Carcass traits and meat quality of male lambs fed monensin or lasalocid

Male lambs, crossbred Merino × Ille de France, were fed a diet supplemented with 31 mg monensin or 32 mg lasalocid per kg of feed dry matter from an initial body weight of 23·6–23·9 kg to the slaughter weight of approximately 40 kg.

Carcass traits and meat quality were evaluated after slaughter; the values obtained were compared with those of the control group fed the same diet without the ionophore supplement.

There were few significant differences among the individual groups in carcass value, sensoric and technological properties of meat and of its composition. However, the lasalocid-fed group seemed to be somewhat better in muscling and in the composition of meat.     

Honest confidence regions and optimality in high-dimensional precision matrix estimation

We propose methodology for estimation of sparse precision matrices and statistical inference for their low-dimensional parameters in a high-dimensional setting where the number of parameters p can be much larger than the sample size. We show that the novel estimator achieves minimax rates in supremum norm and the low-dimensional components of the estimator have a Gaussian limiting distribution. These results hold uniformly over the class of precision matrices with row sparsity of small order \(\sqrt{n}/\log p\) and spectrum uniformly bounded, under a sub-Gaussian tail assumption on the margins of the true underlying distribution. Consequently, our results lead to uniformly valid confidence regions for low-dimensional parameters of the precision matrix. Thresholding the estimator leads to variable selection without imposing irrepresentability conditions. The performance of the method is demonstrated in a simulation study and on real data.     

Identifying the Criteria Set for Multicriteria Decision Making Based on SWOT/PESTLE Analysis: A Case Study of Reconstructing A Water Intake Structure

This paper proposes an approach for defining the criteria set required for multicriteria decision making. An approach is developed for a specific class of water management problems, and a SWOT/PESTLE analysis is recommended for identifying the internal and external factors that influence a given water system. The factors are grouped into six categories: political, economic/financial, social, technical, legal, and environmental (PESTLE), and separated afterwards according to their positive or negative influence on the system. All factors are filtered by a proposed elimination algorithm to identify the non-inferior factors and declare them as candidates for inclusion into the criteria set. An approach is applied to the real-life problem of how to define the criteria set and enable the selection of the optimal reconstruction solution of a water intake structure within a regional hydro-system in Vojvodina Province, Serbia. To verify the developed approach, an independent expert is invited to asses all factors identified by the SWOT/PESTLE analysis using the analytic hierarchy process (AHP) and to rank factors by order of preference. The outcome is satisfactory because the seven top-ranked factors from AHP completely matched the list of factors derived from the elimination algorithm.     

Review of adaptation mechanisms for data-driven soft sensors

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In order to be able to provide a comprehensive overview of the adaptation techniques, adaptive soft sensing methods are reviewed from the perspective of machine learning theory for adaptive learning systems. In particular, the concept drift theory is exploited to classify the algorithms into three different types, which are: (i) moving windows techniques; (ii) recursive adaptation techniques; and (iii) ensemble-based methods. The most significant algorithms are described in some detail and critically reviewed in this work. We also provide a comprehensive list of publications where adaptive soft sensors were proposed and applied to practical problems. Furthermore in order to enable the comparison of different methods to standard soft sensor applications, a list of publicly available data sets for the development of data-driven soft sensors is presented.     

Comprehensive modeling of optoelectronic nanostructures

This paper gives an overview of physics-based modeling of optoelectronic nanostructures, driven by diverse applications such as photovoltaics, solid-state lighting, communications and sensing. Despite this broad field of applications, some common challenges can be identified: accurate modeling of light-matter interaction, semi-coherent carrier transport in the presence of strong recombination, calculation of material properties and electromagnetic characteristics. In this contribution, the general purpose simulation framework tdkp/AQUA/LUMI is presented, with the focus on non-planar nanowire devices.     

Acid‐formed pectin gel delays major incomplete kiwi fruit allergen Act c 1 proteolysis in in vitro gastrointestinal digestion

