ABSTRACT
The Russia-Ukraine conflict represents a humanitarian crisis and causes several socio-economic consequences, being Russia a key supplier of energy and food commodities. After a few weeks of war, the prices of the essential goods, already increased due to the COVID-19 pandemic crisis, have continued to boost. The present research applies the material flow analysis to assess the sustainability of the artisan bread production, comparing a baseline and a war scenario, before and during the aforementioned conflict, and estimates the economic costs associated with natural and energy resources. The analysis is based on primary data collected through semi-structured interviews among nine artisan bakeries and secondary data collected before and during the conflict. The economic assessment, which is applied to enhance the environmental management of sociometabolic systems, is conducted on the entire artisan bread produced in Italy in 2021 and the system boundaries consider a cradle-to-gate approach. The highest upsurge in input costs has been estimated in electricity (+400%), N fertilizer (+233%) and K2O (+152%). The average input cost variation has been evaluated at +232%. Possible opportunities to support production costs include the adoption of an alternative bread recipe, which reduces the supply of impacting resources without affecting the quality of the finished product, as well as the introduction of structural interventions to lower energy costs. The research could help both artisan bakers, to better manage resources, waste and related impacts under the economic and the environmental perspective, and public authorities, to define appropriate strategies to sustain the bread sector. Last, the research provides scholars with an original analysis of the economic costs in the artisan bread production, highlighting its suitability to evaluate the supply chain sustainability from cradle to gate. © 2023 Elsevier Inc.
ABSTRACT
The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.
ABSTRACT
The COVID-19 Pandemic has increased the demands of governments for technologies to estimate the route of infection. In this paper, we propose a new smart city framework that collects anonymized passage information from deployed Bluetooth sensors and analyzes them to reconstruct the multiple trajectories of infected people. We formulate recovering multiple trajectories on the basis of anonymized passage information, including passage time and passage position, obtained by sensors in a smart city as a problem of multiple-trajectory reconstruction in general networks. We propose a new method for reconstructing multiple trajectories on the basis of anonymized passage information. Our method assumes that each trajectory follows a Markov process and estimates transit time for each edge in networks and the transition probability of the Markov process. On the basis of its estimation, our method can find multiple trajectories with maximum likelihood by solving a minimum cost flow problem. We evaluate the performance of our method in experiments using simulation data and actual human trajectory data. © 2022 IEEE.
ABSTRACT
Data from 15 European countries is analysed to provide novel estimates of daily own-price, cross-price and income elasticities of natural-gas-demand from 2016 to 2020. The results show that: first, there is a strong-seasonal component in the October–February period during which residential-demand has a higher share on total demand, and gas price is not a determinant factor for most of the countries. This seasonal profile makes price-based tools more effective modifying end-consumer behaviours from March to August when estimated own-price elasticities present larger values in absolute terms. Second, there are estimated positive own-price elasticities from October to February in Bulgaria, Luxemburg, Poland, the UK, and Portugal. The first four countries present natural gas prices below the EU-28 average during the analysed period and it is argued that positive elasticities may reflect a disconnection between the price traded on the organized markets and the real price perceived by end-customers. For Portugal, who is currently carrying out a very aggressive policy to become coal-free by the end of 2021, natural gas and coal are mainly consumed in power sector to provide flexibility and back up renewable generation. The limited alternatives to provide these services may explain why coal and natural gas are found to be complementary. Finally, it is found that lockdowns due to covid-19 highly impacted on natural gas demand, confirming for the first time in the literature a “double heating effect”. Our results help to find when price-based tools by policymakers will influence more effectively natural-gas-demand following economic and environmental goals. © 2022 The Authors
ABSTRACT
The Covid 19 pandemic has led to considerable destruction of social and economic areas at a global level. This study aims to determine the economic impact of the Covid 19 pandemic on the Turkish forestry sector. In this context, 5 years (from 2017 to 2021) of wood-based product sales of an administrative unit, which carries out regional forestry activities in Turkey, were studied. The data concerning the product groups were subjected to a Laspeyres price index analysis based on the base period weight through the price and estimated price increase rate variables. In addition, correlation analysis was utilized to determine the relationships between the determined variables. The findings showed that the Covid 19 pandemic led to decreases in the Laspeyres price index values for the price and estimated price increase rates when compared with the pre-pandemic period, which was different on a product group’s basis. As a result, it can be said that the Covid 19 pandemic process created a considerable potential for a loss of income in wood-based products, which is one of the primary outputs of the forestry sector, and as of 2021, a recovery process has started. © 2022, North Carolina State University. All rights reserved.
ABSTRACT
The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world’s healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a non-wearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the Channel State Information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional Convolutional Neural Networks (1D-CNN) and Bi-directional long-short term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds. First, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE
ABSTRACT
The overall global death rate for COVID-19 patients has escalated to 2.13% after more than a year of worldwide spread. Despite strong research on the infection pathogenesis, the molecular mechanisms involved in a fatal course are still poorly understood. Machine learning constitutes a perfect tool to develop algorithms for predicting a patient's hospitalization outcome at triage. This paper presents a probabilistic model, referred to as a mortality risk indicator, able to assess the risk of a fatal outcome for new patients. The derivation of the model was done over a database of 2,547 patients from the first COVID-19 wave in Spain. Model learning was tackled through a five multistart configuration that guaranteed good generalization power and low variance error estimators. The training algorithm made use of a class weighting correction to account for the mortality class imbalance and two regularization learners, logistic and lasso regressors. Outcome probabilities were adjusted to obtain cost-sensitive predictions by minimizing the type II error. Our mortality indicator returns both a binary outcome and a three-stage mortality risk level. The estimated AUC across multistarts reaches an average of 0.907. At the optimal cutoff for the binary outcome, the model attains an average sensitivity of 0.898, with a 0.745 specificity. An independent set of 121 patients later released from the same consortium attained perfect sensitivity (1), with a 0.759 specificity when predicted by our model. Best performance for the indicator is achieved when the prediction's time horizon is within two weeks since admission to hospital. In addition to a strong predictive performance, the set of selected features highlights the relevance of several underrated molecules in COVID-19 research, such as blood eosinophils, bilirubin, and urea levels. © 2021 IEEE.
ABSTRACT
Over the years, the number of students who enroll in face-to-face learning typically outnumber those who enroll in online learning, and that most students prefer face-to-face instruction is not new or unknown. Then came the pandemic, and for the first time in our lifetime, the pandemic has posed a unique situation where more students are enrolled in online learning than face-to-face learning. Having the largest population of students to ever enroll in an online learning environment is an opportunity to revisit and learn about students' learning preferences, which could lead educators to find new opportunities to enhance learning in both face-to-face and online environments. This research reports on issues related to the rapid implementation of online or otherwise remote learning due to the COVID-19 pandemic. The research addressed creative solutions to moving coursework to a virtual or online learning format. This research follows a qualitative research method. The research examined students' perceptions of online course delivery for two construction-related courses, methods and supervision, and cost estimating, which are traditionally face-to-face and were shifted online due to COVID-19. The instructor, course delivery formats were consistent between the two courses, but the course content for both courses was different. The research findings show that the students perceived the online course delivery format as effective, yet most students prefer a face-to-face learning environment. Also, the research findings show that an effective online course must provide for and support an interactive and engaging learning environment. The research recommends the use of a synchronous online method, with regular class meetings, provision for breakout groups, and most of all, that educators make themselves available to quickly help students resolve any course-related issues students may run into online. © American Society for Engineering Education, 2021