Typical delay determines waiting time on periodic-food schedules: Static and dynamic tests.

Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food schedules before resuming the food-rewarded response. Under most conditions the steady-state duration of the average waiting time, t, is a linear function of the typical interfood interval. We describe three experiments designed to explore the limits of this process. In all experiments, t was associated with one key color and the subsequent food delay, T, with another. In the first experiment, we compared the relation between t (waiting time) and T (food delay) under two conditions: when T was held constant, and when T was an inverse function of t. The pigeons could maximize the rate of food delivery under the first condition by setting t to a consistently short value; optimal behavior under the second condition required a linear relation with unit slope between t and T. Despite this difference in optimal policy, the pigeons in both cases showed the same linear relation, with slope less than one, between t and T. This result was confirmed in a second parametric experiment that added a third condition, in which T + t was held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third experiment we arranged for a multiplicative relation between t and T (positive feedback), and produced either very short or very long waiting times as predicted by a quasi-dynamic model in which waiting time is strongly determined by the just-preceding food delay.

Comparison of dynamic balance in adolescent male soccer players from rwanda and the United States.

PURPOSE/BACKGROUND: Dynamic balance is an important component of motor skill development. Poor dynamic balance has previously been associated with sport related injury. However, the vast majority of dynamic balance studies as they relate to sport injury have occurred in developed North American or European countries. Thus, the purpose of this study was to compare dynamic balance in adolescent male soccer players from Rwanda to a matched group from the United States. METHODS: Twenty-six adolescent male soccer players from Rwanda and 26 age- and gender-matched control subjects from the United States were screened using the Lower Quarter Y Balance Test during their pre-participation physical. Reach asymmetry (cm) between limbs was examined for all reach directions. In addition, reach distance in each direction (normalized to limb length, %LL) and the composite reach score (also normalized to %LL) were examined. Dependent samples t-tests were performed with significant differences identified at p<0.05. RESULTS: Twenty-six male soccer players from Rwanda (R) were matched to twenty-six male soccer players from the United States (US). The Rwandan soccer players performed better in the anterior (R: 83.9 ± 3.2 %LL; US: 76.5 ± 6.6 %LL, p<0.01), posterolateral (R: 114.4 ± 8.3 %LL ; US: 106.5 ± 8.2 %LL, p<0.01) and composite (R: 105.6 ± 1.3 %LL; US: 97.8 ± 6.2 %LL, p<0.01) reach scores. No significant differences between groups were observed for reach asymmetry. CONCLUSIONS: Adolescent soccer players from Rwanda exhibit superior performance on a standardized dynamic balance test as comparison to similar athletes from the United States. The examination of movement abilities of athletes from countries of various origins may allow for a greater understanding of the range of true normative values for dynamic balance. LEVELS OF EVIDENCE: 3b.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Unipedal balance is affected by lower extremity joint arthroplasty procedure 1 year following surgery.

Lower Extremity Joint Arthroplasty (LEJA) surgery is an effective way to alleviate painful osteoarthritis. Unfortunately, these surgeries do not normalize the loading asymmetry during the single leg stance phase of gait. Therefore, we examined single leg balance in 234 TJA patients (75 hips, 65 knees, 94 ankles) approximately 12 months following surgery. Patients passed if they maintained single leg balance for 10s with their eyes open. Patients one year following total hip arthroplasty (THA-63%) and total knee arthroplasty (TKA-69%) had similar pass rates compared to a total ankle arthroplasty (TAA-9%). Patients following THA and TKA exhibit better unilateral balance in comparison with TAA patients. It may be beneficial to include a rigorous proprioception and balance training program in TAA patients to optimize functional outcomes.

Steady flow dynamics during granular impact.

We study experimentally and computationally the dynamics of granular flow during impacts where intruders strike a collection of disks from above. In the regime where granular force dynamics are much more rapid than the intruder motion, we find that the particle flow near the intruder is proportional to the instantaneous intruder speed; it is essentially constant when normalized by that speed. The granular flow is nearly divergence free and remains in balance with the intruder, despite the latter's rapid deceleration. Simulations indicate that this observation is insensitive to grain properties, which can be explained by the separation of time scales between intergrain force dynamics and intruder dynamics. Assuming there is a comparable separation of time scales, we expect that our results are applicable to a broad class of dynamic or transient granular flows. Our results suggest that descriptions of static-in-time granular flows might be extended or modified to describe these dynamic flows. Additionally, we find that accurate grain-grain interactions are not necessary to correctly capture the granular flow in this regime.