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Stress is a normal part of life for fungi, which can survive in environments considered inhospitable or hostile for other organisms. Due to the ability of fungi to respond to, survive in, and transform the environment, even under severe stresses, many researchers are exploring the mechanisms that enable fungi to adapt to stress. The International Symposium on Fungal Stress (ISFUS) brings together leading scientists from around the world who research fungal stress. This article discusses presentations given at the third ISFUS, held in Sao Jose dos Campos, Sao Paulo, Brazil in 2019, thereby summarizing the state-of-the-art knowledge on fungal stress, a field that includes microbiology, agriculture, ecology, biotechnology, medicine, and astrobiology.
The Jack Rabbit II (JR II) chlorine field trials in 2015 and 2016 involved nine 5–20 ton releases of pressurized liquefied chlorine from a tank mounted 1 m above a broad flat desert sand surface. A model comparison study was initiated, where 17 widely-used dense-gas dispersion models were run by scientists in seven countries.
Predictions were submitted following specified formats, using specified emissions and meteorology inputs. To compare with the model predictions, sets of observations were defined for the arc-maximum 1–3 s averaged concentrations (arc max C) and for cloud widths and heights (to 20 ppm and 200 ppm contours) at distances from 0.2 to 11.0 km from the release. The initial focus is on the three field trials (1, 6, and 7) that have the highest observed concentrations and that have detailed emissions information. It is found that these models are able to satisfactorily simulate (generally within a factor of two) the observed arc max C’s and their variation with downwind distance at this flat desert site. At each downwind distance, the scatter in the arc max C predictions covers about 1 ½ orders of magnitude, but the observed arc max C is within the range of the predictions. The median of the cloud width predictions is about 50% larger than the observed value for the three trials. The median of the cloud height predictions is within about 10% of the observed value. For both cloud width and/or height, there are a few models with large (factor of 3 or higher) overpredictions. Of the 17 models, when compared to observations, there is a core group of 5 or 6 with consistently (across all three trials and all distances) less mean error and scatter in their predictions of arc max C and cloud width and height. However, as a group, the 17 models are performing adequately (using the “factor of two” rule of thumb). An important caveat is that, at the JR II desert site, chlorine deposition is minimal. At a site with vegetation and/or organic-rich soil, the effects of removal of chlorine by deposition are expected to be significant.
To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects.