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Unlocking circularity: the interplay between institutional pressures and supply chain integration
No full textPurposeThis paper investigates the role of institutional pressures (IPs) and supply chain integration (SCI) in driving the adoption of circular economy (CE) practices. It is hypothesised that, responding to IPs, firms might adopt higher levels of SCI in the attempt to implement CE practices.Design/methodology/approachA research model is developed and tested on a cross-sectional sample of 150 multi-national enterprises (MNEs). Textual content from corporate sustainability reports is used to measure the constructs of interest through an advanced coding approach.FindingsFindings show that IPs are driving the adoption of CE practices primarily through the mediation of SCI; the prominent roles of coercive regulatory pressures (CRPs) and normative pressures (NPs) are also highlighted. CRPs influence on CE practices is partially mediated by SCI, with NPs influence being fully mediated by it.Practical implicationsThe study shows that SCI is a key mechanism that lies in between IPs and CE practices; as such, organisations interested in implementing CE practices need to be aware of requirements for achieving higher levels of SCI.Originality/valueThis empirical study is the first large scale analysis that conceptualises how MNE-driven supply chains adopt CE practices. The study empirically validates the model and identifies research avenues in supply chain management (SCM) research to support the adoption of CE practices
KA KORĪ TE HENUA - Report on Initial Design Research and Opportunities for Gamifying Rapa Nui Heritage
No full textThis report was developed by a team of four students from Di-Lab, an engineering and design initiative at the Pontifical Catholic University of Chile, under the direction of Dr Juan Hiriart.The study aimed to understand the design context for a game centred around Rapa Nui heritage and language, utilising qualitative research methods. The students conducted and analysed interviews with Rapa Nui youth, teachers, community leaders, and gamification experts to gather insights into user needs, cultural values, and contextual factors that influence game design.The outcomes of this research included a thematic analysis of the interview data, the creation of user experience (UX) personas representing various player profiles, and the identification of key design opportunities to guide the future stages of the project
Human Idiot Puppet (HIP): Residency at Studio Kura, Itoshima. Project Report
No full textGreat Britain Sasakawa Funded Residency at Studio Kura, Itoshima. Project report listing outputs and outcomes, modes of collaboration and participation by other artists in residence.Project completion date: 30 Sept 2023. Report date 20 December 202
Dynamic Dual‐Level Overcurrent Protection Scheme for Distributed Energy Resource Networks Using Digital Twins Technology
Get PDFThe integration of distributed generators (DGs), particularly renewable energy sources, into conventional distribution networks (DNs) presents significant protection challenges. This study introduces a novel digital twins-based overcurrent relay (OCR) protection scheme with dynamic dual-level characteristic curves for microgrids. By utilizing advanced technologies such as digital-twin technology and hardware-in-the-loop (HIL) testing, the proposed scheme enhances fault management and relay coordination. Key contributions of this work include the integration of advanced technologies and dynamic OCR settings, using digital-twin technology and HIL testing to provide real-time insights and robust validation under practical conditions. Additionally, the research thoroughly examines protection strategies for both grid-connected and islanded modes, ensuring reliable operation during faults or grid failures. The study’s findings show substantial improvements over traditional OCR methods. The traditional OCR recorded a total tripping time of 14.87 s, while the dual-level OCR reduced it to 8.97 s. The results highlight the proposed scheme’s enhanced sensitivity, faster fault isolation capability, and overall superior performance, providing a robust and reliable protection scheme for modern power distribution systems. Further testing results for the digital-twin OCR, comparing both the digital simulation twin relay and the physical twin relay for the traditional single-level OCR scheme and the proposed dual-level scheme, provide valuable insights into the performance and reliability of these protection strategies. The close alignment between the simulation and physical results highlights the robustness and precision of the dual-level scheme
Modern Murders: The Turn-of-the-Century’s Backlash Against Melodramatic and Sensational Representations of Murder, 1880–1914
Get PDFNeighbourhood socioeconomic conditions and emergency admissions for ambulatory care sensitive conditions in children: a longitudinal ecological analysis in England, 2012–2017
Get PDFBackground: Ambulatory care sensitive conditions (ACSCs) are those for which hospital admission could be prevented by interventions in primary care. Children living in socioeconomic disadvantage have higher rates of emergency admissions for ACSCs than their more affluent counterparts. Emergency admissions for ACSCs have been increasing, but few studies have assessed how changing socioeconomic conditions (SECs) have impacted this. This study investigates the association between local SECs and emergency ACS hospital admissions in children in England.Methods: We examined longitudinal trends in emergency admission rates for ACSCs and investigate the association between local SECs and these admissions in children over time in England, using time-varying neighbourhood unemployment as a proxy for SECs. Fixed-effect regression models assessed the relationship between changes in neighbourhood unemployment and admission rates, controlling for unmeasured time-invariant confounding of each neighbourhood. We also explore the extent to which this relationship differs by acute and chronic ACSCs and is explained by access to primary and secondary care.Results: Between 2012 and 2017, paediatric emergency admissions for acute ACSCs increased, while admissions for chronic ACSCs decreased. At the neighbourhood level, each 1% point increase in unemployment was associated with a 3.9% and 2.7% increase in the rate of emergency admissions for acute ACSCs, for children aged 0–9 years and 10–19 years, respectively. A 2.6% increase in admission rates for chronic ACSCs was observed, driven by an association in 0–9 years old. Adjustment for primary and secondary care access did not meaningfully attenuate the magnitude of this association.Conclusions: Increasing trends in neighbourhood unemployment were associated with increases in paediatric emergency admission rates for ACSCs in England. This was not explained by available measures of differential access to care, suggesting policy interventions should address the causes of unemployment and poverty in addition to health system factors to reduce emergency admissions for ACSCs
