Green Public Procurement (GPP) is an essential strategy for achieving goals related to public environmental policy, including sustainable production and consumption, streamlined use of resources and mitigation of climate change. The European Union has adopted policies towards “greening” public procurement for member states in order to promote environmental sustainability. Life Cycle Costing (LCC) is a method that measures the financial impact of an investment over the life cycle of a product. The current EU Procurement Directives (2014) are designed to position LCC centrally to sustainable sourcing. Although the literature identifies the links between the environmental dimension through GPP and the economic dimension through the use of LCC, the interaction between them in the context of public procurement has not been adequately captured. The aim of this paper is to evaluate the results of the LCC tools implementation in the context of GPP in Greece and study the economic impact of green procurement in public organizations. The urgent need of reducing energy consumption in the public sector due to the continuing energy crisis and climate change is an additional incentive to evaluate this impact. LCC tools developed by the EU were used, fed with data from public procurement contracts carried out in the Greek public sector. The results show that the adoption of environmental criteria requires market research, planning and coordination to make it cost-effective, especially under the legislative mandate of GPP in Greece by 2022.

Background: The electronic Freight Transport Information (eFTI) regulation is critical in modernizing freight transport (FT) within the European Union by establishing a framework for the electronic exchange of information. Despite its importance, there is a notable gap in the literature regarding the practical implementation challenges, especially from an empirical perspective. Methods: To address this gap, our study utilized a grounded theory approach, conducting interviews with a diverse group of logistics experts from Greece. The selection of experts was strategic to ensure a comprehensive range of knowledge and expertise, including insights at the policy level as well as practical experiences. Results: Our findings highlight several significant challenges in the implementation of eFTI, including the digital skill gap among the workforce, issues with system interoperability, and diverse capacities and resources of companies of different sizes. Economic factors, regulatory frameworks and the necessity for targeted training and leadership support were also identified as crucial for the digital transition. Conclusions: The study shows that uniform eFTI implementation may not work for all organizations, highlighting the necessity for customized strategies that address specific challenges in the FT chain. Our research deepens the understanding of these issues, providing actionable insights for successful eFTI adoption.

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques to understand the factors influencing maritime transport accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022. Leveraging big data analytics and advanced statistical techniques, the findings reveal significant correlations between vessel size, speed, and specific environmental factors. Furthermore, the study highlights the potential of big data analytics in enhancing predictive modeling, real-time risk assessment, and decision-making processes for maritime traffic management. The integration of big data with intelligent transportation systems (ITSs) can optimize safety strategies, improve accident prevention mechanisms, and enhance the resilience of ocean-going transportation systems. By bridging the gap between big data applications and maritime safety research, this work contributes to the literature by emphasizing the importance of examining both intrinsic and extrinsic factors in predicting maritime accident risks. Additionally, it underscores the transformative role of big data in shaping safer and more efficient waterway transportation systems.

The main objective of this study is to highlight the internal risk factors associated with maritime transportation accidents and the important role of presenting them in the dataset at the time of the incident. Since the study period involves a pre- and post-pandemic timeline, we refer to COVID-19, although it is not part of our analysis. The issue at hand is the appropriate statistical analysis and investigation of the possible correlations between the cause of the incident and internal factors/indicators that may affect the safety of crews on sea routes. We developed a comprehensive study based on advanced econometric modeling, utilizing multifactorial models of robust regression, structural equation modeling (SEM), and Gaussian/mixed-Markov graphical models (GGMs, MGMs) and applying them to a newly compiled dataset covering the 2014–2022 period. Our results bring to the fore important factors that can determine the causes of various accidents and injuries suffered by workers, ranging from work location to work activity and even the rank of the seafarers on board. We do not consider the external factors associated with a maritime transportation accident, as the risk of an accident in this sector due to external factors (i.e., weather conditions, defaults, failures, etc.) is limited. Reducing the number of injuries to seafarers will result not only in better seafarer health but also a reduction in the operating costs of shipping companies due to the reduced insurance premiums they will have to pay. It will also lead to a reduction in the amounts disbursed by Protection and Indemnity (P&I) Clubs to compensate seafarers. In future research, we will use external factors to determine seafaring risks related to, for example, weather conditions, the quality of ships, technology, safety measures, regulations, and more.

In this paper we intoduce Lucas Permutations and we study the connections between them and Fibonacci permutations. Moreover, we show that the number of fibonacci patterns is the fibonacci number F _n+2 .

As cyber threats have increased in both frequency and complexity, the European Union (EU) has taken measures to enhance its cybersecurity framework. This has included the implementation of directives such as NIS1 and NIS2, as well as the establishment of databases such as the European Repository of Cyber Incidents (EuRepoC) and the Cyber Events Database from the University of Maryland. This research investigates the relationships between different types of cyber actors and the corresponding incidents, as well as the correlation between industry sectors and types of cyber incidents. Utilizing chi-square testing on a dataset comprising over 14,000 cyberattack incidents, we identified statistically significant correlations that substantiate the hypotheses proposed. The results of the study indicated that specific actor types, such as nation-states and criminals, were more likely to be associated with particular incident types. In contrast, industry sectors like healthcare and public administration exhibited distinct vulnerabilities. The findings underscore the imperative for data-driven and sector-specific cybersecurity policies within the EU, underscoring the role of comprehensive data repositories in informing effective governance against cyber threats. It is imperative that member states continue to collaborate in order to leverage these insights and enhance resilience and safeguard critical services across Europe.