'The Degree of Despair': The Disjointed Labour Market, the Impact of the Pandemics, the Expansion of Precarious Work among Youth and Its Effects on Young People's Life Trajectories, Life Chances and Political Mentalities - Public Trust; The Case of Greece

This paper focuses on the expansion of precarious forms of employment (temporary, seasonal, part-time jobs etc.) and on the impact this expansion has on young peoples' key determinants of life course. Based on both secondary quantitative-data analysis and primary qualitative research, the paper analyzes the state of play regarding precarious work among youth both in the EU and (mainly) in Greece, while it highlights the impact of the pandemic and the subsequent Recession in the abovementioned. The paper also explores the various aspects and facets of the impact of precarious employment in young peoples' life trajectories. Key findings include: a) the strong correlation between precarious employment, social vulnerability and risk of poverty, b) the fact that, during the pandemic, the "labour market slack" in Greece hit young people aged 15-24 more than people aged 25-54, further widening their precariousness, c) that there is a wider tendency to expand and "normalize" the forms of precarious employment among youth, concerning, especially, the combination of declared and undeclared work, d) that a new labour market dualization is formed, e) that both the pandemic and the subsequent restrictive measures have had a significant impact on the majority of precarious young people, effectively causing a rupture in their already precarious life course and f) that all the abovementioned have a severe impact on key determinants of political behavior - mentalities as well as on public trust among young people. The paper is based on an ongoing Research Project, co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme "Human Resources Development, Education and Lifelong Learning 2014-2020" in the context of the project "Precarious Work and Youth in today's Greece: secondary quantitative analysis, qualitative filed research and research-based policy proposals" (MIS 5048510)

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

An educational setup for a Laser Induced Breakdown Spectroscopy (LIBS) system and its usage for the characterization of cultural heritage objects

An experimental setup for laser induced breakdown spectroscopy (LIBS) has been developed for educational purposes, to be used in the physics curriculum of science students and of students who are specializing in the field of cultural heritage. The setup comprises basically a Q-switched Nd:YAG laser and a fiber optic spectrometer. All components were already existing equipment at the physics laboratories at the TEI of Athens, so that they could be assembled in-house to a considerably economic LIBS setup. The proposed laboratory exercises are focused on one hand on imparting the knowledge about physical principles and phenomena associated with the creation of plasma and the radiation processes, while on the other hand, the students will be trained in the operation and handling the actual analytical process, in terms of specific applications. Various parameters are examined, concerning the laser-matter interaction and the process issues, such as calibration, interpretation of spectra and evaluation of results. Exemplary measurements as an autonomous learning and teaching module were implemented, demonstrating the qualitative and quantitative analysis of various materials typically associated with cultural heritage objects, such as metal standards and original objects and replicas of mural paintings

An educational setup for a Laser Induced Breakdown Spectroscopy (LIBS) system and its usage for the characterization of cultural heritage objects

An experimental setup for laser induced breakdown spectroscopy (LIBS) has been developed for educational purposes, to be used in the physics curriculum of science students and of students who are specializing in the field of cultural heritage. The setup comprises basically a Q-switched Nd:YAG laser and a fiber optic spectrometer. All components were already existing equipment at the physics laboratories at the TEI of Athens, so that they could be assembled in-house to a considerably economic LIBS setup. The proposed laboratory exercises are focused on one hand on imparting the knowledge about physical principles and phenomena associated with the creation of plasma and the radiation processes, while on the other hand, the students will be trained in the operation and handling the actual analytical process, in terms of specific applications. Various parameters are examined, concerning the laser-matter interaction and the process issues, such as calibration, interpretation of spectra and evaluation of results. Exemplary measurements as an autonomous learning and teaching module were implemented, demonstrating the qualitative and quantitative analysis of various materials typically associated with cultural heritage objects, such as metal standards and original objects and replicas of mural paintings

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties

Development and Implementation of a Low Cost μC- Based Brushless DC Motor Sensorless Controller: A Practical Analysis of Hardware and Software Aspects

The ongoing technological advancements of brushless DC motors (BLDCMs) have found a wide range of applications. For instance, ground-based electric vehicles, aerial drones and underwater scooters have already adopted high-performance BLDCMs. Nevertheless their adoption demands control systems to monitor torque, speed and other performance characteristics. Precise design structure and the particular motor functional characteristics are essential for the suitable configuration and implementation of an appropriate controller to suit a wide range of applications. Techniques which do not use Hall sensors should be used then. This paper deals with the analysis of hardware and software aspects during the development of such a microcontroller based and low cost speed controller for motors up to 500 W, along with its practical implementation. The sensorless method employed is based on the zero crossing point (ZCP) detection of the back-electromotive forces’ (back-EMF) differences, as the ZCPs of these quantities match to the time points at which the commutation sequence changes. Additionally, the study presents hardware and software details through calculations, figures, flowcharts and code, providing an insight of the practical issues that may arise in such a low cost prototype. Finally, results obtained by experiments validate the presented hardware/software architecture of the controller