Knowledge Transfer
Knowledge Transfer - Related Videos
05:12
Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
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Cited by 11 •
2017
We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.
06:08
Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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2025
This study aims to analyze how people reason with and about normative rules, deontic reasoning, and the importance of world knowledge in subjects´ inferences. For this purpose, one of the main experimental paradigms in reasoning research was used: Wason´s selection task.
11:05
Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Cited by 10 •
2016
The following paper presents a novel FE simulation technique (KBC-FE), which reduces computational cost by performing simulations on a cloud computing environment, through the application of individual modules. Moreover, it establishes a seamless collaborative network between world leading scientists, enabling the integration of cutting edge knowledge modules into FE simulations.
01:20
Transfer Pricing
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2026
Transfer pricing is the process of setting prices for goods, services, or intangible assets exchanged between divisions, subsidiaries, or related entities within the same organization. Although these transactions occur within a single company, transfer prices are important for evaluating divisional performance, allocating resources, and supporting managerial decision-making. They affect the reported revenues, costs, and profitability of individual business units.Organizations use several...
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Cited by 1 •
2025
This article describes RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), which integrates Large Language Model (LLM) inference with Retrieval-Augmented Generation (RAG). It draws evidence from expert-curated biomedical knowledge bases and peer-reviewed biomedical publications to synthesize new knowledge from up-to-date information, identify explainable and actionable predictions, and pinpoint promising directions for hypothesis-driven investigations.
FAQs
Effective transfer depends on preserving more than instructions. Engineering records should retain design rationale, operating conditions, lessons learned, and safety considerations because these details explain why a solution works and when it can be applied. Capturing that context converts personal experience into capability that another engineer or team can use without repeating the original discovery process.
Transferred information often needs adaptation to fit a new project, workforce, or operating setting. The receiving group must understand the original conditions and then determine how the methods, data, or technology apply in its own context. This preserves useful engineering knowledge while avoiding inappropriate direct reuse that could overlook changed requirements or safety considerations.
These approaches address different parts of the transfer process. Documentation preserves methods, data, decisions, and lessons learned; training communicates established practices; mentoring conveys individual experience; and collaborative problem-solving allows people to apply knowledge together. Combining them helps move both recorded information and practical expertise into shared capability rather than relying on a single communication channel.
Standardized procedures make validated engineering solutions more consistent across people, teams, and project phases. They provide a repeatable reference when personnel change or responsibilities shift, while documented lessons learned and safety considerations preserve important context. This combination supports continuity, reduces duplicated effort, and helps organizations apply established practices reliably instead of rebuilding them from memory.
A practical effort can begin by identifying the expertise, methods, data, or technology that must move to a new person, team, organization, or setting. The organization can then document relevant rationale and conditions, use training or mentoring to explain application, and support collaborative problem-solving. Standardized procedures help embed the resulting capability into ongoing engineering work.
It is especially useful when technology moves into deployment, when interdisciplinary teams must collaborate, or when work passes between project phases. It also becomes important when personnel change, because documentation, training, mentoring, and shared problem-solving can preserve expertise. In each situation, the approach helps receiving groups apply engineering methods and solutions without unnecessary duplication.
Useful outcomes include stronger continuity across personnel or project phases, less duplicated effort, and more consistent application of validated engineering solutions. The receiving team should also gain access to the relevant design rationale, operating conditions, lessons learned, and safety considerations. These results show that knowledge has become usable organizational capability rather than remaining with one individual or group.