The objective of this study was to determine if feeding behavior traits are correlated with performance and feed efficiency traits in growing heifers. Individual dry matter intake (DMI) was measured in Brangus heifers (n = 115) fed a roughage-based diet (ME = 2.1 Mcal/kg) for 70 d using Calan gate feeders (6 heifers/pen). Residual feed intake (RFI) was computed as the residuals from linear regression of DMI on mid-test BW0.75 and average daily gain (ADG). Heifers with the highest (n = 18) and lowest (n = 18) RFI were identified for feeding behavior measurements. During days 28 through 56 of the 70-d feeding trial, continuous video recordings were obtained for all heifers. Video images of two sets of four 24-h periods, two weeks apart, were analyzed for the focal animals. All occurrences of feeding were timed and counted per day, and the eight 24-h periods averaged to derive the overall feeding event (FE) and meal duration and frequency for each focal heifer. Total feeding event duration was defined as the total min per day the animal’s head was down in the feed bunk. A meal included all visits an animal made to the feed bunk that were separated by less than 5 min. The mean RFI values for the low and high RFI heifers were (mean ± SE) - 1.03 and 1.00 ± 0.03 kg/d, respectively. Low RFI heifers consumed 21.9% less (P < 0.0001) DMI, but had similar BW and ADG compared to high RFI heifers. Heifers with low RFI spent more time (P < 0.0001) eating (152 vs 124 ± 4.26 min/d) at a lower eating rate (62.8 vs 99.6 ± 3.28 g/min), but had similar FE frequencies compared to high RFI heifers. Feeding event duration was negatively correlated with RFI while FE frequency and FE eating rate were positively correlated with RFI. However, meal duration and frequency were not correlated with RFI. Therefore, measuring FE characteristics could prove more useful than analyzing meals when trying to predict RFI. Additionally, eating rate appeared to be more closely related to RFI than any of the other feeding behavior traits measured.

No description available.

Certaines données satellitaires ne sont pas utilisées à cause des acquisitions bruitées qui ne reflètent pas les distributions des grandeurs géophysiques du sol, telle que l’humidité du sol. La cause primordiale dans les micro-ondes passives vient des interférences radio fréquence (RFI). Ainsi, les températures apparentes mesurées par un satellite comme SMOS par exemple atteignent souvent des valeurs qui conduisent à des échecs d’inversion de l’humidité du sol. L’objectif de notre projet est d’étudier le phénomène des RFI à petite échelle, son impact sur les micro-ondes passives en bande L à partir des mesures au sol réalisées à l’aide de radiomètres. Une fois l’impact caractérisé de manière rigoureuse, une méthode de filtrage adaptatif a été développée pour corriger les effets. Le projet est composé de trois parties principales. La mise en place d’une expérimentation est réalisée afin de faire des mesures au sol à l’aide de deux radiomètres en bande L. Les mesures sont faites dans des conditions variables et plusieurs scénarios ont été considérés. Ensuite, les données sont collectées et analysées. Cette phase a abouti au développement d’un filtre qui permet d’atténuer l’effet des RFI sur les températures de brillance bruitées. Enfin, le filtre proposé dans le projet a été appliqué sur des données aéroportées en bande L prises sur le site Boreal Ecosystem Research and Monitoring Sites (BERMS) en Saskatchewan. L’expérimentation s’est déroulée à la station SIRENE de l’Université de Sherbrooke. Les instruments ont été mis en place et les radiomètres ont été calibrés en premier lieu pour s’assurer de la fiabilité des mesures. L’émetteur a servi comme une source d’interférence pour les radiomètres. Il était placé à des positions différentes vis-à-vis de ces derniers, et émettait à des puissances variables. Les différents scénarios considérés étaient utiles pour étudier l’effet de la position de la source RFI, ainsi que l’effet de la puissance émise par celle-là sur les températures mesurées par les radiomètres. Pour les mesures, nous avons utilisé un radiomètre multi-bandes qui nous a permis d’étudier l’impact de la bande passante sur les RFI. L’analyse et le traitement des données prises ont conduit au développement d’un filtre coupe-bande permettant de corriger les températures bruitées lorsque les caractéristiques du bruit sont connues. Ce filtre a été appliqué sur des données aéroportées bruitées. Le bruit a pu être atténué pour les températures en polarisation V. Les résultats de l’application du filtre sont satisfaisants dans l’ensemble malgré le volume important de données bruitées sur la zone d’étude. En ce qui concerne les données de la polarisation H, elles n’ont pu être corrigées, car elles étaient presque entièrement bruitées. Le mémoire porte sur une expérimentation originale, car les expériences du genre sont très rares dans la littérature. L’étude s’appuie sur deux radiomètres en bande L, ce qui est très particulier, compte tenu de la rareté de ces instruments.

