University of Colorado Colorado Springs

About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.

Business strategy, marketing organization culture, and performance

Abstract: Drawing on configuration theory, we develop and test a model which posits that overall firm performance will be influenced by how well the marketing organization’s cultural orientation (i.e., market, adhocracy, hierarchy, or clan) complements alternative business strategies (i.e., Prospector, Analyzer, Low-Cost Defender, Differentiated Defender) after controlling for other, key firm-level variables. Responses from a sample of senior marketing managers provide partial support for the model and demonstrate that high-performing businesses of one strategy type have a different cultural orientation than high-performing businesses of the other strategy types. And, contrary to previous research, the results of this study show that each of the cultural orientations may play a role in creating superior performance. We conclude with a discussion of the implications for scholars and for managers.

Sexual networks and sexually transmitted infections: A tale of two cities

Abstract: Research on risk behaviors for sexually transmitted infections (STIs) has revealed that they seldom correspond with actual risk of infection. Core groups of people with high-risk behavior who form networks of people linked by sexual contact are essential for STI transmission, but have been overlooked in epidemiological studies. Social network analysis, a subdiscipline of sociology, provides both the methods and analytical techniques to describe and illustrate the effects of sexual networks on STI transmission. Sexual networks of people from Colorado Springs, Colorado, and from Winnipeg, Manitoba, Canada, infected with chlamydia during a 6-month period were compared. In Winnipeg, 442 networks were identified, comprising 571 cases and 663 contacts, ranging in size from 2 to 20 individuals; Colorado Springs data yielded 401 networks, comprising 468 cases and 700 contacts, ranging in size from 2 to 12 individuals. Taking differing partner notification methods and the slightly smaller population size in Colorado Springs into account, the networks from both places were similar in both size and structure. These smaller, sparsely linked networks, peripheral to the core, may form the mechanism by which chlamydia can remain endemic, in contrast with larger, more densely connected networks, closer to the core, which are associated with steep rises in incidence.

Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma.

Abstract: BACKGROUND. Currently, the standard for predicting pathologic stage from information available at the time of prostate biopsy is the Partin nomograms that were derived using logistic regression analysis. The authors retrospectively reviewed a large series of men with clinically localized prostate carcinoma who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy. They then utilized pathologic and clinical data at the time of prostate biopsy to develop and test an artificial neural network (ANN) to predict the final pathologic stage for this group of men. They then compared the results of ANN with the previous nomograms. METHODS. Five thousand seven hundred forty-four men were treated at the authors' institution from 1985 to 1998. An ANN was developed using two randomly selected training and validation sets for predicting pathologic stage. Input variables included age, preoperative serum prostate specific antigen level, clinical TNM (tumor, lymph node, and metastasis) classification, and Gleason score from the biopsy specimen. Outcomes included organ confinement and lymph node involvement status. RESULTS. The ANN was slightly superior to the nomograms in predicting pathologic stage, such as organ confinement and lymph node involvement status. CONCLUSIONS. In predicting organ confinement and lymph node involvement status, ANN was more accurate and had a larger area under ROC than the nomograms based on the logistic regression method. Artificial neural network models can be developed and used to better predict final pathologic stage when preoperative pathologic and clinical features are known.

Dielectric breakdown in high-ε films for ULSI DRAMs: II. barium-strontium titanate ceramics

Abstract: We present a study of breakdown fields in BaxSr1−xTiO3 ceramics as functions of thickness, temperature, applied voltage ramp rate, electrode material and cross-sectional area. The data show evidence of Zener-like breakdown mechanisms. The d.c. leakage currents at voltages below breakdown are dominated by tunneling injection and image forces at low voltages (≤3 V), Schottky behavior at intermediate voltages V a (3 V ≤ V a ≤ 6 V; 150 kV/cm ≤ E ≤ 300 kV/cm), and Fowler-Nordheim tunneling at high voltages; it is shown that both the Schottky emission regime and the ultimate breakdown arise from inter-grain effects rather than the BST/electrode interface, in accord with the theory of Neumann and Arlt and of Waser and Klee. In contrast to PbZrxTi1−xO3 (PZT), which displays avalanche breakdown and space-charge limited d.c. leakage currents at normal 5 V silicon IC voltages, BST exhibits Schottky-dominated leakage currents in this 5 V regime, and is dominated by Fowler-Nordheim tunneling in its breakdown ...

Pseudo-two-dimensional model and impedance diagnosis of micro internal short circuit in lithium-ion cells

Abstract: The early detection of micro internal short circuit (ISCr) cells can provide sufficient response time for preventing accidents such as spontaneous thermal runaway in battery packs of electric vehicles, and greatly improve safety. Because the existing models describing ISCr are mainly equivalent circuit models and three-dimensional physics-based models, we build a pseudo-two-dimensional model of micro ISCr cells to make up for the gaps. Using the calculation results of this model, we reveal the phenomenon of electric quantity depletion and the variation of internal electrochemical parameters when a micro ISCr occurs in the cell. We find the effective electrical conductivity of the separator is a crucial parameter describing the ISCr severity and determine reasonable values for this effective conductivity for fault diagnosis and battery design. Moreover, we propose an impedance-identification method that can be used for ISCr diagnostics. Through the simulation and experimental results, we find that the impedance of micro ISCr cells is different from that of normal cells and shows a certain regularity with the increase of ISCr severity.