Effects of the correction of particle microbial contamination and particle transit model in the rumen on in situ protein evaluation of grass hays.

Effects of considering the particle comminution rate -kc- in addition to particle rumen outflow -kp- and the ruminal microbial contamination on estimates of by-pass and intestinal digestibility of DM, organic matter and crude protein were examined in perennial ryegrass and oat hays. By-pass kc-kp-based values of amino acids were also determined. This study was performed using particle transit, in situ and 15N techniques on three rumen and duodenum-cannulated wethers. The above estimates were determined using composite samples from rumen-incubated residues representative of feed by-pass. Considering the comminution rate, kc, modified the contribution of the incubated residues to these samples in both hays and revealed a higher microbial contamination, consistently in oat hay and only as a tendency for crude protein in ryegrass hay. Not considering kc or rumen microbial contamination overvalued by-pass and intestinal digestibility in both hays. Therefore, non-microbial-corrected kp-based values of intestinal digested crude protein were overestimated as compared with corrected and kc-kp-based values in ryegrass hay -17.4 vs 4.40%- and in oat hay -5.73 vs 0.19%-. Both factors should be considered to obtain accurate in situ estimates in grasses, as the protein value of grasses is very conditioned by the microbial synthesis derived from their ruminal fermentation. Consistent overvaluations of amino acid by-pass due to not correcting microbial contamination were detected in both hays, with large variable errors among amino acids. A similar degradation pattern of amino acids was recorded in both hays. Cysteine, methionine, leucine and valine were the most degradation-resistant amino acids.

Extending and validating the is-impact model in Saudi Arabia : accounting for computer network quality

Organizations from every industry sector seek to enhance their business performance and competitiveness through the deployment of contemporary information systems (IS), such as Enterprise Systems (ERP). Investments in ERP are complex and costly, attracting scrutiny and pressure to justify their cost. Thus, IS researchers highlight the need for systematic evaluation of information system success, or impact, which has resulted in the introduction of varied models for evaluating information systems. One of these systematic measurement approaches is the IS-Impact Model introduced by a team of researchers at Queensland University of technology (QUT) (Gable, Sedera, & Chan, 2008). The IS-Impact Model is conceptualized as a formative, multidimensional index that consists of four dimensions. Gable et al. (2008) define IS-Impact as "a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups" (p.381). The IT Evaluation Research Program (ITE-Program) at QUT has grown the IS-Impact Research Track with the central goal of conducting further studies to enhance and extend the IS-Impact Model. The overall goal of the IS-Impact research track at QUT is "to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice" (Gable, 2009). In order to achieve that, the IS-Impact research track advocates programmatic research having the principles of tenacity, holism, and generalizability through extension research strategies. This study was conducted within the IS-Impact Research Track, to further generalize the IS-Impact Model by extending it to the Saudi Arabian context. According to Hofsted (2012), the national culture of Saudi Arabia is significantly different from the Australian national culture making the Saudi Arabian culture an interesting context for testing the external validity of the IS-Impact Model. The study re-visits the IS-Impact Model from the ground up. Rather than assume the existing instrument is valid in the new context, or simply assess its validity through quantitative data collection, the study takes a qualitative, inductive approach to re-assessing the necessity and completeness of existing dimensions and measures. This is done in two phases: Exploratory Phase and Confirmatory Phase. The exploratory phase addresses the first research question of the study "Is the IS-Impact Model complete and able to capture the impact of information systems in Saudi Arabian Organization?". The content analysis, used to analyze the Identification Survey data, indicated that 2 of the 37 measures of the IS-Impact Model are not applicable for the Saudi Arabian Context. Moreover, no new measures or dimensions were identified, evidencing the completeness and content validity of the IS-Impact Model. In addition, the Identification Survey data suggested several concepts related to IS-Impact, the most prominent of which was "Computer Network Quality" (CNQ). The literature supported the existence of a theoretical link between IS-Impact and CNQ (CNQ is viewed as an antecedent of IS-Impact). With the primary goal of validating the IS-Impact model within its extended nomological network, CNQ was introduced to the research model. The Confirmatory Phase addresses the second research question of the study "Is the Extended IS-Impact Model Valid as a Hierarchical Multidimensional Formative Measurement Model?". The objective of the Confirmatory Phase was to test the validity of IS-Impact Model and CNQ Model. To achieve that, IS-Impact, CNQ, and IS-Satisfaction were operationalized in a survey instrument, and then the research model was assessed by employing the Partial Least Squares (PLS) approach. The CNQ model was validated as a formative model. Similarly, the IS-Impact Model was validated as a hierarchical multidimensional formative construct. However, the analysis indicated that one of the IS-Impact Model indicators was insignificant and can be removed from the model. Thus, the resulting Extended IS-Impact Model consists of 4 dimensions and 34 measures. Finally, the structural model was also assessed against two aspects: explanatory and predictive power. The analysis revealed that the path coefficient between CNQ and IS-Impact is significant with t-value= (4.826) and relatively strong with â = (0.426) with CNQ explaining 18% of the variance in IS-Impact. These results supported the hypothesis that CNQ is antecedent of IS-Impact. The study demonstrates that the quality of Computer Network affects the quality of the Enterprise System (ERP) and consequently the impacts of the system. Therefore, practitioners should pay attention to the Computer Network quality. Similarly, the path coefficient between IS-Impact and IS-Satisfaction was significant t-value = (17.79) and strong â = (0.744), with IS-Impact alone explaining 55% of the variance in Satisfaction, consistent with results of the original IS-Impact study (Gable et al., 2008). The research contributions include: (a) supporting the completeness and validity of IS-Impact Model as a Hierarchical Multi-dimensional Formative Measurement Model in the Saudi Arabian context, (b) operationalizing Computer Network Quality as conceptualized in the ITU-T Recommendation E.800 (ITU-T, 1993), (c) validating CNQ as a formative measurement model and as an antecedent of IS Impact, and (d) conceptualizing and validating IS-Satisfaction as a reflective measurement model and as an immediate consequence of IS Impact. The CNQ model provides a framework to perceptually measure Computer Network Quality from multiple perspectives. The CNQ model features an easy-to-understand, easy-to-use, and economical survey instrument.

