The competition in the global market is accelerating rapidly because of less technological gap, matured manufacturing level, and various changing customer needs. Increasingly customers choose products in terms of experience desires, psychological desires and whether the products can reflect their values, in addition to the main product functions. Moreover, there are a large number of small and medium sized manufacturing companies in the developing countries. OEM (Original Equipment Manufacturer) and simple mass production cannot generate good value for these manufacture companies, and they have been seeking new opportunities to create higher value for their products/services and satisfy different needs of customers. Mass customization is one of the main business forms in the future, which can best meet the needs of individual customer, especially psychological needs. The key to mass customization is to provide enough modules to meet individual needs with a limited cost increase. The problem has been how to identify the real user needs and individual differences. The purpose of this research is to develop a sound design methodology based upon the current product design theories and practices for future product innovation and sustainable growth of small and medium sized manufacturing enterprises. The research focuses on the user-product cognitive behaviours and the relationship between human attributes and product features. Orthogonal experiment, eye tracking technology and artificial neural network have been successfully applied in this research. The research has developed a user needs hierarchy model and added value hierarchy model, and a robust theoretical basis to predict and evaluate (individual) user needs for product design. The research has further made the following contributions: 1) The relationship between human attributes and product features has been established, which can help designers understand the differences of various customer groups; 2) The different effects of various influence factors on people’s cognition and preference choice based on vision have been analysed and discussed; 3) A new method to identify, cluster, and combine common needs and personalized needs in early design stage for mass customization has been developed; 4) The research results can be reused in the future design of the same or similar kind of products.

Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.

Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average Prevision (MAP) measure of the resulted recommendations. We demonstrate the feasibility of HPEM with two prototype applications that use contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.

This thesis introduces an efficient and robust approach for 3D reconstruction of complete head models with consistent parameterization and personalized shapes from several possible inputs. The system input consists of Cyberware laser-scanned data where we perform scanning task as well as publically available face data where (i) facial expression may or may not exist and (ii) only partial information of head may exist, for instance only front face part without back part of the head. Our method starts with a surface reconstruction approach to either transfer point clouds to a mesh structure or to fill missing points on a triangular mesh. Then, it is followed by a registration process which unifies the representation of all meshes. Afterward, a photo-cloning method is used to extract an adequate set of features in a semi-automatic way on snapshots taken from front and left views of provided range data. We modify Radial Basis Functions (RBFs) deformation so that it would be based on not only distance, but also regional information. Using feature point sets and modified RBFs deformation, a generic mesh can be manipulated in a way that closed eyes and mouth movements like separating upper lip and lower lip can be properly handled. In other word, such mesh modification method makes construction of various facial expressions possible. Moreover, new functions are added where a generic model can be manipulated based on feature point sets to consequently recover missing parts such as ears, back of the head and neck in the input face. After feature-based deformation using modified radial basis functions, a fine mesh modification method based on model points follows to extract the fine details from the available range data. Then, some post refinement procedures employing RBFs deformation and averaging neighboring points are carried out to make the surface of reconstructed 3D head smoother and uniform. Due to existence of flaws and defects on the mesh surface such as flipped triangles, self-intersections or degenerate faces, an automatic repairing approach is leveraged to clean up the entire surface of the mesh. The experiments which are performed on various models show that our method is robust and efficient in terms of accurate full head reconstruction from input data and execution time, respectively. In our method, it is also aimed to use minimum user interaction as much as possible.

This thesis was created for the purpose of processing methodology course named "Communication between the parents and the kindergarten" for employees of nursery schools. Participants after finishing the course should be able to respond more appropriately to often difficult situations that may arise. A higher level of communication skills should increase the competibility of the kindergarten and create a friendly atmosphere. The work is aimed at kindergartens with personalized education which have their own origin in Spain. The author looks at the problem through benchmarking (continuous and systematic comparison to the top of this branch-- Spain, Poland). Data collection was conducted through questionnaires, interviews and observation. The result is a course methodology which includes six exemplary studies and their solutions with some other advice for a mutual communication.

<p>Current online learning approaches are sometimes criticized for a “one- size- fits -all” approach, low levels of interactivity, and insufficient feedback, which may result in low levels of learning satisfaction and high dropout rates. To mitigate these shortcomings, this study proposed a set of rules to design personalized motivational feedback based on students’ personal achievement goals. The researcher expected this specially designed personalized feedback to be able to improve student motivation and learning outcomes. </p> <p>To examine the effectiveness of such feedback, an explanatory mixed-methods study was implemented, which included two consecutive phases. The first phase was a quasi-experimental study. A 2018 online master’s degree program course offered by a large R-1 University in the U.S. served as the study context. Twenty-eight students were selected as the test group where personalized motivational feedback based on the proposed rules was delivered along with regular instructor feedback. Another forty students were selected as the control group who only received regular instructor feedback. Students’ motivation and perceived satisfaction were measured by using pre and post surveys. Students’ learning performance was measured by using the collected assignment scores after the semester ended. The second phase was a set of post interviews, in which 13 students from the two groups were asked about their perceptions of the impact of the feedback they received and how they used feedback in their learning process during the study.</p> <p>In the first study phase, ANCOVA F test results indicated the post-test scores of learner motivation and perceived satisfaction in the test group were significantly higher than those of the control group. The mean value of the cumulative assignment scores in the test group was somewhat higher than that of the control group, but this difference was not statistically significant based on the results of Wilcoxon Two-Sample test and ANCOVA F test. In the second study phase, the post-interviews showed that students in the test group expressed more consistently and strongly that they had an overall positive perception of the feedback received in the course. The participants from the test group further explained the underlying mechanism of this personalized motivational feedback was that it affected students’ learning positively by helping them set and regulate learning goals, activate self-regulation mechanisms, and adjust their learning behaviors.</p> <p>Based on the results and the features of the study design, the researcher concluded that the personalized feedback designed by following the set of rules proposed in this study has the potential to improve learner motivation in the online learning context. While its effect on learning outcomes was not significant, the researcher speculated that learning outcomes might have been affected by more complex factors, such as ceiling effects and predominant class structures. </p> <p>The researcher suggested online instructors and instructional designers consider students’ achievement goals when conducting learner analysis and creating learner profiles. She also suggested developers of next-generation LMSs include achievement goals in the learner model and include such rules in a personalization mechanism. One primary limitation of this study was that a ceiling effect on learning performance emerged leading to insufficient variation for the researcher to detect a statistically significant difference in learning performance. Therefore, the researcher suggests future researchers in this area replicate this approach by using automated feedback delivery tools and consider employing personalized feedback in different types of classes and using specific instructional approaches, such as problem-based learning and competency-based learning. Future research should also consider achievement goal’s mediating factors, such as students’ self-regulation skills, in learner analysis. </p>

