On the Impact of COVID-19-Related Uncertainty

3noCOVID-19 has generated a substantial increase in the level of economic policy uncertainty (EPU) around the World. Recent empirical investigations suggest that the COVID-19 has played a key role in amplifying the overall level of political uncertainty. In Italy, where anti-COVID-19 measures were implemented with some delay and were badly communicated, EPU rose dramatically. We examine the implications of rising COVID-19-related uncertainty for company revenues, gross operating margin and employment in 16 different Italian sectors. Our findings indicate construction, education, manufacturing activities and hospitality as the most hit sectors, with an average short-term drop in company revenues of around 4% in annual terms and a recovery time of almost two years. Thus, COVID-19-related uncertainty is found to be a significant business cycle driver.openopenGufler, Ivan; Donadelli, Michael; Castellini, MartaGufler, Ivan; Donadelli, Michael; Castellini, Mart

Convolutional Neural Network and Stochastic Variational Gaussian Process for Heating Load Forecasting

Heating load forecasting is a key task for operational planning in district heating networks. In this work we present two advanced models for this purpose, namely a Convolutional Neural Network (CNN) and a Stochastic Variational Gaussian Process (SVGP). Both models are extensions of an autoregressive linear model available in the literature. The CNN outperforms the linear model in terms of 48-h prediction accuracy and its parameters are interpretable. The SVGP has performance comparable to the linear model but it intrinsically deals with prediction uncertainty, hence it provides both load estimations and confidence intervals. Models and performance are analyzed and compared on a real dataset of heating load collected in an Italian network

EXPO-AGRI: Smart Automatic Greenhouse Control

Predicting and controlling plant behavior in con- trolled environments is a growing requirement in precision agri- culture. In this context sensor networks and artificial intelligence methods represent key aspects for optimizing the processes of data acquisition, mathematical modeling and decision making. In this paper we present a general architecture for automatic greenhouse control. In particular, we focus on a preliminary model for predicting the risk of new infections of downy mildew of basil (Peronospora belbahrii) on sweet basil. The architecture has three main elements of innovation: new kinds of sensors are used to extract information about the state of the plants, model predictors are generated from this information by non-trivial processing methods, and informative predictors are automatically selected using regularization techniques

European green policy announcements and sectoral stock returns

To fulfill the Paris Agreement commitments and stimulated by an unprecedented amount of public resources put in place to recover from the COVID-induced recession, European governments have recently announced sizable green policy plans. In this paper, we examine the behavior of green and brown portfolios around green policy-related announcements (GPAs) made by major European governments in 2020 via a standard event study analysis and the use of returns of stocks listed in the “STOXX 100 All Europe”. Our main empirical findings indicate the presence of positive cumulative abnormal returns (CARs) both in the green and brown sectors following GPAs. However, the estimated positive sentiment effect is stronger in the former sector. A size effect in terms of the amount of resources announced to be allocated for a specific category of policy is also observed. We find that the observed positive sentiment is mainly driven by announcements on climate change mitigation-related policies, which account for 70% of the total allocated funds. At the sector level, positive and significant CARs due to GPAs are found in the (i) energy, (ii) financial and (iii) industrial sectors. At the country level, GPAs are found to drive a significant positive sentiment effect in the following European countries: Switzerland, Spain, UK, Ireland and Italy. Sector- and country-level analyses confirm the presence of larger benefits from GPAs among more sustainable portfolios

Surgical Treatment for a Relapsing Malleolar Bursitis in a Professional Figure Skating: Case Report

The increase of popularity in sports as Roller Skating, Figure Skating and Ice Hockey has simultaneously increased the number of skating-related injuries. Lesions of chronic dermatological nature and muscle and tendon lesions have high number of case reports. Ankle and foot are peculiarly involved and this condition suggests the correlation with the use of the skating boot, which with its high-cut and hard-fit changes the joint biomechanics and function and, as a result, the intergumentary system is continuously damaged by the compression and rubbing on the foot. The frequent onset of non-septic chronic relapsing bursitis at the level of the malleolar region is common in all sports requiring the use of skating boots. The conservative treatment is usually compromised by the repetition of the traumatic event due to the use of the skating boot. The case report of a 19 year old professional figure skater affected by non-septic relapsing malleolar bursitis at the level of the malleolar region is presented

XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks

Forecasting is an important task for intelligent agents involved in dynamical processes. A specific application domain concerns district heating networks, in which the future heating load generated by centralized power plants and distributed to buildings must be optimized for better plant maintenance, energy consumption and environmental impact. In this paper we present XM_HeatForecast a Python tool designed to support district heating network operators. The tool provides an integrated architecture for i) generating and updating in real-time predictive models of heating load, ii) supporting the analysis of prediction performance and errors, iii) inspecting model parameters and analyzing the historical dataset from which models are trained. A case study is presented in which the software is used on a synthetic dataset of heat loads and weather forecast from which a regression model is generated and updated every 24 h, while predictions of load in the next 48 h are performed every hour. Software available at: https://github.com/XModeling Video available at: https://youtu.be/JtInizI4e_s

