This review presents "a state of the art" report on sustainability in construction materials. The authors propose different solutions to make the concrete industry more environmentally friendly in order to reduce greenhouse gases emissions and consumption of non-renewable resources. Part 1-the present paper-focuses on the use of binders alternative to

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The paper represents the "state of the art" on sustainability in construction materials. In Part 1 of the paper, issues related to production, microstructures, chemical nature, engineering properties, and durability of mixtures based on binders alternative to Portland cement were presented. This second part of the paper concerns the use of traditional and innovative Portland-free lime-based mortars in the conservation of cultural heritage, and the recycling and management of wastes to reduce consumption of natural resources in the production of construction materials. The latter is one of the main concerns in terms of sustainability since nowadays more than 75% of wastes are disposed of in landfills.

This study aims to investigate the effect of the stepwise marine fuel oil regulations on the concentrations of vanadium (V) and nickel (Ni) in ambient air based on a 4-y (2017–2020) online measurement in Shanghai, a coastal city in China. The annual concentration of V was reduced by 58% due to the switch from Domestic Emission Control Area (DECA) 1.0 to DECA 2.0 and further by 74% after the implementation of the International Maritime Organization (IMO) 2020 regulation, while the reduction rate for Ni was only 27% and then 18% respectively. Consistently, a reduction of 84% in V content and a negligible change in Ni content were measured in 180cst ship oil samples from 2010 to 2020. The similar increasing trend of Ni/V ratios (from <0.4 to >2.0) in both ambient measurement and heavy fuel oil samples suggests that the DECA and IMO 2020 regulations effectively reduced the ambient V. However, nickel content is still enriched in the in-use desulfurized residual oils and ship-emitted particles in coastal China. Meanwhile, the previous ratio between V and Ni cannot be used as a tracer for identifying ship-emitted particles due to its large variation in oils. Further updating of the source profile of ship traffic emissions in coastal cities is necessary in the future.

In the field of human-machine interaction, gesture recognition using sparse multichannel surface electromyography (sEMG) remains a challenge. Based on the Hilbert filling curve, a dual-view multi-scale convolutional neural network (DVMSCNN) is designed to enhance gesture recognition performance in this paper. The network consists of two parts. In the first part, sEMG is filled using Hilbert filling curve, and the obtained images in the time and electrode domain are used as inputs to the block. In the second part, the depth features learned by block are fused and classified by a "layer fusion" based view aggregation network. The evaluation of the architecture in the four databases of Ninapro-DB1, DB2, DB3 and DB4 shows that DVMSCNN is more than 7% more accurate than other state-of-the-art methods. When validated using a home-grown dataset, DVMSCNN was able to achieve a recognition rate of 0.8848.