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Valorising cassava peel waste into plasticized polyhydroxyalkanoates blended with polycaprolactone with controllable thermal and mechanical properties
No full textApproximately 99% of plastics produced worldwide were produced by the petrochemical industry in 2019 and it is predicted that plastic consumption may double between 2023 and 2050. The use of biodegradable bioplastics represents an alternative solution to petroleum-based plastics. However, the production cost of biopolymers hinders their real-world use. The use of waste biomass as a primary carbon source for biopolymers may enable a cost-effective production of bioplastics whilst providing a solution to waste management towards a carbon–neutral and circular plastics economy. Here, we report for the first time the production of poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) with a controlled molar ratio of 2:1 3-hydroxybutyrate:3-hydroxvalerate (3HB:3HV) through an integrated pre-treatment and fermentation process followed by alkaline digestion of cassava peel waste, a renewable low-cost substrate, through Cupriavidus necator biotransformation. PHBV was subsequently melt blended with a biodegradable polymer, polycaprolactone (PCL), whereby the 30:70 (mol%) PHBV:PCL blend exhibited an excellent balance of mechanical properties and higher degradation temperatures than PHBV alone, thus providing enhanced stability and controllable properties. This work represents a potential environmental solution to waste management that can benefit cassava processing industries (or other crop processing industries) whilst developing new bioplastic materials that can be applied, for example, to packaging and biomedical engineering
Towards quantum ray tracing
Get PDFRendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This paper investigates hybrid quantum-classical algorithms for ray trac- ing, a core component of most rendering techniques. Through
a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results obtained using a simulator for both
Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination
Low-complexity channel estimation for V2X systems using feed-forward neural networks
No full textResearch on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach
Thermonuclear explosions on neutron stars reveal the speed of their jets
No full textRelativistic jets are observed from accreting and cataclysmic transients throughout the Universe, and have a profound impact on their surroundings . Despite their importance, their launch mechanism is not known. For accreting neutron stars, the speed of their compact jets can reveal whether the jets are powered by magnetic fields anchored in the accretion flow or in the star itself , but so far no such measurements exist. These objects can show bright explosions on their surface due to unstable thermonuclear burning of recently accreted material, called type-I X-ray bursts , during which the mass-accretion rate increases . Here, we report on bright flares in the jet emission for a few minutes after each X-ray burst, attributed to the increased accretion rate. With these flares, we measure the speed of a neutron star compact jet to be , much slower than those from black holes at similar luminosities. This discovery provides a powerful new tool in which we can determine the role that individual system properties have on the jet speed, revealing the dominant jet launching mechanism. [Abstract copyright: © 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Dynamic electoral competition with voter loss-aversion and imperfect recall
Get PDFThis paper explores the implications of voter loss-aversion and imperfect recall for the dynamics of electoral competition in a simple Downsian model of repeated elections. The interplay between the median voter’s reference point and political parties’ choice of platforms generates a dynamic process of (de)polarization, following an initial shift in party ideology. This is consistent with the gradual nature of long-term trends in polarization in the US Congress
Run-time monitoring of 3D object detection in automated driving systems using early layer neural activation patterns
No full textMonitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors, particularly in the less-explored realm of 3D object detection, remains a significant concern. State-of-the-art integrity monitoring (also known as introspection) mechanisms in 2D object detection mainly utilise the activation patterns in the final layer of the DNN-based detector’s backbone. However, that may not sufficiently address the complexities and sparsity of data in 3D object detection. To this end, we conduct, in this article, an extensive investigation into the effects of activation patterns extracted from various layers of the backbone network for introspecting the operation of 3D object detectors. Through a comparative analysis using Kitti and NuScenes datasets with PointPillars and CenterPoint detectors, we demonstrate that using earlier layers’ activation patterns enhances the error detection performance of the integrity monitoring system, yet increases computational complexity. To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector’s backbone and report its performance
The impact of the COVID-19 pandemic on non-COVID-associated mortality : a descriptive longitudinal study of UK data
Get PDFIt has been previously reported in the literature that the COVID-19 pandemic resulted in overall excess deaths and an increase in non-COVID deaths during the pandemic period.Specifically, our research elucidates the impact of the COVID-19 pandemic on non-COVID associated mortality. To compare mortality rates in non-COVID conditions before and after the onset of the COVID-19 pandemic in England and Wales. Annual mortality data for the years 2011-2019 (pre-pandemic) and 2020 (pandemic) in England and Wales were retrieved from the Office for National Statistics (ONS). These data were filtered by ICD-10 codes for nine conditions with high associated mortality. We calculated mortality numbers - overall and age stratified (20-64 and 65+ years) and rates per 100 000, using annual mid-year population estimates. Interrupted time series analyses were conducted using segmented quasi-Poisson regression to identify whether there was a statistically significant change (p < 0.05) in condition-specific death rates following the pandemic onset. Eight of the nine conditions investigated in this study had significant changes in mortality rate during the pandemic period (2020). All-age mortality rate was significantly increased in: 'Symptoms Signs and Ill-defined conditions', 'Cirrhosis and Other Diseases of the Liver', and 'Malignant Neoplasm of the Breast', whereas 'Chronic Lower Respiratory Disorders' saw a significant decrease. Age-stratified analyses also revealed significant increases in the 20-64 age-group in: 'Cerebrovascular Disorders', 'Dementia and Alzheimer's Disease', and 'Ischaemic Heart Diseases'. Trends in non-COVID condition-specific mortality rates from 2011 to 2020 revealed that some non-COVID conditions were disproportionately affected during the pandemic. This may be due to the direct impact COVID-19 had on these conditions or the effect the public health response had on non-COVID risk factor development and condition-related management. Further work is required to understand the reasons behind these disproportionate changes. [Abstract copyright: © 2024 The Authors.