Interlayer-confined Cu(II) complex as an efficient and long-lasting catalyst for oxidation of H2s on montmorillonite

Removal of highly toxic H2S for pollution control and operational safety is a pressing need. For this purpose, a montmorillonite intercalated with Cu(II)-phenanthroline complex [Cu[(Phen)(H2O)2]2+ (Mt-CuPhen) was prepared to capture gaseous H2S under mild conditions. This hybrid material was simple to obtain and demonstrated an outstanding ability to entrap H2S at room temperature, retaining high efficiency for a very long time (up to 36.8 g of S/100 g Mt-CuPhen after 3 months of exposure). Sorbent and H2S uptake were investigated by elemental analysis, X-ray powder diffraction measurements, diffuse reflectance (DR) UV\u2013Vis and infrared spectroscopy, thermal analysis and evolved gas mass spectrometry, scanning electron microscopy equipped with energy-dispersive X-ray spectrometer, and X-ray absorption spectroscopy. The H2S capture was studied over time and a mechanism of action was proposed. The entrapping involves a catalytic mechanism in which [Cu[(Phen)(H2O)2]2+ acts as catalyst for H2S oxidation to S0 by atmospheric oxygen. The low cost and the long-lasting performance for H2S removal render Mt-CuPhen an extremely appealing trap for H2S removal and a promising material for many technological applications

Prevalence and associated factors of COVID-19 across Italian regions: a secondary analysis from a national survey on physiotherapists

Background: Coronavirus disease 2019 (COVID-19) broke out in China in December 2019 and now is a pandemic all around the world. In Italy, Northern regions were hit the hardest during the first wave. We aim to explore the prevalence and the exposure characteristics of physiotherapists (PTs) working in different Italian regions during the first wave of COVID-19. Methods: Between April and May 2020 a structured anonymous online survey was distributed to all PTs registered in the National Professional Registry to collect prevalence data of a confirmed diagnosis of COVID-19 (i.e., nasopharyngeal swab and/or serological test). A bottom-up agglomerative nesting hierarchical clustering method was applied to identify groups of regions based on response rate. Multivariable logistic regression was used to explore personal and work-related factors associated with a confirmed diagnosis of COVID-19. Results: A total of 15,566 PTs completed the survey (response rate 43.3%). The majority of respondents (57.7%) were from Northern regions. Considering all respondents, the number of confirmed COVID-19 cases in Northern and Central Italy, was higher compared to those in Southern Italy (6.9% vs. 1.8%, P < 0.001); focusing the analysis on respondents who underwent nasopharyngeal swab and/or serological test led to similar findings (14.1% vs. 6.4%, P < 0.001). Working in Northern and Central regions was associated with a higher risk of confirmed diagnosis of COVID-19 compared to Southern regions (OR 3.4, 95%CI 2.6 to 4.3). PTs working in Northern and Central regions were more likely to be reallocated to a different unit and changing job tasks, compared to their colleagues working in the Southern regions (10.5% vs 3.7%, P < 0.001). Conclusions: Work-related risk factors were differently distributed between Italian regions at the time of first pandemic wave, and PTs working in the Northern and Central regions were more at risk of a confirmed diagnosis of COVID-19, especially when working in hospitals. Preventive and organizational measures should be applied to harmonize physiotherapy services in the national context. Registration: https://osf.io/x7ch

A new material based on montmorillonite and Cu(II)-phenanthroline complex for effective capture of ammonia from gas phase

The intercalation of [Cu(Phen)(H2O)2]2+ (CuPhen) in montmorillonite (Mt) produces a stable hybrid material that is very efficient in removing NH3 from gas phase even at extremely low pressures. The process was studied by elemental analysis, X-ray powder diffraction, thermal analysis coupled with evolved gas mass spectrometry and DR UV–Vis, NMR and X-ray absorption spectroscopy. The adsorption of CuPhen on Mt consists of two consecutive steps. During the first one, CuPhen intercalates alone into Mt through a cation exchange process, afterwards CuPhen and SO42− ions entry jointly into the mineral interlayer. The two-steps adsorption process is described by a VI-type isotherm, successfully fitted by two independent Frumkin isotherms. NH3 trapping is long-lasting, easy, fast even at extremely low gas pressure and reversible under mild conditions. Mt containing CuPhen always results well performant in removing ammonia from gas phase, but an appreciably higher adsorption capacity of NH3 is obtained when SO42− ion is absent from the interlayer. This hybrid montmorillonite is thus a promising material to be used in industrial or environmental contexts, as an efficient air-cleaner