Computational and Experimental Investigation of the Grooved Roll in Paper Machine Environment

In the paper machine, it is not a desired feature for the boundary layer flows in the fabric and the roll surfaces to travel into the closing nips, creating overpressure. In this thesis, the aerodynamic behavior of the grooved roll and smooth rolls is compared in order to understand the nip flow phenomena, which is the main reason why vacuum and grooved roll constructions are designed. A common method to remove the boundary layer flow from the closing nip is to use the vacuum roll construction. The downside of the use of vacuum rolls is high operational costs due to pressure losses in the vacuum roll shell. The deep grooved roll has the same goal, to create a pressure difference over the paper web and keep the paper attached to the roll or fabric surface in the drying pocket of the paper machine. A literature review revealed that the aerodynamic functionality of the grooved roll is not very well known. In this thesis, the aerodynamic functionality of the grooved roll in interaction with a permeable or impermeable wall is studied by varying the groove properties. Computational fluid dynamics simulations are utilized as the research tool. The simulations have been performed with commercial fluid dynamics software, ANSYS Fluent. Simulation results made with 3- and 2-dimensional fluid dynamics models are compared to laboratory scale measurements. The measurements have been made with a grooved roll simulator designed for the research. The variables in the comparison are the paper or fabric wrap angle, surface velocities, groove geometry and wall permeability. Present-day computational and modeling resources limit grooved roll fluid dynamics simulations in the paper machine scale. Based on the analysis of the aerodynamic functionality of the grooved roll, a grooved roll simulation tool is proposed. The smooth roll simulations show that the closing nip pressure does not depend on the length of boundary layer development. The surface velocity increase affects the pressure distribution in the closing and opening nips. The 3D grooved roll model reveals the aerodynamic functionality of the grooved roll. With the optimal groove size it is possible to avoid closing nip overpressure and keep the web attached to the fabric surface in the area of the wrap angle. The groove flow friction and minor losses play a different role when the wrap angle is changed. The proposed 2D grooved roll simulation tool is able to replicate the grooved aerodynamic behavior with reasonable accuracy. A small wrap angle predicts the pressure distribution correctly with the chosen approach for calculating the groove friction losses. With a large wrap angle, the groove friction loss shows too large pressure gradients, and the way of calculating the air flow friction losses in the groove has to be reconsidered. The aerodynamic functionality of the grooved roll is based on minor and viscous losses in the closing and opening nips as well as in the grooves. The proposed 2D grooved roll model is a simplification in order to reduce computational and modeling efforts. The simulation tool makes it possible to simulate complex paper machine constructions in the paper machine scale. In order to use the grooved roll as a replacement for the vacuum roll, the grooved roll properties have to be considered on the basis of the web handling application.

Machine vision methods for process measurements in pulping

The papermaking industry has been continuously developing intelligent solutions to characterize the raw materials it uses, to control the manufacturing process in a robust way, and to guarantee the desired quality of the end product. Based on the much improved imaging techniques and image-based analysis methods, it has become possible to look inside the manufacturing pipeline and propose more effective alternatives to human expertise. This study is focused on the development of image analyses methods for the pulping process of papermaking. Pulping starts with wood disintegration and forming the fiber suspension that is subsequently bleached, mixed with additives and chemicals, and finally dried and shipped to the papermaking mills. At each stage of the process it is important to analyze the properties of the raw material to guarantee the product quality. In order to evaluate properties of fibers, the main component of the pulp suspension, a framework for fiber characterization based on microscopic images is proposed in this thesis as the first contribution. The framework allows computation of fiber length and curl index correlating well with the ground truth values. The bubble detection method, the second contribution, was developed in order to estimate the gas volume at the delignification stage of the pulping process based on high-resolution in-line imaging. The gas volume was estimated accurately and the solution enabled just-in-time process termination whereas the accurate estimation of bubble size categories still remained challenging. As the third contribution of the study, optical flow computation was studied and the methods were successfully applied to pulp flow velocity estimation based on double-exposed images. Finally, a framework for classifying dirt particles in dried pulp sheets, including the semisynthetic ground truth generation, feature selection, and performance comparison of the state-of-the-art classification techniques, was proposed as the fourth contribution. The framework was successfully tested on the semisynthetic and real-world pulp sheet images. These four contributions assist in developing an integrated factory-level vision-based process control.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Quality assurance of refuse derived fuel with machine vision

This thesis studies the use of machine vision in RDF quality assurance and manufacturing. Currently machine vision is used in recycling and material detection and some commer- cial products are available in the market. In this thesis an on-line machine vision system is proposed for characterizing particle size. The proposed machine vision system is based on the mapping between image segmenta- tion and the ground truth of the particle size. The results shows that the implementation of such machine vision system is feasible.

Expectations of elderly people on the nurse-client relationship and interaction in home care : "When I see the nurse's smiling face it feels likes sunshine on a rainy and gloomy day"

The purpose of the thesis was to explore expectations of elderly people on the nurse-client relationship and interaction in home care. The aim is to improve the quality of care to better meet the needs of the clients. A qualitative approach was adopted. Semi-structured theme interviews were used for data collection. The interviews were conducted during spring 2006. Six elderly clients of a private home care company in Southern Finland acted as informants. Content analysis was used as the method of data analysis. The findings suggest that clients expect nurses to provide professional care with loving-kindness. Trust and mutual, active interaction were expected from the nurse-client relationship. Clients considered it important that the nurse recognizes each client's individual needs. The nurse was expected to perform duties efficiently, but in a calm and unrushed manner. A mechanic performance of tasks was considered negative. Humanity was viewed as a crucial element in the nurse-client relationship. Clients expressed their need to be seen as human beings. Seeing beyond the illness was considered important. A smiling nurse was described to be able to alleviate pain and anxiety. Clients hoped to have a close relationship with the nurse. The development of a close relationship was considered to be more likely if the nurse is familiar and genuine. Clients wish the nurses to have a more attending presence. Clients suggested that the work areas of the nurses could be limited so that they would have more time to transfer from one place to another. Clients felt that they would benefit from this as well. The nurses were expected to be more considerate. Clients wished for more information regarding changes that affect their care. They wished to be informed about changes in schedules and plans. Clients hoped for continuity from the nurse-client relationship. Considering the expectations of clients promotes client satisfaction. Home care providers have an opportunity to reflect their own care behaviour on the findings. To better meet the needs of the clients, nurses could apply the concept of loving-kindness in their work, and strive for a more attending presence.

Worldly Saints : Social Interaction of Dominican Penitent Women in Italy, 1200-1500

Doctoral dissertation, University of Turku

Copresence video conference mediated interaction

Abstract

Interaction of prechilling, temperature, osmotic stress, and light in Picea abies seed germination

Summary

Sensitivity of temperate grassland species to elevated atmospheric CO2 and the interaction with temperature and water stress

Selostus: Kohonneen hiilidioksidipitoisuuden, lämpötilan ja kuivuuden vaikutus nurmikasveihin