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Investigation of nonlocal granular fluidity models using nuclear magnetic resonance
No full textNonlocal rheology models describe features in granular flows, such as scale dependence and flow below the yield point, that are not captured by local rheology models. It has been proposed that these features may be described by the transport of a property known as the granular fluidity. In this article, we studied an annular Couette shear cell of lobelia seeds using nuclear magnetic resonance to collect detailed measurements of the velocity distribution and volume fraction. These data were used to study nonlocal granular rheology models. We found that the nonlocal granular fluidity model was capable of accurately describing the decay in the velocity profile along the shear gradient direction. We also measured the dimensionless fluidity and validated the general form of the relation between this quantity and the volume fraction
COVID-19, global public health justice, and the culture of organized irresponsibility
No full textThis article deploys the language of risk to offer a sociological perspective on the discourse of responsibility in the context of the governance and mishandling of the COVID-19 pandemic. While current debates about global public health justice often tend to overemphasize the role of legal action as a key measure in today’s global public health justice initiatives, the article argues that lack of adequate legal mechanisms – such as a global statute on public health crimes – constitutes only one barrier to the attainment of global public health justice. By and large, the failed administration of public health during global pandemics will not induce criminal prosecution on a worldwide scale and this is not because of lack of adequate legal channels but mostly because of the way in which world risk society reshapes the meaning of responsibility. The article argues that the COVID-19 pandemic is a manufactured risk that is being dealt with within a culture of organized irresponsibility that obfuscates accountability and liability for risk-creation and risk-management and transforms culpability for such risk-creation and risk-management into acquittal. Effective approaches to global public health justice, then, cannot be limited to the introduction of international legal safeguards but need to include a project for the social redistribution of bads and reallocation of global responsibility for risk-creation and risk-management
An Inventor, a Cyborg and an AI Walk into a Bar: Changing Gendered Narratives in Patent Law?
No full textNo description supplie
Metagenomic domain substitution for the high-throughput modification of nonribosomal peptides
No full textThe modular nature of nonribosomal peptide biosynthesis has driven efforts to generate peptide analogs by substituting amino acid-specifying domains within nonribosomal peptide synthetase (NRPS) enzymes. Rational NRPS engineering has increasingly focused on finding evolutionarily favored recombination sites for domain substitution. Here we present an alternative evolution-inspired approach that involves large-scale diversification and screening. By amplifying amino acid-specifying domains en masse from soil metagenomic DNA, we substitute more than 1,000 unique domains into a pyoverdine NRPS. Initial fluorescence and mass spectrometry screens followed by sequencing reveal more than 100 functional domain substitutions, collectively yielding 16 distinct pyoverdines as major products. This metagenomic approach does not require the high success rates demanded by rational NRPS engineering but instead enables the exploration of large numbers of substitutions in parallel. This opens possibilities for the discovery and production of nonribosomal peptides with diverse biological activities. [Figure not available: see fulltext.]
Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)
No full textMassive Open Online Courses (MOOCs) have witnessed a surge in popularity among learners and providers, prompting researchers to explore the enhancement of MOOC utilisation through recommendation systems. However, while existing efforts primarily focus on designing recommender systems capable of providing recommendations across diverse areas of MOOCs, less attention is given to essential characteristics of a robust recommender system, such as practicality and scalability. This literature gap leads to a lack of practical and scalable recommender systems capable of effectively handling increasing volumes of data. Furthermore, current systems leveraging neural networks and deep learning for user behavioural data-based recommendations face challenges in accommodating new data points without retraining. Consequently, scalable and practical recommendation algorithms for MOOCs remain limited in the current literature.To address these shortcomings, this research aims to contribute to the development of scalable and practical recommender systems for MOOCs by utilising demographic and behavioural data of learners, alongside the unique characteristics of MOOCs, for personalised recommendations. In line with these objectives, the study introduced the Novel online Recommender system for MOOCs (NoR-MOOCs) specifically designed for course recommendation. Preliminary studies on the COCO dataset demonstrate the algorithm's promising performance. NoR-MOOCs constructs user profiles based on course ratings provided by users, and its performance evaluation employs predictive and classification accuracy metrics (RMSE, ROC, precision, recall and coverage). A comparison with traditional K-Means and collaborative Filtering algorithms validates the effectiveness of NoR-MOOCs.Additionally, considering the abundant user behavioural and demographic data available in MOOCs, a Novel online Multi-attribute-based Recommendation algorithm for MOOCs (NoM-MOOCs) is designed. This algorithm creates user profiles based on their behavioural and demographic characteristics. Extensive experiments on the Edx and CAN datasets demonstrate the algorithm's significant out-performance compared to the Kernal Mapping recommender system (KMR) in terms of predictive and classification accuracy metrics. Moreover, the analysis of behavioural data from MOOCs logs contributes to forming valuable learning pathways for learners. Existing literature has extensively explored the design and implementation of learning pathway recommendation systems, with such recommender systems being intricately designed using neural networks and deep learning. To this end, a fast online learning path recommender system that incorporates demographic and behavioural data of learners to recommend learning paths is designed. This system identifies similar learners by leveraging demographics and behavioural data and recommends pats paths taken by successful students to struggling students with similar behavioural and demographic profiles. To evaluate the results of this proposed algorithm and the potential impact of the learning path recommender system on learners, an interview with the MOOC professor is conducted.Overall, this research strives to contribute to the advancement of scalable and practical recommender systems for MOOCs, enriching the learning experiences of users and promoting the effective utilisation of MOOCs platforms.</p
