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The purpose of this paper is to present an efficient algorithm based on a multi‐level adaptive mesh refinement strategy for the solution of ill‐posed inverse heat conduction problems arising in pool boiling using few temperature observations.

The stable solution of the inverse problem is obtained by applying the conjugate gradient method for the normal equation method together with a discrepancy stopping rule. The resulting three‐dimensional direct, adjoin and sensitivity problems are solved numerically by a space‐time finite element method. A multi‐level computational approach, which uses an a posteriori error estimator to adaptively refine the meshes on different levels, is proposed to speed up the entire inverse solution procedure.

This systematic approach can efficiently solve the large‐scale inverse problem considered without losing necessary detail in the estimated quantities. It is shown that the choice of different termination parameters in the discrepancy stopping conditions for each level is crucial for obtaining a good overall estimation quality. The proposed algorithm has also been applied to real experimental data in pool boiling. It shows high computational efficiency and good estimation quality.

The high efficiency of the approach presented in the paper allows the fast processing of experimental data at many operating conditions along the entire boiling curve, which has been considered previously as computationally intractable. The present study is the authors' first step towards a systematic approach to consider an adaptive mesh refinement for the solution of large‐scale inverse boiling problems.

This chapter introduces a new theoretical framework for developing emotion-related abilities according to the emotional intelligence (EI) construct definition of Mayer, Salovey, and Caruso (2006). The awareness, reflection, and management (ARM) model has been devised and demonstrates a triadic cycle of emotional ARM relating to affect, cognition, and behavior. The ARM model constitutes an approach to nurture emotion-related abilities (ability EI) and responds to criticism raised by Zeidner, Matthews, and Roberts (2009). The ARM Theory was corroborated by both learning theory and schools of counselling (SOC). The potential to develop emotion-related abilities in emotional awareness, reflection and reasoning, coping and management is discussed.

Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.

The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

The uncertainty evaluation for coordinate measuring machine metrology is problematic due to the diversity of the parameters that can influence the measurement result. From discrete coordinate data taken on curve (or surface) the software of these machines proceeds to an identification of the measured feature, the parameters of the substitute feature serve in the phase of calculation to estimate the form error of form, and the decisions made based on the result measurement may be outliers when the uncertainty associated to the measurement result is not taken into account. The paper aims to discuss these issues.

The authors relied on the orthogonal distance regression (ODR) algorithm to estimate the parameters of the substitute geometrical elements and their uncertainties. The solution of the problem is resolved by an iterative calculation according to the Levenberg Marquard optimization method. The authors have also presented in this paper the propagation model of uncertainties to the circularity error. This model is based on the law of propagation of the uncertainties defined in the GUM.

This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and their uncertainties. This contribution allows us to estimate the uncertaintof the form error by applying the law of propagation of uncertainties. An example of calculating the circularity error and the associated uncertainty is explained. This method can be applied to others geometry type: line, plan, sphere, cylinder and cone.

This work interested manufacturing firms by allowing them: to meet the normative, which requires that each measurement must be accompanied by its uncertainty-in conformity assessment, the decision-making must take account of this uncertainty to avoid the aberrant decisions. Informing the operators on the capability of their measurement process

This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and its uncertainties. without the hypothesis of small displacements torsor, this method integrates the uncertainty on the coordinates of points and can be applied in any reference placemark. This contribution allows us also to estimate the uncertainty of the form error by applying the law of propagation of uncertainties.

The purpose of the paper is to analyse empirically the causal relationships between quality management, service quality and business success in German logistics companies.

The paper develops a measurement instrument of logistics service quality by combining conceptual approaches from service marketing with quality indicators from operations management. This measure is verified through factor analysis on a sample of 229 German logistics providers. Relationships between logistics service quality, quality management and business success are investigated in a structural equation model.

The results support measuring logistics service quality by the three dimensions: service potential, process and outcome. The effect of quality management on these constructs is confirmed. Likewise, the positive effect of service quality on business success is confirmed, with the notable exception of outcome quality.

This research only involves German logistics service providers. Further studies in other countries are needed to generalise the results.