Physiological traits that are biologically associated with feed efficiency may be useful indicator traits residual feed intake (RFI). The objective of this study was to examine the relationships between RFI, temperament, serum hormones and serum metabolites in growing heifers. A 4 yr study (n = 114-119 heifers/yr) was conducted with Brangus heifers (Initial BW = 271 ± 26 kg) that were weaned for 25.5 ± 8.6 d prior to high roughage diet adaptation (ME = 2.0 Mcal/kg DM). Individual dry matter intakes (DMI) were measured using Calan gate feeders and BW measured at 7-d intervals during the 70-d studies. RFI was calculated as the residual from the linear regression of DMI on mid-test BW0.75 and average daily gain (ADG). Temperament scores and exit velocity (EV) were taken at 0-d. Temperament index (TI) was calculated as the average of EV and chute score. On 0-d, blood samples were collected and assayed for partial blood counts (WBC, RBC, hemoglobin, HB), metabolites (total protein, TP; glucose; creatinine; blood urea nitrogen, BUN; β-hydroxybutyrate, BHB) and hormones (cortisol; insulin-like growth hormone I, IGF-I). Across all heifers, RFI was positively correlated with DMI (0.70) and feed:gain (0.59). Heifers with low RFI (< 0.5 SD from mean RFI 0.00 ± 0.71 kg/d) consumed 16 percent less DMI and had 16 percent lower feed:gain than heifers with high RFI (> 0.50 SD from mean RFI). RFI was weakly correlated (P < 0.05) with WBC (0.15), HB (-0.11), total protein (-0.10), BUN (0.10), creatinine (-0.11) and BHB (0.13). Hemoglobin and BHB were weakly correlated with all feed efficiency traits except feed conversion ratio (FCR). No phenotypic correlation was found between cortisol and IGF-I with RFI. Temperament was not correlated with RFI. Cortisol, creatinine and glucose were moderately correlated with all temperament traits. Low TI heifers (calm) had significantly higher Final BW, ADG and DMI than high TI heifers. Calm animals had significantly lower cortisol, HB, creatinine and glucose and higher BHB. These results suggest that the temperament and serum metabolites evaluated in this study have limited utility as indicator traits for RFI in growing heifers.

No description available.

Feed efficiency and metabolism affect profitability of the various components of the beef industry by modulating distribution and use of nutrients within cattle. Separate studies were conducted to determine the 1) repeatability of feed efficiency measurements over time as beef heifers mature into cows, and 2) whether the production and regulation of glucose in heifers is affected by temperament. The influence of temperament on glucoregulatory hormones was studied in Angus crossbred heifers and Brahman heifers whose temperament was determined at weaning. The 6 most calm and 6 most temperamental heifers of each breed were fitted with jugular cannulas. Blood was collected at cannulation and then via the cannula during a 90-min rest period. Following 90 min, dextrose was infused (0.5 mg/kg BW) and blood samples were collected at specific intervals for 3 h total. In the crossbred heifers cortisol (P = 0.0560) and glucose (P = 0.0485) concentrations during the challenge were higher in temperamental relative to calm crossbred heifers. Insulin concentrations tended (P = 0.0737) to be higher in temperamental crossbred heifers. Cortisol (P = 0.0282) and glucose (P = 0.0011) concentrations were significantly higher in temperamental Brahman heifers. Insulin concentrations tended (P = 0.0793) to be greater for calm Brahman heifers. Temperamental cattle had a greater HPA axis response, which led to greater concentrations of cortisol and glucose, possibly because the glucose was being utilized differently by the temperamental cattle. Mature Brahman cow feed efficiency data was collected over two years, on two different cohorts of cows that had previous residual feed intake data as post-weaning heifers. In 2009 and 2010, 37 and 41 cows, respectively, in their first trimester of gestation were evaluated for RFI via the Calan gate system. Cows were fed 2.6% BW for 70 d with BW recorded weekly. Cows were classified according to their RFI values as either efficient or inefficient. Heifer RFI was not correlated to mature cow RFI based on assessment of the Pearson‟s correlation coefficient (r = -0.06, P = 0.57). This study indicates that establishment of RFI in heifers may not accurately predict their feed efficiency as mature cows.

Improving feed efficiency has become a top priority in beef cattle production because of the rapidly increasing cost of feed provision. However, because of the expense associated with collecting individual animal feed intake data, only a relatively small number of animals have been tested, leading to low accuracies of estimated breeding values (EBV). Three studies were conducted to demonstrate the usefulness of including DNA marker information in RFI genetic evaluations. In the first study, the effect of period of testing on RFI was assessed. Beef cattle steers were tested for feed intake, with different cohorts tested in the fall-winter and winter-spring seasons. Seasonal differences were detected although these were confounded by differences in age and weight among the seasons. Additionally, mean EBV accuracy obtained was low, ranging between 0.47 and 0.51, implying that strategies to increase this accuracy are necessary. In the 2nd study, a suite of genetic markers predictive of RFI, DMI and ADG were pre-selected using single marker regression analysis and the top 100 SNPs analyzed further in 5 replicates of the training data to provide prediction equations for RFI, DMI and ADG. Cumulative marker phenotypes (CMP) were used to predict trait phenotypes and accuracy of prediction ranged between 0.007 and 0.414. Given that this prediction accuracy was lower than the polygenic EBV accuracy, the CMP would need to be combined with EBV for effective marker assisted selection. In study 3, genomic selection (GS) theory and methodology were used to derive genomic breeding values (GEBV) for RFI, DMI and ADG. The accuracy of prediction obtained with GEBV was low, ranging from 0.223 to 0.479 for marker panel with 200 SNPs, and 0.114 to 0.246 for a marker panel with 37,959 SNPs, depending on the GS method used. The results from these studies demonstrate that the utility of genetic markers for genomic prediction of RFI in beef cattle may be possible, but will likely be more effective if a tool that combines GEBV with traditional BLUP EBV is used for selection. / Animal Science

