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Neutrosophy studies the origin, nature, scope of neutralities, and their interactions with different ideational spectra. It is a new philosophy that extends fuzzy logic and is the basis of neutrosophic logic, neutrosophic probability, neutrosophic set theory, and neutrosophic statistics. Because the world is full of indeterminacy, the imperfection of knowledge that a human receives/observes from the external world also causes imprecision. Neutrosophy introduces a new concept , which is the representation of indeterminacy. However, this theory is mostly discussed in physiology and mathematics. Thus, applications to prove this theory can solve real problems are needed. Image segmentation is the first and key step in image processing. It is a critical and essential component of image analysis and pattern recognition. In this dissertation, I apply neutrosophy to three types of image segmentation: gray level images, breast ultrasound images, and color images. In gray level image segmentation, neutrosophy helps reduce noise and extend the watershed method to normal images. In breast ultrasound image segmentation, neutrosophy integrates two controversial opinions about speckle: speckle is noise versus speckle includes pattern information. In color image segmentation, neutrosophy integrates color and spatial information, global and local information in two different color spaces: RGB and CIE (L*u*v*), respectively. The experiments show the advantage of using neutrosophy.

Implicit in the use of the target audience concept is the assumption that audiences are highly segmented. Yet media don't deliver the 'unique' audience they claim to. This research provides evidence that genuine audience niches are often hard to find, thus is expected to challenge entrenched assumptions about audience targeting.

The Chinese car market has, through the last decade evolved into the major market in the world. Its car market from has become the world’s largest market from 2009 until today. With the emerging market that is China, the demand for cars is supposed to grow even more in the next decade.The thesis starts by studying the theories of consumer market segmentation with a hybrid and dynamic aspect. A quantitative investigation was conducted with the help of a survey. The survey was sent out to car consumers and potential car consumers who are residing in China. From this study the authors were able to anticipate possible preferential profiles.

The vertebrate hindbrain is divided early in embryogenesis along its anterior-posterior axis into eight segments known as rhombomeres. This provides an excellent model for studying early segmentation and region-specific transcriptional domains. MafB, a basic domain leucine zipper transcription factor, is the first gene known to be expressed in the presumptive rhombomere 5 and 6 domain (r5-r6). MafB expression is directly activated by the homeodomain protein vHnf1. vHnf1 and MafB share an anterior expression limit at the r4/r5 boundary but vHnf1 expression extends beyond the posterior limit of MafB and, therefore, cannot establish the posterior expression boundary of MafB. Through the use of in situ hybridization, immunofluorescence, and chromatin immunoprecipitation analyses, I have determined that the caudal-related homeodomain protein Cdx1 establishes the posterior boundary of MafB by directly inhibiting expression beyond the r6/r7 boundary. My results indicate that MafB is one of the earliest direct targets of Cdx1.

The vertebrate hindbrain is divided early in embryogenesis along its anterior-posterior axis into eight segments known as rhombomeres. This provides an excellent model for studying early segmentation and region-specific transcriptional domains. MafB, a basic domain leucine zipper transcription factor, is the first gene known to be expressed in the presumptive rhombomere 5 and 6 domain (r5-r6). MafB expression is directly activated by the homeodomain protein vHnf1. vHnf1 and MafB share an anterior expression limit at the r4/r5 boundary but vHnf1 expression extends beyond the posterior limit of MafB and, therefore, cannot establish the posterior expression boundary of MafB. Through the use of in situ hybridization, immunofluorescence, and chromatin immunoprecipitation analyses, I have determined that the caudal-related homeodomain protein Cdx1 establishes the posterior boundary of MafB by directly inhibiting expression beyond the r6/r7 boundary. My results indicate that MafB is one of the earliest direct targets of Cdx1.

