The Effects of Biochar and Fat Supplementation on Microbial Fermentation in Batch Cultures

Biochar is a byproduct formed by burning green waste for carbon sequestering in a process called pyrolysis. This product can be used as a soil amendment to benefit plant yield. It has also been used as a supplement for cattle, though benefits in ruminants are still being explored. Hansen et al. (2012) noted a numerical decrease in methane production in vitro. Ruminants erupt methane as a hydrogen sink in the reduced rumen environment. Without a way to remove hydrogen, the microbial ecosystem cannot function normally. Polyunsaturated fatty acids have been used to decrease methane production; however, they often depress NDF digestibility. Therefore, more research is necessary to confirm that decreasing methane production with biochar does not also result from depressing neutral detergent fiber (NDF) digestibility, a major component of dairy cattle diets. The objectives of this study were to observe NDF disappearance (NDFD), volatile fatty acid (VFA) production, and methane gas output with supplementation of a biochar product in rumen fluid batch cultures. The treatments (Trt) were biochar (BC) or biochar bolus (BCB, biochar with electrolytes). The diet provided was a high forage (HF) diet with concentrate pellets (33.3%), orchard grass (44.4%), alfalfa (22.2%), and either no supplemented fat or 3% dry matter (DM) as corn oil (CO). The BC and BCB were dosed (Inc) at either 0, 1, 2 or 4% of total DM. Separately, four round bottom flasks were used for gas production measurements because smaller culture tubes would not produce enough gas volume. The flasks were fed either HF or HF with BC at 2%. Data were analyzed utilizing PROC MIXED (v. 9.4, SAS Institute 2015) with the fixed effects of Trt, CO, Inc, and their interactions. The random effects were run and order of inoculation. BC did not decrease NDFD and with 2% - CO and 1% + CO NDFD increased. BCB also did not decrease NDFD and with 1% - CO, 4% - CO, and 1% + CO NDFD increased (P = 0.07, Trt*CO*Inc). For total VFA production, BC increased the concentration with 2% - CO, 4% - CO, and 4% + CO. BCB also increased total VFA with 4% - CO and 4% + CO (P = 0.02, Trt*CO*Inc). Although methane gas production was not significant, there was numerical reduction of 23.08 mg produced in 24 hours (P = 0.16). Methane (g/kg NDFD) decreased (P = 0.022) by 17.21 g/kg NDFD. A numerical decrease (P = 0.23) of 0.10 mg/d was also seen in hydrogen gas production. Therefore, BC could reduce methane output without depressing NDFD and VFA when implemented as a feed additive. With the current stress on agricultural practices to decrease environmental impacts, feeding biochar as a methane mitigation strategy could be crucial to the dairy industry while simultaneously utilizing a waste product.No embargoAcademic Major: Animal Science

Solving headswitching translation cases in LFG-DOT

It has been shown that LFG-MT (Kaplan et al., 1989) has difficulties with Headswitching data (Sadler et al., 1989, 1990; Sadler & Thompson, 1991). We revisit these arguments in this paper. Despite attempts at solving these problematic constructions using approaches based on linear logic (Van Genabith et al., 1998) and restriction (Kaplan & Wedekind, 1993), we point out further problems which are introduced. We then show how LFG-DOP (Bod & Kaplan, 1998) can be extended to serve as a novel hybrid model for MT, LFG-DOT (Way, 1999, 2001), which promises to improve upon the DOT model of translation (Poutsma 1998, 2000) as well as LFG-MT. LFG-DOT improves the robustness of LFG-MT through the use of the LFG-DOP Discard operator, which produces generalized fragments by discarding certain f-structure features. LFG-DOT can, therefore, deal with ill-formed or previously unseen input where LFG-MT cannot. Finally, we demonstrate that LFG-DOT can cope with such translational phenomena which prove problematic for other LFG-based models of translation

