Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

Economic optimization by compromise programming: The joint production model

In a basic microeconomic model (joint production and consumer's utility curves) three key optima appear: the best technological mix, the maximum profit point and the consumer's utility optimum. Compromise analysis can help to clarify their connections. This paper and other recent research attempt to specify the conditions which guarantee that these three optima coincide. Thus compromise programming becomes a valuable surrogate of economic optimization.     

Interactive learning in the paradise fish (Macropodus opercularis): an ethological interpretation of the second-order conditioning paradigm

This study was aimed at the examination of ‘mental construction’ in paradise fish by interactive learning, which is suggested as an alternative hypothesis for backward or second-order conditioning. Avoidance of goldfish was established in paradise fish by presenting a harmless goldfish (a novel stimulus) after an aversive event (mild electric shocks) in the dark compartment of a shuttle tank. It was found that this avoidance depended on context pre-exposure. Experiment 1 was designed to study the effect of mild shocks on shuttling activity in a familiar context. Experiment 2 aimed at establishing fear-conditioning to the goldfish in a higher-order conditioning paradigm. In the course of training, unpaired stimuli were presented in the dark compartment of the shuttle tank in such a manner that the presentation of mild shocks (20 mA) preceded the encounter with a harmless fish (goldfish). Experiment 3 demonstrated the role of context pre-exposure in interactive learning. Results indicate that (1) while 60 mA shocks resulted in avoidance of the dark compartment, the 20 mA mild shocks affected exploratory behaviour; (2) after pre-exposure to the training environment, paradise fish avoided the dark compartment containing goldfish provided that subjects had previously encountered mild, explorative shocks there; (3) this conditioned fear, a ‘mental construction’ of a potential danger, was proved to be transferable to another context and was consequently aimed specifically at the goldfish, a living being, rather than the training context; (4) the pre-exposure to the shuttle tank was an important part of this training procedure, that is, only subjects habituated to the shuttle tank avoided the goldfish. Results are discussed in the framework of the Interactive Learning Hypothesis, which has been developed as an ethological approach to a higher-order conditioning paradigm. Received: 15 January 1999 / Accepted after revision: 18 August 1999     

Practical Implementation Science: Developing and Piloting the Quality Implementation Tool

According to the Interactive Systems Framework for Dissemination and Implementation, implementation is a major mechanism and concern in bridging research and practice. The growing number of implementation frameworks need to be synthesized and translated so that the science and practice of quality implementation can be furthered. In this article, we: (1) use the synthesis of frameworks developed by Meyers et al. (Am J Commun Psychol, 2012) and translate the results into a practical implementation science tool to use for improving quality of implementation (i.e., the Quality Implementation Tool; QIT), and (2) present some of the benefits and limitations of the tool by describing how the QIT was implemented in two different pilot projects. We discuss how the QIT can be used to guide collaborative planning, monitoring, and evaluation of how an innovation is implemented.     

Using a Training-of-Trainers Approach and Proactive Technical Assistance to Bring Evidence Based Programs to Scale: An Operationalization of the Interactive Systems Framework’s Support System

Bringing evidence based programs to scale was a major initial impetus for the development of the Interactive Systems Framework for Dissemination and Implementation (ISF). The ISF demonstrates the importance of the Support System in facilitating the uptake of innovations in the community (the Delivery System). Two strategies that members of the Support System commonly use are training‐of‐trainers (TOT) models and technical assistance (TA). In this article, we focus on the role of the Support System in bringing evidence‐based programs (EBPs) to scale in the Delivery System using a case example, with special attention on two strategies employed by Support Systems—training‐of‐trainers (TOT) and proactive technical assistance. We then report on findings from a case example from the Promoting Science Based Approaches to Teen Pregnancy Prevention project that furthers our conceptualization of these strategies and the evidence base for them. We also report on the limitations in the literature regarding research on TOTs and proactive TA and provide suggestions for future research on TOTs and proactive TA that will enhance the science and practice of support in the ISF. Special Issue: Advances in Bridging Research and Practice Using the Interactive System Framework for Dissemination and Implementation; Guest Editors: Abraham Wandersman, Paul Flaspohler, Catherine A. Lesesne, Richard Puddy; Action Editor: Emilie Phillips Smith     

Using the Interactive Systems Framework in Understanding the Relation Between General Program Capacity and Implementation in Afterschool Settings

The present study uses the Interactive Systems Framework (ISF) to understand how general capacity influences the implementation of prevention programs in afterschool settings. Eight afterschool sites received the Good Behavior Game (GBG) intervention, a program designed to foster supportive behavioral management and positive youth behavior. In line with the Prevention Support System component of ISF, the intervention afterschool staff were trained and received weekly on-site support from coaches in implementing the GBG. It was found that GBG implementation scores were greatest in afterschool programs that rated high on both organizational- and community-levels of general capacity; high scores on only one level of general capacity resulted in lower implementation scores. Thus, afterschool sites that were more organized, maintained adequate facilities, and developed strong linkages to individuals or organizations in the community scored highest in implementation fidelity and quality. This study highlights the importance of considering interactions among multiple levels of general capacity in efforts to promote evidence-based practices in afterschool settings. Caution should be taken in generalizing findings due to the small sample in this study.     

A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections

Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.     

Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain

On rough terrain, excessive wheel slippage is easily generated by changes of surface conditions such as soil types and geometries. It induces considerable loss of wheel traction and battery energy. To prevent this, wheeled robots should consistently recognize the current situation generated between wheel and surface. And also wheeled robots are required to optimally control wheel motion in limited wheel traction and battery capacity. Therefore, this paper proposes a novel wheel control algorithm based on slip optimization of traction and energy, which is adaptive to change of surface condition. Proposed wheel control algorithm is called Traction-Energy Balancing Adaptive Control (TEB) in this paper and TEB assigns optimized rotation speed to each wheel by observing wheel slip ratio which is a key parameter of TEB. As functions of TEB, TEB is largely divided into three main parts; (1) slip optimizer (2) slip controller (3) SC-compensator. In the slip optimizer, two optimal slip models were derived as a function of slip ratios regarding maximum traction and tractive efficiency using experimental data about wheel-terrain interaction in three types of soil (grass, gravel and sand). And the optimal slip models were employed in order to determine a desired slip value of wheel with observation of a change in actual robot velocity as control input in the slip controller. For optimal slip control, the proposed slip controller is based on conventional PID controller with compensating disturbance in the controller (SC-compensator) which occurs by change of surface shapes. In the SC-compensator, radial function networks (RBFN) was applied in the slip controller and RBFN was of help to readjust previously set PID gains depending on occurred slip error. Finally, TEB was experimentally verified by controlling a real robot having four wheels on various terrain types.