Search

The paper concerns the role of e-leadership in organizations operating in crisis caused by COVID-19 pandemic. Based on the extensive literature review, it introduces a new model of organizational performance based on e-leadership, as a response for sudden growth of dependence of modern organizations on IT. The influence of e-leadership on organizational performance is assumed to be mediated by work engagement, employees' dynamic capabilities, work motivation, P-J fit and job performance. The theoretical model is verified by empirical research performed among 1160 organizations operating in Poland, Italy and USA during an active wave of COVID-19 pandemic. The obtained results confirmed the proposed hypotheses, allowing to conclude that e-leadership activates work engagement of employees, who are more willing to use their dynamic capabilities to shape work motivation and reestablish P-J fit, which in turn influences positively job and organizational performance. [ABSTRACT FROM AUTHOR]

We present a new continuous lifetime model with four parameters by combining the Lomax and the Weibull distributions. The extended odd Weibull Lomax (EOWL) distribution is what we'll call it. This new distribution possesses several desirable properties thanks to the simple linear representation of its hazard rate function, moments, and moment -generating function, with stress-strength reliability that are provided in a simple closed forms. The parameters of the EOWL model are estimated using classical methods such as the maximum likelihood (MLE) and the maximum product of spacing (MPS) and estimated also but using a non-classical method such as Bayesian analytical approaches. Bayesian estimation is performed using the Monte Carlo Markov Chain method. Monte Carlo simulation are used to assess the effectiveness of the estimation methods throughout the Metropolis Hasting (MH) algorithm. To illustrate the suggested distribution's effectiveness and suitability for simulating real-world pandemics, we used three existing COVID-19 data sets from the United Kingdom, the United States of America, and Italy which are studied to serve as illustrative examples. We graphed the P-P plots and TTT plots for the proposed distribution proving its superiority in a graphical manner for modelling the three data sets in the paper. [ABSTRACT FROM AUTHOR]

This paper investigates the heterogeneous impact of school closures during Covid-19 pandemic in Italy on academic performance across different schools, grades, subjects and groups of students. Our analysis utilises an innovative dataset that combines administrative data on standardised tests in grades 5 and 8 with a specifically-designed survey that collects information about teachers' practices between February and June 2020. Firstly, by employing a multilevel (mixed-effects) model, we estimate the extent of learning loss and examine its variability across schools, for students in primary and middle levels during the school year 2020/21. The findings confirm that learning loss has been considerable (between 0.05 and 0.27 SD) although heterogeneity across disciplines and grades exists – higher in English in grade 5, and in mathematics and reading in grade 8. Secondly, as a main contribution of the paper, we explore the mechanisms behind the substantial differences observed across schools, which can be explained by the ability of teachers in using digital tools and evaluating their students, as well as by the leadership role exerted by school principals. [ABSTRACT FROM AUTHOR]

While many efforts are currently devoted to vaccines development and administration, social distancing measures, including severe restrictions such as lockdowns, remain fundamental tools to contain the spread of COVID-19. A crucial point for any government is to understand, on the basis of the epidemic curve, the right temporal instant to set up a lockdown and then to remove it. Different strategies are being adopted with distinct shades of intensity. USA and Europe tend to introduce restrictions of considerable temporal length. They vary in time: a severe lockdown may be reached and then gradually relaxed. An interesting alternative is the Australian model where short and sharp responses have repeatedly tackled the virus and allowed people a return to near normalcy. After a few positive cases are detected, a lockdown is immediately set. In this paper we show that the Australian model can be generalized and given a rigorous mathematical analysis, casting strategies of the type short-term pain for collective gain in the context of sliding-mode control , an important branch of nonlinear control theory. This allows us to gain important insights regarding how to implement short-term lockdowns, obtaining a better understanding of their merits and possible limitations. Effects of vaccines administration in improving the control law's effectiveness are also illustrated. Our model predicts the duration of the severe lockdown to be set to maintain e.g. the number of people in intensive care under a certain threshold. After tuning our strategy exploiting data collected in Italy, it turns out that COVID-19 epidemic could be e.g. controlled by alternating one or two weeks of complete lockdown with one or two months of freedom, respectively. Control strategies of this kind, where the lockdown's duration is well circumscribed, could be important also to alleviate coronavirus impact on economy. [ABSTRACT FROM AUTHOR]

