A digital twin (DT), the digital counterpart of a physical entity, process, or system, is a pivotal innovation driving the manufacturing industry's digital transformation. DT plays a significant role in product lifecycle management (PLM) and product condition monitoring. However, the diversity of systems and processes involved poses challenges in DT and data management within PLM, particularly regarding efficiency, standardized data mapping, and latency.The paper presents a solution architecture to address these challenges and contribute towards an efficient and cost-effective product lifecycle management system. The architecture focuses on DT's data management and communication aspects, utilizing the edge-based, decentralized Eclipse Arrowhead Framework and EDMtruePLM (Enterprise Data Management True Product Lifecycle Management) for standardized data management and condition monitoring of products.Integrating the ISO 10303 STEP standard for data modeling and the Open Platform Communications Unified Architecture (OPC UA) standard for communication is emphasized, improving the contextual significance of the data and the system's interoperability. A use case implementation is presented, where a fischertechnik assembly line is monitored, capturing sensor data through the PLC's OPC UA server. The sensor data is then aligned with the STEP standard and stored in the EDMTruePLM database for monitoring. 

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

The era of extensive digitalization marked by the fourth industrial revolution has ushered in significant advancements in technologies like automation, artificial intelligence, and the Internet of Things (IoT). These innovations are revolutionizing manufacturing processes. Industry 4.0 (I4.0) and the subsequent Industry 5.0 (I5.0) emerged as comprehensive representations of the physical world in the information world, with goals to establish smart factories and promote human-machine coexistence. However, the implementation of I4.0 and I5.0 applications faces challenges related to engineering effort, interoperability, and efficient service discovery and binding.

This thesis seeks to address these challenges by exploring potential strategies to develop an efficient System of Systems (SoS) that comprises individual, autonomous systems collaborating to achieve a shared goal. This research examines methods to enhance the efficacy of SoS by refining its engineering procedures, promoting interoperability between standardized protocols, and employing dynamic adaption mechanisms. It aims to achieve automatic service discovery and interoperability between diverse industrial standards by integrating the Eclipse Arrowhead Framework. This IoT framework facilitates secure and seamless communication and collaboration among devices, machines, and systems.

Moreover, this work delves into saving energy consumption in distributed SoS environments. The thesis aims to optimize energy usage patterns, diminish peak loads, and bolster energy distribution and stability. This is achieved through the Demand Response (DR) mechanism combined with the Eclipse Arrowhead framework. The overarching objective is to pave the way for flexible production processes characterized by minimal resource waste, optimized energy consumption, and sustainable solutions. Through this endeavor, the thesis contributes to shaping a more efficient, interoperable, and sustainable manufacturing landscape in the context of Industry 4.0 and beyond.

Today’s manufacturing industries use a large suite of protocols and technologies to operate heterogeneous devices and software modules. Some of the most widely used technologies in industrial production are OPC UA (Open Platform CommunicationsUnified Architecture) and ROS (Robot Operating System). Hence, enabling interoperability across these technologies is critical to ensure a smooth production flow. We propose a local cloud-based approach to achieve interoperability between ROSand OPC UA by integrating them with the Eclipse ArrowheadFramework. This integration allows these technologies to operate as independent systems while communicating securely at runtime. In addition to achieving interoperability, this integration supports important industrial aspects such as loose coupling, late binding, and cyber-security, making it a flexible solution.

The OPC UA (Open Platform CommunicationsUnified Architecture) technology is found in many industrial applications as it addresses many of Industry 4.0’s requirements. One of its appeals is its service-oriented architecture. Nonetheless, it requires engineering efforts during deployment and maintenance o bind or associates the correct services to a client or consumer system. We propose the integration of OPC UA with the Eclipse Arrowhead Framework (EAF) to enable automatic service discovery and binding at runtime, reducing delays, costs, and errors. The integration also enables the client system to get the service endpoints by querying the service attributes or metadata. Moreover, this forms a bridge to other industrial communication technologies such as Modbus TCP (TransmissionControl Protocol) as the framework is not limited to a specific protocol. To demonstrate the idea, an indexed line with an industrial PLC (programmable logic controller) with an OPCUA server is used to show that the desired services endpoints are revealed at runtime when querying their descriptive attributes or metadata through the EAF’s Orchestrator system.

We present a fairly large, Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. The challenges with NLP systems with regards to tasks such as Machine Translation (MT), word sense disambiguation (WSD) and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. To the best of the authors’ knowledge, this is the first idioms corpus with classes of idioms beyond the literal and the general idioms classification. Inparticular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement (IAA) score, between two independent annotators, of 88.89%. Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). The corpus may also be extended by researchers to meet specific needs. The corpus has part of speech (PoS) tagging from the NLTK library. Classification experiments performed on the corpus to obtain a baseline and comparison among three common models, including the state-of-the-art (SoTA) BERT model, give good results. We also make publicly available the corpus and the relevant codes for working with it for NLP tasks.