Effects of temperature and vapor pressure deficit on genotypic responses to nitrogen nutrition and weed competition in lowland rice

Since rice is the major food for more than half of the worlds population, rice production and productivity have significant implications for food security. In adaptation to increasing water scarcity, as well as to reduce greenhouse gas emissions, water-saving irrigation measures (e.g., alternate wetting and drying AWD) have been introduced in many rice growing regions. Previous studies have shown that AWD increases water use efficiency and reduces methane (CH4) emissions, while grain yield remains equal or is slightly increased compared to continuous flooding. However, the absence of a ponded water layer in formerly flooded rice fields creates new challenges, such as altered root zone temperature (RZT), enhanced nitrification leading to higher nitrate (NO3-) concentrations in the soil, or stimulated weed germination leading to changes in weed flora. All these factors may affect nutrient uptake and assimilation of rice plants and thus plant growth. Further, vapor pressure deficit (VPD) drives transpiration and water flux through plants, so nutrient uptake and assimilation by plants may be subject to adjustment under varying VPD conditions. As VPD varies largely between rice growing regions and seasons, and is also predicted to continuously increase under global warming, it was included as a factor in this study. The overall objective of the study was to evaluate the response of different rice varieties to arising challenges under water-saving irrigation. Experiments were conducted in the greenhouse and VPD chambers at the University of Hohenheim, where plants were grown in hydroponics. Both during day and night, nutrient uptake rates of rice increased linearly with RZT in the observed temperature range up to 29°C, implying that the optimum temperature for nutrient uptake of rice must be above 29°C. However, the uptake rates of different nutrient elements responded differently to RZT, with the increase in nitrogen (N) uptake per °C being greater than that of phosphorus (PO43-) and potassium (K+), which can potentially lead to an imbalance in plant nutrition. Therefore, the increase in RZT either due to climate change or water management may call for an adjusted fertilizer management. In general, the increase in nutrient uptake per °C was more pronounced during the day than during the night, while the amino acid concentration in the leaves both during the day and night was positively correlated with N uptake during the day, suggesting that plants may benefit more from increased temperature during the day. When both ammonium (NH4+) and NO3- were supplied, rice plants took up a higher share of NH4+. However, after depletion of NH4+ in the nutrient solution, plants took up NO3- without decreasing the total N uptake. The N form taken up by the rice plant had no effect on leaf gas exchange at low VPD, whereas NO3- uptake and assimilation increased stomatal conductance in some rice varieties at high VPD, resulting in a significantly higher photosynthetic rate. However, the increase in photosynthesis did not always result in an increase in dry matter, probably due to a higher energy requirement for NO3- assimilation than for NH4+. The effect of N form on leaf