An integrated model for optimal reservoir operation for irrigation of multiple crops

An integratedm odel is developed,b asedo n seasonailn puts of reservoiri nflow and rainfall in the irrigated area, to determine the optimal reservoir release policies and irrigation allocationst o multiple crops.T he model is conceptuallym ade up of two modules. Module 1 is an intraseasonal allocation model to maximize the sum of relative yieldso f all crops,f or a givens tateo f the systemu, singl inear programming(L P). The module takes into account reservoir storage continuity, soil moisture balance, and crop root growthw ith time. Module 2 is a seasonaal llocationm odel to derive the steadys tate reservoiro peratingp olicyu sings tochastidc ynamicp rogramming(S DP). Reservoir storage, seasonal inflow, and seasonal rainfall are the state variables in the SDP. The objective in SDP is to maximize the expected sum of relative yields of all crops in a year.The resultso f module 1 and the transitionp robabilitieso f seasonailn flow and rainfall form the input for module 2. The use of seasonailn puts coupledw ith the LP-SDP solution strategy in the present formulation facilitates in relaxing the limitations of an earlier study,w hile affectinga dditionali mprovementsT. he model is applied to an existing reservoir in Karnataka State, India.

A nonlinear model for assessing multiple probabilistic risks: A case study in South five-island of Changdao National Nature Reserve in China

Several methods for estimating the potential impacts caused by multiple probabilistic risks have been suggested. These existing methods mostly rely on the weight sum algorithm to address the need for integrated risk assessment. This paper develops a nonlinear model to perform such an assessment. The joint probability algorithm has been applied to the model development. An application of the developed model in South five-island of Changdao National Nature Reserve, China, combining remote sensing data and a GIS technique, provides a reasonable risk assessment. Based on the case study, we discuss the feasibility of the model. We propose that the model has the potential for use in identifying the regional primary stressor, investigating the most vulnerable habitat, and assessing the integrated impact of multiple stressors. (C) 2006 Elsevier Ltd. All rights reserved.

