No description available.

Precision medicine (PM) is “the treatment and prevention of disease that takes into account individual variability in genes, environment, and lifestyle for each person” (NIH, 2015). PM was poised to transform clinical practice in 2003 when the Human Genome Project reached completion but resistance to implementation at virtually all health care providers provides the basis for novel study on the diffusion of innovation as well as operational strategy. Existing studies on resistance to PM explore the role of reimbursement, economics, regulatory affairs, and public policies. Investigation into the antecedent conditions for implementation at the physician and organizational levels has been overlooked, a gap this study fills. The research captures the reasons for resistance at the physician and organizational levels and identifies operational strategies for successful implementation at three health care institutions with fully integrated PM programs. The research produced 42 findings with managerial implications and six testable propositions for future research. The dynamics of resistance to PM has revealed key implications for theories of organizational change. These include the observation that the formulation processes of clinical standards of practice in PM are not predicted by prevailing organizational theory; that conventional theories of resistance to change do not fully anticipate the effects of Kuhnian level historic paradigm shifts; and, that communities of practice play a critical role in transformational clinical change. Further, the research demonstrated that PM implementation is characterizable through reproducible organizational and cultural actions; that positive clinical outcomes are measurable and persuasive; and that the needs of stakeholders can be reconciled by aligning physician standards of practice with patient expectations and organizational needs. / Business Administration/Strategic Management

With the rise of online retailer giants like Amazon, and enhancements in internet and mobile technologies, online shopping is becoming increasingly popular. This has lead to new opportunities in online price optimization. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions. In Chapter 2 a multi-period revenue maximization and pricing optimization problem in the presence of reference prices is formulated as a mixed integer nonlinear program. Two algorithms are developed to solve the optimization problem: a generalized Benders' decomposition algorithm and a myopic heuristic. This is followed by numerical computations to illustrate the effciency of the solution approaches as well as some managerial pricing insights. In Chapter 3 a data-driven quadratic programming optimization model for online pricing in the presence of customer ratings is proposed. A new demand function is developed for a multi-product, nite horizon, online retail environment. To solve the optimization problem, a myopic pricing heuristic as well as exact solution approaches are introduced. Using customer reviews ratings data from Amazon.com, a new customer rating forecasting model is validated. This is followed by several analytical and numerical insights. In Chapter 4 a multinomial choice model is used for customer purchase decision to find optimal personalized price discounts for an online retailer that incorporates customer locations and feedback from their reviews. Closed form solutions are derived for two special cases of this problem. To gain some analytical insights extensive numerical experiments are carried followed by several analytical and numerical insights. / Thesis / Doctor of Philosophy (PhD) / The increase in online retail and the improvements in mobile technologies has lead to advantages and opportunities for both customers and retailers. One of these advantages is the ability to keep and efficiently access records of historical orders for both customers and retailers. In addition, online retailing has dramatically decreased the cost of price adjustments and discounts compared to the brick and mortar environment. At the same time, with the increase in online retailing we are witnessing proliferations of online reviews in e-commerce platforms. Given this availability of data and the new capabilities in an online retail environment, there is a need to develop pricing optimization models that integrate all these new features. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions.

Doctor of Philosophy / Architects have long been focused on designing eco-friendly buildings, but there's a growing realization that focusing solely on energy efficiency isn't enough. Occupants spend the majority of their time indoors, and the quality of these indoor spaces profoundly impacts their well-being and productivity. Yet, often overlooked, are factors like lighting, air quality, and noise that can significantly affect how occupants feel and perform. With rising urbanization and recent experiences during the COVID-19 pandemic highlighting the importance of indoor environments, there's a renewed emphasis on user-centric design. Biophilic design, which incorporates elements of nature into buildings, has emerged as a promising approach to enhancing occupants' health, wellness, and well-being. Airflow, a critical aspect of biophilic design, plays a key role in creating healthier indoor spaces. This study aims to develop a prototype system that mimics natural airflow patterns indoors to promote occupants' health and well-being. By analyzing weather data, natural airflow features were identified and used to inform the design of a mechanical system. The goal is to create settings that replicate natural airflow patterns in indoor environments. Ultimately, this research lays the groundwork for future studies to explore how such biophilic systems impact occupants' physiological and psychological health. By prioritizing user experience in building design, indoor spaces that not only conserve energy but also enhance the quality of life could be developed.

ABSTRACT Date: 2024 Level: Bachelor Thesis in Business Administration, 15 cr Institution: School of Business, Society and Engineering, Mälardalen University Authors: Tatiana Bernard, Alexia; Parvin, Maryam; Ram, Mahabad Title: Beyond the Click: Unveiling the Influence of AI Personalization on E-commerce Impulse Buys Supervisor: Emre Yildiz Keywords: AI personalized recommendations, impulsive buying behavior, perceived value, perceived scarcity, perceived positive emotions. Research question: How does AI-powered personalized product recommendations influence impulsive buying behavior in e-commerce, potentially mediated by underlying psychological mechanisms? Purpose: The purpose of this study is to examine the link between AI-powered personalized product recommendations and consumers’ impulsive buying on e-commerce platforms. The research explores how these personalized recommendations might influence customers towards impulsive purchases through perceived value, scarcity, and positive emotions as mediating factors. Method: This study utilized a quantitative research methodology, employing online surveys to collect data from active online shoppers. To ensure a comprehensive sample, both convenience and purposive sampling techniques were implemented, aiming to represent a broad spectrum of online shopping behaviors. Conclusion: This study reveals that AI-powered personalized product recommendations significantly influence impulsive buying behaviors on e-commerce platforms. By examining the mediating effects of perceived value, scarcity, and positive emotions, the research highlights how personalized recommendations can drive impulsive purchases. The findings underscore the importance of sophisticated recommendation systems in enhancing consumer engagement and satisfaction while raising important ethical considerations about consumer manipulation and long-term satisfaction. This study provides valuable insights for businesses looking to balance effective marketing strategies with ethical consumer practices.

