ALBERT

Description: Background: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen. Results: This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy. Conclusions: The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.

Description: This paper aspires to examine the environmental effects of financial market development (FMD), foreign direct investment (FDI), and trade openness on the CO2 emissions per capita along with the environmental Kuznets curve (EKC) hypothesis in six East Asian countries from 1991–2014. For this purpose, spatial econometrics is applied to consider the spillover effects from neighboring countries. The results of the study corroborate the spillover effects from neighboring countries’ CO2 emissions per capita, FMD, FDI, and trade openness, and the EKC hypothesis is proven true in this region. Local FDI inflows, trade openness, and energy intensity are found to be responsible for local environmental degradation. Local FMD has an insignificant environmental effect, but neighboring countries’ FMD has contributed to the local CO2 emissions per capita. Further, positive (negative) environmental spillover effects are found from neighboring countries’ FDI (trade openness).

Description: Iron carbide (Fe3C) is a ceramic magnetic material with high potential for applications in different fields, including catalysis, medicine imaging, coatings, and sensors. Despite its interesting properties, it is still somehow largely unexplored, probably due to challenging synthetic conditions. In this contribution, we present a sol-gel-based method that allows preparing different Fe3C@C nanocomposites with tailored properties for specific applications, in particular, we have focused on and discussed potential uses for adsorption of noxious gas and waste removal. Nanocomposites were prepared using readily available and “green” sources, such as urea, simple and complex sugars, and chitosan. The nanocomposite prepared from chitosan was found to be more efficient for CO2 uptake, while the sample synthetized from cellulose had optimal capability for dye absorption and waste oil removal from water.

Description: Background: The oxidative transformation of benzoin to benzil has been accomplished by the use of a wide variety of reagents or catalysts and different reaction procedures. The conventional oxidizing agents yielded mainly benzaldehyde or/and benzoic acid and only a trace amount of benzil. The limits of practical utilization of these reagents involves the use of stoichiometric amounts of corrosive acids or toxic metallic reagents, which in turn produce undesirable waste materials and required high reaction temperatures.In recent years, vanadium complexes have attracted much attention for their potential utility as catalysts for various types of reactions. Results: Active and selective catalytic systems of new unsymmetrical oxovanadium(IV) Schiff base complexes for the oxidation of benzoin is reported. The Schiff base ligands are derived between 2-aminoethanol and 2-hydroxy-1-naphthaldehyde (H2L1) or 3-ethoxy salicylaldehyde (H2L3); and 2-aminophenol and 3-ethoxysalicylaldehyde (H2L2) or 2-hydroxy-1-naphthaldehyde (H2L4). The unsymmetrical Schiff bases behave as tridentate dibasic ONO donor ligands. Reaction of these Schiff base ligands with oxovanadyl sulphate afforded the mononuclear oxovanadium(IV) complexes (VIVOLx.H2O), which are characterized by various physico-chemical techniques.The catalytic oxidation activities of these complexes for benzoin were evaluated using H2O2 as an oxidant. The best reaction conditions are obtained by considering the effect of solvent, reaction time and temperature. Under the optimized reaction conditions, VOL4 catalyst showed high conversion (〉99 %) with excellent selectivity to benzil (~100 %) in a shorter reaction time compared to the other catalysts considered. Conclusion: Four tridentate ONO type Schiff base ligands were synthesized. Complexation of these ligands with vanadyl(IV) sulphate leads to the formation of new oxovanadium(IV) complexes of type VIVOL.H2O.Elemental analyses and spectral data of the free ligands and their oxovanadium(IV) complexes were found to be in good agreement with their structures, indicating high purity of all the compounds.Oxovanadium complexes were screened for the oxidation of benzoin to benzil using H2O2 as oxidant. The effect of time, solvent and temperature were optimized to obtain maximum yield. The catalytic activity results demonstrate that these catalytic systems are both highly active and selective for the oxidation of benzoin under mild reaction conditions.

Description: Remote Sensing, Vol. 9, Pages 1155: Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco Remote Sensing doi: 10.3390/rs9111155 Authors: Omar Ali Eweys Maria José Escorihuela Josep M. Villar Salah Er-Raki Abdelhakim Amazirh Luis Olivera Lionel Jarlan Saïd Khabba Olivier Merlin The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m−3).