The efficient and sustainable exploitation of energy resources may secure a sustainable economic growth for different regions. However, the peripheries are subject to social, economic, and political constraints, with limited power over energy management. The present work examines regional convergence in exploitation efficiency as synopsized in the GDP generated by energy and minerals in an era of the country’s efforts to shut down the lignite-run power production. With the assistance of panel unit root tests, we confirm non convergence of the variables employed, an expected result given the fact that different energy sources are being used for energy production by each different periphery, generating different economic results. In the second stage the methodology employed is a Bayesian vector auto-regressive model (BVAR) with an informative prior on the steady state. The particular methodology outperforms the conventional VAR methodology due to limited degrees of freedom. The Impulse response analysis and the Variance Decomposition analysis confirmed interlinkages among the regions studied. This result implies that the growth generated by different energy and mineral resources are interconnected. Furthermore, the energy transition taking place in Megalopoli and West Macedonia, where the two greatest lignite industries were located until recently, affects the growth generated by energy and resource exploitation for all the other peripheries, according to our findings. The novelty of the present work stands on the concept to detect interlinkages of energy and resources-based growth for the peripheries in Greece with the assistance of the Bayesian Var. The results of the present work are significant, since our findings suggest to policy makers tools to promote economic growth generated by energy based on alternative energy sources, including the environmentally friendly ones, by taking into consideration the interlinkages established by the existing infrastructure and the conventional energy sources used.

This study introduces an innovative, resource-efficient architecture that incorporates advanced technologies to create a lightweight, flexible, and scalable framework for remote and limited data collecting and processing in next-generation Internet of Things (IoT) applications. It specifically proposes an integrated Federated LoRaWAN (LoRA-FL) system that combines hierarchical, privacy-preserving Federated Learning (FL), Knowledge Distillation (KD), and a customised Medium Access Control (MAC) protocol, identified as FCA-LoRa, to address the significant limitations of LoRaWAN networks. These encompass strict duty cycle rules, restricted bandwidth, and energy limitations, elements that conventionally hinder the implementation of intelligent IoT devices in rural or isolated settings. The proposed architecture features a hierarchical FL approach enabling multi-tier Artificial Intelligence (AI) model aggregation across edge nodes, gateways, and a central server. The effectiveness of the proposed system is confirmed by two practical applications, smart agriculture and smart livestock farming, which exemplify standard situations for far-edge intelligence. The results indicate that the distilled model consistently attains over 90% packet delivery success, illustrating the architecture’s capacity to provide scalable and energy-efficient intelligence at the edge. This research addresses a significant gap in previous studies that frequently examine FL, communication optimisation, and model compression independently. This study offers a comprehensive, implementable approach that tackles model scalability, and network-layer issues within a cohesive architecture, enhancing the practical implementation of AI-driven IoT deployments over LoRaWAN.

Purpose: Although talent management is widely applied across various business sectors, the comprehensive literature focusing on talent management within the context of migration flows remains limited. This paper addresses this gap by conducting an extensive review of the studies published between 2018 and 2023, with particular attention given to the absence of talent management frameworks tailored to refugees. By synthesizing the existing literature, this study aims to highlight the areas where talent management practices can be developed to better address the unique challenges faced by migrant populations. Methodology: This research utilizes the PRISMA methodology and VOSviewer for bibliometric analysis. A total of 62 studies were included, providing insights into the role of talent management across different contexts. Analysis categorizes the studies by the research field, the keywords, the methods, the statistical measures, the geographic origin (countries/universities), and the publication year. Findings: Analysis revealed distinct categorization criteria, including the research field, the keywords, the methodologies, the statistical approaches, the country of origin, and the publication year. This study also identified critical research gaps, particularly in talent management practices related to refugee populations. These findings point to future research directions, emphasizing the need to develop talent management frameworks that can address migration-related challenges. Originality/value: This literature review is innovative due to its cross-disciplinary examination of the talent management literature, focusing on a previously unexplored intersection between talent management and migration. To the best of the authors’ knowledge, this is the first study to systematically analyze the gap in migration-related talent management, underscoring the need for further research in this important and emerging field.