Creditworthiness and Matching Principles

You are creditworthy for φ-ing only if φ-ing is the right thing to do. Famously though, further conditions are needed too – Kant’s shopkeeper did the right thing, but is not creditworthy for doing so. This case shows that creditworthiness requires that there be a certain kind of explanation of why you did the right thing. The reasons for which you act – your motivating reasons – must meet some further conditions. In this paper, I defend a new account of these conditions. On this account, creditworthiness requires that your motivating reasons be normative reasons, and that the principles from which you act match normative principles

A puzzle about enkratic reasoning

Enkratic reasoning—reasoning from believing that you ought to do something to an intention to do that thing—seems good. But there is a puzzle about how it could be. Good reasoning preserves correctness, other things equal. But enkratic reasoning does not preserve correctness. This is because what you ought to do depends on your epistemic position, but what it is correct to intend does not. In this paper, I motivate these claims and thus show that there is a puzzle. I then argue that the best solution is to deny that correctness is always independent of your epistemic position. As I explain, a notable upshot is that a central epistemic norm directs us to believe, not simply what is true, but what we are in a position to know

Edge functionalisation of graphene nanoribbons with a boron dipyrrin complex : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Nanoscience at Massey University, Manawatū, New Zealand

Chemical modification can be used to tune the properties of graphene and graphene nanoribbons, making them promising candidates for carbon-based electronics. The control of edge chemistry provides a route to controlling the properties of graphene nanoribbons, and their self-assembly into larger structures. Mechanically fractured graphene nanoribbons are assumed to contain oxygen functionalities, which enable chemical modification at the nanoribbon edge. The development of graphene nanoribbon edge chemistry is difficult using traditional techniques due to limitations on the characterisation of graphene materials. Through the use of a chromophore with well-defined chemistry, the reactivity of the edges has been investigated. Small aromatic systems were used to understand the reactivity of the boron dipyrrin Cl-BODIPY, and with the aid of spectroscopic and computational methods, the substitution mechanism and properties of the compounds have been investigated. The synthetic procedure was then applied to graphene nanoribbons. Results from infrared and Raman spectroscopy studies show that edge-functionalisation of graphene nanoribbons with BODIPY was successful, and no modifications to the basal plane have been observed

Learning labelled dependencies in machine translation evaluation

Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate better than other existing metrics with human judgements. Other research in this area has presented machine learning methods which learn directly from human judgements. In this paper, we present a novel combination of dependency- and machine learning-based approaches to automatic MT evaluation, and demonstrate greater correlations with human judgement than the existing state-of-the-art methods. In addition, we examine the extent to which our novel method can be generalised across different tasks and domains

Joining hands: developing a sign language machine translation system with and for the deaf community

This paper discusses the development of an automatic machine translation (MT) system for translating spoken language text into signed languages (SLs). The motivation for our work is the improvement of accessibility to airport information announcements for D/deaf and hard of hearing people. This paper demonstrates the involvement of Deaf colleagues and members of the D/deaf community in Ireland in three areas of our research: the choice of a domain for automatic translation that has a practical use for the D/deaf community; the human translation of English text into Irish Sign Language (ISL) as well as advice on ISL grammar and linguistics; and the importance of native ISL signers as manual evaluators of our translated output

A three-pass system combination framework by combining multiple hypothesis alignment methods

So far, many effective hypothesis alignment metrics have been proposed and applied to the system combination, such as TER, HMM, ITER and IHMM. In addition, the Minimum Bayes-risk (MBR) decoding and the confusion network (CN) have become the state-of-the art techniques in system combination. In this paper, we present a three-pass system combination strategy that can combine hypothesis alignment results derived from different alignment metrics to generate a better translation. Firstly the different alignment metrics are carried out to align the backbone and hypotheses, and the individual CN is built corresponding to each alignment results; then we construct a super network by merging the multiple metric-based CN and generate a consensus output. Finally a modified consensus network MBR (ConMBR) approach is employed to search a best translation. Our proposed strategy out performs the best single CN as well as the best single system in our experiments on NIST Chinese-to-English test set