Judgment in business and management research : shedding new light on a familiar concept
No full textJudgment is an important concept in business and management research and related to several subfields, ranging from staff appraisal and entrepreneurship to strategic decision-making and business ethics. The popularity of the concept has given rise to a diversity of understandings, which, in some instances, lack theoretical precision or conceptual clarity. Our review offers a comprehensive overview and consolidates existing research on judgment in business and management research by identifying three perspectives: variance, prediction, and wisdom. We show how these perspectives converge by highlighting shared characteristics of judgment, such as it being evaluative, personal, and key to coping with uncertainty. In addition, our theoretical synthesis demonstrates how the three perspectives diverge along four central characteristics—theoretical inspiration, purpose, onto-epistemological orientation, and mode of reasoning—that shape how judgment is conceptualized and operationalized in business and management research. By developing a theoretical platform that configures judgment research into three distinct perspectives, our review opens up pathways for assessing the conceptual coherence and methodological implications of each perspective. Building on the latter, we explore how the three perspectives can complement each other and conclude by proposing future directions for the advancement of judgment research
A sequential modelling approach to determine process capability space during laser welding of high-strength aluminium alloys
Get PDFRemote laser welding (RLW) technology has become a prominent joining technology in automotive industries, offering high production throughput and cost-effectiveness. Recent advancements in RLW processes such as beam oscillation have led to an increased number of input process parameters, enabling precise control over the heat input to weld metallic materials. A critical necessity in laser welding entails selecting robust process parameters that satisfy all weld quality indicators or key performance indicators (KPIs) during two stages: production stage (often implemented as robotic welding); and repair/rework stage (implemented as cobotic/manual welding to identify process parameters for weld defects) as addressing these factors in both stages is necessary to satisfy near-zero-defect strategy for some e-mobility products.. This research presents a comprehensive methodology that encompasses the following key elements: (i) the development of physics-based simulations to establish the correlation between KPIs and process parameters; (ii) the integration of a sequential modelling approach that strikes a balance between accuracy and computation time to survey the parameter space; and (iii) development of the process capability space for the quick selection of robust process parameters.
Three physical phenomena are considered in the development of numerical models, which are (i) heat transfer, (ii) fluid flow and (iii) material diffusion to investigate the effect of process parameters on the weld thermal cycle, solidification parameters and solute intermixing layer during laser welding of dissimilar high-strength aluminium alloys. The governing physical phenomena are decoupled sequentially, and KPIs are estimated based on the governing phenomena. At each step, the process capability space is defined over the parameters space based on the constraints specific to the current physical phenomena. The process capability space is determined by the constraints based on the KPIs. The process capability space provides the initial combination of process parameter space during the early design stage, which satisfies all the KPIs, thus decreasing the number of experiments. The proposed methodology provides a unique capability to (i) simulate the effect of process variation as generated by the manufacturing process, (ii) model quality requirements with multiple and coupled quality requirements, and (iii) optimise process parameters under competing quality requirements
Could 'ungrading' promote equity and social justice in higher education?
No full textThe traditional grading system in higher education has long been scrutinized for its potential to perpetuate inequality and hinder the pursuit of social justice (Link & Guskey, 2019). This nano-presentation explores the transformative concept of ungrading as a means to promote equity in higher education. Ungrading challenges the conventional assessment norms by emphasizing personalized and holistic approaches to evaluating student learning (Crogman et al., 2023).
This presentation delves into Bloom’s (1976) theoretical framework supporting ungrading, drawing connections between its implementation and the overarching goal of fostering equitable educational environments. By moving away from rigid grading structures, ungrading seeks to provide students with the space and freedom to engage deeply with course material, promoting a more inclusive and student-centred learning experience.
The presentation will address the potential impact of ungrading on marginalized and underrepresented student populations, considering the ways in which this innovative approach may mitigate existing disparities in academic achievement. The presentation will examine how ungrading aligns with the principles of social justice, offering a pathway to dismantle systemic barriers that hinder educational access and success.
Furthermore, the presentation will explore the role of instructors in implementing ungrading strategies, examining the challenges and benefits associated with this pedagogical shift. Insights will be shared regarding the cultivation of a supportive learning environment that encourages collaboration, critical thinking, and a sense of agency among students, ultimately contributing to a more equitable higher education landscape.
This nano-presentation advocates for a paradigm shift in the assessment practices of higher education institutions, emphasizing the potential of ungrading to create more inclusive and just learning environments. The insights presented aim to stimulate further discussion and exploration of alternative assessment methods that prioritize equity, diversity, and social justice in the pursuit of academic excellence