No abstract

Sabkha soil is abundant along the Arabian Gulf and Red Sea coasts and is a problematic soil due to its acute water sensitivity and chemical aggressiveness. In many situations, it is required to improve the load carrying capacity of sabkha, and the use of geotextiles was found appropriate. The objectives of this research were to study frictional characteristics of sand-geotextile-sand and sabkha-geotextile-sand interfaces and to compare the pull-out resistance of locally available nonwoven geotextiles taking into account different test parameters. An experimental setup was developed to conduct the pull-out tests. These test results have indicated the existence of three stages of deformation in the geotextile under pull-out testing, which ultimately lead to the slippage of the entire geotextile strip. The use of the pull-out plate reduces the effects of the lateral earth pressure developed on the front wall of the pull-out box and ensures that the free geotextile is kept within the box and, thus, under the required confinement throughout the test. The pull-out tests results indicated that high tensile strength geotextiles require a large pull-out force in the case of the sand-geotextiles and interface, whereas the least extensible geotextile requires the maximum pull-out force in the case of the sabkha-geotextile-sand interface. It was also found that the geotextile surface texture and extensibility are the two main factors, in addition to the mass per unit area of the geotextile, in the case of sabkha-geotextile-sand interface.

Background Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons’ knowledge and perception of using AI-based tools in clinical decision-making processes. Methods An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society’s website and Twitter profile. Results 650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust. Discussion The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document. In this paper, we compare and contrast two recently introduced approaches to document clustering based on suffix tree data model. The first is an Efficient Phrase based document clustering, which extracts phrases from documents to form compact document representation and uses a similarity measure based on common suffix tree to cluster the documents. The second approach is a frequent word/word meaning sequence based document clustering, it similarly extracts the common word sequence from the document and uses the common sequence/ common word meaning sequence to perform the compact representation, and finally, it uses document clustering approach to cluster the compact documents. These algorithms are using agglomerative hierarchical document clustering to perform the actual clustering step, the difference in these approaches are mainly based on extraction of phrases, model representation as a compact document, and the similarity measures used for clustering. This paper investigates the computational aspect of the two algorithms, and the quality of results they produced.

No abstract