Optical properties of hybrid T3Pyr/SiO2/3C-SiC nanowires

A new class of nanostructured hybrid materials is developed by direct grafting of a model thiophene-based organic dye on the surface of 3C-SiC/SiO₂ core/shell nanowires. TEM-EDX analysis reveals that the carbon distribution is more spread than it would be, considering only the SiC core size, suggesting a main contribution from C of the oligothiophene framework. Further, the sulfur signal found along the treated wires is not detected in the as-grown samples. In addition, the fluorescent spectra are similar for the functionalized nanostructures and T3Pyr in solution, confirming homogeneous molecule grafting on the nanowire surface. Chemical and luminescence characterizations confirm a homogeneous functionalization of the nanowires. In particular, the fluorophore retains its optical properties after functionalization.     

Liquid Chromatography–Hybrid Linear Ion Trap–High-Resolution Mass Spectrometry (LTQ-Orbitrap) Method for the Determination of Glycoalkaloids and Their Aglycons in Potato Samples

Potatoes produce biologically active secondary metabolites like glycoalkaloids and their aglycons, which may have both adverse and beneficial effects in the diet. A new analytical method that uses liquid chromatography–mass spectrometry (LTQ-Orbitrap) has been developed for the analysis of glycoalkaloids and their aglycons in potato samples. Two glycoalkaloids, α-solanine and α-chaconine, and two aglycons, demissidine and solasodine, were quantified in potato samples. Samples were extracted using methanol, purified on an SPE Strata C₁₈ cartridge, and then analyzed in HPLC–mass spectrometry (LTQ-Orbitrap) with the FTMS operating in full scan at a resolving power of 30,000 (FWHM), enabling the detection and accurate mass measurement and with the ITMS mode operating in MRM (multiple reaction monitoring) for glycoalkaloids and their aglycons using the [M + H]⁺ ions and their optimized collision energies. After validation, the method was applied to screen different type of potatoes, and some cooking experiment were conducted.     

The magnitude of a product recall: offshore outsourcing vs. captive offshoring effects

The escalation in product recalls in recent years is attributed to the rise of globalisation and associated challenges of offshoring. Extant SCM research suggests that product recalls have a significant negative impact on financial performance, but gaps exist relative to the managerial actions to minimise their impact. Recall response strategies have become more important in the press, given that a single recall may result in the mandatory withdrawal of millions of products from the market, with firms incurring enormous logistics costs and brand damage. In this study, we address this gap in the research, and using a measure of product recall defined as the volume of products withdrawn from the market due to product quality failure. We explore the scale of the recall in the context of pharmaceutical sector global sourcing strategies, exploring whether the variation in global sourcing decisions not only increases the likelihood of a recall, but also influences the capability to minimise the total cost of recall. Our results suggest that offshore outsourcing and captive offshoring have opposite effects in terms of their influence on the magnitude of product recall. We summarise the implications through a compelling set of insights for future global sourcing strategy research themes.     

Measuring institutional research productivity for the life sciences: the importance of accounting for the order of authors in the byline

Accurate measurement of institutional research productivity should account for the real contribution of the research staff to the output produced in collaboration with other organizations. In the framework of bibliometric measurement, this implies accounting for both the number of co-authors and each individual’s real contribution to scientific publications. Common practice in the life sciences is to indicate such contribution through the order of author names in the byline. In this work, we measure the distortion introduced to university-level bibliometric productivity rankings when the number of co-authors or their position in the byline is ignored. The field of observation consists of all Italian universities active in the life sciences (Biology and Medicine). The analysis is based on the research output of the university staff over the period 2004–2008. Based on the results, we recommend against the use of bibliometric indicators that ignore co-authorship and real contribution of each author to research outputs.     

Local demand shocks and firms' survival: An application to the Italian economy during the Great Recession

The Italian economy is characterised by a large number of micro-firms and small firms and by a long-lasting gap between North and South Italy. Therefore, global shocks – such as the Great Recession – have had a heterogeneous impact at the local level: the collapse in private demand was unequally distributed across different products, services and regions. The aim of this paper, therefore, is to evaluate the impact of the Great Recession on firm's survival for the universe of Italian firms in the light of two relevant indicators: local exposure to crisis and local demand shocks. The results suggest a strong negative impact of local demand shocks on the survival of firms, whereas the degree of exposure to the crisis generally has a weak effect.     

Assessment of extreme and metocean conditions in the Maldives for OTEC applications

The Maldives is a group of tropical atolls, considered globally to be one of the most desirable holiday destinations. There is an urgent requirement to decrease their dependency on fossil fuels that are currently the main source of energy, and a number of renewable energy alternatives are being evaluated. Among these, due to the favorable oceanographic and bathymetric conditions, ocean thermal energy conversion (OTEC) systems represent a viable opportunity for clean and reliable power. However, the stresses the OTEC platform will need to endure during adverse environmental conditions are not well defined. The magnitude of these stresses will then have a direct influence on the design of the OTEC device. In order to overcome this uncertainty, this paper uses hindcast data sets from global weather and ocean models to assess the metocean conditions of the Maldives, with particular reference to extreme conditions. After selecting a suitable location for the deployment of the devices, return values calculated using the peaks‐over‐threshold (POT) methodology are estimated for wind, waves, and currents. The 100‐year return value for the significant wave height is found to be 4.5 m, with a joint occurrence of energy periods between 7.5 and 8.5 seconds, whereas the 100‐year return wind has a velocity of 17.8 m/s and the 100‐year return current of 1.9 m/s. The directionality of these extreme events is also considered, showing the southern and western sub‐quadrants as the prevailing sources, which provides essential information for positioning of the platform. Additional evaluations of tropical revolving storms (TRS) and variations in temperature and salinity patterns are also provided over a 1500‐m water column; temperature varies by approximately 24°C, and salinity by around 2 ppt, showing the suitability of OTEC platforms in the Maldives. This work is therefore of interest to offshore renewable energy stakeholders interested in developing a project in the Maldives or those conducting an analogous analysis in other locations.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

The proton iron-sulfur cluster environment of the [FeFe]-hydrogenase maturation protein HydF from Thermotoga neapolitana

[FeFe]-hydrogenases contain a complex [4Fe–4S]-2Fe cluster (H-cluster) and are able to efficiently reduce protons to H₂. Due to their potential exploitation for renewable energy production biotechnologies, significant efforts have been put into understanding the mechanisms driving the H-cluster assembly, which involves three conserved proteins. Among them, HydF works as scaffold upon which the H-cluster precursor is synthesized and carrier to deliver it to the hydrogenase, resulting in its activation. A FeS cluster binding sequence (CxHx₄₆-₅₃HCxxC) is conserved in all HydF proteins and should in principle provide four ligands to coordinate the Fe atom. However, we found that alternative metal coordination may exist in different HydF proteins and that only the three cysteines are strictly required, whereas the fourth ligand may vary and is, in any case, readily exchangeable. In this work we analyzed by EPR/HYSCORE the FeS cluster proton environment of HydF from Thermotoga neapolitana to determine the possible role of surrounding residues in the non-cysteinyl iron ligation of the protein.