Nanostructured Metallo‐Dielectric Quasi‐Crystals: Towards Photonic‐Plasmonic Resonance Engineering

The first evidence of out‐of‐plane resonances in hybrid metallo‐dielectric quasi‐crystal (QC) nanostructures composed of metal‐backed aperiodically patterned low‐contrast dielectric layers is reported. Via experimental measurements and full‐wave numerical simulations, these resonant phenomena are characterized with specific reference to the Ammann‐Beenker (quasi‐ periodic, octagonal) tiling lattice geometry and the underlying physics is investigated. In particular, it is shown that, by comparison with standard periodic structures, a moderately richer spectrum of resonant modes may be excited, due to the easier achievement of phase‐matching conditions endowed by its denser Bragg spectrum. Such modes are characterized by a distinctive plasmonic or photonic behavior, discriminated by their field distribution and dependence on the metal film thickness. Moreover, the response is accurately predicted via computationally affordable periodic‐approximant‐based numerical modeling. The enhanced capability of QCs to control number, spectral position, and mode distribution of hybrid resonances may be exploited in a variety of possible applications. To assess this aspect, label‐free biosensing is studied via characterization of the surface sensitivity of the proposed structures with respect to local refractive index changes. Moreover, it is also shown that the resonance‐engineering capabilities of QC nanostructures may be effectively exploited in order to enhance the absorption efficiency of thin‐film solar cells.     

A genetic approach to joint routing and link scheduling for wireless mesh networks

Wireless mesh networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self-organized networks in places where wired infrastructures are not available or not deemed to be worth deploying. This paper investigates the joint link scheduling and routing issues involved in the delivery of a given backlog from any node of a wireless mesh network towards a specific node (which acts as a gateway), within a given deadline. Scheduling and routing are assumed to be aware of the physical interference among nodes, which is modeled in the paper by means of a signal-to-interference ratio. Firstly, we present a theoretical model which allows us to formulate the task of deriving joint routing and scheduling as an integer linear programming problem. Secondly, since the problem cannot be dealt with using exact methods, we propose and use a technique based on genetic algorithms. To the best of our knowledge, these algorithms have never been used before for working out these kinds of optimization problems in a wireless mesh environment. We show that our technique is suitable for this purpose as it provides a good trade-off between fast computation and the overall goodness of the solution found. Our experience has in fact shown that genetic algorithms would seem to be quite promising for solving more complex models than the one dealt with in this paper, such as those including multiple flows and multi-radio multi-channels.     

Frequency recovery techniques for TM/TC satellite modem in critical scenarios

Reliability and effectiveness are essential features of satellite transceivers for telemetry and telecommand applications. Modem performance has a strong impact on the success of a satellite mission, in particular, during critical scenarios as the early operation phase, the disposal of a satellite at the end of its life, or the deep‐space missions. In these specific mission critical scenarios, fast and correct data reception is even more important than high channel capacity. An unknown and fast variable channel condition, which can be caused by uncertain spacecraft attitude and large Doppler shift with respect to the data rate, requires efficient and innovative receiver architecture. This paper introduces a complete digital implementation of a transceiver for TM/TC application in low Earth orbit mission that is perfectly compliant with aforementioned requirements. Particular attention is dedicated to the definition and selection of the most appropriate frequency recovery technique; 2 open‐loop techniques that are derived from ML optimal estimator are presented and compared. Additionally, the performance of the proposed receiver is extensively studied and compared with an incoherent technique that is based on the double differential PSK modulation and is known to be suitable for sat‐com in critical scenarios.     

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.     

Short communication: Comparison of growth kinetics at different temperatures of Streptococcus macedonicus and Streptococcus thermophilus strains of dairy origin

Within the genus Streptococcus, S. thermophilus and S. macedonicus are the 2 known species related to foods. Streptococci are widely used as starter cultures to rapidly lower milk pH. As S. macedonicus has been introduced quite recently, much less information is available on its technological potential. Because temperature is an important factor in fermented food production, we compared the growth kinetics over 24 h of 8 S. thermophilus and 7 S. macedonicus strains isolated from various dairy environments in Italy, at 4 temperatures, 30°C, 34°C, 37°C and 42°C. We used the Gompertz model to estimate the 3 main growth parameters; namely, lag phase duration (λ), maximum growth rate (µ_max), and maximum cell number at the stationary phase (N_max). Our results showed significant differences in average growth kinetics between the 2 species. Among the strains tested, 37°C appeared to be the optimal temperature for the growth of both species, particularly for S. macedonicus strains, which showed mean shorter lag phases and higher cell numbers compared with S. thermophilus. Overall, the growth curves of S. macedonicus strains were more similar to each other whereas S. thermophilus strains grew very differently. These results help to better define and compare technological characteristics of the 2 species, in view of the potential use of S. macedonicus in place of S. thermophilus in selected technological applications.     

Soft-Seeded Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L.) Varieties: Preliminary Characterization and Quality Changes of Minimally Processed Arils During Storage

A preliminary characterization of two new soft-seeded pomegranate varieties (MR-100® and KINGDOM®) based on their main physico-chemical and nutritional parameters was reported. The two varieties showed significant differences (p ≤ 0.05) in polyphenols, anthocyanins and antioxidant activity. Kingdom pomegranate had higher polyphenols (2524.73 mg GAE/L), anthocyanins (752.49 mg C₃gE/L) and antioxidant activity (EC₅₀ 13.58 μL/mL) than MR-100 (1792.74 mg GAE/L, 141.29 mg C₃gE/L and EC₅₀ 47.53 μL/mL, respectively). Moreover, minimally processed arils of the two varieties were packaged in semipermeable and micro-perforated film at 5 °C, and the quality changes that occurred during storage condition (15 days) were investigated. During storage, Kingdom arils exhibited a better performance in terms of antioxidant activity, polyphenols and anthocyanin content with respect to MR-100. Furthermore, the packaging systems did not affect the estimated quality parameters for both varieties. Based on the sensory evaluation and microbial counts, both aril varieties reached, at 15-day storage, suitable values for commercial purpose.     

On-road emissions of light-duty vehicles in europe

For obtaining type approval in the European Union, light-duty vehicles have to comply with emission limits during standardized laboratory emissions testing. Although emission limits have become more stringent in past decades, light-duty vehicles remain an important source of nitrogen oxides and carbon monoxide emissions in Europe. Furthermore, persisting air quality problems in many urban areas suggest that laboratory emissions testing may not accurately capture the on-road emissions of light-duty vehicles. To address this issue, we conduct the first comprehensive on-road emissions test of light-duty vehicles with state-of-the-art Portable Emission Measurement Systems. We find that nitrogen oxides emissions of gasoline vehicles as well as carbon monoxide and total hydrocarbon emissions of both diesel and gasoline vehicles generally remain below the respective emission limits. By contrast, nitrogen oxides emissions of diesel vehicles (0.93 ± 0.39 grams per kilometer [g/km]), including modern Euro 5 diesel vehicles (0.62 ± 0.19 g/km), exceed emission limits by 320 ± 90%. On-road carbon dioxide emissions surpass laboratory emission levels by 21 ± 9%, suggesting that the current laboratory emissions testing fails to accurately capture the on-road emissions of light-duty vehicles. Our findings provide the empirical foundation for the European Commission to establish a complementary emissions test procedure for light-duty vehicles. This procedure could be implemented together with more stringent Euro 6 emission limits in 2014. The envisaged measures should improve urban air quality and provide incentive for innovation in the automotive industry.