gas exchange of some rice varieties was only found at high VPD, indicating genotype-specific adaptation strategies to high VPD. However, maintenance of high stomatal conductance at high VPD will only be beneficial at sufficient levels of water supply. Therefore, we hypothesize that with increasing VPD, intensified nitrification under water-saving irrigation may improve leaf gas exchange of rice plants, provided a careful choice of variety and good water management. Furthermore, N form had an effect on the competition between rice and weeds. In mixed culture with rice, a large share of NO3- increased the growth and competitiveness of upland weeds but reduced the growth and competitiveness of lowland weeds. Consequently, enhanced nitrification under AWD may reduce the competitive pressure of lowland weeds, but increase the competition of upland weeds. In contrast to rice, growth of the upland weed was not reduced by high VPD, while its nutrient uptake was correlated with water uptake, suggesting that upland weeds will more successfully compete with rice for nutrients as VPD increases. Selection of rice varieties better adapted to NO3- uptake will improve rice growth and its competitiveness against weeds under AWD. The cumulative effects of RZT and soil nitrification on rice growth should be considered when evaluating the effects of climate change on rice growth.Da Reis das Hauptnahrungsmittel für mehr als die Hälfte der Weltbevölkerung ist, haben Reisproduktion und Produktivität des Anbaus erhebliche Auswirkungen auf die Ernährungssicherheit. In Anpassung an die zunehmende Wasserknappheit sowie zur Reduzierung der Treibhausgasemissionen wurden in vielen Reisanbaugebieten wassersparende Bewässerungsmaßnahmen (z. B. alternierende Bewässerung und Trocknung - AWD) eingeführt. Frühere Studien haben gezeigt, dass AWD die Wassernutzungseffizienz erhöht und die Methan (CH4)-Emissionen reduziert, während der Kornertrag im Vergleich zur kontinuierlichen Überstauung gleich bleibt oder leicht erhöht wird. Das Fehlen einer Wasserschicht in ehemals gefluteten Reisfeldern schafft jedoch neue Herausforderungen, wie z. B. eine veränderte Wurzelraumtemperatur (RZT), eine verstärkte Nitrifikation, die zu höheren Nitrat (NO3-)-Konzentrationen im Boden führt, oder eine stimulierte Unkrautkeimung, die zu Veränderungen in der Unkrautflora führt. All diese Faktoren können die Nährstoffaufnahme und -assimilation der Reispflanzen und damit das Pflanzenwachstum beeinflussen. Darüber hinaus steuert das Dampfdruckdefizit (VPD) die Transpiration und den Wasserfluss durch die Pflanzen, so dass die Nährstoffaufnahme und -assimilation durch die Pflanzen unter variierenden VPD-Bedingungen einer Anpassung unterliegen kann. Da das VPD zwischen den Reisanbaugebieten und den Jahreszeiten stark variiert und außerdem eine kontinuierliche Zunahme unter der globalen Erwärmung vorhergesagt wird, wurde es als Faktor in diese Studie aufgenommen. Das übergeordnete Ziel der Studie war es, die Reaktion verschiedener Reissorten auf die entstehenden Herausforderungen unter wassersparender Bewässerung zu bewerten. Die Experimente wurden im Gewächshaus und in VPD-Kammern an der Universität Hohenheim durchgeführt, wo die Pflanzen in Hydroponik kultiviert wurden. Sowohl tagsüber als auch nachts stiegen die Nährstoffaufnahmeraten von Reis