Development of Single-Phase Microbial Cementation Method and to Investigate its Efficacy on Bearing Capacity, UCS, and Permeability of Sandy Soils

Microbially induced calcite precipitation (MICP) is a method based on collaborative knowledge of microbiology, chemistry and geotechnical engineering. The objective of this study was to investigate the increase of the bearing capacity and the unconfined compressive strength (UCS) as well as the reduction of the permeability of sandy soil using MICP. Experiments were carried out using Bacillus Pasteurii, on three different types of sand. The admixture of bacterial culture and cementation (BCC) solution all-in-one with sand by single-phase injection was applied to induce cementation. Three samples of the selected sand were treated with varied concentrations of BCC solution, ranging from 0.05 to 0.2 L/kg, with a curing period of 3, 7 and 14 days. The test results indicated an enhancement of 55% in UCS for sand treated with a BCC content of 0.05 to 0.2 L/Kg and a reduction of 40% in permeability for untreated sand with an effective diameter of 0.5 mm treated with 0.2 L/kg of BCC solution after 14 days of curing. The results of a plate load test (PLT) on MICP treated sand showed an increase in the ultimate bearing capacity (qu) by about 2.95 to 5.8 times and a 1.7 to 3.31-fold reduction in settlement corresponding to the same load applied on untreated footing. Further investigation of the size and shape of the bearing plate on bearing capacity and settlement was carried out through a plate load test. The higher and more favorable results shown by a rectangular plate compared to a circular plate indicate that the first is preferable.

A feminist ethos for caring knowledge production in transdisciplinary sustainability science

Transdisciplinary Sustainability Science has emerged as a viable answer to current sustainability crises with the aim to strengthen collaborative knowledge production. To expand its transformative potential, we argue that Transdisciplinary Sustainability Science needs to thoroughly engage with questions of unequal power relations and hierarchical scientific constructs. Drawing on the work of the feminist philosopher María Puig de la Bellacasa, we examine a feminist ethos of care which might provide useful guidance for sustainability researchers who are interested in generating critical-emancipatory knowledge. A feminist ethos of care is constituted by three interrelated modes of knowledge production: (1) thinking-with, (2) dissenting-within and (3) thinking-for. These modes of thinking and knowing enrich knowledge co-production in Transdisciplinary Sustainability Science by (i) embracing relational ontologies, (ii) relating to the ‘other than human’, (iii) cultivating caring academic cultures, (iv) taking care of non-academic research partners, (v) engaging with conflict and difference, (vi) interrogating positionalities and power relations through reflexivity, (vii) building upon marginalised knowledges via feminist standpoints and (viii) countering epistemic violence within and beyond academia. With our paper, we aim to make a specific feminist contribution to the field of Transdisciplinary Sustainability Science and emphasise its potentials to advance this field.

An open source library to parse and analyze online collaborative knowledge-building portals

With the success of collaborative knowledge-building portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons; first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building dynamics.We describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark analysis.Link of the repository: Verma et al. (2020)Video Tutorial: Verma et al. (2020)Supplementary Material: Verma et al. (2020)

A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.

What do we know about the impact of the Covid-19 pandemic on hospices? A collaborative multi-stakeholder knowledge synthesis

Background: Prior to undertaking a study looking at the effects of the COVID-19 pandemic upon lived experiences of hospice services in the West Midlands, we sought to identify the range of issues that hospice service users and providers faced between March 2020 and July 2021, and to provide a report that can be accessed and understood by all interested stakeholders. Methods: We undertook a collaborative multi-stakeholder approach for scoping the range of potential issues and synthesising knowledge. This involved a review of available literature; a focus group with hospice stakeholders; and a collaborative knowledge exchange panel. Results: The literature on the effects of the COVID-19 pandemic on hospices remains limited, but it is developing a picture of a service that has had to rapidly adapt the way it provides care and support to its service users, during a period when it faced many fundamental challenges to established ways of providing these services. Conclusions: The impacts of many of the changes on hospices have not been fully assessed. It is also not known what the effects upon the quality of care and support are for those with life-limiting conditions and those that care for them. We found that the pandemic has presented a new normative and service context in which quality of care and life itself was valued that is, as yet, poorly understood.

Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata

Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.