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Employers are increasingly selecting and developing employees based on skills rather than qualifications. Governments now have a growing focus on skilling, reskilling and upskilling the workforce through skills-based development rather than qualifications as a way of improving productivity. Both these changes are leading to a much stronger interest in digital badging and micro-credentialing that enables a more granular, skills-based development of learner-earners. This paper explores the use of an online skills profiling tool that can be used by designers, educators, researchers, employers and governments to understand how badges and micro-credentials can be incorporated within existing qualifications and how skills developed within learning can be compared and aligned to those sought in job roles. This work, and lessons learnt from the case study examples of computing-related degree programmes in the UK, also highlights exciting opportunities for educational providers to develop and accommodate personalised learning into existing formal education structures across a range of settings and contexts.

Abstract Laser powder bed fusion of high-strength aluminium alloys remains challenging due to the formation of hot cracks during the printing process. Hot cracking is a complex phenomenon involving a complex thermo-mechanical and metallurgical interplay. Such relationship needs to be fully understood to reap the benefits of advanced laser modulation (temporal and spatial) capabilities, now becoming available in PBF-LB. To this end, we explore the formation of hot cracks in single tracks produced using a simple spatial–temporal laser modulation characterised by laser pulses of various distance. The formation of cracks is then rationalised by physical and numerical modelling using multi-physics CFD simulation. We demonstrate that the area of the mushy zone at the back of the moving melt pool, dynamically contracts and expands according to the laser temporal regimes and find maxima in correspondence to the largest laser pulse distance and the time steps between exposures. By then analysing in detail in the crack observed in the printed parts fabricated with the same laser modulation, it is possible to conclude that the mechanisms leading to hot cracks in these specimens is analogous. In turn, the insights on single tracks can be extended, for the most part, to the case of bulk specimens. Nevertheless, printed specimens appear to be more sensitive to the erratic movement of the melt pool, due to the presence of a larger number of highly energetical grain boundaries and unintended microstructural defects (voids and inclusions), from which cracks can nucleate. Graphical Abstract

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

ABSTRACTWe conducted a comparative evaluation of the FDA-cleared Simplexa GBS Direct and ARIES GBS molecular assays for the detection of Streptococcus agalactiae (Group B Streptococcus, GBS) in 386 prospectively collected, broth-enriched vaginal/rectal swab specimens. The sensitivity of each test was 96.2% and specificity was ≥98.7% when compared to a combined direct and enriched culture method using chromogenic culture medium. A total of four specimens were called positive by both molecular assays but negative by culture, likely representing specimens with a low burden of GBS in these specimens. Two specimens were reported positive by culture but negative by both molecular assays. One of these specimens demonstrated atypically colored colonies on chromogenic agar; the other yielded typically colored colonies only observed after broth enrichment. Our data demonstrate equivalent performance of Simplexa and ARIES molecular assays for the detection of GBS in clinical specimens.IMPORTANCEClinical laboratories often face decisions regarding which of the multiple available molecular platforms would best fit their needs based on cost, workflow, menu, and diagnostic performance. Therefore, objective clinical comparisons of similar molecular tests are valuable resources to aid these decisions. We provide a clinical comparison of two FDA-cleared tests to routine culture and to each other that can be used by clinical laboratories when determining which of the available molecular platforms would best fit their laboratory in terms of workflow, cost, and performance.

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45-52N, 119-123W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 C hotter than a 1-in-1000-year heat wave would have been in 1850-1900, when global mean temperatures were 1.2 C cooler than today. Looking into the future, in a world with 2 C of global warming (0.8 C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.