Rapid Dissolution of Lignocellulosic Plant Materials in an Ionic Liquid

Concerns regarding the non-renewable nature of, and pollution from, petroleum derived energy and commercial products has led to the concept of a biomass economy. As part of this vision for a society based on sustainable biomaterials, proposed biorefineries need to tackle the challenges of taking a wide diversity of raw biomass and rapidly and effectively transforming it into functionalizable platform molecules that can be derivatized into industrial and consumer products, or converted into biofuels. A substantial research effort is underway focussed on degrading biomass into smaller constituents using a variety of physical, chemical, and biological processes. One promising technology for the solubilization of biomass is ionic liquids (ILs), which has received considerable attention as a medium for efficient solubilization of a variety of materials. ILs also allow fractional separation when combined with solvent extraction (conventional, and green technologies such as supercritical CO2), precipitation, and adsorption/absorption methods, and to conduct a wide range of chemical reactions using thermal, electrochemical, photochemical, and biocatalytic processes. As a potential pretreatment technology for the biorefineries of the future, we report herein the first rapid dissolution of a range of coniferous and deciduous woods and grassy lignocellulosic plant materials in an IL using microwave radiation

Biological Activities of Extracts from Sumac (Rhus spp.): A Review

Sumac is the common name for a genus (Rhus) that contains over 250 individual species of flowering plants in the family Anacardiaceae. These plants are found in temperate and tropical regions worldwide, often grow in areas of marginal agricultural capacity, and have a long history of use by indigenous peoples for medicinal and other uses. The research efforts on sumac extracts to date indicate a promising potential for this plant family to provide renewable bioproducts with the following reported desirable bioactivities: antifibrogenic, antifungal, antiinflammatory, antimalarial, antimicrobial, antimutagenic, antioxidant, antithrombin, antitumorigenic, antiviral, cytotoxic, hypoglycaemic, and leukopenic. As well, the bioactive components can be extracted from the plant material using environmentally benign solvents that allow for both food and industrial end-uses. The favorable worldwide distribution of sumac also suggests that desirable bioproducts may be obtained at source, with minimal transportation requirements from the source through processing to end consumer. However, previous work has focussed on only a few members of this large plant family. In addition, not all of the species studied to date have been fully characterized for potential bioactive components and bioactivities. Thus, there remains a significant research gap spanning the range from lead chemical discovery through process development and optimization in order to better understand the full potential of the Rhus genus as part of global green technology based bioproduct and bioprocess research programs

Trichoderma reesei derived cellulase activity in three N,N-dimethylethanolammonium akylcarboxylate ionic liquids

The activity and denaturation extent of cellulase from Trichoderma reesei (E.C. # 3.2.1.4) was investigated in three representative N,N-dimethylethanolammonium akylcarboxylate ionic liquids. Significant cellulase activity and absence of enzyme unfolding was found in all concentrations of N,N-dimethylethanolammonium acetate (DMEAA), including the pure liquid. Activities in 20% and 40% (v/v) solutions of DMEAA were equal to citrate buffer controls. Lower enzymatic activities and denaturation were observed in solutions of the corresponding formate and octanoate ionic liquids, although cellulose hydrolysis still proceeded at a substantial rate. The results provide the first proof-of-principle that cellulose can be enzymatically hydrolyzed in the presence of high ionic liquid concentrations

Immigrants’ settlement patterns in the city of Naples

ABSTRACT. Residential segregation is the outcome of both economic inhomogeneities within the urban space and attraction among individuals sharing the same ethnicity. Here we focus on the settlement patterns originated by different groups of immigrants in the city of Naples. We use the inhomogeneous L-function for measuring segregation due to spatial attraction, while adjusting for the effects of inhomogeneity. Monte Carlo simulations have been used to build confidence envelopes for the null hypothesis of absence of spatial attraction. All nationalities exhibited significant spatial attraction at all considered distances, except for Romania and Poland. However, spatial attraction resulted much stronger for immigrants from Pakistan, China, and Sri Lanka. RIASSUNTO. La segregazione residenziale è la risultante di disomogeneità economiche all’interno dello spazio urbano e dell’attrazione tra individui della stessa etnia. In questo lavoro, noi considerazione i modelli insediativi originati da diversi gruppi di immigrati stranieri nella città di Napoli ed impieghiamo la funzione L disomogenea per misurare la segregazione causata dall’attrazione spaziale, tenendo sotto controllo le disomogeneità spaziali. Tramite simulazioni Monte Carlo abbiamo costruito degli intervalli di confidenza per l’ipotesi nulla di assenza di attrazione. Tutte le nazionalità analizzate hanno mostrato un’attrazione spaziale significativa a tutte le distanze considerate, ad eccezione di Rumeni e Polacchi. Un’attrazione più forte è stata osservata per gli immigrati provenienti dal Pakistan, Cina e Sri Lanka

Alien shades of grey: New occurrences and relevant spread of Sciurus carolinensis in Italy

The eastern grey squirrel is listed among the worst invasive species throughout the world. This species of American origin is currently replacing the native Eurasian red squirrel in most of the Great Britain, as well as in parts of Ireland and Italy. It may debark trees and exert damages to woodlands and tree plantations. Therefore, its spread may be deleterious for biodiversity and environment, emphasising the need for a rapid detection in new areas of occurrence. In this work, we reported for the first time, the presence of new populations of this invasive species in Tuscany (Central Italy) and some updates and analyses regarding the status of this species in Veneto (North-Eastern Italy). Occurrences were collected through citizen-science contributory approach supported by photos, road-kills, and/or hair-tube sampling. Field investigations ad hoc were carried out in Veneto and Tuscany   to confirm the repeated reports in the surroundings of Arezzo and in the province of Siena. Although records can be possibly related to erratic or single individuals escaped from captivity, reproductive nuclei have also been detected in both regions, with the observations of juveniles and/or lactating females. The occurrence of the  species in these regions is still scarce and localised, but considering the surrounding favourable wooded habitats, a rapid removal of the animals would be required to prevent their spread

Effect of concrete tensile strength in non linear analyses of 2D structures - a comparison between three commercial finite element softwares

Non-linear finite element method (FEM) allows to take into account material and geometrical non-linearities in the simulation of the behaviour of reinforced concrete structures. However, the accuracy of the numerical solution with respect to experimental tests is often questionable, especially in the case of 2D and 3D structures. Several competitions showed in the past significant scatter of the predicted results with respect to the correct ones. Even though internationally well-known computer softwares can be used to predict the structural response, the uncertainty of the numerical simulation cannot be neglected. Therefore, the application of finite element models to the assessment of concrete structures requires a proper investigation of the uncertainty related to the results of the simulations. This paper presents a comparison of numerical simulations of sixteen case studies taken from past experimental tests and modelled with three commercial non-linear softwares. The purpose of the investigation is to show how significant could be the difference between the experimental and numerically evaluated failure load and displacement in function of the code used and the variation of only one material parameter

A CNN-based fusion method for feature extraction from sentinel data

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators