Clonal integration in Ochthochloa compressa under extreme environmental conditions

While multiple studies have indicated the benefit of clonal integration and its role in resisting harsh environmental conditions, many researchers have indicated the need for further studies to understand fully the role of clonal integration and what determines the optimal strategies in various environments. In this project a series of studies were carried out in an extremely arid area (Al Thumam area in the Arabian Peninsula) to contribute to the knowledge of the benefits of clonal integration and understanding the behaviour of the clonal grass Ochthochloa compressa. This study is unique because it has investigated the benefits of clonality in one of the harshest environments where clonal plants are found. Experiment 1. I aimed to understand the patterns of spread and expansion of stolons, particularly whether stolons grew in random directions or are directed to better patches by the mother ramets to establish new daughter ramets maximizing their chances of success. I measured nutrient contents (N, P and K) in patches where mother plants grew, and where daughter ramets had established. In addition, I sampled nearby unoccupied patches. Mother plants were found in patches with higher N concentrations than where the daughter ramets were found. There were no differences in concentration of P; while K was the lowest where not fully rooted daughters were found. The results suggest that daughter ramets did not establish in the best areas, indicating that the spread and expansion of stolons in the O. compressa occurs randomly. Experiment 2. I investigated the effects of the addition of fertilizer to mother and daughter ramets, including addition of nutrients to daughters disconnected from the mother ramets. Cutting the stolons caused to death of the daughter because these ramets were still dependent on the mother ramets. When connected, mothers that received nutrients affected some transference nutrients to daughter ramets. In contrast, the daughters accumulated the nutrients in above ground tissue when receiving added fertilizer, and there was no sign of transference to the mother ramet. Nutrient addition did not affect in any case the efficiency photosynthetic in both mother and daughter ramets. Experiment 3. In this experiment, I focused on the effect of the distance between mother and daughter ramets on the performance of daughter ramets. Daughters located close to the mother ramets could suffer competition by the mother if they are within the area of the root system. The results showed no significant differences between mother and daughter ramets in the concentration of nitrogen, phosphorus, and potassium nor in photosynthetic activity. This indicates that the daughters have the ability to resist competition through continued support from their mothers despite the scarcity of resources and the harsh environmental conditions in the study area. Experiment 4. In this experiment, I studied the effect of simulated grazing on both mother and daughter ramets when one of them was clipped and 50-60% of the leaves were removed, while remaining connected. Clipping did not affect the N content of mother ramets, but the concentration of phosphorus was decreased by clipping. K was lower in mother ramets connected to clipped daughters. Daughters connected to clipped mothers had higher N concentration but K and P did not change. Clipping of daughters did not have any effect on mother ramets concentration of nutrients. Photosynthetic efficiency did not record any significant differences when ramets were clipped. The results indicate that O. compressa strategy to resist grazing consists mainly in continuing to support daughter ramets. As far as I know, this is the first study of the phenomenon of clonal integration for O. compressa. This study revealed the importance of clonal integration for O. compressa to resistance of the harsh environmental conditions. Under the harsh conditions these plants live, the preferred strategy seems to be for the mother ramets to expand by producing ramets in random sites and heavily subsidize their growth with nutrient, and almost certainly water. Indeed, I documented strong evidence of transfer of nutrients through the stolons from mother ramets to daughter ramets but no evidence of transfer in the other direction even when nutrients were supplied to daughters. Further, seemingly surplus N available to the mother when clipping limited their foliar tissues was directed to daughters rather than to re-sprouting (which was probably limited by water availability). The insights obtained on the biology of O. compressa are critical as it is a native plant in a harsh environment, and it is suitable for fodder for pastoral animals, as well as having potential for restoration of degraded areas. Further, it provides new insights into the phenomenon of clonal integration in harsh habitats, and area which still needs further study and research.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 201

Efficient implementation of volume/surface integrated average based multi-moment method

We investigated discretization strategies of the conservation equation in VSIAM3 (volume/surface integrated average based multi-moment method) which is a numerical framework for incompressible and compressible flows based on a multi-moment concept. We investigated these strategies through the lid-driven cavity flow problem, shock tube problems, 2D explosion test and droplet splashing on a superhydrophobic substrate. We found that the use of the CIP-CSLR (constrained interpolation profile-conservative semi-Lagrangian with rational function) method as the conservation equation solver is critically important for the robustness of incompressible flow simulations using VSIAM3 and that numerical results are sensitive to discretization techniques of the divergence term in the conservation equation. Based on these results, we proposed efficient implementation techniques of VSIAM3

Integrated Bayesian Framework for Remaining Useful Life Prediction.

