IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency.

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring.

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Embedded Vision Intelligence for the Safety of Smart Cities.

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities' safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.

Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications.

Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a necessity in an effort to alleviate the communication burden of high-resolution video streaming, to provide flying aids, such as obstacle avoidance and automated landing, and to create autonomous machines. Thus, there is a growing interest on the part of many researchers in developing and validating solutions that are suitable for deployment on a UAV system by following the general trend of edge processing and airborne computing, which transforms UAVs from moving sensors into intelligent nodes that are capable of local processing. In this paper, we present, in a rigorous way, the design and implementation of a 12.85 kg UAV system equipped with the necessary computational power and sensors to serve as a testbed for image processing and machine learning applications, explain the rationale behind our decisions, highlight selected implementation details, and showcase the usefulness of our system by providing an example of how a sample computer vision application can be deployed on our platform.

Augmented Reality (AR) Supporting Citizen Engagement in Circular Economy.

Improving the public's awareness of and engagement with sustainable practices is essential for transitioning from a linear to a circular economy (CE). This task is not trivial; however, research into new ways of effectively supporting awareness and engagement, especially in view of digitalization trends, is still missing. In this study, we investigate the ability of augmented reality (AR) technology to improve awareness of CE practices and to engage citizens, inclusively, in the CE approach. Specifically, we report on the development of a novel AR engagement tool and present the results of two studies undertaken using this tool to assess the reliability and validity of this engagement practice. The first study focuses on the citizens of a municipality in Greece, and the second focuses on the general public outside the municipality boundaries. Attributes related to user engagement, such as novelty, aesthetics, perceived usability, endurability, focused attention, self-efficacy, perceived learning, and interest in the topic, were assessed, as were factors affecting these attributes related to the participants' knowledge of CE principles and exposure to similar technologies. The two studies indicate that the AR engagement tool increased social inclusion and community cohesion and was able to successfully bring CE principles and benefits to the public's attention. Higher engagement levels were observed in participants who had limited exposure to the concept of CE prior to using the AR tool, and its use was found to improve their confidence and interest in CE. This study aspires to contribute to the ongoing discourse on the use of mobile AR and to support further evidence-based development of digital tools for public awareness and engagement. Supplementary Information: The online version contains supplementary material available at 10.1007/s43615-021-00137-7.

Industrial symbiosis platforms for synergy identification and their most important data points: a systematic review.

Background: Industrial symbiosis (IS) primarily involves interfirm utilization of industrial residual resources. An important factor determining the success of IS is the identification and matching of cooperation opportunities. Digital tools, including IS platforms, are considered facilitators of this process. This systematic literature review addresses the research question: ' Which are the most important data points of an IS platform and how can they be used for the promotion of IS?'. Methods: The review is based on scientific publications from the following academic research databases: ScienceDirect, Scopus, SpringerLink, Wiley Online Library, AISel and IEEE (via Google Scholar), and grey literature obtained through a customized Google search technique, last performed on 9/3/2021. Records were included according to their scientific content, namely if the document: i) examined the identification of synergies utilizing ICT tools, ii) data requirements or platform related information were presented or iii) the impact of a digital tool in promoting IS was discussed. Exclusion criteria were: articles not written in English, not peer-reviewed, published before 2016 or document type other than scientific article, conference paper or EU project deliverable. Two independent reviewers performed title scanning and abstract reading of the documents to reduce the risk of bias. Results: The total number of records included after abstract and full text reading was 32. The main results of this review suggest that two significant types of data points are encountered in IS platforms; i) data required for synergy identification and ii) platform related information. Conclusions: A possible limitation of the study is a minor risk of bias due to one reviewer performing full text reading and synthesis of results; however, they reported to and consulted with the supervising reviewer. Overall, the results indicate that several types of data points are required for effective matching and successful promotion of IS through digital tools.

Driver-Vehicle-Environment monitoring for on-board driver support systems: lessons learned from design and implementation.

This paper is presenting the efforts to implement in real time and for on-board applications a set of Driver-Vehicle-Environment (DVE) monitoring modules based on the theoretical work done in DVE modelling within the EC 6th FW co funded AIDE Integrated Project. First the need for such an implementation will be discussed. Then the basic DVE modelling principles will be introduced and analysed. Based on that and on the overview of the theoretical work performed around the DVE modelling, the real time DVE monitoring modules developed in this project will be presented and analysed. To do this the DVE parameters needed to allow the required functionalities will be discussed and analysed. Special attention will be given to the use cases and scenarios of use for the real time DVE modules. This allows the reader to understand the functionalities that these modules enable in tomorrow's vehicles that will integrate a large degree of automation supported by advanced integrated and adaptive human machine interfaces (HMIs). The paper will also present examples of the functional and technical tests and validation results for some of the DVE modules. The paper will conclude with a discussion around the lessons learned about the design and implementation of such systems. This will include also the next steps and open issues for research in order for these systems to become standard modules in tomorrow's vehicles.

Influence of the refractive index core profile on modal scattering of terminated two-dimensional waveguides.

We examine the influence of the refractive index core profile on the modal scattering of abruptly terminated slab waveguides. The analysis is based on the integral equation method with accelerating parameters, while for the field description in the waveguide core, an appropriate Lanczos-Fourier expansion is employed. The electric-field distribution on the terminal plane, the reflection and transformation coefficient of the TE guided modes, and the far-field radiation pattern are computed. Numerical results are presented for slab waveguides with step, linear, and parabolic refractive index profiles of the core. Finally, several approximate analytical solutions are derived to study the problem in question and to explain the results obtained.

Reflectivity properties of an anisotropic slab waveguide with isolated substrate.

The scattering properties for both TE and TM modes of an abruptly ended two-layered slab waveguide with anisotropic core and isolated substrate are examined by an improved iteration technique, which is based on the integral equation method with accelerating parameters. The relative dielectric constants of the core for the three Cartesian directions are considered to be different, but cases with isotropic core are also considered. The electric field distribution on the terminal plane and the reflection coefficients of the dominant TE and TM guided modes, as well as the near-field distribution and the far-field radiation pattern, are computed, while numerical results are presented for several cases of the core anisotropy.

Metal iris influence on guided-mode diffraction.

The effects of the presence of metal irises on guided-mode propagation through a symmetrical three-layer slab waveguide are examined by using the integral equation method. The aperture electric field distribution is expressed in terms of a finite series of Chebyshev polynomials. The modal reflection and transmission coefficients, the near-field structure, and the far-field radiation pattern are calculated, while numerical results are presented for several iris apertures.

Reflectivity of buried slab waveguides.

The scattering properties of an abruptly ended buried slab waveguide for both TE and TM modes are examined by an improved iteration technique that is based on the integral equation method with "accelerating" parameters. The waveguide is considered a symmetrical slab, for which the weakly guiding conditions are invalid, and it is embedded in a different dielectric material. The tangential electric field distribution on the terminal plane, the reflection coefficient of the first TE and TM guided modes, and the far-field radiation pattern are computed. Numerical results are presented for several ended waveguides, while special attention is given to the far-field radiation pattern rotation and the terminal field distributions.