Using aspiration levels in an interior primal-dual multiobjective linear programming algorithm

We introduce in this paper a new multiple-objective linear programming (MOLP) algorithm. The algorithm is based on the single-objective path-following primal—dual linear programming algorithm and combines it with aspiration levels and the use of achievement scalarizing functions. The resulting algorithm falls in the class of interactive MOLP algorithms, as it requires interaction with the decision maker (DM) during the iterative process to obtain statements of aspirations for levels of objectives of the MOLP problem. The interior point algorithm is then used to trace a path of interates from a current (interior) solution and approach as closely as desired a non-dominated solution corresponding to the optimum of the achievement scalarizing function. The timing of the interaction with the DM is dependent on the progress of the interior algorithm. It can take place every few, pre-specified, iterations or after the duality gap achieved for the stated aspirations has fallen below a certain threshold. It is expected that an interior algorithm will speed up the overall process of searching and finding the most preferred MOLP solution—especially in large-scale problems—by avoiding the need for numerous pivot operations and their corresponding interactive sessions inherent in simplex-based algorithms.     

The Development of Early Attachment Behaviours

The normative development of attachment behaviours and the changing relationship between the level of activation of the attachment system and the intensity of subsequent attachment behaviours were studied longitudinally from 3 to 12 months of age. The study was based on Bowlby's notion about increasing goal directedness of the attachment system. The sample consisted of two groups of children, 33 children born with cleft lip and palate and 34 children without this congenital anomaly. To circumvent the age-limited applicability of the Strange Situation procedure, a new observational instrument was developed, the so-called Induced Stress at Home procedure. It appeared that the ISH procedure is a valid alternative to induce and measure the attachment behaviours proximity seeking, contact maintenance and avoidance. Using the multilevel model for longitudinal data, it was shown that attachment behaviours are not very stable across age. An elaborated model revealed that the relationship between activation of the attachment system and subsequent contact maintenance and resistant behaviour become less strong with age. No differences between children with and without cleft lip and palate were found.     

Indifference sets of reference points in multi‐objective integer linear programming

Reference point approaches for multi‐objective problems rely on the definition of an achievement scalarizing function that projects reference points onto the non‐dominated solution set. In this paper, we investigate the behaviour of reference points using a Tchebycheff metric‐based scalarizing function in multi‐objective pure integer linear programming (MOILP). Since the non‐dominated solutions are discrete in MOILP, there are multiple reference points that lead to the same solution, i.e. there are indifference sets on the reference point space. We investigate some properties of the reference points in MOILP and also the graphical representation of indifference sets for tri‐objective problems. We further investigate properties of the reference points when additional limitations on the objective function values are introduced. Copyright © 2001 John Wiley & Sons, Ltd.     

Material handling device selection in cellular manufacturing

This paper presents a new multi‐criteria decision model for the material handling device (MHD) selection problem in cellular manufacturing systems. Given a set of manufacturing cells based on several automatic work‐centres, the technique makes it possible to select a particular MHD for each cell in an integrated way, with different constraints being taken into consideration. The approach is based on two different multi‐attribute analyses executed with analytic hierarchy process (AHP) methodology, and a final integer linear programming including important limitations faced by the designer when making MHD investment decisions. An example using real data is provided to illustrate this methodology. Copyright © 2002 John Wiley & Sons, Ltd.     

The operation of Lake Kariba: A multiobjective decision analysis

The paper describes a successful application of Bayesian decision analysis to the operation of the Lake Kariba hydropower system. This management problem is complicated by the high uncertainty of the inflow process, multiple and conflicting objectives and the influence of time on some of the parameters in the management task. Inflows to the reservoir are forecast through dynamic linear models. Managerial preferences are modelled through a multiattribute utility function. Since the solution of the exact model is computationally too demanding, a heuristic method is applied to find a feasible control strategy. A comparison with results obtained by methods used previously demonstrates the superiority of the methodology presented here.     