linear mit der RZT im beobachteten Temperaturbereich bis 29°C an, was bedeutet, dass die optimale Temperatur für die Nährstoffaufnahme von Reis über 29°C liegen muss. Die Aufnahmeraten der verschiedenen Nährstoffelemente reagierten jedoch unterschiedlich auf RZT, wobei die Zunahme der Stickstoff (N)-Aufnahme pro °C größer war als die von Phosphor (PO43-) und Kalium (K+), was möglicherweise zu einem Ungleichgewicht in der Pflanzenernährung führen kann. Daher kann der Anstieg der RZT entweder durch den Klimawandel oder durch das Wassermanagement ein angepasstes Düngemanagement erforderlich machen. Im Allgemeinen war der Anstieg der Nährstoffaufnahme pro °C am Tag stärker ausgeprägt als in der Nacht, während die Aminosäurekonzentration in den Blättern sowohl am Tag als auch in der Nacht positiv mit der N-Aufnahme am Tag korreliert war, was darauf hindeutet, dass die Pflanzen möglicherweise mehr von einer erhöhten Temperatur am Tag profitieren. Wenn sowohl Ammonium (NH4+) als auch NO3- zugeführt wurden, nahmen die Reispflanzen einen höheren Anteil an NH4+ auf. Nach Verarmung an NH4+ in der Nährlösung nahmen die Pflanzen jedoch NO3- auf, ohne dass die Gesamt-N-Aufnahme abnahm. Die von der Reispflanze aufgenommene N form hatte bei niedrigem VPD keinen Einfluss auf den Blattgasaustausch, während die NO3--Aufnahme und -Assimilation bei einigen Reissorten bei hohem VPD die stomatäre Leitfähigkeit erhöhte, was zu einer signifikant höheren Photosyntheserate führte. Die Zunahme der Photosynthese führte jedoch nicht immer zu einer Zunahme der Trockensubstanz, wahrscheinlich aufgrund eines höheren Energiebedarfs für die NO3--Assimilation als für NH4+. Der Effekt der N form auf den Blattgasaustausch bei einigen Reissorten wurde nur bei hohem VPD gefunden, was auf genotypspezifische Anpassungsstrategien an hohes VPD hinweist. Die Aufrechterhaltung einer hohen stomatären Leitfähigkeit bei hohem VPD ist jedoch nur bei ausreichender Wasserversorgung von Vorteil. Daher stellen wir die Hypothese auf, dass mit zunehmendem VPD eine verstärkte Nitrifikation unter wassersparender Bewässerung den Blattgasaustausch von Reispflanzen verbessern kann, eine sorgfältige Sortenwahl und ein gutes Wassermanagement vorausgesetzt. Außerdem hatte die N form einen Einfluss auf die Konkurrenz zwischen Reis und Unkraut. In Mischkultur mit Reis erhöhte ein hoher Anteil an NO3- das Wachstum und die Konkurrenzfähigkeit von Trockenreis-Unkräutern, reduzierte aber das Wachstum und die Konkurrenzfähigkeit von Naßreis-Unkräutern. Folglich kann eine erhöhte Nitrifikation unter AWD den Konkurrenzdruck von Naßreis-Unkräutern verringern, aber die Konkurrenz durch Trockenreis-Unkräuter erhöhen. Im Gegensatz zu Reis wurde das Wachstum der Trockenreis-Unkräuter durch hohes VPD nicht reduziert, wobei ihre Nährstoffaufnahme mit der Wasseraufnahme korreliert war, was darauf hindeutet, dass Trockenreis-Unkräuter mit steigendem VPD erfolgreicher mit Reis um Nährstoffe konkurrieren. Die Selektion von Reissorten, die besser an die NO3--Aufnahme angepasst sind, wird das Wachstum von Reis und seine Konkurrenzfähigkeit gegenüber Unkräutern unter AWD verbessern. Die kumulativen Effekte von RZT und Bodennitrifikation auf das Reiswachstum sollten berücksichtigt werden, wenn die Auswirkungen des Klimawandels auf das Reiswachstum bewertet werden

