Effects of mass and body composition on fasting fuel utilisation in grey seal pups (Halichoerus grypus Fabricius): an experimental study using supplementary feeding

This study used supplementary feeding to test the hypothesis that fuel partitioning during the postweaning fast in grey seal pups is affected by size and composition of energy reserves at weaning, and by extra provisioning. Mass and body composition changes were measured during suckling and fasting to investigate the effect of natural differences in energy reserves at weaning on subsequent allocation of fat and protein to energy use. We fed seven pups for 5 days after weaning, to investigate the effect of increased fuel availability, and particularly protein, on fuel utilisation. After correcting for protein used during the moult, the proportional contribution of fat was 86–99% of total energy use. Pups with greater energy reserves, i.e. those that were heavier and fatter at weaning, had higher rates of fat and energy use. There was no significant relationship between adiposity at weaning and proportional contribution of fat to energy use, perhaps due to a limited sample size or range of body masses and adiposity. Supplemented individuals used energy, specifically fat, much faster and utilised proportionally less of their endogenous protein by departure than non-supplemented individuals. Fat metabolism contributed a similar percentage to daily energy use in both groups. These findings show that pups spare protein, even when energy use is dramatically increased. Pups that receive greater maternal provisioning and lay down more protein may have increased survival chances at sea. This study highlights the importance of protein reserves in first year survival of grey seal pups.

Feeding strategy with alternate fasting and refeeding: effects on farmed pacu production

P>A 36-day trial was conducted to determine the effects of repetitive periods of food restriction and refeeding on growth and energy metabolism in pacu (Piaractus mesopotamicus). A total 264 juvenile fish (36.9 +/- 2.8 g) were fed with the experimental diet for 36 days using three regimes: (i) feeding daily to satiation (FD); (ii) no feed for 3 days, then feeding the same amount offered to the control groups for the next 3 days (NF/R controlled); and (iii) no feed for 3 days, then feeding to apparent satiation for the next 3 days (NF/R at satiation). The treatments were distributed into four tanks each. WG and SGR were higher in FD group. Fish refed showed hyperphagia just up to the second day of refeeding. The worst feed conversion rate and the lowest protein efficiency ratio were found in fish NF/R controlled. The lowest values of visceral fat somatic index were found in both fasted fish groups, particularly in NF/R at satiation. The LL and glycogen concentrations, and the hepatosomatic index were all elevated in both feed restricted fish. Muscle lipid showed a tendency to decrease after the cycle of fasting and refeeding. Plasma free fatty acids and glucose levels were elevated in fish subjected to feeding restrictions while serum triglycerides levels were reduced. Triiodothyronine levels were significantly depressed in fish from the NF/R-controlled group and remained at the same levels as the control fish in fish NF/R at satiation. Results indicated that fish subjected to cyclic periods of 3-day satiation or controlled feeding after 3-days of fasting were unable to achieve the final body weight of fish fed to satiation after 36 days.

A sequencing approach for creating new train timetables

Train scheduling is a complex and time consuming task of vital importance. To schedule trains more accurately and efficiently than permitted by current techniques a novel hybrid job shop approach has been proposed and implemented. Unique characteristics of train scheduling are first incorporated into a disjunctive graph model of train operations. A constructive algorithm that utilises this model is then developed. The constructive algorithm is a general procedure that constructs a schedule using insertion, backtracking and dynamic route selection mechanisms. It provides a significant search capability and is valid for any objective criteria. Simulated Annealing and Local Search meta-heuristic improvement algorithms are also adapted and extended. An important feature of these approaches is a new compound perturbation operator that consists of many unitary moves that allows trains to be shifted feasibly and more easily within the solution. A numerical investigation and case study is provided and demonstrates that high quality solutions are obtainable on real sized applications.

Intradialytic exercise : a feasibility study

Background: Exercise is known to improve mental and physical functioning and to improve quality of life. The obstacles faced by individuals with chronic kidney disease on maintenance haemodialysis include increased levels of fatigue, decreased motivation, and the inability to schedule exercise around daily activities and dialysis schedules. Aim: This pilot study was undertaken to determine the feasibility and potential efficacy of an individually-tailored exercise program for in-centre haemodialysis patients. Method: A 16 week program was designed and evaluated in relation to changes in physical capacity, the extent of exercise undertaken, and quality of life indicators. Results and Conclusion: The resultant recommendations regarding the level of motivational support, the time and physical requirements in implementing an exercise program will provide useful information for others embarking on similar studies.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

WebPut : Efficient web-based data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is also proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Experiments based on several real-world data collections demonstrate that WebPut outperforms existing approaches.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

A comparative study of prognostic model for bearing failure

An effective prognostics program will provide ample lead time for maintenance engineers to schedule a repair and to acquire replacement components before catastrophic failures occur. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique. For comparative study of the proposed model with the proportional hazard model (PHM), experimental bearing failure data from an accelerated bearing test rig were used. The result shows that the proposed prognostic model based on health state probability estimation can provide a more accurate prediction capability than the commonly used PHM in bearing failure case study.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.