A nonlinear model for the long-term hydro-thermal generation scheduling problem over multiple areas with transmission constraints

This paper presents a nonlinear model with individual representation of plants for the centralized long-term hydrothermal scheduling problem over multiple areas. In addition to common aspects of long-term scheduling, this model takes transmission constraints into account. The ability to optimize hydropower exchange among multiple areas is important because it enables further minimization of complementary thermal generation costs. Also, by considering transmission constraints for long-term scheduling, a more precise coupling with shorter horizon schedules can be expected. This is an important characteristic from both operational and economic viewpoints. The proposed model is solved by a sequential quadratic programming approach in the form of a prototype system for different case studies. An analysis of the benefits provided by the model is also presented. ©2009 IEEE.

Freeway traffic estimation in Beijing based on particle filter

Short-term traffic flow data is characterized by rapid and dramatic fluctuations. It reflects the nature of the frequent congestion in the lane, which shows a strong nonlinear feature. Traffic state estimation based on the data gained by electronic sensors is critical for much intelligent traffic management and the traffic control. In this paper, a solution to freeway traffic estimation in Beijing is proposed using a particle filter, based on macroscopic traffic flow model, which estimates both traffic density and speed.Particle filter is a nonlinear prediction method, which has obvious advantages for traffic flows prediction. However, with the increase of sampling period, the volatility of the traffic state curve will be much dramatic. Therefore, the prediction accuracy will be affected and difficulty of forecasting is raised. In this paper, particle filter model is applied to estimate the short-term traffic flow. Numerical study is conducted based on the Beijing freeway data with the sampling period of 2 min. The relatively high accuracy of the results indicates the superiority of the proposed model.

Three-phase model of consumers' behaviours : a synthesis of the theory of planned behaviour, self-determination theory and planning

Using a longitudinal study, an overall behavioural model with three related phases (cognitive, motivational and volitional phase) across three studies was examined to identify the factors that most prominently drive consumer environmental behaviour. This thesis provides empirical evidence to support the behavioural model in an environmental consumption context and shows a new avenue for promoting consumer environmental behaviour.

Applying the ecological model of behavior change to a physical activity trial in retirement communities : description of the study protocol

Objectives To describe the intervention protocol for the first multilevel ecological intervention for physical activity in retirement communities that addresses individual, interpersonal and community influences on behavior change. Design A cluster randomized controlled trial design was employed with two study arms: a physical activity intervention and an attention control successful aging condition. Setting Sixteen continuing care retirement communities in San Diego County. Participants Three hundred twenty older adults, aged 65 years and older, are being recruited to participate in the trial. In addition, peer leaders are being recruited to lead some study activities, especially to sustain the intervention after study activities ceased. Intervention Participants in the physical activity trial receive individual, interpersonal and community intervention components. The individual level components include pedometers, goal setting and individual phone counseling. The interpersonal level components include group education sessions and peer-led activities. The community level components include resource audits and enumeration, tailored walking maps, and community improvement projects. The successful aging group receives individual and group attention about successful aging topics. Measurements The main outcome is light to moderate physical activity, measured objectively by accelerometry. Other objective outcomes included physical functioning, blood pressure, physical fitness, and cognitive functioning. Self report measures include depressive symptoms and health related quality of life. Results The intervention is being delivered successfully in the communities and compliance rates are high. Conclusion Ecological Models call for interventions that address multiple levels of the model. Previous studies have not included components at each level and retirement communities provide a model environment to demonstrate how to implement such an intervention.

Model for internal porosity development of different shaped foods

Three particular geometrical shapes of foods were prepared from food materials. Cuboidal (aspect ratio = 1:1, 2:1, 3:1) , cylindrical (length: dameter = 1:1, 2:1, 3:1) and spheres were selected from potato, beans and peas respectively. Internal porosity was determined from solid density (theoretical)and particle density (experimental) during fluidised bed drying at different moisture contents. Solid density was calculated using formulae (conservation of mass and volume) already published in the literature by previous researchers. Determined porosity values were correlated with moisture ratio for different geometrical shapes.

