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This paper aims at the improvement of the cost‐effectiveness of brushless DC motor (BDCM) drives integrated in electric and hybrid propulsion systems.

The cost‐effectiveness improvement is gained through the reduction of the topology of the inverter in the armature which turns to have two legs (four switches) rather than three legs (six switches) in conventional inverters. This has been made possible thanks to the availability of the battery pack in automotive applications.

It has been found that the four‐switch three‐phase inverter (FSTPI) fed BDCM drive has almost the same performance as the six‐switch three‐phase inverter (SSTPI) fed BDCM.

This works should be extended by an experimental validation of the established results.

The reduction of the topology of the inverter in the armature of the BDCM opens up crucial cost benefits especially in large‐scale production industries, such as the automotive one.

The implementation of a simple self‐control strategy in a FSTPI fed BDCM drive yields almost the same dynamic and steady state performance as those obtained by a SSTPI fed BDCM drive. An analytical assessment of the steady state features of the FSTPI‐fed BDCM drive has been confirmed by simulation.

Addressing integrity, flexibility and interoperability challenges in enterprise information systems (EISs) is often hindered by the “three-fit” barrier, which encompasses vertical, horizontal and transversal fit problems. To overcome these obstacles, we propose solutions aimed at defining the business view of EIS. This study addresses these issues by proposing solutions tailored to the business view of EIS. Specifically, it introduces the core ontology of sensitive business processes (COSBP), a conceptual framework designed to formalize and define the multidimensional dimensions of sensitive business processes (SBPs). By providing a unified structure of central concepts and semantic relationships, COSBP enhances both knowledge management (KM) and business process management (BPM) in organizational contexts.

This paper adopts the design science research methodology covering the phases of a design-oriented research project that develops new artifacts, such as the COSBP ontology, based on SBP modeling requirements. Following a formal multi-level, multi-component approach, COSBP is structured into sub-ontologies across different abstraction levels. Built upon the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) foundational ontology, COSBP integrates and extends core concepts from core domain ontologies in business processes. The framework specifies six key modeling dimensions of SBPs – functional, organizational, behavioral, informational, intentional and knowledge – each represented as a distinct class of ontological modules (OMs).

COSBP offers a semantically rich and precise framework for modeling SBPs, addressing complexity and ambiguity in conceptual modeling. It supports the creation of expressive and effective SBP models while enabling consensus-driven representation at a generic level. Additionally, COSBP serves as a foundation for extending modeling notations and developing tools that align with these notations. Its application in enterprise environments improves the integration, adaptability and interoperability of EISs, ultimately enhancing organizational processes and decision-making.

The development of the COSBP ontology holds considerable potential for application in various industries beyond its original focus on business process management and KM. The ontology’s capability to semantically model sensitive, knowledge-intensive and dynamic processes can be extended to other real-life scenarios in other complex domains and sectors – for example, finance and banking, government and public services, insurance, manufacturing and supply chain management, retail, E-commerce, logistics and transportation crisis management, government and public services, higher education and so on. By integrating artificial intelligence (AI) with the COSBP ontology, we aim to enable more intelligent decision-making, process monitoring and improved management of SBPs in knowledge-driven domains.