Fault detection at the early stage is very important in modern industrial processes to avoid failure with life-threatening results and to reduce the cost of maintenance and machine downtime. In this paper, we present a workflow for building a fault diagnosis system based on acoustic emission (AE) using machine learning (ML) techniques. Our fault diagnosis approach is implemented on an embedded device with the internet of things (IoT) connectivity for real-time faults detection and classification in rotating machines. The achieved accuracy for our approach with a fine decision tree ML model is 96.1%.

Digital Twin (DT) stands as the premier technology within Industry 4.0 used in multiple sectors. Although there has been considerable recent literature on DT, there has been limited focus on the life cycle methodologies for its development. Lacking a well-defined process can significantly complicate DT development. This paper proposes an augmented Agile Software Development Life Cycle as a methodology for DT development.

Identity federation allows one to link a user’s digital identities across several identity management systems. Federated identity management (FIM) ensures that users have easy access to the available resources. However, scaling FIM to numerous partners is a challenging process due to the interoperability issue between different federation architectures. This study proposes a dynamic identity federation model to eliminate the manual configuration steps needed to establish an organizational identity federation by utilizing the OpenID Connect (OIDC) framework. The proposed model consists of three major steps to establish dynamic FIM: first, the discovery of the OpenID service provider, which indicates the location of the partner organization; second, the registration of the OpenID relying party, which allows the organization and its partner to negotiate information for establishing the federation; finally, establishing the dynamic trust federation. The proposed dynamic FIM model allows applications to provide services to end-users coming from various domains while maintaining a trust between clients and service providers. Through our proposed dynamic identity federation model, organizations can save hundreds of hours by achieving dynamic federation in runtime and serving a large number of end-users.

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. Cardiovascular Magnetic Resonance (CMR) imaging is widely recognized as the gold standard for the non-invasive assessment of cardiac structure and function. Among the key clinical biomarkers, left ventricular volume and ejection fraction are routinely estimated from CMR images to evaluate cardiac performance. This paper presents both fully automated and semi-automated frameworks for LVEF estimation at end-systole and end-diastole, with the primary distinction lying in their segmentation methodologies. The proposed pipeline consists of image enhancement, heart localization using Fourier transform techniques, segmentation, and volume estimation. In the semi-automated system, segmentation is achieved through a learning-free region growing algorithm, augmented by global thresholding and an optimized seed point selection strategy. The automated approach employs a deep convolutional neural network based on the U-Net architecture. Both systems incorporate inter-slice spatial continuity to enhance segmentation consistency across short-axis slices of the left ventricle. Experimental evaluation was conducted on a dataset comprising 4,400 short-axis CMR images. The proposed methods achieved Continuous Ranked Probability Score (CRPS) values of 0.053 and 0.015 for the automated and semi-automated systems, respectively. The results demonstrate the effectiveness and robustness of the proposed approaches, particularly the learning-free method, in handling anatomical and imaging variability. These findings underscore the potential of the proposed systems to support reliable and efficient LVEF quantification, thereby contributing to improved clinical workflows and decision-making in cardiovascular diagnostics.