The Impact of Brand Love, Brand Attachment, and Electronic Shopping Experience Satisfaction (eSES) on Consumer Willingness to Write Reviews

The purpose of this study is to examine the willingness of a consumer to write an online review in relation to their love for a brand, their attachment to a brand, and their satisfaction with shopping online for that brand. There are limited empirical studies that investigate the impact of Brand Love, Brand Attachment, and Electronic Shopping Experience Satisfaction (eSES) on online reviews. Brand Love is the extent of emotional attachment that a content consumer feels for a certain brand or trade name whereas Brand Attachment measures the degree or strength to which consumers connects themselves with a brand. Lastly, a consumer’s satisfaction with their online shopping experience encompasses a variety of factors, such as product performance and product price. Data was gathered via a Qualtrics survey. A total of 206 online shoppers submitted usable data. These shoppers had all shopped and bought an item they love online in the past six months. The item that shoppers bought fell into one of two categories: clothing or electronics. Multiple regression was used to test the proposed hypotheses regarding intentions to write an online review and the three constructs: Brand Love, Brand Attachment, and Electronic Shopping Experience Satisfaction. Results show that only Brand Attachment may have an influence on a consumer’s willingness to write online reviews. Online retailers would benefit from this study because the study examines how consumers come to the intention to write an online review; therefore, results may provide insight for marketers and practitioners to use for potential online marketing efforts

High-throughput Scientific Workflow Scheduling under Deadline Constraint in Clouds

Cloud computing is a paradigm shift in service delivery that promises a leap in efficiency and flexibility in using computing resources. As cloud infrastructures are widely deployed around the globe, many data- and computeintensive scientific workflows have been moved from traditional high-performance computing platforms and grids to clouds. With the rapidly increasing number of cloud users in various science domains, it has become a critical task for the cloud service provider to perform efficient job scheduling while still guaranteeing the workflow completion time as specified in the Service Level Agreement (SLA). Based on practical models for cloud utilization, we formulate a delay-constrained workflow optimization problem to maximize resource utilization for high system throughput and propose a two-step scheduling algorithm to minimize the cloud overhead under a user-specified execution time bound. Extensive simulation results illustrate that the proposed algorithm achieves lower computing overhead or higher resource utilization than existing methods under the execution time bound, and also significantly reduces the total workflow execution time by strategically selecting appropriate mapping nodes for prioritized modules

Staying the Course: Toward Strong HQIM Implementation in Delaware

With the implementation of high-quality instructional materials (HQIM) and curriculum-based professional learning, Delaware educators, students, and families have ventured into promising, challenging new territory. HQIM ask a great deal of their users. Educators are called upon to abandon traditional approaches to instruction, allowing kids to loudly drive classroom discourse rather than passively taking notes on teacher lectures. Students are asked to grapple with rigorous, problem-based subject matter that offers no easy answers and requires deep analytical thinking and collaboration. Families are asked to support their children’s learning when the materials and resources that come home may feel unfamiliar and overwhelming. For all stakeholders, implementation can, at times, feel like an arduous journey with no clear destination

Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators

Extensive efforts in automated approaches for content moderation have been focused on developing models to identify toxic, offensive, and hateful content -- with the aim of lightening the load for moderators. Yet, it remains uncertain whether improvements on those tasks truly address the needs that moderators have in accomplishing their work. In this paper, we surface the gaps between past research efforts that have aimed to provide automation for aspects of the content moderation task, and the needs of volunteer content moderators. To do so, we conduct a model review on Hugging Face to reveal the availability of models to cover various moderation rules and guidelines. We further put state-of-the-art LLMs to the test (GPT-4 and Llama-2), evaluating how well these models perform in flagging violations of platform rules. Overall, we observe a non-trivial gap, as missing developed models and LLMs exhibit low recall on a significant portion of the rules

Quantification of liver function by linearization of a two‐compartment model of gadoxetic acid uptake using dynamic contrast‐enhanced magnetic resonance imaging

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144228/1/nbm3913.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144228/2/nbm3913_am.pd

Master\u27s Recital

List of performers and performances

Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics

A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.Comment: Under revie

Dynamically Programmable Magnetic Fields for Controlled Movement of Cells Loaded with Iron Oxide Nanoparticles

Cell-based therapies are becoming increasingly prominent in numerous medical contexts, particularly in regenerative medicine and the treatment of cancer. However, since the efficacy of the therapy is largely dependent on the concentration of therapeutic cells at the treatment area, a major challenge associated with cell-based therapies is the ability to move and localize therapeutic cells within the body. In this article, a technique based on dynamically programmable magnetic fields is successfully demonstrated to noninvasively aggregate therapeutic cells at a desired location. Various types of therapeutically relevant cells (neural stem cells, monocytes/macrophages, and chimeric antigen receptor T cells) are loaded with iron oxide nanoparticles and then focused at a particular site using externally controlled electromagnets. These experimental results serve as a readily scalable prototype for designing an apparatus that patients can wear to focus therapeutic cells at the anatomical sites needed for treatment