The cucurbit[8]uril (CB8) synthetic receptor is shown to form high-affinity host–guest complexes with dicationic dithienylethene (DTE) photoswitches in water. ITC experiments combined with computational studies suggest that the formation of the inclusion complexes is mainly driven by a combination of hydrophobic effects, ion–dipole, hydrogen- and chalcogen-bonding interactions. The binding affinities were observed to be much higher for the DTE closed isomers, reaching values in the picomolar range (up to 1011 M−1) while the open isomers display up to 10 000-fold lower affinities, setting ideal conditions for the development of robust photoswitchable host–guest complexes. The light-responsive affinity of these photoswitches toward CB8 was explored to control the encapsulation and release of nanomolar affinity steroids via competitive guest replacement.

Elemental content plays an important role in biological processes, and so, the multielemental analysis of human tissue samples is required in biomedical research. Still, the small amount of available biological samples and the adipose content of the samples can be major setbacks for the accurate determination of elemental content. In this study, we explored the potential of several analytical approaches combined with total reflection X-ray fluorescence spectrometry (TXRF) for multielemental analysis of human tissues with different adipose content (colon, heart, liver, lung, muscle, intestine, skin, stomach, uterus, bladder and aorta). The capabilities and limitations of different sample treatment procedures (suspension and acidic digestion) and two TXRF systems with different anode configurations (Mo and W X-ray tubes) have been evaluated for such purpose. Results showed that for tissues with a higher fat content (e.g., skin, and intestine) the best strategy was the acidic digestion of the sample before TXRF analysis. However, for other tissues, acceptable results were obtained by suspending 20 mg of powdered material in 1 mL of 2 M nitric acid. A further enhancement of the limits of detection and accuracy of the results was achieved if using Mo-TXRF systems, especially for the determination of low Z elements (e.g., K, and Ca) and of elements present at low concentrations (e.g., Cu) in the human tissues. Finally, results by TXRF analysis were compared with those obtained with μ-EDXRF and ICP-OES, and a good agreement was obtained.

Recent developments in automation and Artificial Intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the ‘second machine age’, arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. Findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects about technology and unemployment were not substantiated by our results

https://ideas.repec.org/p/mde/wpaper/0165.html

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New technologies, sustainability policies, protectionism and consumers preferences are pushing for the reorganisation of the automotive cluster. The emergence of artificial intelligence (AI) has the potential to create disruptive effects in the employment systems across the world. The future deployment of broad-spectrum algorithms capable of being used in wide areas of application (e.g., industrial robotics, software and data communication) can lead to considerable changes in current work patterns, swiftly render many unemployed across the globe and profoundly destabilise labour relations. In this paper, we identify the probable penetration of AI in the automotive sector and to study its effects on work organisation, employment, and industrial relations systems, in Portugal. These changes are put in place to enhance the product quality, control costs, and improve productivity. We study these implications on productivity and industrial relations collecting new data and obtain results based on secondary statistical analyses and case studies in the automotive industry. Finally, changes in the productivity and labour market will be discussed considering the employment and skills changes in the automotive sector when investment on automation becomes a clear trend in the automotive sector.