Internationalisation, cultural distance and country characteristics: a Bayesian analysis of SME's financial performance

Relying on the accounting data of a panel of 403 Italian manufacturing SMEs collected over a period of 5 years, we find results suggesting that multinationality per se does not impact on the economic performance of international small and medium sized firms. It is the characteristics of the country selected i.e. the political hazard, the financial stability and the economic performance that significantly influence SMEs financial performance. The management implication for small and medium sized firms selecting and entering new geographic markets is significant, since our results show that for SMEs it is the market selection process that really matters and not the degree of multinationality

Co-evaluation of climate services. A case study for hydropower generation

Climate services are attracting growing attention and interest as instruments to promote climate change adaptation. The transparent assessment of the potential value brought by the services can play a major role. It can foster the commitment of the user towards a co-generation process increasingly central to climate services creation, can provide developers important information to better tailor the service to the user needs, and can finally increase recognition of the value of the service boosting confidence and trust in the tool. This study presents and then demonstrates the applicability of an evaluation methodology based on the Bayesian framework derived from the information value theory. The specific case study is the Smart Climate Hydropower Tool (SCHT), a climate service designed to support management decisions in hydropower generation. The service uses freely available seasonal forecasts and machine learning algorithms to predict incoming discharge to hydropower reservoirs. The user is ENEL Green Power Italy, and the testing environments are two water basins in Colombia. The study defines the expected value of perfect information, the expected value of the information currently used by the hydropower producer and the expected value of the service information. It then discusses pros and cons of the applicability of the method

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

The Role of Obesity, Body Composition, and Nutrition in COVID-19 Pandemia: A Narrative Review

The coronavirus disease 2019 (COVID-19) pandemic has spread worldwide, infecting nearly 500 million people, with more than 6 million deaths recorded globally. Obesity leads people to be more vulnerable, developing worse outcomes that can require hospitalization in intensive care units (ICU). This review focused on the available findings that investigated the link between COVID-19, body composition, and nutritional status. Most studies showed that not only body fat quantity but also its distribution seems to play a crucial role in COVID-19 severity. Compared to the body mass index (BMI), visceral adipose tissue and intrathoracic fat are better predictors of COVID-19 severity and indicate the need for hospitalization in ICU and invasive mechanical ventilation. High volumes of epicardial adipose tissue and its thickness can cause an infection located in the myocardial tissue, thereby enhancing severe COVID-related myocardial damage with impairments in coronary flow reserve and thromboembolism. Other important components such as sarcopenia and intermuscular fat augment the vulnerability in contracting COVID-19 and increase mortality, inflammation, and muscle damage. Malnutrition is prevalent in this population, but a lack of knowledge remains regarding the beneficial effects aimed at optimizing nutritional status to limit catabolism and preserve muscle mass. Finally, with the increase in patients recovering from COVID-19, evaluation and treatment in those with Long COVID syndrome may become highly relevant

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Sub-surface modifications in silicon with ultra-short pulsed lasers above 2 µm

Nonlinear optical phenomena in silicon such as self-focusing and multi-photon absorption are strongly dependent on the wavelength, energy, and duration of the exciting pulse, especially for wavelengths >2µm. We investigate the sub-surface modification of silicon using ultra-short pulsed lasers at wavelengths in the range of 1950–2400 nm, at a pulse duration between 2 and 10 ps and pulse energy varying from 1 µJ to 1 mJ. We perform numerical simulations and experiments using fiber-based lasers built in-house that operate in this wavelength range for the surface and sub-surface processing of Si-wafers. The results are compared to the literature data at 1550 nm. Due to a dip in the nonlinear absorption spectrum and a peak in the spectrum of the third-order nonlinearity, the wavelengths between 2000 and 2200 nm prove to be more favorable for creating sub-surface modifications in silicon. This is the case even though those wavelengths do not allow as tight focusing as those at 1550 nm. This is compensated for by an increased self-focusing due to the nonlinear Kerr-effect around 2100 nm at high light intensities, characteristic for ultra-short pulses

Large-scale response of the Eastern Mediterranean thermohaline circulation to African monsoon intensification during sapropel S1 formation

This study was supported by Shell International Exploration and Production Inc. We thank the R/V URANIA crew for at sea assistance. This is the ISMAR contribution n. 1914. We thank Dr. L. Capotondi and Dr. L. Vigliotti for their constructive comments on the first draft of the manuscript. We also thank Dr. Daria Pasqual (University of Padova, Dept. of Geosciences) for her assistance in XRF analyses. We thank two anonymous reviewers and the Editor H. Bauch for their constructive comments. We also acknowledge Prof. Gerhard Schmiedl (Universität Hamburg) and Associate Prof. Syee Weldeab (Earth Science, UC Santa Barbara) for providing published data used in this study.Peer reviewedPostprin

FATZ, a filamin-, actinin-, and telethonin-binding protein of the Z-disc of skeletal muscle

We report the identification and characterization of a novel 32-kDa protein expressed in skeletal muscle and located in the Z-disc of the sarcomere. We found that this protein binds to three other Z-disc proteins; therefore, we have-named it FATZ, gamma -filamin/ABP-L, alpha -actinin and telethonin binding protein of the Z-disc. From yeast two-hybrid experiments we are able to show that the SR3-SR4 domains of alpha -actinin 2 are required to bind the COOH-terminal region of the FATZ as does gamma -filamin/ABP-L, Furthermore, by using a glutathione S-transferase overlay assay we find that FATZ also binds telethonin. The level of FATZ protein in muscle cells increases during differentiation, being clearly detectable before the onset of myosin, Although FATZ has no known interaction domains, it would appear to be involved in a complex network of interactions with other Z-band components. On the basis of the information known about its binding partners, we could envisage a central role for FATZ in the: myofibrillogenesis, After screening our muscle expressed sequence tag data base and the public expressed sequence tag data bases, we were able to assemble two other muscle transcripts that show a high level of identity with FATZ in two different domains. Therefore, FATZ may be the first member of a small family of novel muscle proteins