On the characterization of materials and masonry walls of historical buildings: Use of optical system to obtain displacement maps in double-flat jack tests

Among the testing techniques aiming at the mechanical characterization of masonry, the double flat-jack testing method is widely adopted to identify the local value of significant parameters needed to perform structural analyses, such as elastic modulus, Poisson’s ratio and compressive strength. The experience gained from many applications has allowed not only to collect experimental data concerning different types of masonry, but also to highlight the difficulty in the interpretation of the results and the limitations of both single and double flat-jack tests. Although the accuracy of the flat-jack technique in detecting strength and deformability behavior of masonry is still debated in the technical literature and practical activities, changes in the testing procedure aiming at ascertaining the validity of the test results have not been formally defined yet. After a brief description of the standard test procedure and its uncertainties, the present paper proposes an upgrade of the test procedure for improving the level of reliability of the test results. In particular, an experimental case study related to a historical brick masonry building located in Italy is presented to point out the additional information necessary to validate the results of the testing process

A Drought Alert system based on seasonal forecasts

Water resources are under stress in many areas of the world, because of a combination of climatic and anthropogenic factors. The Mediterranean area is one of the regions mostly vulnerable to climate alterations. These alterations have direct impacts on the surface water balance and groundwater recharge, and thus changes in the reservoir inputs and the management of water utilities (WUs) are severe challenges for water resources in the future. However, WUs management routines scarcely consider climate information and are based on the stationarity assumption, working on weekly or daily time scale. The use of seasonal forecasts for guiding a strategic planning of the resources has been increasing across several climate-sensitive sectors, including water management and energy. This is due to the fact that it is generally preferred to focus on the upcoming season rather than taking decisions on the basis of a 100-year climate projection. The project EUPORIAS promoted the use of climate information for decision support by involving both providers and potential users of seasonal data. It was demonstrated that seasonal forecasts may give important contributions in the fields of drought-risk assessment and mid-term reservoir management. This study aims at providing some insights in using seasonal forecasts to derive supporting information for water management decision-makers based on drought assessment. Indeed, the exploitation of climate information as precipitation in a mid-term scale, as the seasonal scale, allows for understanding the possible shifts in water resource availability. In this study we describe some results obtained for a case study in Greece

SOON: The Station Observation Outlier finder

In the climate change era, it is fundamental to monitor the availability of water resources. One of the possible causes for a change in the water availability is related to variations in the meteorological conditions. To track this change, ground-based observations are one of the commonly used measurements. However, these datasets might include both extreme but realistic values and erroneous information. A necessary but not trivial preliminary process for exploiting the observations is to filter the former while retailing the latter. The Station Observation Outlier fiNder (SOON) is a highly innovative algorithm, that identifies errors in large dataflows. SOON can be used on historical datasets as well as in real-time dataflows. A first prototype has been tested on 8 years (2007-2014) of hourly data recorded by about 10000 stations around Europe, which includes 7 meteorological variables: temperature, dewpoint temperature, pressure, precipitation, wind speed, wind gusts, and cloudiness. The dataset belongs to the Ubimet archive and has been provided within the EDI incubator programme

Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7\ua0months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period

An innovative approach for detecting the effect of climate change on the hydrometeorological extremes

In a future climate, extreme hydrometeorological events are expected to increase in magnitude and frequency. However, changes in the extreme event characteristics on a relatively short time-scale could be attributed to either climate fluctuations or the effect of anthropogenic climate change. How to distinguish between these two cases is still a field of research. This study presents a novel technique to detect systematic changes in the hydrometeorological extremes in Africa, as part of the eXtreme Climate Facilities project (XCF) lead by the African Risk Capacity (ARC). In a first step, we introduce the Extreme Climate Index (ECI), an objective, multi-hazard index constructed to identify intense droughts, storms, and heat weaves. Subsequently, a new method that estimates the probability of anthropogenic climate change to be the cause of the changes in the hydrometeorological extremes is introduced. This technique is applied to the case of XCF, which is aimed at designing a new financial tool to mitigate the anthropogenic effect on extremes. The method is calibrated with synthetic datasets as well as with the results of the pre-industrial experiment of the CMIP5 database. At the same time, this analysis explores the extent to which such a technique is generally applicable to the identification of systematic changes in the hydrometeorological extremes

Protein extraction from grape tissues by two-dimensional electrophoresis

At the onset of proteomic studies protein samples have to be accurately separated by two dimensional electrophoresis (2-DE); subsequently polypeptides are identified. Grape tissues, in particular roots, can be very problematic due to their hardness and to the high content of compounds that interfere in classical protein extraction. We have used a phenol-based extraction method in the presence of a protease inhibitor and Polyvinylpolypyrrolidone (PVPP). In this paper we demonstrate that this extraction method gives satisfactory and reproducible protein separation allowing the identification of some proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

