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Mark D. Agars is an associate professor of psychology at California State University, San Bernardino. He received his Ph.D. from the Pennsylvania State University in industrial and organizational psychology, where he worked with James L. Farr.

Aims to look at the way women in Western industrialized countries have made significant career advancements but barriers remain which necessitate organizational intervention. A number of initiatives that have commonly been introduced to help women in their career development have produced only modest gains in women's advancement.

Argues that four basic underlying processes – social cognitions, justice, threat, and utility – must be considered and managed as part of the development, implementation, and evaluation of initiatives targeting women's career advancement, if such initiatives are to have their desired impact. These underlying processes and their implications are presented with recommendations for organizational leaders and human resource professionals.

The goal of women's advancement is both noble and vital to long‐term organizational success, and initiatives to further the cause are needed. These goals are not, however, without their obstacles. By understanding how social cognitions, perceptions of fairness, threat, and utility can individually and collectively impact the success or failure of programs to advance women, organizational leaders can develop and implement informed practices that are embraced by all members of the organization and which, consequently, further the advancement of all organizational members.

The article focuses on the key underlying processes that are believed to be critical for understanding the barriers to women's career advancement, but it is necessary to note that, as with any organizational initiative, there are other organizational factors that must be considered, such as top management support, and the history of the organization with regard to gender.

Organizational creativity and innovation are inherently complex phenomena, and subject to a myriad of broad contextual and social influences. As the evidence grows for the link between innovation and organizational effectiveness and, ultimately, organizational survival, there is no doubting the need for theoretical and practical advances in our understanding. The complex nature of these constructs, however, requires that such efforts utilize a multi-level lens. This chapter discusses key aspects of creativity and innovation in organizations, including fundamental construct definition issues, which underscore the need for a multi-level perspective. It also reviews extant theoretical perspectives for their contributions to a multi-level understanding, and the research in two key areas of social influence – group factors and leadership – that have received substantial attention in the organizational literature. The review and discussion of these areas reveal not only numerous advances, but also substantial limitations that must be resolved through more complex and comprehensive (i.e., multi-level) approaches. The chapter concludes with several recommendations intended to guide and inform future work in the organizational creativity and innovation field.

As the preceding chapter and commentaries reveal, the field of organizational creativity and innovation is both complex and multifaceted. Many core constructs are ambiguously defined, and levels issues are often confounded within constructs and among proposed relationships. When attempting to advance the understanding of social influence factors and organizational creativity and innovation, multi-level perspectives are particularly well suited to address these complexities. The commentaries of Dionne and Runco represent alternative approaches to facing these challenges. This response to their commentaries discusses our perspective on their recommendations and presents some final thoughts on how multi-level approaches should form the basis for moving the field forward.

The purpose of this paper is to define the construct of confidence in top leadership and to develop a brief measure of that construct.

Two studies were conducted in which 674 working adults completed measures of work‐related attitudes. In both studies, the scale demonstrated convergent and discriminant validity with like and unrelated constructs.

The results provide evidence for the construct validity of the new measure.

Respondents were relatively young and drawn predominantly from a working student population in a single region of the USA. Additional research should be conducted with additional working populations, especially in the context of addressing organizational change.

The short five‐item scale would be useful as a diagnostic tool for assessing the confidence of organizational members in their top leadership.

This study supports a concept, confidence in top leadership, that as a followership variable, can be used to identify if top leadership has the support of its followers.

This commentary is intended to complement the chapter written by Agars, Kaufman, and Locke (this volume). Agars et al. (this volume) are correct in stating that innovation is a vital part of business and that much can be learned about innovation from studies of creativity. This commentary underscores several of the more important points made by Agars et al., questions others, and fills in several gaps found in their chapter. Perhaps most important are the qualifications offered in the present commentary, which are intended to constrain some of the claims made in Agars et al.'s chapter. Related to this is the need to consider the larger picture of scholarship dealing with innovation and creativity. For example, there may be an originality bias among creativity researchers and an unfortunate tendency to ignore relevant but older research. This commentary does have a critical tone, but only because many of Agars et al.'s arguments are entirely tenable and need not be repeated. The gaps, however, should be filled, and some of the claims qualified. Nevertheless, some ambiguity is useful with a complex topic, such as creativity, and it is important to recognize that creativity is not entirely a social process.

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise.

This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity.

Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages.

EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

As has been said, the commercial “infants' food,” leaving on one side the condensed milk, is almost invariably a powder, and, as such, should conform in composition as nearly as possible to a dried human milk. Of course, the preparations are not altogether free from water. From a large number of analyses it appears that the percentage in the great majority of cases varies between 4 and 8, the maximum being 13.9 and the minimum under 1. The average is approximately 6.5 per cent.

This study aims to apply an open-source approach to protect the 3D printing industry from innovation stagnation due to broad patenting of obvious materials.

To do this, first an open-source implementation of the first five conditions of an open-source algorithm developed to identify all obvious 3-D printing materials was implemented in Python, and the compound combinations of two and three constituents were tested on ten natural and synthetic compounds. The time complexity for combinations composed of two constituents and three constituents is determined to be O(n²) and O(n³), respectively.

Generating all combinations of materials available on the Chemical Abstracts Services (CAS) registry on the fastest processor on the market will require at least 73.9 h for the latter, but as the number of constituents increases the time needed becomes prohibitive (e.g. 3 constituents is 1.65 million years). To demonstrate how machine learning (ML) could help prioritize both theoretical as well as experimental efforts a three-part biomaterial consisting of water, agar and glycerin was used as a case study. A decision tree model is trained with the experimental data and is used to fill in missing physical properties, including Young's modulus and yield strength, with 84.9 and 85.1% accuracy, respectively.

The results are promising for an open-source system that can theoretically generate all possible combinations of materials for 3-D printing that can then be used to identify suitable printing material for specific business cases based on desired material properties.