International audienceIn this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL. The proposed method extracts monotonic trends from offline sensor signals, which are used to build reference models. From online signals the method represents the uncertainty about the current status, using discrete Bayesian filter. Finally, the method predicts RUL of the monitored component using integrated method based on K-nearest neighbor (k-NN) and Gaussian process regression (GPR). The performance of the algorithm is demonstrated using two real data sets from NASA Ames prognostics data repository. The results show that the algorithm obtain good results for both application

The cytogenetic effect of the new asthma and allergy drug montelukast on albino mice: chromosomal studies

Montelukast or Singulair has recently been introduced to treat asthma. Because it is apparently free of serious side-effects, the present work aimed to investigate its effects on chromosomes of bone marrow cells of mice. The percentage of structural chromosomal aberrations was highly elevated due to treatment with Montelukast. The aberrationsincreased successively with increasing the time and the dose of the therapy. Numerical chromosomal aberrations were also increased, and interstitial deletions of certain bands were detected in G/T-banded karyotypes of the treated samples. Montelukast appears to have potential genotoxicity in the somatic cells of mice in vivo

Structural evaluation of concrete expanded polystyrene sandwich panels for slab applications

Sandwich panels are being extensively and increasingly used in building construction because they are light in weight, energy efficient, aesthetically attractive and can be easily handled and erected. This paper presents a structural evaluation of Concrete-Expanded Polystyrene (CEPS) sandwich panels for slab applications using finite element modeling approach. CEPS panels are made of expanded polystyrene foam sandwiched between concrete skins. The use of foam in the middle of sandwich panel reduces the weight of the structure and also acts as insulation against thermal, acoustics and vibration. Applying reinforced concrete skin to both sides of panel takes the advantages of the sandwich concept where the reinforced concrete skins take compressive and tensile loads resulting in higher stiffness and strength and the core transfers shear loads between the faces. This research uses structural software Strand7, which is based on finite element method, to predict the load deformation behaviour of the CEPS sandwich slab panels. Non linear static analysis was used in the numerical investigations. Predicted results were compared with the existing experimental results to validate the numerical approach used

Nonparametric time series modelling for industrial prognostics and health management.

International audiencePrognostics and health management (PHM) methods aim at detecting the degradation, diagnosing the faults and predicting the time at which a system or a component will no longer perform its desired function. PHM is based on access to a model of a system or a component using one or combination of physical or data driven models. In physical based models one has to gather a lot of knowledge about the desired system, and then build analytical model of the system function of the degradation mechanism that is used as a reference during system operation. On the other hand data-driven models are based on the exploitation of symptoms or indicators of degradations using statistical or Artifcial Intelligence (AI) methods on the monitored system once it is operational and learn the normal behaviour. Trend extraction is one of the methods used to extract important information contained in the sensory signals, which can be used for data driven models. However, extraction of such information from collected data in a practical working environment is always a great challenge as sensory signals are usually multidimensional and obscured by noise. Also, the extracted trends should represent the nominal behaviour of the system as well as should represent the health status evolution. This paper presents a method for nonparametric trend modelling from multidimensional sensory data so as to use such trends in machinery health prognostics. The goal of this work is to develop a method that can extract features representing the nominal behaviour of the monitored component and from these features extract smooth trends to represent the critical component's health evolution over the time. The proposed method starts by multidimensional feature extraction from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the number of the extracted features. The selected features can be used to represent the nominal behaviour of the system and hence detect any deviation. Then, empirical mode decomposition algorithm (EMD) is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Finally, ridge regression is applied to the extracted trend for modelling and can be used later for remaining useful life prediction. The method is demonstrated on accelerated degradation dataset of bearings acquired from PRONOSTIA experimental platform and another dataset downloaded form NASA repository where it is shown to be able to extract signal trends

Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction.

International audienceReliability of prognostics and health management systems (PHM) relies upon accurate understanding of critical components' degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of data or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HI) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline health indicator, to the online health indicator, using k-nearest neighbors (k-NN) classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications

Stress–strain model for concrete confined by FRP composites

In this paper, a stress–strain model for concrete confined by fiber reinforced polymer (FRP) composites is developed. The model is based on the results of a comprehensive experimental program including large-scale circular, square and rectangular short columns confined by carbon/epoxy and E-glass/epoxy jackets providing a wide range of confinement ratios. Ultimate stress, rupture strain, jacket parameters, and cross-sectional geometry were found to be significant factors affecting the stress–strain behavior of FRP-confined concrete. Such parameters were analyzed statistically based on the experimental data, and equations to theoretically predict these parameters are presented. Experimental results from this study were compared to the proposed semi-empirical model as well as others from the literature