Purpose: This paper aims to design a vulnerability assessment model considering the multidimensional and systematic approach to disaster risk and vulnerability. This model serves to both risk mitigation and disaster preparedness phases of humanitarian logistics.Design/methodology/approach: A survey of 27,218 households in Pueblo Rico and Dosquebradas was conducted to obtain information about disaster risk for landslides, floods and collapses. We adopted a cross entropy-based approach for the measure of disaster vulnerability (Kullback–Leibler divergence), and a maximum-entropy estimation for the reconstruction of riska prioricategorization (logistic regression). The capabilities approach of Sen supported theoretically our multidimensional assessment of disaster vulnerability.Findings: Disaster vulnerability is shaped by economic, such as physical attributes of households, and health indicators, which are in specific morbidity indicators that seem to affect vulnerability outputs. Vulnerability is heterogeneous between communities/districts according to formal comparisons of Kullback–Leibler divergence. Nor social dimension, neither chronic illness indicators seem to shape vulnerability, at least for Pueblo Rico and Dosquebradas.Research limitations/implications: The results need a qualitative or case study validation at the community/district level.Practical implications: We discuss how risk mitigation policies and disaster preparedness strategies can be driven by empirical results. For example, the type of stock to preposition can vary according to the disaster or the kind of alternative policies that can be formulated on the basis of the strong relationship between morbidity and disaster risk.Originality/value: Entropy-based metrics are not widely used in humanitarian logistics literature, as well as empirical data-driven techniques.

The world is living its fourth industrial revolution, consisting in the digitalization of industrial processes and incorporating the use of different technologies, leading to significant changes in future workplaces and future jobs. For these reasons, the next generation of business & engineering professionals must strengthen their technical knowledge and personal competencies to face an adequate adaptation to the challenges brought by this new digital era. In this research, we present different learning methods that reinforce professional competencies focused on Integration, Trust, Communication, and Collaboration (ITC2) which are pointed as some of the main soft competencies needed to face this digital revolution in future businesses. The objective of this article is to propose a new challenge-based learning course-pack including the use of different methodologies such as serious games, gamification, simulation, and case studies to reinforce these ITC2 competencies. The results, composed of individual scores, testimonies, and focus groups, revealed evidence on ITC2 capacities training.

In recent years, agriculture has become an essential activity in Colombia, despite the challenges faced by farmers due to low yields and insufficient resources to improve their main activities, such as irrigation systems, agricultural practices, and industrial machinery. This Hass avocado approach has been addressed in previous research considering system dynamics simulation to evaluate farmers’ behavior strategies and improve their competitiveness. However, these studies typically examine a single strategy effect and avoid multiple integrated strategies. Other studies focused on the complex interactions between different factors in the production chain and their feedback effects on farmers’ productivity and cash flow. For these reasons, this research provides a comprehensively dynamic model and evaluates long-term strategies and their effects on supporting and improving small farmers’ productivity and profitability. A system dynamics methodology was used to model complex systems processing Hass avocado farmer association data and explore their effects on competitiveness for long-term sustainable and profitable agriculture. This research proposes optimal scenarios for small farmers, including strategies such as low-interest credit access, logistics practices, and government technical support. The scenarios provide a proactive tool for decision makers and promote rural farmers’ development, aligning high-quality fresh product supply and demand.

In this article, we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determine the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means high economic activity, which leads to more deaths due to COVID-19. There is a lack of supply in the set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated with COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

Long-term and short-term term national power demand forecasting is a well known and open issue for many countries. In this paper, we focus and study the short-term Peruvian national power demand forecasting. Thus, we tackle this problem using indirect and direct method for prediction. The former method relies on Social Network Activity to estimate national needs using regression models. The latter method is based on Artificial Neural Networks (ANNs). The network was used subsequently for predictions of the power for the last day of April, May and June 2016. The result was highly satisfactory with a mean absolute percentage error (MAPE) of 0.36 % for April and 0.34% in May and June. The ANN cumulative model proved to be a fast, reliable and accurate method for predicting power demand in Peril. In the case of the social activity generated by tweets, there is an increase in the MAPE values of an order of magnitude, reaching a maximum value of 7.3% for June. Nevertheless, the power demand forecasting using Twitter posts is a good indicator as a first approximation.

We investigate the iridescence exhibited by Ceroglossus suturalis beetles, which mostly live endemically in the southern end of South America. Two differently colored specimens have been studied. We observed and characterized the samples by different microscopy techniques, which revealed a multilayer structure within their cuticle. Using measured reflectance spectra as input data, we applied heuristic optimization techniques to estimate the refractive index values of the constituent materials, to be introduced within the theoretical model. The color of the samples was calculated for different incidence angles, showing that multilayer interference is the mechanism responsible for the observed iridescence.