Iskorištavanje hrane, metaboliti u krvi i ponašanje pri unosu hrane u teladi sahival pasmina odabrane s obzirom na visoki ili niski ostatni unos hrane

This study aimed to evaluate differences in feed utilization between low and high residual feed intake (RFI) in Sahiwal calves by comparing performance, ingestive behavior and blood metabolites. Eighteen, growing, female Sahiwal calves (aged 10-14 months; body weight (BW) 100-125 kg) were fed ad libitum on a total mixed ration for 90 d. RFI varied from -0.53 to 0.40 kg dry matter (DM)/d with a mean RFI of -0.27 to 0.17 kg DM/d in low and high RFI Sahiwal calves, respectively. Calves with low RFI consumed 26% less DM and required 35% less metabolizable energy for body maintenance (MEm) compared to high RFI, yet gained at a similar rate. Low RFI calves digest feed more efficiently than less efficient calves. Conventional efficiency measures also showed better efficiency in low RFI than high RFI calves. Low RFI calves spent less time in feeding, rumination, and chewing. Higher plasma concentrations of insulin-like growth factor-1 (IGF-1), growth hormone (GH), and creatinine, and lower concentrations of albumin, plasma urea nitrogen (PUN), and triglycerides were observed in the low RFI group than the high RFI group. However, plasma total protein, glucose, cholesterol, non esterified fatty acid (NEFA), beta-hydroxy butyric acid (BHBA), calcium (Ca), and phosphorus (P) concentrations were similar in both groups. In summary, low RFI calves utilized feed more efficiently by spending less time and energy in feeding, and the variability in blood metabolites might be due to differences in body metabolism.Ovo istraživanje imalo je za cilj, na temelju proizvodnje, ponašanja kod unosa hrane i metabolita u krvi, procijeniti razlike u iskorištavanju hrane između sahival teladi s niskim ostatnim unosom hrane i visokim ostatnim unosom hrane (Residual Feed Intake - RFI). Osamnaest sahival teladi ženskog spola (u dobi od 10 do -14 mjeseci i tjelesnoj masi od 100 do 125 kg) hranjeno je 90 dana, ad libitum, kompletnim mješovitim obrokom. Ostatni unos hrane kretao se od -0,53 do 0,40 kg suhe tvari/d, sa srednjom vrijednošću od -0,27 kod sahival teladi s niskim ostatnim unosom i srednjom vrijednošću od 0,17 kg kod sahival teladi visokim ostatnim unosom hrane. Iako je telad s niskim ostatnim unosom hrane u odnosu na onu s visokim ostatnim unosom hrane konzumirala 26% manje suhe tvari i zahtijevala 35 % manje uzdržne energije za metabolizam tijela, prirast obje skupne teladi kretao se po sličnoj stopi. Telad s niskim ostatnim unosom hrane imala je učinkovitiju hranidbu što su pokazali i standarni pokazatelji prema kojima je ta telad provela hranidbu u kraćem vremenu, uz kraće žvakanje i preživanje. U usporedbi s teladi koja ima viši ostatni unos hrane, telad s niskim ostatnim unosom hrane imala je u plazmi veće koncentracije inzulinu-sličnog faktora rasta-1 (IGF-1), hormona rasta (GH) i kreatinina, te niže koncentracije albumina, dušika iz ureje i triglicerida. Koncentracije ukupnih proteina, glukoze, kolesterola, neesterificirane masne kiseline (NEFA), betahidroksi maslačne kiseline (BHBA), kalcija (Ca) i fosfora (P) bile su slične u obje skupine teladi. Sažeto, telad s niskim ostatnim unosom hrane iskorištavala je hranu učinkovitije, provodeći kraće vrijeme i trošeći manje energije prilikom hranjenja, a varijacije metabolita u krvi mogle bi biti posljedica razlika u metabolizmu

Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

Working with Young People Who Offend : An Examination of the Literature Regarding Violence, Substance Misuse and Harmful Sexual Behaviour

This paper presents a review of the recent literature relating to effective practice with young people displaying harmful sexual behaviour (HSB), violence or risky substance misuse. The intention is to build upon and update the 2007 literature review Research and practice in risk assessment and risk management of children and young people engaging in offending behaviour, funded by the Risk Management Authority (RMA) and carried out by the Scottish Centre for Crime and Justice Research (SCCJR)