Collective Cargo Transport and Sorting with Molecular Swarms

Recent work has demonstrated the viability of DNA robotics and artificial molecular machines for molecular transportation and cargo sorting with potential applications in manufacturing responsive molecular devices, programmable therapeutics, and autonomous chemical synthesis. We extend previous work on cooperative molecular transportation using artificial molecular machines, where we similarly functionalize DNA-conjugated microtubules driven by kinesin motor proteins. DNA-functionalized microtubules propelled by surface-adhered kinesin motors enable the self-organization of molecular swarms, where such swarms load and transport cargo (microbead) in a simulated chemical environment. We demonstrate programmable molecular swarms for cargo sorting and cooperative transport. Cargo loading occurs when sufficient microtubules are at the same location as the cargo, and cargo unloading occurs at specific points in the environment through interaction with localized DNA species. Our contribution is the design of a chemotaxis molecular controller, forcing the swarm to tumble (random change direction) when the system is not following a molecular gradient corresponding to the cargo type, thus directing it to specific points for cargo unloading. This work thus contributes to the open problem of how to best design programmable molecular machines for various tasks in microscopic environments

Automating the Generation of Competency Questions for Ontologies with AgOCQs

Competency Questions (CQs) are natural language questions drawn from a chosen subject domain and are intended for use in ontology engineering processes. Authoring good quality and answerable CQs has been shown to be difficult and time-consuming, due to, among others, manual authoring, relevance, answerability, and re-usability. As a result, few ontologies are accompanied by few CQs and their uptake among ontology developers remains low. We aim to address the challenges with manual CQ authoring through automating CQ generation. This novel process, called AgOCQs, leverages a combination of Natural Language Processing (NLP) techniques, corpus and transfer learning methods, and an existing controlled natural language for CQs. AgOCQs was applied to CQ generation from a corpus of Covid-19 research articles, and a selection of the generated questions was evaluated in a survey. 70% of the CQs were judged as being grammatically correct by at least 70% of the participants. For 12 of the 20 evaluated CQs, the ontology expert participants deemed the CQs to be answerable by an ontology at a range of 50$$\backslash$$-85$$\backslash$$across the CQs, with the remainder uncertain. This same group of ontology experts found the CQs to be relevant between 70$$\backslash$$-93$$\backslash$$across the 12 CQs. Finally, 73$$\backslash$$of the users group and 69$$\backslash$$of the ontology experts judged all the CQs to provide clear domain coverage. These findings are promising for the automation of CQs authoring, which should reduce development time for ontology developers

Surface realisation architecture for low-resourced African languages

There has been growing interest in building surface realisation systems to support the automatic generation of text in African languages. Such tools focus on converting abstract representations of meaning to a text. Since African languages are low-resourced, economical use of resources and general maintainability are key considerations. However, there is no existing surface realiser architecture that possesses most of the maintainability characteristics (e.g., modularity, reusability, and analysability) that will lead to maintainable software that can be used for the languages. Moreover, there is no consensus surface realisation architecture created for other languages that can be adapted for the languages in question. In this work, we solve this by creating a novel surface realiser architecture suitable for low-resourced African languages that abides by the features of maintainable software. Its design comes after a granular analysis, classification, and comparison of the architectures used by 77 existing NLG systems. We compare our architecture to existing architectures and show that it supports the most features of a maintainable software product

Reflections on Digital Maternal and Child Health Support for Mothers and Community Health Workers in Rural Areas of Limpopo Province, South Africa

Introduction: Digital health support using mobile and digital technologies, such as MomConnect and WhatsApp, is providing opportunities to improve maternal and child healthcare in low- and middle-income countries. Yet, the perspective of health service providers, pregnant women, and mothers as recipients of digital health support is under-researched in rural areas. Material and Methods: An exploratory-descriptive qualitative research approach was adopted to reflect on the experiences of mothers, community leaders, and community health workers on mobile health opportunities in the context of maternal and child health in rural areas. Purposive sampling was used to select 18 participants who participated in the two focus groups and individual semi-structured interviews for data collection about digital maternal and child health support. The thematic open coding method of data analysis assisted authors in making sense of the given reflections of mothers, community leaders, and healthcare workers about digital health support. Results: Participants commented on different existing digital support apps and their importance for maternal and child health. For example, MoMConnect, Pregnancy+, WhatsApp, and non-digital resources were perceived as useful ways of communication that assist in improving maternal and child health. However, participants reported several challenges related to the use of digital platforms, which affect following the health instructions given to pregnant women and mothers. Conclusions: Participants expressed the significant role of digital support apps in maternal and child health, which is impacted by various challenges. Addressing the lack of digital resources could improve access to health instructions for pregnant women and mothers

Focused Crawling for Automated IsiXhosa Corpus Building

IsiXhosa is a low-resource language, which means that it does not have many large, high-quality corpora. This makes it difficult to perform many kinds of research with the language. This paper examines the use of focused Web crawling for automatic corpus generation. The resulting corpus is characterised using statistical methods: its vocabulary growth has been found to fit Heaps’ Law, and its word frequency has been found to be heavy-tailed. In addition, as expected, the corpus statistics did not match expectations from non-agglutinative language

The Impact of Morphological Diversity in Robot Swarms

In nature, morphological diversity enhances functional diversity, however, there is little swarm (collective) robotics research on the impact of morphological and behavioral (body-brain) diversity that emerges in response to changing environments. This study investigates the impact of increasingly complex task environments on the artificial evolution of body-brain diversity in simulated robot swarms. We investigate whether increasing task environment complexity (collective behavior tasks requiring increasing degrees of cooperative behavior) mandates concurrent increases in behavioral, morphological, or coupled increases in body-brain diversity in robotic swarms. Experiments compared three variants of collective behavior evolution across increasingly complex task environments: two behavioral diversity maintenance variants and body-brain diversity maintenance. Results indicate that body-brain diversity maintenance yielded a significantly higher behavioral and morphological diversity in evolved swarms overall, which was beneficial in the most complex task environment

Milk Matters 4.0: Challenges in deploying university-led mobile application development for small NGOs

Milk Matters is a small Cape Town based non-profit milk bank. Their primary role is to collect expressed breastmilk from donor mothers, pasteurize it and distribute it to recipient infants in need. Previous postgraduate projects from the University of Cape Town (UCT) have co-designed a donor facing mobile application with Milk Matters, however no mobile application is currently deployed or promoted by the non-governmental organization (NGO). This project will build upon the work already done with Milk Matters and aims to update the full system for deployment. While post-deployment evaluation will also analyse the uptake and usage of the application, this poster will focus on discussing the challenges in the deployment of university-led mobile application development for small NGOs

Reconsidering Priorities for Digital Maternal and Child Health: Community-Centered Perspectives from South Africa

Especially in developing regions, parents are rarely given a direct voice in the design of digital maternal and child health (MCH) interventions. Instead, MCH needs and requirements are driven by organizations and health workers. In this research, we engage with both rural and urban parents and community leaders to better understand their challenges and priorities for digital MCH and propose a parent-centered agenda for human-computer interaction research. This paper reports on the community-based, digital MCH priorities identified in our research, and describes how we approached community discourse and co-design of digital initiatives for these priorities, through parent-centered workshops with low-resource South African communities. Furthermore, we provide the parent-centered design opportunities and tensions we discovered for digital MCH in South African contexts, such as designing for local contexts and languages, designing for accessibility and connectedness, and highlighting the underdeveloped digital MCH niches. Finally, we highlight the importance of including facilitators for co-design workshops, such as using intermediaries and design cards