Why are very short times so long and very long times so short in elastic waves?

In a first study of thermoelastic waves, such as on the textbook of Landau and Lifshitz, one might at first glance understand that when the given period is very short, waves are isentropic because heat conduction does not set in, while if the given period is very long waves are isothermal because there is enough time for thermalization to be thoroughly accomplished. When one pursues the study of these waves further, by the mathematical inspection of the complete thermoelastic wave equation he finds that if the period is very short, much shorter than a characteristic time of the material, the wave is isothermal, while if it is very long, much longer than the characteristic time, the wave is isentropic. One also learns that this fact is supported by experiments: at low frequencies the elastic waves are isentropic, while they are isothermal when the frequencies are so high that can be attained in few cases. The authors show that there is no contradiction between the first glance understanding and the mathematical treatment of the elastic wave equation: for thermal effects very long periods are so short and very short periods are so long.Comment: 7 pages, submitted to European Journal of Physic

Improvement of insulin sensitivity in diabetic and non diabetic patients with chronic hepatitis C treated with direct antiviral agents

The increased incidence of type 2 diabetes mellitus among hepatitis C virus (HCV) infected patients is likely due to viral-induced insulin resistance (IR). Indeed, control of diabetes in these patients benefits of successful antiviral treatment; whether the same applies to subtler alterations of glucose metabolism is unknown. We aimed to fill this gap

Climate and landscape composition explain agronomic practices, pesticide use and grape yield in vineyards across Italy

Context Worldwide, organic farming is being promoted as one of the main alternatives to intensive conventional farming. However, the benefits of organic agriculture are still controversial and need to be tested across wide environmental gradients. Objective Here, we carried out an observational study to test how agronomic practices, pest management, environmental impact and yield of conventional and organic vineyards changed along wide climatic and landscape gradients across Italy. Methods We used a block design with 38 pairs of conventional and organic vineyards across Italy. Results and conclusions Most agronomic practices did not differ between conventional and organic vineyards. By contrast, landscape composition and climate were strong predictors of management in both systems. First, increasing semi-natural areas around the vineyards reduced pesticide pressure and related environmental impacts, but was also associated with lower yield. Second, irrespective of the farming system, a warm and dry climate was associated with reduced fungicide pressure. Conventional farming had a yield gain of 40% in cold and wet climate compared to organic but the yield gap disappeared in the warmest regions. Significance In both farming systems, we observed a large variability in management practices that was mainly explained by climate and landscape composition. This large variability should be considered when evaluating the benefits and drawbacks of different farming systems under contrasting environmental contexts

A Multivariate Time Series Analysis of Ground Deformation Using Persistent Scatterer Interferometry

Ground deformations in urban areas can be the result of a combination of multiple factors and pose several hazards to infrastructures and human lives. In order to monitor these phenomena, Interferometric Synthetic Aperture Radar (InSAR) techniques are applied. The obtained signals record the overlapping of the phenomena, and their separation is a relevant issue. In this framework, we explored a new multi-method approach based on the combination of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Hierarchal Clustering (HC) on the standardized results to distinguish the main trends and seasonal signals embedded in the time series of ground displacements, to understand spatial-temporal patterns, to correlate ground deformation phenomena with geological and anthropogenic factors, and to recognize the specific footprints of different ground deformation phenomena. This method allows us to classify the ground deformations at the site scale in the metropolitan area of Naples, which is affected by uplift cycles, subsidence, cavity instabilities and sinkholes. At the local scale, the results allow a kinematic classification using the extracted components and considering the effect of the radius of influence generated by each cavity, as it is performed from a theoretical point of view when the draw angle is considered. According to the results, among the classified cavities, 2% were assigned to subsidence and 11% to uplift kinematics, while the remaining were found to be stable. Furthermore, our results show that the centering of the Spatial-PCA (S-PCA) is representative of the region’s main trend, whereas Temporal-PCA (T-PCA) gives information about the displacement rates identified by each component

A multivariate time series analysis of underground gas storage deformations using InSAR data

Underground gas storage (UGS) is of strategic importance both in terms of security of supply and to ensure the operational continuity of primary industrial basins. UGS reservoirs make it possible to guarantee the country a continuous and reliable supply of natural gas. It is well known that UGS activities can induce ground deformations, in response to gas injection and extraction cycles. The Lombardy region (Italy) has a predominant part in the Italian national policy of UGS in depleted reservoirs. In this work, five UGS reservoirs located in Lombardy and three additional ones in Italy, which differ in geometric and geo-lithological features, were considered.In this context, the InSAR (Interferometric Synthetic Aperture Radar) technique plays a key role in monitoring ground deformations induced by UGS activities, providing precise measurements of ground displacement.In this contribution, we present (i) an application of a multi-method approach for the analysis of trends and seasonal signals in the EGMS InSAR time series of ground displacements in the proximity of UGS reservoirs to recognise specific footprints and spatial-temporal patterns of ground deformation. For this purpose, large datasets of ground displacements covering the UGS area in Lombardy (25 km2) from 2015 to 2022 were analysed; and (ii) an interpretation of the possible causal relationship between displacement and gas injection and extraction time series using cross-correlation approach and wavelet tools in the time-frequency domain.The multi-method approach involves the application and optimization of Principal Component (PCA) and Independent Component Analyses (ICA) in temporal (T-) and spatial (S-) modes on both ascending and descending InSAR time series, as well as on the vertical and horizontal ones, allowing for a spatial-temporal separation of the original data into a set of limited components. Among them, it is possible to isolate those related to the USG deformations, from other signals typical of the region. Subsequently, clustering analysis is performed to group the InSAR time series and identify characteristic ground deformation patterns, which could also be related to differences in grain size properties.As a result, it was possible to recognize and separate a limited number of signal components, describing long-term displacement and seasonal fluctuations, and the derived maps allowed the characterization of the area of influence relative to each UGS reservoir. Finally, cross-correlation approach and wavelet tools made it possible to identify and interpret the time lag between the peaks and, consequently to improve the correlation between displacements and anthropogenic triggers.To validate the deformation patterns resulting from the approach, numerical analyses were performed in which the gas injection and extraction time series were considered as input variables