Sustainability performance measurement for Libyan Iron and Steel Company using Rough AHP

The iron and steel industry plays a major role in Libyan urbanization. Iron and steel products are the main driving forces in the construction manufacturing sector in Libya. This research suggested a set of indicators to evaluate the sustainability of the iron and steel industry in Libya using a rough AHP model. Rough AHP analyses the relative importance of the criteria based on their preferences given by experts. The research results show that the most important criterion is costs followed by emission and waste. We have found that the rough AHP model can play an important role in improving indicators that quantify the advance towards sustainable development, especially when it is in a situation where complex environments (i.e., Libya) exist.

Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.