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Background Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies. Methods Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset. Results The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression. Conclusion The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers' expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano). [ABSTRACT FROM AUTHOR]

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device. [ABSTRACT FROM AUTHOR]

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction. [ABSTRACT FROM AUTHOR]

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated. We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper. [ABSTRACT FROM AUTHOR]

The IoT landscape is characterized by a fragmentation of standards, platforms and technologies, often scattered among different vertical domains. To prevent the market to continue to be fragmented and power-less, a protocol-independent semantic layer can serve as enabler of interoperability among the various smart devices from different manufacturers that co-exist in a specific industry domain, but also across different domains. To that end, the SAREF ontology was created in 2015 with the intention to interconnect data, enabling the communication between IoT devices that use different protocols and standards. A number of industrial sectors consequently expressed their interest to extend SAREF into their domains in order to fill the gaps of the semantics not yet covered by their communication protocols. Therefore, the SAREF4INMA ontology was recently created to extend SAREF for describing the Smart Industry & Manufacturing domain. SAREF4INMA is based on several standards and IoT initiatives, as well as on real use cases, and includes classes, properties and instances specifically created to cover the industry and manufacturing domain. This work describes the approach followed to develop this ontology, specifies its requirements and also includes a practical example of how to use it. [ABSTRACT FROM AUTHOR]

Semantically rich descriptions of manufacturing machines, offered in a machine-interpretable code, can provide interesting benefits in Industry 4.0 scenarios. However, the lack of that type of descriptions is evident. In this paper we present the development effort made to build an ontology, called ExtruOnt, for describing a type of manufacturing machine, more precisely, a type that performs an extrusion process (extruder). Although the scope of the ontology is restricted to a concrete domain, it could be used as a model for the development of other ontologies for describing manufacturing machines in Industry 4.0 scenarios. The terms of the ExtruOnt ontology provide different types of information related with an extruder, which are reflected in distinct modules that constitute the ontology. Thus, it contains classes and properties for expressing descriptions about components of an extruder, spatial connections, features, and 3D representations of those components, and finally the sensors used to capture indicators about the performance of this type of machine. The ontology development process has been carried out in close collaboration with domain experts. [ABSTRACT FROM AUTHOR]

Nowadays it is becoming increasingly necessary to query data stored in different datasets of public access, such as those included in the Linked Data environment, in order to get as much information as possible on distinct topics. However, users have difficulty to query those datasets with different vocabularies and data structures. For this reason it is interesting to develop systems that can produce on demand rewritings of queries. Moreover, a semantics preserving rewriting cannot often be guaranteed by those systems due to heterogeneity of the vocabularies. It is at this point where the quality estimation of the produced rewriting becomes crucial. In this paper we present a novel framework that, given a query written in the vocabulary the user is more familiar with, the system rewrites the query in terms of the vocabulary of a target dataset. Moreover, it informs about the quality of the rewritten query with two scores: a similarity factor which is based on the rewriting process itself, and a quality score offered by a predictive model. This Machine Learning based model learns from a set of queries and their intended (gold standard) rewritings. The feasibility of the framework has been validated in a real scenario. [ABSTRACT FROM AUTHOR]

RDF Graph Summarization pertains to the process of extracting concise but meaningful summaries from RDF Knowledge Bases (KBs) representing as close as possible the actual contents of the KB both in terms of structure and data. RDF Summarization allows for better exploration and visualization of the underlying RDF graphs, optimization of queries or query evaluation in multiple steps, better understanding of connections in Linked Datasets and many other applications. In the literature, there are efforts reported presenting algorithms for extracting summaries from RDF KBs. These efforts though provide different results while applied on the same KB, thus a way to compare the produced summaries and decide on their quality and best-fitness for specific tasks, in the form of a quality framework, is necessary. So in this work, we propose a comprehensive Quality Framework for RDF Graph Summarization that would allow a better, deeper and more complete understanding of the quality of the different summaries and facilitate their comparison. We work at two levels: the level of the ideal summary of the KB that could be provided by an expert user and the level of the instances contained by the KB. For the first level, we are computing how close the proposed summary is to the ideal solution (when this is available) by defining and computing its precision, recall and F-measure against the ideal solution. For the second level, we are computing if the existing instances are covered (i.e. can be retrieved) and at which degree by the proposed summary. Again we define and compute its precision, recall and F-measure against the data contained in the original KB. We also compute the connectivity of the proposed summary compared to the ideal one, since in many cases (like, e.g., when we want to query) this is an important factor and in general in RDF, linked datasets are usually used. We use our quality framework to test the results of three of the best RDF Graph Summarization algorithms, when summarizing different (in terms of content) and diverse (in terms of total size and number of instances, classes and predicates) KBs and we present comparative results for them. We conclude this work by discussing these results and the suitability of the proposed quality framework in order to get useful insights for the quality of the presented results. [ABSTRACT FROM AUTHOR]

Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles three technical contributions that significantly advance the state-of-the-art in exploration of semantic data using semantic web techniques and technologies. [ABSTRACT FROM AUTHOR]

Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles four technical contributions that significantly advance the state-of-the-art in Semantic Web of Things for Industry 4.0 including the use of Semantic Web technologies and techniques in Industry 4.0 solutions. [ABSTRACT FROM AUTHOR]