The Personalized System of Instruction (PSI) is designed to individualize instruction based on traditional learning theories. Students are required to demonstrate mastery before advancing to new material. A self-pacing feature allows students to dictate their rate of progress. Compared to lecture-discussion instruction, PSI courses have demonstrated superior examination performance as well as increased ratings of course quality. However, studies have been criticized for testing only basic skills while ignoring more complex processes. In this research project, the PSI study guides were designed to emphasize complex processes and mastery test and review examination questions reflected increased item-level complexity. Results showed that students were able to master these complex items at the required 90% criterion. Performance on the comprehensive review examinations was slightly lower for complex items. Expected differences relating to the three group sequence requirements were not obtained. Nevertheless, mastery performance on the complex items was achieved by all students regardless of experimental group.

Chimeric antigen receptors (CAR) are modular genetically modified receptors that consist of an extracellular antigen binding domain fused to intracellular T-cell signaling domains. CAR therapy broadly consists of engineering a patient’s own T-cells to express a CAR directed against a tumor cell surface antigen. This therapy has been extremely successful in treating B-cell neoplasms by targeting CD19 and is paradigm changing in developing personalized immunotherapy for oncology applications. Although impressive response rates are observed, the durability of therapeutic response remains a concern and relapse mechanisms frequently center around issues of antigen loss. In addition, heterogeneous disease and solid tumors present formidable barriers toward extending the applicability of CAR technology as a result of compounding issues of tumor microenvironment and cell trafficking. In this thesis we review the current thought on the state of CAR therapy and the challenges to therapeutic efficacy, therapeutic manufacture, and clinical safety in the context of each other with an overall emphasis on identifying the fundamental goal of making fit-for-purpose CARs for different diseases.

The subgroup identification field which sometimes is called personalized medicine, tries to group individuals such that the effects of a treatment are the most beneficial for them. One of the methods developed for this purpose is called PSICA. Currently this method works in a setting of multiple treatments and real valued response variables. In this thesis, this methodology is extended to the degree that it can also handle ordinal response variables that can take a finite number of values. It is also compared to a competitor method which results in similar performance but with the added value of a probabilistic output and a model that is interpretable and ready for policy making. This is achieved at the expense of a higher execution time. Finally, this extension is applied to a longitudinal study done in Nicaragua in the los Cuatro Santos population in which some interventions were applied in order to reduce poverty. The results showed which were the most beneficial treatments for different population subgroups.

This study explores the subject of providing personalized services to mobile users, by exploiting relevant domain knowledge (i.e. contextual information). Although the process of gathering, modelling and processing of context has been extensively researched, there are only a few studies in the literature showing how such context can be effectively utilized to provide services valuable to the general public. Instead, there exist a multitude of examples of services targeted towards specialized audiences, either because the scope of each service is not of broad interest, or because of custom software and/or hardware requirements. Part of the reason why the scope of such services is so narrow can also be attributed to short service life cycle. While all such services offer relative value to interested audiences, we support that contemporary mainstream mobile devices are now more than ever capable of running large-scale context-aware applications as the required combination of hardware and software is available. This licentiate thesis challenges the current state-of-the art in context aware services by proposing an alternative perspective, driven from the appreciation of the user rather than from the ideas of a system designer. The potential impact of this work lies in the set of diverse applications which have been implemented using existing mainstream technology, targeting large and diverse sets of audiences. In order to realize the vision, we have implemented a context-aware system featuring a flexible architecture that is able to scale to the requirements of different services. In order to demonstrate the flexibility of this architecture as well as to prove the aforementioned claims, we have implemented support for two context aware services which have demonstratively had a large appeal to users. These scenarios not only include full implementation and exposure to public use, but they also differ from each other in terms of their functionality: A printing service where the printing resources are scattered within a workspace environment. The system selects the most appropriate printer for a mobile user to print his or her document on, based on the user's location and nature of the document relative to the capabilities of each of the printers. A recommender system service where mobile users are forwarded Web feeds of related interest, based on each user's social signature on the web (i.e. social context). The reader should note the tangible nature of context used in the services above, as context is not only associated - by tradition - with knowledge relative to physical stimuli (e.g. location), but is also related to information present on contemporary media such as the World Wide Web. / QC 20110516