Exploiting Text and Network Context for Geolocation of Social Media Users

Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text- and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets

Optimisation for image processing

The main purpose of optimisation in image processing is to compensate for missing, corrupted image data, or to find good correspondences between input images. We note that image data essentially has infinite dimensionality that needs to be discretised at certain levels of resolution. Most image processing methods find a suboptimal solution, given the characteristics of the problem. While the general optimisation literature is vast, there does not seem to be an accepted universal method for all image problems. In this thesis, we consider three interrelated optimisation approaches to exploit problem structures of various relaxations to three common image processing problems: 1. The first approach to the image registration problem is based on the nonlinear programming model. Image registration is an ill-posed problem and suffers from many undesired local optima. In order to remove these unwanted solutions, certain regularisers or constraints are needed. In this thesis, prior knowledge of rigid structures of the images is included in the problem using linear and bilinear constraints. The aim is to match two images while maintaining the rigid structure of certain parts of the images. A sequential quadratic programming algorithm is used, employing dimensional reduction, to solve the resulting discretised constrained optimisation problem. We show that pre-processing of the constraints can reduce problem dimensionality. Experimental results demonstrate better performance of our proposed algorithm compare to the current methods. 2. The second approach is based on discrete Markov Random Fields (MRF). MRF has been successfully used in machine learning, artificial intelligence, image processing, including the image registration problem. In the discrete MRF model, the domain of the image problem is fixed (relaxed) to a certain range. Therefore, the optimal solution to the relaxed problem could be found in the predefined domain. The original discrete MRF is NP hard and relaxations are needed to obtain a suboptimal solution in polynomial time. One popular approach is the linear programming (LP) relaxation. However, the LP relaxation of MRF (LP-MRF) is excessively high dimensional and contains sophisticated constraints. Therefore, even one iteration of a standard LP solver (e.g. interior-point algorithm), may take too long to terminate. Dual decomposition technique has been used to formulate a convex-nondifferentiable dual LP-MRF that has geometrical advantages. This has led to the development of first order methods that take into account the MRF structure. The methods considered in this thesis for solving the dual LP-MRF are the projected subgradient and mirror descent using nonlinear weighted distance functions. An analysis of the convergence properties of the method is provided, along with improved convergence rate estimates. The experiments on synthetic data and an image segmentation problem show promising results. 3. The third approach employs a hierarchy of problem's models for computing the search directions. The first two approaches are specialised methods for image problems at a certain level of discretisation. As input images are infinite-dimensional, all computational methods require their discretisation at some levels. Clearly, high resolution images carry more information but they lead to very large scale and ill-posed optimisation problems. By contrast, although low level discretisation suffers from the loss of information, it benefits from low computational cost. In addition, a coarser representation of a fine image problem could be treated as a relaxation to the problem, i.e. the coarse problem is less ill-conditioned. Therefore, propagating a solution of a good coarse approximation to the fine problem could potentially improve the fine level. With the aim of utilising low level information within the high level process, we propose a multilevel optimisation method to solve the convex composite optimisation problem. This problem consists of the minimisation of the sum of a smooth convex function and a simple non-smooth convex function. The method iterates between fine and coarse levels of discretisation in the sense that the search direction is computed using information from either the gradient or a solution of the coarse model. We show that the proposed algorithm is a contraction on the optimal solution and demonstrate excellent performance on experiments with image restoration problems.Open Acces

Probabilistic uncertainty quantification and experiment design for nonlinear models: Applications in systems biology

Despite the ever-increasing interest in understanding biology at the system level, there are several factors that hinder studies and analyses of biological systems. First, unlike systems from other applied fields whose parameters can be effectively identified, biological systems are usually unidentifiable, even in the ideal case when all possible system outputs are known with high accuracy. Second, the presence of multivariate bifurcations often leads the system to behaviors that are completely different in nature. In such cases, system outputs (as function of parameters/inputs) are usually discontinuous or have sharp transitions across domains with different behaviors. Finally, models from systems biology are usually strongly nonlinear with large numbers of parameters and complex interactions. This results in high computational costs of model simulations that are required to study the systems, an issue that becomes more and more problematic when the dimensionality of the system increases. Similarly, wet-lab experiments to gather information about the biological model of interest are usually strictly constrained by research budget and experimental settings. The choice of experiments/simulations for inference, therefore, needs to be carefully addressed. ^ The work presented in this dissertation develops strategies to address theoretical and practical limitations in uncertainty quantification and experimental design of non-linear mathematical models, applied in the context of systems biology. This work resolves those issues by focusing on three separate but related approaches: (i) the use of probabilistic frameworks for uncertainty quantification in the face of unidentifiability (ii) the use of behavior discrimination algorithms to study systems with discontinuous model responses and (iii) the use of effective sampling schemes and optimal experimental design to reduce the computational/experimental costs. ^ This cumulative work also places strong emphasis on providing theoretical foundations for the use of the proposed framework: theoretical properties of algorithms at each step in the process are investigated carefully to give more insights about how the algorithms perform, and in many cases, to provide feedback to improve the performance of existing approaches. Through the newly developed procedures, we successfully created a general probabilistic framework for uncertainty quantification and experiment design for non-linear models in the face of unidentifiability, sharp model responses with limited number of model simulations, constraints on experimental setting, and even in the absence of data. The proposed methods have strong theoretical foundations and have also proven to be effective in studies of expensive high-dimensional biological systems in various contexts

