Defiance College

This case study offers a comprehensive analysis of the contemporary global athletic footwear market, illustrating how the shifting marketing strategies, distribution channel configurations, and evolving consumer preferences have refined the traditional competitive playbook. It identifies and contrasts the strategic maneuvers of three groups of global brands: dominant giants (Nike and Adidas), resilient legacy players (Puma, ASICS, and Under Armour), and fast-growing challenger brands (Hoka, ON, Li-Ning, and Anta).Through a lens of global business development, the case study highlights the excellence and balance in product and marketing strategies that sports brands must constantly and delicately harness to expand and sustain their footwear market shares. This includes the balance between superior athletic performance (e.g., innovation in footwear cushioning technology) and lifestyle resonance (e.g., national culture and retro designs) in product strategy, as well as the balance in the brand's distribution strategy, between the high-margin direct-to-customer (DTC) sales model and the wide-reaching, partnership-based wholesale retail network.The narrative explores how market leaders could stumble when over-relying on a single strategy - such as Nike's aggressive pivoting in 2020 to digital DTC and data-driven performance marketing at the expense of its superior brand storytelling. Conversely, it shows how agile underdogs, such as Hoka and ON, had grown rapidly in the global market since circa 2010 by initially capturing high-end niche markets and progressively offering innovative footwear technologies. The study also discusses the developing paths of legacy sports brands, for example, Puma and Under Armour opting to persist in very different strategies on product portfolio and marketing mix. In addition, two local brands going global from an emerging market (Li-Ning and Anta from China) were examined in terms of how they leveraged domestic culture and aggressive international multi-brand acquisitions to challenge Western dominance in the sports goods market.

OBJECTIVE:This study examines the impact of early sport specialization on mental health in NCAA athletes. Specialized athletes report higher stress, weight changes, fatigue, decreased sport enjoyment, and greater burnout. Few studies have explored this link, and none have focused on NCAA athletes who specialized before college. This is the first multicenter study to assess these mental health domains related to specialization. DESIGN:Retrospective cohort study. SETTING:Sport, Action, Finding, Evaluation Consortium. PARTICIPANTS:In total, 257 collegiate athletes from the NCAA's 3 levels of competition: Divisions I, II, and III. INTERVENTION:N/A. MAIN OUTCOME MEASURES:Athletes completed a questionnaire about their demographics, specialization status, burnout-related perceptions, academic outcomes, weight loss, and sleep. Results were analyzed based on specialization status, which was classified using a published 3-point scale. RESULTS:Highly specialized athletes reported less enjoyment ( P = 0.033), reduced athletic accomplishment ( P = 0.01), poorer academic performance ( P = 0.043), more weight loss ( P = 0.043), and worse sleep ( P < 0.001) than low-specialized athletes. Moderately specialized athletes had a reduced sense of athletic accomplishment ( P = 0.002) and more daytime sleepiness ( P = 0.002) than low-specialized athletes. Female athletes reported more unintentional weight loss ( P < 0.01) and less sport enjoyment ( P = 0.036) than male athletes. Pressure to specialize before college was linked to worse outcomes in all domains except weight loss ( P = 0.871). CONCLUSIONS:Higher specialization correlates with negative mental health outcomes, including reduced sport enjoyment and accomplishment, sleep, and more weight loss. Pressure to focus on 1 sport before college is a stronger predictor of adverse outcomes than specialization itself.

BACKGROUND:Distinguishing individuals with cognitive decline (CD), including early Alzheimer's disease, from cognitively normal (CN) individuals is essential for improving diagnostic accuracy and enabling timely intervention. Positron emission tomography (PET) captures metabolic brain alterations associated with CD, but its broader application is often limited by cost and radiation exposure. To enhance the clinical utility of PET while addressing data limitations, we propose a data-efficient framework that integrates complementary multi-scale PET representations at voxel-level and region-level. METHODS:Voxel-level features were extracted using convolutional neural networks (CNN) or principal component analysis networks (PCANet) from [¹⁸F]FDG PET imaging. Region-level features were derived from standardized uptake value ratio measurements across predefined brain regions and processed using a deep neural network (DNN). These voxel- and region-level information are integrated through direct concatenation. For the final prediction, different machine learning models and ensemble technique were applied. The models were trained and validated using 5-fold cross-validation on PET scans from 252 participants in the Alzheimer's Disease Neuroimaging Initiative, comprising 118 CN and 134 CD subjects. Additional correlation analysis and disease classification comparison with the Mini-Mental State Examination (MMSE) were also performed. RESULTS:In 5-fold cross-validation, CNN, PCANet, and DNN models achieved classification accuracies of 0.69 ± 0.04, 0.69 ± 0.06, and 0.82 ± 0.06, respectively. The integrated DNN-CNN model using direct concatenation yielded the highest accuracy (0.87 ± 0.05), with a 6.33% improvement in accuracy and reduced standard deviation relative to the DNN-only model. Overall, there were an increase of 14.22% in Recall (0.77 to 0.88) and an increase of 7.92% in F1-Score (0.82 to 0.88). Moreover, the predicted probability of CD showed a significant correlation with MMSE scores, and the model achieved higher accuracy, recall, and F1-score than MMSE-based classification. CONCLUSION:Combining complementary voxel-level and region-level PET representations with deep learning improved classification performance over single-representation models, particularly by enhancing sensitivity to cognitive decline. These findings support the potential utility of multi-scale FDG-PET representations for machine learning-based cognitive decline detection.

Textbook-centred mathematics teaching rarely allows teachers to draw on their valuable learning experiences. This narrative study explored how novice mathematics teachers draw on their learning experiences in their current teaching practice. The participants include three mathematics teachers – two from private schools and one from a public school in the Kathmandu Valley, Nepal. They participated in at least two in-depth qualitative interviews that collected information about their experiences as mathematics learners and how those experiences shaped their teaching. The findings indicate that novice teachers often draw on their prior learning experiences as a form of hidden curriculum that implicitly guides their classroom practices. The underprepared and unconfident novice teachers tend to reproduce the inherited, traditional approaches to mathematics teaching. Nevertheless, once novice teachers gradually collect some experiences and merge these experiences with their emerging knowledge, they gradually emulate their role model mathematics teachers and replicate the practices that they appreciated as learners. These results suggest that recognizing and critically engaging novice and in-service teachers with their own learning experiences can support student-centred teaching.