Aeroacoustics and psychoacoustics characterization of a boundary layer ingesting ducted fan
Get PDFA comprehensive wind tunnel investigation was conducted to analyze noise generation, propagation, and perception mechanisms in a boundary layer ingesting (BLI) ducted fan through integrated aeroacoustic and psychoacoustic assessments. The study examines interactions between an incoming adverse pressure gradient turbulent boundary layer flow, developed over a curved wall, and the ducted fan. The fundamental investigation confirms that the fan thrust regime influences aerodynamic, aeroacoustic, and psychoacoustic characteristics, exhibiting various haystacking phenomena. High-thrust operation induces a pronounced upstream suction effect, accelerating the boundary layer flow, amplifying bulk momentum, and intensifying turbulence ingestion, leading to fan aeroacoustics and associated fan haystacking in noise spectrum. In contrast, low-thrust operation minimally alters the boundary layer flow, with reduced suction and noise dominated by duct aeroacoustics and the associated duct haystacking due to interactions between ingested turbulence and the duct’s acoustic field. The psychoacoustic assessments indicate that both fan and duct haystacking contribute to higher perceived noise in the high- and low-thrust regime, respectively
The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss
Get PDFListening to music can be an issue for those with a hearing impairment, and hearing aids are not a universal solution. This paper details the first use of an open challenge methodology to improve the audio quality of music for those with hearing loss through machine learning. The first challenge (CAD1) had 9 participants. The second was a 2024 ICASSP grand challenge (ICASSP24), which attracted 17 entrants. The challenge tasks concerned demixing and remixing pop/rock music to allow a personalized rebalancing of the instruments in the mix, along with amplification to correct for raised hearing thresholds. The software baselines provided for entrants to build upon used two state-of-the-art demix algorithms: Hybrid Demucs and Open-Unmix. Objective evaluation used HAAQI, the Hearing-Aid Audio Quality Index. No entries improved on the best baseline in CAD1. It is suggested that this arose because demixing algorithms are relatively mature, and recent work has shown that access to large (private) datasets is needed to further improve performance. Learning from this, for ICASSP24 the scenario was made more difficult by using loudspeaker reproduction and specifying gains to be applied before remixing. This also made the scenario more useful for listening through hearing aids. Nine entrants scored better than the best ICASSP24 baseline. Most of the entrants used a refined version of Hybrid Demucs and NAL-R amplification. The highest scoring system combined the outputs of several demixing algorithms in an ensemble approach. These challenges are now open benchmarks for future research with freely available software and data
An image dataset for use in detecting unwanted bolt rotation.
Get PDFIn any industrial system, ensuring that the engineered components therein are in working order is essential for the safety of workers and for efficient and cost-effective running. However, due to factors such as stress, deformation, and corrosion, individual components degrade over time, eventually leading to failure. Whilst there exist several public training datasets for use in bolt detection, there is none in the area of bolts or other mechanical fixings changing over time. We prepared a novel dataset of over 1,100 images depicting a bolted apparatus from different angles, and with varying degrees of bolt rotation. The images were taken in laboratory conditions, with carefully measured variations. As far as we know, no other such dataset exists
GAN-enhanced deep learning for improved Alzheimer's disease classification and longitudinal brain change analysis
Get PDFAlzheimer's disease (AD) is commonly defined by a progressive decline in cognitive functions and memory. Early detection is crucial to mitigate the devastating impacts of AD, which can significantly impair a person's quality of life. Traditional methods for diagnosing AD, while still in use, often involve time-consuming processes that are prone to errors and inefficiencies. These manual techniques are limited in their ability to handle the vast amount of data associated with the disease, leading to slower diagnosis and potential misclassification. Advancements in artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offer promising solutions to these challenges. AI techniques can process large datasets with high accuracy, significantly improving the speed and precision of AD detection. However, despite these advancements, issues such as limited accuracy, computational complexity, and the risk of overfitting still pose challenges in the field of AD classification. To address these challenges, the proposed study integrates deep learning architectures, particularly ResNet101 and long short-term memory (LSTM) networks, to enhance both feature extraction and classification of AD. The ResNet101 model is augmented with innovative layers such as the pattern descriptor parsing operation (PDPO) and the detection convolutional kernel layer (DCK), which are designed to extract the most relevant features from datasets such as ADNI and OASIS. These features are then processed through the LSTM model, which classifies individuals into categories such as cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Another key aspect of the research is the use of generative adversarial networks (GANs) to identify the progressive or non-progressive nature of AD. By employing both a generator and a discriminator, the GAN model detects whether the AD state is advancing. If the original and predicted classes align, AD is deemed non-progressive; if they differ, the disease is progressing. This innovative approach provides a nuanced view of AD, which could lead to more precise and personalized treatment plans. The numerical outcome obtained by the proposed model for ADNI dataset is 0.9931, and for OASIS dataset, the accuracy gained by the model is 0.9985. Ultimately, this research aims to offer significant contributions to the medical field, helping healthcare professionals diagnose AD more accurately and efficiently, thus improving patient outcomes. Furthermore, brain simulation models are integrated into this framework to provide deeper insights into the underlying neural mechanisms of AD. These brain simulation models help visualize and predict how AD may evolve in different regions of the brain, enhancing both diagnosis and treatment planning
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