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The purpose of this paper is the increase the flexibility of robots used for handling of 3D (food) objects handling by the development and evaluation of a novel 3D Bernoulli gripper.

A new gripper technology have been designed and evaluated. A deformable surface have been used to enable individual product handling. The lift force generated and the force exerted on the product during gripping is measured using a material tester instrument. Various products are tested with the gripper. A experimental/theoretical approach is used to explain the results.

A deformable surface can be used to generate a lift force using the Bernoulli principle on 3D objects. Using a small forming a significant increase in the lift force generated is recorded. Increasing the forming further was shown to have little or even negative effects. The forces exerted on the product during forming was measured to be sufficiently low to avoid product damage.

To be able to improve the grippers lift strength a better model and understanding of the flow is needed.

A novel Bernoulli gripper for 3D Bernoulli gripping have been designed and evaluated. The gripper enables flexible and delicate handling of various product shapes, 3D as well as 2D. Increased utilization of robots in the food industry can be gained.

This study aims to evaluate and compare by 100 hours engine bench tests the tribological performances of two types of lubrication oils, which were sulfur-based, boron succinimide-containing antiwear package (NP-3) oil and conventional zinc dialkyldithiophosphate (ZDDP)-containing (R-1) oil.

The tribological performances of the oils were evaluated in three main contexts, including engine tests, physical/chemical changes and surface analysis.

Results showed that NP-3 lubrication oil, which was environment- and catalyst-friendly, can be an alternative lubrication oil with its tribological performance due to similar antiwear characteristics with the ZDDP.

Attempts to develop catalysis- and environment-friendly antiwear additive packages have not presented popular or commonly used ZDDP-free products for the vehicle industry. This study presents tribological characterization of a newly developed ZDDP-free lubricating oil by engine bench tests.

Interim managers (IMs) are consultants who take on managerial positions during limited periods to perform changes, handle crises or cover vacancies. The increasing use of these short-term outsiders shapes new conditions for organizational learning in contemporary work life. The purpose of this paper is to contribute to research-based knowledge and theoretical understanding of the relationship between interim management and organizational learning.

The paper presents a literature review on interim management published within the years 2000–2020 and analyzes it through the lens of organizational learning.

An interim management assignment is characterized by a period of uncertainty, a limited time frame, knowledge from the outside and rather invisible outcomes. The concepts of shared mental models, dialogue, knowledge creation and organizational culture shed light on possibilities and constraints for organizational learning in these arrangements. The findings highlight the IM’s position as central for transforming the organizational culture, put a question mark for the establishment of the IM’s knowledge, show the need for defining outcomes in terms of learning processes and indicate tensions between opportunities for dialogue and the exercise of power.

The study provides a new conceptual understanding of interim management, laying the foundation for empirical studies on this topic from an organizational learning perspective.

In the telecommunication industry, companies gain a competitive edge through the competence of their employees, making issues of learning critical. The study aims to identify specific learning processes necessary when working at the edge both of one's own knowledge and of that of the branch.

This research draws on theories of learning through experience and interaction, and looks at software development engineers working at the interface between tele‐ and datacom within one company, Ericsson, Sweden. Data were collected in 2000 in four software‐engineering teams, through semi‐structured interviews, reflection groups and observations. Data were analyzed in an interplay between empirical findings and theoretical concepts.

The research identified three kinds of learning processes in which employees engage to accomplish their tasks: learning basic knowledge; co‐creating new knowledge; and learning changing‐knowledge. Learning basic knowledge was a frequent returning to a state of knowing nothing among skilled workers. The co‐creation of new knowledge implied close interaction processes in the midst of carrying out difficult work tasks. Learning changing‐knowledge questioned hitherto acquired knowledge through the necessity of taking in new facts and aspects in relation to already existing deep and extensive knowing.

Differentiating these learning processes has theoretical implications and a practical significance for organizations wanting to focus on competence and learning issues.

When organizing for learning it is of crucial importance to be aware of the kind of actual learning processes that are ongoing and need support and infrastructure.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.