Water leakage is acknowledged as one of the most important issues that drinking water supply systems are facing worldwide. Non-Revenue Water is estimated to 346 million m3 per day and its cost/value is estimated to 39 billion USD per year. At the same time drinking water quality is jeopardized from the water intake points to the consumer’s tap, even during normal operating conditions.ICT support water utility operators to improve the operational capacity of their water supply system. A smart green system to control water leakage and monitor drinking water quality in the water supply system of Paramythia city will be built in the context of SMASH project. It consists of: (a) IoT system comprising three local control stations, installed in selected parts of the water supply network, monitoring water quantity&quality parameters in real time; (b) the hydraulic simulation model of the water supply system of Paramythia; (c) a virtual sensors system, which will be used for water quality prediction; (d) a Decision Support System (DSS) for leakage detection and optimal management of water supply system parameters in an automated manner.The DSS will detect and locate water leakages within the DMA zone and inform the operators for excessive values in drinking water quality parameters. The DSS will use as inputs the data from the IoT system, will interact with the hydraulic simulation model, and obtain the water quality data from the virtual sensors. All these data will be processed by the DSS logic in the backend subsystem. The IoT and the hydraulic simulation data, based on the digital twin of the water supply system, are used for the calculation of specific performance indicators related to water leakage, such as well-known IWA indicators: water losses, ILI, etc. Calculating the divergences between the PI values observed & the ones representing the optimal operation of the water network without leakages and setting appropriate thresholds, the DSS will detect the leakage, while several different scenarios will run in hydraulic simulation. The frontend subsystem of the DSS will be able to visualize the water distribution network, statistical values of water quantitative & qualitative parameters. It will provide alarms in case of leakage or exceedance of water quality parameters’ values and it will show the leakage location in a map. The architecture of the smart green system, currently under development, is depicted in Fig.1.Figure 1. The DSS for the water parameters management in the water supply systemKeywords: Drinking water; water quality; leakage; virtual sensors; smart system; decision support.Acknowledgement: This work is co-financed by EEA Grants 2014 – 2021 and Greek Public Investments Program.Liemberger, R., & Wyatt, A. (2019). Quantifying the global non-revenue water problem. Water Supply, 19(3), 831-837. Antzoulatos G., Mourtzios C., et al (2020), Making urban water smart: the SMART-WATER solution. Water Science & Technology, 82(12), 2691–2710. Alegre, H., Baptista, et al (2016). Performance indicators for water supply services. 3rd IWA publishing.

The conventional utilization of fossil fuels precipitates uncontrolled carbon dioxide and sulfur oxides emissions, thereby engendering pronounced atmospheric pollution and global health ramifications. Within the maritime domain, concerted global initiatives aspire to mitigate emissions by 2050, centering on the adaptation of engines, alteration of fuel compositions, and amelioration of exhaust gas treatment protocols. This investigation pioneers experimentation with marine gas oil augmented by methanol, a practice conventionally encumbered by prohibitively expensive additives. Successful amalgamation of methanol, animal-derived biodiesel, and marine gas oil (MGO) is empirically demonstrated under meticulously controlled thermal conditions, creating a homogeneous blend with virtually zero sulfur content and reduced carbon content, featuring characteristics akin to conventional marine gas oil but with no use of expensive emulsifiers. This new blend is suitable for employment in maritime engines utilizing Delaval technology, yet with significantly lower energy requirements compared to those necessitated using conventional very low sulfur fuel oil (VLSFO) with a maximum sulfur content of 0.5% w/w.

This paper describes the development mode for a software application about management of inland water ecosystems that will help scientists and management authorities to take decisions related to the management of Inland Water Ecosystems. In addition, the above software will also provide the user with the capacity to create hypothetical (what-if) scenarios in order to achieve the best form of intervention. Furthermore the aim of this project is to construct an application that will provide a database with ichthyology and environmental data. Finally, the materials and methodology to be used and the possible results, will be discussed