Material Visualisation for Virtual Reality: The Perceptual Investigations

Material representation plays a significant role in design visualisation and evaluation. On one hand, the simulated material properties determine the appearance of product prototypes in digitally rendered scenes. On the other hand, those properties are perceived by the viewers in order to make important design decisions. As an approach to simulate a more realistic environment, Virtual Reality (VR) provides users a vivid impression of depth and embodies them into an immersive environment. However, the scientific understanding of material perception and its applications in VR is still fairly limited. This leads to this thesis’s research question on whether the material perception in VR is different from that in traditional 2D displays, as well as the potential of using VR as a design tool to facilitate material evaluation.       This thesis is initiated from studying the perceptual difference of rendered materials between VR and traditional 2D viewing modes. Firstly, through a pilot study, it is confirmed that users have different perceptual experiences of the same material in the two viewing modes. Following that initial finding, the research investigates in more details the perceptual difference with psychophysics methods, which help in quantifying the users’ perceptual responses. Using the perceptual scale as a measuring means, the research analyses the users’ judgment and recognition of the material properties under VR and traditional 2D display environments. In addition, the research also elicits the perceptual evaluation criteria to analyse the emotional aspects of materials. The six perceptual criteria are in semantic forms, including rigidity, formality, fineness, softness, modernity, and irregularity.       The results showed that VR could support users in making a more refined judgment of material properties. That is to say, the users perceive better the minute changes of material properties under immersive viewing conditions. In terms of emotional aspects, VR is advantageous in signifying the effects induced by visual textures, while the 2D viewing mode is more effective for expressing the characteristics of plain surfaces. This thesis has contributed to the deeper understanding of users’ perception of material appearances in Virtual Reality, which is critical in achieving an effective design visualisation using such a display medium

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: a meta-analysis

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Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes

Evaluation of ammonia pretreatment of four fibrous biowastes and its effect on black soldier fly larvae rearing performance

Biowaste treatment with black soldier fly larvae (BSFL, Hermetia illucens L.) can promote a more sustainable food system by reusing nutrients that would otherwise be wasted. However, many agri-food wastes and byproducts are typically high in lignocellulosic fibers (i.e., cellulose, hemicellulose, and lignin), making it resistant to efficient larval and/or microbial degradation. Ammonia pretreatment could be used to partially degrade lignocellulose, making the biowaste more easily degradable by the larvae and/or microorganisms. This study evaluated ammonia pretreatment for lignocellulose degradation and its effect on BSFL performance on four fibrous biowastes: brewers spent grain, cow manure, oat pulp, and grass clippings. First, the optimal ammonia dose (1 % or 5 % dry mass) and pretreatment time (three or seven days) were assessed by measuring fibers after treatment and further examined using Fourier transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) images. Second, BSFL rearing performance on ammonia-pretreated substrates was assessed with a 9-day feeding experiment. Three-day pretreatment with 5 % ammonia was chosen as it decreased the total fiber content by 8–23 % for all substrates except cow manure. Contrary to expectations, ammonia pretreatment with all substates decreased BSFL rearing performance metrics by more than half compared to the untreated control. Follow-up experiments suggested that ammonia pretreatment had a dose-dependent toxicity to BSFL. Interestingly, three-day fermentation of cow manure and oat pulp increased bioconversion rate by 25–31 %. This study shows that ammonia pretreatment is not suitable before BSFL rearing. Ammonia toxicity to BSFL and other pretreatments, such as fermentation, should be further studied.ISSN:0956-053XISSN:1879-245

Ammonia pretreatment of agri-food wastes to enhance black soldier fly larvae bioprocessing performance

Black soldier fly larvae (BSFL) bioconversion is a promising bioprocessing technology. However, one major challenge in BSFL waste processing, is that agri-food wastes (e.g. animal manure, agricultural crops and residues) tend to be low in macronutrients and high in lignocellulosic fibre (e.g. cellulose, hemicellulose and lignin), resulting in longer larvae development times and lower larval mass compared to when reared on high-value substrates (e.g. food waste). This study aimed to increase BSFL process performance on four fibre-rich substrates by ammonia substrate pretreatment: brewers spent grain, grass clippings, oat drink-by-product and cow manure. We first conducted experiments to identify optimal aqueous ammonia dose (1 vs 5%, 25% conc.) and pretreatment time (3 vs 7 days) by comparing its effect on the lignocellulosic substrate composition (neutral, acid and lignin detergent fibre) compared to an untreated control. Once we determined the optimal pretreatment condition, we completed controlled feeding experiments with four replicates, using ammonia pretreated and untreated substrates in a climate chamber (9 days, 2.5 larvae/cm2, 28 °C and 44-70% relative humidity). Results demonstrated that for most substrates a dose of 5% achieved improved lignocellulosic degradation, decreasing neutral detergent fibre by 6 to 22%, when compared to the untreated control. A pretreatment time of 3 days performed best for all substrates, except grass clippings, which demonstrated better results at 7 days. Based on our results, aqueous ammonia can result in fibre degradation of BSFL substrates and influence larvae performance. Although not fully evident at this stage of the experiments, ammonia pretreatment of such fibrous wastes shows mixed results of larvae performance depending on the substrates in question. This research works to increase performance of abundant fibrous low-value waste streams for recycling within the food system.ISSN:2352-458

