After this article introduction, we review the prevailing theory of organizations, and what it means to organizational science and the new discipline of Quantum Interaction to have an uncertainty principle (ir.dcs.gla.ac.uk/qi2008; the corresponding author is one of the organizers). Further into the background, we review control theory for organizations and its importance to machine and human agents; we review the hypothesis for the uncertainty principle; and we review the status of the field and laboratory evidence so far collected to establish the uncertainty principle for organizations. Then we review future trends and provide the conclusion.
The lack of first principles linking organizational theory and empirical evidence puts the future of autonomous multi-agent system (MAS) missions and their interactions with humans at risk. Characterizing this issue as N increases are the significant trade-offs between costs and computational power to interact among agents and humans. In contrast to the extreme of command or consensus decision-making approaches to manage these tradeoffs, quantizing the pro-con positions in decision-making may produce a robust model of interaction that better integrates social theory with experiment and increases computational power with N. We have found that optimum solutions of ill-defined problems (idp’s) occurred when incommensurable beliefs interacting before neutral decision makers generated sufficient emotion to process information, I, but insufficient to impair the interaction, unexpectedly producing more trust than under the game-theory model of cooperation. We have extended our model to a mathematical theory of organizations, especially mergers; and we introduce random exploration into our model with the goal of revising rational theory to achieve autonomy with an MAS.
Abstract Faculty mentoring is a process/activity that can occur early, mid-career, or even when administrators are returning to a teaching role. Mentoring can take on numerous forms to include classroom observation, discussions on content within a course, formal/informal review of course content, review of individual lesson notes, and the sharing of lesson notes, homework, exams, design problems, syllabus, and study guides or a portion of any listed before. This paper presents the results of one faculty member completing a combination of the above as a form of mentoring with a new faculty member, a lecturer, and two mid-term faculty. The analysis examines student assessments and faculty reflections on the mentoring process and how it improved their current methods to prepare and then teach follow-on courses.
For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike’s Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention.
Background and purpose Some symptoms of multiple sclerosis ( MS ) affect driving. In a recent study, performance on five cognitive tests predicted the on‐road test performance of individuals with relapsing‐remitting MS with 91% accuracy, 70% sensitivity and 97% specificity. However, the accuracy with which the battery will predict the driving performance of a different cohort that includes all types of MS is unknown. Methods Participants ( n = 118; 48 ± 9 years of age; 97 females) performed a comprehensive off‐road evaluation that lasted about 3 h and a standardized on‐road test that lasted approximately 45 min over a 2‐day period within the same week. Performance on the five cognitive tests was used to predict participants’ performance on the standardized on‐road test. Results Performance on the five tests together predicted outcome of the on‐road test with 82% accuracy, 42% sensitivity and 90% specificity. Conclusions The accuracy of predicting the on‐road performance of a new MS cohort using performance on the battery of five cognitive tests remained very high (82%). The battery, which was administrable in <45 min and cost ~$150, was better at identifying those who actually passed the on‐road test (90% specificity). The sensitivity (42%) of the battery indicated that it should not be used as the sole determinant of poor driving‐related cognitive skills. A fail performance on the battery should only imply that more comprehensive testing is warranted.
This paper proposes a modular framework utilized to assess the risk of building envelope failures due to hurricane wind hazards. A component-based approach is taken to develop an integrated building envelope model that is based on previous research of individual component and system capacities. Key modules of the proposed framework include a wind-borne debris generation module and an impact-tracking module that will interact with a hurricane simulation module capable of simulating synthetic hurricanes for various return periods and historical hurricanes. One major difference between the proposed framework and other risk assessment models is that the proposed framework is extremely flexible in allowing the user to define the building stock within the area of study, which will provide the user with the ability to investigate an unlimited number of "what if" scenarios. Another distinction of the proposed framework is that it is driven by a three-dimensional probabilistic debris trajectory model developed by the authors, rather than using damage curves developed from observed post-hurricane assessments or insurance claim data. Debris impact risk plots presented in a polar coordinate system are developed using the framework and can be utilized either pre- or post-construction to mitigate the damage to the building envelope of homes within a subdivision during a hurricane event.
