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First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Ioannis Xanthis is first author on ‘β1 integrin is a sensor of blood flow direction’, published in JCS. Ioannis conducted the research described in this articlewhile aPhDstudent in Prof. Paul Evans’s lab at IICD, University of Sheffield Medical School, Sheffield, UK. He is now a postdoctoral research assistant in lab of Dr Thomas Iskratsch at the School of Engineering and Materials Science, Queen Mary University of London, London, UK, investigating vascular biomechanics and bioengineering. [ABSTRACT FROM AUTHOR]

The author's provides a review of the literature using a systematic literature review of the lean tool and lean construction papers published in the time frame from 2008 to 2018. The papers selected from the Scopus database by using the following filters: ten top-rated journals selected on the basis of cite score form Scopus, the only article having a key words "lean construction", or "Lean tool" were selected, only engineering and material science article selected, and the last only journal article was selected. The search result filters the 48 articles out of 944 articles. The findings of the study conclude that only a few tool and techniques were explored a number of times by the researchers. On the other hand, few countries were dominating in terms of publication were the UK, and the USA has 15, 14 articles respectively. There is not a single article from India in the selected papers. [ABSTRACT FROM AUTHOR]

Featured Application: This compact but precision highly stable NMR time-domain relaxation spectrometer is most useful for quantitative material science measurements of both the mass and the mobility/dynamics/stiffness/viscosity/rigidity of hydrocarbons and polymers, both in the bulk and in sub-nano-meter and upward sized pores. It is highly useful for studying the properties of hydrocarbons in pores in applications such as recovered porous rock samples from oil reservoirs, and in fired biochar porous carbon samples, to give just two examples. In addition, applicable to both these fields of study is the ability to make NMR Cryoporometric measurements of pore-size distributions in porous materials, for sub-nano- to over micrometer sized pores. NMR Relaxation (NMRR) is an extremely useful quantitative technique for material science, particularly for studying polymers and porous materials. NMR Cryoporometry (NMRC) is a powerful technique for the measurement of pore-size distributions and total porosities. This paper discusses the use, capabilities and application of a newly available compact NMR time-domain relaxation spectrometer, the Lab-Tools Mk3 NMR Relaxometer & Cryoporometer [Lab-Tools (nano-science), Ramsgate, Kent, UK (2019)]. Being Field Programmable Gate Array based means that it is unusually compact, which makes it particularly suitable for the lab bench-top, in the field and also mobile use. Its use with a variable-temperature NMR probe such as the Lab-Tools Peltier thermo-electrically cooled variable-temperature (V-T) probe is also discussed. This enables the NMRC measurement of pore-size distributions in porous materials, from sub-nano- to over 1 micron sized pores. These techniques are suitable for a wide range of porous materials and also polymers. This instrument comes with a Graphical User Interface (GUI) for control, which also enables both online and offline analysis of the measured data. This makes it is easy to use for material science studies both in the field and in university, research institute, company and even school laboratories. The Peltier cooling gives the precision temperature control and smoothness needed by NMR Cryoporometry, particularly near the probe liquid bulk melting point. Results from example NMR Relaxation and NMR Cryoporometric measurements are given. [ABSTRACT FROM AUTHOR]

This paper reviews some of the challenges posed by the huge growth of experimental data generated by the new generation of large-scale experiments at UK national facilities at the Rutherford Appleton Laboratory (RAL) site at Harwell near Oxford. Such 'Big Scientific Data' comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility. Increasingly, scientists are now required to use advanced machine learning and other AI technologies both to automate parts of the data pipeline and to help find new scientific discoveries in the analysis of their data. For commercially important applications, such as object recognition, natural language processing and automatic translation, deep learning has made dramatic breakthroughs. Google's DeepMind has now used the deep learning technology to develop their AlphaFold tool to make predictions for protein folding. Remarkably, it has been able to achieve some spectacular results for this specific scientific problem. Can deep learning be similarly transformative for other scientific problems? After a brief review of some initial applications of machine learning at the RAL, we focus on challenges and opportunities for AI in advancing materials science. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains. We conclude with some initial examples of our 'scientific machine learning' benchmark suite and of the research challenges these benchmarks will enable. [ABSTRACT FROM AUTHOR]

The development of synchrotron science over the last 50 years is reviewed from the perspective of the authors' own scientific programmes. [ABSTRACT FROM AUTHOR]

The authors reflect on the contribution of the Synchrotron Radiation Source (SRS) at Daresbury Laboratory in Great Britain, which is the first X-ray synchrotron radiation ring, to material science. The contend that SRS was the fist to delivered uninterrupted beams of intense X-rays, and its use has led to major advances in both fundamental and applied science, ranging from the structure of glass to catalysts in operation and from the crystallography of proteins to elements at high pressure.

This paper reports on a historical materials and energy flow analysis of the UK steel sector. The flow of raw materials, steel, steel products and steel scrap are quantified for the period from 1954 to 1994. On the basis of this analysis, the authors calculate the consumption of exergy (or available energy) associated with the UK steel sectortaking into account steel production, transport, generation of wastesteel, trade and recycling. The study finds that overall exergy consumption in the sector has declined almost twofold from 700 to 380 PJ p.a. over the study period, indicating a similar reduction in resource consumption. This is mainly due to improvements in the exergetic efficiency of steel production and the phasing out of the less-efficient open hearth method. Contrary to the overall decline, the amount of waste steel and the transportation exergy consumption due to the import of raw materials has increased significantly. There appears to be considerable potential, therefore, to reduce resource consumption in the steel sector still further by increasing the recovery of post-usescrap. [ABSTRACT FROM AUTHOR]