A coupled SPH-DEM model for micro-scale structural deformations of plant cells during drying

A single plant cell was modeled with smoothed particle hydrodynamics (SPH) and a discrete element method (DEM) to study the basic micromechanics that govern the cellular structural deformations during drying. This two-dimensional particle-based model consists of two components: a cell fluid model and a cell wall model. The cell fluid was approximated to a highly viscous Newtonian fluid and modeled with SPH. The cell wall was treated as a stiff semi-permeable solid membrane with visco-elastic properties and modeled as a neo-Hookean solid material using a DEM. Compared to existing meshfree particle-based plant cell models, we have specifically introduced cell wall–fluid attraction forces and cell wall bending stiffness effects to address the critical shrinkage characteristics of the plant cells during drying. Also, a moisture domain-based novel approach was used to simulate drying mechanisms within the particle scheme. The model performance was found to be mainly influenced by the particle resolution, initial gap between the outermost fluid particles and wall particles and number of particles in the SPH influence domain. A higher order smoothing kernel was used with adaptive smoothing length to improve the stability and accuracy of the model. Cell deformations at different states of cell dryness were qualitatively and quantitatively compared with microscopic experimental findings on apple cells and a fairly good agreement was observed with some exceptions. The wall–fluid attraction forces and cell wall bending stiffness were found to be significantly improving the model predictions. A detailed sensitivity analysis was also done to further investigate the influence of wall–fluid attraction forces, cell wall bending stiffness, cell wall stiffness and the particle resolution. This novel meshfree based modeling approach is highly applicable for cellular level deformation studies of plant food materials during drying, which characterize large deformations.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

Calibrating the Gaussian multi-target tracking model

We present novel batch and online (sequential) versions of the expectation-maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. Online EM is also suited to MTT applications that demand real-time processing of the data. Performance is assessed in numerical examples using simulated data for various scenarios. For batch estimation our method significantly outperforms an existing gradient based maximum likelihood technique, which we show to be significantly biased. © 2014 Springer Science+Business Media New York.

Grid Cell Hexagonal Patterns Formed by Fast Self-Organized Learning within Entorhinal Cortex

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. How these hexagonal patterns arise has excited intense interest. It has previously been shown how a selforganizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? A neural model is proposed that converts path integration signals into hexagonal grid cell patterns of multiple scales. This GRID model creates only grid cell patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support a unified computational framework for explaining how entorhinal-hippocampal interactions support spatial navigation.

Antisense Gene Silencing and Bacteriophages as Novel Disinfection Processes for Engineered Systems

The growth and proliferation of invasive bacteria in engineered systems is an ongoing problem. While there are a variety of physical and chemical processes to remove and inactivate bacterial pathogens, there are many situations in which these tools are no longer effective or appropriate for the treatment of a microbial target. For example, certain strains of bacteria are becoming resistant to commonly used disinfectants, such as chlorine and UV. Additionally, the overuse of antibiotics has contributed to the spread of antibiotic resistance, and there is concern that wastewater treatment processes are contributing to the spread of antibiotic resistant bacteria.

Due to the continually evolving nature of bacteria, it is difficult to develop methods for universal bacterial control in a wide range of engineered systems, as many of our treatment processes are static in nature. Still, invasive bacteria are present in many natural and engineered systems, where the application of broad acting disinfectants is impractical, because their use may inhibit the original desired bioprocesses. Therefore, to better control the growth of treatment resistant bacteria and to address limitations with the current disinfection processes, novel tools that are both specific and adaptable need to be developed and characterized.

In this dissertation, two possible biological disinfection processes were investigated for use in controlling invasive bacteria in engineered systems. First, antisense gene silencing, which is the specific use of oligonucleotides to silence gene expression, was investigated. This work was followed by the investigation of bacteriophages (phages), which are viruses that are specific to bacteria, in engineered systems.