BACKGROUND: It is thought that food sensitisers must be able to reach the intestine in order to sensitise patients. Pectin is a gel‐forming plant polysaccharide that can protect allergens from in vivo gastric digestion and in vitro pepsin digestion. The aim of this study was to examine if pectin gel formed in the acidic environment of the stomach can protect labile allergen from in vitro gastrointestinal digestion. RESULTS: Pectin forms a gel in the acidic conditions of gastric fluid up to a concentration of 1.0 ± 0.14 g L^?1. Four allergenic fruits (kiwi, cherry, apple and banana) form gels in the same manner at the dilutions 14.8 ± 0.4; 8.4 ± 0.2, 9.4 ± 0.35 and 29.1 ± 0.2, respectively. The time necessary for dissolution of 50 g L^?1 pectin gel in intestinal fluid was found to be 70 ± 0.2 min. Pectin gel formed in situ was able to protect Act c 1 from pepsin digestion for 1 h and from further intestinal digestion for one additional hour. CONCLUSION: Pectin gel in an acidic environment protects Act c 1 from pepsin digestion and dissolves slowly in the slightly basic environment of the intestine allowing the survival of fruit allergen for additional time and possible interaction with the gut immune system. Copyright © 2008 Society of Chemical Industry     

Removal of N-terminal peptides from β-lactoglobulin by proteolytic contaminants in a commercial phenol oxidase preparation

The use of enzymes may improve the functional properties of various food ingredients. The aim of this study was to examine the effects of proteolytic contaminants in phenol oxidases on β-lactoglobulin (BLG). In the presence of Trametes versicolor laccase and Agaricus bisporus tyrosinase, both variants of BLG (A and B) underwent removal of a peptide from the N-terminus. The truncated forms were more susceptible to digestion by pepsin. The truncation of BLG resulted from contaminating proteases and not due to the action of phenol oxidases. The removal of N-terminal peptides proceeded quickly, while the rest of the globular protein remained resistant to proteolysis for up to 3 h. In the case of the application of enzymes in food bioprocessing, it may be important to carefully monitor the effects of contaminating proteases in enzyme preparations used.     

Chaos-Based transmitted-reference ultra-wideband communications

Transmitted-reference (TR) ultra-wideband (UWB) communication systems have gained increasing popularity for the usage in the low data rate application, due to its non-coherent receiver structure. In conventional TR system, non-coherency at the receiver is achieved by sending reference pulses prior to the data-bearing pulses. Then, at the receiver side, reference pulses are used as template signals for correlation with data-bearing pulses. Therefore, the orthogonality between reference and data pulses is obtained in time division multiple access (TDMA) fashion. However, the implementation of a wideband delay line is very difficult in the current low power integrated circuits. In this paper, a TR method called Chaos-Based TR (CB-TR) is proposed. In the proposed method, chaotic sequences are used to separate the reference and data pulses. Such approach exploits the benefits of chaotic signals, such as non-periodicity, easy-to-generate, impulse-like autocorrelation value and low cross-correlation value. Furthermore, in order to decrease the influence of some negative properties of conventional chaotic maps, a modified chaotic generator (MCS) is proposed. Simulation results over the IEEE 802.15.4a channel model show comparable bit error rate performance to other TR methods.     

Amino acid composition of meat, fatty acid composition of fat and content of some chemical elements in the tissues of male lambs fed monensin or lasalocid

Tissues of slaughtered male lambs fed for 56 days on a feed mixture, supplemented with either monensin or lasalocid, were analysed and compared with tissues of control lambs fed similarly but without ionophore supplement.

The analyses carried out were for the amino acid composition of meat, fatty acid composition of intramuscular and perirenal fat, and for some chemical elements in meat, liver and kidneys.

In meat of both supplemented groups of lambs a significant increase in tyrosine was observed.

In the fat tissues of monensin-fed lambs a higher proportion of odd-carbon acid (heptadecanoic acid) was observed; differences in some other fatty acids among the groups were also found.

Some significant differences in the chemical elements analysed were observed, particularly in liver, few in kidneys and none in the muscle of ionophore-fed groups.

The quality of meat, as judged by the analyses for the substances mentioned, was little changed by supplementation of monensin or lasalocid in the feed of male lambs.     

Stream water temperature prediction based on Gaussian process regression

The prediction of stream water temperature presents an interesting topic since the water temperature has a significant ecological and economical role, such as in species distribution, fishery, industry and agriculture water exploitation. The prediction of stream water temperature is usually based on appropriate mathematical model and measurements of different atmospheric factors. In this paper, a probabilistic approach to daily mean water temperature prediction is proposed. The resulting model is a combination of two Gaussian process regression models where the first model describes the long-term component of water temperature and the other model describes the short-term variations in water temperature. The proposed approach is developed even further by modeling the short-term variations with multiple Gaussian process regression models instead with a single one. Apart from that, variable selection procedure based on mutual information is presented which is suitable for input variable selection when nonlinear models for stream water prediction are developed. The proposed approach is compared with traditional modeling approaches on the measurements obtained on the Drava river in Croatia. The presented methodology can be used as a basis of the predictive tools for water resource managers.