Recommendation systems have been widely utilized in e-commerce settings to aid users through their shopping experiences. The principal advantage of these systems is their ability to narrow down the purchase options in addition to marketing items to customers. However, a number of challenges remain, notably those related to obtaining a clearer understanding of users, their profiles, and their preferences in terms of purchased items. Specifically, recommender systems based on collaborative filtering recommend items that have been rated by other users with preferences similar to those of the targeted users. Intuitively, the more information and ratings collected about the user, the more accurate are the recommendations such systems suggest. In a typical recommender systems database, the data are sparse. Sparsity occurs when the number of ratings obtained by the users is much lower than the number required to build a prediction model. This usually occurs because of the users’ reluctance to share their reviews, either due to privacy issues or an unwillingness to make the extra effort. Grey-sheep users pose another challenge. These are users who shared their reviews and ratings yet disagree with the majority in the systems. The current state-of-the-art typically treats these users as outliers and removes them from the system. Our goal is to determine whether keeping these users in the system may benefit learning. Thirdly, cold-start problems refer to the scenario whereby a new item or user enters the system and is another area of active research. In this case, the system will have no information about the new user or item, making it problematic to find a correlation with others in the system. This thesis addresses the three above-mentioned research challenges through the development of machine learning methods for use within the recommendation system setting. First, we focus on the label and data sparsity though the development of the Hybrid Cluster analysis and Classification learning (HCC-Learn) framework, combining supervised and unsupervised learning methods. We show that combining classification algorithms such as k-nearest neighbors and ensembles based on feature subspaces with cluster analysis algorithms such as expectation maximization, hierarchical clustering, canopy, k-means, and cascade k-means methods, generally produces high-quality results when applied to benchmark datasets. That is, cluster analysis clearly benefits the learning process, leading to high predictive accuracies for existing users. Second, to address the cold-start problem, we present the Popular Users Personalized Predictions (PUPP-DA) framework. This framework combines cluster analysis and active learning, or so-called user-in-the-loop, to assign new customers to the most appropriate groups in our framework. Based on our findings from the HCC-Learn framework, we employ the expectation maximization soft clustering technique to create our user segmentations in the PUPP-DA framework, and we further incorporate Convolutional Neural Networks into our design. Our results show the benefits of user segmentation based on soft clustering and the use of active learning to improve predictions for new users. Furthermore, our findings show that focusing on frequent or popular users clearly improves classification accuracy. In addition, we demonstrate that deep learning outperforms machine learning techniques, notably resulting in more accurate predictions for individual users. Thirdly, we address the grey-sheep problem in our Grey-sheep One-class Recommendations (GSOR) framework. The existence of grey-sheep users in the system results in a class imbalance whereby the majority of users will belong to one class and a small portion (grey-sheep users) will fall into the minority class. In this framework, we use one-class classification to provide a class structure for the training examples. As a pre-assessment stage, we assess the characteristics of grey-sheep users and study their impact on model accuracy. Next, as mentioned above, we utilize one-class learning, whereby we focus on the majority class to first learn the decision boundary in order to generate prediction lists for the grey-sheep (minority class). Our results indicate that including grey-sheep users in the training step, as opposed to treating them as outliers and removing them prior to learning, has a positive impact on the general predictive accuracy.