Exhaled volatile fatty acids, ruminal methane emission, and their diurnal patterns in lactating dairy cows

To date, the commonly used methods to assess rumen fermentation are invasive. Exhaled breath contains hundreds of volatile organic compounds (VOC) that can reflect animal physiological processes. In the present study, for the first time, we aimed to use a non-invasive metabolomics approach based on high-resolution mass spectrometry to identify rumen fermentation parameters in dairy cows. Enteric methane (CH4) production from 7 lactating cows was measured 8 times over 2 consecutive days using the GreenFeed system. Simultaneously, exhalome samples were collected in Tedlar gas sampling bags and analyzed offline using a secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) system. In total, 1,298 features were detected, among them targeted exhaled volatile fatty acids (eVFA, i.e., acetate, propionate, butyrate), which were putatively annotated using their exact mass-to-charge ratio. The intensity of eVFA, in particular acetate, increased immediately after feeding and followed a similar pattern observed for ruminal CH4 production. The average total eVFA concentration was 35.4 count-per-second (CPS), and among the individual eVFA, acetate had the greatest concentration, averaging 21.0 CPS followed by propionate at 11.5 CPS, and butyrate at 2.82 CPS. Further, exhaled acetate was on average the most abundant of the individual eVFA at around 59.3%, followed by 32.5 and 7.9% of the total eVFA for propionate and butyrate, respectively. This corresponds well with the previously reported proportions of these VFA in the rumen. The diurnal patterns of ruminal CH4 emission and individual eVFA were characterized using a linear mixed model with cosine function fit. The model characterized similar diurnal patterns for eVFA and ruminal CH4 and H2 production. Regarding the diurnal patterns of eVFA, the phase (time of peak) of butyrate occurred first, followed by that of acetate and propionate. Importantly, the phase of total eVFA occurred around 1 h before that of ruminal CH4. This corresponds well with existing data on the relationship between rumen VFA production and CH4 formation. Results from the present study revealed a great potential to assess the rumen fermentation of dairy cows using exhaled metabolites as a non-invasive proxy for rumen VFA. Further validation, with comparisons to rumen fluid, and establishment of the proposed method are required.ISSN:0022-0302ISSN:1525-319

Effect of diets enriched in n-6 or n-3 fatty acid on dry matter intake, energy balance, oxidative stress, and milk fat profile of transition cows

The objective of this study was to determine the effect of dietary supplementation of n-3 polyunsaturated fatty acids (PUFA) and n-6 PUFA on dry matter intake (DMI), energy balance, oxidative stress, and performance of transition cows. Forty-five multiparous Holstein dairy cows with similar parity, body weight (BW), body condition score (BCS), and milk yield were used in a completely randomized design during a 56-d experimental period including 28 d prepartum and 28 d postpartum. At 240 d of pregnancy, cows were randomly assigned to one of the 3 isoenergetic and isoprotein dietary treatments, including a control ration containing 1% hydrogenated fatty acid (CON), a ration with 8% extruded soybean (HN6, high n-6 PUFA source), and a ration with 3.5% extruded flaxseed (HN3; high n-3 PUFA source). The HN6 and HN3 diets had an n-6/n-3 ratio of 3.05:1 and 0.64:1 in prepartum cows and 8.16:1 and 1.59:1 in postpartum cows, respectively. During the prepartum period (3, 2, and 1 wk before calving), DMI, DMI per unit of BW, total net energy intake, and net energy balance were higher in the HN3 than in the CON and NH6 groups. During the postpartum period (2, 3, and 4 wk after calving), cows fed HN3 and HN6 diets both showed increasing DMI, DMI as a percentage of BW, and total net energy intake compared with those fed the CON diet. The BW of calves in the HN3 group was 12.91% higher than those in the CON group. Yield and nutrient composition of colostrum (first milking after calving) were not affected by HN6 or HN3 but milk yield from 1 to 4 wk of milking was significantly improved compared with CON. During the transition period, BW, BCS, and BCS changes were not affected. Cows fed the HN6 diet had a higher plasma NEFA concentration compared with the CON cows during the prepartum period. Feeding HN3 reduced the proportion of de novo fatty acids and increased the proportion of preformed long-chain fatty acids in regular milk. In addition, the n-3 PUFA-enriched diet reduced the n-6/n-3 PUFA ratio in milk. In conclusion, increasing the n-3 fatty acids concentration in the diet increased both DMI during the transition period and milk production after calving, and supplementing n-3 fatty acids was more effective in mitigating the net energy balance after calving.ISSN:0022-0302ISSN:1525-319