After the analysis and forecast of COVID-19 spreading in China, Italy, and France the WHO has declared the COVID-19 a pandemic. There are around 100 research groups across the world trying to develop a vaccine for this coronavirus. Therefore, the quantitative and qualitative analysis of the COVID–19 pandemic is needed along with the effect of rapid test infection identification on controlling the spread of COVID-19. Mathematical models with computational simulations are the effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. In this paper, we develop the models for coronavirus disease at different stages with the addition of more parameters due to interactions among the individuals. Then, some key computational simulations and sensitivity analysis are investigated. Further, the local sensitivities for each model state concerning the model parameters are computed using the model reduction techniques: the dynamical models are eventually changed with the change of parameters are represented graphically. [ABSTRACT FROM AUTHOR]

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems. • We quantify economic resilience of Italian municipalities through bank transaction data. • We model territories with a set of socioeconomic and network-based covariates. • Machine learning classifiers accurately predict economic resilience. • We use SHAP values to identify most relevant predictors. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. [ABSTRACT FROM AUTHOR]

• A mathematical model is developed for COVID-19 pandemic. • The parameters of the model are estimated using basis functions. • It's possible to fit the model to real data by time-varying estimation of parameters. • The time-varying parameters result in COVID-19 Linear Parameter Varying (LPV) model. • The LPV model is useful for stability assessment and controller design. Current outbreaks of the COIVD-19 pandemic demonstrate a global threat. In this paper, a conceptual model is developed for the COVID-19 pandemic, in which the people in society are divided into Susceptible, Exposed, Minor infected (Those who need to be quarantined at home), Hospitalized (Those who are in need of hospitalization), Intensive infected (ventilator-in-need infected), Recovered and Deceased. In this paper, first, the model that is briefly called SEMHIRD for a sample country (Italy as an example) is considered. Then, exploiting the real data of the country, the parameters of the model are obtained by assuming some basis functions on the collected data and solving linear least square problems in each window of data to estimate the time-varying parameters of the model. Thus, the parameters are updated every few days, and the system behavior is modeled according to the changes in the parameters. Then, the Linear Parameter Varying (LPV) Model of COVID19 is derived, and its stability analysis is presented. In the end, the influence of different levels of social distancing and quarantine on the variation of severely infected and hospitalized people is studied. [ABSTRACT FROM AUTHOR]

• The study described the case of a 7-week-old suckling baby infected withSARS-CoV-2 virus. • Only culture isolation allowed the identification of the cytopathogenic agent. • Cell culture still remains the only reference method for emerging viruses. SARS-CoV-2 emerged in China in December 2019 and has now been declared a pandemic by the World Health Organization. This paper described the case of a 7-week-old suckling baby from Italy who was SARS-CoV-2-positive only by the cell culture method, with no clinical suspicion of and/or risk factors for SARS-CoV-2 infection. The baby was referred to hospital, with signs and symptoms of upper respiratory tract infection, before the virus had spread to the province. Nasal and pharyngeal swabs and a nasopharyngeal aspirate were used for conventional and molecular diagnostic assays not including the SARS-CoV-2 virus. Bacteria referred to the resident population were revealed in nasal and pharyngeal swabs. No viruses were detected using both immunofluorescence assay and nucleic acid amplification assays in the nasopharyngeal aspirate. The baby was discharged in good condition after 3 days of hospitalisation. Later, a cytopathic effect on the cell monolayers currently used for respiratory viruses was observed and the viral particles were identified as Coronaviridae by transmission electron microscopy. SARS-CoV-2 was identified by RT-PCR performed both on cell culture and on the stored aliquot of the original sample. The virus isolate was named SARS-Cov-2/human/Parma/1/2020. Cell culture still remains the only reference diagnostic method also for emerging viruses, allowing it to reveal cytopathogenic viruses and demonstrate their infectivity. [ABSTRACT FROM AUTHOR]

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus. [Display omitted] • Environmental impacts in Italy due to energy metabolism were assessed from Jan-2015. • Covid-19 related lockdowns in 2020 favoured a reduction by ∼5% of those impacts. • Examples of impact savings are ∼54 Mt CO 2 -eq., ∼120 kt NO x -eq. and ∼39 kt PM 2.5 -eq. • ∼66 Disability Adjusted Life Years per 100,000 inhabitants are estimated to be saved. • These beneficial effects are however far from balance Covid-19 related harmful effects. [ABSTRACT FROM AUTHOR]