Study objective - The aim was to determine whether areas in the periaqueductal grey matter, medial dorsal raphé, or ventrolateral medulla might be involved with the integration of blood pressure and heart rate during isometric exercise.Design - Cats were anaesthetised with α chloralose (75 mg·kg-1) and catheters inserted into the right jugular vein and carotid artery. Isometric contractions were generated using a microprocessor controlled stimulator and sleeve electrode around the tibial nerve. Bilateral lesions were made in the dorsal periaqueductal grey matter (P1.0, LR 2.0, HD + 1.5 mm) or two sites in the ventrolateral medulla (P12.0, RL 2.0, HD -10 mm; or P12.0, RL 2.0, HD -8.5 mm). Lesions were also made in the medial dorsal raphé nuclei (P1.0, RL 0.0, HD +1.5 mm). Clonidine was injected into the cerebral aqueduct to determine whether it would exert an antipressor effect during muscle contraction after the lesions were made. Only one site of lesion was made in a group of animals. Bilateral injections of clonidine (250 ng in 0.5 μl) were made into the intact ventrolateral medulla (P11.5, RL 4.0, HD -8.5 mm) to explore its role further. Fatiguing contractions were performed before and after the lesions were made, or clonidine was injected, and changes in arterial blood pressure and heart rate were measured. Verification of the lesion sites or the microinjection sites, and the extent of the lesion or spread of the clonidine, was made from histological examination of brain tissue after each experiment.Experimental material - Adult cats of either sex, n = 20, weight 2.4 (SD 0.4) kg, were used.Measurements and main results - Fatiguing isometric contractions in control conditions caused mean arterial pressure to increase by 45-50 mm Hg and heart rates by 20-25 beats·min-1. Bilateral lesions in the dorsal periaqueductal grey matter did not alter resting mean arterial pressure but attenuated the pressor response during contractions. Injections of clonidine into the cerebral aqueduct had no further antipressor effects after the lesions. Lesions of the medial dorsal raphé nuclei or injections of clonidine into the intact medial dorsal raphé nuclei did not affect the pressor response to fatiguing isometric contractions. Injections of clonidine into the intact ventrolateral medulla eliminated the pressor response to isometric contractions. Bilateral lesions of the ventrolateral medulla near the rostral lateral border of the inferior olivary tract nuclei (P12.0, LR 2.0, HD -10 mm) also attenuated the muscle pressor response, while subsequent injections of clonidine into the cerebral aqueduct depressed the changes in blood pressure further.Conclusions - Ergoreceptor information may be processed through the periaqueductal grey matter through the ventrolateral medulla to control arterial blood pressure during isometric exercise to fatigue.

This project mainly investigated how the niobium(Nb) content influences the microstructure and mechanical properties of grey cast iron. Considering the mechanism, the study also analyzes the relationship between microstructure and mechanical properties. Generally, the work is based on 127 test bars/samples from two cylinder heads and three batches of plates, which were studied by measuring tensile strength, microhardness, graphite size, carbide amount and chemistry. The result data has been evaluated with statistical methods. The experiments mainly included the preparation of the samples for test and analysis. The mechanical properties in this study are evaluated by the tensile strength of the grey cast iron. Meanwhile, various microscopies were applied to observe how niobium and cooling rate influence the microstructure. Finally, from the analysis results, it tells that the niobium does affect the tensile strength of grey cast iron. Higher the niobium content is, higher the tensile strength is. The computed result based on the data also shows niobium’s strong effect. The faster cooling rate will increase the tensile strength and pearlite microhardness of grey cast iron as well. The carbide amount of grey cast iron can be increased by the addition of niobium content. Furthermore, some future work needs to be done to explain the unsolved problem in this result. The reasons of why a specific position A-2-d of plates has high values of tensile strength demand more microstructure investigation. For the niobium influence, more experiments and data containing a larger range of niobium content also need to be done to prove the mathematics results in this report. / Detta projekt undersökte huvudsakligen hur innehållet av niob (Nb) påverkar gråstålens mikrostruktur och mekaniska egenskaper. Med tanke på mekanismen analyserar undersökningen även förhållandet mellan mikrostruktur och mekaniska egenskaper. Arbetet baseras i allmänhet på 127 provstänger / prover från två cylinderhuvuden och tre satser av plattor, vilka studerades genom mätning av draghållfasthet, mikrohårdhet, grafitstorlek, karbidmängd och kemi. Resultatdata har utvärderats med statistiska metoder. Experimenten inbegriper huvudsakligen beredningen av proven för test och analys. De mekaniska egenskaperna i denna studie utvärderas av gråstålets draghållfasthet. Under tiden applicerades olika mikroskopier för att observera hur niob- och kylhastigheten påverkar mikrostrukturen. Slutligen, från analysresultaten, berättar den att niobet påverkar draghållfastheten hos grågjutjärn. Ju högre niobinnehållet är, desto högre är draghållfastheten. Det beräknade resultatet baserat på data visar också niobins starka effekt. Den snabbare kylhastigheten ökar också draghållfastheten och pearliten-mikrohårdheten hos grågjutjärn. Karbidmängden av grågjutjärn kan ökas genom tillsats av niobhalt. Vidare måste vissa framtida arbeten göras för att förklara det olösta problemet i detta resultat. Skälen till varför en specifik position A-2-d av plattor har höga dragkrafter kräver mer mikrostrukturundersökning. För niobinpåverkan måste fler experiment och data som innehåller ett större antal niobinnehåll också göras för att bevisa matematikresultaten i denna rapport.