Improving feed efficiency has become a top priority in beef cattle production because of the rapidly increasing cost of feed provision. However, because of the expense associated with collecting individual animal feed intake data, only a relatively small number of animals have been tested, leading to low accuracies of estimated breeding values (EBV). Three studies were conducted to demonstrate the usefulness of including DNA marker information in RFI genetic evaluations. In the first study, the effect of period of testing on RFI was assessed. Beef cattle steers were tested for feed intake, with different cohorts tested in the fall-winter and winter-spring seasons. Seasonal differences were detected although these were confounded by differences in age and weight among the seasons. Additionally, mean EBV accuracy obtained was low, ranging between 0.47 and 0.51, implying that strategies to increase this accuracy are necessary. In the 2nd study, a suite of genetic markers predictive of RFI, DMI and ADG were pre-selected using single marker regression analysis and the top 100 SNPs analyzed further in 5 replicates of the training data to provide prediction equations for RFI, DMI and ADG. Cumulative marker phenotypes (CMP) were used to predict trait phenotypes and accuracy of prediction ranged between 0.007 and 0.414. Given that this prediction accuracy was lower than the polygenic EBV accuracy, the CMP would need to be combined with EBV for effective marker assisted selection. In study 3, genomic selection (GS) theory and methodology were used to derive genomic breeding values (GEBV) for RFI, DMI and ADG. The accuracy of prediction obtained with GEBV was low, ranging from 0.223 to 0.479 for marker panel with 200 SNPs, and 0.114 to 0.246 for a marker panel with 37,959 SNPs, depending on the GS method used. The results from these studies demonstrate that the utility of genetic markers for genomic prediction of RFI in beef cattle may be possible, but will likely be more effective if a tool that combines GEBV with traditional BLUP EBV is used for selection. / Animal Science

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Currently, the construction industry is characterized by a high competition between its companies. Due to this, companies must guarantee that the performance of their projects is possible by using an appropriate management system which allows an adequate planning of activities, since selecting an inadequate system can cause serious inconveniences such as having rework and increase cost and time in projects. Therefore, the construction industry is constantly searching for management models that can minimize the previously mentioned inconveniences in order to differentiate themselves and take advantage of their competitors. Precisely, the Scrum framework, conceived in the software engineering industry, is an alternative to traditional projects management, offering an exponential improvement in communication for project teams and improving the planning of activities, through an iterative framework that allows flexibility for changing requirements, reduces uncertainty and creates reliability for what was planned. Thus, this research adapts and applies the Scrum framework within a construction project, optimizing considerably the execution time.

The evolution from the fifth generation (5G) networks to 6G promises to revolution- ize connectivity, supporting a vast array of applications from high-definition video streaming and immersive augmented reality experiences to critical machine-type communications. However, this progression brings along the challenge of efficiently managing the limited radio spectrum (RF) resources to accommodate the diverse quality of service (QoS) requirements of a variety of service and user types. Another problem gaining traction with the advances of wireless communications technology is the coexistence between active communication systems and passive RF sensing operating in the same or adjacent bands. Central to addressing these challenges is the proposed application of deep reinforcement learning (DRL), which emerges as a tool for adaptive and intel- ligent radio resource management (RRM) in the face of the increasingly complex and dynamic RF system requirements. This dissertation investigates the application of DRL for service, user, and network management of advanced wireless networks operating in dedicated and shared spectrum. Through a series of innovative DRL-based frameworks and solutions to a variety of emerging RRM problems, this work contributes to the optimization of spectrum, transmission power, and band- width allocation, as well as network configuration. We contribute to the integration of cutting-edge technologies such as unmanned aerial vehicles as aerial base stations, reconfigurable intelligent sur- faces, and multi-user multiple input, multiple output systems for a seamless user experience. The core of the dissertation explores how DRL can adaptively manage spectrum resources that satisfy the QoS requirements of different 5G service classes, specifically enhanced mobile broadband and ultra-reliable low-latency communications, while also facilitating the integration of terrestrial and aerial network nodes to enhance coverage and capacity. This dissertation further extends into the domain of coexistence between active wireless communication systems and passive remote sensing technologies. We collect radiometric measurement data in a custom-built software-defined radio testbed for which we design different 5G downlink transmission patters and data sets. Based on the collected and processed data from the testbed’s radiometer, we propose a DRL-based strategy to manage 5G communications while reducing the RF interference impact on co-channel radiometric measurements. Through simulations, the proposed solution demonstrates the tradeoffs between communications and sensing operations in terms of common wireless network performance met- rics, such as sum data rate and user fairness, and brightness temperature readings obtained by the radiometer.