[Display omitted] • An Open Science integration approach for vessel tracking data is presented. • It combines heterogeneous fisheries, biodiversity, and environmental FAIR data. • It infers fishing activity and risks to potentially impacted species. • The impact of Italian reduced fishing activity during the 2020 lockdown is estimated. • Three use cases demonstrate consistency and potential applications. Vessel tracking data help study the potential impact of fisheries on biodiversity and produce risk assessments. Existing workflows process vessel tracks to identify fishing activity and integrate information on species vulnerability. However, there are significant data integration challenges across the data sources needed for an integrated impact assessment due to heterogeneous nomenclatures, data accessibility issues, geographical and computational scalability of the processes, and confidentiality and transparency towards decision making authorities. This paper presents an Open Science data integration approach to use vessel tracking data in integrated impact assessments. Our approach combines heterogeneous knowledge sources from fisheries, biodiversity, and environmental observations to infer fishing activity and risks to potentially impacted species. An Open Science e-Infrastructure facilitates access to data sources and maximises the reproducibility of the results and the method's reusability across several application domains. Our method's quality is assessed through three case studies: The first demonstrates cross-dataset consistency by comparing the results obtained from two different vessel data sources. The second performs a temporal pattern analysis of fishing activity and potentially impacted species over time. The third assesses the potential impact of reduced fishing pressure on marine biodiversity and threatened species due to the 2020 COVID-19 lockdown in Italy. The method is meant to be integrated with other systems through its Open Science-oriented features and can rapidly use new sources of findable, accessible, interoperable, and reusable (FAIR) data. Other systems can use it to (i) classify vessel activity in data-limited scenarios, (ii) identify bycatch species (when catchability data are available), and (iii) study the effects of fisheries on habitats and populations' growth. [ABSTRACT FROM AUTHOR]

• Two-phase training algorithm able to boost the Machine Learning accuracy. • Accurate bankruptcy prediction up to 60 months by a Machine Learning algorithm. • Integration of the Machine Learning algorithm in a DSS for policy making. • Test of the DSS on the Italian SMEs, with a focus on the COVID-19 effects. In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making. [ABSTRACT FROM AUTHOR]

• University buildings must be safe, secure and suitable for an "in-presence" education. • Architectural and technological retrofits are necessary to face the COVID-19. • HVAC-related measures anti-contagion need studies in both time and space domains. • A numerical model of a University building, calibrated against measurements, is proposed. • The design of refurbishment is performed by coupled numerical methods, BES and CFD. The proposed investigation is aimed at providing useful suggestions and guidelines for the renovation of educational buildings, in order to do University classrooms safe and sustainable indoor places, with respect to the 2020 SARS-CoV-2 global pandemic. Classrooms and common spaces have to be thought again, for a new "in-presence" life, after the recent worldwide emergency following the spring 2020 pandemic diffusion of COVID-19. In this paper, starting from a real case study, and thus the architectural and technological refurbishment of an Italian University building (Campobasso, South Italy, cold climate), with the aims of improving the classrooms' quality and safety, a comprehensive approach for the retrofit design is proposed. By taking into account the necessary come back to classrooms starting, hopefully, from the next months (Autumn 2020), experimental studies (monitoring and investigations of the current energy performances) are followed by the coupling of different numerical methods of investigations, and thus building performance simulations, under transient conditions of heat transfer, and computational fluid dynamics studies, to evidence criticalities and potentialities to designers involved in the re-thinking of indoor spaces hosting multiple persons, with quite high occupancy patterns. Both energy impacts, in terms of monthly and annual increase of energy demands due to higher mechanical ventilation, and indoor distribution of microclimatic parameters (i.e., temperature, airspeed, age of air) are here investigated, by proposing new scenarios and evidencing the usefulness of HVAC systems, equipment (e.g., sensible heat recovery, without flows' contamination) and suitability of some strategies for the air distribution systems (ceiling squared and linear slot diffusers) compared to traditional ones. [ABSTRACT FROM AUTHOR]