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Data files included: 1\) all.pres_global.csv: is a classic plot (on rows) x species (in column) dataset of presences for all alien species recorded at the global scale. These contain the Global Biodiversity Information Facility (GBIF) and European Vegetation Archive (EVA) presences of alien species recorded globally in the global buffer. Columns correspond to: * source: can be either GBIF or EVA, depending on the original dataset from which data are sourced * Longitude\Latitude: two columns to georeference plots, coordinates in geographic WGS 84 * cells: a unique identifier shared among all the datasets to identify the raster cell to which all other columns refer * 93 columns of species names: these columns display 1 if the corresponding alien species have been found in the corresponding cell, and 0 if at least one other alien species has been found in the same cell. In this case, 0 does not correspond to absences but should be interpreted as a table filler * 7 columns for the environmental variables: these represent the environmental variables extracted for the relevant cells in which at least one alien species was observed. Variable names match the original ones, refer to ly.names.def.csv for a more intuitive description 2\) all.pres_regional.csv: is a classic plot (on rows) x species (in column) dataset of presences for all alien species recorded at the local/regional scale, i.e., in Mediterranean Europe. These contain the Global Biodiversity Information Facility (GBIF) and European Vegetation Archive (EVA) presence of alien species in the regional buffer. Columns correspond to: * source: can be either GBIF or EVA, depending on the original dataset from which data are sourced * Longitude\Latitude: two columns to georeference plots, coordinates in geographic WGS 84 * cells: a unique identifier shared among all the datasets to identify the raster cell to which all other columns refer * 93 columns of species names: these columns display 1 if the corresponding alien species have been found in the corresponding cell, and 0 if at least one other alien species has been found in the same cell. In this case, 0 does not correspond to absences but should be interpreted as a table filler * 7 columns for the environmental variables: these represent the environmental variables extracted for the relevant cells in which at least one alien species was observed. Variable names match the original ones, refer to ly.names.def.csv for a more intuitive description 3\) ly.names.def.csv: is a character vector file (just one row) to define intuitive names of the environmental variables. 4\) Global_BKG.csv: represents all background points used to fit the global model. These were used to extract three sets of background points, after weithging by the regional sampling intensity. Columns correspond to: * Longitude\Latitude: two columns to georeference plots, coordinates in geographic WGS 84 * EVA_Nr._plots: the number of sampled EVA plots in the corresponding cell * cells: a unique identifier shared among all the datasets to identify the raster cell to which all other columns refer * GBIF_Nr._plots: the number of sampled GBIF plots in the corresponding cell. The two cell's number (i.e., EVA_Nr._plots and GBIF_Nr._plots) were summed and used to weigh absences (only once for all species) that were then used to randomly extract the three background point samples in the global background * 7 columns for the environmental variables: these represent the environmental variables extracted for the relevant cells in which at least one alien species was observed. Variable names match the original ones, refer to ly.names.def.csv for a more intuitive description 5\) Local_BKG.csv: represents all background points used to fit the local/regional model. These were used to extract three sets of background points, after weithging by the regional sampling intensity. Columns correspond to: * Longitude\Latitude: two columns to georeference plots, coordinates in geographic WGS 84 * EVA_Nr._plots: the number of sampled EVA plots in the corresponding cell * cells: a unique identifier shared among all the datasets to identify the raster cell to which all other columns refer * GBIF_Nr._plots: the number of sampled GBIF plots in the corresponding cell . The two cells (i.e., EVA_Nr._plots and GBIF_Nr._plots) were summed and used to weight absences (only once for all species) that were then used to randomly extract the three background point samples in the local/regional background * 7 columns for the environmental variables: these represent the environmental variables extracted for the relevant cells in which at least one alien species was observed. Variable names match the original ones, refer to ly.names.def.csv for a more intuitive description 6\) myexpl.var30... : by their extended name, represent the environmental variables used to project the model in the current and future environmental conditions of Mediterranean Europe. This is a raster stack and each layer name is defined by the file ly.names.def.csv, which order is matched. 7\) Distance_to_cities: is a raster file that can be uploaded in R using the raster (function), and represents a cellwise distance to the major cities. 8\) Distance_to_coast: is a raster file that can be uploaded in R using the raster (function), and represents a cellwise distance to the European coastline. 9\) Distance_to_ports: is a raster file that can be uploaded in R using the raster (function), and represents a cellwise distance to the major European ports. These are the raw data that can be used to reproduce results of the paper: "Plant invasion in Mediterranean Europe: current invasion hotspots and future scenarios". The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry and densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders. We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change. Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far. With these data (environmental variables, species presences and background points, and distance to ports cities and to the coast) and using the R software following the ODMAP protocol attached to the original paper all results meet the criteria of reproducible science. Datasets from the EVA and GBIF were processed following the Material and Methods section of the paper, to derive the attached files of regional and global presences and background points. The environmental variables used were processed as explained in the paper. Files of distances to the cities, ports and coast were elaborated from the raw data downloadable as reported in the data availability statement. The data is in .csv format and can be read by any text editor file. We recommend their usage in R. To reproduce analyses please use Biomod 2 R package. 