Effective Presentations of Mathematical Structures
No full textFor several algebraic structures, we show that they have a punctual dimension either 1 or ∞. The punctual degree structures of rigid structures and the ordered integers are dense. We consider some classes where every punctual structure from the class can be punctually embedded into its punctual (existential, algebraic) closure. We prove that the space C[0, 1] and the Urysohn space U are computably and punctually universal, and that the Urysohn space is not punctually categorical. And finally, we show that effectively compact punctual presentations of a Stone space are punctually homeomorphically embeddable into Cantor space, and that there is a compact totally disconnected punctual Polish space which is not computably homeomorphically embeddable into Cantor space.</p
The Implication Of Transformable Space Saving Furniture On The Interior Design In Small Living Units
No full textThis project reviews some excellent and successful design projects that providing the same space different usage scenarios as promoted by the explosion of increased urbanisation, growing population and the reduction of per capita living area. The explosion of the COVID-19 epidemic has eventually changed many people's living situations and working habits; however, most people find it challenging to buy large residential spaces in the city that can meet all their living and working needs. Therefore, the popularity of multi-functional living spaces and folding furniture is increasing significantly. This report aims to study the impact of deformable space-saving furniture in small apartments on the interior design of small living units, focusing on the transformation of small apartments to meet the needs of people living and working at home. The design location is a small apartment in one of the most densely populated areas in Hong Kong. Transforming small apartments into commercial and residential buildings provides new options for people living in small apartments regarding living experience and lifestyle.</p
Optimal Valuation of Variable Annuity Guaranteed Lifetime Withdrawal Benefits with Embedded Top-up Option
No full textThis paper generalizes earlier works on the valuation of variable annuity guaranteed lifetime withdrawal benefits (VAGLWB) to an extent in which the policyholders are given an option to change the parameters of the contract, i.e., the withdrawal and premium rates, at any time during the duration of the contract. Under this framework, we propose an embedded top-up option which gives the buyer an opportunity to change the contract to a new one with a larger withdrawal rate and reduced premium payment rate subject to paying a switching cost. The valuation is formulated in terms of the optimal stopping problem of finding an exercise time of the option which maximizes the contract value. We provide optimal solution to the stopping problem in which the asset’s price evolves according to a geometric Brownian motion. Moreover, we show that the value function of optimal stopping problem is given explicitly in terms of confluent hypergeometric function which satisfies continuous and smooth pasting conditions. Majorant and (super) harmonic properties of the value function are established to show optimality of the solution. Numerical examples are discussed to exemplify the main results and the sensitivity analysis of several parameters in the contract.</p
Climate Change Dispute Resolution
No full textClimate change is the biggest threat humankind has been called to face. It threatens the very survival of our species and planet. This thesis considers the climate crisis from a dispute resolution (DR) perspective. More specifically, the disputes arising as a consequence of climate change and the way in which they are, and should be, addressed. These disputes are unique and require specific consideration given they concern an imminent threat to human survival, involve highly vulnerable parties and fundamental power imbalances, and are burgeoning in complexity and volume. A cursory consideration suggests that the current approach to these climate change disputes is not effective, as climate change worsens and related disputes increase. This assumption, however, has not yet been demonstrated by evidence-based examination. Although there is research considering particular types of climate change disputes (such as, those based on human rights), specific DR processes (such as, negotiation or adjudication) and aspects of effectiveness (such as, the impact of adjudication on mitigation) there is no work that examines and assesses the full scope of climate change disputes and DR processes. As a result, there is no substantiated basis on which climate change disputes can be most effectively identified, understood, resolved or prevented. In order to address these problems, this thesis provides a comprehensive map of climate change disputes and the current DR system for addressing them. It also formulates and applies a mechanism for assessing the effectiveness of that system, one that includes and prioritises addressing climate change itself. On the basis of the resulting assessment, which demonstrates deficiencies in the current climate change DR system, this thesis proceeds to recommend specific improvements to enhance that system’s efficacy. It concludes that the most effective way to address climate change disputes is through a system that supports the climate response, is comprehensive, cohesive, deliberate, adaptable, preventative, and, in large part, relies on more and better use of innovative alternative DR processes. Although this recommended approach requires changes in the way DR is conceived and delivered, it is vital this occur given that climate change disputes are escalating, and will continue to do so, as more individuals, communities, states, and ecosystems are impacted, and the urgency with which we must face the climate crisis grows. Climate change requires bold action on every front, including through our DR system.</p
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