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In 2008, Paul De Knop (Vrije Universiteit Brussels) stated that “in spite of the social value of sport and its role as a policy tool, human sport sciences still lack a fulfilling position in the academic world.” In Belgium and in Flanders (the northern and Dutch-speaking part of the country), the sociology of sport is still a small field of research among the sport sciences. The discipline is institutionalized within the institutes of physical education of the three universities (University of Ghent; Katholieke Universiteit Leuven; Vrije Universiteit Brussels). The scarcity of academic funding streams resulted in a focus on more applied, policy-based research in Flanders. Additionally, all institutes emphasize increasingly an interdisciplinary cooperation to connect with stronger research fields (e.g., health sciences, social studies, or international studies on sport participation). Even though each university has its own research tradition, the universities and the government cooperate in a longitudinal study on sport participation in Flanders. De Knop, who became rector of the Vrije Universiteit Brussels (VUB) in 2008, was the first lecturer of the course sociology of sport at his university. He graduated in 1975 as licentiate in physical education and his career at the university converged with the development of the discipline. Together with Roland Renson and Bart Vanreusel (KU Leuven), he was one of the academic pioneers for the sociology of sport in Flanders.

The threat posed to the environment by CFC cleaning processes for printed circuit boards has led to an investigation of possible alternatives. In a preselection procedure, surprising results were obtained using propylene glycol ethers (PGE), solvents for the paint industry. In this study these ethers are compared with five other groups of cleaners, which can be divided into four water‐based classes: weak acid, neutral, weak and strong alkaline, and water miscible organic solvent cleaners which are non halogen‐containing and biodegradable. The cleaning power of PGE and other cleaners is tested on fluxes for wave and reflow soldering. Comparative results for the different groups are given, combined with surface insulation resistance measurements. Good results can be obtained using alkaline or solvent cleaners. However, it appears that the cleaning results depend heavily on the type of flux used and the choice of a matching cleaning process.

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

During reflow soldering the applied solder paste is melted and the components, previously placed on the solder paste, move into their final position. This process, however, may be accompanied by various unwanted movements of components and solder. Components may move horizontally along the surface of the board (this is called swimming or floating), or may move vertically and stand on their ends (this is called drawbridging or Manhattan effect). On the other hand, the molten solder may move to places other than those intended, e.g., into metallised holes (PTH) connected to the solder lands, or upwards along component leads away from the joint area; this effect is called solder wicking. Moreover, isolated small solder balls are often found on the board surface after melting of the paste. Experiments show that all these effects depend on the heating method, vapour phase soldering often being the most prone. The driving forces of the displacements can be explained in terms of forces and pressure caused by the surface tension of the molten solder, whereas the observed influences of the heating method are the result of the direction from which the heat is transported to the solder paste to be melted. From this, important conclusions for vapour phase soldering, infra‐red soldering and hot‐belt soldering may be drawn.

Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

The authors contribute to the literature by contrasting the cognitive with a contextual view on learning and development to address preconceptions about aging in order to broaden employers’ views on learning and performance across the working life and careers of their employees. The authors furthermore identify a number of opportunities that exist to support and sustain learning capability and performance in older workers. The paper aims to discuss these issues.

The authors conducted a selective literature search to identify literature that best exemplifies both the cognitive and contextual view.

Age-related declines in performance are observed on very specific and limited tasks, unlikely to be encountered in the workplace. Due to its focus on assessing limits and age differences, such studies will not normally take experience, job and personal resources into account – these are more likely to be present in the workplace. The authors find that much of the more applied evidence suggests older workers can perform at a similar level to their younger colleagues – when the workplace or training context does not restrain them from using their own strategies and resources to complete tasks.

The current overview of research that is more representative of the more cognitive view outlines a number of issues regarding the transferability as well as generalisability of these well publicised findings. Many of the laboratory-based cognitive ageing research focuses on “testing the limits” of cognitive processing and efficiency. This paradigm is not compatible with the specific tasks and learning that takes place in the workplace, which led us to a more contextual view.

The authors provide several starting points for broader and more inclusive training and development, particularly the role of supervisory practices, climate, self-efficacy and learning competency, resources and strategies.

Taking a more contextual view may foster a re-evaluation of how inclusive current managerial practice, policies and current development initiatives are. Distinguishing job and personal resources can help in identifying the aspects that can be influenced by workers themselves and those aspects that are the primary responsibility of organisations and their managers.