An exploratory study on the aspects of vocabulary knowledge addressed in EAP textbooks

Vocabulary knowledge, which plays an important role in foreign or second language (L2) learning, involves a range of aspects such as form and meaning, grammatical functions, or word parts. Little research, however, has investigated how aspects of vocabulary knowledge are addressed in L2 textbooks. This study aims to fill that gap by examining the aspects of vocabulary knowledge that English for Academic Purposes (EAP) textbooks pay attention to. To that end, four EAP textbooks of upper-intermediate and advanced levels were investigated. A total of 873 vocabulary activities were identified and analysed based on Nation (2013) and Brown’s (2011) frameworks. Results show that grammatical functions, associations, and word parts receive the most attention in the EAP textbooks while written form, constraints on use, and spoken form receive the least attention. The findings also demonstrate variations among the EAP textbooks in their amounts of attention to different aspects of word knowledge.status: accepte

Performance of Concrete Beams Reinforced with Various Ratios of Hybrid GFRP/Steel Bars

This paper aims to study the flexural behavior of concrete beams reinforced with hybrid combinations of GFRP/steel bars. To this purpose an experimental program was carried out on four concrete beams reinforced with Glass Fiber Reinforced Polymer (GFRP) and twelve hybrid GFRP/steel Reinforced Concrete (RC) beams. Flexural behavior of the tested beams such as stages of response, failure modes, crack patterns, stiffness, toughness and ductility were analyzed. The experimental results showed that depending on GFRP/steel reinforcement configurations, the behavior of hybrid GFRP/steel RC beams undergoes three or four stages, namely: pre-cracking stage; after concrete cracking and before steel yielding; post-yield stage of the steel bar until peak load and failure stage. Totally six failure modes of hybrid RC beams are reported depending on reinforcement rations and configuration. The effect of reinforcement configuration and ratio of GFRP to steel (ρg) on the crack patterns, stiffness, ductility and toughness of hybrid RC beams are significant. Based on the non-linear deformation model, an analytical model has been developed and validated to determine the steel yielding moment and ultimate moment of hybrid GFRP/steel RC beams. It could be seen that the experimental values were in good agreement with the predicted values

A new Bayesian approach for determining the number of components in a finite mixture

This article evaluates a new Bayesian approach to determining the number of components in a finite mixture. We evaluate through simulation studies mixtures of normals and latent class mixtures of Bernoulli responses. For normal mixtures we use a “gold standard” set of population models based on a well-known “testbed” data set – the galaxy recession velocity data set of Roeder (1990). For Bernoulli latent class mixtures we consider models for psychiatric diagnosis (Berkhof, van Mechelen and Gelman 2003). The new approach is based on comparing models with different numbers of components through their posterior deviance distributions, based on non-informative or diffuse priors. Simulations show that even large numbers of closely spaced normal components can be identified with sufficiently large samples, while for atent classes with Bernoulli responses identification is more complex, though it again improves with increasing sample size

DESIGNING HEDGE ALGEBRAIC CONTROLLER AND OPTIMIZING BY GENETIC ALGORITHM FOR SERIAL ROBOTS ADHERING TRAJECTORIES

In recent years, the application of hedge algebras in the field of control has been studied. The results show that this approach has many advantages. In additions, industrial robots are being well-developed and extensively used, especially in the industrial revolution 4.0. Accurate control of industrial robots is a class of problems that many scientists are interested in. In this paper, we design a controller based on hedge algebra for serial robots. The control rule is given by linguistic rule base system. The goal is to accurately control the moving robot arm which adheres given trajectories. Optimization of fuzzy parameters for the controller is done by genetic algorithms. The system has been simulated on the Matlab-Simulink software. The simulation results show that the orbital deviation is very small. Moreover, the controller worked well with correct control quality. This result once presents the simplicity and efficiency of the hedge algebras approach to control