This paper postulates that the effect of e-commerce on firm performance is not direct and needs to be examined using mediating factors. The Ordinary Least-Squares (OLS) model was employed with the data of the Flash Eurobarometer 439 Survey entitled The Use of Online Marketplaces and Search Engines by small and medium enterprises. The obtained findings provide support for the mediating hypothesis. To be more precise, while the relationship between e-commerce and firm performance is negative, it is positively mediated by certain types of internet sales channels. In particular, the benefits of e-commerce in terms of higher sales are more pronounced when firms use commercial websites and online marketplaces. On the other hand, the interaction between e-commerce and search engines has an insignificant effect on firm performance. This study advances research on e-commerce by emphasizing the importance of mediating effect.

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

This study explores the effect of alkali treatment at ambient (25 °C) and elevated temperatures (100 °C) on the physicochemical, structural, morphological, and thermal properties of Calamus tenuis cane fibers (CTCFs) for the first time. Our purpose is to investigate their potential use as reinforcement in polymer composites, since cane fibers are generally known for their accurate and consistent geometrical orientation. Treatment with 8% (w/v) sodium hydroxide (NaOH) is carried out at ambient temperature and at 100 °C for 4 h. Chemical analysis and Fourier transform IR spectroscopy (FTIR) indicate some removal of non-cellulosic elements from CTCFs during alkali treatment, resulting in increased surface roughness, as confirmed by using SEM micrographs. This removal of non-cellulosic elements leads to an enhancement in the density of the treated CTCFs. Untreated and treated fibers are analyzed for maximum degradation temperature, thermal stability, and kinetic activation energy (E_a) using thermogravimetric analysis (TGA). In particular, E_a was considerably diminished with treatment and temperature. X-ray diffraction (XRD) results show an improved crystallinity index (37.38% to 44.02%) and crystallite size (2.73 nm to 2.98 nm) for fibers treated with 8% NaOH at ambient temperature. In conclusion, a general benefit was achieved by treating CTCFs, though the influence of increasing temperature treatment appears controversial.