Yes / Widespread generation and analysis of omics data have revolutionized molecular medicine on Earth, yet its power to yield new mechanistic insights and improve occupational health during spaceflight is still to be fully realized in humans. Nevertheless, rapid technological advancements and ever-regular spaceflight programs mean that longitudinal, standardized, and cost-effective collection of human space omics data are firmly within reach. Here, we consider the practicality and scientific return of different sampling methods and omic types in the context of human spaceflight. We also appraise ethical and legal considerations pertinent to omics data derived from European astronauts and spaceflight participants (SFPs). Ultimately, we propose that a routine omics collection program in spaceflight and analog environments presents a golden opportunity. Unlocking this bright future of artificial intelligence (AI)-driven analyses and personalized medicine approaches will require further investigation into best practices, including policy design and standardization of omics data, metadata, and sampling methods. / H.C., R.H., J.B., D.B., S.G., T.E., and N.J.S. are members of the ESA Space Omics Topical Team, funded by the ESA grant/contract 4000131202/20/NL/PG/pt “Space Omics: Towards an integrated ESA/NASA –omics database for spaceflight and ground facilities experiments” awarded to R.H., which was the main funding source for this work. H.C. is also supported by the Horizon Center for Doctoral Training at the University of Nottingham (UKRI grant no. EP/S023305/1). S.G. is supported by the Swedish Research Council VR grant 2020-04864. L.A.R. and M.M. represent the Omics Subgroup of the Japan Society for the Promotion of Science KAKENHI funding group “Living in Space” and are supported by JP15K21745, JP20H03234, and 20F20382. L.A.R. is also supported by the JSPS postdoctoral fellowship P20382. We thank Dr. Sarah Castro-Wallace, the NASA GeneLab Animal AWG, ISSOP, ESA Space Omics Topical Team, ESA Personalized Medicine Topical Team, and Global Alliance for Genomic Health (GA4GH) for useful discussions.

Cancer may be treated with personalized medicine, meaning that specific patientsmight respond better to specific treatments instead of having a common treatment. TheReference Drug-based Neural Network (RefDNN) predicts whether a particular cancer cellline will resist a determined drug, but it fails to provide an explanations for this prediction.The thesis objective is to research on explainable machine learning methods to extractrule-based explanations from the RefDNN predictions and conclude on how confident wecan be about these explanations and whether they make sense from a biological point ofview. One of such explainable machine learning methods is Local Rule-based Explanation(LORE), which extracts rule-based explanations from any black box model using localdecision trees. In this thesis LORE is applied to explain the predictions of the RefDNNon a drug sensitivity dataset and three experiments are set up. First experiment tests theaccuracy and general performance of the extracted rule-based explanations. Second experimentstests the robustness of the rule-based explanations. Third experiments checks theglobal fidelity of the local decision trees used by LORE to mimic the RefDNN behaviour.Finally, one rule-based decision is explained from a biological point of view and conclusionsare made on the obtained results.

The Affectionate Gesture Planner (AGP) is a smartphone application (i.e., “app”) that aims to increase relationship satisfaction for couples in long-term relationships by prompting couples to complete loving acts for each other on a daily basis. The AGP app is informed by concepts from social exchange theory and the investment model (Rusbult, 1983), which predict that increasing the mutual exchange of beneficial investments to the relationship improves the quality and stability of the relationship. The present study is a formative evaluation on the prototype of the AGP app. Based on participant feedback, the AGP app will be improved prior to future trials. Future models of the AGP app will be used to compare the efficacy of using standardized suggestions versus personalized prompts.

No description available.

This study analyzed participants in an online professional development and certification program can to see if they could predict the learning value of individual distance education tools. The Texas Center for Educational Technology (TCET) funded by the Texas Telecommunications Infrastructure Fund (TIF) designed the Technology Applications Certification Program (TACP). In the TACP, students are offered four graduate level classes which, when combined, meet the standards for the State Board for Educator Certification (SBEC) Technology Applications certification. The four courses that comprise the TACP are Computers in Education, Introduction to the Internet, Multimedia in Technology Applications, and Introduction to Video Technologies. The first course started in January 2002 with approximately 706 participants in 40 cohorts across the state of Texas. The TACP combines two different worlds of technology training. Half of the coursework was completed through asynchronous content and discussions, while the remaining classes were hands-on classes in local district computer labs. These face-to-face meetings enabled learners to get hands-on training with direct assistance. Between the online and face-to-face segments, a variety of learning tools were introduced to the participants. Participants were surveyed through the online Snapshot Survey in January and again in September.