Stability analysis of an adaptive packet access scheme for mobile communication systems with high propagation delays

In this paper, we investigate a packet access scheme that is able to support mixed traffics in the presence of high propagation delays. Referring to a Time‐Code Division Multiple Access air interface, we propose a Medium Access Control (MAC) protocol based on a random access scheme. A successful attempt grants the use of a slot‐code resource. This protocol is named Adaptive Time Code‐Packet Reservation Multiple Access (ATC‐PRMA), since the access parameters are changed, depending on the traffic load conditions, so as to fulfil Quality of Service requirements. Numerical examples are carried out for the Low Earth Orbit (LEO)‐Mobile Satellite System (MSS) scenario, but all these considerations could be applied to High‐Altitude Platform Stations (HAPSs) as well. In both cases, high propagation delays prevent an immediate feedback to users. An analytical approach is proposed to study the stability of our MAC scheme. Accordingly, we define a criterion for optimizing system performance. The predicted ATC‐PRMA behaviour is supported by simulation results. Finally, we show the performance improvement of ATC‐PRMA with respect to a MAC protocol not employing adaptive parameters. Copyright © 2003 John Wiley & Sons, Ltd.     

Analysis of an absorption machine driven by the heat recovery on an I.C. reciprocating engine

The present work studies an absorption machine driven by the heat recovery on an internal combustion (i.c.) reciprocating engine. The thermal energy recovered from the i.c. engine exhaust is used to drive a double‐effect water–lithium bromide cycle, while the heat recovered from the cooling jacket of the engine drives a single‐effect water–lithium bromide cycle. The two absorption cycles are integrated into a single unit with a common evaporator and absorber. The absorption unit was first evaluated by a cycle analysis determining the sensitivity to the main boundary conditions and to the internal parameters. Then a specific simulation code of all the different devices of the absorption machine was developed to evaluate the real performance and size of the unit together with its operating condition limits. The absorption machine shows a coefficient of performance around 1, very close to the performance of a traditional double‐effect absorption chiller driven by steam or by a gas burner. The absorption unit could operate with cooling water inlet temperature lower than 35–36°C and refrigerated outlet temperature higher than 3°C. Copyright © 2005 John Wiley & Sons, Ltd.     

REMARKS ON HEAT AND WORK TRANSFERS IN SYSTEMS WITH UNIFORM INTENSIVE PROPERTIES

The differential forms of heat and work, owing to their dependence upon parameters characterizing the interaction between system and environment (and not only upon the state functions of the system), differ basically from the usual non-exact differentials.

The main aim of this paper is to examine these differences considering quasi-static irreversible processes with the uniformity of internal temperature and pressure. Besides, the specific heat is defined referring only to the properties of the system and to the path of the process and not to the actual heat transfer     

An effective preprocessor for structured FORTRAN: The HENTRAN system

In the paper the new HENTRAN preprocessor for structured FORTRAN is illustrated. The motivations and the goals of the project are first outlined. The extended FORTRAN language implemented through HENTRAN is then illustrated and its adequacy and flexibility for structured programming are discussed. The basic architecture of the HENTRAN translator is described and its main features concerning reliability, portability, and efficiency are discussed in comparison with other similar systems.     

Calcium: A comprehensive review on quantification,interaction with milk proteins and implications for processing of dairy products

Calcium (Ca) is a key micronutrient of high relevance for human nutrition that also influences the texture and taste of dairy products and their processability. In bovine milk, Ca is presented in several speciation forms, such as complexed with other milk components or free as ionic calcium while being distributed between colloidal and serum phases of milk. Partitioning of Ca between these phases is highly dynamic and influenced by factors, such as temperature, ionic strength, pH, and milk composition. Processing steps used during the manufacture of dairy products, such as preconditioning, concentration, acidification, salting, cooling, and heating, all contribute to modify Ca speciation and partition, thereby influencing product functionality, product yield, and fouling of equipment. This review aims to provide a comprehensive understanding of the influence of Ca partition on dairy products properties to support the development of kinetics models to reduce product losses and develop added-value products with improved functionality. To achieve this objective, approaches to separate milk phases, analytical approaches to determine Ca partition and speciation, the role of Ca on protein–protein interactions, and their influence on processing of dairy products are discussed.     

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.     

Effect of goat milk composition on cheesemaking traits and daily cheese production

Cheese yield is strongly influenced by the composition of milk, especially fat and protein contents, and by the efficiency of the recovery of each milk component in the curd. The real effect of milk composition on cheesemaking ability of goat milk is still unknown. The aims of this study were to quantify the effects of milk composition; namely, fat, protein, and casein contents, on milk nutrient recovery in the curd, cheese yield, and average daily yield. Individual milk samples were collected from 560 goats of 6 different breeds. Each sample was analyzed in duplicate using the 9-laboratory milk cheesemaking assessment, a laboratory method that mimicked cheesemaking procedures, with milk heating, rennet addition, coagulation, curd cutting, and draining. Data were submitted to statistical analysis; results showed that the increase of milk fat content was associated with a large improvement of cheese yield because of the higher recovery of all milk nutrients in the curd, and thus a higher individual daily cheese yield. The increase of milk protein content affected the recovery of fat, total solids, and energy in the curd. Casein number, calculated as casein-to-protein ratio, did not affect protein recovery but strongly influenced the recovery of fat, showing a curvilinear pattern and the most favorable data for the intermediate values of casein number. In conclusion, increased fat and protein contents in the milk had an effect on cheese yield not only for the greater quantity of nutrients available but also for the improved efficiency of the recovery in the curd of all nutrients. These results are useful to improve knowledge on cheesemaking processes in the caprine dairy industry.     

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.