Revealing the energy paradox: Assessing the asymmetric impact of pandemic uncertainty on consumption of renewable and nonrenewable energy.

Uncertainties have grown around the world during the last few decades. Pandemic uncertainty has a substantial impact on economic activities, which may have a big influence on energy consumption. The goal of this investigation is to appraise the asymmetric influence of pandemic uncertainty on nonrenewable and renewable energy consumption in the top 10 energy consumer economies of the European Union (Germany, Poland, Spain, Netherlands, France, Italy, Belgium, Sweden, Czech Republic, and Finland). Previously, panel data approaches were utilized to obtain reliable outcomes on the pandemic-energy consumption nexus, regardless of the fact that various nations did not autonomously exhibit similar relationship. This investigation, on the other hand, implements a special technique "Quantile-on-Quantile" that supports us to appraise time-series interdependence in each economy by providing international yet nation-specific perceptions of the connection among the variables. Estimates show that pandemic uncertainty reduces both nonrenewable and renewable energy consumption in most selected nations at stated quantiles of the data distribution. Nonrenewable energy consumption is much more influenced by pandemic uncertainty than renewable energy consumption. Furthermore, the rank of asymmetries across our variables differentiates by the economy, emphasizing the need for decisionmakers to pay much attention to pandemics-related uncertainty and the energy sector.

Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.