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Yes / In engineering design, there are many functional relationships which are difficult to express into a simple and exact mathematical formula. Instead they are documented within a form of line graphs (or plot charts or curve diagrams) in engineering handbooks or text books. Because the information in such a form cannot be used directly in the modern computer aided design (CAD) process, it is necessary to find a way to numerically represent the information. In this paper, a data processing system for numerical representation of line graphs in mechanical design is developed, which incorporates the process cycle from the initial data acquisition to the final output of required information. As well as containing the capability for curve fitting through Cubic spline and Neural network techniques, the system also adapts a novel methodology for use in this application: Grey Models. Grey theory have been used in various applications, normally involved with time-series data, and have the characteristic of being able to handle sparse data sets and data forecasting. Two case studies were then utilized to investigate the feasibility of Grey models for curve fitting. Furthermore, comparisons with the other two established techniques show that the accuracy was better than the Cubic spline function method, but slightly less accurate than the Neural network method. These results are highly encouraging and future work to fully investigate the capability of Grey theory, as well as exploiting its sparse data handling capabilities is recommended.

Perrault's Bluebeard tale is a story of domestic abuse: Bluebeard tries to control his wife's movements by prohibiting her from entering a specific room in their chateau and attempts to kill his wife as punishment when she ultimately disobeys him. Bluebeard's violence towards his wife clearly marks him as an abuser. There have been countless other versions of the Bluebeard tale including Elizabeth Gaskell's short story "The Grey Woman." Unlike other versions of the tale that emphasize Bluebeard's physically abusive behavior, Gaskell's version focuses on a more subtle form of abuse: emotional abuse. Emotional abuse has remained an obscure topic within Victorian scholarship, and my paper attempts to address this gap in the literature by exploring the emotionally abusive marriage between Anna Scherer and, her personal Bluebeard, M. de la Tourelle. The term "emotional abuse did not exist during Gaskell's time, and yet, she skillfully portrays an emotionally abusive relationship. M. de la Tourelle isolates Anna from her family, controls her movements within her own home, and unexpectedly rages at her. Anna records the events of her abusive marriage in a letter to her daughter years after the events originally take place. As Anna writes her narrative, she attempts to articulate the abuse she endured. Without access to our 21st-century lexicon of abuse, Anna instead settles on physiognomy as a language that allows her to make sense of her husband's behavior. Physiognomy was a popular pseudoscience at the time, and it teaches that physical characteristics are indicative of personality traits. So, in her writing, Anna analyzes the curve of her husband's mouth, the light in his eyes, and the color of his cheeks all in an attempt to explain the emotional abuse she endured throughout her marriage. Anna also subjects herself to a physiognomic reading as she depicts how drastically her coloring has changed during her brief marriage to M. de la Tourelle. When Anna first married M. de la Tourelle, she had bright, lily-like skin and blonde hair. However, after enduring abuse in her marriage, her hair and skin both turn unnaturally and permanently gray. Anna depicts herself as being forever changed because of her abusive marriage. Gaskell's short story uses physiognomy as a tool to discuss emotional abuse long before the term "emotional abuse" existed. Studying the role physiognomy plays in "The Grey Woman," allows for new insights on how emotional abuse operates within the text. Ultimately, physiognomy provides a way of understanding how Victorian authors may have depicted both abusers and victims.

In today's market, companies are faced with pressure to increase variety in product offerings. While increasing variety can help increase market share and sales growth, the costs of doing so can be significant. Ultimately, variety causes complexity in products and processes to soar, which negatively impacts product development, quality, production scheduling, efficiency and more. Product variety is just one common cause of product complexity, a topic that several researchers have tackled with several sources of product complexity now identified. However, even with such progress, product complexity continues to be a theoretical concept, making it difficult for companies to fully implement advances and fully manage product complexity. More and more companies are relying on product family design to handle product variety. Broadly, a product family can be defined as a group of products sharing common elements. The advantages for companies using product family strategies can be significant: they enable efficient derivation of product variants, reduce inventory and handling costs, as well as setup and retooling time. The design challenge however, is to select the product platform to generate a variety of products with minimum deviation from individual requirements. Accordingly, the structure of product families makes designing and evaluating them a challenging process. In order to fully embrace the relationships between variety, product complexity, and product families an understanding of product complexity causes and impacts is essential. This research begins by introducing four main dimensions of product complexity within the context of a generalized definition. Product complexity indicators suitable in product design, development and production are derived. By establishing measurements for the identified indicators and using clustering techniques, a complexity evaluation approach for product family designs is also developed in this research. The evaluation approach is also applied on a component basis, to identify Critical Components that are main sources and contributors of complexity within product families. By standardizing identified Critical Components, product complexity levels and associated costs can be managed. A case application of three product families from a tire manufacturing company is used to verify that this research approach is suitable for evaluating and managing product complexity in product families. / Ph. D.