Circuit optimization via sequential computer experiments: design of an output buffer

In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit's performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator's deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.     

Interactional synchrony: Genuine or spurious? A critique of recent research

In two recent articles, McDowall (1978a, 1978b) has challenged the micro-analytic work of W. S. Condon and Adam Kendon. Specifically, he has argued on the basis of his work that interactional synchrony is not a genuine phenomenon, but rather a statistically expectable noise in social interaction. In this paper, we demonstrate that McDowall's results are inconclusive because of confusion as to what constitutes interactional synchrony. We clarify these issues and place McDowall's experiments in their proper perspective.     

Micromomentary movement and the deconding of face and body cues

A new version of the Profile of Nonverbal Sensitivity Test (PONS)—a standardized test of sensitivity to nonverbal cues—was developed to determine the effects of five levels or sequences of micromomentary movement on accuracy in decoding nonverbal face and body cues, presented for 125 microseconds (msecs). The five sequences were: backward movement, partially backward movement, no movement, partially forward movement, and fully forward movement. Two versions of this test—the Test of Micromomentary Movement Effects (TOMME)—were administered to two samples of female high school and college students. The results, averaged over face and body cues, showed that as the movement sequences more closely approximated the fully forward sequence, decoding accuracy increased. These results, showing the benefits of adding properly sequenced information at split second (42 msec) exposures, not only support the findings of previous researchers that micromomentary facial affect displays may be helpful in decoding nonverbal facial cues, but also suggest that micromomentary movements may be important for decoding body cues as well.     

Specification of nonverbal behaviors for clinical assessment

Nine categories of nonverbal behavior (extremity movements, self-manipulations, facial expression, posture, orienting, gestures, voice quality/tone, speech rate/pressure, and sense of timing) were tested in a standardized role play situation of social skills. Each category was judged using a new midi-level system of assessment which permitted specification of component behaviors but allowed observers to make single ratings at the ends of videotaped episodes. The midi-level measurements were as reliable and practical as more traditional global measures of social skill and social anxiety. Midis were superior to globals (i.e., single overall ratings of skill and anxiety) in terms of predicting physiological indices of social anxiety. Voice quality/tone and sense of timing appeared to be the best predictors of criterion social skill measures and self-manipulations, extremity movements, and gestures had the highest weights in predicting criterion measures of social anxiety.     

Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

Strategic learning with scenarios

Since the oil shocks upset the business world in the 1970s, the use of multiple scenario analysis has been increasingly propagated as an approach to deal effectively with the many long-run uncertainties that surround business organisations. Since its introduction, the scenario approach has undergone some considerable changes and it is now claimed fulfils a diverse range of functions. Newly-added functions include the stretching of managers' mental models and the triggering and acceleration of processes of organisational learning. Although these functions currently get most of the attention in academic and management journals in recent years, a satisfying explanation of how scenarios fulfil these functions is still missing in the scenario literature. The scenario methodology seems to tell only part of the story suggesting that construing and using scenarios ‘simply’ consists of sequentially completing several distinct phases. If multiple scenario analysis really is able to fulfil the wide range of functions ascribed to it another, more dynamic process has to be hidden behind the rather static phase model. The scenario literature does not give any insight into this latter process. This article aims to increase the understanding of multiple scenario analysis by unravelling some of the mysteries surrounding it. For this purpose, the role of scenarios in strategic management is studied from a cognitive perspective. It appears that scenarios can deal effectively with several bottlenecks that potentially hinder organisational learning on a strategic level in organisations.     

Data relaying with constraints in hierarchical sensor networks

In this paper we present and discuss several optimisation problems that arise in the management of data flow in wireless sensor networks (WSNets). We consider a hierarchical architecture for WSNets composed of sensors, relays, and relay gateways. Sensors send data they generate at a known average bit rate to relays in one hop. The relay nodes use a multi-hop mechanism to reach a set of assigned gateways which then forward the data directly to the base station. We are concerned with finding an assignment of relay gateways to relays so that certain constraints are satisfied. We define a unified model in which constraints such as lifetime, data delay, and data flow splitting are formulated in terms of four optimisation problem in graphs.     

Strategic Alliance Outcomes: a Transaction‐Cost Economics Perspective

Empirical research on strategic alliances has been limited because previous studies examined alliance outcomes, and the factors associated with them, from a single partner in a manufacturing alliance. Furthermore, many of these studies have been done from a transaction cost perspective and researchers have inferred opportunistic behavior, rather than directly measuring it and observing its actual relationship with alliance performance. Building on previous transaction cost theory and research, this study seeks to address these gaps by analyzing factors associated with both opportunistic behavior and alliance performance within a major service sector, namely the US healthcare industry. After controlling for asset specificity and alliance age, we found that partner trustworthiness and contractual safeguards were negatively related to opportunistic behavior. Furthermore, opportunistic behavior was negatively related to alliance performance, as hypothesized. Interestingly, mutual equity investments were found to be unrelated to opportunistic behavior, counter to transaction‐cost logic. These findings refine and extend the transaction‐cost economics perspective regarding our understanding of strategic alliance behavior and outcomes, and offer executives in service‐based industries some practical ideas for assuring favorable strategic alliance outcomes.