Abstract
Background: The estimation of the real number of COVID-19 infected people is one of the concerns of the governments around the world. In this sense, this study seeks to assess the incidence and fatality of COVID-19 in Europe considering the expected number of the infected cases. Methods: A quantitative exploratory study was performed on the top 10 countries most affected by COVID-19 by 9th June in Europe. Furthermore, this study presents three propagation estimation models of the COVID-19 that help us to understand the real incidence of the pandemic in each country. Each model is briefly explained and applied. Results: The findings revealed a great heterogeneity of COVID-19 cases and deaths among the countries. The indicator of the number of deaths reveals the greatest disparity between other countries with the United Kingdom, recording about 6 or 7 times more deaths than Russia or Germany. Infection fatality rate (IFR) tends to be a more reliable indicator when analyzing data because it is less dependent on the number of tests performed. Conclusion: Several estimation models can be used to determine the incidence of COVID-19. However, their results in European countries are still quite asymmetrical although they are more reliable than just looking at the perspective of the number of cases or deaths recorded. The infection fatality rate (IFR) emerges as a more accurate indicator by estimating the expected number of registered cases, which includes asymptomatic cases and patients with mild symptoms that are not known and reported by health authorities. © 2020 The authors.

Abstract
Onshore wind energy (WE) has achieved a significant diffusion worldwide, in spite of the existence of multiple barriers to the large-scale implementation of wind farms. These barriers have been reported in a large number of studies, but the literature is lacking a systematized overview of their categories and locations. Based on a framework for the analysis of barriers to the penetration of renewable energy sources proposed by Painuly [363], this systematic literature review contributes to addressing this gap, identifying barriers to the large-scale implementation of onshore wind farms by category (market failures, market distortions, economic and financial, institutional, technical, social and other barriers) and location (countries around the world), and characterizing them by the level of economic development (least developed, developing, in transition, and developed) and stage of diffusion (recent or advanced) in their locations. The framework showed a high level of fit with the case of WE and allowed the identification of 31 barriers in 159 countries. The barriers were found to be mostly present in developing economies with recent diffusion, although some barriers were found to occur broadly across developed economies, regardless of the stage of diffusion. The three most frequently observed barriers were the inadequate consideration of externalities, uncertain and unsupportive governmental policies, and insufficient transmission grids.

Abstract
The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.

Abstract
Purpose - Supply Chain Management (SCM) is one of the most important parts of business, which includes supply chain quality management (SCQM) and supply chain risk management (SCRM). One of the consequences of an epidemic outbreak can be a lack of reliable data and difficulty in accessing this information, which can simultaneously disrupt supply and demand. Because epidemics of infectious diseases such as Covid-19 cause many deaths worldwide. Therefore, in order to effectively control these epidemics and also to prevent the failure of health systems and laboratory services, having a quality management program and supply chain risk management seems to be essential. The main purpose of this article is to carefully review the studies that have analyzed the results of SCQM, SCRM techniques of different countries and industries in response to the COVID-19 crisis. Design/methodology/approach - In this research, studies pursue and assess the problems and solutions based on a systematic literature review analysis. Findings - By considering the researches which have been done related to disruptions of COVID-19, - important disruptions and risk management plans are mentioned to provide a better comprehension of this issue. Research limitations/implications - Since this global pandemic is a completely new issue, analyzing and gathering reliable statics from companies was very a complicated task. In a different circumstance, exploring hidden disruptions costs and other related issues is continuing since thspread of this disease is not finished yet. Therefore, access to the related data for experts is limited that leads to publishing fewer case studies researches in this filed. Originality/value - In this paper, the implication of the pandemic situation (COVID-19) is investigated for SCQM.

Abstract

Abstract
Recently, developing bio-based carbon materials due to the surface chemistry and a large spectrum of pore structures have received much attention. In the present work, a series of activated carbon (AC) adsorbents were synthesized from the compost derived by the mechanical/biological treatment of municipal solid wastes and evaluated regarding their CO2 uptake. The AC samples were characterized by sulfuric acid and calcination by N-2 at 400 and 800 degrees C. Then, the CO2 uptake capacities were evaluated by dynamic breakthrough experiments in a temperature range of 40-100 degrees C and pressures up to 3 bar. The presented data were properly described by Langmuir model and it was revealed that the CMSW-S-800 sample, treated with sulfuric acid and activated at 800 degrees C, has the highest CO2 uptake capacity with an amount adsorbed around 2.6 mol/kg at 40 degrees C. In the next step, a mathematical model has been developed to match the experimental dynamic breakthrough data and design a pressure swing adsorption (PSA) cyclic process to evaluate the capacity and potential of the best AC sample for CO2 adsorption. The results arising from this work showed a possible route for the application of the compost as a source of activated carbon for the sorption of greenhouse gases.