Logistics service providers should devote more attention to quality management than they currently do. To enhance quality, they should focus their efforts on service potential and the service process. The paper offers them a way to measure these quality dimensions.

Research into logistics services has so far been mostly descriptive. The present study is the first to validate empirically a measure of logistics service quality and relate it to other phenomena. The relationship between quality management, service quality and business success found by structural modelling helps to understand the role of quality in logistics services.

The rapidly emerging digital work system, accentuated by technological innovation, has dramatically changed the nature of skill-sets required for employees to perform their tasks effectively at the workplaces. This paper aims to examine the skills development strategies that organizations in South Africa are adopting in the transitioning of their respective workforces to fit the skills dynamics of the emerging work system.

The paper uses the interpretive qualitative research strategy to draw evidence from semi-structured interviews conducted on 38 respondents, using the thematic analytical process to derive themes embedded in the data set.

Based on the strength of data analysis, this paper identified two broad themes and six sub-themes that are critical for the transformation and transition of existing pool of skills for the emerging work system in South Africa.

The research lacks ability to be generalized, which is a methodological limitation that is inherently associated with cross-sectional design and qualitative strategy in terms of causality and generalizability of findings.

The main value-add of this paper is the development of evidence-based research outcomes that provide both theoretical and practical framework for skills development and transition initiatives that are imperative for policy formulation. The paper responded to, and advanced the respective works of Hirschi (2017), Sharma et al. (2021) and Barley et al. (2017), by establishing the following strategic themes that are critical for skills development and transition mechanisms in the emerging work system: stakeholder relationship, media and public perception, learning organization, higher education system, continuous skills development and technology and job losses.

The purpose of this paper is to analyse the global scientific outputs of ontology research, an important emerging discipline that has huge potential to improve information understanding, organization, and management.

This study collected literature published during 1900-2012 from the Web of Science database. The bibliometric analysis was performed from authorial, institutional, national, spatiotemporal, and topical aspects. Basic statistical analysis, visualization of geographic distribution, co-word analysis, and a new index were applied to the selected data.

Characteristics of publication outputs suggested that ontology research has entered into the soaring stage, along with increased participation and collaboration. The authors identified the leading authors, institutions, nations, and articles in ontology research. Authors were more from North America, Europe, and East Asia. The USA took the lead, while China grew fastest. Four major categories of frequently used keywords were identified: applications in Semantic Web, applications in bioinformatics, philosophy theories, and common supporting technology. Semantic Web research played a core role, and gene ontology study was well-developed. The study focus of ontology has shifted from philosophy to information science.

This is the first study to quantify global research patterns and trends in ontology, which might provide a potential guide for the future research. The new index provides an alternative way to evaluate the multidisciplinary influence of researchers.

When CEOs are publicly weighing in on sociopolitical debates, this is known as CEO activism. The steadily growing number of such statements made in recent years has been subject to a flourishing academic debate. This field offers first profound findings from observational studies. However, the discussion of CEO activism lacks a thorough theoretical grounding, such as a shared concept accounting for the heterogeneity of sociopolitical incidents. Thus, the aim of this paper is to provide an archetypal framework for CEO activism.

The authors used a multiple case study approach on 145 activism cases stated by CEOs and found seven distinct statement archetypes.

The study identifies four main structural design elements accounting for the heterogeneity of activism, i.e. the addressed meta-category of the statement, the targeted outcome, the used tonality and the orientation of the CEOs’ positions. Further, the authors found seven distinguishable archetypes of CEO activism statements: “Climate Alerts”, “Economy Visions”, “Political Comments”, “Self-reflections and Social Concerns”, “Tech Designs”, “Unclouded Evaluations” and “Descriptive Explanations”.

This typology classifies the heterogeneity of CEO activism. It will enable the analysis of interrelationships, mechanisms and motivations on a differentiated level and raise the comprehensibility of research-results.

The framework supports executives in understanding the heterogeneity of CEO activism and to analyse personality-fits.

To the authors’ knowledge, this marks the first conceptualisation of activism developed cross-thematically. The work supports further theory-building on CEO activism.