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State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction template applicability for small organic molecules to supplement the rare reaction examples of relevance to energetic materials. The training data for the neural network was generated by brute force determination of template subgraph matching for product molecules from a database of reactions in U.S. patent literature. This data generation strategy successfully augmented the information about template applicability for rare reaction mechanisms in the reaction database. The increased ability to recognize rare reaction templates was demonstrated for reaction templates of interest for energetic material synthesis such as heterocycle ring formation.The following article has been submitted to by the 21st Biennial APS Conference on Shock Compression of Condensed Matter. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/.

This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access datasets of organic reactions with artificially calculated template applicability and pretraining a template relevance neural network on this augmented applicability dataset, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small dataset of well curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating these strategies can be very useful for small datasets.

BACKGROUND Type 2 diabetes is a significant cause of morbidity and mortality in the United States. Lifestyle modifications including increasing physical activity and losing weight have been demonstrated to improve glycemic control. However, most patients struggle to make these changes. Many stakeholders are interested in using gamification and social incentives to increase engagement in healthy behaviors. However, these approaches often do not appropriately leverage insights from behavioral economics that could be used to address predictable barriers to behavior change. OBJECTIVE This study aimed to describe the protocol for the Influencing DIabetics to Adapt Behaviors related to Exercise and weighT by Enhancing Social incentives (iDiabetes) trial, which aimed to evaluate the effectiveness of gamification interventions that leverage insights from behavioral economics to enhance supportive, competitive, or collaborative social incentives to improve glycemic control, promote weight loss, and increase physical activity among overweight and obese adults with type 2 diabetes. METHODS We are conducting a one-year four-arm randomized controlled trial of 361 overweight and obese patients with type 2 diabetes and a glycated hemoglobin (HbA1c) level ≥8.0. Wireless weight scales and wearable devices are provided to remotely monitor weight and physical activity and transmit data to the study team. Patients are recruited by email, following which they establish a baseline measure of weight, daily step count, HbA1c level, and low-density lipoprotein cholesterol level and then repeat these measures at 6 and 12 months. The control arm receives no other interventions. Patients randomized to one of the three intervention arms are entered into a game designed using insights from behavioral economics to enhance supportive, competitive, or collaborative social incentives. To examine predictors of strong or poor performance, participants completed validated questionnaires on a range of areas including their personality, risk preferences, and social network. RESULTS Enrollment of 361 patients was completed in January 2019. Results are expected in 2020. CONCLUSIONS The iDiabetes trial represents a scalable model to remotely monitor the daily health behaviors of adults with type 2 diabetes. Results from this trial will help provide insights into how to improve management of patients with type 2 diabetes. CLINICALTRIAL ClinicalTrials.gov NCT02961192; https://clinicaltrials.gov/ct2/show/NCT02961192

New perspectives in entrepreneurship are emerging that view the concept of opportunity as central in the domain of entrepreneurship research. However, a robust understanding of how entrepreneurs and local institutions perceive opportunity in the community is still nascent in the empirical literature. We wish to contribute to the debate by presenting a testable empirical framework that views entrepreneurship in the community primarily as embedded within individual entrepreneurs, local institutions, and perceptions about local opportunity. A better understanding of this complex relationship may clarify why many local entrepreneurship assistance programs are developed in the absence of entrepreneur input, why many local entrepreneurs have little or no interaction with institutions in their community intended to help small businesspeople, and who entrepreneurs and local institutional leaders believe should be responsible for identifying and exploiting opportunity. The objective of this work is to identify differences in how individuals and entrepreneurs think about opportunity, and to develop strategies to bolster collaboration and opportunity exploitation within and between these groups in order to enhance local economic development through entrepreneurship. This paper presents the preliminary findings of face-to-face interviews and written surveys with entrepreneurs and community institutional leaders in three American states (Maine, Pennsylvania, and Wisconsin) about their views on entrepreneurship, opportunity enhancement, and local economic development. Results regarding the process of entrepreneurship and individual-institutional interaction are compared in high- and low-entrepreneurship areas to identify opportunity-oriented strategies that seem to be working at the local level. Finally, strategies are developed to overcome barriers between individual entrepreneurs and their institutional environment to enhance opportunity identification and exploitation.

Pennsylvania is a state whose rural cities and towns - once prosperous centers of manufacturing and agricultural production - have seen steady economic declines in these sectors due to macro-economic restructuring. Global economic forces have resulted in the loss or downgrade of many local jobs, population declines, the loss of educated youth to growing metropolitan areas, and the abandonment of small businesses from many small downtowns. Despite these trends, most small towns and cities still retain a substantial core of new business activity. These entrepreneurs hold the promise of reviving the lagging local economy by providing new jobs and critical products and services that are targeted to local tastes and needs. Understanding what led them to start a business in a declining area can lead to the development of new strategies to entice more entrepreneurs to locate a business in the area to stimulate local economic development. This report uses interviews with forty-two entrepreneurs in eight small towns across Pennsylvania to understand what specific factors motivated their decision to locate in areas traditionally marked by economic decline. The report reveals the surprising fact that most small town entrepreneurs are not natives, but were attracted to the area for other reasons. The report also explores which factors were more important to the location decision - personal, individualist factors or factors within the local social and physical environment. A conceptual framework of the location decision in Pennsylvania is developed, and real strategies for bolstering local entrepreneurship, especially in areas formerly engaged in manufacturing, are drawn from the findings.

The theory of dynamic limit pricing implies that a firm maximizes its wealth by gradually sacrificing its dominant market share. We extend the theory by simulation methods to show that higher structural entry barriers generally result in both higher profits and a slower sacrifice of market share. The model is applied to 42 once-dominant firms in U. S. manufacturing to explain jointly the declines of their market shares and the profit rates earned during 1905–1929. The statistical results agree substantially with the hypothesis that these firms behaved consistently with maximizing their wealth through dynamic limit pricing.