The (almost) ubiquitous ASA framework

Ben Schneider’s ASA framework and the associated idea of homogeneity is a theoretical cornerstone on which most organisational person-environment (PE) fit studies are built. However, whilst it is commonly used to justify studies and to explain empirical findings, very few PE fit studies have moved the underlying ASA framework forward. More than 20 years on, the theory remains intact and has barely evolved. This paper begins with a short citation review that illustrates how the ASA framework is used in empirical studies. Following this, the body of the paper explores the reasons why the empirical studies that use the ASA framework as their theoretical underpinning are ineffective in revising Schneider’s ideas. The paper concludes with a discussion of the ASA framework’s rhetorical role and its inappropriateness as a theoretical justification of empirical PE fit studies.

Individual variation of in-air female ‘pup contact’ calls in Weddell seals, Leptonychotes weddellii

Vocal recognition may function as a critical factor in maintaining the phocid mother-pup bond during lactation. For vocal recognition to function, the caller must produce individually distinct calls that are recognised by their intended recipient. Mother-pup vocal recognition has been studied extensively in colonial otariids and appears to be characteristic of this family. Although less numerous, empirical studies of phocid species have revealed a range of recognition abilities. This study investigated whether Weddell seal (Leptonychotes weddellii) females produce individually distinct 'pup contact' calls that function during natural pair reunions. Fifteen calls from each of nine females recorded in the Vestfold Hills, Antarctica were analysed. One temporal, nine fundamental frequency and five spectral characteristics were measured. Results of the cross-validated Discriminant Function Analysis revealed that mothers produce individually distinct calls with 56% of calls assigned to the correct individual. The probability of achieving this level of discrimination on novel data by chance alone is highly improbable. Analysis of eight mother-pup reunions recorded near McMurdo Sound, Antarctica further demonstrated that these 'pup contact' calls function during natural pair reunions. Behavioural analysis also revealed that pups were chiefly responsible for establishing and maintaining close contact throughout the reunion process. Our study therefore demonstrates that Weddell seal females produce calls with sufficient stereotypy to allow pups to identify them during pair reunions, providing evidence of a functioning mother-pup vocal recognition system.

Measurement of the deformation and adhesion of solids in contact

Two curved surfaces of molecularly smooth mica are brought into contact under a controlled load, and their deformed shape is measured using optical interference fringes of equal chromatic order. This technique allows exceptionally accurate measurements of surface profiles at separations down to a molecular scale. Contact between the surfaces can be made either adhesive or nonadhesive by suitable choice of the medium between them. Results have been obtained in both cases and compared with the theories of Hertz (nonadhesive contact) and Johnson, Kendall, and Roberts (adhesive contact).

Contact electrification and adhesion between dissimilar materials

Simultaneous measurements of surface force and surface charge demonstrate strong attraction due to the spontaneous transfer of electrical charge from one smooth insulator (mica) to another (silica) as a result of simple, nonsliding contact in dry nitrogen. The measured surface charge densities are 5 to 20 millicoulombs per square meter after contact. The work required to separate the charged surfaces is typically 6 to 9 joules per square meter, comparable to the fracture energies of ionic-covalent materials. Observation of partial gas discharges when the surfaces are approximately 1 micrometer apart gives valuable insight into the charge separation processes underlying static electrical phenomena in general.

Contact electrification induced by monolayer modification of a surface and relation to acid-base interactions

Electrical charge separation following contact between two materials (contact electrification or the triboelectric effect) is well known to occur between different materials as a consequence of their different electronic structures. Here we show that the phenomenon occurs between two surfaces of the same material if one is coated with a single chemisorbed monolayer. We use the surface force apparatus to study contact electrification and adhesion between two silica surfaces, one coated with an amino-silane. The presence of this monolayer results in significantly enhanced adhesion between the surfaces, owing to electrostatic attraction following contact electrification, in accord with Derjaguin's electrostatic theory of adhesion. At the same time, the observed increase in adhesion is consistent with Fowkes' acid-base model (in which acid-base interactions between surface groups are considered to be the predominant factor determining adhesion), as the monolayer converts the originally acidic silica surface to a basic (amine-terminated) one. These observations demonstrate a link between acid- base interactions and contact electrification.

Electrical charge separation following contact between two materials (contact electrification or the triboelectric effect) is well known to occur between different materials as a consequence of their different electronic structures. Here we show that the phenomenon occurs between two surfaces of the same material if one is coated with a single chemisorbed monolayer. We use the surface force apparatus to study contact electrification and adhesion between two silica surfaces, one coated with an amino-silane. The presence of this monolayer results in significantly enhanced adhesion between the surfaces, owing to electrostatic attraction following contact electrification, in accord with Derjaguin's electrostatic theory of adhesion. At the same time, the observed increase in adhesion is consistent with Fowkes' acid-base model (in which acid-base interactions between surface groups are considered to be the predominant factor determining adhesion), as the monolayer converts the originally acidic silica surface to a basic (amine-terminated) one. These observations demonstrate a link between acid-base interactions and contact electrification.

Data-driven computation of contact dynamics during two-point manipulation of deformable objects

This study represents a preliminary step towards data-driven computation of contact dynamics during manipulation of deformable objects at two points of contact. A modeling approach is proposed that characterizes the individual interaction at both points and the mutual effects of the two interactions on each other via a set of parameters. Both global as well as local coordinate systems are tested for encoding the contact mechanics. Artificial neural networks are trained on simulated data to capture the object behavior. A comparison of test data with the output of the trained system reveals a mean squared error percentage between 1% and 3% for simple interactions.

Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.