This research investigates how the impact of persuasive messages in the political domain can be improved when fit is created by subliminally priming recipients’ regulatory focus (either promotion or prevention) and by linguistic framing of the message (either strategic approach framing or strategic avoidance framing). Results of two studies show that regulatory fit: a) increases the impact of a political message favoring nuclear energy on implicit attitudes of the target audience (Study 1); and b) induces a more positive evaluation of, and intentions to vote for, the political candidate who is delivering a message concerning immigration policies (Study 2).

Abstract Increasing electrification of final uses can be a viable solution towards low-carbon energy systems, when coupled with local renewable power generation. Mountain areas can already benefit from high shares of hydro-power generation, but, at the same time, rely on oil products for transport and for the heating sector in remote areas where natural gas infrastructures are not available. This research work evaluates potential scenarios for the electrification of transport and heating sectors, by coupling the simulation tool EnergyPLAN with a multi-objective optimization algorithm to analyse economic and environmental aspects. Results show that the largest benefits are expected from the electrification of the heating sector. Indeed, a CO2 emissions reduction up to 30% can be reached by acting on the transport sector alone, while up to 65% combining it with measures on heating, industry and agriculture sectors and additional electricity generation from photovoltaic systems. Moreover, the use of heat pumps can lead to significant CO2 emissions decrease with only to a slight increase in the overall annual costs thanks to lower variable costs that partly compensate the higher required initial investment and electricity storage deployment. The optimization analyses also highlight the effect of progressive penetration of electric vehicles in the private cars fleet and hydrogen trucks in the light-duty vehicles one.

Disproportionation of superoxide to peroxide and O2 generates the highly reactive singlet O2, which needs to be avoided for highly reversible metal–O2 batteries.

AbstractRecently, great attention has been given to the transition from centralized to distributed generation energy production systems. There is a growing potential regarding the use of trigeneration systems in the residential sector because they have the ability to produce thermal energy and electricity from a single source of fuel.This study has the goal to determine the best systems able to satisfy the demand for electricity and thermal energy for a complex of historic buildings in Rome. Such analysis has been conducted using a specific tool conceived for energetic and financial analysis: the RETScreen software.

Abstract The paper addresses the potential heat-to-power application of supercritical CO2 (sCO2) plants to the cement industry, thereby reducing their electricity demand and improving energy efficiency. The research was conducted as part of the European project CO2OLHEAT (G.A. 101022831), which involves the installation of a 2 MW Waste-Heat-to-Power (WH2P) skid based on a sCO2 cycle in a cement plant, the first of its kind with a MW-scale power output. The paper summarizes technologies and processes employed in the Italian cement production sector, detecting where the waste heat can be successfully extracted to feed the recovery plant without compromising the industrial process. Moreover, the paper discusses the national cement market and explores the potential advantages and limitations of integrating sCO2 recovery plants within the national cement context, considering production and energy-related data. The final finding reveals the percentage of recoverable electricity per technological class for the cement production sector in Italy with a potential application of the sCO2 recovery plant, aiming at identifying the potential market penetration of the CO2OLHEAT installation.

Abstract The optimum management of the engine system has a crucial role in order to achieve high efficiency and reduced pollutant emissions. Advanced methods have been proposed, in which several types of sensors are used to directly/indirectly sense the combustion and to provide a feedback signal to optimize the engine management. In turbocharged engines, it has been demonstrated that a relationship exists between the rotational speed of the turbocharger and the thermo-fluid dynamic condition of the gases at the exhaust valve opening. Such a relation allows to establish a link between the engine operating conditions in terms of speed, load and injection settings and the turbocharger speed. A research activity was performed aimed at developing a methodology in which the signal from an accelerometer mounted on the compressor housing was used to extract information about the turbocharger speed value. The activity was organized in two subsequent steps, each one focused on one specific objective: – estimation of the mean turbocharger rotational speed – evaluation of the turbocharger speed fluctuations. Tests were performed on a small displacement two-cylinder diesel engine mainly used in urban vehicles that was equipped with a turbocharger. The results obtained during the first step of activity demonstrated the opportunity of further investigations in order to compute the turbocharger speed fluctuation from the accelerometer signal processing. This paper is devoted to present the results of the second step of the research activity, with the final aim of realizing a non intrusive control of combustion process, in which the variation of combustion development as regards nominal condition is detected via the estimation of the turbocharger speed.

This work has been supported by grant FC-GRUPIN-IDI/2018/000241, from Asturian Regional Government and grant MINECO-17-ENE2016-77919-R, from the Spanish Ministry of Economy and Competitiveness.

Industrial facilities release a large amount of heat as a by-product of their processes. To improve environmental performance and increase process profitability, a portion of the waste heat can be recovered and employed for power generation by recovery systems. Supercritical carbon dioxide (sCO2) plants are emerging as potential alternatives to the well-established technologies for waste heat recovery (WHR) power generation in heavy industry. This paper offers a preliminary techno-economic analysis of a waste heat-to-power system based on a sCO2 closed-loop for a heavy-industrial process.By conducting a parametric investigation on the WHR sCO2 system’s key design parameters, a number of preferable configurations from a thermodynamic perspective were initially identified; they were subsequently analyzed from the economic point of view in terms of net present value (NPV) and pay-back period (PBP). The privileged WHR system configuration achieved an overall efficiency of 30.4% and a power output of 21.6 kWe, providing an NPV of almost US k$ 376 with a PBP of approximately 4.5 years.