Trehalose Toxicity in Cuscuta reflexa

a,a-Trehalose induced a rapid blackening of the terminal 2.5-centimeter region of excised Cuscuta relexa Roxb. vine. The incorporation of radioactivity from [I'C]glucose into alkali-insoluble fraction of shoot tip was markedly inhibited by 12 hours of trehalose feeding to an excised vine. This inhibition was confied to the apical segment of the vine in which cell elongation occurred. The rate of blackening of shoot tip explants was hastened by the addition of gibberellic acid A3, which promoted elongation growth of isolated Cuscuta shoot tips. The symptom of trehalose toxicity was duplicated by 2-deoxygucose, which has been shown to be a potent inhibitor of ceD wall synthesis in yeast. The observations suggest that trehalose interferes with the synthesis of ceDl wail polysaccharides, the chief component of which was presumed to be cellulose.

Commercial application of polychaete sand filters for wastewater remediation and broodstock feeds

Summary Prototype sand-worm filtration beds were constructed at two prawn farms and one fish farm to assess and demonstrate their polychaete (marine worm) production and wastewater remediation capacities at semi-commercial scale. Wastewater treatment properties were monitored and worms produced were assessed and either sold for bait or used by the farms’ hatcheries as broodstock (prawn or fish breeder) feed. More than 34 megalitres of prawn- and fish-pond water was beneficially treated in the 116-319-d trial. The design of the polychaete-assisted sand filters (PASFs) constructed at each farm affected their water handling rates, which on average ranged from 315 to 1000 L m-2 d-1 at the three farms. A low profile design incorporating shallow bunded ponds made from polyethylene liner and timber stakes provided the easiest method of construction. This simple design applied at broad scale facilitated the highest quantities of treated water and the greatest worm production. Designs with higher sides increased the head pressure above the sand bed surface, thus increasing the amount of water that could be treated each day. Most water qualities were affected in a similar way to that demonstrated in the previous tank trials: dissolved oxygen, pH, total suspended solids and chlorophyll a levels were all consistently significantly lowered as pond water percolated through the sand bed, and dissolved forms of nitrogen and phosphorus were marginally increased on several occasions. However, unlike the previous smaller-scale tank trials, total nitrogen (TN) and total phosphorus (TP) levels were both significantly lowered by these larger-scale PASFs. The reasons for this are still unclear and require further research. Maximum TN and TP removals detected in the trial were 48.8% and 67.5%, respectively, and average removals (in unfed beds) at the three farms ranged from 20.0 to 27.7% for TN and from 22.8 to 40.8% for TP. Collectively, these results demonstrate the best suspended solids, chlorophyll and macronutrient removal capacities so far reported for any mariculture wastewater treatment methodology to date. Supplemental feeding of PASFs with fish meal was also investigated at one farm as a potential means of increasing their polychaete biomass production. Whilst fed beds produced higher biomass (152 ± 35 g m-2) compared with unfed beds (89 ± 17 g m-2) after 3.7 months of operation, the low number of replicates (2) prevented statistically significant differences from being demonstrated for either growth or survival. At harvest several months later, worm biomass production was estimated to be similar to, or in slight excess of, previously reported production levels (300-400 g m-2). Several qualities of filtered water appear to have been affected by supplemental feeding: it appeared to marginally lower dissolved oxygen and pH levels, and increased the TN and TP levels though not so much to eliminate significant beneficial water treatment effects. Periodic sampling during an artificial-tide demonstrated the tendency for treated-water quality changes during the first hour of filtration. Total nitrogen and ammonia peaked early in the tidal flow and then fell to more stable levels for the remainder of the filtration period. Other dissolved nutrients also showed signs of this sand-bed-flushing pattern, and dissolved oxygen tended to climb during the first hour and become more stable thereafter. These patterns suggest that the routine sampling of treated water undertaken at mid-inflow during the majority of the wider study would likely have overestimated the levels of TN and dissolved nutrients discharged from the beds, and hence underestimated the PASFs treatment efficacies in this regard. Analyses of polychaete biomass collected from each bed in the study revealed that the worms were free from contamination with the main prawn viruses that would create concerns for their feeding to commercial prawn broodstock in Australia. Their documented proximal and nutritional contents also provide a guide for hatchery operators when using live or frozen stock. Their dry matter content ranged from 18.3 to 22.3%, ash ranged from 10.2 to 14.0%, gross energy from 20.2 to 21.5 MJ kg-1, and fat from 5.0 to 9.2%. Their cholesterol levels ranged from 0.86 to 1.03% of dry matter, whilst total phospholipids range from 0.41 to 0.72%. Thirty-one different fatty acids were present at detectable (≥0.005% of dry matter) levels in the sampled worm biomass. Palmitic acid was by far the most prevalent fatty acid detected (1.21 ± 0.18%), followed by eicosapentaenoic (EPA) (0.48 ± 0.03%), stearic (0.46 ± 0.04%), vaccenic (0.38 ± 0.05%), adrenic (0.35 ± 0.02%), docosadienoic (0.28 ± 0.02%), arachidonic (AA) (0.22 ± 0.01%), palmitoleic (0.20 ± 0.04%) and 23 other fatty acids with average contents of less than 0.2% of dry matter. Supplemental feeding with fish meal at one farm appeared to increase the docosahexaenoic acid (DHA) content of the worms considerably, and modify the average AA : EPA : DHA from 1.0 : 2.7 : 0.3 to 1.0 : 2.0 : 1.1. Consistent with previous results, the three most heavily represented amino acids in the dry matter of sampled worms were glutamic acid (8.5 ± 0.2%), aspartic acid (5.5 ± 0.1%) and glycine (4.9 ± 0.5%). These biomass content results suggest that worms produced in PASF systems are well suited to feeding to prawn and fish broodstock, and provide further strong evidence of the potential to modify their contents for specific nutritional uses. The falling wild-fishery production of marine bloodworms in Queensland is typical of diminishing polychaete resources world-wide and demonstrates the need to develop sustainable production methods here and overseas. PASF systems offer the dual benefits of wastewater treatment for environmental management and increased productivity through a valuable secondary crop grown exclusively on waste nutrients.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Peripartum management of glycemia in women with type 1 diabetes

