The attention to environmental impacts of cement production has grown fast in recent decades. The cement industry is a significant greenhouse gases emitter mainly due to the calcinations of raw materials and the combustions of fuels. This paper investigates on the environmental performances of cement production and on the identification of factors driving emissions. For this purpose, a sample of 193 different recipes of gray cement produced in Italy from 2014 to 2019 according to the European standard EN 197-1. This paper identifies the consumption impact categories (e.g., fossil fuels, renewable and non-renewable secondary fuels) that explain the assessment of the Global Warming Potential, one of the most crucial impacts of cement production. Having regard to the overall examined dataset and each cement type, a set of predictive models is implemented and evaluated. A similar approach has been adopted to produce accurate predictive models for further environmental impact categories that quantify emissions to air. The obtained results provide important information that can support cement producers to develop low-impacting cement recipes.

Scale-discretised wavelets yield a directional wavelet framework on the sphere where a signal can be probed not only in scale and position but also in orientation. Furthermore, a signal can be synthesised from its wavelet coefficients exactly, in theory and practice (to machine precision). Scale-discretised wavelets are closely related to spherical needlets (both were developed independently at about the same time) but relax the axisymmetric property of needlets so that directional signal content can be probed. Needlets have been shown to satisfy important quasi-exponential localisation and asymptotic uncorrelation properties. We show that these properties also hold for directional scale-discretised wavelets on the sphere and derive similar localisation and uncorrelation bounds in both the scalar and spin settings. Scale-discretised wavelets can thus be considered as directional needlets.

An early and accurate detection of different subtypes of tumors is crucial for an effective guidance to personalized therapy and in predicting the ability of tumor to metastasize. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated silicon nanowires (Ag/SiNWs), to efficiently discriminate genomic DNA of different subtypes of melanoma and colon tumors. The diagnostic information is obtained by performing label free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat and leveraging the classification ability of learning models to reveal the specific and distinct physico-chemical interaction of tumor DNA molecules with the Ag/NW, here supposed to be partly caused by a different DNA methylation degree.