings:AI and big data analytics technologies improve SDEI outcomes by transforming procurement processes and organisational subsystems. We identified six digitally enabled process changes spanning DEI-supplier prioritisation, identification, standardisation, onboarding and engagement, development and performance tracking. Using socio-technical systems (STS) theory, we also found that digitalisation functions as a system-level integrator, helping to resolve misalignments across technical, social and environmental subsystems and enabling more scalable, coherent and long-term SDEI procurement outcomes.Originality:This paper theorises that digitalisation enables the systemic embedding of SDEI in procurement. It adopts a distinctive dyadic lens by foregrounding the role of TSPs as active co-creators of inclusive procurement infrastructures. The paper identifies six new digitally enabled procurement process improvements; also, by extending STS theory, it introduces subsystem boundary blurring to explain how digitalisation enables SDEI outcomes regarding both volume and value. By offering a systemic lens for exploring DEI transformations across procurement ecosystems, this paper lays the foundation for a research agenda at the intersection of digitalisation, STS theory and inclusive procurement
Digitalisation as a Catalyst for Supplier Diversity, Equity and Inclusion
Full text linkPurpose:This article investigates digital technologies’ potential to enable supplier diversity, equity and inclusion (SDEI) outcomes by improving procurement processes.Design/Methodology/Approach:An in-depth qualitative case study approach was used to analyse a global energy organisation (Energy-Co) and two of its technology service providers (TSPs), which form two dyads embedded in one case. Thirty-nine interviews were conducted, and internal secondary data was also examined. Line-by-line inductive coding was employed to extract 873 participant insights, leading to first-order codes and six second-order themes.Findings:AI and big data analytics technologies improve SDEI outcomes by transforming procurement processes and organisational subsystems. We identified six digitally enabled process changes spanning DEI-supplier prioritisation, identification, standardisation, onboarding and engagement, development and performance tracking. Using socio-technical systems (STS) theory, we also found that digitalisation functions as a system-level integrator, helping to resolve misalignments across technical, social and environmental subsystems and enabling more scalable, coherent and long-term SDEI procurement outcomes.Originality:This paper theorises that digitalisation enables the systemic embedding of SDEI in procurement. It adopts a distinctive dyadic lens by foregrounding the role of TSPs as active co-creators of inclusive procurement infrastructures. The paper identifies six new digitally enabled procurement process improvements; also, by extending STS theory, it introduces subsystem boundary blurring to explain how digitalisation enables SDEI outcomes regarding both volume and value. By offering a systemic lens for exploring DEI transformations across procurement ecosystems, this paper lays the foundation for a research agenda at the intersection of digitalisation, STS theory and inclusive procurement
Digitalisation as a Catalyst for Supplier Diversity, Equity and Inclusion
No full textPurpose:This article investigates digital technologies’ potential to enable supplier diversity, equity and inclusion (SDEI) outcomes by improving procurement processes.Design/Methodology/Approach:An in-depth qualitative case study approach was used to analyse a global energy organisation (Energy-Co) and two of its technology service providers (TSPs), which form two dyads embedded in one case. Thirty-nine interviews were conducted, and internal secondary data was also examined. Line-by-line inductive coding was employed to extract 873 participant insights, leading to first-order codes and six second-order themes.Findings:AI and big data analytics technologies improve SDEI outcomes by transforming procurement processes and organisational subsystems. We identified six digitally enabled process changes spanning DEI-supplier prioritisation, identification, standardisation, onboarding and engagement, development and performance tracking. Using socio-technical systems (STS) theory, we also found that digitalisation functions as a system-level integrator, helping to resolve misalignments across technical, social and environmental subsystems and enabling more scalable, coherent and long-term SDEI procurement outcomes.Originality:This paper theorises that digitalisation enables the systemic embedding of SDEI in procurement. It adopts a distinctive dyadic lens by foregrounding the role of TSPs as active co-creators of inclusive procurement infrastructures. The paper identifies six new digitally enabled procurement process improvements; also, by extending STS theory, it introduces subsystem boundary blurring to explain how digitalisation enables SDEI outcomes regarding both volume and value. By offering a systemic lens for exploring DEI transformations across procurement ecosystems, this paper lays the foundation for a research agenda at the intersection of digitalisation, STS theory and inclusive procurement
