One of the great threats of earthquakes is soil liquefaction which can put many lives in danger and damage important structures. The evaluation of soil liquefaction potential is of great importance for geotechnical engineers in order to avoid future costs. The most common approach for such an evaluation has been using historical records of soil liquefaction, gathered from different locations. Most of the historical records include SPT and CPT data. This paper focuses on the evaluation of liquefaction potential using shear wave velocity (SVW) database, including 415 site observations. Among many models for liquefaction potential assessment, ANN and SVM are popular and used by different researchers to better predict the future hazards of liquefaction. In this paper, we introduce Random Forest as a method for liquefaction assessment to classify soils with respect to their potential of liquefaction when exposed to certain earthquakes. The details of feature selection and model development is stated. It is shown that Random Forest, tested on 83 unseen cases (20% of database), is able to predict the liquefaction potential in soils with 92.77% accuracy. Also, Random Forest can provide the relative importance of the predictors, among which the normalized shear wave velocity (Vs1) obtained the highest significance score for liquefaction potential prediction.

Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analysed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect. We develop a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH) approach to modelling the dynamical behaviour of turbidity over different locations and at different water depths. We use daily values of turbidity during the years 2017-2018 to fit the model. We show that the results of our fitted model are in line with the observed data and that the uncertainties, measured through Bayesian credible intervals, are well calibrated. Furthermore, we show that the daily effects of dredging and dumping on turbidity are negligible in comparison to that of wind speed.

As digital business practices become increasingly prevalent, new concerns about the impact of digitalization on society have emerged. As businesses’ relationships with society and the environment are reshaped by digital technologies, digital social responsibility (DSR) emerged as a subset of corporate social responsibility (CSR). This chapter covers the transition from CSR to DSR parallel to digital transition and introduces six key components of DSR that are responsibility for data privacy and protection, algorithm unbiasedness, the future of work in the digital era, digital inclusion and bridging the digital gap, responsibility for environmentally-friendly digital technologies, and protection from fake news. For each of them, potential solutions are proposed.

Fashion supply chain management is challenging due to demand uncertainty, short product life cycle, fierce global competition, increasing expectations for diversity in production, and keeping pace with the current and emerging trends. In this paper, a brief review of the published works in the field of fashion and clothing supply chain is presented and an integrated linear model is developed to minimize the costs of a fashion brand, including production and logistics costs, enabling the firm to make better decisions with regard to its production and replenishment policies and to respond in a reasonable time to customers demands for various designed items. We show that our model can successfully account for the complex structure of the supply chain consisting of designers, manufacturers, distributors, and stores. The model is validated using the General Algebraic Modeling System (GAMS) with the data collected from an Iranian fashion brand.

Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated models for forecasting and understanding key drivers. However, the data collected from sensors often contain missing values due to faulty equipment or maintenance issues. The missing values rarely occur simultaneously leading to data that are multivariate misaligned sparse time series. We propose two models that are capable of performing multivariate spatio-temporal forecasting while handling missing data naturally without the need for imputation. The first model is a transformer-based model, which we name SERT (Spatio-temporal Encoder Representations from Transformers). The second is a simpler model named SST-ANN (Sparse Spatio-Temporal Artificial Neural Network) which is capable of providing interpretable results. We conduct extensive experiments on two different datasets for multivariate spatio-temporal forecasting and show that our models have competitive or superior performance to those at the state-of-the-art.