Weld joint characteristics by nano-materials addition

The need for industries to remain competitive in the welding business, has created necessity to develop innovative processes that can exceed customer’s demand. Significant development in improving weld efficiency, during the past decades, still have their drawbacks, specifically in the weld strength properties. The recent innovative technologies have created smallest possible solid material known as nanomaterial and their introduction in welding production has improved the weld strength properties and to overcome unstable microstructures in the weld. This study utilizes a qualitative research method, to elaborate the methods of introducing nanomaterial to the weldments and the characteristic of the welds produced by different welding processes. The study mainly focuses on changes in the microstructural formation and strength properties on the welded joint and also discusses those factors influencing such improvements, due to the addition of nanomaterials. The effect of nanomaterial addition in welding process modifies the physics of joining region, thereby, resulting in significant improvement in the strength properties, with stable microstructure in the weld. The addition of nanomaterials in the welding processes are, through coating on base metal, addition in filler metal and utilizing nanostructured base metal. However, due to its insignificant size, the addition of nanomaterials directly to the weld, would poses complications. The factors having major influence on the joint integrity are dispersion of nanomaterials, characteristics of the nanomaterials, quantity of nanomaterials and selection of nanomaterials. The addition of nanomaterials does not affect the fundamental properties and characteristics of base metals and the filler metal. However, in some cases, the addition of nanomaterials lead to the deterioration of the joint properties by unstable microstructural formations. Still research are ongoing to achieve high joint integrity, in various materials through different welding processes and also on other factors that influence the joint strength.

Grain yield, net blotch and scald of barley in Finnish official variety trials

Selostus: Ohrasato ja verkko- ja rengaslaikku virallisissa lajikekokeissa

Feeding whole grain with pelleted diets to growing broiler chickens

Selostus: Kokojyväviljan syöttäminen broilereille rakeisen rehun seassa

Effect of annually repeated undersowing on cereal grain yields

Selostus: Aluskasveja voi käyttää toistuvasti viljan viljelyssä

Reducing grain damage in naked oat through gentle harvesting

Selostus: Paljasjyväisen kauran hellävarainen sadonkorjuu

Impact dehulling oat grain to improve quality of on-farm produced feed 2

Selostus: Kauran kuorinnan aiheuttama jyvien rikkoutuminen ei heikennä säilyvyyttä

Impact dehulling oat grain to improve quality of on-farm produced feed 1

Selostus: Kotoisen kauran ravitsemuksellista arvoa ja energiapitoisuutta voidaan parantaa kuorinnalla

Relationships between climate and winter cereal grain quality in Finland and their potential for forecasting

Selostus: Sään vaikutus syysviljojen hehtolitran painoon ennusteen laadinnan näkökulmasta

Quantitative refinement of reaction-based biomodels

In the field of molecular biology, scientists adopted for decades a reductionist perspective in their inquiries, being predominantly concerned with the intricate mechanistic details of subcellular regulatory systems. However, integrative thinking was still applied at a smaller scale in molecular biology to understand the underlying processes of cellular behaviour for at least half a century. It was not until the genomic revolution at the end of the previous century that we required model building to account for systemic properties of cellular activity. Our system-level understanding of cellular function is to this day hindered by drastic limitations in our capability of predicting cellular behaviour to reflect system dynamics and system structures. To this end, systems biology aims for a system-level understanding of functional intraand inter-cellular activity. Modern biology brings about a high volume of data, whose comprehension we cannot even aim for in the absence of computational support. Computational modelling, hence, bridges modern biology to computer science, enabling a number of assets, which prove to be invaluable in the analysis of complex biological systems, such as: a rigorous characterization of the system structure, simulation techniques, perturbations analysis, etc. Computational biomodels augmented in size considerably in the past years, major contributions being made towards the simulation and analysis of large-scale models, starting with signalling pathways and culminating with whole-cell models, tissue-level models, organ models and full-scale patient models. The simulation and analysis of models of such complexity very often requires, in fact, the integration of various sub-models, entwined at different levels of resolution and whose organization spans over several levels of hierarchy. This thesis revolves around the concept of quantitative model refinement in relation to the process of model building in computational systems biology. The thesis proposes a sound computational framework for the stepwise augmentation of a biomodel. One starts with an abstract, high-level representation of a biological phenomenon, which is materialised into an initial model that is validated against a set of existing data. Consequently, the model is refined to include more details regarding its species and/or reactions. The framework is employed in the development of two models, one for the heat shock response in eukaryotes and the second for the ErbB signalling pathway. The thesis spans over several formalisms used in computational systems biology, inherently quantitative: reaction-network models, rule-based models and Petri net models, as well as a recent formalism intrinsically qualitative: reaction systems. The choice of modelling formalism is, however, determined by the nature of the question the modeler aims to answer. Quantitative model refinement turns out to be not only essential in the model development cycle, but also beneficial for the compilation of large-scale models, whose development requires the integration of several sub-models across various levels of resolution and underlying formal representations.

Implementation of Quantitative Reaction-based Model Refinement

Building a computational model for complex biological systems is an iterative process. It starts from an abstraction of the process and then incorporates more details regarding the specific biochemical reactions which results in the change of the model fit. Meanwhile, the model’s numerical properties such as its numerical fit and validation should be preserved. However, refitting the model after each refinement iteration is computationally expensive resource-wise. There is an alternative approach which ensures the model fit preservation without the need to refit the model after each refinement iteration. And this approach is known as quantitative model refinement. The aim of this thesis is to develop and implement a tool called ModelRef which does the quantitative model refinement automatically. It is both implemented as a stand-alone Java application and as one of Anduril framework components. ModelRef performs data refinement of a model and generates the results in two different well known formats (SBML and CPS formats). The development of this tool successfully reduces the time and resource needed and the errors generated as well by traditional reiteration of the whole model to perform the fitting procedure.