OBJECTIVE We aimed to 1) describe the peripartum management of type 1 diabetes at an Australian teaching hospital and 2) discuss factors influencing the apparent transient insulin independence postpartum. RESEARCH DESIGN AND METHODS We conducted a retrospective review of women with type 1 diabetes delivering singleton pregnancies from 2005 to 2010. Information was collected regarding demographics, medical history, peripartum management and outcome, and breast-feeding. To detect a difference in time to first postpartum blood glucose level (BGL) >8 mmol/L between women with an early (<4 h) and late (>12 h) requirement for insulin postpartum, with a power of 80% and a type 1 error of 0.05, at least 24 patients were required. RESULTS An intravenous insulin infusion was commenced in almost 95% of women. Univariate analysis showed that increased BMI at term, lower creatinine at term, longer duration from last dose of long- or intermediate-acting insulin, and discontinuation of an insulin infusion postpartum were associated with a shorter time to first requirement of insulin postpartum (P = 0.005, 0.026, 0.026, and <0.001, respectively). There was a correlation between higher doses of insulin commenced postpartum and number of out-of-range BGLs (r[36] = 0.358, P = 0.030) and hypoglycemia (r[36] = 0.434, P = 0.007). Almost 60% had at least one BGL <3.5 mmol/L between delivery and discharge. CONCLUSIONS Changes in the pharmacodynamic profile of insulin may contribute to the transient insulin independence sometimes observed postpartum in type 1 diabetes. A dose of 50–60% of the prepregnancy insulin requirement resulted in the lowest rate of hypoglycemia and glucose excursions. These results require validation in a larger, prospective study.

Incremental attribute evaluation through recursive procedures

Incremental semantic analysis in a programming environment based on Attribute Grammars is performed by an Incremental Attribute Evaluator (IAE). Current IAEs are either table-driven or make extensive use of graph structures to schedule reevaluation of attributes. A method of compiling an Ordered Attribute Grammar into mutually recursive procedures is proposed. These procedures form an optimal time Incremental Attribute Evaluator for the attribute grammar, which does not require any graphs or tables.

Link Conditions for Simplifying Meshes with Embedded Structures

Interactive visualization applications benefit from simplification techniques that generate good-quality coarse meshes from high-resolution meshes that represent the domain. These meshes often contain interesting substructures, called embedded structures, and it is desirable to preserve the topology of the embedded structures during simplification, in addition to preserving the topology of the domain. This paper describes a proof that link conditions, proposed earlier, are sufficient to ensure that edge contractions preserve the topology of the embedded structures and the domain. Excluding two specific configurations, the link conditions are also shown to be necessary for topology preservation. Repeated application of edge contraction on an extended complex produces a coarser representation of the domain and the embedded structures. An extension of the quadric error metric is used to schedule edge contractions, resulting in a good-quality coarse mesh that closely approximates the input domain and the embedded structures.