AbstractCities are microcosms representing a diversity of human experience. The complexity of urban systems arises from this diversity, where the services that cities offer to their inhabitants have to be tailored for their unique requirements. This paper studies the complexity of urban environments in terms of the assimilation of its communities. We examine the urban assimilation complexity with respect to the foreignness between communities and formalize the level of complexity using information-theoretic measures. Our findings contribute to a sociological perspective of the relationship between urban complex systems and the diversity of communities that make up urban systems.

2021-10-01·Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Thermal neutron measurements with an unpowered, miniature, solid-state device

作者: Clayton Fullwood ; Peter Hedlesky ; Steven Block ; Tracy Tipping ; John Rabaey ; Will Flanagan ; Tim Hossain ; Aidan Medcalf

A prototype neutron detector has been created through modification to a com. non-volatile flash memory device.Studies are being performed to modify this prototype into a purpose-built device with greater performance and functionality.This paper describes a demonstration of this technol. using a thermal neutron beam produced by a TRIGA research reactor.With a 4x4 array of 16 prototype devices, the full widths of the beam dimensions at half maximum are measured to be 2.2x2.1 cm².

2021-09-01·Journal of Marketing Analytics

Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

作者: Stella Park ; John V. Colias ; Elizabeth Horn

In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts.

项与 University of Dallas 相关的新闻（医药）

2021-06-29

·GlobeNewswire

ProntoForms Corporation Names Michael Kramer as CRO

OTTAWA, June 29, 2021 (GLOBE NEWSWIRE) -- ProntoForms Corporation (TSXV: PFM), the global leader in field-focused low-code application platforms for enterprise, announced today that Michael Kramer has joined the company as the Chief Revenue Officer, reporting directly to Alvaro Pombo, CEO of ProntoForms. Mr. Kramer is a revenue executive with over 20 years of experience in the software as a service (SaaS) industry. He previously held CRO roles at both BlueRidge.AI and Steelwedge Software, as well as executive sales roles at SAP, Infor, Workday, Fuze, and Accruent. He is continuing his learning in Innovation and Entrepreneurship at Stanford University Graduate School of Business, holds an MBA at the University of Dallas, and has a Bachelor of Science in Engineering at Purdue University. “We’re very happy to have Michael join our executive team,” said Alvaro Pombo, CEO of ProntoForms. “He brings a wealth of SaaS experience, as well as a proven track record of consistently exceeding revenue targets—we see Michael as an important, strategic fit for our growth.” “I’m delighted to join the ProntoForms team and look forward to playing a key role in the growth of the company,” said Michael Kramer. “My experience selling SaaS to enterprise customers helped me truly understand the value of low-code software platforms. I appreciate the unique position ProntoForms holds as the leading platform for agile, custom mobile apps for the field.” The appointment of all officers are subject to necessary regulatory approvals. About ProntoForms CorporationProntoForms is the global leader in field-focused low-code application platforms for enterprise. The Company's solution is used to create apps and forms to collect and analyze field data with smartphones and tablets – either as a standalone solution or as a mobile front-end to enterprise systems of record. The Company’s 100,000+ subscribers harness the intuitive, secure, and scalable solution to increase productivity, improve quality of service, and mitigate risks. The Company is based in Ottawa, Canada, and trades on the TSXV under the symbol PFM. ProntoForms is the registered trademark of ProntoForms Inc., a wholly owned subsidiary of ProntoForms Corporation. For additional information, please contact: Certain information in this press release may constitute forward-looking information. For example, statements about the Company's expectations as to how the hiring described above will benefit the Company and statements about future growth or value are forward-looking information. This information is based on current expectations that are subject to significant risks and uncertainties that are difficult to predict. Actual results might differ materially from results suggested in any forward-looking statements. The Company assumes no obligation to update the forward-looking statements, or to update the reasons why actual results could differ from those reflected in the forward looking-statements unless and until required by securities laws applicable to the Company. Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

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