This thesis explores the experiences of managers who work flexibly. Flexible working policies are prevalent in all organizations in the UK because of the legislation giving specific groups of parents and carers the right to request flexible working. Many organizations extend the policies to all employees, yet the take-up is not as high as expected, particularly among staff at managerial levels. This thesis explores how managers construe and experience flexible working arrangements while successfully fulfilling their roles as managers of people. The exploratory study consisted of interviews with eight managers with unique flexible working patterns. Analysis of the interview transcripts identified concepts of consistency and adaptability. Consistency refers to meeting fixed needs from the work and non-work domains, and adaptability refers to the adjustment of schedules to meet the changing demands from those domains. The concepts of consistency and adaptability were further explored in the main study which is based on interviews with 24 women and 10 men who held managerial positions and had a flexible working arrangement which reduced their face time in the workplace. The research offers three main contributions to the literature. At a theoretical level, I propose a model which demonstrates how individuals use consistency and adaptability to meet the fixed and changing demands from the work and non-work domains. This model extends understanding of the complexity of the segmentation/integration continuum of boundary theory, explaining how and why managers use flexible working arrangements as a means of managing boundaries and achieving desired goals in both domains. Four distinct clusters emerged among the managerial participants in terms of the type and direction of adaptability, indicating the range of strategies used by managers to ensure the success of their flexible working arrangements. A detailed description of managers’ flexible working experiences is provided, adding to what is known about the role of manager through the exploration of the enactment of that role when working flexibly.

In many image-processing applications, one object of interest must be segmented. The techniques used for segmentation vary depending on the particular situation and the specifications of the problem at hand. In methods that rely on a learning process, the lack of a sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. The performance of some other methods may suffer from frequent user interactions to determine the critical segmentation parameters. Also, none of the existing approaches use online (permanent) feedback, from the user, in order to evaluate the generated results. Considering the above factors, a new multi-stage image segmentation system, based on Reinforcement Learning (RL) is introduced as the main contribution of this research. In this system, the RL agent takes specific actions, such as changing the tasks parameters, to modify the quality of the segmented image. The approach starts with a limited number of training samples and improves its performance in the course of time. In this system, the expert knowledge is continuously incorporated to increase the segmentation capabilities of the method. Learning occurs based on interactions with an offline simulation environment, and later online through interactions with the user. The offline mode is performed using a limited number of manually segmented samples, to provide the segmentation agent with basic information about the application domain. After this mode, the agent can choose the appropriate parameter values for different processing tasks, based on its accumulated knowledge. The online mode, consequently, guarantees that the system is continuously training and can increase its accuracy, the more the user works with it. During this mode, the agent captures the user preferences and learns how it must change the segmentation parameters, so that the best result is achieved. By using these two learning modes, the RL agent allows us to optimally recognize the decisive parameters for the entire segmentation process.

In many image-processing applications, one object of interest must be segmented. The techniques used for segmentation vary depending on the particular situation and the specifications of the problem at hand. In methods that rely on a learning process, the lack of a sufficient number of training samples is usually an obstacle, especially when the samples need to be manually prepared by an expert. The performance of some other methods may suffer from frequent user interactions to determine the critical segmentation parameters. Also, none of the existing approaches use online (permanent) feedback, from the user, in order to evaluate the generated results. Considering the above factors, a new multi-stage image segmentation system, based on Reinforcement Learning (RL) is introduced as the main contribution of this research. In this system, the RL agent takes specific actions, such as changing the tasks parameters, to modify the quality of the segmented image. The approach starts with a limited number of training samples and improves its performance in the course of time. In this system, the expert knowledge is continuously incorporated to increase the segmentation capabilities of the method. Learning occurs based on interactions with an offline simulation environment, and later online through interactions with the user. The offline mode is performed using a limited number of manually segmented samples, to provide the segmentation agent with basic information about the application domain. After this mode, the agent can choose the appropriate parameter values for different processing tasks, based on its accumulated knowledge. The online mode, consequently, guarantees that the system is continuously training and can increase its accuracy, the more the user works with it. During this mode, the agent captures the user preferences and learns how it must change the segmentation parameters, so that the best result is achieved. By using these two learning modes, the RL agent allows us to optimally recognize the decisive parameters for the entire segmentation process.