Should metacognition be measured by logistic regression?

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria.     

Towards a conceptual and empirical differentiation between workplace bullying and interpersonal conflict

This study investigates the defining features that distinguish workplace bullying from interpersonal conflict – being frequency, negative social behaviour, power imbalance, length and perceived intent – by contrasting the characteristics of conflict incidents in a group of workplace bullying victims versus a group of non-victims. A group of 47 victims and 62 non-victims were identified based on a questionnaire time 1 and time 2 (time lag of 6 months). The conflict incidents were assessed between time 1 and time 2 using an event-based diary study that was filled out for a period of two times 20 working days with a break of 4 months in between. Hierarchical linear modelling (HLM) showed that conflict incidents differed for victims versus non-victims, in line with the defining aspects of workplace bullying: victims’ conflict incidents related more to the work context and included more personal and work-related negative social behaviour. Victims perceived more inferiority and less control in the conflicts, indicated more continuation of previous conflict incidents and reported more negative intentions from their opponent. These findings validate the conceptual differentiation between interpersonal conflict and workplace bullying, while at the same time adhering to their related nature.     

Identification of abnormal movements with 3D accelerometer sensors for seizure recognition

Human-activity recognition and seizure-detection techniques have gathered pace with the widespread availability of wearable devices. A study of the literature shows various studies for 3D accelerometer-based seizure detection that describe the selection of acceleration variables and controlled transformations, while discarding the remaining input variable contributions. The aim of this research is to evaluate feature extraction based on different techniques and with the advantage of an overview of all information on the problem. Three feature extraction techniques – namely, Locally Linear Embedding, Principal Component Analysis (PCA) and a Distance-Based PCA – are analyzed and their outcomes compared against K-Nearest Neighbor and Decision Trees. A realistic experimentation simulating epileptic mioclonic convulsions was performed. The PCA-based methods were found to produce solutions that managed the problem perfectly well, either learning specific models for each individual or learning generalized models.     

BANOVA: Bayesian Analysis of Experiments in Consumer Psychology

This article introduces a Bayesian extension of ANOVA for the analysis of experimental data in consumer psychology. The approach, called BANOVA (Bayesian ANOVA), addresses some common challenges that consumer psychologists encounter in their experimental work, and is specifically suited for the analysis of repeated measures designs. There appears to be a recent surge in interest in those designs based on the recognition that they are sensitive to individual differences in response to experimental treatments and that they offer advantages for assessing causal mediating mechanisms, even at the individual level. BANOVA enables the analysis of repeated measures data derived from mixed within–between‐subjects experiments with Normal and nonNormal‐dependent variables and accommodates unobserved individual differences. It allows for the calculation of effect sizes, planned comparisons, simple effects, spotlight and floodlight analyses, and includes a wide range of mediation, moderation, and moderated mediation analyses. An R software package implements these analyses, and aims to provide a one‐stop shop for the analysis of experiments in consumer psychology. The package is illustrated through applications to a number of data sets from previously published studies.     

Effects of classroom organization on letter-word reading in first grade

Teacher organization is a crucial part of classroom functioning; however, its relation to student achievement has not been investigated as extensively as that of instruction. In this study, organization is defined as the amount of time teachers spend explaining the purpose and procedures of learning activities and daily routines. Data from first-grade classrooms (N = 44) observed three times during the school year (fall, winter, and spring) are analyzed, along with students' (N = 108) literacy skills at fall and spring. Hierarchical Linear Modeling reveals that, controlling for students' fall word reading and vocabulary skills, as well as amount of language arts instruction they receive, both amount and change in amount over time in classroom organization significantly predicts spring word reading skills. Specifically, children in classrooms observed in higher amounts of classroom time in organization at the beginning of the school year, followed by sharp decreases over the school year, demonstrated stronger letter and word reading skills by spring, and this was a main effect (p < .05). Practical and research implications are discussed.