Sustainability transition in industry 4.0 and smart manufacturing with the triple-layered business model canvas

Sustainability transition is becoming increasingly relevant at a manufacturing level, especially for resource- and energy-intensive industries. In addition, the 4.0 industry paradigm opens new opportunities in terms of sustainable development. The aim of this research is to analyze the introduction of sustainability in the corporate value proposition, through the evolution from a traditional to a sustainable business model. The business model innovation will be investigated in the case of a ceramic tile producer in the district of Sassuolo, Italy. The company has introduced several sustainability practices over the years and, through investments in Industry 4.0 technologies, is able to conduct impact assessments of its production process. The applied tool for the business model transition will be the Triple-Layered Business Model Canvas by Joyce and Paquin. The results illustrate the new company's sustainable value proposition, considering all three pillars of sustainability: environment, economy, and society. Despite the limitations resulting from the individual case study, the findings can be easily adapted to other ceramic tile companies in the sector. Besides, the paper could inspire other manufacturing companies in the drafting of a sustainable business model. The paper explores the still limited literature on the application of sustainable business models in operational scenarios

Seasonal forecasts to support water management decisions

6noWater resources are under stress in many areas of the world, because of a combination of climatic and anthropogenic factors. One of the regions mostly vulnerable to climate change is the Mediterranean area, where alterations in temperature, precipitation and frequency of extreme events have been experienced. In recent years, water shortage has become an increasing concern and water availability is expected to decline in southern Europe. These alterations have direct impacts on the surface water balance and groundwater recharge, and thus changes in the reservoir inputs and the management of water utilities are severe challenges for water resources in the future. Water Utilities (WUs) management routines scarcely consider climate information and are based on the stationarity assumption, working on weekly or daily time scale. This study aims at providing some insights in using seasonal forecasts to support a decision system for water management based on long term planning. The integration of long term climate information with water balance modeling will produce suitable seasonal hydrological forecasts for understanding the possible shifts in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills, but the results of more recent studies are promising although the predictability varies depending on seasons and areas of application. In this study we describe the preliminary results of the use of the seasonal forecast products released by the Copernicus Climate Change Service (C3S), mainly air temperature and precipitation, in two study areas, i.e. Italy and Greece. The forecasts are updated every month and cover a time range of 6 months.openopenE. Arnone, S. Dal Gesso, M. Cucchi, L. Ortolani, M. Petitta, S. CamantiArnone, E.; Dal Gesso, S.; Cucchi, M.; Ortolani, L.; Petitta, M.; Camanti, S

When a Politician Disappoints: The Role of Gender Stereotypical Expectations in Post-Scandal Judgment

This study examines how evaluations of male and female politicians are worsened by corruption scandals that disappoint expectations of honesty. Participants evaluated a fictitious politician before and after watching a video about a corruption scandal involving that politician. The manipulated variables were the politician’s sex and whether they shared participants’ political affiliations. Results showed that a female politician affiliated with the participants’ preferred party was the most damaged by the scandal because she had the highest expectations of honesty placed upon her

Influence of the exhaust gas turbocharger on nano-scale particulate matter emissions from a GDI spark ignition engine

The influence of the exhaust gas turbocharger on nano-scale Particulate Matter (PM) number emissions from a Gasoline Direct Injected (GDI) engine is investigated at fixed exhaust gas dilution ratio for a matrix of three engine speeds and four engine load operating points. Experimental repeatability is assessed by means of the Coefficient of Variation (CoV) from three independent measurements for every test point. A hypothesis test on the difference between total number count before and after the turbine shows that there are statistically relevant variations for most operating points. A reduction in PM total number count at low load is observed, and an increment at high load. It is conjectured that as fuel injection pressure and duration increase with load, a larger share of volatile particulate matter is produced, which then undergoes nucleation as the exhaust gas expands through the turbine. At the same time, the centrifugal action within the turbocharger is believed to promote particle agglomeration and growth, and fragmentation of micro-scale particles. Experiments with variable dilution ratio at a fixed engine test point show that changes in dilution ratio affect repeatability of the emissions measurements only marginally. Yet, a hypothesis test on the variation of total number count with dilution shows that PM number counts are systematically affected by changes in dilution ratio. Furthermore, a hypothesis test also shows that the impact of the turbocharger on total number emissions is statistically relevant regardless of the dilution ratio adopted