Performance and Design of Punching-Shear Reinforcing Systems

Punching shear reinforcement is increasingly used in flat slabs as an effective solution to increase their strength and deformation capacity. Several punching shear reinforcing systems have been developed in the past, such as studs, stirrups or bent-up bars. The efficiency of such systems is strongly influenced by their development conditions (anchorage, bond) and detailing rules. Codes of practice, however, do not typically acknowledge such differences, proposing the same set of design formulas for all systems. This approach is detrimental for some systems (with better detailing rules and anchorage characteristics) and does not provide enough guidance for design of others (not respecting codes’ detailing rules). In this paper, the fundamentals of the critical shear crack theory are explained with respect to the design of punching shear reinforcing systems. It is shown that this theory provides a consistent basis for design of shear reinforcing systems accounting for their particularities and modes of failure. The results of 6 tests on full scale slabs (3.0× 3.0× 0.25 m) with same flexural and shear reinforcing ratio but with different punching shear reinforcing systems are presented and discussed. The experimental results confirm that the strength and deformation capacity are strongly influenced by the characteristics of the shear reinforcing system. The results for the various systems are finally investigated within the frame of the critical shear crack theory, leading to a series of recommendations for desig

Head Gasket Finite Element Model Correlation

Finite element analysis studies are increasingly being relied upon to improve the design and decrease overall production time of powertrain components. Multi-layer steel head gaskets are important, passive sealing components that exist in almost all internal combustion engines and are crucial for proper engine performance. In industry there currently exist many different approaches for studying this component using finite element analysis. This study attempted to give insight into what finite element methods are currently being used by analysts and if their results correlate with physical test results. The category of finite elements studied for use in the gasket assembly were dependent on the type of results required and included conventional shell, continuum shell, gasket type and three-dimensional solid elements. By use of ABAQUS software and Fuji Pressure Film comparisons, it was found that each element type has strengths and limitations regarding real world correlation, computational expense and ease of procedure

BI para el pronóstico de ventas con visualización móvil para la empresa Inversiones DRB S.A.C

El objetivo de esta presente investigación fue determinar la influencia que tiene un BI después de su implantación en la empresa Inversiones D.B.R para el área de ventas enfocada al pronostico de ventas mejorando su rentabilidad, no solo incrementando las ventas sino su margen bruto respecto a las ganancias. Para los resultados se aplicaron el pre test y post test de cada indicador Margen Bruto y Crecimiento de ventas, también se evaluó la normalidad y la prueba paramétrica de t-student donde se logró aceptar la hipótesis alternativa rechazado la nula. Demostrando en los resultados En la presente investigación se logró ver que para el crecimiento de ventas aumento un 12,7% y el margen bruto también aumento un 11,4% frente a lo obtenido antes de implementar la solución. Todo lo anterior nos llevó a validar que un BI si ayuda a la gerencia en la toma de decisiones además de presentar visualizaciones interactivas con el flujo de la información en un tiempo, por el lado móvil se demostró que pueden estar conectados a tiempo real con la parte de escritorio. Las recomendaciones que se llevaron a cabo fueron evaluadas con investigaciones posteriores

Levels of Visual Information Processing: Perception of Dynamic Properties and Events

Objective: The here presented studies explore automatic and controlled per- ceptual processes in two dynamic paradigms and support a rationale of a multi- level approach to dynamic visual perception. Method: I investigate different perceptual levels of dynamic scenes, including factors within the perceiver, within the objects, and within the environment. Automatic processes are explored with a simple 3-D tracking task and an event perception recognition task; controlled processes are observed in a modified tracking task with specific object properties and an identification task. Results: Through analysis of tracking and report errors measured in the two paradigms, I observed similarities in the automatic processing of artificial 3-D tracking environments (Study 1: the scene-based relations are more important than positions of individual objects) and real-life video clips (Study 2: core as- pects are preferred over fine details). Despite the assumption that tracking is a cognitive-impenetrable mechanism, results of the modified tracking task (Study 3) point towards the ability of participants to strategically weigh visual information based on task-demands. Conclusion: The results of this dissertation illustrate that the identification of influential internal and external factors is important to enhance our under- standing of the multidimensional nature of perception – an understanding that will eventually and hopefully bring research to move beyond questions of how resources are limited, and start to focus on fundamental issues like how we can use mental resources to our benefit

Wind Power

This book is the result of inspirations and contributions from many researchers of different fields. A wide verity of research results are merged together to make this book useful for students and researchers who will take contribution for further development of the existing technology. I hope you will enjoy the book, so that my effort to bringing it together for you will be successful. In my capacity, as the Editor of this book, I would like to thanks and appreciate the chapter authors, who ensured the quality of the material as well as submitting their best works. Most of the results presented in to the book have already been published on international journals and appreciated in many international conferences

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings

Analysing functional genomics data using novel ensemble, consensus and data fusion techniques

Motivation: A rapid technological development in the biosciences and in computer science in the last decade has enabled the analysis of high-dimensional biological datasets on standard desktop computers. However, in spite of these technical advances, common properties of the new high-throughput experimental data, like small sample sizes in relation to the number of features, high noise levels and outliers, also pose novel challenges. Ensemble and consensus machine learning techniques and data integration methods can alleviate these issues, but often provide overly complex models which lack generalization capability and interpretability. The goal of this thesis was therefore to develop new approaches to combine algorithms and large-scale biological datasets, including novel approaches to integrate analysis types from different domains (e.g. statistics, topological network analysis, machine learning and text mining), to exploit their synergies in a manner that provides compact and interpretable models for inferring new biological knowledge. Main results: The main contributions of the doctoral project are new ensemble, consensus and cross-domain bioinformatics algorithms, and new analysis pipelines combining these techniques within a general framework. This framework is designed to enable the integrative analysis of both large- scale gene and protein expression data (including the tools ArrayMining, Top-scoring pathway pairs and RNAnalyze) and general gene and protein sets (including the tools TopoGSA , EnrichNet and PathExpand), by combining algorithms for different statistical learning tasks (feature selection, classification and clustering) in a modular fashion. Ensemble and consensus analysis techniques employed within the modules are redesigned such that the compactness and interpretability of the resulting models is optimized in addition to the predictive accuracy and robustness. The framework was applied to real-word biomedical problems, with a focus on cancer biology, providing the following main results: (1) The identification of a novel tumour marker gene in collaboration with the Nottingham Queens Medical Centre, facilitating the distinction between two clinically important breast cancer subtypes (framework tool: ArrayMining) (2) The prediction of novel candidate disease genes for Alzheimer’s disease and pancreatic cancer using an integrative analysis of cellular pathway definitions and protein interaction data (framework tool: PathExpand, collaboration with the Spanish National Cancer Centre) (3) The prioritization of associations between disease-related processes and other cellular pathways using a new rule-based classification method integrating gene expression data and pathway definitions (framework tool: Top-scoring pathway pairs) (4) The discovery of topological similarities between differentially expressed genes in cancers and cellular pathway definitions mapped to a molecular interaction network (framework tool: TopoGSA, collaboration with the Spanish National Cancer Centre) In summary, the framework combines the synergies of multiple cross-domain analysis techniques within a single easy-to-use software and has provided new biological insights in a wide variety of practical settings