B-fields do not suppress star formation in low metallicity dwarf galaxies
No full textMany studies have looked at the impact of magnetic fields on star formation in molecular clouds and Milky Way like galaxies, concluding that the field suppresses star formation. However, most of these studies are based on fully developed fields that have reached the saturation level, with little work on investigating how the growth phase of a primordial field affects star formation in low metallicity environments. In this paper, we investigate the impact of the growth phase of a primordial field on low metallicity dwarf galaxies. We perform high-resolution arepo simulations of 5 isolated dwarf galaxies. Two models are hydrodynamical, two start with a primordial B-field of 10−6µG, and one with a saturated B-field of 10−2µG. All models include a non-equilibrium, time-dependent chemical network that includes the effects of gas shielding from the ambient UV field. Sink particles form directly from the gravitational collapse of gas and are treated as star-forming clumps that can accrete gas. We vary the metallicity, UV-field, and cosmic ray ionization rate between 1% and 10% of solar values. We find that the magnetic field has little impact on the star formation rate, which is in tension with previously published results. We show that an increase in the mass fractions of both molecular hydrogen and cold gas, along with changes in the perpendicular gas velocity dispersion’s and the B-field acting in the weak-field model overcomes the expected suppression in star formation.<br/
Semimetallic and semiconducting graphene-hBN multilayers with parallel or reverse stacking
No full textWe theoretically investigate 3D layered crystals of alternating graphene and hBN layers with different symmetries. Depending on the hopping parameters between the graphene layers, we find that these synthetic 3D materials can feature semimetallic, gapped, or Weyl semimetal phases. Using first-principles calculations to parameterize the low-energy Hamiltonians we establish the most likely electronic phases. Our results demonstrate that 3D crystals stacked from individual 2D materials represent a new materials class with emergent properties different from their constituents
Semimetallic and semiconducting graphene-hBN multilayers with parallel or reverse stacking
No full textWe theoretically investigate 3D layered crystals of alternating graphene and hBN layers with different symmetries. Depending on the hopping parameters between the graphene layers, we find that these synthetic 3D materials can feature semimetallic, gapped, or Weyl semimetal phases. Using first-principles calculations to parameterize the low-energy Hamiltonians we establish the most likely electronic phases. Our results demonstrate that 3D crystals stacked from individual 2D materials represent a new materials class with emergent properties different from their constituents
Impact of Two-Phase Flow Pattern on Solvent Vapour Extraction
No full textSolvent Vapour extraction (Vapex) is a promising technology for in-situ heavy oil recovery from oil sands deposits. The prediction of oil recovery rates requires fundamental understanding of the pore-scale mechanisms and their impact on mass transfer and oil production. To bridge the gap between pore-scale mechanisms and macro scale recovery, a dynamic pore-network model for two-phase flow with mass transfer is developed. The impact of pressure gradient on two-phase flow pattern, mass transfer and oil production are investigated. It is found that at high capillary numbers, in viscous dominated flow, dissolved oil is moved in intermittent liquid clusters to the outlet of the network. This mechanism of interface renewal maintains a steep solvent mole fraction gradient at the interface and enhances mass transfer, resulting in high oil production. In capillary dominated flow, capillary fingering with low mass transfer and oil production are observed
Zero-Shot Human-Object Interaction Recognition via Affordance Graphs
No full textWe propose a new approach for Zero-Shot Human-Object Interaction Recognition in the challenging setting that involves interactions with unseen actions (as opposed to just unseen combinations of seen actions and objects). Our approach makes use of knowledge external to the image content in the form of a graph that models affordance relations between actions and objects, i.e., whether an action can be performed on the given object or not. We propose a loss function with the aim of distilling the knowledge contained in the graph into the model, while also using the graph to regularise learnt representations by imposing a local structure on the latent space. We evaluate our approach on several datasets (including the popular HICO and HICO-DET) and show that it outperforms the current state of the art