For the antisense gene silencing work, a computational approach was used to quantify the number of off-targets and to determine the effects of off-targets in prokaryotic organisms. For the organisms of Escherichia coli K-12 MG1655 and Mycobacterium tuberculosis H37Rv the mean number of off-targets was found to be 15.0 + 13.2 and 38.2 + 61.4, respectively, which results in a reduction of greater than 90% of the effective oligonucleotide concentration. It was also demonstrated that there was a high variability in the number of off-targets over the length of a gene, but that on average, there was no general gene location that could be targeted to reduce off-targets. Therefore, this analysis needs to be performed for each gene in question. It was also demonstrated that the thermodynamic binding energy between the oligonucleotide and the mRNA accounted for 83% of the variation in the silencing efficiency, compared to the number of off-targets, which explained 43% of the variance of the silencing efficiency. This suggests that optimizing thermodynamic parameters must be prioritized over minimizing the number of off-targets. In conclusion for the antisense work, these results suggest that off-target hybrids can account for a greater than 90% reduction in the concentration of the silencing oligonucleotides, and that the effective concentration can be increased through the rational design of silencing targets by minimizing off-target hybrids.

Regarding the work with phages, the disinfection rates of bacteria in the presence of phages was determined. The disinfection rates of E. coli K12 MG1655 in the presence of coliphage Ec2 ranged up to 2 h-1, and were dependent on both the initial phage and bacterial concentrations. Increasing initial phage concentrations resulted in increasing disinfection rates, and generally, increasing initial bacterial concentrations resulted in increasing disinfection rates. However, disinfection rates were found to plateau at higher bacterial and phage concentrations. A multiple linear regression model was used to predict the disinfection rates as a function of the initial phage and bacterial concentrations, and this model was able to explain 93% of the variance in the disinfection rates. The disinfection rates were also modeled with a particle aggregation model. The results from these model simulations suggested that at lower phage and bacterial concentrations there are not enough collisions to support active disinfection rates, which therefore, limits the conditions and systems where phage based bacterial disinfection is possible. Additionally, the particle aggregation model over predicted the disinfection rates at higher phage and bacterial concentrations of 108 PFU/mL and 108 CFU/mL, suggesting other interactions were occurring at these higher concentrations. Overall, this work highlights the need for the development of alternative models to more accurately describe the dynamics of this system at a variety of phage and bacterial concentrations. Finally, the minimum required hydraulic residence time was calculated for a continuous stirred-tank reactor and a plug flow reactor (PFR) as a function of both the initial phage and bacterial concentrations, which suggested that phage treatment in a PFR is theoretically possible.

In addition to determining disinfection rates, the long-term bacterial growth inhibition potential was determined for a variety of phages with both Gram-negative and Gram-positive bacteria. It was determined, that on average, phages can be used to inhibit bacterial growth for up to 24 h, and that this effect was concentration dependent for various phages at specific time points. Additionally, it was found that a phage cocktail was no more effective at inhibiting bacterial growth over the long-term than the best performing phage in isolation.

Finally, for an industrial application, the use of phages to inhibit invasive Lactobacilli in ethanol fermentations was investigated. It was demonstrated that phage 8014-B2 can achieve a greater than 3-log inactivation of Lactobacillus plantarum during a 48 h fermentation. Additionally, it was shown that phages can be used to protect final product yields and maintain yeast viability. Through modeling the fermentation system with differential equations it was determined that there was a 10 h window in the beginning of the fermentation run, where the addition of phages can be used to protect final product yields, and after 20 h no additional benefit of the phage addition was observed.

In conclusion, this dissertation improved the current methods for designing antisense gene silencing targets for prokaryotic organisms, and characterized phages from an engineering perspective. First, the current design strategy for antisense targets in prokaryotic organisms was improved through the development of an algorithm that minimized the number of off-targets. For the phage work, a framework was developed to predict the disinfection rates in terms of the initial phage and bacterial concentrations. In addition, the long-term bacterial growth inhibition potential of multiple phages was determined for several bacteria. In regard to the phage application, phages were shown to protect both final product yields and yeast concentrations during fermentation. Taken together, this work suggests that the rational design of phage treatment is possible and further work is needed to expand on this foundation.